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0000000000000000000000000000000000000000..567be97b7cd8631e71367e713dc2f0ef23bd76f5 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/mctct/__init__.py @@ -0,0 +1,56 @@ +# Copyright 2022 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available + + +_import_structure = { + "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"], + "feature_extraction_mctct": ["MCTCTFeatureExtractor"], + "processing_mctct": ["MCTCTProcessor"], +} + + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_mctct"] = [ + "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST", + "MCTCTForCTC", + "MCTCTModel", + "MCTCTPreTrainedModel", + ] + + +if TYPE_CHECKING: + from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig + from .feature_extraction_mctct import MCTCTFeatureExtractor + from .processing_mctct import MCTCTProcessor + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/mctct/__pycache__/processing_mctct.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/mctct/__pycache__/processing_mctct.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3d52489f86fc8338b7e61cc73b2f7047b9f106a0 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/mctct/__pycache__/processing_mctct.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/mctct/configuration_mctct.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/mctct/configuration_mctct.py new file mode 100644 index 0000000000000000000000000000000000000000..6546b18eab0522ce8c618da7bf3a8baab005dd0d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/mctct/configuration_mctct.py @@ -0,0 +1,184 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""M-CTC-T model configuration""" + +from ....configuration_utils import PretrainedConfig +from ....utils import logging + + +logger = logging.get_logger(__name__) + + +from .._archive_maps import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class MCTCTConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`MCTCTModel`]. It is used to instantiate an + M-CTC-T model according to the specified arguments, defining the model architecture. Instantiating a configuration + with the defaults will yield a similar configuration to that of the M-CTC-T + [speechbrain/m-ctc-t-large](https://huggingface.co/speechbrain/m-ctc-t-large) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 8065): + Vocabulary size of the M-CTC-T model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`MCTCTModel`]. + hidden_size (`int`, *optional*, defaults to 1536): + Dimension of the encoder layers and the pooler layer. + num_hidden_layers (`int`, *optional*, defaults to 36): + Number of hidden layers in the Transformer encoder. + intermediate_size (`int`, *optional*, defaults to 6144): + Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 4): + Number of attention heads for each attention layer in the Transformer encoder. + attention_head_dim (`int`, *optional*, defaults to 384): + Dimensions of each attention head for each attention layer in the Transformer encoder. + max_position_embeddings (`int`, *optional*, defaults to 920): + The maximum sequence length that this model might ever be used with (after log-mel spectrogram extraction). + layer_norm_eps (`float`, *optional*, defaults to 1e-05): + The epsilon used by the layer normalization layers. + layerdrop (`float`, *optional*, defaults to 0.3): + The probability of dropping an encoder layer during training. The default 0.3 value is used in the original + implementation. + hidden_act (`str` or `function`, *optional*, defaults to `"relu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"` and `"gelu_new"` are supported. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + hidden_dropout_prob (`float`, *optional*, defaults to 0.3): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + attention_probs_dropout_prob (`float`, *optional*, defaults to 0.3): + The dropout ratio for the attention probabilities. + pad_token_id (`int`, *optional*, defaults to 1): + The tokenizer index of the pad token. + bos_token_id (`int`, *optional*, defaults to 0): + The tokenizer index of the bos token. + eos_token_id (`int`, *optional*, defaults to 2): + The tokenizer index of the eos token. + conv_glu_dim (`int`, *optional*, defaults to 1): + The dimension of the output of the `Conv1dSubsampler` layer in which GLU is applied on. Though the original + Flashlight code uses the value of 2, here it's adapted to 1 due to transposition differences. + conv_dropout (`int`, *optional*, defaults to 0.3): + The probability of randomly dropping the `Conv1dSubsampler` layer during training. + num_conv_layers (`int`, *optional*, defaults to 1): + Number of convolution layers before applying transformer encoder layers. + conv_kernel (`Sequence[int]`, *optional*, defaults to `(7,)`): + The kernel size of the 1D convolution applied before transformer layers. `len(conv_kernel)` must be equal + to `num_conv_layers`. + conv_stride (`Sequence[int]`, *optional*, defaults to `(3,)`): + The stride length of the 1D convolution applied before transformer layers. `len(conv_stride)` must be equal + to `num_conv_layers`. + input_feat_per_channel (`int`, *optional*, defaults to 80): + Feature dimensions of the channels of the input to the Conv1D layer. + input_channels (`int`, *optional*, defaults to 1): + Number of input channels of the input to the Conv1D layer. + conv_channels (`List[int]`, *optional*): + Channel sizes of intermediate Conv1D layers. + ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`): + Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an + instance of [`MCTCTForCTC`]. + ctc_zero_infinity (`bool`, *optional*, defaults to `False`): + Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly + occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance + of [`MCTCTForCTC`]. + + Example: + + ```python + >>> from transformers import MCTCTConfig, MCTCTModel + + >>> # Initializing a M-CTC-T mctct-large style configuration + >>> configuration = MCTCTConfig() + + >>> # Initializing a model (with random weights) from the mctct-large style configuration + >>> model = MCTCTModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "mctct" + + def __init__( + self, + vocab_size=8065, + hidden_size=1536, + num_hidden_layers=36, + intermediate_size=6144, + num_attention_heads=4, + attention_head_dim=384, + max_position_embeddings=920, + layer_norm_eps=1e-5, + layerdrop=0.3, + hidden_act="relu", + initializer_range=0.02, + hidden_dropout_prob=0.3, + attention_probs_dropout_prob=0.3, + pad_token_id=1, + bos_token_id=0, + eos_token_id=2, + conv_glu_dim=1, + conv_dropout=0.3, + num_conv_layers=1, + conv_kernel=(7,), + conv_stride=(3,), + input_feat_per_channel=80, + input_channels=1, + conv_channels=None, + ctc_loss_reduction="sum", + ctc_zero_infinity=False, + **kwargs, + ): + super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id) + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.intermediate_size = intermediate_size + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + self.max_position_embeddings = max_position_embeddings + self.layer_norm_eps = layer_norm_eps + self.layerdrop = layerdrop + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.pad_token_id = pad_token_id + self.bos_token_id = bos_token_id + self.eos_token_id = eos_token_id + self.conv_glu_dim = conv_glu_dim + self.conv_dropout = conv_dropout + self.num_conv_layers = num_conv_layers + self.input_feat_per_channel = input_feat_per_channel + self.input_channels = input_channels + self.conv_channels = conv_channels + self.ctc_loss_reduction = ctc_loss_reduction + self.ctc_zero_infinity = ctc_zero_infinity + + # prevents config testing fail with exporting to json + self.conv_kernel = list(conv_kernel) + self.conv_stride = list(conv_stride) + + if len(self.conv_kernel) != self.num_conv_layers: + raise ValueError( + "Configuration for convolutional module is incorrect. " + "It is required that `len(config.conv_kernel)` == `config.num_conv_layers` " + f"but is `len(config.conv_kernel) = {len(self.conv_kernel)}`, " + f"`config.num_conv_layers = {self.num_conv_layers}`." + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/mmbt/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/mmbt/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e467090cb4fbfa55ec51ec8232a54180c532ad6c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/mmbt/__init__.py @@ -0,0 +1,45 @@ +# Copyright 2020 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available + + +_import_structure = {"configuration_mmbt": ["MMBTConfig"]} + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_mmbt"] = ["MMBTForClassification", "MMBTModel", "ModalEmbeddings"] + + +if TYPE_CHECKING: + from .configuration_mmbt import MMBTConfig + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/mmbt/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/mmbt/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5b634025c927d89e893195a8bd85e3ce80107418 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/mmbt/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/mmbt/configuration_mmbt.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/mmbt/configuration_mmbt.py new file mode 100644 index 0000000000000000000000000000000000000000..df5161b0927ad26279a273216d1d9ab6d465063a --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/mmbt/configuration_mmbt.py @@ -0,0 +1,42 @@ +# coding=utf-8 +# Copyright (c) Facebook, Inc. and its affiliates. +# Copyright (c) HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" MMBT configuration""" + +from ....utils import logging + + +logger = logging.get_logger(__name__) + + +class MMBTConfig(object): + """ + This is the configuration class to store the configuration of a [`MMBTModel`]. It is used to instantiate a MMBT + model according to the specified arguments, defining the model architecture. + + Args: + config ([`PreTrainedConfig`]): + Config of the underlying Transformer models. Its values are copied over to use a single config. + num_labels (`int`, *optional*): + Size of final Linear layer for classification. + modal_hidden_size (`int`, *optional*, defaults to 2048): + Embedding dimension of the non-text modality encoder. + """ + + def __init__(self, config, num_labels=None, modal_hidden_size=2048): + self.__dict__ = config.__dict__ + self.modal_hidden_size = modal_hidden_size + if num_labels: + self.num_labels = num_labels diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/open_llama/__pycache__/configuration_open_llama.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/open_llama/__pycache__/configuration_open_llama.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..65c44fd23eab46bf95e120e9a7a4b4b465868e2c Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/open_llama/__pycache__/configuration_open_llama.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/open_llama/modeling_open_llama.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/open_llama/modeling_open_llama.py new file mode 100644 index 0000000000000000000000000000000000000000..098f8c7da50d5e3b737ad6a5e12ccb456e71356d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/open_llama/modeling_open_llama.py @@ -0,0 +1,969 @@ +# coding=utf-8 +# Copyright 2023 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch Open-Llama model.""" +import math +from typing import List, Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ....activations import ACT2FN +from ....modeling_attn_mask_utils import _prepare_4d_causal_attention_mask +from ....modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast +from ....modeling_utils import PreTrainedModel +from ....utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings +from .configuration_open_llama import OpenLlamaConfig + + +logger = logging.get_logger(__name__) + +try: + from xformers import ops as xops +except ImportError: + xops = None + + +_CONFIG_FOR_DOC = "OpenLlamaConfig" + + +# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->OpenLlama +class OpenLlamaRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + OpenLlamaRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + +# Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->OpenLlama +class OpenLlamaRotaryEmbedding(nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + # Build here to make `torch.jit.trace` work. + self._set_cos_sin_cache( + seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() + ) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq) + + freqs = torch.outer(t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + if seq_len > self.max_seq_len_cached: + self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) + + return ( + self.cos_cached[:seq_len].to(dtype=x.dtype), + self.sin_cached[:seq_len].to(dtype=x.dtype), + ) + + +# Copied from transformers.models.falcon.modeling_falcon.FalconLinearScalingRotaryEmbedding with Falcon->OpenLlama +class OpenLlamaLinearScalingRotaryEmbedding(OpenLlamaRotaryEmbedding): + """OpenLlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" + + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): + self.scaling_factor = scaling_factor + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq) + t = t / self.scaling_factor + + freqs = torch.outer(t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + +# Copied from transformers.models.falcon.modeling_falcon.FalconDynamicNTKScalingRotaryEmbedding with Falcon->OpenLlama +class OpenLlamaDynamicNTKScalingRotaryEmbedding(OpenLlamaRotaryEmbedding): + """OpenLlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" + + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): + self.scaling_factor = scaling_factor + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + + if seq_len > self.max_position_embeddings: + base = self.base * ( + (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) + ) ** (self.dim / (self.dim - 2)) + inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq) + + freqs = torch.outer(t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +# Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb +def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + position_ids (`torch.Tensor`): + The position indices of the tokens corresponding to the query and key tensors. For example, this can be + used to pass offsetted position ids when working with a KV-cache. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos[position_ids].unsqueeze(unsqueeze_dim) + sin = sin[position_ids].unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +class OpenLlamaMLP(nn.Module): + def __init__( + self, + hidden_size: int, + intermediate_size: int, + hidden_act: str, + dropout_prob: float, + ): + super().__init__() + self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False) + self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False) + self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False) + self.act_fn = ACT2FN[hidden_act] + self.dropout = nn.Dropout(dropout_prob) + + def forward(self, x): + out = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return self.dropout(out) + + +class OpenLlamaAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: OpenLlamaConfig): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.max_position_embeddings = config.max_position_embeddings + self.dropout_prob = config.attention_dropout_prob + self.rope_theta = config.rope_theta + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) + self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) + self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + self._init_rope() + + # Copied from transformers.models.llama.modeling_llama.LlamaAttention._init_rope with Llama->OpenLlama + def _init_rope(self): + if self.config.rope_scaling is None: + self.rotary_emb = OpenLlamaRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + base=self.rope_theta, + ) + else: + scaling_type = self.config.rope_scaling["type"] + scaling_factor = self.config.rope_scaling["factor"] + if scaling_type == "linear": + self.rotary_emb = OpenLlamaLinearScalingRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + scaling_factor=scaling_factor, + base=self.rope_theta, + ) + elif scaling_type == "dynamic": + self.rotary_emb = OpenLlamaDynamicNTKScalingRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + scaling_factor=scaling_factor, + base=self.rope_theta, + ) + else: + raise ValueError(f"Unknown RoPE scaling type {scaling_type}") + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + # [bsz, nh, t, hd] + + if past_key_value is not None: + # reuse k, v, self_attention + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + + past_key_value = (key_states, value_states) if use_cache else None + + if self.config.use_memory_efficient_attention and xops is not None and self.training: + attn_weights = None + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + attn_output = xops.memory_efficient_attention( + query_states, key_states, value_states, attn_bias=xops.LowerTriangularMask(), p=self.dropout_prob + ) + else: + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights + attention_mask + attn_weights = torch.max( + attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min, device=attn_weights.device) + ) + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2) + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class OpenLlamaDecoderLayer(nn.Module): + def __init__(self, config: OpenLlamaConfig): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = OpenLlamaAttention(config=config) + self.mlp = OpenLlamaMLP( + hidden_size=self.hidden_size, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + dropout_prob=config.hidden_dropout_prob, + ) + self.input_layernorm = OpenLlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = OpenLlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + """ + + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +OPEN_LLAMA_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`OpenLlamaConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare Open-Llama Model outputting raw hidden-states without any specific head on top.", + OPEN_LLAMA_START_DOCSTRING, +) +class OpenLlamaPreTrainedModel(PreTrainedModel): + config_class = OpenLlamaConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["OpenLlamaDecoderLayer"] + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + if self.config.use_stable_embedding: + torch.nn.init.xavier_normal_(module.weight.data) + else: + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + +OPEN_LLAMA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. + + [What are position IDs?](../glossary#position-ids) + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape + `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare Open-Llama Model outputting raw hidden-states without any specific head on top.", + OPEN_LLAMA_START_DOCSTRING, +) +class OpenLlamaModel(OpenLlamaPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OpenLlamaDecoderLayer`] + + Args: + config: OpenLlamaConfig + """ + + def __init__(self, config: OpenLlamaConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + if config.use_stable_embedding: + self.embed_layer_norm = nn.LayerNorm(config.hidden_size) + else: + self.embed_layer_norm = None + self.layers = nn.ModuleList([OpenLlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.norm = OpenLlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + @add_start_docstrings_to_model_forward(OPEN_LLAMA_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + seq_length_with_past = seq_length + past_key_values_length = 0 + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + if past_key_values is not None: + past_key_values_length = past_key_values[0][0].shape[2] + seq_length_with_past = seq_length_with_past + past_key_values_length + + if position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0) + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + if self.embed_layer_norm: + inputs_embeds = self.embed_layer_norm(inputs_embeds) + # embed positions + if self.config.use_memory_efficient_attention and self.training: + attention_mask = None + elif attention_mask is None: + attention_mask = torch.ones( + (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device + ) + + input_shape = (batch_size, seq_length) + attention_mask = _prepare_4d_causal_attention_mask( + attention_mask, input_shape, inputs_embeds, past_key_values_length + ) + + hidden_states = inputs_embeds + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = () if use_cache else None + + for idx, decoder_layer in enumerate(self.layers): + if output_hidden_states: + all_hidden_states += (hidden_states,) + + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + attention_mask, + position_ids, + None, + output_attentions, + None, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + +class OpenLlamaForCausalLM(OpenLlamaPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.model = OpenLlamaModel(config) + if config.shared_input_output_embedding: + self.lm_head = None + else: + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @add_start_docstrings_to_model_forward(OPEN_LLAMA_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, OpenLlamaForCausalLM + + >>> model = OpenLlamaForCausalLM.from_pretrained("openlm-research/open_llama_7b") + >>> tokenizer = AutoTokenizer.from_pretrained("openlm-research/open_llama_7b") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + if self.config.shared_input_output_embedding: + logits = torch.einsum( + "blh,vh->blv", hidden_states.to(self.model.embed_tokens.weight.device), self.model.embed_tokens.weight + ) + else: + logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + # move labels to correct device to enable model parallelism + labels = labels.to(logits.device) + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs + ): + if past_key_values is not None: + past_length = past_key_values[0][0].shape[2] + + # Some generation methods already pass only the last input ID + if input_ids.shape[1] > past_length: + remove_prefix_length = past_length + else: + # Default to old behavior: keep only final ID + remove_prefix_length = input_ids.shape[1] - 1 + + input_ids = input_ids[:, remove_prefix_length:] + + position_ids = kwargs.get("position_ids", None) + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -input_ids.shape[1] :] + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += ( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), + ) + return reordered_past + + +@add_start_docstrings( + """ + The LLaMa Model transformer with a sequence classification head on top (linear layer). + + [`OpenLlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal + models (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + OPEN_LLAMA_START_DOCSTRING, +) +class OpenLlamaForSequenceClassification(OpenLlamaPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = OpenLlamaModel(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + @add_start_docstrings_to_model_forward(OPEN_LLAMA_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility + sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 + sequence_lengths = sequence_lengths % input_ids.shape[-1] + sequence_lengths = sequence_lengths.to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/retribert/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/retribert/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..dba5e14594e16c19fc1a269a92e968fec35afc26 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/retribert/__init__.py @@ -0,0 +1,73 @@ +# Copyright 2020 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available + + +_import_structure = { + "configuration_retribert": ["RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RetriBertConfig"], + "tokenization_retribert": ["RetriBertTokenizer"], +} + +try: + if not is_tokenizers_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["tokenization_retribert_fast"] = ["RetriBertTokenizerFast"] + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_retribert"] = [ + "RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "RetriBertModel", + "RetriBertPreTrainedModel", + ] + + +if TYPE_CHECKING: + from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig + from .tokenization_retribert import RetriBertTokenizer + + try: + if not is_tokenizers_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .tokenization_retribert_fast import RetriBertTokenizerFast + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_retribert import ( + RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST, + RetriBertModel, + RetriBertPreTrainedModel, + ) + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/retribert/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/retribert/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..433adb8c9ab4a866b6983362c176635bd1a55d57 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/retribert/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/retribert/modeling_retribert.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/retribert/modeling_retribert.py new file mode 100644 index 0000000000000000000000000000000000000000..7dba8a276eeb56c379f782ea2bd5e1be7119cfe7 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/retribert/modeling_retribert.py @@ -0,0 +1,218 @@ +# coding=utf-8 +# Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +RetriBERT model +""" + + +import math +from typing import Optional + +import torch +import torch.utils.checkpoint as checkpoint +from torch import nn + +from ....modeling_utils import PreTrainedModel +from ....utils import add_start_docstrings, logging +from ...bert.modeling_bert import BertModel +from .configuration_retribert import RetriBertConfig + + +logger = logging.get_logger(__name__) + + +from .._archive_maps import RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +# INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL # +class RetriBertPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RetriBertConfig + load_tf_weights = None + base_model_prefix = "retribert" + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + +RETRIBERT_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + """Bert Based model to embed queries or document for document retrieval.""", + RETRIBERT_START_DOCSTRING, +) +class RetriBertModel(RetriBertPreTrainedModel): + def __init__(self, config: RetriBertConfig) -> None: + super().__init__(config) + self.projection_dim = config.projection_dim + + self.bert_query = BertModel(config) + self.bert_doc = None if config.share_encoders else BertModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.project_query = nn.Linear(config.hidden_size, config.projection_dim, bias=False) + self.project_doc = nn.Linear(config.hidden_size, config.projection_dim, bias=False) + + self.ce_loss = nn.CrossEntropyLoss(reduction="mean") + + # Initialize weights and apply final processing + self.post_init() + + def embed_sentences_checkpointed( + self, + input_ids, + attention_mask, + sent_encoder, + checkpoint_batch_size=-1, + ): + # reproduces BERT forward pass with checkpointing + if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size: + return sent_encoder(input_ids, attention_mask=attention_mask)[1] + else: + # prepare implicit variables + device = input_ids.device + input_shape = input_ids.size() + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) + head_mask = [None] * sent_encoder.config.num_hidden_layers + extended_attention_mask: torch.Tensor = sent_encoder.get_extended_attention_mask( + attention_mask, input_shape + ) + + # define function for checkpointing + def partial_encode(*inputs): + encoder_outputs = sent_encoder.encoder( + inputs[0], + attention_mask=inputs[1], + head_mask=head_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = sent_encoder.pooler(sequence_output) + return pooled_output + + # run embedding layer on everything at once + embedding_output = sent_encoder.embeddings( + input_ids=input_ids, position_ids=None, token_type_ids=token_type_ids, inputs_embeds=None + ) + # run encoding and pooling on one mini-batch at a time + pooled_output_list = [] + for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size)): + b_embedding_output = embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size] + b_attention_mask = extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size] + pooled_output = checkpoint.checkpoint(partial_encode, b_embedding_output, b_attention_mask) + pooled_output_list.append(pooled_output) + return torch.cat(pooled_output_list, dim=0) + + def embed_questions( + self, + input_ids, + attention_mask=None, + checkpoint_batch_size=-1, + ): + q_reps = self.embed_sentences_checkpointed( + input_ids, + attention_mask, + self.bert_query, + checkpoint_batch_size, + ) + return self.project_query(q_reps) + + def embed_answers( + self, + input_ids, + attention_mask=None, + checkpoint_batch_size=-1, + ): + a_reps = self.embed_sentences_checkpointed( + input_ids, + attention_mask, + self.bert_query if self.bert_doc is None else self.bert_doc, + checkpoint_batch_size, + ) + return self.project_doc(a_reps) + + def forward( + self, + input_ids_query: torch.LongTensor, + attention_mask_query: Optional[torch.FloatTensor], + input_ids_doc: torch.LongTensor, + attention_mask_doc: Optional[torch.FloatTensor], + checkpoint_batch_size: int = -1, + ) -> torch.FloatTensor: + r""" + Args: + input_ids_query (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary for the queries in a batch. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask_query (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + input_ids_doc (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary for the documents in a batch. + attention_mask_doc (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on documents padding token indices. + checkpoint_batch_size (`int`, *optional*, defaults to `-1`): + If greater than 0, uses gradient checkpointing to only compute sequence representation on + `checkpoint_batch_size` examples at a time on the GPU. All query representations are still compared to + all document representations in the batch. + + Return: + `torch.FloatTensor``: The bidirectional cross-entropy loss obtained while trying to match each query to its + corresponding document and each document to its corresponding query in the batch + """ + device = input_ids_query.device + q_reps = self.embed_questions(input_ids_query, attention_mask_query, checkpoint_batch_size) + a_reps = self.embed_answers(input_ids_doc, attention_mask_doc, checkpoint_batch_size) + compare_scores = torch.mm(q_reps, a_reps.t()) + loss_qa = self.ce_loss(compare_scores, torch.arange(compare_scores.shape[1]).to(device)) + loss_aq = self.ce_loss(compare_scores.t(), torch.arange(compare_scores.shape[0]).to(device)) + loss = (loss_qa + loss_aq) / 2 + return loss diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/retribert/tokenization_retribert.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/retribert/tokenization_retribert.py new file mode 100644 index 0000000000000000000000000000000000000000..c991f3972230bd9027f43137b2a51550d4655f4d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/retribert/tokenization_retribert.py @@ -0,0 +1,517 @@ +# coding=utf-8 +# Copyright 2018 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization classes for RetriBERT.""" + +import collections +import os +import unicodedata +from typing import List, Optional, Tuple + +from ....tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace +from ....utils import logging + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"} + + +# Copied from transformers.models.bert.tokenization_bert.load_vocab +def load_vocab(vocab_file): + """Loads a vocabulary file into a dictionary.""" + vocab = collections.OrderedDict() + with open(vocab_file, "r", encoding="utf-8") as reader: + tokens = reader.readlines() + for index, token in enumerate(tokens): + token = token.rstrip("\n") + vocab[token] = index + return vocab + + +# Copied from transformers.models.bert.tokenization_bert.whitespace_tokenize +def whitespace_tokenize(text): + """Runs basic whitespace cleaning and splitting on a piece of text.""" + text = text.strip() + if not text: + return [] + tokens = text.split() + return tokens + + +class RetriBertTokenizer(PreTrainedTokenizer): + r""" + Constructs a RetriBERT tokenizer. + + [`RetriBertTokenizer`] is identical to [`BertTokenizer`] and runs end-to-end tokenization: punctuation splitting + and wordpiece. + + This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer + to: this superclass for more information regarding those methods. + + Args: + vocab_file (`str`): + File containing the vocabulary. + do_lower_case (`bool`, *optional*, defaults to `True`): + Whether or not to lowercase the input when tokenizing. + do_basic_tokenize (`bool`, *optional*, defaults to `True`): + Whether or not to do basic tokenization before WordPiece. + never_split (`Iterable`, *optional*): + Collection of tokens which will never be split during tokenization. Only has an effect when + `do_basic_tokenize=True` + unk_token (`str`, *optional*, defaults to `"[UNK]"`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + sep_token (`str`, *optional*, defaults to `"[SEP]"`): + The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for + sequence classification or for a text and a question for question answering. It is also used as the last + token of a sequence built with special tokens. + pad_token (`str`, *optional*, defaults to `"[PAD]"`): + The token used for padding, for example when batching sequences of different lengths. + cls_token (`str`, *optional*, defaults to `"[CLS]"`): + The classifier token which is used when doing sequence classification (classification of the whole sequence + instead of per-token classification). It is the first token of the sequence when built with special tokens. + mask_token (`str`, *optional*, defaults to `"[MASK]"`): + The token used for masking values. This is the token used when training this model with masked language + modeling. This is the token which the model will try to predict. + tokenize_chinese_chars (`bool`, *optional*, defaults to `True`): + Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see this + [issue](https://github.com/huggingface/transformers/issues/328)). + strip_accents (`bool`, *optional*): + Whether or not to strip all accents. If this option is not specified, then it will be determined by the + value for `lowercase` (as in the original BERT). + """ + + vocab_files_names = VOCAB_FILES_NAMES + model_input_names = ["input_ids", "attention_mask"] + + # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.__init__ + def __init__( + self, + vocab_file, + do_lower_case=True, + do_basic_tokenize=True, + never_split=None, + unk_token="[UNK]", + sep_token="[SEP]", + pad_token="[PAD]", + cls_token="[CLS]", + mask_token="[MASK]", + tokenize_chinese_chars=True, + strip_accents=None, + **kwargs, + ): + if not os.path.isfile(vocab_file): + raise ValueError( + f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained" + " model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" + ) + self.vocab = load_vocab(vocab_file) + self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()]) + self.do_basic_tokenize = do_basic_tokenize + if do_basic_tokenize: + self.basic_tokenizer = BasicTokenizer( + do_lower_case=do_lower_case, + never_split=never_split, + tokenize_chinese_chars=tokenize_chinese_chars, + strip_accents=strip_accents, + ) + + self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token)) + + super().__init__( + do_lower_case=do_lower_case, + do_basic_tokenize=do_basic_tokenize, + never_split=never_split, + unk_token=unk_token, + sep_token=sep_token, + pad_token=pad_token, + cls_token=cls_token, + mask_token=mask_token, + tokenize_chinese_chars=tokenize_chinese_chars, + strip_accents=strip_accents, + **kwargs, + ) + + @property + # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.do_lower_case + def do_lower_case(self): + return self.basic_tokenizer.do_lower_case + + @property + # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.vocab_size + def vocab_size(self): + return len(self.vocab) + + # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.get_vocab + def get_vocab(self): + return dict(self.vocab, **self.added_tokens_encoder) + + # Copied from transformers.models.bert.tokenization_bert.BertTokenizer._tokenize + def _tokenize(self, text, split_special_tokens=False): + split_tokens = [] + if self.do_basic_tokenize: + for token in self.basic_tokenizer.tokenize( + text, never_split=self.all_special_tokens if not split_special_tokens else None + ): + # If the token is part of the never_split set + if token in self.basic_tokenizer.never_split: + split_tokens.append(token) + else: + split_tokens += self.wordpiece_tokenizer.tokenize(token) + else: + split_tokens = self.wordpiece_tokenizer.tokenize(text) + return split_tokens + + # Copied from transformers.models.bert.tokenization_bert.BertTokenizer._convert_token_to_id + def _convert_token_to_id(self, token): + """Converts a token (str) in an id using the vocab.""" + return self.vocab.get(token, self.vocab.get(self.unk_token)) + + # Copied from transformers.models.bert.tokenization_bert.BertTokenizer._convert_id_to_token + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + return self.ids_to_tokens.get(index, self.unk_token) + + # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.convert_tokens_to_string + def convert_tokens_to_string(self, tokens): + """Converts a sequence of tokens (string) in a single string.""" + out_string = " ".join(tokens).replace(" ##", "").strip() + return out_string + + # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.build_inputs_with_special_tokens + def build_inputs_with_special_tokens( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and + adding special tokens. A BERT sequence has the following format: + + - single sequence: `[CLS] X [SEP]` + - pair of sequences: `[CLS] A [SEP] B [SEP]` + + Args: + token_ids_0 (`List[int]`): + List of IDs to which the special tokens will be added. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. + """ + if token_ids_1 is None: + return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] + cls = [self.cls_token_id] + sep = [self.sep_token_id] + return cls + token_ids_0 + sep + token_ids_1 + sep + + # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.get_special_tokens_mask + def get_special_tokens_mask( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False + ) -> List[int]: + """ + Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding + special tokens using the tokenizer `prepare_for_model` method. + + Args: + token_ids_0 (`List[int]`): + List of IDs. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + already_has_special_tokens (`bool`, *optional*, defaults to `False`): + Whether or not the token list is already formatted with special tokens for the model. + + Returns: + `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. + """ + + if already_has_special_tokens: + return super().get_special_tokens_mask( + token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True + ) + + if token_ids_1 is not None: + return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] + return [1] + ([0] * len(token_ids_0)) + [1] + + # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.create_token_type_ids_from_sequences + def create_token_type_ids_from_sequences( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence + pair mask has the following format: + + ``` + 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 + | first sequence | second sequence | + ``` + + If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). + + Args: + token_ids_0 (`List[int]`): + List of IDs. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). + """ + sep = [self.sep_token_id] + cls = [self.cls_token_id] + if token_ids_1 is None: + return len(cls + token_ids_0 + sep) * [0] + return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] + + # Copied from transformers.models.bert.tokenization_bert.BertTokenizer.save_vocabulary + def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: + index = 0 + if os.path.isdir(save_directory): + vocab_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] + ) + else: + vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory + with open(vocab_file, "w", encoding="utf-8") as writer: + for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]): + if index != token_index: + logger.warning( + f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive." + " Please check that the vocabulary is not corrupted!" + ) + index = token_index + writer.write(token + "\n") + index += 1 + return (vocab_file,) + + +# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer +class BasicTokenizer(object): + """ + Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.). + + Args: + do_lower_case (`bool`, *optional*, defaults to `True`): + Whether or not to lowercase the input when tokenizing. + never_split (`Iterable`, *optional*): + Collection of tokens which will never be split during tokenization. Only has an effect when + `do_basic_tokenize=True` + tokenize_chinese_chars (`bool`, *optional*, defaults to `True`): + Whether or not to tokenize Chinese characters. + + This should likely be deactivated for Japanese (see this + [issue](https://github.com/huggingface/transformers/issues/328)). + strip_accents (`bool`, *optional*): + Whether or not to strip all accents. If this option is not specified, then it will be determined by the + value for `lowercase` (as in the original BERT). + do_split_on_punc (`bool`, *optional*, defaults to `True`): + In some instances we want to skip the basic punctuation splitting so that later tokenization can capture + the full context of the words, such as contractions. + """ + + def __init__( + self, + do_lower_case=True, + never_split=None, + tokenize_chinese_chars=True, + strip_accents=None, + do_split_on_punc=True, + ): + if never_split is None: + never_split = [] + self.do_lower_case = do_lower_case + self.never_split = set(never_split) + self.tokenize_chinese_chars = tokenize_chinese_chars + self.strip_accents = strip_accents + self.do_split_on_punc = do_split_on_punc + + def tokenize(self, text, never_split=None): + """ + Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer. + + Args: + never_split (`List[str]`, *optional*) + Kept for backward compatibility purposes. Now implemented directly at the base class level (see + [`PreTrainedTokenizer.tokenize`]) List of token not to split. + """ + # union() returns a new set by concatenating the two sets. + never_split = self.never_split.union(set(never_split)) if never_split else self.never_split + text = self._clean_text(text) + + # This was added on November 1st, 2018 for the multilingual and Chinese + # models. This is also applied to the English models now, but it doesn't + # matter since the English models were not trained on any Chinese data + # and generally don't have any Chinese data in them (there are Chinese + # characters in the vocabulary because Wikipedia does have some Chinese + # words in the English Wikipedia.). + if self.tokenize_chinese_chars: + text = self._tokenize_chinese_chars(text) + # prevents treating the same character with different unicode codepoints as different characters + unicode_normalized_text = unicodedata.normalize("NFC", text) + orig_tokens = whitespace_tokenize(unicode_normalized_text) + split_tokens = [] + for token in orig_tokens: + if token not in never_split: + if self.do_lower_case: + token = token.lower() + if self.strip_accents is not False: + token = self._run_strip_accents(token) + elif self.strip_accents: + token = self._run_strip_accents(token) + split_tokens.extend(self._run_split_on_punc(token, never_split)) + + output_tokens = whitespace_tokenize(" ".join(split_tokens)) + return output_tokens + + def _run_strip_accents(self, text): + """Strips accents from a piece of text.""" + text = unicodedata.normalize("NFD", text) + output = [] + for char in text: + cat = unicodedata.category(char) + if cat == "Mn": + continue + output.append(char) + return "".join(output) + + def _run_split_on_punc(self, text, never_split=None): + """Splits punctuation on a piece of text.""" + if not self.do_split_on_punc or (never_split is not None and text in never_split): + return [text] + chars = list(text) + i = 0 + start_new_word = True + output = [] + while i < len(chars): + char = chars[i] + if _is_punctuation(char): + output.append([char]) + start_new_word = True + else: + if start_new_word: + output.append([]) + start_new_word = False + output[-1].append(char) + i += 1 + + return ["".join(x) for x in output] + + def _tokenize_chinese_chars(self, text): + """Adds whitespace around any CJK character.""" + output = [] + for char in text: + cp = ord(char) + if self._is_chinese_char(cp): + output.append(" ") + output.append(char) + output.append(" ") + else: + output.append(char) + return "".join(output) + + def _is_chinese_char(self, cp): + """Checks whether CP is the codepoint of a CJK character.""" + # This defines a "chinese character" as anything in the CJK Unicode block: + # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) + # + # Note that the CJK Unicode block is NOT all Japanese and Korean characters, + # despite its name. The modern Korean Hangul alphabet is a different block, + # as is Japanese Hiragana and Katakana. Those alphabets are used to write + # space-separated words, so they are not treated specially and handled + # like the all of the other languages. + if ( + (cp >= 0x4E00 and cp <= 0x9FFF) + or (cp >= 0x3400 and cp <= 0x4DBF) # + or (cp >= 0x20000 and cp <= 0x2A6DF) # + or (cp >= 0x2A700 and cp <= 0x2B73F) # + or (cp >= 0x2B740 and cp <= 0x2B81F) # + or (cp >= 0x2B820 and cp <= 0x2CEAF) # + or (cp >= 0xF900 and cp <= 0xFAFF) + or (cp >= 0x2F800 and cp <= 0x2FA1F) # + ): # + return True + + return False + + def _clean_text(self, text): + """Performs invalid character removal and whitespace cleanup on text.""" + output = [] + for char in text: + cp = ord(char) + if cp == 0 or cp == 0xFFFD or _is_control(char): + continue + if _is_whitespace(char): + output.append(" ") + else: + output.append(char) + return "".join(output) + + +# Copied from transformers.models.bert.tokenization_bert.WordpieceTokenizer +class WordpieceTokenizer(object): + """Runs WordPiece tokenization.""" + + def __init__(self, vocab, unk_token, max_input_chars_per_word=100): + self.vocab = vocab + self.unk_token = unk_token + self.max_input_chars_per_word = max_input_chars_per_word + + def tokenize(self, text): + """ + Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform + tokenization using the given vocabulary. + + For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`. + + Args: + text: A single token or whitespace separated tokens. This should have + already been passed through *BasicTokenizer*. + + Returns: + A list of wordpiece tokens. + """ + + output_tokens = [] + for token in whitespace_tokenize(text): + chars = list(token) + if len(chars) > self.max_input_chars_per_word: + output_tokens.append(self.unk_token) + continue + + is_bad = False + start = 0 + sub_tokens = [] + while start < len(chars): + end = len(chars) + cur_substr = None + while start < end: + substr = "".join(chars[start:end]) + if start > 0: + substr = "##" + substr + if substr in self.vocab: + cur_substr = substr + break + end -= 1 + if cur_substr is None: + is_bad = True + break + sub_tokens.append(cur_substr) + start = end + + if is_bad: + output_tokens.append(self.unk_token) + else: + output_tokens.extend(sub_tokens) + return output_tokens diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/retribert/tokenization_retribert_fast.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/retribert/tokenization_retribert_fast.py new file mode 100644 index 0000000000000000000000000000000000000000..97fbfc07d30ca65cd4d31dc2e221d0ef22073175 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/retribert/tokenization_retribert_fast.py @@ -0,0 +1,180 @@ +# coding=utf-8 +# Copyright 2018 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization classes for RetriBERT.""" + +import json +from typing import List, Optional, Tuple + +from tokenizers import normalizers + +from ....tokenization_utils_fast import PreTrainedTokenizerFast +from ....utils import logging +from .tokenization_retribert import RetriBertTokenizer + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"} + + +class RetriBertTokenizerFast(PreTrainedTokenizerFast): + r""" + Construct a "fast" RetriBERT tokenizer (backed by HuggingFace's *tokenizers* library). + + [`RetriBertTokenizerFast`] is identical to [`BertTokenizerFast`] and runs end-to-end tokenization: punctuation + splitting and wordpiece. + + This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should + refer to this superclass for more information regarding those methods. + + Args: + vocab_file (`str`): + File containing the vocabulary. + do_lower_case (`bool`, *optional*, defaults to `True`): + Whether or not to lowercase the input when tokenizing. + unk_token (`str`, *optional*, defaults to `"[UNK]"`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + sep_token (`str`, *optional*, defaults to `"[SEP]"`): + The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for + sequence classification or for a text and a question for question answering. It is also used as the last + token of a sequence built with special tokens. + pad_token (`str`, *optional*, defaults to `"[PAD]"`): + The token used for padding, for example when batching sequences of different lengths. + cls_token (`str`, *optional*, defaults to `"[CLS]"`): + The classifier token which is used when doing sequence classification (classification of the whole sequence + instead of per-token classification). It is the first token of the sequence when built with special tokens. + mask_token (`str`, *optional*, defaults to `"[MASK]"`): + The token used for masking values. This is the token used when training this model with masked language + modeling. This is the token which the model will try to predict. + clean_text (`bool`, *optional*, defaults to `True`): + Whether or not to clean the text before tokenization by removing any control characters and replacing all + whitespaces by the classic one. + tokenize_chinese_chars (`bool`, *optional*, defaults to `True`): + Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see [this + issue](https://github.com/huggingface/transformers/issues/328)). + strip_accents (`bool`, *optional*): + Whether or not to strip all accents. If this option is not specified, then it will be determined by the + value for `lowercase` (as in the original BERT). + wordpieces_prefix (`str`, *optional*, defaults to `"##"`): + The prefix for subwords. + """ + + vocab_files_names = VOCAB_FILES_NAMES + slow_tokenizer_class = RetriBertTokenizer + model_input_names = ["input_ids", "attention_mask"] + + # Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast.__init__ + def __init__( + self, + vocab_file=None, + tokenizer_file=None, + do_lower_case=True, + unk_token="[UNK]", + sep_token="[SEP]", + pad_token="[PAD]", + cls_token="[CLS]", + mask_token="[MASK]", + tokenize_chinese_chars=True, + strip_accents=None, + **kwargs, + ): + super().__init__( + vocab_file, + tokenizer_file=tokenizer_file, + do_lower_case=do_lower_case, + unk_token=unk_token, + sep_token=sep_token, + pad_token=pad_token, + cls_token=cls_token, + mask_token=mask_token, + tokenize_chinese_chars=tokenize_chinese_chars, + strip_accents=strip_accents, + **kwargs, + ) + + normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__()) + if ( + normalizer_state.get("lowercase", do_lower_case) != do_lower_case + or normalizer_state.get("strip_accents", strip_accents) != strip_accents + or normalizer_state.get("handle_chinese_chars", tokenize_chinese_chars) != tokenize_chinese_chars + ): + normalizer_class = getattr(normalizers, normalizer_state.pop("type")) + normalizer_state["lowercase"] = do_lower_case + normalizer_state["strip_accents"] = strip_accents + normalizer_state["handle_chinese_chars"] = tokenize_chinese_chars + self.backend_tokenizer.normalizer = normalizer_class(**normalizer_state) + + self.do_lower_case = do_lower_case + + # Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast.build_inputs_with_special_tokens + def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and + adding special tokens. A BERT sequence has the following format: + + - single sequence: `[CLS] X [SEP]` + - pair of sequences: `[CLS] A [SEP] B [SEP]` + + Args: + token_ids_0 (`List[int]`): + List of IDs to which the special tokens will be added. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. + """ + output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id] + + if token_ids_1 is not None: + output += token_ids_1 + [self.sep_token_id] + + return output + + # Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast.create_token_type_ids_from_sequences + def create_token_type_ids_from_sequences( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence + pair mask has the following format: + + ``` + 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 + | first sequence | second sequence | + ``` + + If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). + + Args: + token_ids_0 (`List[int]`): + List of IDs. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). + """ + sep = [self.sep_token_id] + cls = [self.cls_token_id] + if token_ids_1 is None: + return len(cls + token_ids_0 + sep) * [0] + return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] + + # Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast.save_vocabulary + def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: + files = self._tokenizer.model.save(save_directory, name=filename_prefix) + return tuple(files) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/tapex/__pycache__/tokenization_tapex.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/tapex/__pycache__/tokenization_tapex.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fdf15a1bf54a88aa0ba9cb6982a19f7ae2a0ebaf Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/tapex/__pycache__/tokenization_tapex.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..5c98aa45dc2739336b75e72167c739160cf5d11f --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py @@ -0,0 +1,606 @@ +# coding=utf-8 +# Copyright 2022 The Trajectory Transformers paper authors and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch TrajectoryTransformer model.""" + +import math +import os +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import numpy as np +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import functional as F + +from ....modeling_utils import PreTrainedModel +from ....utils import ( + ModelOutput, + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) +from .configuration_trajectory_transformer import TrajectoryTransformerConfig + + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "CarlCochet/trajectory-transformer-halfcheetah-medium-v2" +_CONFIG_FOR_DOC = "TrajectoryTransformerConfig" + + +from .._archive_maps import TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +def load_tf_weights_in_trajectory_transformer(model, config, tf_checkpoint_path): + """Load tf checkpoints in a pytorch model.""" + try: + import re + + import numpy as np + import tensorflow as tf + except ImportError: + logger.error( + "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " + "https://www.tensorflow.org/install/ for installation instructions." + ) + raise + tf_path = os.path.abspath(tf_checkpoint_path) + logger.info(f"Converting TensorFlow checkpoint from {tf_path}") + # Load weights from TF model + init_vars = tf.train.list_variables(tf_path) + names = [] + arrays = [] + for name, shape in init_vars: + logger.info(f"Loading TF weight {name} with shape {shape}") + array = tf.train.load_variable(tf_path, name) + names.append(name) + arrays.append(array) + + for name, array in zip(names, arrays): + name = name.split("/") + # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v + # which are not required for using pretrained model + if any( + n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"] + for n in name + ): + logger.info(f"Skipping {'/'.join(name)}") + continue + pointer = model + for m_name in name: + if re.fullmatch(r"[A-Za-z]+_\d+", m_name): + scope_names = re.split(r"_(\d+)", m_name) + else: + scope_names = [m_name] + if scope_names[0] == "kernel" or scope_names[0] == "gamma": + pointer = getattr(pointer, "weight") + elif scope_names[0] == "output_bias" or scope_names[0] == "beta": + pointer = getattr(pointer, "bias") + elif scope_names[0] == "output_weights": + pointer = getattr(pointer, "weight") + elif scope_names[0] == "squad": + pointer = getattr(pointer, "classifier") + else: + try: + pointer = getattr(pointer, scope_names[0]) + except AttributeError: + logger.info(f"Skipping {'/'.join(name)}") + continue + if len(scope_names) >= 2: + num = int(scope_names[1]) + pointer = pointer[num] + if m_name[-11:] == "_embeddings": + pointer = getattr(pointer, "weight") + elif m_name == "kernel": + array = np.transpose(array) + try: + if pointer.shape != array.shape: + raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched") + except AssertionError as e: + e.args += (pointer.shape, array.shape) + raise + logger.info(f"Initialize PyTorch weight {name}") + pointer.data = torch.from_numpy(array) + return model + + +@dataclass +class TrajectoryTransformerOutput(ModelOutput): + """ + Base class for model's outputs that also contains a pooling of the last hidden states. + + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss. + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`Tuple[Tuple[torch.Tensor]]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of length `config.n_layers`, containing tuples of tensors of shape `(batch_size, num_heads, + sequence_length, embed_size_per_head)`). Contains pre-computed hidden-states (key and values in the + attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer + plus the initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. GPT2Attentions weights after the attention softmax, used to compute the weighted average + in the self-attention heads. + """ + + loss: Optional[torch.FloatTensor] = None + logits: torch.FloatTensor = None + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + + +class TrajectoryTransformerPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = TrajectoryTransformerConfig + load_tf_weights = load_tf_weights_in_trajectory_transformer + base_model_prefix = "trajectory_transformer" + main_input_name = "trajectories" + supports_gradient_checkpointing = True + + def _init_weights(self, module): + if isinstance(module, (nn.Linear, nn.Embedding)): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + elif isinstance(module, EinLinear): + for i in range(module.n_models): + nn.init.kaiming_uniform_(module.weight[i], a=math.sqrt(5) / self.config.kaiming_initializer_range) + if module.bias is not None: + fan_in, _ = nn.init._calculate_fan_in_and_fan_out(module.weight[i]) + bound = (1 / math.sqrt(fan_in)) * self.config.initializer_range + nn.init.uniform_(module.bias[i], -bound, bound) + + +TRAJECTORY_TRANSFORMER_START_DOCSTRING = r""" + This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use + it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and + behavior. + + Parameters: + config ([`TrajectoryTransformerConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +TRAJECTORY_TRANSFORMER_INPUTS_DOCSTRING = r""" + Args: + trajectories (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Batch of trajectories, where a trajectory is a sequence of states, actions and rewards. + past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.n_layers`, *optional*): + Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see + `past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have + their past given to this model should not be passed as `input_ids` as they have already been computed. + targets (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Desired targets used to compute the loss. + attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class EinLinear(nn.Module): + def __init__(self, n_models, in_features, out_features, bias): + super().__init__() + self.n_models = n_models + self.out_features = out_features + self.in_features = in_features + self.weight = nn.Parameter(torch.Tensor(n_models, out_features, in_features)) + if bias: + self.bias = nn.Parameter(torch.Tensor(n_models, out_features)) + else: + self.register_parameter("bias", None) + + def reset_parameters(self): + for i in range(self.n_models): + nn.init.kaiming_uniform_(self.weight[i], a=math.sqrt(5)) + if self.bias is not None: + fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight[i]) + bound = 1 / math.sqrt(fan_in) + nn.init.uniform_(self.bias[i], -bound, bound) + + def forward(self, input): + """ + Args: + input (`torch.FloatTensor` of shape `(B, n_models, input_dim)`): + The input to the layer. + """ + # [ batch_size x n_models x output_dim ] + output = torch.einsum("eoi,bei->beo", self.weight, input) + if self.bias is not None: + raise RuntimeError() + return output + + +class CausalSelfAttention(nn.Module): + def __init__(self, config): + super().__init__() + + if config.n_embd % config.n_head != 0: + raise ValueError(f"n_head ({config.n_head}) should be a divisor of n_embd ({config.n_embd})") + + # key, query, value projections for all heads + self.key = nn.Linear(config.n_embd, config.n_embd) + self.query = nn.Linear(config.n_embd, config.n_embd) + self.value = nn.Linear(config.n_embd, config.n_embd) + + # regularization + self.attn_drop = nn.Dropout(config.attn_pdrop) + self.resid_drop = nn.Dropout(config.resid_pdrop) + + # output projection + self.proj = nn.Linear(config.n_embd, config.n_embd) + + # causal mask to ensure that attention is only applied to the left in the input sequence + self.register_buffer( + "mask", + torch.tril(torch.ones(config.block_size, config.block_size)).view( + 1, 1, config.block_size, config.block_size + ), + persistent=False, + ) + + # mask previous value estimates + joined_dim = config.observation_dim + config.action_dim + 2 + self.mask.squeeze()[:, joined_dim - 1 :: joined_dim] = 0 + + self.n_head = config.n_head + + def forward( + self, + hidden_states: Optional[Tuple[torch.FloatTensor]], + layer_past: Optional[Tuple[torch.Tensor]] = None, + use_cache: Optional[bool] = False, + output_attentions: Optional[bool] = False, + ): + batch_size, sequence_length, embedding_dim = hidden_states.size() + + # calculate query, key, values for all heads in batch and move head forward to be the batch dim + # [ batch_size x n_heads x sequence_length x head_dim ] + key = ( + self.key(hidden_states) + .view(batch_size, sequence_length, self.n_head, embedding_dim // self.n_head) + .transpose(1, 2) + ) + query = ( + self.query(hidden_states) + .view(batch_size, sequence_length, self.n_head, embedding_dim // self.n_head) + .transpose(1, 2) + ) + value = ( + self.value(hidden_states) + .view(batch_size, sequence_length, self.n_head, embedding_dim // self.n_head) + .transpose(1, 2) + ) + + if layer_past is not None: + past_key, past_value = layer_past + key = torch.cat((past_key, key), dim=-2) + value = torch.cat((past_value, value), dim=-2) + + if use_cache is True: + present = (key, value) + else: + present = None + + # causal self-attention + # [ batch_size x n_heads x sequence_length x sequence_length ] + attn_weights = (torch.matmul(query, key.transpose(-2, -1))) * (1.0 / math.sqrt(key.size(-1))) + attn_weights = attn_weights.masked_fill( + self.mask[:, :, :sequence_length, :sequence_length] == 0, torch.finfo(attn_weights.dtype).min + ) + attn_weights = F.softmax(attn_weights, dim=-1) + self._attn_map = attn_weights.clone() + attn_weights = self.attn_drop(attn_weights) + + output = torch.matmul(attn_weights, value) + # [ batch_size x sequence_length x embedding_dim ] + # re-assemble all head outputs side by side + output = output.transpose(1, 2).contiguous().view(batch_size, sequence_length, embedding_dim) + + # output projection + output = self.resid_drop(self.proj(output)) + + outputs = (output, present) + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +class Block(nn.Module): + def __init__(self, config): + super().__init__() + self.ln1 = nn.LayerNorm(config.n_embd) + self.ln2 = nn.LayerNorm(config.n_embd) + self.attn = CausalSelfAttention(config) + + # MLP + self.l1 = nn.Linear(config.n_embd, 4 * config.n_embd) + self.act = nn.GELU() + self.l2 = nn.Linear(4 * config.n_embd, config.n_embd) + self.drop = nn.Dropout(config.resid_pdrop) + + def forward( + self, + hidden_states: Optional[Tuple[torch.FloatTensor]], + layer_past: Optional[Tuple[torch.Tensor]] = None, + use_cache: Optional[bool] = False, + output_attentions: Optional[bool] = False, + ): + residual = hidden_states + hidden_states = self.ln1(hidden_states) + + attn_outputs = self.attn( + hidden_states, layer_past=layer_past, use_cache=use_cache, output_attentions=output_attentions + ) + attn_output = attn_outputs[0] + outputs = attn_outputs[1:] + hidden_states = attn_output + residual + + residual = hidden_states + hidden_states = self.ln2(hidden_states) + hidden_states = self.l1(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.l2(hidden_states) + hidden_states = residual + self.drop(hidden_states) + + if use_cache: + outputs = (hidden_states,) + outputs + else: + outputs = (hidden_states,) + outputs[1:] + + return outputs + + +@add_start_docstrings( + "The bare TrajectoryTransformer Model transformer outputting raw hidden-states without any specific head on top.", + TRAJECTORY_TRANSFORMER_START_DOCSTRING, +) +class TrajectoryTransformerModel(TrajectoryTransformerPreTrainedModel): + """the full GPT language model, with a context size of block_size""" + + def __init__(self, config): + super().__init__(config) + + # input embedding stem (+1 for stop token) + self.tok_emb = nn.Embedding(config.vocab_size * config.transition_dim + 1, config.n_embd) + + self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd)) + self.drop = nn.Dropout(config.embd_pdrop) + # transformer + self.blocks = nn.ModuleList([Block(config) for _ in range(config.n_layer)]) + # decoder head + self.ln_f = nn.LayerNorm(config.n_embd) + self.head = EinLinear(config.transition_dim, config.n_embd, config.vocab_size + 1, bias=False) + + self.vocab_size = config.vocab_size + self.stop_token = config.vocab_size * config.transition_dim + self.block_size = config.block_size + + self.observation_dim = config.observation_dim + self.action_dim = config.action_dim + self.transition_dim = config.transition_dim + self.embedding_dim = config.n_embd + + self.action_weight = config.action_weight + self.reward_weight = config.reward_weight + self.value_weight = config.value_weight + + self.gradient_checkpointing = False + + self.post_init() + + def get_block_size(self): + return self.block_size + + def offset_tokens(self, trajectories): + _, sequence_length = trajectories.shape + + n_states = int(np.ceil(sequence_length / self.transition_dim)) + + offsets = torch.arange(self.transition_dim) * self.vocab_size + offsets = offsets.repeat(n_states).to(trajectories.device) + + offset_trajectories = trajectories + offsets[:sequence_length] + offset_trajectories[trajectories == self.vocab_size] = self.stop_token + return offset_trajectories + + def pad_to_full_observation(self, hidden_states): + batch_size, sequence_length, _ = hidden_states.shape + + n_pad = (self.transition_dim - sequence_length % self.transition_dim) % self.transition_dim + padding = torch.zeros(batch_size, n_pad, self.embedding_dim, device=hidden_states.device) + + # [ batch_size x padded_sequence_length' x embedding_dim ] + hidden_states_pad = torch.cat([hidden_states, padding], dim=1) + hidden_states_pad = hidden_states_pad.view(-1, self.transition_dim, self.embedding_dim) + + return hidden_states_pad, n_pad + + @add_start_docstrings_to_model_forward( + TRAJECTORY_TRANSFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length") + ) + @replace_return_docstrings(output_type=TrajectoryTransformerOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + trajectories: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + targets: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor], TrajectoryTransformerOutput]: + r""" + Returns: + + Examples: + + ```python + >>> from transformers import TrajectoryTransformerModel + >>> import torch + + >>> model = TrajectoryTransformerModel.from_pretrained( + ... "CarlCochet/trajectory-transformer-halfcheetah-medium-v2" + ... ) + >>> model.to(device) + >>> model.eval() + + >>> observations_dim, action_dim, batch_size = 17, 6, 256 + >>> seq_length = observations_dim + action_dim + 1 + + >>> trajectories = torch.LongTensor([np.random.permutation(self.seq_length) for _ in range(batch_size)]).to( + ... device + ... ) + >>> targets = torch.LongTensor([np.random.permutation(self.seq_length) for _ in range(batch_size)]).to(device) + + >>> outputs = model( + ... trajectories, + ... targets=targets, + ... use_cache=True, + ... output_attentions=True, + ... output_hidden_states=True, + ... return_dict=True, + ... ) + ``` + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + + if past_key_values is None: + past_key_values = tuple([None] * len(self.blocks)) + + batch_size, sequence_length = trajectories.size() + + if sequence_length > self.block_size: + raise ValueError("Cannot forward, model block size is exhausted.") + + offset_trajectories = self.offset_tokens(trajectories) + # [ batch_size x sequence_length x embedding_dim ] + # forward the GPT model + token_embeddings = self.tok_emb(offset_trajectories) # each index maps to a (learnable) vector + position_embeddings = self.pos_emb[:, :sequence_length, :] # each position maps to a (learnable) vector + + hidden_states = self.drop(token_embeddings + position_embeddings) + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + presents = () if use_cache else None + all_self_attentions = () if output_attentions else None + all_hidden_states = () if output_hidden_states else None + + for i, (block, layer_past) in enumerate(zip(self.blocks, past_key_values)): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if self.gradient_checkpointing and self.training: + outputs = self._gradient_checkpointing_func( + block.__call__, + hidden_states, + layer_past, + use_cache, + output_attentions, + ) + else: + outputs = block(hidden_states, layer_past, use_cache, output_attentions) + + hidden_states = outputs[0] + if use_cache is True: + presents = presents + (outputs[1],) + + if output_attentions: + all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) + + # [ batch_size x sequence_length x embedding_dim ] + hidden_state = self.ln_f(hidden_states) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + hidden_states_pad, n_pad = self.pad_to_full_observation(hidden_state) + + logits = self.head(hidden_states_pad) + logits = logits.reshape(batch_size, sequence_length + n_pad, self.vocab_size + 1) + logits = logits[:, :sequence_length] + + # if we are given some desired targets also calculate the loss + if targets is not None: + loss = F.cross_entropy(logits.reshape(-1, logits.size(-1)), targets.view(-1), reduction="none") + if self.action_weight != 1 or self.reward_weight != 1 or self.value_weight != 1: + # make weights + n_states = int(np.ceil(sequence_length / self.transition_dim)) + weights = torch.cat( + [ + torch.ones(self.observation_dim, device=trajectories.device), + torch.ones(self.action_dim, device=trajectories.device) * self.action_weight, + torch.ones(1, device=trajectories.device) * self.reward_weight, + torch.ones(1, device=trajectories.device) * self.value_weight, + ] + ) + weights = weights.repeat(n_states) + weights = weights[1:].repeat(batch_size, 1) + loss = loss * weights.view(-1) + loss = (loss * attention_mask.view(-1)).mean() + else: + loss = None + + if not return_dict: + return tuple(v for v in [loss, logits, presents, all_hidden_states, all_self_attentions] if v is not None) + + return TrajectoryTransformerOutput( + loss=loss, + logits=logits, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/transfo_xl/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/transfo_xl/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f3674e19665ca74e1e6ee3ac92ca812e54580007 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/transfo_xl/__init__.py @@ -0,0 +1,97 @@ +# Copyright 2020 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available + + +_import_structure = { + "configuration_transfo_xl": ["TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP", "TransfoXLConfig"], + "tokenization_transfo_xl": ["TransfoXLCorpus", "TransfoXLTokenizer"], +} + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_transfo_xl"] = [ + "TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST", + "AdaptiveEmbedding", + "TransfoXLForSequenceClassification", + "TransfoXLLMHeadModel", + "TransfoXLModel", + "TransfoXLPreTrainedModel", + "load_tf_weights_in_transfo_xl", + ] + +try: + if not is_tf_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_tf_transfo_xl"] = [ + "TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFAdaptiveEmbedding", + "TFTransfoXLForSequenceClassification", + "TFTransfoXLLMHeadModel", + "TFTransfoXLMainLayer", + "TFTransfoXLModel", + "TFTransfoXLPreTrainedModel", + ] + + +if TYPE_CHECKING: + from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig + from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_transfo_xl import ( + TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST, + AdaptiveEmbedding, + TransfoXLForSequenceClassification, + TransfoXLLMHeadModel, + TransfoXLModel, + TransfoXLPreTrainedModel, + load_tf_weights_in_transfo_xl, + ) + + try: + if not is_tf_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_tf_transfo_xl import ( + TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST, + TFAdaptiveEmbedding, + TFTransfoXLForSequenceClassification, + TFTransfoXLLMHeadModel, + TFTransfoXLMainLayer, + TFTransfoXLModel, + TFTransfoXLPreTrainedModel, + ) + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/transfo_xl/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/transfo_xl/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..10b1d11d9d529abab9ffc3ed68250e26a17b1db0 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/transfo_xl/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/transfo_xl/__pycache__/configuration_transfo_xl.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/transfo_xl/__pycache__/configuration_transfo_xl.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b3ab31f68a40080bafe9d195f9c01dd45d281abd Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/transfo_xl/__pycache__/configuration_transfo_xl.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/transfo_xl/configuration_transfo_xl.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/transfo_xl/configuration_transfo_xl.py new file mode 100644 index 0000000000000000000000000000000000000000..50bf94ae7ea3983d40dcf3d03c2e4e1027c23c80 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/transfo_xl/configuration_transfo_xl.py @@ -0,0 +1,189 @@ +# coding=utf-8 +# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Transformer XL configuration""" + +from ....configuration_utils import PretrainedConfig +from ....utils import logging + + +logger = logging.get_logger(__name__) + + +from .._archive_maps import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class TransfoXLConfig(PretrainedConfig): + """ + This is the configuration class to store the configuration of a [`TransfoXLModel`] or a [`TFTransfoXLModel`]. It is + used to instantiate a Transformer-XL model according to the specified arguments, defining the model architecture. + Instantiating a configuration with the defaults will yield a similar configuration to that of the TransfoXL + [transfo-xl/transfo-xl-wt103](https://huggingface.co/transfo-xl/transfo-xl-wt103) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + vocab_size (`int`, *optional*, defaults to 267735): + Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`TransfoXLModel`] or [`TFTransfoXLModel`]. + cutoffs (`List[int]`, *optional*, defaults to `[20000, 40000, 200000]`): + Cutoffs for the adaptive softmax. + d_model (`int`, *optional*, defaults to 1024): + Dimensionality of the model's hidden states. + d_embed (`int`, *optional*, defaults to 1024): + Dimensionality of the embeddings + n_head (`int`, *optional*, defaults to 16): + Number of attention heads for each attention layer in the Transformer encoder. + d_head (`int`, *optional*, defaults to 64): + Dimensionality of the model's heads. + d_inner (`int`, *optional*, defaults to 4096): + Inner dimension in FF + div_val (`int`, *optional*, defaults to 4): + Divident value for adapative input and softmax + pre_lnorm (`boolean`, *optional*, defaults to `False`): + Whether or not to apply LayerNorm to the input instead of the output in the blocks. + n_layer (`int`, *optional*, defaults to 18): + Number of hidden layers in the Transformer encoder. + mem_len (`int`, *optional*, defaults to 1600): + Length of the retained previous heads. + clamp_len (`int`, *optional*, defaults to 1000): + Use the same pos embeddings after clamp_len. + same_length (`boolean`, *optional*, defaults to `True`): + Whether or not to use the same attn length for all tokens + proj_share_all_but_first (`boolean`, *optional*, defaults to `True`): + True to share all but first projs, False not to share. + attn_type (`int`, *optional*, defaults to 0): + Attention type. 0 for Transformer-XL, 1 for Shaw et al, 2 for Vaswani et al, 3 for Al Rfou et al. + sample_softmax (`int`, *optional*, defaults to -1): + Number of samples in the sampled softmax. + adaptive (`boolean`, *optional*, defaults to `True`): + Whether or not to use adaptive softmax. + dropout (`float`, *optional*, defaults to 0.1): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + dropatt (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + untie_r (`boolean`, *optional*, defaults to `True`): + Whether ot not to untie relative position biases. + init (`str`, *optional*, defaults to `"normal"`): + Parameter initializer to use. + init_range (`float`, *optional*, defaults to 0.01): + Parameters initialized by U(-init_range, init_range). + proj_init_std (`float`, *optional*, defaults to 0.01): + Parameters initialized by N(0, init_std) + init_std (`float`, *optional*, defaults to 0.02): + Parameters initialized by N(0, init_std) + layer_norm_epsilon (`float`, *optional*, defaults to 1e-05): + The epsilon to use in the layer normalization layers + eos_token_id (`int`, *optional*, defaults to 0): + End of stream token id. + + Examples: + + ```python + >>> from transformers import TransfoXLConfig, TransfoXLModel + + >>> # Initializing a Transformer XL configuration + >>> configuration = TransfoXLConfig() + + >>> # Initializing a model (with random weights) from the configuration + >>> model = TransfoXLModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "transfo-xl" + keys_to_ignore_at_inference = ["mems"] + attribute_map = { + "n_token": "vocab_size", + "hidden_size": "d_model", + "num_attention_heads": "n_head", + "num_hidden_layers": "n_layer", + } + + def __init__( + self, + vocab_size=267735, + cutoffs=[20000, 40000, 200000], + d_model=1024, + d_embed=1024, + n_head=16, + d_head=64, + d_inner=4096, + div_val=4, + pre_lnorm=False, + n_layer=18, + mem_len=1600, + clamp_len=1000, + same_length=True, + proj_share_all_but_first=True, + attn_type=0, + sample_softmax=-1, + adaptive=True, + dropout=0.1, + dropatt=0.0, + untie_r=True, + init="normal", + init_range=0.01, + proj_init_std=0.01, + init_std=0.02, + layer_norm_epsilon=1e-5, + eos_token_id=0, + **kwargs, + ): + self.vocab_size = vocab_size + self.cutoffs = [] + self.cutoffs.extend(cutoffs) + if proj_share_all_but_first: + self.tie_projs = [False] + [True] * len(self.cutoffs) + else: + self.tie_projs = [False] + [False] * len(self.cutoffs) + self.d_model = d_model + self.d_embed = d_embed + self.d_head = d_head + self.d_inner = d_inner + self.div_val = div_val + self.pre_lnorm = pre_lnorm + self.n_layer = n_layer + self.n_head = n_head + self.mem_len = mem_len + self.same_length = same_length + self.attn_type = attn_type + self.clamp_len = clamp_len + self.sample_softmax = sample_softmax + self.adaptive = adaptive + self.dropout = dropout + self.dropatt = dropatt + self.untie_r = untie_r + self.init = init + self.init_range = init_range + self.proj_init_std = proj_init_std + self.init_std = init_std + self.layer_norm_epsilon = layer_norm_epsilon + super().__init__(eos_token_id=eos_token_id, **kwargs) + + @property + def max_position_embeddings(self): + # Message copied from Transformer-XL documentation + logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit.") + return -1 + + @max_position_embeddings.setter + def max_position_embeddings(self, value): + # Message copied from Transformer-XL documentation + raise NotImplementedError( + f"The model {self.model_type} is one of the few models that has no sequence length limit." + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl_utilities.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl_utilities.py new file mode 100644 index 0000000000000000000000000000000000000000..ed1488d5595cb8f36eb540992fb4ca46534a60fb --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl_utilities.py @@ -0,0 +1,179 @@ +# coding=utf-8 +# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" + A TF 2.0 Adaptive Softmax for Transformer XL model. +""" + + +import tensorflow as tf + +from ....modeling_tf_utils import keras +from ....tf_utils import shape_list + + +class TFAdaptiveSoftmaxMask(keras.layers.Layer): + def __init__(self, vocab_size, d_embed, d_proj, cutoffs, div_val=1, keep_order=False, **kwargs): + super().__init__(**kwargs) + + self.vocab_size = vocab_size + self.d_embed = d_embed + self.d_proj = d_proj + + self.cutoffs = cutoffs + [vocab_size] + self.cutoff_ends = [0] + self.cutoffs + self.div_val = div_val + + self.shortlist_size = self.cutoffs[0] + self.n_clusters = len(self.cutoffs) - 1 + self.head_size = self.shortlist_size + self.n_clusters + self.keep_order = keep_order + + self.out_layers = [] + self.out_projs = [] + + def build(self, input_shape): + if self.n_clusters > 0: + self.cluster_weight = self.add_weight( + shape=(self.n_clusters, self.d_embed), initializer="zeros", trainable=True, name="cluster_weight" + ) + self.cluster_bias = self.add_weight( + shape=(self.n_clusters,), initializer="zeros", trainable=True, name="cluster_bias" + ) + + if self.div_val == 1: + for i in range(len(self.cutoffs)): + if self.d_proj != self.d_embed: + weight = self.add_weight( + shape=(self.d_embed, self.d_proj), + initializer="zeros", + trainable=True, + name=f"out_projs_._{i}", + ) + self.out_projs.append(weight) + else: + self.out_projs.append(None) + weight = self.add_weight( + shape=(self.vocab_size, self.d_embed), + initializer="zeros", + trainable=True, + name=f"out_layers_._{i}_._weight", + ) + bias = self.add_weight( + shape=(self.vocab_size,), + initializer="zeros", + trainable=True, + name=f"out_layers_._{i}_._bias", + ) + self.out_layers.append((weight, bias)) + else: + for i in range(len(self.cutoffs)): + l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] + d_emb_i = self.d_embed // (self.div_val**i) + + weight = self.add_weight( + shape=(d_emb_i, self.d_proj), initializer="zeros", trainable=True, name=f"out_projs_._{i}" + ) + self.out_projs.append(weight) + weight = self.add_weight( + shape=(r_idx - l_idx, d_emb_i), + initializer="zeros", + trainable=True, + name=f"out_layers_._{i}_._weight", + ) + bias = self.add_weight( + shape=(r_idx - l_idx,), + initializer="zeros", + trainable=True, + name=f"out_layers_._{i}_._bias", + ) + self.out_layers.append((weight, bias)) + super().build(input_shape) + + @staticmethod + def _logit(x, W, b, proj=None): + y = x + if proj is not None: + y = tf.einsum("ibd,ed->ibe", y, proj) + return tf.einsum("ibd,nd->ibn", y, W) + b + + @staticmethod + def _gather_logprob(logprob, target): + lp_size = shape_list(logprob) + r = tf.range(lp_size[0], dtype=target.dtype) + idx = tf.stack([r, target], 1) + return tf.gather_nd(logprob, idx) + + def call(self, hidden, target, return_mean=True, training=False): + head_logprob = 0 + if self.n_clusters == 0: + output = self._logit(hidden, self.out_layers[0][0], self.out_layers[0][1], self.out_projs[0]) + if target is not None: + loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=target, logits=output) + out = tf.nn.log_softmax(output, axis=-1) + else: + hidden_sizes = shape_list(hidden) + out = [] + loss = tf.zeros(hidden_sizes[:2]) + for i in range(len(self.cutoffs)): + l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] + if target is not None: + mask = (target >= l_idx) & (target < r_idx) + mask_idx = tf.where(mask) + cur_target = tf.boolean_mask(target, mask) - l_idx + + if self.div_val == 1: + cur_W = self.out_layers[0][0][l_idx:r_idx] + cur_b = self.out_layers[0][1][l_idx:r_idx] + else: + cur_W = self.out_layers[i][0] + cur_b = self.out_layers[i][1] + + if i == 0: + cur_W = tf.concat([cur_W, self.cluster_weight], 0) + cur_b = tf.concat([cur_b, self.cluster_bias], 0) + + head_logit = self._logit(hidden, cur_W, cur_b, self.out_projs[0]) + head_logprob = tf.nn.log_softmax(head_logit) + out.append(head_logprob[..., : self.cutoffs[0]]) + if target is not None: + cur_head_logprob = tf.boolean_mask(head_logprob, mask) + cur_logprob = self._gather_logprob(cur_head_logprob, cur_target) + else: + tail_logit = self._logit(hidden, cur_W, cur_b, self.out_projs[i]) + tail_logprob = tf.nn.log_softmax(tail_logit) + cluster_prob_idx = self.cutoffs[0] + i - 1 # No probability for the head cluster + logprob_i = head_logprob[..., cluster_prob_idx, None] + tail_logprob + out.append(logprob_i) + if target is not None: + cur_head_logprob = tf.boolean_mask(head_logprob, mask) + cur_tail_logprob = tf.boolean_mask(tail_logprob, mask) + cur_logprob = self._gather_logprob(cur_tail_logprob, cur_target) + cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1] + if target is not None: + loss += tf.scatter_nd(mask_idx, -cur_logprob, shape_list(loss)) + out = tf.concat(out, axis=-1) + + if target is not None: + if return_mean: + loss = tf.reduce_mean(loss) + # Add the training-time loss value to the layer using `self.add_loss()`. + self.add_loss(loss) + + # Log the loss as a metric (we could log arbitrary metrics, + # including different metrics for training and inference. + self.add_metric(loss, name=self.name, aggregation="mean" if return_mean else "") + + return out diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py new file mode 100644 index 0000000000000000000000000000000000000000..7290a7a83b85660069bc2b88e4ba2734114f3f9b --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py @@ -0,0 +1,819 @@ +# coding=utf-8 +# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" + Tokenization classes for Transformer XL model. Adapted from https://github.com/kimiyoung/transformer-xl. +""" + + +import glob +import os +import pickle +import re +from collections import Counter, OrderedDict +from typing import List, Optional, Tuple + +import numpy as np + +from ....tokenization_utils import PreTrainedTokenizer +from ....utils import ( + cached_file, + is_sacremoses_available, + is_torch_available, + logging, + requires_backends, + strtobool, + torch_only_method, +) + + +if is_sacremoses_available(): + import sacremoses as sm + + +if is_torch_available(): + import torch + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = { + "pretrained_vocab_file": "vocab.pkl", + "pretrained_vocab_file_torch": "vocab.bin", + "vocab_file": "vocab.txt", +} + + +PRETRAINED_CORPUS_ARCHIVE_MAP = { + "transfo-xl/transfo-xl-wt103": "https://huggingface.co/transfo-xl/transfo-xl-wt103/resolve/main/corpus.bin", +} +CORPUS_NAME = "corpus.bin" + +MATCH_NUMBERS = r"(?<=\d)[,.](?=\d)", r" @\g<0>@ " +DETOKENIZE_NUMBERS = [(r" @\,@ ", r","), (r" @\.@ ", r".")] + + +def tokenize_numbers(text_array: List[str]) -> List[str]: + """ + Splits large comma-separated numbers and floating point values. This is done by replacing commas with ' @,@ ' and + dots with ' @.@ '. + + Args: + text_array: An already tokenized text as list. + + Returns: + A list of strings with tokenized numbers. + + Example: + + ```python + >>> tokenize_numbers(["$", "5,000", "1.73", "m"]) + ['$', '5', '@,@', '000', '1', '@.@', '73', 'm'] + ```""" + tokenized = [] + for i in range(len(text_array)): + reg, sub = MATCH_NUMBERS + replaced = re.sub(reg, sub, text_array[i]).split() + tokenized.extend(replaced) + + return tokenized + + +def detokenize_numbers(text: str) -> str: + """ + Inverts the operation of *tokenize_numbers*. This is replacing ' @,@ ' and ' @.@' by ',' and '.'. + + Args: + text: A string where the number should be detokenized. + + Returns: + A detokenized string. + + Example: + + ```python + >>> detokenize_numbers("$ 5 @,@ 000 1 @.@ 73 m") + '$ 5,000 1.73 m' + ```""" + for reg, sub in DETOKENIZE_NUMBERS: + text = re.sub(reg, sub, text) + return text + + +class TransfoXLTokenizer(PreTrainedTokenizer): + """ + Construct a Transformer-XL tokenizer adapted from Vocab class in [the original + code](https://github.com/kimiyoung/transformer-xl). The Transformer-XL tokenizer is a word-level tokenizer (no + sub-word tokenization). + + This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to + this superclass for more information regarding those methods. + + Args: + special (`List[str]`, *optional*): + A list of special tokens (to be treated by the original implementation of this tokenizer). + min_freq (`int`, *optional*, defaults to 0): + The minimum number of times a token has to be present in order to be kept in the vocabulary (otherwise it + will be mapped to `unk_token`). + max_size (`int`, *optional*): + The maximum size of the vocabulary. If left unset, it will default to the size of the vocabulary found + after excluding the tokens according to the `min_freq` rule. + lower_case (`bool`, *optional*, defaults to `False`): + Whether or not to lowercase the input when tokenizing. + delimiter (`str`, *optional*): + The delimiter used between tokens. + vocab_file (`str`, *optional*): + File containing the vocabulary (from the original implementation). + pretrained_vocab_file (`str`, *optional*): + File containing the vocabulary as saved with the `save_pretrained()` method. + never_split (`List[str]`, *optional*): + List of tokens that should never be split. If no list is specified, will simply use the existing special + tokens. + unk_token (`str`, *optional*, defaults to `""`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + eos_token (`str`, *optional*, defaults to `""`): + The end of sequence token. + additional_special_tokens (`List[str]`, *optional*, defaults to `['']`): + A list of additional special tokens (for the HuggingFace functionality). + language (`str`, *optional*, defaults to `"en"`): + The language of this tokenizer (used for mose preprocessing). + """ + + vocab_files_names = VOCAB_FILES_NAMES + model_input_names = ["input_ids"] + + def __init__( + self, + special=None, + min_freq=0, + max_size=None, + lower_case=False, + delimiter=None, + vocab_file=None, + pretrained_vocab_file: str = None, + never_split=None, + unk_token="", + eos_token="", + additional_special_tokens=[""], + language="en", + **kwargs, + ): + logger.error( + "`TransfoXL` was deprecated due to security issues linked to `pickle.load` in `TransfoXLTokenizer`. " + "See more details on this model's documentation page: " + "`https://github.com/huggingface/transformers/blob/main/docs/source/en/model_doc/transfo-xl.md`." + ) + + requires_backends(self, "sacremoses") + if special is None: + special = [] + self.counter = Counter() + self.special = special + self.min_freq = min_freq + self.max_size = max_size + self.lower_case = lower_case + self.delimiter = delimiter + self.vocab_file = vocab_file + self.punctuation_symbols = '!"#$%&()*+,-./\\:;<=>?@[\\]^_`{|}~' + self.punction_without_space_before_pattern = re.compile(rf"[^\s][{self.punctuation_symbols}]") + self.punctuation_with_space_around_pattern = self._compile_space_around_punctuation_pattern() + self.language = language + self.moses_punct_normalizer = sm.MosesPunctNormalizer(language) + self.moses_tokenizer = sm.MosesTokenizer(language) + self.moses_detokenizer = sm.MosesDetokenizer(language) + self.idx2sym = [] + self.sym2idx = OrderedDict() + # This try... catch... is not beautiful but honestly this tokenizer was not made to be used + # in a library like ours, at all. + try: + vocab_dict = None + if pretrained_vocab_file is not None: + # Priority on pickle files (support PyTorch and TF) + if not strtobool(os.environ.get("TRUST_REMOTE_CODE", "False")): + raise ValueError( + "This part uses `pickle.load` which is insecure and will execute arbitrary code that is " + "potentially malicious. It's recommended to never unpickle data that could have come from an " + "untrusted source, or that could have been tampered with. If you already verified the pickle " + "data and decided to use it, you can set the environment variable " + "`TRUST_REMOTE_CODE` to `True` to allow it." + ) + with open(pretrained_vocab_file, "rb") as f: + vocab_dict = pickle.load(f) + + # Loading a torch-saved transfo-xl vocab dict with pickle results in an integer + # Entering this if statement means that we tried to load a torch-saved file with pickle, and we failed. + # We therefore load it with torch, if it's available. + if isinstance(vocab_dict, int): + if not is_torch_available(): + raise ImportError( + "Not trying to load dict with PyTorch as you need to install pytorch to load " + "from a PyTorch pretrained vocabulary, " + "or activate it with environment variables USE_TORCH=1 and USE_TF=0." + ) + vocab_dict = torch.load(pretrained_vocab_file) + + if vocab_dict is not None: + for key, value in vocab_dict.items(): + if key not in self.__dict__ or key in ["sym2idx", "idx2sym"]: + self.__dict__[key] = value + elif vocab_file is not None: + self.build_vocab() + + except Exception as e: + raise ValueError( + f"Unable to parse file {pretrained_vocab_file}. Unknown format. " + "If you tried to load a model saved through TransfoXLTokenizerFast, " + "please note they are not compatible." + ) from e + + if vocab_file is not None: + self.build_vocab() + + super().__init__( + special=special, + min_freq=min_freq, + max_size=max_size, + lower_case=lower_case, + delimiter=delimiter, + vocab_file=vocab_file, + pretrained_vocab_file=pretrained_vocab_file, + never_split=never_split, + unk_token=unk_token, + eos_token=eos_token, + additional_special_tokens=additional_special_tokens, + language=language, + **kwargs, + ) + + # these are not required to initialize the parent class as only used when tokenizing. + if never_split is None: + never_split = self.all_special_tokens + self.never_split = never_split + + @property + def do_lower_case(self): + return self.lower_case + + def _compile_space_around_punctuation_pattern(self): + look_ahead_for_special_token = f"(?=[{self.punctuation_symbols}])" + look_ahead_to_match_all_except_space = r"(?=[^\s])" + return re.compile(r"" + look_ahead_for_special_token + look_ahead_to_match_all_except_space) + + def count_file(self, path, verbose=False, add_eos=False): + if verbose: + logger.info(f"counting file {path} ...") + assert os.path.exists(path), f"Input file {path} not found" + + sents = [] + with open(path, "r", encoding="utf-8") as f: + for idx, line in enumerate(f): + if verbose and idx > 0 and idx % 500000 == 0: + logger.info(f" line {idx}") + symbols = self.tokenize(line, add_eos=add_eos) + self.counter.update(symbols) + sents.append(symbols) + + return sents + + def count_sents(self, sents, verbose=False): + """ + sents : a list of sentences, each a list of tokenized symbols + """ + if verbose: + logger.info(f"counting {len(sents)} sents ...") + for idx, symbols in enumerate(sents): + if verbose and idx > 0 and idx % 500000 == 0: + logger.info(f" line {idx}") + self.counter.update(symbols) + + def _build_from_file(self, vocab_file): + self.idx2sym = [] + self.sym2idx = OrderedDict() + + with open(vocab_file, "r", encoding="utf-8") as f: + for line in f: + symb = line.strip().split()[0] + self.add_symbol(symb) + if "" in self.sym2idx: + self.unk_idx = self.sym2idx[""] + elif "" in self.sym2idx: + self.unk_idx = self.sym2idx[""] + else: + raise ValueError("Token not in vocabulary and no token in vocabulary for replacement.") + + def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: + if os.path.isdir(save_directory): + vocab_file = os.path.join( + save_directory, + (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["pretrained_vocab_file"], + ) + else: + vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory + with open(vocab_file, "wb") as f: + pickle.dump(self.__dict__, f) + return (vocab_file,) + + def build_vocab(self): + if self.vocab_file: + logger.info(f"building vocab from {self.vocab_file}") + self._build_from_file(self.vocab_file) + logger.info(f"Final vocab size {len(self.sym2idx)}") + else: + logger.info(f"building vocab with min_freq={self.min_freq}, max_size={self.max_size}") + self.idx2sym = [] + self.sym2idx = OrderedDict() + + for sym in self.special: + self.add_special(sym) + + for sym, cnt in self.counter.most_common(self.max_size): + if cnt < self.min_freq: + break + self.add_symbol(sym) + + logger.info(f"Final vocab size {len(self.sym2idx)} from {len(self.counter)} unique tokens") + + @torch_only_method + def encode_file(self, path, ordered=False, verbose=False, add_eos=True, add_double_eos=False): + if verbose: + logger.info(f"encoding file {path} ...") + assert os.path.exists(path), f"Output file {path} not found" + encoded = [] + with open(path, "r", encoding="utf-8") as f: + for idx, line in enumerate(f): + if verbose and idx > 0 and idx % 500000 == 0: + logger.info(f" line {idx}") + symbols = self.tokenize(line, add_eos=add_eos, add_double_eos=add_double_eos) + encoded.append(self.convert_to_tensor(symbols)) + + if ordered: + encoded = torch.cat(encoded) + + return encoded + + @torch_only_method + def encode_sents(self, sents, ordered=False, verbose=False): + if verbose: + logger.info(f"encoding {len(sents)} sents ...") + encoded = [] + for idx, symbols in enumerate(sents): + if verbose and idx > 0 and idx % 500000 == 0: + logger.info(f" line {idx}") + encoded.append(self.convert_to_tensor(symbols)) + + if ordered: + encoded = torch.cat(encoded) + + return encoded + + def add_special(self, sym): + if sym not in self.sym2idx: + self.idx2sym.append(sym) + self.sym2idx[sym] = len(self.idx2sym) - 1 + setattr(self, f"{sym.strip('<>')}_idx", self.sym2idx[sym]) + + def add_symbol(self, sym): + if sym not in self.sym2idx: + self.idx2sym.append(sym) + self.sym2idx[sym] = len(self.idx2sym) - 1 + + def move_added_token(self, token: str, target_idx: int): + """ + Moves an added token to a specific position in the vocab. This method should be used when resizing an embedding + layer other than the last one in the `AdaptiveEmbedding` in order to move the token in the tokenizer from the + default position (at the very end) to the desired one. + + Args: + token: The token to move to a specific position in the vocab. + target_idx: The position where the token should be moved to. + """ + assert token in self.added_tokens_encoder, "Token which should be moved has to be an added token" + assert token not in self.idx2sym, "Token which should be moved is already in vocab" + + # Insert sym into vocab + self.idx2sym.insert(target_idx, token) + self.sym2idx[token] = target_idx + + # Shift following indices in sym2idx + for idx in range(target_idx + 1, len(self.idx2sym)): + current_sym = self.idx2sym[idx] + self.sym2idx[current_sym] = idx + + # Delete token from added_tokens + old_index = self._added_tokens_encoder.pop(token) + self._added_tokens_decoder.pop(old_index) + + def moses_punct_norm(self, text): + return self.moses_punct_normalizer.normalize(text) + + def moses_tokenize(self, text): + return self.moses_tokenizer.tokenize( + text, aggressive_dash_splits=True, return_str=False, escape=False, protected_patterns=self.never_split + ) + + def moses_pipeline(self, text: str) -> List[str]: + """ + Does basic tokenization using [`sacremoses.MosesPunctNormalizer`] and [`sacremoses.MosesTokenizer`] with + *aggressive_dash_splits=True* (see [`sacremoses.tokenize.MosesTokenizer.tokenize`]). Additionally, large + comma-separated numbers and floating point values are split. E.g. "23,000 people are 1.80m tall" -> "23 @,@ 000 + people are 1 @.@ 80m tall" + + Args: + text: Text to be tokenize + + Returns: + A list of tokenized string + + Example: + + ```python + >>> tokenizer = TransfoXLTokenizer.from_pretrained("transfo-xl/transfo-xl-wt103") + >>> tokenizer.moses_pipeline("23,000 people are 1.80 m tall") + ['23', '@,@', '000', 'people', 'are', '1', '@.@', '80', 'm', 'tall'] + ```""" + text = self.moses_punct_norm(text) + text = self.moses_tokenize(text) + text = tokenize_numbers(text) + return text + + def _convert_id_to_token(self, idx): + """Converts an id in a token (BPE) using the vocab.""" + assert 0 <= idx < len(self), f"Index {idx} out of vocabulary range" + return self.idx2sym[idx] + + def _convert_token_to_id(self, sym): + """Converts a token (str) in an id using the vocab.""" + if sym in self.sym2idx: + return self.sym2idx[sym] + else: + # logger.info(f'encounter unk {sym}') + # assert '' not in sym + if hasattr(self, "unk_idx"): + return self.sym2idx.get(sym, self.unk_idx) + # Backward compatibility with pre-trained models + elif "" in self.sym2idx: + return self.sym2idx[""] + elif "" in self.sym2idx: + return self.sym2idx[""] + else: + raise ValueError("Token not in vocabulary and no token in vocabulary for replacement.") + + def convert_tokens_to_string(self, tokens): + """ + Converts a sequence of tokens (string) in a single string. Additionally, the split numbers are converted back + into it's original form. + """ + out_string = self.moses_detokenizer.detokenize(tokens) + return detokenize_numbers(out_string).strip() + + @torch_only_method + def convert_to_tensor(self, symbols): + return torch.LongTensor(self.convert_tokens_to_ids(symbols)) + + @property + def vocab_size(self): + return len(self.idx2sym) + + def get_vocab(self): + vocab = self.sym2idx.copy() + vocab.update(self.added_tokens_encoder) + return vocab + + def _tokenize(self, line, add_eos=False, add_double_eos=False): + line = line.strip() + # convert to lower case + if self.lower_case: + line = line.lower() + + # empty delimiter '' will evaluate False + if self.delimiter == "": + symbols = line + else: + symbols = self.moses_pipeline(line) + + if add_double_eos: # lm1b + return [""] + symbols + [""] + elif add_eos: + return symbols + [""] + else: + return symbols + + +class LMOrderedIterator(object): + def __init__(self, data, bsz, bptt, device="cpu", ext_len=None): + """ + data -- LongTensor -- the LongTensor is strictly ordered + """ + self.bsz = bsz + self.bptt = bptt + self.ext_len = ext_len if ext_len is not None else 0 + + self.device = device + + # Work out how cleanly we can divide the dataset into bsz parts. + self.n_step = data.size(0) // bsz + + # Trim off any extra elements that wouldn't cleanly fit (remainders). + data = data.narrow(0, 0, self.n_step * bsz) + + # Evenly divide the data across the bsz batches. + self.data = data.view(bsz, -1).t().contiguous().to(device) + + # Number of mini-batches + self.n_batch = (self.n_step + self.bptt - 1) // self.bptt + + def get_batch(self, i, bptt=None): + if bptt is None: + bptt = self.bptt + seq_len = min(bptt, self.data.size(0) - 1 - i) + + end_idx = i + seq_len + beg_idx = max(0, i - self.ext_len) + + data = self.data[beg_idx:end_idx] + target = self.data[i + 1 : i + 1 + seq_len] + + data_out = data.transpose(0, 1).contiguous().to(self.device) + target_out = target.transpose(0, 1).contiguous().to(self.device) + + return data_out, target_out, seq_len + + def get_fixlen_iter(self, start=0): + for i in range(start, self.data.size(0) - 1, self.bptt): + yield self.get_batch(i) + + def get_varlen_iter(self, start=0, std=5, min_len=5, max_deviation=3): + max_len = self.bptt + max_deviation * std + i = start + while True: + bptt = self.bptt if np.random.random() < 0.95 else self.bptt / 2.0 + bptt = min(max_len, max(min_len, int(np.random.normal(bptt, std)))) + data, target, seq_len = self.get_batch(i, bptt) + i += seq_len + yield data, target, seq_len + if i >= self.data.size(0) - 2: + break + + def __iter__(self): + return self.get_fixlen_iter() + + +class LMShuffledIterator(object): + def __init__(self, data, bsz, bptt, device="cpu", ext_len=None, shuffle=False): + """ + data -- list[LongTensor] -- there is no order among the LongTensors + """ + self.data = data + + self.bsz = bsz + self.bptt = bptt + self.ext_len = ext_len if ext_len is not None else 0 + + self.device = device + self.shuffle = shuffle + + def get_sent_stream(self): + # index iterator + epoch_indices = np.random.permutation(len(self.data)) if self.shuffle else np.array(range(len(self.data))) + + # sentence iterator + for idx in epoch_indices: + yield self.data[idx] + + @torch_only_method + def stream_iterator(self, sent_stream): + # streams for each data in the batch + streams = [None] * self.bsz + + data = torch.LongTensor(self.bptt, self.bsz) + target = torch.LongTensor(self.bptt, self.bsz) + + n_retain = 0 + + while True: + # data : [n_retain+bptt x bsz] + # target : [bptt x bsz] + data[n_retain:].fill_(-1) + target.fill_(-1) + + valid_batch = True + + for i in range(self.bsz): + n_filled = 0 + try: + while n_filled < self.bptt: + if streams[i] is None or len(streams[i]) <= 1: + streams[i] = next(sent_stream) + # number of new tokens to fill in + n_new = min(len(streams[i]) - 1, self.bptt - n_filled) + # first n_retain tokens are retained from last batch + data[n_retain + n_filled : n_retain + n_filled + n_new, i] = streams[i][:n_new] + target[n_filled : n_filled + n_new, i] = streams[i][1 : n_new + 1] + streams[i] = streams[i][n_new:] + n_filled += n_new + except StopIteration: + valid_batch = False + break + + if not valid_batch: + return + + data_out = data.transpose(0, 1).contiguous().to(self.device) + target_out = target.transpose(0, 1).contiguous().to(self.device) + + yield data_out, target_out, self.bptt + + n_retain = min(data.size(0), self.ext_len) + if n_retain > 0: + data[:n_retain] = data[-n_retain:] + data.resize_(n_retain + self.bptt, data.size(1)) + + def __iter__(self): + # sent_stream is an iterator + sent_stream = self.get_sent_stream() + + for batch in self.stream_iterator(sent_stream): + yield batch + + +class LMMultiFileIterator(LMShuffledIterator): + def __init__(self, paths, vocab, bsz, bptt, device="cpu", ext_len=None, shuffle=False): + self.paths = paths + self.vocab = vocab + + self.bsz = bsz + self.bptt = bptt + self.ext_len = ext_len if ext_len is not None else 0 + + self.device = device + self.shuffle = shuffle + + def get_sent_stream(self, path): + sents = self.vocab.encode_file(path, add_double_eos=True) + if self.shuffle: + np.random.shuffle(sents) + sent_stream = iter(sents) + + return sent_stream + + def __iter__(self): + if self.shuffle: + np.random.shuffle(self.paths) + + for path in self.paths: + # sent_stream is an iterator + sent_stream = self.get_sent_stream(path) + for batch in self.stream_iterator(sent_stream): + yield batch + + +class TransfoXLCorpus(object): + @classmethod + @torch_only_method + def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs): + """ + Instantiate a pre-processed corpus. + """ + vocab = TransfoXLTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) + is_local = os.path.isdir(pretrained_model_name_or_path) + # redirect to the cache, if necessary + try: + resolved_corpus_file = cached_file(pretrained_model_name_or_path, CORPUS_NAME, cache_dir=cache_dir) + except EnvironmentError: + logger.error( + f"Corpus '{pretrained_model_name_or_path}' was not found in corpus list" + f" ({', '.join(PRETRAINED_CORPUS_ARCHIVE_MAP.keys())}. We assumed '{pretrained_model_name_or_path}'" + f" was a path or url but couldn't find files {CORPUS_NAME} at this path or url." + ) + return None + if is_local: + logger.info(f"loading corpus file {resolved_corpus_file}") + else: + logger.info(f"loading corpus file {CORPUS_NAME} from cache at {resolved_corpus_file}") + + # Instantiate tokenizer. + corpus = cls(*inputs, **kwargs) + corpus_dict = torch.load(resolved_corpus_file) + for key, value in corpus_dict.items(): + corpus.__dict__[key] = value + corpus.vocab = vocab + if corpus.train is not None: + corpus.train = torch.tensor(corpus.train, dtype=torch.long) + if corpus.valid is not None: + corpus.valid = torch.tensor(corpus.valid, dtype=torch.long) + if corpus.test is not None: + corpus.test = torch.tensor(corpus.test, dtype=torch.long) + return corpus + + def __init__(self, *args, **kwargs): + self.vocab = TransfoXLTokenizer(*args, **kwargs) + self.dataset = None + self.train = None + self.valid = None + self.test = None + + def build_corpus(self, path, dataset): + self.dataset = dataset + + if self.dataset in ["ptb", "wt2", "enwik8", "text8"]: + self.vocab.count_file(os.path.join(path, "train.txt")) + self.vocab.count_file(os.path.join(path, "valid.txt")) + self.vocab.count_file(os.path.join(path, "test.txt")) + elif self.dataset == "wt103": + self.vocab.count_file(os.path.join(path, "train.txt")) + elif self.dataset == "lm1b": + train_path_pattern = os.path.join( + path, + "1-billion-word-language-modeling-benchmark-r13output", + "training-monolingual.tokenized.shuffled", + "news.en-*", + ) + train_paths = glob.glob(train_path_pattern) + # the vocab will load from file when build_vocab() is called + + self.vocab.build_vocab() + + if self.dataset in ["ptb", "wt2", "wt103"]: + self.train = self.vocab.encode_file(os.path.join(path, "train.txt"), ordered=True) + self.valid = self.vocab.encode_file(os.path.join(path, "valid.txt"), ordered=True) + self.test = self.vocab.encode_file(os.path.join(path, "test.txt"), ordered=True) + elif self.dataset in ["enwik8", "text8"]: + self.train = self.vocab.encode_file(os.path.join(path, "train.txt"), ordered=True, add_eos=False) + self.valid = self.vocab.encode_file(os.path.join(path, "valid.txt"), ordered=True, add_eos=False) + self.test = self.vocab.encode_file(os.path.join(path, "test.txt"), ordered=True, add_eos=False) + elif self.dataset == "lm1b": + self.train = train_paths + self.valid = self.vocab.encode_file(os.path.join(path, "valid.txt"), ordered=False, add_double_eos=True) + self.test = self.vocab.encode_file(os.path.join(path, "test.txt"), ordered=False, add_double_eos=True) + + def get_iterator(self, split, *args, **kwargs): + if split == "train": + if self.dataset in ["ptb", "wt2", "wt103", "enwik8", "text8"]: + data_iter = LMOrderedIterator(self.train, *args, **kwargs) + elif self.dataset == "lm1b": + kwargs["shuffle"] = True + data_iter = LMMultiFileIterator(self.train, self.vocab, *args, **kwargs) + elif split in ["valid", "test"]: + data = self.valid if split == "valid" else self.test + if self.dataset in ["ptb", "wt2", "wt103", "enwik8", "text8"]: + data_iter = LMOrderedIterator(data, *args, **kwargs) + elif self.dataset == "lm1b": + data_iter = LMShuffledIterator(data, *args, **kwargs) + else: + data_iter = None + raise ValueError(f"Split not recognized: {split}") + + return data_iter + + +@torch_only_method +def get_lm_corpus(datadir, dataset): + fn = os.path.join(datadir, "cache.pt") + fn_pickle = os.path.join(datadir, "cache.pkl") + if os.path.exists(fn): + logger.info("Loading cached dataset...") + corpus = torch.load(fn_pickle) + elif os.path.exists(fn): + logger.info("Loading cached dataset from pickle...") + if not strtobool(os.environ.get("TRUST_REMOTE_CODE", "False")): + raise ValueError( + "This part uses `pickle.load` which is insecure and will execute arbitrary code that is potentially " + "malicious. It's recommended to never unpickle data that could have come from an untrusted source, or " + "that could have been tampered with. If you already verified the pickle data and decided to use it, " + "you can set the environment variable `TRUST_REMOTE_CODE` to `True` to allow it." + ) + with open(fn, "rb") as fp: + corpus = pickle.load(fp) + else: + logger.info(f"Producing dataset {dataset}...") + kwargs = {} + if dataset in ["wt103", "wt2"]: + kwargs["special"] = [""] + kwargs["lower_case"] = False + elif dataset == "ptb": + kwargs["special"] = [""] + kwargs["lower_case"] = True + elif dataset == "lm1b": + kwargs["special"] = [] + kwargs["lower_case"] = False + kwargs["vocab_file"] = os.path.join(datadir, "1b_word_vocab.txt") + elif dataset in ["enwik8", "text8"]: + pass + + corpus = TransfoXLCorpus(datadir, dataset, **kwargs) + torch.save(corpus, fn) + + return corpus diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/depth_anything/__pycache__/configuration_depth_anything.cpython-312.pyc 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frames_and_literature_positions_to_atom14_pos, torsion_angles_to_frames +from .loss import compute_predicted_aligned_error, compute_tm +from .protein import Protein as OFProtein +from .protein import to_pdb +from .rigid_utils import Rigid, Rotation +from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/esm/openfold_utils/__pycache__/feats.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/esm/openfold_utils/__pycache__/feats.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9c06dfca2883f224925bf2bcb0a172ddc41972e8 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/esm/openfold_utils/__pycache__/feats.cpython-312.pyc differ diff --git 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not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import logging +import math +from functools import partial +from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union + +import torch + +from .tensor_utils import tensor_tree_map, tree_map + + +def _fetch_dims(tree: Union[dict, list, tuple, torch.Tensor]) -> List[Tuple[int, ...]]: + shapes = [] + if isinstance(tree, dict): + for v in tree.values(): + shapes.extend(_fetch_dims(v)) + elif isinstance(tree, (list, tuple)): + for t in tree: + shapes.extend(_fetch_dims(t)) + elif isinstance(tree, torch.Tensor): + shapes.append(tree.shape) + else: + raise ValueError("Not supported") + + return shapes + + +@torch.jit.ignore +def _flat_idx_to_idx(flat_idx: int, dims: Tuple[int, ...]) -> Tuple[int, ...]: + idx = [] + for d in reversed(dims): + idx.append(flat_idx % d) + flat_idx = flat_idx // d + + return tuple(reversed(idx)) + + +@torch.jit.ignore +def _get_minimal_slice_set( + start: Sequence[int], + end: Sequence[int], + dims: Sequence[int], + start_edges: Optional[Sequence[bool]] = None, + end_edges: Optional[Sequence[bool]] = None, +) -> List[Tuple[slice, ...]]: + """ + Produces an ordered sequence of tensor slices that, when used in sequence on a tensor with shape dims, yields + tensors that contain every leaf in the contiguous range [start, end]. Care is taken to yield a short sequence of + slices, and perhaps even the shortest possible (I'm pretty sure it's the latter). + + end is INCLUSIVE. + """ + + # start_edges and end_edges both indicate whether, starting from any given + # dimension, the start/end index is at the top/bottom edge of the + # corresponding tensor, modeled as a tree + def reduce_edge_list(l: List[bool]) -> None: + tally = True + for i in range(len(l)): + reversed_idx = -1 * (i + 1) + l[reversed_idx] &= tally + tally = l[reversed_idx] + + if start_edges is None: + start_edges = [s == 0 for s in start] + reduce_edge_list(start_edges) + if end_edges is None: + end_edges = [e == (d - 1) for e, d in zip(end, dims)] + reduce_edge_list(end_edges) + + # Base cases. Either start/end are empty and we're done, or the final, + # one-dimensional tensor can be simply sliced + if len(start) == 0: + return [()] + elif len(start) == 1: + return [(slice(start[0], end[0] + 1),)] + + slices: List[Tuple[slice, ...]] = [] + path_list: List[slice] = [] + + # Dimensions common to start and end can be selected directly + for s, e in zip(start, end): + if s == e: + path_list.append(slice(s, s + 1)) + else: + break + + path: Tuple[slice, ...] = tuple(path_list) + divergence_idx = len(path) + + # start == end, and we're done + if divergence_idx == len(dims): + return [path] + + def upper() -> Tuple[Tuple[slice, ...], ...]: + assert start_edges is not None + assert end_edges is not None + + sdi = start[divergence_idx] + return tuple( + path + (slice(sdi, sdi + 1),) + s + for s in _get_minimal_slice_set( + start[divergence_idx + 1 :], + [d - 1 for d in dims[divergence_idx + 1 :]], + dims[divergence_idx + 1 :], + start_edges=start_edges[divergence_idx + 1 :], + end_edges=[True for _ in end_edges[divergence_idx + 1 :]], + ) + ) + + def lower() -> Tuple[Tuple[slice, ...], ...]: + assert start_edges is not None + assert end_edges is not None + + edi = end[divergence_idx] + return tuple( + path + (slice(edi, edi + 1),) + s + for s in _get_minimal_slice_set( + [0 for _ in start[divergence_idx + 1 :]], + end[divergence_idx + 1 :], + dims[divergence_idx + 1 :], + start_edges=[True for _ in start_edges[divergence_idx + 1 :]], + end_edges=end_edges[divergence_idx + 1 :], + ) + ) + + # If both start and end are at the edges of the subtree rooted at + # divergence_idx, we can just select the whole subtree at once + if start_edges[divergence_idx] and end_edges[divergence_idx]: + slices.append(path + (slice(start[divergence_idx], end[divergence_idx] + 1),)) + # If just start is at the edge, we can grab almost all of the subtree, + # treating only the ragged bottom edge as an edge case + elif start_edges[divergence_idx]: + slices.append(path + (slice(start[divergence_idx], end[divergence_idx]),)) + slices.extend(lower()) + # Analogous to the previous case, but the top is ragged this time + elif end_edges[divergence_idx]: + slices.extend(upper()) + slices.append(path + (slice(start[divergence_idx] + 1, end[divergence_idx] + 1),)) + # If both sides of the range are ragged, we need to handle both sides + # separately. If there's contiguous meat in between them, we can index it + # in one big chunk + else: + slices.extend(upper()) + middle_ground = end[divergence_idx] - start[divergence_idx] + if middle_ground > 1: + slices.append(path + (slice(start[divergence_idx] + 1, end[divergence_idx]),)) + slices.extend(lower()) + + return slices + + +@torch.jit.ignore +def _chunk_slice(t: torch.Tensor, flat_start: int, flat_end: int, no_batch_dims: int) -> torch.Tensor: + """ + Equivalent to + + t.reshape((-1,) + t.shape[no_batch_dims:])[flat_start:flat_end] + + but without the need for the initial reshape call, which can be memory-intensive in certain situations. The only + reshape operations in this function are performed on sub-tensors that scale with (flat_end - flat_start), the chunk + size. + """ + + batch_dims = t.shape[:no_batch_dims] + start_idx = list(_flat_idx_to_idx(flat_start, batch_dims)) + # _get_minimal_slice_set is inclusive + end_idx = list(_flat_idx_to_idx(flat_end - 1, batch_dims)) + + # Get an ordered list of slices to perform + slices = _get_minimal_slice_set( + start_idx, + end_idx, + batch_dims, + ) + + sliced_tensors = [t[s] for s in slices] + + return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]) for s in sliced_tensors]) + + +def chunk_layer( + layer: Callable, + inputs: Dict[str, Any], + chunk_size: int, + no_batch_dims: int, + low_mem: bool = False, + _out: Any = None, + _add_into_out: bool = False, +) -> Any: + """ + Implements the "chunking" procedure described in section 1.11.8. + + Layer outputs and inputs are assumed to be simple "pytrees," consisting only of (arbitrarily nested) lists, tuples, + and dicts with torch.Tensor leaves. + + Args: + layer: + The layer to be applied chunk-wise + inputs: + A (non-nested) dictionary of keyworded inputs. All leaves must be tensors and must share the same batch + dimensions. + chunk_size: + The number of sub-batches per chunk. If multiple batch dimensions are specified, a "sub-batch" is defined + as a single indexing of all batch dimensions simultaneously (s.t. the number of sub-batches is the product + of the batch dimensions). + no_batch_dims: + How many of the initial dimensions of each input tensor can be considered batch dimensions. + low_mem: + Avoids flattening potentially large input tensors. Unnecessary in most cases, and is ever so slightly + slower than the default setting. + Returns: + The reassembled output of the layer on the inputs. + """ + if not (len(inputs) > 0): + raise ValueError("Must provide at least one input") + + initial_dims = [shape[:no_batch_dims] for shape in _fetch_dims(inputs)] + orig_batch_dims = tuple([max(s) for s in zip(*initial_dims)]) + + def _prep_inputs(t: torch.Tensor) -> torch.Tensor: + if not low_mem: + if not sum(t.shape[:no_batch_dims]) == no_batch_dims: + t = t.expand(orig_batch_dims + t.shape[no_batch_dims:]) + t = t.reshape(-1, *t.shape[no_batch_dims:]) + else: + t = t.expand(orig_batch_dims + t.shape[no_batch_dims:]) + return t + + prepped_inputs: Dict[str, Any] = tensor_tree_map(_prep_inputs, inputs) + prepped_outputs = None + if _out is not None: + prepped_outputs = tensor_tree_map(lambda t: t.view([-1] + list(t.shape[no_batch_dims:])), _out) + + flat_batch_dim = 1 + for d in orig_batch_dims: + flat_batch_dim *= d + + no_chunks = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0) + + def _select_chunk(t: torch.Tensor) -> torch.Tensor: + return t[i : i + chunk_size] if t.shape[0] != 1 else t + + i = 0 + out = prepped_outputs + for _ in range(no_chunks): + # Chunk the input + if not low_mem: + select_chunk = _select_chunk + else: + select_chunk = partial( + _chunk_slice, + flat_start=i, + flat_end=min(flat_batch_dim, i + chunk_size), + no_batch_dims=len(orig_batch_dims), + ) + + chunks: Dict[str, Any] = tensor_tree_map(select_chunk, prepped_inputs) + + # Run the layer on the chunk + output_chunk = layer(**chunks) + + # Allocate space for the output + if out is None: + out = tensor_tree_map(lambda t: t.new_zeros((flat_batch_dim,) + t.shape[1:]), output_chunk) + + # Put the chunk in its pre-allocated space + if isinstance(output_chunk, dict): + + def assign(d1: dict, d2: dict) -> None: + for k, v in d1.items(): + if isinstance(v, dict): + assign(v, d2[k]) + else: + if _add_into_out: + v[i : i + chunk_size] += d2[k] + else: + v[i : i + chunk_size] = d2[k] + + assign(out, output_chunk) + elif isinstance(output_chunk, tuple): + for x1, x2 in zip(out, output_chunk): + if _add_into_out: + x1[i : i + chunk_size] += x2 + else: + x1[i : i + chunk_size] = x2 + elif isinstance(output_chunk, torch.Tensor): + if _add_into_out: + out[i : i + chunk_size] += output_chunk + else: + out[i : i + chunk_size] = output_chunk + else: + raise ValueError("Not supported") + + i += chunk_size + + out = tensor_tree_map(lambda t: t.view(orig_batch_dims + t.shape[1:]), out) + + return out + + +class ChunkSizeTuner: + def __init__( + self, + # Heuristically, runtimes for most of the modules in the network + # plateau earlier than this on all GPUs I've run the model on. + max_chunk_size: int = 512, + ): + self.max_chunk_size = max_chunk_size + self.cached_chunk_size: Optional[int] = None + self.cached_arg_data: Optional[tuple] = None + + def _determine_favorable_chunk_size(self, fn: Callable, args: tuple, min_chunk_size: int) -> int: + logging.info("Tuning chunk size...") + + if min_chunk_size >= self.max_chunk_size: + return min_chunk_size + + candidates: List[int] = [2**l for l in range(int(math.log(self.max_chunk_size, 2)) + 1)] + candidates = [c for c in candidates if c > min_chunk_size] + candidates = [min_chunk_size] + candidates + candidates[-1] += 4 + + def test_chunk_size(chunk_size: int) -> bool: + try: + with torch.no_grad(): + fn(*args, chunk_size=chunk_size) + return True + except RuntimeError: + return False + + min_viable_chunk_size_index = 0 + i = len(candidates) - 1 + while i > min_viable_chunk_size_index: + viable = test_chunk_size(candidates[i]) + if not viable: + i = (min_viable_chunk_size_index + i) // 2 + else: + min_viable_chunk_size_index = i + i = (i + len(candidates) - 1) // 2 + + return candidates[min_viable_chunk_size_index] + + def _compare_arg_caches(self, ac1: Iterable, ac2: Iterable) -> bool: + consistent = True + for a1, a2 in zip(ac1, ac2): + assert type(ac1) == type(ac2) + if isinstance(ac1, (list, tuple)): + consistent &= self._compare_arg_caches(a1, a2) + elif isinstance(ac1, dict): + a1_items = [v for _, v in sorted(a1.items(), key=lambda x: x[0])] + a2_items = [v for _, v in sorted(a2.items(), key=lambda x: x[0])] + consistent &= self._compare_arg_caches(a1_items, a2_items) + else: + consistent &= a1 == a2 + + return consistent + + def tune_chunk_size( + self, + representative_fn: Callable, + args: tuple, + min_chunk_size: int, + ) -> int: + consistent = True + arg_data: tuple = tree_map(lambda a: a.shape if isinstance(a, torch.Tensor) else a, args, object) + if self.cached_arg_data is not None: + # If args have changed shape/value, we need to re-tune + assert len(self.cached_arg_data) == len(arg_data) + consistent = self._compare_arg_caches(self.cached_arg_data, arg_data) + else: + # Otherwise, we can reuse the precomputed value + consistent = False + + if not consistent: + self.cached_chunk_size = self._determine_favorable_chunk_size( + representative_fn, + args, + min_chunk_size, + ) + self.cached_arg_data = arg_data + + assert self.cached_chunk_size is not None + + return self.cached_chunk_size diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/esm/openfold_utils/feats.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/esm/openfold_utils/feats.py new file mode 100644 index 0000000000000000000000000000000000000000..18b01a1fecaccfaafd93f8a269eff6ede752ccb1 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/esm/openfold_utils/feats.py @@ -0,0 +1,255 @@ +# Copyright 2021 AlQuraishi Laboratory +# Copyright 2021 DeepMind Technologies Limited +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict, Tuple, overload + +import torch +import torch.types +from torch import nn + +from . import residue_constants as rc +from .rigid_utils import Rigid, Rotation +from .tensor_utils import batched_gather + + +@overload +def pseudo_beta_fn(aatype: torch.Tensor, all_atom_positions: torch.Tensor, all_atom_masks: None) -> torch.Tensor: + ... + + +@overload +def pseudo_beta_fn( + aatype: torch.Tensor, all_atom_positions: torch.Tensor, all_atom_masks: torch.Tensor +) -> Tuple[torch.Tensor, torch.Tensor]: + ... + + +def pseudo_beta_fn(aatype, all_atom_positions, all_atom_masks): + is_gly = aatype == rc.restype_order["G"] + ca_idx = rc.atom_order["CA"] + cb_idx = rc.atom_order["CB"] + pseudo_beta = torch.where( + is_gly[..., None].expand(*((-1,) * len(is_gly.shape)), 3), + all_atom_positions[..., ca_idx, :], + all_atom_positions[..., cb_idx, :], + ) + + if all_atom_masks is not None: + pseudo_beta_mask = torch.where( + is_gly, + all_atom_masks[..., ca_idx], + all_atom_masks[..., cb_idx], + ) + return pseudo_beta, pseudo_beta_mask + else: + return pseudo_beta + + +def atom14_to_atom37(atom14: torch.Tensor, batch: Dict[str, torch.Tensor]) -> torch.Tensor: + atom37_data = batched_gather( + atom14, + batch["residx_atom37_to_atom14"], + dim=-2, + no_batch_dims=len(atom14.shape[:-2]), + ) + + atom37_data = atom37_data * batch["atom37_atom_exists"][..., None] + + return atom37_data + + +def build_template_angle_feat(template_feats: Dict[str, torch.Tensor]) -> torch.Tensor: + template_aatype = template_feats["template_aatype"] + torsion_angles_sin_cos = template_feats["template_torsion_angles_sin_cos"] + alt_torsion_angles_sin_cos = template_feats["template_alt_torsion_angles_sin_cos"] + torsion_angles_mask = template_feats["template_torsion_angles_mask"] + template_angle_feat = torch.cat( + [ + nn.functional.one_hot(template_aatype, 22), + torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2], 14), + alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2], 14), + torsion_angles_mask, + ], + dim=-1, + ) + + return template_angle_feat + + +def build_template_pair_feat( + batch: Dict[str, torch.Tensor], + min_bin: torch.types.Number, + max_bin: torch.types.Number, + no_bins: int, + use_unit_vector: bool = False, + eps: float = 1e-20, + inf: float = 1e8, +) -> torch.Tensor: + template_mask = batch["template_pseudo_beta_mask"] + template_mask_2d = template_mask[..., None] * template_mask[..., None, :] + + # Compute distogram (this seems to differ slightly from Alg. 5) + tpb = batch["template_pseudo_beta"] + dgram = torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2, dim=-1, keepdim=True) + lower = torch.linspace(min_bin, max_bin, no_bins, device=tpb.device) ** 2 + upper = torch.cat([lower[1:], lower.new_tensor([inf])], dim=-1) + dgram = ((dgram > lower) * (dgram < upper)).type(dgram.dtype) + + to_concat = [dgram, template_mask_2d[..., None]] + + aatype_one_hot: torch.LongTensor = nn.functional.one_hot( + batch["template_aatype"], + rc.restype_num + 2, + ) + + n_res = batch["template_aatype"].shape[-1] + to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2], n_res, -1, -1)) + to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2], -1, n_res, -1)) + + n, ca, c = [rc.atom_order[a] for a in ["N", "CA", "C"]] + rigids = Rigid.make_transform_from_reference( + n_xyz=batch["template_all_atom_positions"][..., n, :], + ca_xyz=batch["template_all_atom_positions"][..., ca, :], + c_xyz=batch["template_all_atom_positions"][..., c, :], + eps=eps, + ) + points = rigids.get_trans()[..., None, :, :] + rigid_vec = rigids[..., None].invert_apply(points) + + inv_distance_scalar = torch.rsqrt(eps + torch.sum(rigid_vec**2, dim=-1)) + + t_aa_masks = batch["template_all_atom_mask"] + template_mask = t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c] + template_mask_2d = template_mask[..., None] * template_mask[..., None, :] + + inv_distance_scalar = inv_distance_scalar * template_mask_2d + unit_vector = rigid_vec * inv_distance_scalar[..., None] + + if not use_unit_vector: + unit_vector = unit_vector * 0.0 + + to_concat.extend(torch.unbind(unit_vector[..., None, :], dim=-1)) + to_concat.append(template_mask_2d[..., None]) + + act = torch.cat(to_concat, dim=-1) + act = act * template_mask_2d[..., None] + + return act + + +def build_extra_msa_feat(batch: Dict[str, torch.Tensor]) -> torch.Tensor: + msa_1hot: torch.LongTensor = nn.functional.one_hot(batch["extra_msa"], 23) + msa_feat = [ + msa_1hot, + batch["extra_has_deletion"].unsqueeze(-1), + batch["extra_deletion_value"].unsqueeze(-1), + ] + return torch.cat(msa_feat, dim=-1) + + +def torsion_angles_to_frames( + r: Rigid, + alpha: torch.Tensor, + aatype: torch.Tensor, + rrgdf: torch.Tensor, +) -> Rigid: + # [*, N, 8, 4, 4] + default_4x4 = rrgdf[aatype, ...] + + # [*, N, 8] transformations, i.e. + # One [*, N, 8, 3, 3] rotation matrix and + # One [*, N, 8, 3] translation matrix + default_r = r.from_tensor_4x4(default_4x4) + + bb_rot = alpha.new_zeros((*((1,) * len(alpha.shape[:-1])), 2)) + bb_rot[..., 1] = 1 + + # [*, N, 8, 2] + alpha = torch.cat([bb_rot.expand(*alpha.shape[:-2], -1, -1), alpha], dim=-2) + + # [*, N, 8, 3, 3] + # Produces rotation matrices of the form: + # [ + # [1, 0 , 0 ], + # [0, a_2,-a_1], + # [0, a_1, a_2] + # ] + # This follows the original code rather than the supplement, which uses + # different indices. + + all_rots = alpha.new_zeros(default_r.get_rots().get_rot_mats().shape) + all_rots[..., 0, 0] = 1 + all_rots[..., 1, 1] = alpha[..., 1] + all_rots[..., 1, 2] = -alpha[..., 0] + all_rots[..., 2, 1:] = alpha + + all_frames = default_r.compose(Rigid(Rotation(rot_mats=all_rots), None)) + + chi2_frame_to_frame = all_frames[..., 5] + chi3_frame_to_frame = all_frames[..., 6] + chi4_frame_to_frame = all_frames[..., 7] + + chi1_frame_to_bb = all_frames[..., 4] + chi2_frame_to_bb = chi1_frame_to_bb.compose(chi2_frame_to_frame) + chi3_frame_to_bb = chi2_frame_to_bb.compose(chi3_frame_to_frame) + chi4_frame_to_bb = chi3_frame_to_bb.compose(chi4_frame_to_frame) + + all_frames_to_bb = Rigid.cat( + [ + all_frames[..., :5], + chi2_frame_to_bb.unsqueeze(-1), + chi3_frame_to_bb.unsqueeze(-1), + chi4_frame_to_bb.unsqueeze(-1), + ], + dim=-1, + ) + + all_frames_to_global = r[..., None].compose(all_frames_to_bb) + + return all_frames_to_global + + +def frames_and_literature_positions_to_atom14_pos( + r: Rigid, + aatype: torch.Tensor, + default_frames: torch.Tensor, + group_idx: torch.Tensor, + atom_mask: torch.Tensor, + lit_positions: torch.Tensor, +) -> torch.Tensor: + # [*, N, 14] + group_mask = group_idx[aatype, ...] + + # [*, N, 14, 8] + group_mask_one_hot: torch.LongTensor = nn.functional.one_hot( + group_mask, + num_classes=default_frames.shape[-3], + ) + + # [*, N, 14, 8] + t_atoms_to_global = r[..., None, :] * group_mask_one_hot + + # [*, N, 14] + t_atoms_to_global = t_atoms_to_global.map_tensor_fn(lambda x: torch.sum(x, dim=-1)) + + # [*, N, 14, 1] + atom_mask = atom_mask[aatype, ...].unsqueeze(-1) + + # [*, N, 14, 3] + lit_positions = lit_positions[aatype, ...] + pred_positions = t_atoms_to_global.apply(lit_positions) + pred_positions = pred_positions * atom_mask + + return pred_positions diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/esm/openfold_utils/rigid_utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/esm/openfold_utils/rigid_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..2bc2fe5f5c4ebff888e2d66eae3647073be89b4f --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/esm/openfold_utils/rigid_utils.py @@ -0,0 +1,1242 @@ +# Copyright 2021 AlQuraishi Laboratory +# Copyright 2021 DeepMind Technologies Limited +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from functools import lru_cache +from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple + +import numpy as np +import torch + + +def rot_matmul(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: + """ + Performs matrix multiplication of two rotation matrix tensors. Written out by hand to avoid AMP downcasting. + + Args: + a: [*, 3, 3] left multiplicand + b: [*, 3, 3] right multiplicand + Returns: + The product ab + """ + + def row_mul(i: int) -> torch.Tensor: + return torch.stack( + [ + a[..., i, 0] * b[..., 0, 0] + a[..., i, 1] * b[..., 1, 0] + a[..., i, 2] * b[..., 2, 0], + a[..., i, 0] * b[..., 0, 1] + a[..., i, 1] * b[..., 1, 1] + a[..., i, 2] * b[..., 2, 1], + a[..., i, 0] * b[..., 0, 2] + a[..., i, 1] * b[..., 1, 2] + a[..., i, 2] * b[..., 2, 2], + ], + dim=-1, + ) + + return torch.stack( + [ + row_mul(0), + row_mul(1), + row_mul(2), + ], + dim=-2, + ) + + +def rot_vec_mul(r: torch.Tensor, t: torch.Tensor) -> torch.Tensor: + """ + Applies a rotation to a vector. Written out by hand to avoid transfer to avoid AMP downcasting. + + Args: + r: [*, 3, 3] rotation matrices + t: [*, 3] coordinate tensors + Returns: + [*, 3] rotated coordinates + """ + x, y, z = torch.unbind(t, dim=-1) + return torch.stack( + [ + r[..., 0, 0] * x + r[..., 0, 1] * y + r[..., 0, 2] * z, + r[..., 1, 0] * x + r[..., 1, 1] * y + r[..., 1, 2] * z, + r[..., 2, 0] * x + r[..., 2, 1] * y + r[..., 2, 2] * z, + ], + dim=-1, + ) + + +@lru_cache(maxsize=None) +def identity_rot_mats( + batch_dims: Tuple[int, ...], + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + requires_grad: bool = True, +) -> torch.Tensor: + rots = torch.eye(3, dtype=dtype, device=device, requires_grad=requires_grad) + rots = rots.view(*((1,) * len(batch_dims)), 3, 3) + rots = rots.expand(*batch_dims, -1, -1) + rots = rots.contiguous() + + return rots + + +@lru_cache(maxsize=None) +def identity_trans( + batch_dims: Tuple[int, ...], + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + requires_grad: bool = True, +) -> torch.Tensor: + trans = torch.zeros((*batch_dims, 3), dtype=dtype, device=device, requires_grad=requires_grad) + return trans + + +@lru_cache(maxsize=None) +def identity_quats( + batch_dims: Tuple[int, ...], + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + requires_grad: bool = True, +) -> torch.Tensor: + quat = torch.zeros((*batch_dims, 4), dtype=dtype, device=device, requires_grad=requires_grad) + + with torch.no_grad(): + quat[..., 0] = 1 + + return quat + + +_quat_elements: List[str] = ["a", "b", "c", "d"] +_qtr_keys: List[str] = [l1 + l2 for l1 in _quat_elements for l2 in _quat_elements] +_qtr_ind_dict: Dict[str, int] = {key: ind for ind, key in enumerate(_qtr_keys)} + + +def _to_mat(pairs: List[Tuple[str, int]]) -> np.ndarray: + mat = np.zeros((4, 4)) + for key, value in pairs: + ind = _qtr_ind_dict[key] + mat[ind // 4][ind % 4] = value + + return mat + + +_QTR_MAT = np.zeros((4, 4, 3, 3)) +_QTR_MAT[..., 0, 0] = _to_mat([("aa", 1), ("bb", 1), ("cc", -1), ("dd", -1)]) +_QTR_MAT[..., 0, 1] = _to_mat([("bc", 2), ("ad", -2)]) +_QTR_MAT[..., 0, 2] = _to_mat([("bd", 2), ("ac", 2)]) +_QTR_MAT[..., 1, 0] = _to_mat([("bc", 2), ("ad", 2)]) +_QTR_MAT[..., 1, 1] = _to_mat([("aa", 1), ("bb", -1), ("cc", 1), ("dd", -1)]) +_QTR_MAT[..., 1, 2] = _to_mat([("cd", 2), ("ab", -2)]) +_QTR_MAT[..., 2, 0] = _to_mat([("bd", 2), ("ac", -2)]) +_QTR_MAT[..., 2, 1] = _to_mat([("cd", 2), ("ab", 2)]) +_QTR_MAT[..., 2, 2] = _to_mat([("aa", 1), ("bb", -1), ("cc", -1), ("dd", 1)]) + + +def quat_to_rot(quat: torch.Tensor) -> torch.Tensor: + """ + Converts a quaternion to a rotation matrix. + + Args: + quat: [*, 4] quaternions + Returns: + [*, 3, 3] rotation matrices + """ + # [*, 4, 4] + quat = quat[..., None] * quat[..., None, :] + + # [4, 4, 3, 3] + mat = _get_quat("_QTR_MAT", dtype=quat.dtype, device=quat.device) + + # [*, 4, 4, 3, 3] + shaped_qtr_mat = mat.view((1,) * len(quat.shape[:-2]) + mat.shape) + quat = quat[..., None, None] * shaped_qtr_mat + + # [*, 3, 3] + return torch.sum(quat, dim=(-3, -4)) + + +def rot_to_quat(rot: torch.Tensor) -> torch.Tensor: + if rot.shape[-2:] != (3, 3): + raise ValueError("Input rotation is incorrectly shaped") + + [[xx, xy, xz], [yx, yy, yz], [zx, zy, zz]] = [[rot[..., i, j] for j in range(3)] for i in range(3)] + + k = [ + [ + xx + yy + zz, + zy - yz, + xz - zx, + yx - xy, + ], + [ + zy - yz, + xx - yy - zz, + xy + yx, + xz + zx, + ], + [ + xz - zx, + xy + yx, + yy - xx - zz, + yz + zy, + ], + [ + yx - xy, + xz + zx, + yz + zy, + zz - xx - yy, + ], + ] + + _, vectors = torch.linalg.eigh((1.0 / 3.0) * torch.stack([torch.stack(t, dim=-1) for t in k], dim=-2)) + return vectors[..., -1] + + +_QUAT_MULTIPLY = np.zeros((4, 4, 4)) +_QUAT_MULTIPLY[:, :, 0] = [[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, -1]] + +_QUAT_MULTIPLY[:, :, 1] = [[0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1], [0, 0, -1, 0]] + +_QUAT_MULTIPLY[:, :, 2] = [[0, 0, 1, 0], [0, 0, 0, -1], [1, 0, 0, 0], [0, 1, 0, 0]] + +_QUAT_MULTIPLY[:, :, 3] = [[0, 0, 0, 1], [0, 0, 1, 0], [0, -1, 0, 0], [1, 0, 0, 0]] + +_QUAT_MULTIPLY_BY_VEC = _QUAT_MULTIPLY[:, 1:, :] + +_CACHED_QUATS: Dict[str, np.ndarray] = { + "_QTR_MAT": _QTR_MAT, + "_QUAT_MULTIPLY": _QUAT_MULTIPLY, + "_QUAT_MULTIPLY_BY_VEC": _QUAT_MULTIPLY_BY_VEC, +} + + +@lru_cache(maxsize=None) +def _get_quat(quat_key: str, dtype: torch.dtype, device: torch.device) -> torch.Tensor: + return torch.tensor(_CACHED_QUATS[quat_key], dtype=dtype, device=device) + + +def quat_multiply(quat1: torch.Tensor, quat2: torch.Tensor) -> torch.Tensor: + """Multiply a quaternion by another quaternion.""" + mat = _get_quat("_QUAT_MULTIPLY", dtype=quat1.dtype, device=quat1.device) + reshaped_mat = mat.view((1,) * len(quat1.shape[:-1]) + mat.shape) + return torch.sum(reshaped_mat * quat1[..., :, None, None] * quat2[..., None, :, None], dim=(-3, -2)) + + +def quat_multiply_by_vec(quat: torch.Tensor, vec: torch.Tensor) -> torch.Tensor: + """Multiply a quaternion by a pure-vector quaternion.""" + mat = _get_quat("_QUAT_MULTIPLY_BY_VEC", dtype=quat.dtype, device=quat.device) + reshaped_mat = mat.view((1,) * len(quat.shape[:-1]) + mat.shape) + return torch.sum(reshaped_mat * quat[..., :, None, None] * vec[..., None, :, None], dim=(-3, -2)) + + +def invert_rot_mat(rot_mat: torch.Tensor) -> torch.Tensor: + return rot_mat.transpose(-1, -2) + + +def invert_quat(quat: torch.Tensor) -> torch.Tensor: + quat_prime = quat.clone() + quat_prime[..., 1:] *= -1 + inv = quat_prime / torch.sum(quat**2, dim=-1, keepdim=True) + return inv + + +class Rotation: + """ + A 3D rotation. Depending on how the object is initialized, the rotation is represented by either a rotation matrix + or a quaternion, though both formats are made available by helper functions. To simplify gradient computation, the + underlying format of the rotation cannot be changed in-place. Like Rigid, the class is designed to mimic the + behavior of a torch Tensor, almost as if each Rotation object were a tensor of rotations, in one format or another. + """ + + def __init__( + self, + rot_mats: Optional[torch.Tensor] = None, + quats: Optional[torch.Tensor] = None, + normalize_quats: bool = True, + ): + """ + Args: + rot_mats: + A [*, 3, 3] rotation matrix tensor. Mutually exclusive with quats + quats: + A [*, 4] quaternion. Mutually exclusive with rot_mats. If normalize_quats is not True, must be a unit + quaternion + normalize_quats: + If quats is specified, whether to normalize quats + """ + if (rot_mats is None and quats is None) or (rot_mats is not None and quats is not None): + raise ValueError("Exactly one input argument must be specified") + + if (rot_mats is not None and rot_mats.shape[-2:] != (3, 3)) or (quats is not None and quats.shape[-1] != 4): + raise ValueError("Incorrectly shaped rotation matrix or quaternion") + + # Force full-precision + if quats is not None: + quats = quats.to(dtype=torch.float32) + if rot_mats is not None: + rot_mats = rot_mats.to(dtype=torch.float32) + + if quats is not None and normalize_quats: + quats = quats / torch.linalg.norm(quats, dim=-1, keepdim=True) + + self._rot_mats = rot_mats + self._quats = quats + + @staticmethod + def identity( + shape, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + requires_grad: bool = True, + fmt: str = "quat", + ) -> Rotation: + """ + Returns an identity Rotation. + + Args: + shape: + The "shape" of the resulting Rotation object. See documentation for the shape property + dtype: + The torch dtype for the rotation + device: + The torch device for the new rotation + requires_grad: + Whether the underlying tensors in the new rotation object should require gradient computation + fmt: + One of "quat" or "rot_mat". Determines the underlying format of the new object's rotation + Returns: + A new identity rotation + """ + if fmt == "rot_mat": + rot_mats = identity_rot_mats( + shape, + dtype, + device, + requires_grad, + ) + return Rotation(rot_mats=rot_mats, quats=None) + elif fmt == "quat": + quats = identity_quats(shape, dtype, device, requires_grad) + return Rotation(rot_mats=None, quats=quats, normalize_quats=False) + else: + raise ValueError(f"Invalid format: f{fmt}") + + # Magic methods + + def __getitem__(self, index: Any) -> Rotation: + """ + Allows torch-style indexing over the virtual shape of the rotation object. See documentation for the shape + property. + + Args: + index: + A torch index. E.g. (1, 3, 2), or (slice(None,)) + Returns: + The indexed rotation + """ + if type(index) != tuple: + index = (index,) + + if self._rot_mats is not None: + rot_mats = self._rot_mats[index + (slice(None), slice(None))] + return Rotation(rot_mats=rot_mats) + elif self._quats is not None: + quats = self._quats[index + (slice(None),)] + return Rotation(quats=quats, normalize_quats=False) + else: + raise ValueError("Both rotations are None") + + def __mul__(self, right: torch.Tensor) -> Rotation: + """ + Pointwise left multiplication of the rotation with a tensor. Can be used to e.g. mask the Rotation. + + Args: + right: + The tensor multiplicand + Returns: + The product + """ + if not (isinstance(right, torch.Tensor)): + raise TypeError("The other multiplicand must be a Tensor") + + if self._rot_mats is not None: + rot_mats = self._rot_mats * right[..., None, None] + return Rotation(rot_mats=rot_mats, quats=None) + elif self._quats is not None: + quats = self._quats * right[..., None] + return Rotation(rot_mats=None, quats=quats, normalize_quats=False) + else: + raise ValueError("Both rotations are None") + + def __rmul__(self, left: torch.Tensor) -> Rotation: + """ + Reverse pointwise multiplication of the rotation with a tensor. + + Args: + left: + The left multiplicand + Returns: + The product + """ + return self.__mul__(left) + + # Properties + + @property + def shape(self) -> torch.Size: + """ + Returns the virtual shape of the rotation object. This shape is defined as the batch dimensions of the + underlying rotation matrix or quaternion. If the Rotation was initialized with a [10, 3, 3] rotation matrix + tensor, for example, the resulting shape would be [10]. + + Returns: + The virtual shape of the rotation object + """ + if self._rot_mats is not None: + return self._rot_mats.shape[:-2] + elif self._quats is not None: + return self._quats.shape[:-1] + else: + raise ValueError("Both rotations are None") + + @property + def dtype(self) -> torch.dtype: + """ + Returns the dtype of the underlying rotation. + + Returns: + The dtype of the underlying rotation + """ + if self._rot_mats is not None: + return self._rot_mats.dtype + elif self._quats is not None: + return self._quats.dtype + else: + raise ValueError("Both rotations are None") + + @property + def device(self) -> torch.device: + """ + The device of the underlying rotation + + Returns: + The device of the underlying rotation + """ + if self._rot_mats is not None: + return self._rot_mats.device + elif self._quats is not None: + return self._quats.device + else: + raise ValueError("Both rotations are None") + + @property + def requires_grad(self) -> bool: + """ + Returns the requires_grad property of the underlying rotation + + Returns: + The requires_grad property of the underlying tensor + """ + if self._rot_mats is not None: + return self._rot_mats.requires_grad + elif self._quats is not None: + return self._quats.requires_grad + else: + raise ValueError("Both rotations are None") + + def get_rot_mats(self) -> torch.Tensor: + """ + Returns the underlying rotation as a rotation matrix tensor. + + Returns: + The rotation as a rotation matrix tensor + """ + if self._rot_mats is not None: + return self._rot_mats + elif self._quats is not None: + return quat_to_rot(self._quats) + else: + raise ValueError("Both rotations are None") + + def get_quats(self) -> torch.Tensor: + """ + Returns the underlying rotation as a quaternion tensor. + + Depending on whether the Rotation was initialized with a quaternion, this function may call torch.linalg.eigh. + + Returns: + The rotation as a quaternion tensor. + """ + if self._rot_mats is not None: + return rot_to_quat(self._rot_mats) + elif self._quats is not None: + return self._quats + else: + raise ValueError("Both rotations are None") + + def get_cur_rot(self) -> torch.Tensor: + """ + Return the underlying rotation in its current form + + Returns: + The stored rotation + """ + if self._rot_mats is not None: + return self._rot_mats + elif self._quats is not None: + return self._quats + else: + raise ValueError("Both rotations are None") + + # Rotation functions + + def compose_q_update_vec(self, q_update_vec: torch.Tensor, normalize_quats: bool = True) -> Rotation: + """ + Returns a new quaternion Rotation after updating the current object's underlying rotation with a quaternion + update, formatted as a [*, 3] tensor whose final three columns represent x, y, z such that (1, x, y, z) is the + desired (not necessarily unit) quaternion update. + + Args: + q_update_vec: + A [*, 3] quaternion update tensor + normalize_quats: + Whether to normalize the output quaternion + Returns: + An updated Rotation + """ + quats = self.get_quats() + new_quats = quats + quat_multiply_by_vec(quats, q_update_vec) + return Rotation( + rot_mats=None, + quats=new_quats, + normalize_quats=normalize_quats, + ) + + def compose_r(self, r: Rotation) -> Rotation: + """ + Compose the rotation matrices of the current Rotation object with those of another. + + Args: + r: + An update rotation object + Returns: + An updated rotation object + """ + r1 = self.get_rot_mats() + r2 = r.get_rot_mats() + new_rot_mats = rot_matmul(r1, r2) + return Rotation(rot_mats=new_rot_mats, quats=None) + + def compose_q(self, r: Rotation, normalize_quats: bool = True) -> Rotation: + """ + Compose the quaternions of the current Rotation object with those of another. + + Depending on whether either Rotation was initialized with quaternions, this function may call + torch.linalg.eigh. + + Args: + r: + An update rotation object + Returns: + An updated rotation object + """ + q1 = self.get_quats() + q2 = r.get_quats() + new_quats = quat_multiply(q1, q2) + return Rotation(rot_mats=None, quats=new_quats, normalize_quats=normalize_quats) + + def apply(self, pts: torch.Tensor) -> torch.Tensor: + """ + Apply the current Rotation as a rotation matrix to a set of 3D coordinates. + + Args: + pts: + A [*, 3] set of points + Returns: + [*, 3] rotated points + """ + rot_mats = self.get_rot_mats() + return rot_vec_mul(rot_mats, pts) + + def invert_apply(self, pts: torch.Tensor) -> torch.Tensor: + """ + The inverse of the apply() method. + + Args: + pts: + A [*, 3] set of points + Returns: + [*, 3] inverse-rotated points + """ + rot_mats = self.get_rot_mats() + inv_rot_mats = invert_rot_mat(rot_mats) + return rot_vec_mul(inv_rot_mats, pts) + + def invert(self) -> Rotation: + """ + Returns the inverse of the current Rotation. + + Returns: + The inverse of the current Rotation + """ + if self._rot_mats is not None: + return Rotation(rot_mats=invert_rot_mat(self._rot_mats), quats=None) + elif self._quats is not None: + return Rotation( + rot_mats=None, + quats=invert_quat(self._quats), + normalize_quats=False, + ) + else: + raise ValueError("Both rotations are None") + + # "Tensor" stuff + + def unsqueeze(self, dim: int) -> Rotation: + """ + Analogous to torch.unsqueeze. The dimension is relative to the shape of the Rotation object. + + Args: + dim: A positive or negative dimension index. + Returns: + The unsqueezed Rotation. + """ + if dim >= len(self.shape): + raise ValueError("Invalid dimension") + + if self._rot_mats is not None: + rot_mats = self._rot_mats.unsqueeze(dim if dim >= 0 else dim - 2) + return Rotation(rot_mats=rot_mats, quats=None) + elif self._quats is not None: + quats = self._quats.unsqueeze(dim if dim >= 0 else dim - 1) + return Rotation(rot_mats=None, quats=quats, normalize_quats=False) + else: + raise ValueError("Both rotations are None") + + @staticmethod + def cat(rs: Sequence[Rotation], dim: int) -> Rotation: + """ + Concatenates rotations along one of the batch dimensions. Analogous to torch.cat(). + + Note that the output of this operation is always a rotation matrix, regardless of the format of input + rotations. + + Args: + rs: + A list of rotation objects + dim: + The dimension along which the rotations should be concatenated + Returns: + A concatenated Rotation object in rotation matrix format + """ + rot_mats = torch.cat( + [r.get_rot_mats() for r in rs], + dim=dim if dim >= 0 else dim - 2, + ) + + return Rotation(rot_mats=rot_mats, quats=None) + + def map_tensor_fn(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Rotation: + """ + Apply a Tensor -> Tensor function to underlying rotation tensors, mapping over the rotation dimension(s). Can + be used e.g. to sum out a one-hot batch dimension. + + Args: + fn: + A Tensor -> Tensor function to be mapped over the Rotation + Returns: + The transformed Rotation object + """ + if self._rot_mats is not None: + rot_mats = self._rot_mats.view(self._rot_mats.shape[:-2] + (9,)) + rot_mats = torch.stack(list(map(fn, torch.unbind(rot_mats, dim=-1))), dim=-1) + rot_mats = rot_mats.view(rot_mats.shape[:-1] + (3, 3)) + return Rotation(rot_mats=rot_mats, quats=None) + elif self._quats is not None: + quats = torch.stack(list(map(fn, torch.unbind(self._quats, dim=-1))), dim=-1) + return Rotation(rot_mats=None, quats=quats, normalize_quats=False) + else: + raise ValueError("Both rotations are None") + + def cuda(self) -> Rotation: + """ + Analogous to the cuda() method of torch Tensors + + Returns: + A copy of the Rotation in CUDA memory + """ + if self._rot_mats is not None: + return Rotation(rot_mats=self._rot_mats.cuda(), quats=None) + elif self._quats is not None: + return Rotation(rot_mats=None, quats=self._quats.cuda(), normalize_quats=False) + else: + raise ValueError("Both rotations are None") + + def to(self, device: Optional[torch.device], dtype: Optional[torch.dtype]) -> Rotation: + """ + Analogous to the to() method of torch Tensors + + Args: + device: + A torch device + dtype: + A torch dtype + Returns: + A copy of the Rotation using the new device and dtype + """ + if self._rot_mats is not None: + return Rotation( + rot_mats=self._rot_mats.to(device=device, dtype=dtype), + quats=None, + ) + elif self._quats is not None: + return Rotation( + rot_mats=None, + quats=self._quats.to(device=device, dtype=dtype), + normalize_quats=False, + ) + else: + raise ValueError("Both rotations are None") + + def detach(self) -> Rotation: + """ + Returns a copy of the Rotation whose underlying Tensor has been detached from its torch graph. + + Returns: + A copy of the Rotation whose underlying Tensor has been detached from its torch graph + """ + if self._rot_mats is not None: + return Rotation(rot_mats=self._rot_mats.detach(), quats=None) + elif self._quats is not None: + return Rotation( + rot_mats=None, + quats=self._quats.detach(), + normalize_quats=False, + ) + else: + raise ValueError("Both rotations are None") + + +class Rigid: + """ + A class representing a rigid transformation. Little more than a wrapper around two objects: a Rotation object and a + [*, 3] translation Designed to behave approximately like a single torch tensor with the shape of the shared batch + dimensions of its component parts. + """ + + def __init__(self, rots: Optional[Rotation], trans: Optional[torch.Tensor]): + """ + Args: + rots: A [*, 3, 3] rotation tensor + trans: A corresponding [*, 3] translation tensor + """ + # (we need device, dtype, etc. from at least one input) + + batch_dims, dtype, device, requires_grad = None, None, None, None + if trans is not None: + batch_dims = trans.shape[:-1] + dtype = trans.dtype + device = trans.device + requires_grad = trans.requires_grad + elif rots is not None: + batch_dims = rots.shape + dtype = rots.dtype + device = rots.device + requires_grad = rots.requires_grad + else: + raise ValueError("At least one input argument must be specified") + + if rots is None: + rots = Rotation.identity( + batch_dims, + dtype, + device, + requires_grad, + ) + elif trans is None: + trans = identity_trans( + batch_dims, + dtype, + device, + requires_grad, + ) + + assert rots is not None + assert trans is not None + + if (rots.shape != trans.shape[:-1]) or (rots.device != trans.device): + raise ValueError("Rots and trans incompatible") + + # Force full precision. Happens to the rotations automatically. + trans = trans.to(dtype=torch.float32) + + self._rots = rots + self._trans = trans + + @staticmethod + def identity( + shape: Tuple[int, ...], + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + requires_grad: bool = True, + fmt: str = "quat", + ) -> Rigid: + """ + Constructs an identity transformation. + + Args: + shape: + The desired shape + dtype: + The dtype of both internal tensors + device: + The device of both internal tensors + requires_grad: + Whether grad should be enabled for the internal tensors + Returns: + The identity transformation + """ + return Rigid( + Rotation.identity(shape, dtype, device, requires_grad, fmt=fmt), + identity_trans(shape, dtype, device, requires_grad), + ) + + def __getitem__(self, index: Any) -> Rigid: + """ + Indexes the affine transformation with PyTorch-style indices. The index is applied to the shared dimensions of + both the rotation and the translation. + + E.g.:: + + r = Rotation(rot_mats=torch.rand(10, 10, 3, 3), quats=None) t = Rigid(r, torch.rand(10, 10, 3)) indexed = + t[3, 4:6] assert(indexed.shape == (2,)) assert(indexed.get_rots().shape == (2,)) + assert(indexed.get_trans().shape == (2, 3)) + + Args: + index: A standard torch tensor index. E.g. 8, (10, None, 3), + or (3, slice(0, 1, None)) + Returns: + The indexed tensor + """ + if type(index) != tuple: + index = (index,) + + return Rigid( + self._rots[index], + self._trans[index + (slice(None),)], + ) + + def __mul__(self, right: torch.Tensor) -> Rigid: + """ + Pointwise left multiplication of the transformation with a tensor. Can be used to e.g. mask the Rigid. + + Args: + right: + The tensor multiplicand + Returns: + The product + """ + if not (isinstance(right, torch.Tensor)): + raise TypeError("The other multiplicand must be a Tensor") + + new_rots = self._rots * right + new_trans = self._trans * right[..., None] + + return Rigid(new_rots, new_trans) + + def __rmul__(self, left: torch.Tensor) -> Rigid: + """ + Reverse pointwise multiplication of the transformation with a tensor. + + Args: + left: + The left multiplicand + Returns: + The product + """ + return self.__mul__(left) + + @property + def shape(self) -> torch.Size: + """ + Returns the shape of the shared dimensions of the rotation and the translation. + + Returns: + The shape of the transformation + """ + return self._trans.shape[:-1] + + @property + def device(self) -> torch.device: + """ + Returns the device on which the Rigid's tensors are located. + + Returns: + The device on which the Rigid's tensors are located + """ + return self._trans.device + + def get_rots(self) -> Rotation: + """ + Getter for the rotation. + + Returns: + The rotation object + """ + return self._rots + + def get_trans(self) -> torch.Tensor: + """ + Getter for the translation. + + Returns: + The stored translation + """ + return self._trans + + def compose_q_update_vec(self, q_update_vec: torch.Tensor) -> Rigid: + """ + Composes the transformation with a quaternion update vector of shape [*, 6], where the final 6 columns + represent the x, y, and z values of a quaternion of form (1, x, y, z) followed by a 3D translation. + + Args: + q_vec: The quaternion update vector. + Returns: + The composed transformation. + """ + q_vec, t_vec = q_update_vec[..., :3], q_update_vec[..., 3:] + new_rots = self._rots.compose_q_update_vec(q_vec) + + trans_update = self._rots.apply(t_vec) + new_translation = self._trans + trans_update + + return Rigid(new_rots, new_translation) + + def compose(self, r: Rigid) -> Rigid: + """ + Composes the current rigid object with another. + + Args: + r: + Another Rigid object + Returns: + The composition of the two transformations + """ + new_rot = self._rots.compose_r(r._rots) + new_trans = self._rots.apply(r._trans) + self._trans + return Rigid(new_rot, new_trans) + + def apply(self, pts: torch.Tensor) -> torch.Tensor: + """ + Applies the transformation to a coordinate tensor. + + Args: + pts: A [*, 3] coordinate tensor. + Returns: + The transformed points. + """ + rotated = self._rots.apply(pts) + return rotated + self._trans + + def invert_apply(self, pts: torch.Tensor) -> torch.Tensor: + """ + Applies the inverse of the transformation to a coordinate tensor. + + Args: + pts: A [*, 3] coordinate tensor + Returns: + The transformed points. + """ + pts = pts - self._trans + return self._rots.invert_apply(pts) + + def invert(self) -> Rigid: + """ + Inverts the transformation. + + Returns: + The inverse transformation. + """ + rot_inv = self._rots.invert() + trn_inv = rot_inv.apply(self._trans) + + return Rigid(rot_inv, -1 * trn_inv) + + def map_tensor_fn(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Rigid: + """ + Apply a Tensor -> Tensor function to underlying translation and rotation tensors, mapping over the + translation/rotation dimensions respectively. + + Args: + fn: + A Tensor -> Tensor function to be mapped over the Rigid + Returns: + The transformed Rigid object + """ + new_rots = self._rots.map_tensor_fn(fn) + new_trans = torch.stack(list(map(fn, torch.unbind(self._trans, dim=-1))), dim=-1) + + return Rigid(new_rots, new_trans) + + def to_tensor_4x4(self) -> torch.Tensor: + """ + Converts a transformation to a homogenous transformation tensor. + + Returns: + A [*, 4, 4] homogenous transformation tensor + """ + tensor = self._trans.new_zeros((*self.shape, 4, 4)) + tensor[..., :3, :3] = self._rots.get_rot_mats() + tensor[..., :3, 3] = self._trans + tensor[..., 3, 3] = 1 + return tensor + + @staticmethod + def from_tensor_4x4(t: torch.Tensor) -> Rigid: + """ + Constructs a transformation from a homogenous transformation tensor. + + Args: + t: [*, 4, 4] homogenous transformation tensor + Returns: + T object with shape [*] + """ + if t.shape[-2:] != (4, 4): + raise ValueError("Incorrectly shaped input tensor") + + rots = Rotation(rot_mats=t[..., :3, :3], quats=None) + trans = t[..., :3, 3] + + return Rigid(rots, trans) + + def to_tensor_7(self) -> torch.Tensor: + """ + Converts a transformation to a tensor with 7 final columns, four for the quaternion followed by three for the + translation. + + Returns: + A [*, 7] tensor representation of the transformation + """ + tensor = self._trans.new_zeros((*self.shape, 7)) + tensor[..., :4] = self._rots.get_quats() + tensor[..., 4:] = self._trans + + return tensor + + @staticmethod + def from_tensor_7(t: torch.Tensor, normalize_quats: bool = False) -> Rigid: + if t.shape[-1] != 7: + raise ValueError("Incorrectly shaped input tensor") + + quats, trans = t[..., :4], t[..., 4:] + + rots = Rotation(rot_mats=None, quats=quats, normalize_quats=normalize_quats) + + return Rigid(rots, trans) + + @staticmethod + def from_3_points( + p_neg_x_axis: torch.Tensor, origin: torch.Tensor, p_xy_plane: torch.Tensor, eps: float = 1e-8 + ) -> Rigid: + """ + Implements algorithm 21. Constructs transformations from sets of 3 points using the Gram-Schmidt algorithm. + + Args: + p_neg_x_axis: [*, 3] coordinates + origin: [*, 3] coordinates used as frame origins + p_xy_plane: [*, 3] coordinates + eps: Small epsilon value + Returns: + A transformation object of shape [*] + """ + p_neg_x_axis_unbound = torch.unbind(p_neg_x_axis, dim=-1) + origin_unbound = torch.unbind(origin, dim=-1) + p_xy_plane_unbound = torch.unbind(p_xy_plane, dim=-1) + + e0 = [c1 - c2 for c1, c2 in zip(origin_unbound, p_neg_x_axis_unbound)] + e1 = [c1 - c2 for c1, c2 in zip(p_xy_plane_unbound, origin_unbound)] + + denom = torch.sqrt(sum(c * c for c in e0) + eps * torch.ones_like(e0[0])) + e0 = [c / denom for c in e0] + dot = sum((c1 * c2 for c1, c2 in zip(e0, e1))) + e1 = [c2 - c1 * dot for c1, c2 in zip(e0, e1)] + denom = torch.sqrt(sum((c * c for c in e1)) + eps * torch.ones_like(e1[0])) + e1 = [c / denom for c in e1] + e2 = [ + e0[1] * e1[2] - e0[2] * e1[1], + e0[2] * e1[0] - e0[0] * e1[2], + e0[0] * e1[1] - e0[1] * e1[0], + ] + + rots = torch.stack([c for tup in zip(e0, e1, e2) for c in tup], dim=-1) + rots = rots.reshape(rots.shape[:-1] + (3, 3)) + + rot_obj = Rotation(rot_mats=rots, quats=None) + + return Rigid(rot_obj, torch.stack(origin_unbound, dim=-1)) + + def unsqueeze(self, dim: int) -> Rigid: + """ + Analogous to torch.unsqueeze. The dimension is relative to the shared dimensions of the rotation/translation. + + Args: + dim: A positive or negative dimension index. + Returns: + The unsqueezed transformation. + """ + if dim >= len(self.shape): + raise ValueError("Invalid dimension") + rots = self._rots.unsqueeze(dim) + trans = self._trans.unsqueeze(dim if dim >= 0 else dim - 1) + + return Rigid(rots, trans) + + @staticmethod + def cat(ts: Sequence[Rigid], dim: int) -> Rigid: + """ + Concatenates transformations along a new dimension. + + Args: + ts: + A list of T objects + dim: + The dimension along which the transformations should be concatenated + Returns: + A concatenated transformation object + """ + rots = Rotation.cat([t._rots for t in ts], dim) + trans = torch.cat([t._trans for t in ts], dim=dim if dim >= 0 else dim - 1) + + return Rigid(rots, trans) + + def apply_rot_fn(self, fn: Callable[[Rotation], Rotation]) -> Rigid: + """ + Applies a Rotation -> Rotation function to the stored rotation object. + + Args: + fn: A function of type Rotation -> Rotation + Returns: + A transformation object with a transformed rotation. + """ + return Rigid(fn(self._rots), self._trans) + + def apply_trans_fn(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Rigid: + """ + Applies a Tensor -> Tensor function to the stored translation. + + Args: + fn: + A function of type Tensor -> Tensor to be applied to the translation + Returns: + A transformation object with a transformed translation. + """ + return Rigid(self._rots, fn(self._trans)) + + def scale_translation(self, trans_scale_factor: float) -> Rigid: + """ + Scales the translation by a constant factor. + + Args: + trans_scale_factor: + The constant factor + Returns: + A transformation object with a scaled translation. + """ + return self.apply_trans_fn(lambda t: t * trans_scale_factor) + + def stop_rot_gradient(self) -> Rigid: + """ + Detaches the underlying rotation object + + Returns: + A transformation object with detached rotations + """ + return self.apply_rot_fn(lambda r: r.detach()) + + @staticmethod + def make_transform_from_reference( + n_xyz: torch.Tensor, ca_xyz: torch.Tensor, c_xyz: torch.Tensor, eps: float = 1e-20 + ) -> Rigid: + """ + Returns a transformation object from reference coordinates. + + Note that this method does not take care of symmetries. If you provide the atom positions in the non-standard + way, the N atom will end up not at [-0.527250, 1.359329, 0.0] but instead at [-0.527250, -1.359329, 0.0]. You + need to take care of such cases in your code. + + Args: + n_xyz: A [*, 3] tensor of nitrogen xyz coordinates. + ca_xyz: A [*, 3] tensor of carbon alpha xyz coordinates. + c_xyz: A [*, 3] tensor of carbon xyz coordinates. + Returns: + A transformation object. After applying the translation and rotation to the reference backbone, the + coordinates will approximately equal to the input coordinates. + """ + translation = -1 * ca_xyz + n_xyz = n_xyz + translation + c_xyz = c_xyz + translation + + c_x, c_y, c_z = [c_xyz[..., i] for i in range(3)] + norm = torch.sqrt(eps + c_x**2 + c_y**2) + sin_c1 = -c_y / norm + cos_c1 = c_x / norm + + c1_rots = sin_c1.new_zeros((*sin_c1.shape, 3, 3)) + c1_rots[..., 0, 0] = cos_c1 + c1_rots[..., 0, 1] = -1 * sin_c1 + c1_rots[..., 1, 0] = sin_c1 + c1_rots[..., 1, 1] = cos_c1 + c1_rots[..., 2, 2] = 1 + + norm = torch.sqrt(eps + c_x**2 + c_y**2 + c_z**2) + sin_c2 = c_z / norm + cos_c2 = torch.sqrt(c_x**2 + c_y**2) / norm + + c2_rots = sin_c2.new_zeros((*sin_c2.shape, 3, 3)) + c2_rots[..., 0, 0] = cos_c2 + c2_rots[..., 0, 2] = sin_c2 + c2_rots[..., 1, 1] = 1 + c2_rots[..., 2, 0] = -1 * sin_c2 + c2_rots[..., 2, 2] = cos_c2 + + c_rots = rot_matmul(c2_rots, c1_rots) + n_xyz = rot_vec_mul(c_rots, n_xyz) + + _, n_y, n_z = [n_xyz[..., i] for i in range(3)] + norm = torch.sqrt(eps + n_y**2 + n_z**2) + sin_n = -n_z / norm + cos_n = n_y / norm + + n_rots = sin_c2.new_zeros((*sin_c2.shape, 3, 3)) + n_rots[..., 0, 0] = 1 + n_rots[..., 1, 1] = cos_n + n_rots[..., 1, 2] = -1 * sin_n + n_rots[..., 2, 1] = sin_n + n_rots[..., 2, 2] = cos_n + + rots = rot_matmul(n_rots, c_rots) + + rots = rots.transpose(-1, -2) + translation = -1 * translation + + rot_obj = Rotation(rot_mats=rots, quats=None) + + return Rigid(rot_obj, translation) + + def cuda(self) -> Rigid: + """ + Moves the transformation object to GPU memory + + Returns: + A version of the transformation on GPU + """ + return Rigid(self._rots.cuda(), self._trans.cuda()) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/esm/openfold_utils/tensor_utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/esm/openfold_utils/tensor_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..99dd6dbe47b68247794e51810fd274c6352e5b4f --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/esm/openfold_utils/tensor_utils.py @@ -0,0 +1,144 @@ +# Copyright 2021 AlQuraishi Laboratory +# Copyright 2021 DeepMind Technologies Limited +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from functools import partial +from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload + +import torch +import torch.nn as nn +import torch.types + + +def add(m1: torch.Tensor, m2: torch.Tensor, inplace: bool) -> torch.Tensor: + # The first operation in a checkpoint can't be in-place, but it's + # nice to have in-place addition during inference. Thus... + if not inplace: + m1 = m1 + m2 + else: + m1 += m2 + + return m1 + + +def permute_final_dims(tensor: torch.Tensor, inds: List[int]) -> torch.Tensor: + zero_index = -1 * len(inds) + first_inds = list(range(len(tensor.shape[:zero_index]))) + return tensor.permute(first_inds + [zero_index + i for i in inds]) + + +def flatten_final_dims(t: torch.Tensor, no_dims: int) -> torch.Tensor: + return t.reshape(t.shape[:-no_dims] + (-1,)) + + +def masked_mean(mask: torch.Tensor, value: torch.Tensor, dim: int, eps: float = 1e-4) -> torch.Tensor: + mask = mask.expand(*value.shape) + return torch.sum(mask * value, dim=dim) / (eps + torch.sum(mask, dim=dim)) + + +def pts_to_distogram( + pts: torch.Tensor, min_bin: torch.types.Number = 2.3125, max_bin: torch.types.Number = 21.6875, no_bins: int = 64 +) -> torch.Tensor: + boundaries = torch.linspace(min_bin, max_bin, no_bins - 1, device=pts.device) + dists = torch.sqrt(torch.sum((pts.unsqueeze(-2) - pts.unsqueeze(-3)) ** 2, dim=-1)) + return torch.bucketize(dists, boundaries) + + +def dict_multimap(fn: Callable[[list], Any], dicts: List[dict]) -> dict: + first = dicts[0] + new_dict = {} + for k, v in first.items(): + all_v = [d[k] for d in dicts] + if isinstance(v, dict): + new_dict[k] = dict_multimap(fn, all_v) + else: + new_dict[k] = fn(all_v) + + return new_dict + + +def one_hot(x: torch.Tensor, v_bins: torch.Tensor) -> torch.Tensor: + reshaped_bins = v_bins.view(((1,) * len(x.shape)) + (len(v_bins),)) + diffs = x[..., None] - reshaped_bins + am = torch.argmin(torch.abs(diffs), dim=-1) + return nn.functional.one_hot(am, num_classes=len(v_bins)).float() + + +def batched_gather(data: torch.Tensor, inds: torch.Tensor, dim: int = 0, no_batch_dims: int = 0) -> torch.Tensor: + ranges: List[Union[slice, torch.Tensor]] = [] + for i, s in enumerate(data.shape[:no_batch_dims]): + r = torch.arange(s) + r = r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape) - i - 1)))) + ranges.append(r) + + remaining_dims: List[Union[slice, torch.Tensor]] = [slice(None) for _ in range(len(data.shape) - no_batch_dims)] + remaining_dims[dim - no_batch_dims if dim >= 0 else dim] = inds + ranges.extend(remaining_dims) + # Matt note: Editing this to get around the behaviour of using a list as an array index changing + # in recent Numpy versions + return data[tuple(ranges)] + + +T = TypeVar("T") + + +# With tree_map, a poor man's JAX tree_map +def dict_map( + fn: Callable[[T], Any], dic: Dict[Any, Union[dict, list, tuple, T]], leaf_type: Type[T] +) -> Dict[Any, Union[dict, list, tuple, Any]]: + new_dict: Dict[Any, Union[dict, list, tuple, Any]] = {} + for k, v in dic.items(): + if isinstance(v, dict): + new_dict[k] = dict_map(fn, v, leaf_type) + else: + new_dict[k] = tree_map(fn, v, leaf_type) + + return new_dict + + +@overload +def tree_map(fn: Callable[[T], Any], tree: T, leaf_type: Type[T]) -> Any: + ... + + +@overload +def tree_map(fn: Callable[[T], Any], tree: dict, leaf_type: Type[T]) -> dict: + ... + + +@overload +def 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Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert SegFormer checkpoints.""" + + +import argparse +import json +from collections import OrderedDict +from pathlib import Path + +import requests +import torch +from huggingface_hub import hf_hub_download +from PIL import Image + +from transformers import ( + SegformerConfig, + SegformerForImageClassification, + SegformerForSemanticSegmentation, + SegformerImageProcessor, +) +from transformers.utils import logging + + +logging.set_verbosity_info() +logger = logging.get_logger(__name__) + + +def rename_keys(state_dict, encoder_only=False): + new_state_dict = OrderedDict() + for key, value in state_dict.items(): + if encoder_only and not key.startswith("head"): + key = "segformer.encoder." + key + if key.startswith("backbone"): + key = key.replace("backbone", "segformer.encoder") + if "patch_embed" in key: + # replace for example patch_embed1 by patch_embeddings.0 + idx = key[key.find("patch_embed") + len("patch_embed")] + key = key.replace(f"patch_embed{idx}", f"patch_embeddings.{int(idx)-1}") + if "norm" in key: + key = key.replace("norm", "layer_norm") + if "segformer.encoder.layer_norm" in key: + # replace for example layer_norm1 by layer_norm.0 + idx = key[key.find("segformer.encoder.layer_norm") + len("segformer.encoder.layer_norm")] + key = key.replace(f"layer_norm{idx}", f"layer_norm.{int(idx)-1}") + if "layer_norm1" in key: + key = key.replace("layer_norm1", "layer_norm_1") + if "layer_norm2" in key: + key = key.replace("layer_norm2", "layer_norm_2") + if "block" in key: + # replace for example block1 by block.0 + idx = key[key.find("block") + len("block")] + key = key.replace(f"block{idx}", f"block.{int(idx)-1}") + if "attn.q" in key: + key = key.replace("attn.q", "attention.self.query") + if "attn.proj" in key: + key = key.replace("attn.proj", "attention.output.dense") + if "attn" in key: + key = key.replace("attn", "attention.self") + if "fc1" in key: + key = key.replace("fc1", "dense1") + if "fc2" in key: + key = key.replace("fc2", "dense2") + if "linear_pred" in key: + key = key.replace("linear_pred", "classifier") + if "linear_fuse" in key: + key = key.replace("linear_fuse.conv", "linear_fuse") + key = key.replace("linear_fuse.bn", "batch_norm") + if "linear_c" in key: + # replace for example linear_c4 by linear_c.3 + idx = key[key.find("linear_c") + len("linear_c")] + key = key.replace(f"linear_c{idx}", f"linear_c.{int(idx)-1}") + if key.startswith("head"): + key = key.replace("head", "classifier") + new_state_dict[key] = value + + return new_state_dict + + +def read_in_k_v(state_dict, config): + # for each of the encoder blocks: + for i in range(config.num_encoder_blocks): + for j in range(config.depths[i]): + # read in weights + bias of keys and values (which is a single matrix in the original implementation) + kv_weight = state_dict.pop(f"segformer.encoder.block.{i}.{j}.attention.self.kv.weight") + kv_bias = state_dict.pop(f"segformer.encoder.block.{i}.{j}.attention.self.kv.bias") + # next, add keys and values (in that order) to the state dict + state_dict[f"segformer.encoder.block.{i}.{j}.attention.self.key.weight"] = kv_weight[ + : config.hidden_sizes[i], : + ] + state_dict[f"segformer.encoder.block.{i}.{j}.attention.self.key.bias"] = kv_bias[: config.hidden_sizes[i]] + state_dict[f"segformer.encoder.block.{i}.{j}.attention.self.value.weight"] = kv_weight[ + config.hidden_sizes[i] :, : + ] + state_dict[f"segformer.encoder.block.{i}.{j}.attention.self.value.bias"] = kv_bias[ + config.hidden_sizes[i] : + ] + + +# We will verify our results on a COCO image +def prepare_img(): + url = "http://images.cocodataset.org/val2017/000000039769.jpg" + image = Image.open(requests.get(url, stream=True).raw) + + return image + + +@torch.no_grad() +def convert_segformer_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path): + """ + Copy/paste/tweak model's weights to our SegFormer structure. + """ + + # load default SegFormer configuration + config = SegformerConfig() + encoder_only = False + + # set attributes based on model_name + repo_id = "huggingface/label-files" + if "segformer" in model_name: + size = model_name[len("segformer.") : len("segformer.") + 2] + if "ade" in model_name: + config.num_labels = 150 + filename = "ade20k-id2label.json" + expected_shape = (1, 150, 128, 128) + elif "city" in model_name: + config.num_labels = 19 + filename = "cityscapes-id2label.json" + expected_shape = (1, 19, 128, 128) + else: + raise ValueError(f"Model {model_name} not supported") + elif "mit" in model_name: + encoder_only = True + size = model_name[4:6] + config.num_labels = 1000 + filename = "imagenet-1k-id2label.json" + expected_shape = (1, 1000) + else: + raise ValueError(f"Model {model_name} not supported") + + # set config attributes + id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) + id2label = {int(k): v for k, v in id2label.items()} + config.id2label = id2label + config.label2id = {v: k for k, v in id2label.items()} + if size == "b0": + pass + elif size == "b1": + config.hidden_sizes = [64, 128, 320, 512] + config.decoder_hidden_size = 256 + elif size == "b2": + config.hidden_sizes = [64, 128, 320, 512] + config.decoder_hidden_size = 768 + config.depths = [3, 4, 6, 3] + elif size == "b3": + config.hidden_sizes = [64, 128, 320, 512] + config.decoder_hidden_size = 768 + config.depths = [3, 4, 18, 3] + elif size == "b4": + config.hidden_sizes = [64, 128, 320, 512] + config.decoder_hidden_size = 768 + config.depths = [3, 8, 27, 3] + elif size == "b5": + config.hidden_sizes = [64, 128, 320, 512] + config.decoder_hidden_size = 768 + config.depths = [3, 6, 40, 3] + else: + raise ValueError(f"Size {size} not supported") + + # load image processor (only resize + normalize) + image_processor = SegformerImageProcessor( + image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False + ) + + # prepare image + image = prepare_img() + pixel_values = image_processor(images=image, return_tensors="pt").pixel_values + + logger.info(f"Converting model {model_name}...") + + # load original state dict + if encoder_only: + state_dict = torch.load(checkpoint_path, map_location=torch.device("cpu")) + else: + state_dict = torch.load(checkpoint_path, map_location=torch.device("cpu"))["state_dict"] + + # rename keys + state_dict = rename_keys(state_dict, encoder_only=encoder_only) + if not encoder_only: + del state_dict["decode_head.conv_seg.weight"] + del state_dict["decode_head.conv_seg.bias"] + + # key and value matrices need special treatment + read_in_k_v(state_dict, config) + + # create HuggingFace model and load state dict + if encoder_only: + config.reshape_last_stage = False + model = SegformerForImageClassification(config) + else: + model = SegformerForSemanticSegmentation(config) + model.load_state_dict(state_dict) + model.eval() + + # forward pass + outputs = model(pixel_values) + logits = outputs.logits + + # set expected_slice based on model name + # ADE20k checkpoints + if model_name == "segformer.b0.512x512.ade.160k": + expected_slice = torch.tensor( + [ + [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]], + [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]], + [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]], + ] + ) + elif model_name == "segformer.b1.512x512.ade.160k": + expected_slice = torch.tensor( + [ + [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]], + [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]], + [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]], + ] + ) + elif model_name == "segformer.b2.512x512.ade.160k": + expected_slice = torch.tensor( + [ + [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]], + [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]], + [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]], + ] + ) + elif model_name == "segformer.b3.512x512.ade.160k": + expected_slice = torch.tensor( + [ + [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]], + [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]], + [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]], + ] + ) + elif model_name == "segformer.b4.512x512.ade.160k": + expected_slice = torch.tensor( + [ + [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]], + [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]], + [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]], + ] + ) + elif model_name == "segformer.b5.640x640.ade.160k": + expected_slice = torch.tensor( + [ + [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]], + [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]], + [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]], + ] + ) + # Cityscapes checkpoints + elif model_name == "segformer.b0.1024x1024.city.160k": + expected_slice = torch.tensor( + [ + [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]], + [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]], + [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]], + ] + ) + elif model_name == "segformer.b0.512x1024.city.160k": + expected_slice = torch.tensor( + [ + [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]], + [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]], + [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]], + ] + ) + elif model_name == "segformer.b0.640x1280.city.160k": + expected_slice = torch.tensor( + [ + [ + [-1.1372e01, -1.2787e01, -1.3477e01], + [-1.2536e01, -1.4194e01, -1.4409e01], + [-1.3217e01, -1.4888e01, -1.5327e01], + ], + [ + [-1.4791e01, -1.7122e01, -1.8277e01], + [-1.7163e01, -1.9192e01, -1.9533e01], + [-1.7897e01, -1.9991e01, -2.0315e01], + ], + [ + [7.6723e-01, 4.1921e-01, -7.7878e-02], + [4.7772e-01, 9.5557e-03, -2.8082e-01], + [3.6032e-01, -2.4826e-01, -5.1168e-01], + ], + ] + ) + elif model_name == "segformer.b0.768x768.city.160k": + expected_slice = torch.tensor( + [ + [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]], + [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]], + [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]], + ] + ) + elif model_name == "segformer.b1.1024x1024.city.160k": + expected_slice = torch.tensor( + [ + [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]], + [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]], + [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]], + ] + ) + elif model_name == "segformer.b2.1024x1024.city.160k": + expected_slice = torch.tensor( + [ + [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]], + [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]], + [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]], + ] + ) + elif model_name == "segformer.b3.1024x1024.city.160k": + expected_slice = torch.tensor( + [ + [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]], + [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]], + [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]], + ] + ) + elif model_name == "segformer.b4.1024x1024.city.160k": + expected_slice = torch.tensor( + [ + [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]], + [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]], + [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]], + ] + ) + elif model_name == "segformer.b5.1024x1024.city.160k": + expected_slice = torch.tensor( + [ + [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]], + [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]], + [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]], + ] + ) + else: + predicted_class_idx = logits.argmax(-1).item() + print("Predicted class:", model.config.id2label[predicted_class_idx]) + + # verify logits + if not encoder_only: + assert logits.shape == expected_shape + assert torch.allclose(logits[0, :3, :3, :3], expected_slice, atol=1e-2) + + # finally, save model and image processor + logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}...") + Path(pytorch_dump_folder_path).mkdir(exist_ok=True) + model.save_pretrained(pytorch_dump_folder_path) + image_processor.save_pretrained(pytorch_dump_folder_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--model_name", + default="segformer.b0.512x512.ade.160k", + type=str, + help="Name of the model you'd like to convert.", + ) + parser.add_argument( + "--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)." + ) + parser.add_argument( + "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model." + ) + args = parser.parse_args() + convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path) diff --git 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-0,0 +1,358 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert Wav2Vec2 checkpoint.""" + + +import argparse + +import fairseq +import torch +from torch import nn + +from transformers import ( + MBart50Tokenizer, + MBartConfig, + MBartForCausalLM, + SpeechEncoderDecoderConfig, + SpeechEncoderDecoderModel, + Wav2Vec2Config, + Wav2Vec2FeatureExtractor, + Wav2Vec2Model, + logging, +) + + +logging.set_verbosity_info() +logger = logging.get_logger(__name__) + +MAPPING = { + "post_extract_proj": "feature_projection.projection", + "encoder.pos_conv.0": "encoder.pos_conv_embed.conv", + "self_attn.k_proj": "encoder.layers.*.attention.k_proj", + "self_attn.v_proj": "encoder.layers.*.attention.v_proj", + "self_attn.q_proj": "encoder.layers.*.attention.q_proj", + "self_attn.out_proj": "encoder.layers.*.attention.out_proj", + "self_attn_layer_norm": "encoder.layers.*.layer_norm", + "fc1": "encoder.layers.*.feed_forward.intermediate_dense", + "fc2": "encoder.layers.*.feed_forward.output_dense", + "final_layer_norm": "encoder.layers.*.final_layer_norm", + "encoder.layer_norm": "encoder.layer_norm", + "w2v_model.layer_norm": "feature_projection.layer_norm", + "quantizer.weight_proj": "quantizer.weight_proj", + "quantizer.vars": "quantizer.codevectors", + "project_q": "project_q", + "final_proj": "project_hid", + "w2v_encoder.proj": "lm_head", + "mask_emb": "masked_spec_embed", +} +TOP_LEVEL_KEYS = [ + "lm_head", + "quantizer.weight_proj", + "quantizer.codevectors", + "project_q", + "project_hid", +] + + +def set_recursively(hf_pointer, key, value, full_name, weight_type): + for attribute in key.split("."): + hf_pointer = getattr(hf_pointer, attribute) + + if weight_type is not None: + hf_shape = getattr(hf_pointer, weight_type).shape + else: + hf_shape = hf_pointer.shape + + assert hf_shape == value.shape, ( + f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be" + f" {value.shape} for {full_name}" + ) + + if weight_type == "weight": + hf_pointer.weight.data = value + elif weight_type == "weight_g": + hf_pointer.weight_g.data = value + elif weight_type == "weight_v": + hf_pointer.weight_v.data = value + elif weight_type == "bias": + hf_pointer.bias.data = value + else: + hf_pointer.data = value + + logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.") + + +def recursively_load_weights_wav2vec2(fairseq_model, hf_model): + unused_weights = [] + fairseq_dict = fairseq_model.state_dict() + + feature_extractor = hf_model.feature_extractor + adapter = hf_model.adapter + + for name, value in fairseq_dict.items(): + is_used = False + if "conv_layers" in name: + load_conv_layer( + name, + value, + feature_extractor, + unused_weights, + hf_model.config.feat_extract_norm == "group", + ) + is_used = True + elif any(x in name for x in ["adaptor", "w2v_encoder.proj.", "w2v_proj_ln."]): + load_adapter(name, value, adapter, unused_weights) + is_used = True + else: + for key, mapped_key in MAPPING.items(): + if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]: + is_used = True + if "*" in mapped_key: + layer_index = name.split(key)[0].split(".")[-2] + mapped_key = mapped_key.replace("*", layer_index) + if "weight_g" in name: + weight_type = "weight_g" + elif "weight_v" in name: + weight_type = "weight_v" + elif "bias" in name: + weight_type = "bias" + elif "weight" in name: + weight_type = "weight" + else: + weight_type = None + set_recursively(hf_model, mapped_key, value, name, weight_type) + continue + if not is_used: + unused_weights.append(name) + + logger.warning(f"Unused weights: {unused_weights}") + + +def load_conv_layer(full_name, value, feature_extractor, unused_weights, use_group_norm): + name = full_name.split("conv_layers.")[-1] + items = name.split(".") + layer_id = int(items[0]) + type_id = int(items[1]) + + if type_id == 0: + if "bias" in name: + assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, ( + f"{full_name} has size {value.shape}, but" + f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found." + ) + feature_extractor.conv_layers[layer_id].conv.bias.data = value + logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.") + elif "weight" in name: + assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, ( + f"{full_name} has size {value.shape}, but" + f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found." + ) + feature_extractor.conv_layers[layer_id].conv.weight.data = value + logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.") + elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): + if "bias" in name: + assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, ( + f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was" + " found." + ) + feature_extractor.conv_layers[layer_id].layer_norm.bias.data = value + logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.") + elif "weight" in name: + assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, ( + f"{full_name} has size {value.shape}, but" + f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found." + ) + feature_extractor.conv_layers[layer_id].layer_norm.weight.data = value + logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.") + else: + unused_weights.append(full_name) + + +def load_adapter(full_name, value, adapter, unused_weights): + name = full_name.split("adaptor.")[-1] + items = name.split(".") + + if items[1].isdigit(): + layer_id = int(items[1]) + else: + layer_id = None + + if "adaptor" not in full_name: + if "proj_ln" in full_name: + # has to be layer norm + if "bias" in name: + assert ( + value.shape == adapter.proj_layer_norm.bias.data.shape + ), f"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found." + adapter.proj_layer_norm.bias.data = value + logger.info(f"Adapter proj layer norm bias was initialized from {full_name}.") + if "weight" in name: + assert ( + value.shape == adapter.proj_layer_norm.weight.data.shape + ), f"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found." + adapter.proj_layer_norm.weight.data = value + else: + # has to be projection layer + if "bias" in name: + assert ( + value.shape == adapter.proj.bias.data.shape + ), f"{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found." + adapter.proj.bias.data = value + logger.info(f"Adapter proj layer bias was initialized from {full_name}.") + if "weight" in name: + assert ( + value.shape == adapter.proj.weight.data.shape + ), f"{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found." + adapter.proj.weight.data = value + logger.info(f"Adapter proj layer weight was initialized from {full_name}.") + elif isinstance(layer_id, int): + if "bias" in name: + assert ( + value.shape == adapter.layers[layer_id].conv.bias.data.shape + ), f"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found." + adapter.layers[layer_id].conv.bias.data = value + logger.info(f"Adapter layer {layer_id} bias was initialized from {full_name}.") + elif "weight" in name: + assert ( + value.shape == adapter.layers[layer_id].conv.weight.data.shape + ), f"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found." + adapter.layers[layer_id].conv.weight.data = value + logger.info(f"Adapter layer {layer_id} bias was initialized from {full_name}.") + else: + unused_weights.append(full_name) + + +def make_linear_from_emb(emb): + vocab_size, emb_size = emb.weight.shape + lin_layer = nn.Linear(vocab_size, emb_size, bias=False) + lin_layer.weight.data = emb.weight.data + return lin_layer + + +@torch.no_grad() +def convert_wav2vec2_checkpoint( + checkpoint_path, + pytorch_dump_folder_path, + dict_path, + config_yaml_path, + encoder_config_path, + decoder_config_path, + add_adapter, + adapter_kernel_size, + adapter_stride, + decoder_start_token_id, + encoder_output_dim, +): + """ + Copy/paste/tweak model's weights to transformers design. + """ + # load configs + encoder_config = Wav2Vec2Config.from_pretrained( + encoder_config_path, + add_adapter=True, + adapter_stride=adapter_stride, + adapter_kernel_size=adapter_kernel_size, + token_token=True, + output_hidden_size=encoder_output_dim, + ) + decoder_config = MBartConfig.from_pretrained(decoder_config_path) + + # load model + model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task( + [checkpoint_path], + arg_overrides={ + "config_yaml": config_yaml_path, + "data": "/".join(dict_path.split("/")[:-1]), + "w2v_path": checkpoint_path, + "load_pretrained_decoder_from": None, + }, + ) + model = model[0].eval() + + # load feature extractor + feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(encoder_config_path, token_token=True) + + # set weights for wav2vec2 encoder + hf_encoder = Wav2Vec2Model(encoder_config) + + recursively_load_weights_wav2vec2(model.encoder, hf_encoder) + + # load decoder weights + hf_decoder = MBartForCausalLM(decoder_config) + missing_keys, unexpected_keys = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(), strict=False) + logger.warning(f"The following keys are missing when loading the decoder weights: {missing_keys}") + logger.warning(f"The following keys are unexpected when loading the decoder weights: {unexpected_keys}") + + hf_wav2vec = SpeechEncoderDecoderModel(encoder=hf_encoder, decoder=hf_decoder) + hf_wav2vec.config.tie_word_embeddings = False + + tokenizer = MBart50Tokenizer(dict_path) + tokenizer.save_pretrained(pytorch_dump_folder_path) + + config = hf_wav2vec.config.to_dict() + config["pad_token_id"] = tokenizer.pad_token_id + config["bos_token_id"] = tokenizer.bos_token_id + config["eos_token_id"] = tokenizer.eos_token_id + config["tokenizer_class"] = "mbart50" + config["feature_extractor_type"] = "wav2vec2" + + config["decoder_start_token_id"] = tokenizer.eos_token_id + config["forced_bos_token_id"] = 250004 + config["forced_eos_token_id"] = tokenizer.eos_token_id + + hf_wav2vec.config = SpeechEncoderDecoderConfig.from_dict(config) + + hf_wav2vec.save_pretrained(pytorch_dump_folder_path) + feature_extractor.save_pretrained(pytorch_dump_folder_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") + parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint") + parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model") + parser.add_argument("--config_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model") + parser.add_argument( + "--encoder_config_path", + default="facebook/wav2vec2-xls-r-1b", + type=str, + help="Path to hf encoder wav2vec2 checkpoint config", + ) + parser.add_argument( + "--decoder_config_path", + default="facebook/mbart-large-50-one-to-many-mmt", + type=str, + help="Path to hf decoder checkpoint config", + ) + parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers") + parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers") + parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers") + parser.add_argument("--encoder_output_dim", default=1024, type=int, help="encoder output dim") + parser.add_argument("--start_token_id", default=250004, type=int, help="`decoder_start_token_id` of model config") + + args = parser.parse_args() + convert_wav2vec2_checkpoint( + args.checkpoint_path, + args.pytorch_dump_folder_path, + args.dict_path, + args.config_yaml_path, + encoder_config_path=args.encoder_config_path, + decoder_config_path=args.decoder_config_path, + add_adapter=args.add_adapter, + 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a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/squeezebert/tokenization_squeezebert.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/squeezebert/tokenization_squeezebert.py new file mode 100644 index 0000000000000000000000000000000000000000..30f866770d2465a2897e548b8356fe9f6e88b911 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/squeezebert/tokenization_squeezebert.py @@ -0,0 +1,503 @@ +# coding=utf-8 +# Copyright 2020 The SqueezeBert authors and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization classes for SqueezeBERT.""" + +import collections +import os +import unicodedata +from typing import List, Optional, Tuple + +from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace +from ...utils import logging + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"} + + +# Copied from transformers.models.bert.tokenization_bert.load_vocab +def load_vocab(vocab_file): + """Loads a vocabulary file into a dictionary.""" + vocab = collections.OrderedDict() + with open(vocab_file, "r", encoding="utf-8") as reader: + tokens = reader.readlines() + for index, token in enumerate(tokens): + token = token.rstrip("\n") + vocab[token] = index + return vocab + + +# Copied from transformers.models.bert.tokenization_bert.whitespace_tokenize +def whitespace_tokenize(text): + """Runs basic whitespace cleaning and splitting on a piece of text.""" + text = text.strip() + if not text: + return [] + tokens = text.split() + return tokens + + +# Copied from transformers.models.bert.tokenization_bert.BertTokenizer with Bert->SqueezeBert,BERT->SqueezeBERT +class SqueezeBertTokenizer(PreTrainedTokenizer): + r""" + Construct a SqueezeBERT tokenizer. Based on WordPiece. + + This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to + this superclass for more information regarding those methods. + + Args: + vocab_file (`str`): + File containing the vocabulary. + do_lower_case (`bool`, *optional*, defaults to `True`): + Whether or not to lowercase the input when tokenizing. + do_basic_tokenize (`bool`, *optional*, defaults to `True`): + Whether or not to do basic tokenization before WordPiece. + never_split (`Iterable`, *optional*): + Collection of tokens which will never be split during tokenization. Only has an effect when + `do_basic_tokenize=True` + unk_token (`str`, *optional*, defaults to `"[UNK]"`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + sep_token (`str`, *optional*, defaults to `"[SEP]"`): + The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for + sequence classification or for a text and a question for question answering. It is also used as the last + token of a sequence built with special tokens. + pad_token (`str`, *optional*, defaults to `"[PAD]"`): + The token used for padding, for example when batching sequences of different lengths. + cls_token (`str`, *optional*, defaults to `"[CLS]"`): + The classifier token which is used when doing sequence classification (classification of the whole sequence + instead of per-token classification). It is the first token of the sequence when built with special tokens. + mask_token (`str`, *optional*, defaults to `"[MASK]"`): + The token used for masking values. This is the token used when training this model with masked language + modeling. This is the token which the model will try to predict. + tokenize_chinese_chars (`bool`, *optional*, defaults to `True`): + Whether or not to tokenize Chinese characters. + + This should likely be deactivated for Japanese (see this + [issue](https://github.com/huggingface/transformers/issues/328)). + strip_accents (`bool`, *optional*): + Whether or not to strip all accents. If this option is not specified, then it will be determined by the + value for `lowercase` (as in the original SqueezeBERT). + """ + + vocab_files_names = VOCAB_FILES_NAMES + + def __init__( + self, + vocab_file, + do_lower_case=True, + do_basic_tokenize=True, + never_split=None, + unk_token="[UNK]", + sep_token="[SEP]", + pad_token="[PAD]", + cls_token="[CLS]", + mask_token="[MASK]", + tokenize_chinese_chars=True, + strip_accents=None, + **kwargs, + ): + if not os.path.isfile(vocab_file): + raise ValueError( + f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained" + " model use `tokenizer = SqueezeBertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" + ) + self.vocab = load_vocab(vocab_file) + self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()]) + self.do_basic_tokenize = do_basic_tokenize + if do_basic_tokenize: + self.basic_tokenizer = BasicTokenizer( + do_lower_case=do_lower_case, + never_split=never_split, + tokenize_chinese_chars=tokenize_chinese_chars, + strip_accents=strip_accents, + ) + + self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token)) + + super().__init__( + do_lower_case=do_lower_case, + do_basic_tokenize=do_basic_tokenize, + never_split=never_split, + unk_token=unk_token, + sep_token=sep_token, + pad_token=pad_token, + cls_token=cls_token, + mask_token=mask_token, + tokenize_chinese_chars=tokenize_chinese_chars, + strip_accents=strip_accents, + **kwargs, + ) + + @property + def do_lower_case(self): + return self.basic_tokenizer.do_lower_case + + @property + def vocab_size(self): + return len(self.vocab) + + def get_vocab(self): + return dict(self.vocab, **self.added_tokens_encoder) + + def _tokenize(self, text, split_special_tokens=False): + split_tokens = [] + if self.do_basic_tokenize: + for token in self.basic_tokenizer.tokenize( + text, never_split=self.all_special_tokens if not split_special_tokens else None + ): + # If the token is part of the never_split set + if token in self.basic_tokenizer.never_split: + split_tokens.append(token) + else: + split_tokens += self.wordpiece_tokenizer.tokenize(token) + else: + split_tokens = self.wordpiece_tokenizer.tokenize(text) + return split_tokens + + def _convert_token_to_id(self, token): + """Converts a token (str) in an id using the vocab.""" + return self.vocab.get(token, self.vocab.get(self.unk_token)) + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + return self.ids_to_tokens.get(index, self.unk_token) + + def convert_tokens_to_string(self, tokens): + """Converts a sequence of tokens (string) in a single string.""" + out_string = " ".join(tokens).replace(" ##", "").strip() + return out_string + + def build_inputs_with_special_tokens( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and + adding special tokens. A SqueezeBERT sequence has the following format: + + - single sequence: `[CLS] X [SEP]` + - pair of sequences: `[CLS] A [SEP] B [SEP]` + + Args: + token_ids_0 (`List[int]`): + List of IDs to which the special tokens will be added. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. + """ + if token_ids_1 is None: + return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] + cls = [self.cls_token_id] + sep = [self.sep_token_id] + return cls + token_ids_0 + sep + token_ids_1 + sep + + def get_special_tokens_mask( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False + ) -> List[int]: + """ + Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding + special tokens using the tokenizer `prepare_for_model` method. + + Args: + token_ids_0 (`List[int]`): + List of IDs. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + already_has_special_tokens (`bool`, *optional*, defaults to `False`): + Whether or not the token list is already formatted with special tokens for the model. + + Returns: + `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. + """ + + if already_has_special_tokens: + return super().get_special_tokens_mask( + token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True + ) + + if token_ids_1 is not None: + return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] + return [1] + ([0] * len(token_ids_0)) + [1] + + def create_token_type_ids_from_sequences( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Create a mask from the two sequences passed to be used in a sequence-pair classification task. A SqueezeBERT sequence + pair mask has the following format: + + ``` + 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 + | first sequence | second sequence | + ``` + + If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). + + Args: + token_ids_0 (`List[int]`): + List of IDs. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). + """ + sep = [self.sep_token_id] + cls = [self.cls_token_id] + if token_ids_1 is None: + return len(cls + token_ids_0 + sep) * [0] + return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] + + def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: + index = 0 + if os.path.isdir(save_directory): + vocab_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] + ) + else: + vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory + with open(vocab_file, "w", encoding="utf-8") as writer: + for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]): + if index != token_index: + logger.warning( + f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive." + " Please check that the vocabulary is not corrupted!" + ) + index = token_index + writer.write(token + "\n") + index += 1 + return (vocab_file,) + + +# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer +class BasicTokenizer(object): + """ + Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.). + + Args: + do_lower_case (`bool`, *optional*, defaults to `True`): + Whether or not to lowercase the input when tokenizing. + never_split (`Iterable`, *optional*): + Collection of tokens which will never be split during tokenization. Only has an effect when + `do_basic_tokenize=True` + tokenize_chinese_chars (`bool`, *optional*, defaults to `True`): + Whether or not to tokenize Chinese characters. + + This should likely be deactivated for Japanese (see this + [issue](https://github.com/huggingface/transformers/issues/328)). + strip_accents (`bool`, *optional*): + Whether or not to strip all accents. If this option is not specified, then it will be determined by the + value for `lowercase` (as in the original BERT). + do_split_on_punc (`bool`, *optional*, defaults to `True`): + In some instances we want to skip the basic punctuation splitting so that later tokenization can capture + the full context of the words, such as contractions. + """ + + def __init__( + self, + do_lower_case=True, + never_split=None, + tokenize_chinese_chars=True, + strip_accents=None, + do_split_on_punc=True, + ): + if never_split is None: + never_split = [] + self.do_lower_case = do_lower_case + self.never_split = set(never_split) + self.tokenize_chinese_chars = tokenize_chinese_chars + self.strip_accents = strip_accents + self.do_split_on_punc = do_split_on_punc + + def tokenize(self, text, never_split=None): + """ + Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer. + + Args: + never_split (`List[str]`, *optional*) + Kept for backward compatibility purposes. Now implemented directly at the base class level (see + [`PreTrainedTokenizer.tokenize`]) List of token not to split. + """ + # union() returns a new set by concatenating the two sets. + never_split = self.never_split.union(set(never_split)) if never_split else self.never_split + text = self._clean_text(text) + + # This was added on November 1st, 2018 for the multilingual and Chinese + # models. This is also applied to the English models now, but it doesn't + # matter since the English models were not trained on any Chinese data + # and generally don't have any Chinese data in them (there are Chinese + # characters in the vocabulary because Wikipedia does have some Chinese + # words in the English Wikipedia.). + if self.tokenize_chinese_chars: + text = self._tokenize_chinese_chars(text) + # prevents treating the same character with different unicode codepoints as different characters + unicode_normalized_text = unicodedata.normalize("NFC", text) + orig_tokens = whitespace_tokenize(unicode_normalized_text) + split_tokens = [] + for token in orig_tokens: + if token not in never_split: + if self.do_lower_case: + token = token.lower() + if self.strip_accents is not False: + token = self._run_strip_accents(token) + elif self.strip_accents: + token = self._run_strip_accents(token) + split_tokens.extend(self._run_split_on_punc(token, never_split)) + + output_tokens = whitespace_tokenize(" ".join(split_tokens)) + return output_tokens + + def _run_strip_accents(self, text): + """Strips accents from a piece of text.""" + text = unicodedata.normalize("NFD", text) + output = [] + for char in text: + cat = unicodedata.category(char) + if cat == "Mn": + continue + output.append(char) + return "".join(output) + + def _run_split_on_punc(self, text, never_split=None): + """Splits punctuation on a piece of text.""" + if not self.do_split_on_punc or (never_split is not None and text in never_split): + return [text] + chars = list(text) + i = 0 + start_new_word = True + output = [] + while i < len(chars): + char = chars[i] + if _is_punctuation(char): + output.append([char]) + start_new_word = True + else: + if start_new_word: + output.append([]) + start_new_word = False + output[-1].append(char) + i += 1 + + return ["".join(x) for x in output] + + def _tokenize_chinese_chars(self, text): + """Adds whitespace around any CJK character.""" + output = [] + for char in text: + cp = ord(char) + if self._is_chinese_char(cp): + output.append(" ") + output.append(char) + output.append(" ") + else: + output.append(char) + return "".join(output) + + def _is_chinese_char(self, cp): + """Checks whether CP is the codepoint of a CJK character.""" + # This defines a "chinese character" as anything in the CJK Unicode block: + # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) + # + # Note that the CJK Unicode block is NOT all Japanese and Korean characters, + # despite its name. The modern Korean Hangul alphabet is a different block, + # as is Japanese Hiragana and Katakana. Those alphabets are used to write + # space-separated words, so they are not treated specially and handled + # like the all of the other languages. + if ( + (cp >= 0x4E00 and cp <= 0x9FFF) + or (cp >= 0x3400 and cp <= 0x4DBF) # + or (cp >= 0x20000 and cp <= 0x2A6DF) # + or (cp >= 0x2A700 and cp <= 0x2B73F) # + or (cp >= 0x2B740 and cp <= 0x2B81F) # + or (cp >= 0x2B820 and cp <= 0x2CEAF) # + or (cp >= 0xF900 and cp <= 0xFAFF) + or (cp >= 0x2F800 and cp <= 0x2FA1F) # + ): # + return True + + return False + + def _clean_text(self, text): + """Performs invalid character removal and whitespace cleanup on text.""" + output = [] + for char in text: + cp = ord(char) + if cp == 0 or cp == 0xFFFD or _is_control(char): + continue + if _is_whitespace(char): + output.append(" ") + else: + output.append(char) + return "".join(output) + + +class WordpieceTokenizer(object): + """Runs WordPiece tokenization.""" + + def __init__(self, vocab, unk_token, max_input_chars_per_word=100): + self.vocab = vocab + self.unk_token = unk_token + self.max_input_chars_per_word = max_input_chars_per_word + + def tokenize(self, text): + """ + Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform + tokenization using the given vocabulary. + + For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`. + + Args: + text: A single token or whitespace separated tokens. This should have + already been passed through *BasicTokenizer*. + + Returns: + A list of wordpiece tokens. + """ + + output_tokens = [] + for token in whitespace_tokenize(text): + chars = list(token) + if len(chars) > self.max_input_chars_per_word: + output_tokens.append(self.unk_token) + continue + + is_bad = False + start = 0 + sub_tokens = [] + while start < len(chars): + end = len(chars) + cur_substr = None + while start < end: + substr = "".join(chars[start:end]) + if start > 0: + substr = "##" + substr + if substr in self.vocab: + cur_substr = substr + break + end -= 1 + if cur_substr is None: + is_bad = True + break + sub_tokens.append(cur_substr) + start = end + + if is_bad: + output_tokens.append(self.unk_token) + else: + output_tokens.extend(sub_tokens) + return output_tokens diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/stablelm/__pycache__/modeling_stablelm.cpython-312.pyc 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All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" TF 2.0 Swin Transformer model.""" + + +from __future__ import annotations + +import collections.abc +import math +import warnings +from dataclasses import dataclass +from functools import partial +from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union + +import tensorflow as tf + +from ...activations_tf import ACT2FN +from ...modeling_tf_utils import ( + TFPreTrainedModel, + TFSequenceClassificationLoss, + get_initializer, + keras, + keras_serializable, + unpack_inputs, +) +from ...tf_utils import shape_list +from ...utils import ( + ModelOutput, + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) +from .configuration_swin import SwinConfig + + +logger = logging.get_logger(__name__) + +# General docstring +_CONFIG_FOR_DOC = "SwinConfig" + +# Base docstring +_CHECKPOINT_FOR_DOC = "microsoft/swin-tiny-patch4-window7-224" +_EXPECTED_OUTPUT_SHAPE = [1, 49, 768] + +# Image classification docstring +_IMAGE_CLASS_CHECKPOINT = "microsoft/swin-tiny-patch4-window7-224" +_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat" + + +from ..deprecated._archive_maps import TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +# drop_path, TFSwinPatchEmbeddings, TFSwinPatchMerging and TFSwinDropPath are tensorflow +# implementations of PyTorch functionalities in the timm library. + + +@dataclass +class TFSwinEncoderOutput(ModelOutput): + """ + Swin encoder's outputs, with potential hidden states and attentions. + + Args: + last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each stage) of shape + `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `tf.Tensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + reshaped_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each stage) of shape + `(batch_size, hidden_size, height, width)`. + + Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to + include the spatial dimensions. + """ + + last_hidden_state: tf.Tensor = None + hidden_states: Tuple[tf.Tensor, ...] | None = None + attentions: Tuple[tf.Tensor, ...] | None = None + reshaped_hidden_states: Tuple[tf.Tensor, ...] | None = None + + +@dataclass +class TFSwinModelOutput(ModelOutput): + """ + Swin model's outputs that also contains a pooling of the last hidden states. + + Args: + last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + pooler_output (`tf.Tensor` of shape `(batch_size, hidden_size)`, *optional*, returned when `add_pooling_layer=True` is passed): + Average pooling of the last layer hidden-state. + hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each stage) of shape + `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `tf.Tensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + reshaped_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each stage) of shape + `(batch_size, hidden_size, height, width)`. + + Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to + include the spatial dimensions. + """ + + last_hidden_state: tf.Tensor = None + pooler_output: tf.Tensor | None = None + hidden_states: Tuple[tf.Tensor, ...] | None = None + attentions: Tuple[tf.Tensor, ...] | None = None + reshaped_hidden_states: Tuple[tf.Tensor, ...] | None = None + + +@dataclass +class TFSwinMaskedImageModelingOutput(ModelOutput): + """ + Swin masked image model outputs. + + Args: + loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided): + Masked image modeling (MLM) loss. + reconstruction (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`): + Reconstructed pixel values. + hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each stage) of shape + `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `tf.Tensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + reshaped_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each stage) of shape + `(batch_size, hidden_size, height, width)`. + + Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to + include the spatial dimensions. + """ + + loss: tf.Tensor | None = None + reconstruction: tf.Tensor = None + hidden_states: Tuple[tf.Tensor, ...] | None = None + attentions: Tuple[tf.Tensor, ...] | None = None + reshaped_hidden_states: Tuple[tf.Tensor, ...] | None = None + + @property + def logits(self): + warnings.warn( + "logits attribute is deprecated and will be removed in version 5 of Transformers." + " Please use the reconstruction attribute to retrieve the final output instead.", + FutureWarning, + ) + return self.reconstruction + + +@dataclass +class TFSwinImageClassifierOutput(ModelOutput): + """ + Swin outputs for image classification. + + Args: + loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Classification (or regression if config.num_labels==1) loss. + logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`): + Classification (or regression if config.num_labels==1) scores (before SoftMax). + hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each stage) of shape + `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `tf.Tensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + reshaped_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each stage) of shape + `(batch_size, hidden_size, height, width)`. + + Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to + include the spatial dimensions. + """ + + loss: tf.Tensor | None = None + logits: tf.Tensor = None + hidden_states: Tuple[tf.Tensor, ...] | None = None + attentions: Tuple[tf.Tensor, ...] | None = None + reshaped_hidden_states: Tuple[tf.Tensor, ...] | None = None + + +def window_partition(input_feature: tf.Tensor, window_size: int) -> tf.Tensor: + """ + Partitions the given input into windows. + """ + batch_size, height, width, num_channels = shape_list(input_feature) + input_feature = tf.reshape( + input_feature, + (batch_size, height // window_size, window_size, width // window_size, window_size, num_channels), + ) + windows = tf.transpose(input_feature, (0, 1, 3, 2, 4, 5)) + windows = tf.reshape(windows, (-1, window_size, window_size, num_channels)) + return windows + + +def window_reverse(windows: tf.Tensor, window_size: int, height: int, width: int) -> tf.Tensor: + """ + Merges windows to produce higher resolution features. + """ + x = tf.shape(windows)[0] + y = tf.cast(height * width / (window_size * window_size), tf.int32) + batch_size = tf.math.floordiv(x, y) + windows = tf.reshape( + windows, (batch_size, height // window_size, width // window_size, window_size, window_size, -1) + ) + windows = tf.transpose(windows, (0, 1, 3, 2, 4, 5)) + windows = tf.reshape(windows, (batch_size, height, width, -1)) + return windows + + +def drop_path( + input: tf.Tensor, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True +) -> tf.Tensor: + """ + Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + if drop_prob == 0.0 or not training: + return input + keep_prob = 1 - drop_prob + input_shape = shape_list(input) + ndim = len(input_shape) + shape = [input_shape[0]] + [1] * (ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = tf.random.uniform(shape) + random_tensor = tf.where(random_tensor <= keep_prob, 1.0, 0.0) + if keep_prob > 0.0 and scale_by_keep: + random_tensor /= keep_prob + return input * random_tensor + + +class TFSwinEmbeddings(keras.layers.Layer): + """ + Construct the patch and position embeddings. Optionally, also the mask token. + """ + + def __init__(self, config: SwinConfig, use_mask_token: bool = False, **kwargs) -> None: + super().__init__(**kwargs) + self.patch_embeddings = TFSwinPatchEmbeddings(config, name="patch_embeddings") + self.num_patches = self.patch_embeddings.num_patches + self.patch_grid = self.patch_embeddings.grid_size + self.embed_dim = config.embed_dim + self.use_mask_token = use_mask_token + self.use_absolute_embeddings = config.use_absolute_embeddings + + self.norm = keras.layers.LayerNormalization(name="norm", epsilon=1e-5) + self.dropout = keras.layers.Dropout(config.hidden_dropout_prob, name="dropout") + self.config = config + + def build(self, input_shape: tf.TensorShape) -> None: + if self.use_mask_token: + self.mask_token = self.add_weight(shape=(1, 1, self.embed_dim), initializer="zeros", name="mask_token") + else: + self.mask_token = None + + if self.use_absolute_embeddings: + self.position_embeddings = self.add_weight( + (1, self.num_patches + 1, self.embed_dim), initializer="zeros", name="positional_embeddings" + ) + else: + self.position_embeddings = None + + if self.built: + return + self.built = True + if getattr(self, "patch_embeddings", None) is not None: + with tf.name_scope(self.patch_embeddings.name): + self.patch_embeddings.build(None) + if getattr(self, "norm", None) is not None: + with tf.name_scope(self.norm.name): + self.norm.build([None, None, self.config.embed_dim]) + if getattr(self, "dropout", None) is not None: + with tf.name_scope(self.dropout.name): + self.dropout.build(None) + + def call( + self, pixel_values: tf.Tensor, bool_masked_pos: bool = None, training: bool = False + ) -> Tuple[tf.Tensor, Tuple[int, int]]: + embeddings, output_dimensions = self.patch_embeddings(pixel_values, training=training) + embeddings = self.norm(embeddings, training=training) + batch_size, seq_len, _ = shape_list(embeddings) + + if bool_masked_pos is not None: + mask_tokens = tf.repeat(self.mask_token, batch_size, 0) + mask_tokens = tf.repeat(mask_tokens, seq_len, 1) + # replace the masked visual tokens by mask_tokens + mask = tf.expand_dims(bool_masked_pos, -1) + mask = tf.cast(mask, mask_tokens.dtype) + + embeddings = embeddings * (1.0 - mask) + mask_tokens * mask + + if self.position_embeddings is not None: + embeddings = embeddings + self.position_embeddings + + embeddings = self.dropout(embeddings, training=training) + + return embeddings, output_dimensions + + +class TFSwinPatchEmbeddings(keras.layers.Layer): + """ + Image to Patch Embedding. + """ + + def __init__(self, config, **kwargs): + super().__init__(**kwargs) + image_size, patch_size = config.image_size, config.patch_size + num_channels, hidden_size = config.num_channels, config.embed_dim + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_patches = num_patches + self.grid_size = (image_size[0] // patch_size[0], image_size[1] // patch_size[1]) + + self.projection = keras.layers.Conv2D( + filters=hidden_size, + kernel_size=self.patch_size, + strides=self.patch_size, + padding="valid", + name="projection", + ) + + def maybe_pad(self, pixel_values: tf.Tensor, height: int, width: int) -> tf.Tensor: + if width % self.patch_size[1] != 0: + pad_values = ((0, 0), (0, 0), (0, 0), (0, self.patch_size[1] - width % self.patch_size[1])) + pixel_values = tf.pad(pixel_values, pad_values) + if height % self.patch_size[0] != 0: + pad_values = ((0, 0), (0, 0), (0, self.patch_size[0] - height % self.patch_size[0]), (0, 0)) + pixel_values = tf.pad(pixel_values, pad_values) + return pixel_values + + def call(self, pixel_values: tf.Tensor, training: bool = False) -> Tuple[tf.Tensor, Tuple[int, int]]: + _, num_channels, height, width = shape_list(pixel_values) + if tf.executing_eagerly() and num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + ) + # pad the input to be divisible by self.patch_size, if needed + pixel_values = self.maybe_pad(pixel_values, height, width) + + # B,C,H,W -> B,H,W,C + pixel_values = tf.transpose(pixel_values, (0, 2, 3, 1)) + + embeddings = self.projection(pixel_values, training=training) + + # B,H,W,C -> B,C,H,W + embeddings = tf.transpose(embeddings, (0, 3, 1, 2)) + + batch_size, channels, height, width = shape_list(embeddings) + output_dimensions = (height, width) + + embeddings = tf.reshape(embeddings, (batch_size, channels, -1)) + embeddings = tf.transpose(embeddings, (0, 2, 1)) + return embeddings, output_dimensions + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "projection", None) is not None: + with tf.name_scope(self.projection.name): + self.projection.build([None, None, None, self.num_channels]) + + +class TFSwinPatchMerging(keras.layers.Layer): + """ + Patch Merging Layer. + + Args: + input_resolution (`Tuple[int]`): + Resolution of input feature. + dim (`int`): + Number of input channels. + norm_layer (`keras.layer.Layer`, *optional*, defaults to `keras.layers.LayerNormalization`): + Normalization layer class. + """ + + def __init__( + self, input_resolution: Tuple[int, int], dim: int, norm_layer: Optional[Callable] = None, **kwargs + ) -> None: + super().__init__(**kwargs) + self.input_resolution = input_resolution + self.dim = dim + self.reduction = keras.layers.Dense(2 * dim, use_bias=False, name="reduction") + if norm_layer is None: + # Use same default epsilon as PyTorch + self.norm = keras.layers.LayerNormalization(epsilon=1e-5, name="norm") + else: + self.norm = norm_layer(name="norm") + + def maybe_pad(self, input_feature: tf.Tensor, height: int, width: int) -> tf.Tensor: + should_pad = (height % 2 == 1) or (width % 2 == 1) + if should_pad: + pad_values = ((0, 0), (0, height % 2), (0, width % 2), (0, 0)) + input_feature = tf.pad(input_feature, pad_values) + + return input_feature + + def call(self, input_feature: tf.Tensor, input_dimensions: Tuple[int, int], training: bool = False) -> tf.Tensor: + height, width = input_dimensions + # `dim` is height * width + batch_size, _, num_channels = shape_list(input_feature) + + input_feature = tf.reshape(input_feature, (batch_size, height, width, num_channels)) + # pad input to be disible by width and height, if needed + input_feature = self.maybe_pad(input_feature, height, width) + # [batch_size, height/2, width/2, num_channels] + input_feature_0 = input_feature[:, 0::2, 0::2, :] + # [batch_size, height/2, width/2, num_channels] + input_feature_1 = input_feature[:, 1::2, 0::2, :] + # [batch_size, height/2, width/2, num_channels] + input_feature_2 = input_feature[:, 0::2, 1::2, :] + # [batch_size, height/2, width/2, num_channels] + input_feature_3 = input_feature[:, 1::2, 1::2, :] + # batch_size height/2 width/2 4*num_channels + input_feature = tf.concat([input_feature_0, input_feature_1, input_feature_2, input_feature_3], -1) + input_feature = tf.reshape( + input_feature, (batch_size, -1, 4 * num_channels) + ) # batch_size height/2*width/2 4*C + + input_feature = self.norm(input_feature, training=training) + input_feature = self.reduction(input_feature, training=training) + + return input_feature + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "reduction", None) is not None: + with tf.name_scope(self.reduction.name): + self.reduction.build([None, None, 4 * self.dim]) + if getattr(self, "norm", None) is not None: + with tf.name_scope(self.norm.name): + self.norm.build([None, None, 4 * self.dim]) + + +class TFSwinDropPath(keras.layers.Layer): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob: float = None, scale_by_keep: bool = True, **kwargs) -> None: + super(TFSwinDropPath, self).__init__(**kwargs) + self.drop_prob = drop_prob + self.scale_by_keep = scale_by_keep + + def call(self, input: tf.Tensor, training: bool = False) -> tf.Tensor: + return drop_path(input, self.drop_prob, training, self.scale_by_keep) + + +class TFSwinSelfAttention(keras.layers.Layer): + def __init__(self, config: SwinConfig, dim: int, num_heads: int, **kwargs) -> None: + super().__init__(**kwargs) + if dim % num_heads != 0: + raise ValueError( + f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})" + ) + + self.num_attention_heads = num_heads + self.attention_head_size = int(dim / num_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + window_size = config.window_size + self.window_size = ( + window_size if isinstance(window_size, collections.abc.Iterable) else (window_size, window_size) + ) + + self.query = keras.layers.Dense( + self.all_head_size, + kernel_initializer=get_initializer(config.initializer_range), + use_bias=config.qkv_bias, + name="query", + ) + self.key = keras.layers.Dense( + self.all_head_size, + kernel_initializer=get_initializer(config.initializer_range), + use_bias=config.qkv_bias, + name="key", + ) + self.value = keras.layers.Dense( + self.all_head_size, + kernel_initializer=get_initializer(config.initializer_range), + use_bias=config.qkv_bias, + name="value", + ) + + self.dropout = keras.layers.Dropout(config.attention_probs_dropout_prob) + + def build(self, input_shape: tf.TensorShape) -> None: + self.relative_position_bias_table = self.add_weight( + shape=(((2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1)), self.num_attention_heads), + initializer="zeros", + name="relative_position_bias_table", + ) + self.relative_position_index = self.add_weight( + shape=(self.window_size[0] ** 2, self.window_size[1] ** 2), + trainable=False, + dtype=tf.int32, + name="relative_position_index", + ) + + # get pair-wise relative position index for each token inside the window + coords_h = tf.range(self.window_size[0]) + coords_w = tf.range(self.window_size[1]) + coords = tf.stack(tf.meshgrid(coords_h, coords_w, indexing="ij")) + coords_flatten = tf.reshape(coords, (shape_list(coords)[0], -1)) + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] + relative_coords = tf.transpose(relative_coords, (1, 2, 0)) + + stack_0, stack_1 = tf.unstack(relative_coords, axis=2) + stack_0 += self.window_size[0] - 1 + stack_0 *= 2 * self.window_size[1] - 1 + stack_1 += self.window_size[1] - 1 + relative_coords = tf.stack([stack_0, stack_1], axis=2) + + self.relative_position_index.assign(tf.cast(tf.reduce_sum(relative_coords, axis=-1), tf.int32)) + + if self.built: + return + self.built = True + if getattr(self, "query", None) is not None: + with tf.name_scope(self.query.name): + self.query.build([None, None, self.all_head_size]) + if getattr(self, "key", None) is not None: + with tf.name_scope(self.key.name): + self.key.build([None, None, self.all_head_size]) + if getattr(self, "value", None) is not None: + with tf.name_scope(self.value.name): + self.value.build([None, None, self.all_head_size]) + + def transpose_for_scores(self, x: tf.Tensor) -> tf.Tensor: + new_x_shape = shape_list(x)[:-1] + [self.num_attention_heads, self.attention_head_size] + x = tf.reshape(x, new_x_shape) + return tf.transpose(x, (0, 2, 1, 3)) + + def call( + self, + hidden_states: tf.Tensor, + attention_mask: tf.Tensor | None = None, + head_mask: tf.Tensor | None = None, + output_attentions: bool = False, + training: bool = False, + ) -> Tuple[tf.Tensor, ...]: + batch_size, dim, _ = shape_list(hidden_states) + mixed_query_layer = self.query(hidden_states) + + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + query_layer = self.transpose_for_scores(mixed_query_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = tf.matmul(query_layer, tf.transpose(key_layer, (0, 1, 3, 2))) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + relative_position_bias = tf.gather( + self.relative_position_bias_table, tf.reshape(self.relative_position_index, (-1,)) + ) + relative_position_bias = tf.reshape( + relative_position_bias, + (self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1), + ) + + relative_position_bias = tf.transpose(relative_position_bias, (2, 0, 1)) + attention_scores = attention_scores + tf.expand_dims(relative_position_bias, 0) + + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in SwinModel call() function) + mask_shape = shape_list(attention_mask)[0] + attention_scores = tf.reshape( + attention_scores, (batch_size // mask_shape, mask_shape, self.num_attention_heads, dim, dim) + ) + attention_mask = tf.expand_dims(attention_mask, 1) + attention_mask = tf.expand_dims(attention_mask, 0) + attention_scores = attention_scores + attention_mask + attention_scores = tf.reshape(attention_scores, (-1, self.num_attention_heads, dim, dim)) + + # Normalize the attention scores to probabilities. + attention_probs = tf.nn.softmax(attention_scores, axis=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs, training=training) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = tf.matmul(attention_probs, value_layer) + context_layer = tf.transpose(context_layer, (0, 2, 1, 3)) + new_context_layer_shape = shape_list(context_layer)[:-2] + [ + self.all_head_size, + ] + context_layer = tf.reshape(context_layer, new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +class TFSwinSelfOutput(keras.layers.Layer): + def __init__(self, config: SwinConfig, dim: int, **kwargs) -> None: + super().__init__(**kwargs) + self.dense = keras.layers.Dense(dim, name="dense") + self.dropout = keras.layers.Dropout(config.attention_probs_dropout_prob, name="dropout") + self.dim = dim + + def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states, training=training) + return hidden_states + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "dense", None) is not None: + with tf.name_scope(self.dense.name): + self.dense.build([None, None, self.dim]) + if getattr(self, "dropout", None) is not None: + with tf.name_scope(self.dropout.name): + self.dropout.build(None) + + +class TFSwinAttention(keras.layers.Layer): + def __init__(self, config: SwinConfig, dim: int, num_heads: int, **kwargs) -> None: + super().__init__(**kwargs) + self.self = TFSwinSelfAttention(config, dim, num_heads, name="self") + self.self_output = TFSwinSelfOutput(config, dim, name="output") + self.pruned_heads = set() + + def prune_heads(self, heads): + """ + Prunes heads of the model. See base class PreTrainedModel heads: dict of {layer_num: list of heads to prune in + this layer} + """ + raise NotImplementedError + + def call( + self, + hidden_states: tf.Tensor, + attention_mask: tf.Tensor | None = None, + head_mask: tf.Tensor | None = None, + output_attentions: bool = False, + training: bool = False, + ) -> tf.Tensor: + self_outputs = self.self(hidden_states, attention_mask, head_mask, output_attentions, training=training) + attention_output = self.self_output(self_outputs[0], hidden_states, training=training) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "self", None) is not None: + with tf.name_scope(self.self.name): + self.self.build(None) + if getattr(self, "self_output", None) is not None: + with tf.name_scope(self.self_output.name): + self.self_output.build(None) + + +class TFSwinIntermediate(keras.layers.Layer): + def __init__(self, config: SwinConfig, dim: int, **kwargs) -> None: + super().__init__(**kwargs) + self.dense = keras.layers.Dense(int(config.mlp_ratio * dim), name="dense") + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + self.dim = dim + + def call(self, hidden_states: tf.Tensor) -> tf.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "dense", None) is not None: + with tf.name_scope(self.dense.name): + self.dense.build([None, None, self.dim]) + + +class TFSwinOutput(keras.layers.Layer): + def __init__(self, config: SwinConfig, dim: int, **kwargs) -> None: + super().__init__(**kwargs) + self.dense = keras.layers.Dense(dim, name="dense") + self.dropout = keras.layers.Dropout(config.hidden_dropout_prob, "dropout") + self.config = config + self.dim = dim + + def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states, training=training) + return hidden_states + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "dense", None) is not None: + with tf.name_scope(self.dense.name): + self.dense.build([None, None, int(self.config.mlp_ratio * self.dim)]) + + +class TFSwinLayer(keras.layers.Layer): + def __init__( + self, config, dim, input_resolution: Tuple[int, int], num_heads: int, shift_size: int = 0, **kwargs + ) -> None: + super().__init__(**kwargs) + self.chunk_size_feed_forward = config.chunk_size_feed_forward + min_res = tf.reduce_min(input_resolution) + self.window_size = min_res if min_res <= config.window_size else config.window_size + self.shift_size = 0 if min_res <= self.window_size else shift_size + self.input_resolution = input_resolution + + self.layernorm_before = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_before") + self.attention = TFSwinAttention(config, dim, num_heads, name="attention") + self.drop_path = ( + TFSwinDropPath(config.drop_path_rate, name="drop_path") + if config.drop_path_rate > 0.0 + else keras.layers.Activation("linear", name="drop_path") + ) + self.layernorm_after = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_after") + self.intermediate = TFSwinIntermediate(config, dim, name="intermediate") + self.swin_output = TFSwinOutput(config, dim, name="output") + self.dim = dim + + def get_attn_mask(self, height: int, width: int, window_size: int, shift_size: int) -> tf.Tensor | None: + img_mask = tf.zeros((height, width)) + height_slices = ((0, -window_size), (-window_size, -shift_size), (-shift_size, -1)) + width_slices = ((0, -window_size), (-window_size, -shift_size), (-shift_size, -1)) + + # calculate attention mask for SW-MSA + if shift_size > 0: + count = 0 + for height_slice in height_slices: + for width_slice in width_slices: + height_inds = tf.range(height_slice[0] % height, height_slice[1] % height + 1) + width_inds = tf.range(width_slice[0] % width, width_slice[1] % width + 1) + indices = tf.reshape(tf.stack(tf.meshgrid(height_inds, width_inds), axis=-1), (-1, 2)) + if len(indices) >= 1: + updates = tf.ones((len(indices),), dtype=img_mask.dtype) * count + img_mask = tf.tensor_scatter_nd_update(img_mask, indices, updates) + count += 1 + + img_mask = tf.expand_dims(img_mask, -1) + img_mask = tf.expand_dims(img_mask, 0) + + mask_windows = window_partition(img_mask, window_size) + mask_windows = tf.reshape(mask_windows, (-1, window_size * window_size)) + attn_mask = tf.expand_dims(mask_windows, 1) - tf.expand_dims(mask_windows, 2) + attn_mask = tf.where(attn_mask != 0, float(-100.0), attn_mask) + attn_mask = tf.where(attn_mask == 0, float(0.0), attn_mask) + return attn_mask + + def maybe_pad( + self, hidden_states: tf.Tensor, window_size: int, height: int, width: int + ) -> Tuple[tf.Tensor, tf.Tensor]: + pad_right = (window_size - width % window_size) % window_size + pad_bottom = (window_size - height % window_size) % window_size + pad_values = [[0, 0], [0, pad_bottom], [0, pad_right], [0, 0]] + hidden_states = tf.pad(hidden_states, pad_values) + pad_values = tf.reshape(pad_values, (-1,)) + return hidden_states, pad_values + + def call( + self, + hidden_states: tf.Tensor, + input_dimensions: Tuple[int, int], + head_mask: tf.Tensor | None = None, + output_attentions: bool = False, + training: bool = False, + ) -> tf.Tensor: + # if window size is larger than input resolution, we don't partition windows + min_res = tf.reduce_min(input_dimensions) + shift_size = 0 if min_res <= self.window_size else self.shift_size + window_size = min_res if min_res <= self.window_size else self.window_size + + height, width = input_dimensions + batch_size, _, channels = shape_list(hidden_states) + shortcut = hidden_states + + hidden_states = self.layernorm_before(hidden_states, training=training) + hidden_states = tf.reshape(hidden_states, (batch_size, height, width, channels)) + # pad hidden_states to multiples of window size + hidden_states, pad_values = self.maybe_pad(hidden_states, window_size, height, width) + + _, height_pad, width_pad, _ = shape_list(hidden_states) + # cyclic shift + if shift_size > 0: + shifted_hidden_states = tf.roll(hidden_states, shift=(-shift_size, -shift_size), axis=(1, 2)) + else: + shifted_hidden_states = hidden_states + + # partition windows + hidden_states_windows = window_partition(shifted_hidden_states, window_size) + hidden_states_windows = tf.reshape(hidden_states_windows, (-1, window_size * window_size, channels)) + attn_mask = self.get_attn_mask( + height=height_pad, width=width_pad, window_size=window_size, shift_size=shift_size + ) + + attention_outputs = self.attention( + hidden_states_windows, attn_mask, head_mask, output_attentions=output_attentions, training=training + ) + + attention_output = attention_outputs[0] + + attention_windows = tf.reshape(attention_output, (-1, window_size, window_size, channels)) + shifted_windows = window_reverse(attention_windows, window_size, height_pad, width_pad) + + # reverse cyclic shift + if shift_size > 0: + attention_windows = tf.roll(shifted_windows, shift=(shift_size, shift_size), axis=(1, 2)) + else: + attention_windows = shifted_windows + + was_padded = pad_values[3] > 0 or pad_values[5] > 0 + if was_padded: + attention_windows = attention_windows[:, :height, :width, :] + + attention_windows = tf.reshape(attention_windows, (batch_size, height * width, channels)) + + hidden_states = shortcut + self.drop_path(attention_windows, training=training) + + layer_output = self.layernorm_after(hidden_states, training=training) + layer_output = self.intermediate(layer_output) + layer_output = hidden_states + self.swin_output(layer_output, training=training) + + layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,) + return layer_outputs + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "layernorm_before", None) is not None: + with tf.name_scope(self.layernorm_before.name): + self.layernorm_before.build([None, None, self.dim]) + if getattr(self, "attention", None) is not None: + with tf.name_scope(self.attention.name): + self.attention.build(None) + if getattr(self, "drop_path", None) is not None: + with tf.name_scope(self.drop_path.name): + self.drop_path.build(None) + if getattr(self, "layernorm_after", None) is not None: + with tf.name_scope(self.layernorm_after.name): + self.layernorm_after.build([None, None, self.dim]) + if getattr(self, "intermediate", None) is not None: + with tf.name_scope(self.intermediate.name): + self.intermediate.build(None) + if getattr(self, "swin_output", None) is not None: + with tf.name_scope(self.swin_output.name): + self.swin_output.build(None) + + +class TFSwinStage(keras.layers.Layer): + def __init__( + self, + config: SwinConfig, + dim: int, + input_resolution: Tuple[int, int], + depth: int, + num_heads: int, + drop_path: List[float], + downsample: Optional[Callable], + **kwargs, + ) -> None: + super().__init__(**kwargs) + self.config = config + self.dim = dim + self.blocks = [ + TFSwinLayer( + config=config, + dim=dim, + input_resolution=input_resolution, + num_heads=num_heads, + shift_size=0 if (i % 2 == 0) else config.window_size // 2, + name=f"blocks.{i}", + ) + for i in range(depth) + ] + + # patch merging layer + if downsample is not None: + self.downsample = downsample( + input_resolution, + dim=dim, + norm_layer=partial(keras.layers.LayerNormalization, epsilon=1e-5), + name="downsample", + ) + else: + self.downsample = None + + self.pointing = False + + def call( + self, + hidden_states: tf.Tensor, + input_dimensions: Tuple[int, int], + head_mask: tf.Tensor | None = None, + output_attentions: Optional[bool] = False, + training: bool = False, + ) -> Tuple[tf.Tensor, ...]: + height, width = input_dimensions + for i, layer_module in enumerate(self.blocks): + layer_head_mask = head_mask[i] if head_mask is not None else None + + layer_outputs = layer_module( + hidden_states, input_dimensions, layer_head_mask, output_attentions, training=training + ) + + hidden_states = layer_outputs[0] + + if self.downsample is not None: + height_downsampled, width_downsampled = (height + 1) // 2, (width + 1) // 2 + output_dimensions = (height, width, height_downsampled, width_downsampled) + hidden_states = self.downsample(layer_outputs[0], input_dimensions, training=training) + else: + output_dimensions = (height, width, height, width) + + stage_outputs = (hidden_states, output_dimensions) + + if output_attentions: + stage_outputs += layer_outputs[1:] + return stage_outputs + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "downsample", None) is not None: + with tf.name_scope(self.downsample.name): + self.downsample.build(None) + if getattr(self, "blocks", None) is not None: + for layer in self.blocks: + with tf.name_scope(layer.name): + layer.build(None) + + +class TFSwinEncoder(keras.layers.Layer): + def __init__(self, config: SwinConfig, grid_size: Tuple[int, int], **kwargs): + super().__init__(**kwargs) + self.num_layers = len(config.depths) + self.config = config + dpr = list((tf.linspace(0, 1, sum(config.depths)) * config.drop_path_rate).numpy()) + self.layers = [ + TFSwinStage( + config=config, + dim=int(config.embed_dim * 2**i_layer), + input_resolution=(grid_size[0] // (2**i_layer), grid_size[1] // (2**i_layer)), + depth=config.depths[i_layer], + num_heads=config.num_heads[i_layer], + drop_path=dpr[sum(config.depths[:i_layer]) : sum(config.depths[: i_layer + 1])], + downsample=TFSwinPatchMerging if (i_layer < self.num_layers - 1) else None, + name=f"layers.{i_layer}", + ) + for i_layer in range(self.num_layers) + ] + + self.gradient_checkpointing = False + + def call( + self, + hidden_states: tf.Tensor, + input_dimensions: Tuple[int, int], + head_mask: tf.Tensor | None = None, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + training: bool = False, + ) -> Union[Tuple[tf.Tensor, ...], TFSwinEncoderOutput]: + all_input_dimensions = () + all_hidden_states = () if output_hidden_states else None + all_reshaped_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + if output_hidden_states: + batch_size, _, hidden_size = shape_list(hidden_states) + # rearrange b (h w) c -> b c h w + reshaped_hidden_state = tf.reshape(hidden_states, (batch_size, *input_dimensions, hidden_size)) + reshaped_hidden_state = tf.transpose(reshaped_hidden_state, (0, 3, 1, 2)) + all_hidden_states += (hidden_states,) + all_reshaped_hidden_states += (reshaped_hidden_state,) + + for i, layer_module in enumerate(self.layers): + layer_head_mask = head_mask[i] if head_mask is not None else None + + layer_outputs = layer_module( + hidden_states, input_dimensions, layer_head_mask, output_attentions, training=training + ) + + hidden_states = layer_outputs[0] + output_dimensions = layer_outputs[1] + + input_dimensions = (output_dimensions[-2], output_dimensions[-1]) + all_input_dimensions += (input_dimensions,) + + if output_hidden_states: + batch_size, _, hidden_size = shape_list(hidden_states) + # rearrange b (h w) c -> b c h w + reshaped_hidden_state = tf.reshape(hidden_states, (batch_size, *input_dimensions, hidden_size)) + reshaped_hidden_state = tf.transpose(reshaped_hidden_state, (0, 3, 1, 2)) + all_hidden_states += (hidden_states,) + all_reshaped_hidden_states += (reshaped_hidden_state,) + + if output_attentions: + all_self_attentions += layer_outputs[2:] + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + + return TFSwinEncoderOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + reshaped_hidden_states=all_reshaped_hidden_states, + ) + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "layers", None) is not None: + for layer in self.layers: + with tf.name_scope(layer.name): + layer.build(None) + + +class TFSwinPreTrainedModel(TFPreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = SwinConfig + base_model_prefix = "swin" + main_input_name = "pixel_values" + + +SWIN_START_DOCSTRING = r""" + This model is a Tensorflow + [keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a + regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and + behavior. + + Parameters: + config ([`SwinConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +SWIN_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`] + for details. + head_mask (`tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +def normalize_data_format(value: str) -> str: + """ + From tensorflow addons + https://github.com/tensorflow/addons/blob/8cec33fcaaf1cf90aec7bdd55a0fcdbb251ce5c2/tensorflow_addons/utils/keras_utils.py#L71 + """ + if value is None: + value = keras.backend.image_data_format() + data_format = value.lower() + if data_format not in {"channels_first", "channels_last"}: + raise ValueError( + 'The `data_format` argument must be one of "channels_first", "channels_last". Received: ' + str(value) + ) + return data_format + + +class AdaptiveAveragePooling1D(keras.layers.Layer): + """ + Args: + Average 1D Pooling with adaptive kernel size. + output_size: An integer or tuple/list of a single integer, specifying pooled_features. + The new size of output channels. + data_format: A string, + one of `channels_last` (default) or `channels_first`. The ordering of the dimensions in the inputs. + `channels_last` corresponds to inputs with shape `(batch, steps, channels)` while `channels_first` corresponds + to inputs with shape `(batch, channels, steps)`. + Input shape: + - If `data_format='channels_last'`: 3D tensor with shape `(batch, steps, channels)`. + - If `data_format='channels_first'`: 3D tensor with shape `(batch, channels, steps)`. + Output shape: + - If `data_format='channels_last'`: 3D tensor with shape `(batch_size, pooled_steps, channels)`. + - If `data_format='channels_first'`: 3D tensor with shape `(batch_size, channels, pooled_steps)`. + + Adapted from [tensorflow-addon's adaptive pooling.py]( + https://github.com/tensorflow/addons/blob/8cec33fcaaf1cf90aec7bdd55a0fcdbb251ce5c2/tensorflow_addons/layers/adaptive_pooling.py#L90-L120 + ) + """ + + def __init__( + self, + output_size: Union[int, Iterable[int]], + reduce_function: Callable = tf.reduce_mean, + data_format: Optional[str] = None, + **kwargs, + ) -> None: + self.data_format = normalize_data_format(data_format) + self.reduce_function = reduce_function + self.output_size = (output_size,) if isinstance(output_size, int) else tuple(output_size) + super().__init__(**kwargs) + + def call(self, inputs: tf.Tensor, *args) -> None: + bins = self.output_size[0] + if self.data_format == "channels_last": + splits = tf.split(inputs, bins, axis=1) + splits = tf.stack(splits, axis=1) + out_vect = self.reduce_function(splits, axis=2) + else: + splits = tf.split(inputs, bins, axis=2) + splits = tf.stack(splits, axis=2) + out_vect = self.reduce_function(splits, axis=3) + return out_vect + + def compute_output_shape(self, input_shape: Iterable[int]) -> tf.TensorShape: + input_shape = tf.TensorShape(input_shape).as_list() + if self.data_format == "channels_last": + shape = tf.TensorShape([input_shape[0], self.output_size[0], input_shape[2]]) + else: + shape = tf.TensorShape([input_shape[0], input_shape[1], self.output_size[0]]) + return shape + + def get_config(self) -> Dict[str, Any]: + config = { + "output_size": self.output_size, + "data_format": self.data_format, + } + base_config = super().get_config() + return {**base_config, **config} + + +@keras_serializable +class TFSwinMainLayer(keras.layers.Layer): + config_class = SwinConfig + + def __init__( + self, config: SwinConfig, add_pooling_layer: bool = True, use_mask_token: bool = False, **kwargs + ) -> None: + super().__init__(**kwargs) + self.config = config + self.num_layers = len(config.depths) + self.num_features = int(config.embed_dim * 2 ** (self.num_layers - 1)) + + self.embeddings = TFSwinEmbeddings(config, use_mask_token=use_mask_token, name="embeddings") + self.encoder = TFSwinEncoder(config, self.embeddings.patch_grid, name="encoder") + + self.layernorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm") + self.pooler = AdaptiveAveragePooling1D(output_size=(1,)) if add_pooling_layer else None + + def get_input_embeddings(self) -> TFSwinPatchEmbeddings: + return self.embeddings.patch_embeddings + + def _prune_heads(self, heads_to_prune: Dict[int, List]): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + def get_head_mask(self, head_mask: Optional[Any]) -> List: + if head_mask is not None: + raise NotImplementedError + return [None] * len(self.config.depths) + + @unpack_inputs + def call( + self, + pixel_values: tf.Tensor | None = None, + bool_masked_pos: tf.Tensor | None = None, + head_mask: tf.Tensor | None = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + training: bool = False, + ) -> Union[TFSwinModelOutput, Tuple[tf.Tensor, ...]]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask) + embedding_output, input_dimensions = self.embeddings( + pixel_values, bool_masked_pos=bool_masked_pos, training=training + ) + + encoder_outputs = self.encoder( + embedding_output, + input_dimensions, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + training=training, + ) + + sequence_output = encoder_outputs[0] + sequence_output = self.layernorm(sequence_output, training=training) + + pooled_output = None + if self.pooler is not None: + batch_size, _, num_features = shape_list(sequence_output) + pooled_output = self.pooler(sequence_output) + pooled_output = tf.reshape(pooled_output, (batch_size, num_features)) + + if not return_dict: + output = (sequence_output, pooled_output) + encoder_outputs[1:] + return output + + return TFSwinModelOutput( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + reshaped_hidden_states=encoder_outputs.reshaped_hidden_states, + ) + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "embeddings", None) is not None: + with tf.name_scope(self.embeddings.name): + self.embeddings.build(None) + if getattr(self, "encoder", None) is not None: + with tf.name_scope(self.encoder.name): + self.encoder.build(None) + if getattr(self, "layernorm", None) is not None: + with tf.name_scope(self.layernorm.name): + self.layernorm.build([None, None, self.num_features]) + + +@add_start_docstrings( + "The bare Swin Model transformer outputting raw hidden-states without any specific head on top.", + SWIN_START_DOCSTRING, +) +class TFSwinModel(TFSwinPreTrainedModel): + def __init__( + self, config: SwinConfig, add_pooling_layer: bool = True, use_mask_token: bool = False, **kwargs + ) -> None: + super().__init__(config, **kwargs) + self.config = config + self.swin = TFSwinMainLayer(config, name="swin") + + @add_start_docstrings_to_model_forward(SWIN_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=TFSwinModelOutput, + config_class=_CONFIG_FOR_DOC, + modality="vision", + expected_output=_EXPECTED_OUTPUT_SHAPE, + ) + @unpack_inputs + def call( + self, + pixel_values: tf.Tensor | None = None, + bool_masked_pos: tf.Tensor | None = None, + head_mask: tf.Tensor | None = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + training: bool = False, + ) -> Union[TFSwinModelOutput, Tuple[tf.Tensor, ...]]: + r""" + bool_masked_pos (`tf.Tensor` of shape `(batch_size, num_patches)`, *optional*): + Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + swin_outputs = self.swin( + pixel_values=pixel_values, + bool_masked_pos=bool_masked_pos, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + training=training, + ) + + return swin_outputs + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "swin", None) is not None: + with tf.name_scope(self.swin.name): + self.swin.build(None) + + +class TFSwinPixelShuffle(keras.layers.Layer): + """TF layer implementation of torch.nn.PixelShuffle""" + + def __init__(self, upscale_factor: int, **kwargs) -> None: + super().__init__(**kwargs) + if not isinstance(upscale_factor, int) or upscale_factor < 2: + raise ValueError(f"upscale_factor must be an integer value >= 2 got {upscale_factor}") + self.upscale_factor = upscale_factor + + def call(self, x: tf.Tensor) -> tf.Tensor: + hidden_states = x + batch_size, _, _, num_input_channels = shape_list(hidden_states) + block_size_squared = self.upscale_factor**2 + output_depth = int(num_input_channels / block_size_squared) + # When the number of output channels >= 2, PyTorch's PixelShuffle and + # TF's depth_to_space differ in their output as the order of channels selected for combining + # is a permutation of the other c.f. + # https://stackoverflow.com/questions/68272502/tf-depth-to-space-not-same-as-torchs-pixelshuffle-when-output-channels-1 + permutation = tf.constant( + [[i + j * block_size_squared for i in range(block_size_squared) for j in range(output_depth)]] + ) + hidden_states = tf.gather(params=hidden_states, indices=tf.tile(permutation, [batch_size, 1]), batch_dims=-1) + hidden_states = tf.nn.depth_to_space(hidden_states, block_size=self.upscale_factor, data_format="NHWC") + return hidden_states + + +class TFSwinDecoder(keras.layers.Layer): + def __init__(self, config: SwinConfig, **kwargs): + super().__init__(**kwargs) + self.conv2d = keras.layers.Conv2D( + filters=config.encoder_stride**2 * config.num_channels, kernel_size=1, strides=1, name="0" + ) + self.pixel_shuffle = TFSwinPixelShuffle(config.encoder_stride, name="1") + self.config = config + + def call(self, x: tf.Tensor) -> tf.Tensor: + hidden_states = x + # B,C,H,W -> B,H,W,C + hidden_states = tf.transpose(hidden_states, (0, 2, 3, 1)) + hidden_states = self.conv2d(hidden_states) + hidden_states = self.pixel_shuffle(hidden_states) + # B,H,W,C -> B,C,H,W + hidden_states = tf.transpose(hidden_states, (0, 3, 1, 2)) + return hidden_states + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "conv2d", None) is not None: + with tf.name_scope(self.conv2d.name): + self.conv2d.build([None, None, None, self.config.hidden_size]) + if getattr(self, "pixel_shuffle", None) is not None: + with tf.name_scope(self.pixel_shuffle.name): + self.pixel_shuffle.build(None) + + +@add_start_docstrings( + "Swin Model with a decoder on top for masked image modeling, as proposed in" + " [SimMIM](https://arxiv.org/abs/2111.09886).", + SWIN_START_DOCSTRING, +) +class TFSwinForMaskedImageModeling(TFSwinPreTrainedModel): + def __init__(self, config: SwinConfig): + super().__init__(config) + + self.swin = TFSwinMainLayer(config, add_pooling_layer=False, use_mask_token=True, name="swin") + + self.decoder = TFSwinDecoder(config, name="decoder") + + @add_start_docstrings_to_model_forward(SWIN_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=TFSwinMaskedImageModelingOutput, config_class=_CONFIG_FOR_DOC) + @unpack_inputs + def call( + self, + pixel_values: tf.Tensor | None = None, + bool_masked_pos: tf.Tensor | None = None, + head_mask: tf.Tensor | None = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + training: bool = False, + ) -> Union[Tuple, TFSwinMaskedImageModelingOutput]: + r""" + bool_masked_pos (`tf.Tensor` of shape `(batch_size, num_patches)`): + Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). + + Returns: + + Examples: + ```python + >>> from transformers import AutoImageProcessor, TFSwinForMaskedImageModeling + >>> import tensorflow as tf + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224") + >>> model = TFSwinForMaskedImageModeling.from_pretrained("microsoft/swin-tiny-patch4-window7-224") + + >>> num_patches = (model.config.image_size // model.config.patch_size) ** 2 + >>> pixel_values = image_processor(images=image, return_tensors="tf").pixel_values + >>> # create random boolean mask of shape (batch_size, num_patches) + >>> bool_masked_pos = tf.random.uniform((1, num_patches)) >= 0.5 + + >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos) + >>> loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction + >>> list(reconstructed_pixel_values.shape) + [1, 3, 224, 224] + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.swin( + pixel_values, + bool_masked_pos=bool_masked_pos, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + training=training, + ) + + sequence_output = outputs[0] + # Reshape to (batch_size, num_channels, height, width) + sequence_output = tf.transpose(sequence_output, (0, 2, 1)) + batch_size, num_channels, sequence_length = shape_list(sequence_output) + height = width = int(sequence_length**0.5) + sequence_output = tf.reshape(sequence_output, (batch_size, num_channels, height, width)) + + # Reconstruct pixel values + reconstructed_pixel_values = self.decoder(sequence_output) + + masked_im_loss = None + if bool_masked_pos is not None: + size = self.config.image_size // self.config.patch_size + bool_masked_pos = tf.reshape(bool_masked_pos, (-1, size, size)) + mask = tf.repeat(bool_masked_pos, self.config.patch_size, 1) + mask = tf.repeat(mask, self.config.patch_size, 2) + mask = tf.expand_dims(mask, 1) + mask = tf.cast(mask, tf.float32) + + reconstruction_loss = keras.losses.mean_absolute_error( + # Swap axes as metric calculation reduces over the final dimension + tf.transpose(pixel_values, (1, 2, 3, 0)), + tf.transpose(reconstructed_pixel_values, (1, 2, 3, 0)), + ) + reconstruction_loss = tf.expand_dims(reconstruction_loss, 0) + total_loss = tf.reduce_sum(reconstruction_loss * mask) + num_masked_pixels = (tf.reduce_sum(mask) + 1e-5) * self.config.num_channels + masked_im_loss = total_loss / num_masked_pixels + masked_im_loss = tf.reshape(masked_im_loss, (1,)) + + if not return_dict: + output = (reconstructed_pixel_values,) + outputs[2:] + return ((masked_im_loss,) + output) if masked_im_loss is not None else output + + return TFSwinMaskedImageModelingOutput( + loss=masked_im_loss, + reconstruction=reconstructed_pixel_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + reshaped_hidden_states=outputs.reshaped_hidden_states, + ) + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "swin", None) is not None: + with tf.name_scope(self.swin.name): + self.swin.build(None) + if getattr(self, "decoder", None) is not None: + with tf.name_scope(self.decoder.name): + self.decoder.build(None) + + +@add_start_docstrings( + """ + Swin Model transformer with an image classification head on top (a linear layer on top of the final hidden state of + the [CLS] token) e.g. for ImageNet. + """, + SWIN_START_DOCSTRING, +) +class TFSwinForImageClassification(TFSwinPreTrainedModel, TFSequenceClassificationLoss): + def __init__(self, config: SwinConfig): + super().__init__(config) + + self.num_labels = config.num_labels + self.swin = TFSwinMainLayer(config, name="swin") + + # Classifier head + self.classifier = ( + keras.layers.Dense(config.num_labels, name="classifier") + if config.num_labels > 0 + else keras.layers.Activation("linear", name="classifier") + ) + + @add_start_docstrings_to_model_forward(SWIN_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_IMAGE_CLASS_CHECKPOINT, + output_type=TFSwinImageClassifierOutput, + config_class=_CONFIG_FOR_DOC, + expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, + ) + @unpack_inputs + def call( + self, + pixel_values: tf.Tensor | None = None, + head_mask: tf.Tensor | None = None, + labels: tf.Tensor | None = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + training: bool = False, + ) -> Union[Tuple[tf.Tensor, ...], TFSwinImageClassifierOutput]: + r""" + labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.swin( + pixel_values, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + training=training, + ) + + pooled_output = outputs[1] + + logits = self.classifier(pooled_output, training=training) + + loss = None if labels is None else self.hf_compute_loss(labels, logits) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return TFSwinImageClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + reshaped_hidden_states=outputs.reshaped_hidden_states, + ) + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "swin", None) is not None: + with tf.name_scope(self.swin.name): + self.swin.build(None) + if getattr(self, "classifier", None) is not None: + if hasattr(self.classifier, "name"): + with tf.name_scope(self.classifier.name): + self.classifier.build([None, None, self.swin.num_features]) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/swin2sr/__pycache__/convert_swin2sr_original_to_pytorch.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/swin2sr/__pycache__/convert_swin2sr_original_to_pytorch.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cb731fc298471bf3768f383e4c19d91ef630a1d1 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/swin2sr/__pycache__/convert_swin2sr_original_to_pytorch.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/swin2sr/__pycache__/image_processing_swin2sr.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/swin2sr/__pycache__/image_processing_swin2sr.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..db2a06825b2ad9904036ec49c06703b621a7b9cf Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/swin2sr/__pycache__/image_processing_swin2sr.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/swin2sr/__pycache__/modeling_swin2sr.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/swin2sr/__pycache__/modeling_swin2sr.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2a615ec87ea5b961ad68165ba2ea30a30ef2301f Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/swin2sr/__pycache__/modeling_swin2sr.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/swin2sr/configuration_swin2sr.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/swin2sr/configuration_swin2sr.py new file mode 100644 index 0000000000000000000000000000000000000000..1858be52a5ab4543483bc958fe20edd40948bcb8 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/swin2sr/configuration_swin2sr.py @@ -0,0 +1,154 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Swin2SR Transformer model configuration""" + +from ...configuration_utils import PretrainedConfig +from ...utils import logging + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class Swin2SRConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`Swin2SRModel`]. It is used to instantiate a Swin + Transformer v2 model according to the specified arguments, defining the model architecture. Instantiating a + configuration with the defaults will yield a similar configuration to that of the Swin Transformer v2 + [caidas/swin2sr-classicalsr-x2-64](https://huggingface.co/caidas/swin2sr-classicalsr-x2-64) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + image_size (`int`, *optional*, defaults to 64): + The size (resolution) of each image. + patch_size (`int`, *optional*, defaults to 1): + The size (resolution) of each patch. + num_channels (`int`, *optional*, defaults to 3): + The number of input channels. + num_channels_out (`int`, *optional*, defaults to `num_channels`): + The number of output channels. If not set, it will be set to `num_channels`. + embed_dim (`int`, *optional*, defaults to 180): + Dimensionality of patch embedding. + depths (`list(int)`, *optional*, defaults to `[6, 6, 6, 6, 6, 6]`): + Depth of each layer in the Transformer encoder. + num_heads (`list(int)`, *optional*, defaults to `[6, 6, 6, 6, 6, 6]`): + Number of attention heads in each layer of the Transformer encoder. + window_size (`int`, *optional*, defaults to 8): + Size of windows. + mlp_ratio (`float`, *optional*, defaults to 2.0): + Ratio of MLP hidden dimensionality to embedding dimensionality. + qkv_bias (`bool`, *optional*, defaults to `True`): + Whether or not a learnable bias should be added to the queries, keys and values. + hidden_dropout_prob (`float`, *optional*, defaults to 0.0): + The dropout probability for all fully connected layers in the embeddings and encoder. + attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + drop_path_rate (`float`, *optional*, defaults to 0.1): + Stochastic depth rate. + hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`, + `"selu"` and `"gelu_new"` are supported. + use_absolute_embeddings (`bool`, *optional*, defaults to `False`): + Whether or not to add absolute position embeddings to the patch embeddings. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + layer_norm_eps (`float`, *optional*, defaults to 1e-05): + The epsilon used by the layer normalization layers. + upscale (`int`, *optional*, defaults to 2): + The upscale factor for the image. 2/3/4/8 for image super resolution, 1 for denoising and compress artifact + reduction + img_range (`float`, *optional*, defaults to 1.0): + The range of the values of the input image. + resi_connection (`str`, *optional*, defaults to `"1conv"`): + The convolutional block to use before the residual connection in each stage. + upsampler (`str`, *optional*, defaults to `"pixelshuffle"`): + The reconstruction reconstruction module. Can be 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None. + + Example: + + ```python + >>> from transformers import Swin2SRConfig, Swin2SRModel + + >>> # Initializing a Swin2SR caidas/swin2sr-classicalsr-x2-64 style configuration + >>> configuration = Swin2SRConfig() + + >>> # Initializing a model (with random weights) from the caidas/swin2sr-classicalsr-x2-64 style configuration + >>> model = Swin2SRModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "swin2sr" + + attribute_map = { + "hidden_size": "embed_dim", + "num_attention_heads": "num_heads", + "num_hidden_layers": "num_layers", + } + + def __init__( + self, + image_size=64, + patch_size=1, + num_channels=3, + num_channels_out=None, + embed_dim=180, + depths=[6, 6, 6, 6, 6, 6], + num_heads=[6, 6, 6, 6, 6, 6], + window_size=8, + mlp_ratio=2.0, + qkv_bias=True, + hidden_dropout_prob=0.0, + attention_probs_dropout_prob=0.0, + drop_path_rate=0.1, + hidden_act="gelu", + use_absolute_embeddings=False, + initializer_range=0.02, + layer_norm_eps=1e-5, + upscale=2, + img_range=1.0, + resi_connection="1conv", + upsampler="pixelshuffle", + **kwargs, + ): + super().__init__(**kwargs) + + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_channels_out = num_channels if num_channels_out is None else num_channels_out + self.embed_dim = embed_dim + self.depths = depths + self.num_layers = len(depths) + self.num_heads = num_heads + self.window_size = window_size + self.mlp_ratio = mlp_ratio + self.qkv_bias = qkv_bias + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.drop_path_rate = drop_path_rate + self.hidden_act = hidden_act + self.use_absolute_embeddings = use_absolute_embeddings + self.layer_norm_eps = layer_norm_eps + self.initializer_range = initializer_range + self.upscale = upscale + self.img_range = img_range + self.resi_connection = resi_connection + self.upsampler = upsampler diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/swin2sr/modeling_swin2sr.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/swin2sr/modeling_swin2sr.py new file mode 100644 index 0000000000000000000000000000000000000000..fb3c0a38f21f4790ebed7b7bb61485bb8c1bee22 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/swin2sr/modeling_swin2sr.py @@ -0,0 +1,1179 @@ +# coding=utf-8 +# Copyright 2022 Microsoft Research and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch Swin2SR Transformer model.""" + + +import collections.abc +import math +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn + +from ...activations import ACT2FN +from ...modeling_outputs import BaseModelOutput, ImageSuperResolutionOutput +from ...modeling_utils import PreTrainedModel +from ...pytorch_utils import find_pruneable_heads_and_indices, meshgrid, prune_linear_layer +from ...utils import ( + ModelOutput, + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) +from .configuration_swin2sr import Swin2SRConfig + + +logger = logging.get_logger(__name__) + +# General docstring +_CONFIG_FOR_DOC = "Swin2SRConfig" + +# Base docstring +_CHECKPOINT_FOR_DOC = "caidas/swin2SR-classical-sr-x2-64" +_EXPECTED_OUTPUT_SHAPE = [1, 180, 488, 648] + + +from ..deprecated._archive_maps import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +@dataclass +class Swin2SREncoderOutput(ModelOutput): + """ + Swin2SR encoder's outputs, with potential hidden states and attentions. + + Args: + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of + shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + """ + + last_hidden_state: torch.FloatTensor = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + + +# Copied from transformers.models.swin.modeling_swin.window_partition +def window_partition(input_feature, window_size): + """ + Partitions the given input into windows. + """ + batch_size, height, width, num_channels = input_feature.shape + input_feature = input_feature.view( + batch_size, height // window_size, window_size, width // window_size, window_size, num_channels + ) + windows = input_feature.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, num_channels) + return windows + + +# Copied from transformers.models.swin.modeling_swin.window_reverse +def window_reverse(windows, window_size, height, width): + """ + Merges windows to produce higher resolution features. + """ + num_channels = windows.shape[-1] + windows = windows.view(-1, height // window_size, width // window_size, window_size, window_size, num_channels) + windows = windows.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, height, width, num_channels) + return windows + + +# Copied from transformers.models.beit.modeling_beit.drop_path +def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor: + """ + Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks, + however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the + layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the + argument. + """ + if drop_prob == 0.0 or not training: + return input + keep_prob = 1 - drop_prob + shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device) + random_tensor.floor_() # binarize + output = input.div(keep_prob) * random_tensor + return output + + +# Copied from transformers.models.swin.modeling_swin.SwinDropPath with Swin->Swin2SR +class Swin2SRDropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob: Optional[float] = None) -> None: + super().__init__() + self.drop_prob = drop_prob + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + return drop_path(hidden_states, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return "p={}".format(self.drop_prob) + + +class Swin2SREmbeddings(nn.Module): + """ + Construct the patch and optional position embeddings. + """ + + def __init__(self, config): + super().__init__() + + self.patch_embeddings = Swin2SRPatchEmbeddings(config) + num_patches = self.patch_embeddings.num_patches + + if config.use_absolute_embeddings: + self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.embed_dim)) + else: + self.position_embeddings = None + + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.window_size = config.window_size + + def forward(self, pixel_values: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor]: + embeddings, output_dimensions = self.patch_embeddings(pixel_values) + + if self.position_embeddings is not None: + embeddings = embeddings + self.position_embeddings + + embeddings = self.dropout(embeddings) + + return embeddings, output_dimensions + + +class Swin2SRPatchEmbeddings(nn.Module): + def __init__(self, config, normalize_patches=True): + super().__init__() + num_channels = config.embed_dim + image_size, patch_size = config.image_size, config.patch_size + + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + patches_resolution = [image_size[0] // patch_size[0], image_size[1] // patch_size[1]] + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.projection = nn.Conv2d(num_channels, config.embed_dim, kernel_size=patch_size, stride=patch_size) + self.layernorm = nn.LayerNorm(config.embed_dim) if normalize_patches else None + + def forward(self, embeddings: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor, Tuple[int]]: + embeddings = self.projection(embeddings) + _, _, height, width = embeddings.shape + output_dimensions = (height, width) + embeddings = embeddings.flatten(2).transpose(1, 2) + + if self.layernorm is not None: + embeddings = self.layernorm(embeddings) + + return embeddings, output_dimensions + + +class Swin2SRPatchUnEmbeddings(nn.Module): + r"""Image to Patch Unembedding""" + + def __init__(self, config): + super().__init__() + + self.embed_dim = config.embed_dim + + def forward(self, embeddings, x_size): + batch_size, height_width, num_channels = embeddings.shape + embeddings = embeddings.transpose(1, 2).view(batch_size, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C + return embeddings + + +# Copied from transformers.models.swinv2.modeling_swinv2.Swinv2PatchMerging with Swinv2->Swin2SR +class Swin2SRPatchMerging(nn.Module): + """ + Patch Merging Layer. + + Args: + input_resolution (`Tuple[int]`): + Resolution of input feature. + dim (`int`): + Number of input channels. + norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`): + Normalization layer class. + """ + + def __init__(self, input_resolution: Tuple[int], dim: int, norm_layer: nn.Module = nn.LayerNorm) -> None: + super().__init__() + self.input_resolution = input_resolution + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(2 * dim) + + def maybe_pad(self, input_feature, height, width): + should_pad = (height % 2 == 1) or (width % 2 == 1) + if should_pad: + pad_values = (0, 0, 0, width % 2, 0, height % 2) + input_feature = nn.functional.pad(input_feature, pad_values) + + return input_feature + + def forward(self, input_feature: torch.Tensor, input_dimensions: Tuple[int, int]) -> torch.Tensor: + height, width = input_dimensions + # `dim` is height * width + batch_size, dim, num_channels = input_feature.shape + + input_feature = input_feature.view(batch_size, height, width, num_channels) + # pad input to be disible by width and height, if needed + input_feature = self.maybe_pad(input_feature, height, width) + # [batch_size, height/2, width/2, num_channels] + input_feature_0 = input_feature[:, 0::2, 0::2, :] + # [batch_size, height/2, width/2, num_channels] + input_feature_1 = input_feature[:, 1::2, 0::2, :] + # [batch_size, height/2, width/2, num_channels] + input_feature_2 = input_feature[:, 0::2, 1::2, :] + # [batch_size, height/2, width/2, num_channels] + input_feature_3 = input_feature[:, 1::2, 1::2, :] + # [batch_size, height/2 * width/2, 4*num_channels] + input_feature = torch.cat([input_feature_0, input_feature_1, input_feature_2, input_feature_3], -1) + input_feature = input_feature.view(batch_size, -1, 4 * num_channels) # [batch_size, height/2 * width/2, 4*C] + + input_feature = self.reduction(input_feature) + input_feature = self.norm(input_feature) + + return input_feature + + +# Copied from transformers.models.swinv2.modeling_swinv2.Swinv2SelfAttention with Swinv2->Swin2SR +class Swin2SRSelfAttention(nn.Module): + def __init__(self, config, dim, num_heads, window_size, pretrained_window_size=[0, 0]): + super().__init__() + if dim % num_heads != 0: + raise ValueError( + f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})" + ) + + self.num_attention_heads = num_heads + self.attention_head_size = int(dim / num_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + self.window_size = ( + window_size if isinstance(window_size, collections.abc.Iterable) else (window_size, window_size) + ) + self.pretrained_window_size = pretrained_window_size + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1)))) + # mlp to generate continuous relative position bias + self.continuous_position_bias_mlp = nn.Sequential( + nn.Linear(2, 512, bias=True), nn.ReLU(inplace=True), nn.Linear(512, num_heads, bias=False) + ) + + # get relative_coords_table + relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.int64).float() + relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.int64).float() + relative_coords_table = ( + torch.stack(meshgrid([relative_coords_h, relative_coords_w], indexing="ij")) + .permute(1, 2, 0) + .contiguous() + .unsqueeze(0) + ) # [1, 2*window_height - 1, 2*window_width - 1, 2] + if pretrained_window_size[0] > 0: + relative_coords_table[:, :, :, 0] /= pretrained_window_size[0] - 1 + relative_coords_table[:, :, :, 1] /= pretrained_window_size[1] - 1 + elif window_size > 1: + relative_coords_table[:, :, :, 0] /= self.window_size[0] - 1 + relative_coords_table[:, :, :, 1] /= self.window_size[1] - 1 + relative_coords_table *= 8 # normalize to -8, 8 + relative_coords_table = ( + torch.sign(relative_coords_table) * torch.log2(torch.abs(relative_coords_table) + 1.0) / math.log2(8) + ) + self.register_buffer("relative_coords_table", relative_coords_table, persistent=False) + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(meshgrid([coords_h, coords_w], indexing="ij")) + coords_flatten = torch.flatten(coords, 1) + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] + relative_coords = relative_coords.permute(1, 2, 0).contiguous() + relative_coords[:, :, 0] += self.window_size[0] - 1 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) + self.register_buffer("relative_position_index", relative_position_index, persistent=False) + + self.query = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias) + self.key = nn.Linear(self.all_head_size, self.all_head_size, bias=False) + self.value = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias) + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = False, + ) -> Tuple[torch.Tensor]: + batch_size, dim, num_channels = hidden_states.shape + mixed_query_layer = self.query(hidden_states) + + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + query_layer = self.transpose_for_scores(mixed_query_layer) + + # cosine attention + attention_scores = nn.functional.normalize(query_layer, dim=-1) @ nn.functional.normalize( + key_layer, dim=-1 + ).transpose(-2, -1) + logit_scale = torch.clamp(self.logit_scale, max=math.log(1.0 / 0.01)).exp() + attention_scores = attention_scores * logit_scale + relative_position_bias_table = self.continuous_position_bias_mlp(self.relative_coords_table).view( + -1, self.num_attention_heads + ) + # [window_height*window_width,window_height*window_width,num_attention_heads] + relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1 + ) + # [num_attention_heads,window_height*window_width,window_height*window_width] + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + relative_position_bias = 16 * torch.sigmoid(relative_position_bias) + attention_scores = attention_scores + relative_position_bias.unsqueeze(0) + + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in Swin2SRModel forward() function) + mask_shape = attention_mask.shape[0] + attention_scores = attention_scores.view( + batch_size // mask_shape, mask_shape, self.num_attention_heads, dim, dim + ) + attention_mask.unsqueeze(1).unsqueeze(0) + attention_scores = attention_scores + attention_mask.unsqueeze(1).unsqueeze(0) + attention_scores = attention_scores.view(-1, self.num_attention_heads, dim, dim) + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +# Copied from transformers.models.swin.modeling_swin.SwinSelfOutput with Swin->Swin2SR +class Swin2SRSelfOutput(nn.Module): + def __init__(self, config, dim): + super().__init__() + self.dense = nn.Linear(dim, dim) + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + + return hidden_states + + +# Copied from transformers.models.swinv2.modeling_swinv2.Swinv2Attention with Swinv2->Swin2SR +class Swin2SRAttention(nn.Module): + def __init__(self, config, dim, num_heads, window_size, pretrained_window_size=0): + super().__init__() + self.self = Swin2SRSelfAttention( + config=config, + dim=dim, + num_heads=num_heads, + window_size=window_size, + pretrained_window_size=pretrained_window_size + if isinstance(pretrained_window_size, collections.abc.Iterable) + else (pretrained_window_size, pretrained_window_size), + ) + self.output = Swin2SRSelfOutput(config, dim) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = False, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self(hidden_states, attention_mask, head_mask, output_attentions) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +# Copied from transformers.models.swin.modeling_swin.SwinIntermediate with Swin->Swin2SR +class Swin2SRIntermediate(nn.Module): + def __init__(self, config, dim): + super().__init__() + self.dense = nn.Linear(dim, int(config.mlp_ratio * dim)) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.swin.modeling_swin.SwinOutput with Swin->Swin2SR +class Swin2SROutput(nn.Module): + def __init__(self, config, dim): + super().__init__() + self.dense = nn.Linear(int(config.mlp_ratio * dim), dim) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + return hidden_states + + +# Copied from transformers.models.swinv2.modeling_swinv2.Swinv2Layer with Swinv2->Swin2SR +class Swin2SRLayer(nn.Module): + def __init__(self, config, dim, input_resolution, num_heads, shift_size=0, pretrained_window_size=0): + super().__init__() + self.input_resolution = input_resolution + window_size, shift_size = self._compute_window_shift( + (config.window_size, config.window_size), (shift_size, shift_size) + ) + self.window_size = window_size[0] + self.shift_size = shift_size[0] + self.attention = Swin2SRAttention( + config=config, + dim=dim, + num_heads=num_heads, + window_size=self.window_size, + pretrained_window_size=pretrained_window_size + if isinstance(pretrained_window_size, collections.abc.Iterable) + else (pretrained_window_size, pretrained_window_size), + ) + self.layernorm_before = nn.LayerNorm(dim, eps=config.layer_norm_eps) + self.drop_path = Swin2SRDropPath(config.drop_path_rate) if config.drop_path_rate > 0.0 else nn.Identity() + self.intermediate = Swin2SRIntermediate(config, dim) + self.output = Swin2SROutput(config, dim) + self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps) + + def _compute_window_shift(self, target_window_size, target_shift_size) -> Tuple[Tuple[int, int], Tuple[int, int]]: + window_size = [r if r <= w else w for r, w in zip(self.input_resolution, target_window_size)] + shift_size = [0 if r <= w else s for r, w, s in zip(self.input_resolution, window_size, target_shift_size)] + return window_size, shift_size + + def get_attn_mask(self, height, width, dtype): + if self.shift_size > 0: + # calculate attention mask for shifted window multihead self attention + img_mask = torch.zeros((1, height, width, 1), dtype=dtype) + height_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + width_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + count = 0 + for height_slice in height_slices: + for width_slice in width_slices: + img_mask[:, height_slice, width_slice, :] = count + count += 1 + + mask_windows = window_partition(img_mask, self.window_size) + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + else: + attn_mask = None + return attn_mask + + def maybe_pad(self, hidden_states, height, width): + pad_right = (self.window_size - width % self.window_size) % self.window_size + pad_bottom = (self.window_size - height % self.window_size) % self.window_size + pad_values = (0, 0, 0, pad_right, 0, pad_bottom) + hidden_states = nn.functional.pad(hidden_states, pad_values) + return hidden_states, pad_values + + def forward( + self, + hidden_states: torch.Tensor, + input_dimensions: Tuple[int, int], + head_mask: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = False, + ) -> Tuple[torch.Tensor, torch.Tensor]: + height, width = input_dimensions + batch_size, _, channels = hidden_states.size() + shortcut = hidden_states + + # pad hidden_states to multiples of window size + hidden_states = hidden_states.view(batch_size, height, width, channels) + hidden_states, pad_values = self.maybe_pad(hidden_states, height, width) + _, height_pad, width_pad, _ = hidden_states.shape + # cyclic shift + if self.shift_size > 0: + shifted_hidden_states = torch.roll(hidden_states, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + else: + shifted_hidden_states = hidden_states + + # partition windows + hidden_states_windows = window_partition(shifted_hidden_states, self.window_size) + hidden_states_windows = hidden_states_windows.view(-1, self.window_size * self.window_size, channels) + attn_mask = self.get_attn_mask(height_pad, width_pad, dtype=hidden_states.dtype) + if attn_mask is not None: + attn_mask = attn_mask.to(hidden_states_windows.device) + + attention_outputs = self.attention( + hidden_states_windows, attn_mask, head_mask, output_attentions=output_attentions + ) + + attention_output = attention_outputs[0] + + attention_windows = attention_output.view(-1, self.window_size, self.window_size, channels) + shifted_windows = window_reverse(attention_windows, self.window_size, height_pad, width_pad) + + # reverse cyclic shift + if self.shift_size > 0: + attention_windows = torch.roll(shifted_windows, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + attention_windows = shifted_windows + + was_padded = pad_values[3] > 0 or pad_values[5] > 0 + if was_padded: + attention_windows = attention_windows[:, :height, :width, :].contiguous() + + attention_windows = attention_windows.view(batch_size, height * width, channels) + hidden_states = self.layernorm_before(attention_windows) + hidden_states = shortcut + self.drop_path(hidden_states) + + layer_output = self.intermediate(hidden_states) + layer_output = self.output(layer_output) + layer_output = hidden_states + self.drop_path(self.layernorm_after(layer_output)) + + layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,) + return layer_outputs + + +class Swin2SRStage(nn.Module): + """ + This corresponds to the Residual Swin Transformer Block (RSTB) in the original implementation. + """ + + def __init__(self, config, dim, input_resolution, depth, num_heads, drop_path, pretrained_window_size=0): + super().__init__() + self.config = config + self.dim = dim + self.layers = nn.ModuleList( + [ + Swin2SRLayer( + config=config, + dim=dim, + input_resolution=input_resolution, + num_heads=num_heads, + shift_size=0 if (i % 2 == 0) else config.window_size // 2, + pretrained_window_size=pretrained_window_size, + ) + for i in range(depth) + ] + ) + + if config.resi_connection == "1conv": + self.conv = nn.Conv2d(dim, dim, 3, 1, 1) + elif config.resi_connection == "3conv": + # to save parameters and memory + self.conv = nn.Sequential( + nn.Conv2d(dim, dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim, 3, 1, 1), + ) + + self.patch_embed = Swin2SRPatchEmbeddings(config, normalize_patches=False) + + self.patch_unembed = Swin2SRPatchUnEmbeddings(config) + + def forward( + self, + hidden_states: torch.Tensor, + input_dimensions: Tuple[int, int], + head_mask: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = False, + ) -> Tuple[torch.Tensor]: + residual = hidden_states + + height, width = input_dimensions + for i, layer_module in enumerate(self.layers): + layer_head_mask = head_mask[i] if head_mask is not None else None + + layer_outputs = layer_module(hidden_states, input_dimensions, layer_head_mask, output_attentions) + + hidden_states = layer_outputs[0] + + output_dimensions = (height, width, height, width) + + hidden_states = self.patch_unembed(hidden_states, input_dimensions) + hidden_states = self.conv(hidden_states) + hidden_states, _ = self.patch_embed(hidden_states) + + hidden_states = hidden_states + residual + + stage_outputs = (hidden_states, output_dimensions) + + if output_attentions: + stage_outputs += layer_outputs[1:] + return stage_outputs + + +class Swin2SREncoder(nn.Module): + def __init__(self, config, grid_size): + super().__init__() + self.num_stages = len(config.depths) + self.config = config + dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))] + self.stages = nn.ModuleList( + [ + Swin2SRStage( + config=config, + dim=config.embed_dim, + input_resolution=(grid_size[0], grid_size[1]), + depth=config.depths[stage_idx], + num_heads=config.num_heads[stage_idx], + drop_path=dpr[sum(config.depths[:stage_idx]) : sum(config.depths[: stage_idx + 1])], + pretrained_window_size=0, + ) + for stage_idx in range(self.num_stages) + ] + ) + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + input_dimensions: Tuple[int, int], + head_mask: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + ) -> Union[Tuple, Swin2SREncoderOutput]: + all_input_dimensions = () + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + if output_hidden_states: + all_hidden_states += (hidden_states,) + + for i, stage_module in enumerate(self.stages): + layer_head_mask = head_mask[i] if head_mask is not None else None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + stage_module.__call__, hidden_states, input_dimensions, layer_head_mask, output_attentions + ) + else: + layer_outputs = stage_module(hidden_states, input_dimensions, layer_head_mask, output_attentions) + + hidden_states = layer_outputs[0] + output_dimensions = layer_outputs[1] + + input_dimensions = (output_dimensions[-2], output_dimensions[-1]) + all_input_dimensions += (input_dimensions,) + + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if output_attentions: + all_self_attentions += layer_outputs[2:] + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + + return Swin2SREncoderOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +class Swin2SRPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = Swin2SRConfig + base_model_prefix = "swin2sr" + main_input_name = "pixel_values" + supports_gradient_checkpointing = True + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, (nn.Linear, nn.Conv2d)): + torch.nn.init.trunc_normal_(module.weight.data, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + +SWIN2SR_START_DOCSTRING = r""" + This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use + it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and + behavior. + + Parameters: + config ([`Swin2SRConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +SWIN2SR_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See + [`Swin2SRImageProcessor.__call__`] for details. + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare Swin2SR Model transformer outputting raw hidden-states without any specific head on top.", + SWIN2SR_START_DOCSTRING, +) +class Swin2SRModel(Swin2SRPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.config = config + + if config.num_channels == 3 and config.num_channels_out == 3: + rgb_mean = (0.4488, 0.4371, 0.4040) + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + else: + self.mean = torch.zeros(1, 1, 1, 1) + self.img_range = config.img_range + + self.first_convolution = nn.Conv2d(config.num_channels, config.embed_dim, 3, 1, 1) + self.embeddings = Swin2SREmbeddings(config) + self.encoder = Swin2SREncoder(config, grid_size=self.embeddings.patch_embeddings.patches_resolution) + + self.layernorm = nn.LayerNorm(config.embed_dim, eps=config.layer_norm_eps) + self.patch_unembed = Swin2SRPatchUnEmbeddings(config) + self.conv_after_body = nn.Conv2d(config.embed_dim, config.embed_dim, 3, 1, 1) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.patch_embeddings + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + def pad_and_normalize(self, pixel_values): + _, _, height, width = pixel_values.size() + + # 1. pad + window_size = self.config.window_size + modulo_pad_height = (window_size - height % window_size) % window_size + modulo_pad_width = (window_size - width % window_size) % window_size + pixel_values = nn.functional.pad(pixel_values, (0, modulo_pad_width, 0, modulo_pad_height), "reflect") + + # 2. normalize + self.mean = self.mean.type_as(pixel_values) + pixel_values = (pixel_values - self.mean) * self.img_range + + return pixel_values + + @add_start_docstrings_to_model_forward(SWIN2SR_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=BaseModelOutput, + config_class=_CONFIG_FOR_DOC, + modality="vision", + expected_output=_EXPECTED_OUTPUT_SHAPE, + ) + def forward( + self, + pixel_values: torch.FloatTensor, + head_mask: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutput]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, len(self.config.depths)) + + _, _, height, width = pixel_values.shape + + # some preprocessing: padding + normalization + pixel_values = self.pad_and_normalize(pixel_values) + + embeddings = self.first_convolution(pixel_values) + embedding_output, input_dimensions = self.embeddings(embeddings) + + encoder_outputs = self.encoder( + embedding_output, + input_dimensions, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = encoder_outputs[0] + sequence_output = self.layernorm(sequence_output) + + sequence_output = self.patch_unembed(sequence_output, (height, width)) + sequence_output = self.conv_after_body(sequence_output) + embeddings + + if not return_dict: + output = (sequence_output,) + encoder_outputs[1:] + + return output + + return BaseModelOutput( + last_hidden_state=sequence_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +class Upsample(nn.Module): + """Upsample module. + + Args: + scale (`int`): + Scale factor. Supported scales: 2^n and 3. + num_features (`int`): + Channel number of intermediate features. + """ + + def __init__(self, scale, num_features): + super().__init__() + + self.scale = scale + if (scale & (scale - 1)) == 0: + # scale = 2^n + for i in range(int(math.log(scale, 2))): + self.add_module(f"convolution_{i}", nn.Conv2d(num_features, 4 * num_features, 3, 1, 1)) + self.add_module(f"pixelshuffle_{i}", nn.PixelShuffle(2)) + elif scale == 3: + self.convolution = nn.Conv2d(num_features, 9 * num_features, 3, 1, 1) + self.pixelshuffle = nn.PixelShuffle(3) + else: + raise ValueError(f"Scale {scale} is not supported. Supported scales: 2^n and 3.") + + def forward(self, hidden_state): + if (self.scale & (self.scale - 1)) == 0: + for i in range(int(math.log(self.scale, 2))): + hidden_state = self.__getattr__(f"convolution_{i}")(hidden_state) + hidden_state = self.__getattr__(f"pixelshuffle_{i}")(hidden_state) + + elif self.scale == 3: + hidden_state = self.convolution(hidden_state) + hidden_state = self.pixelshuffle(hidden_state) + + return hidden_state + + +class UpsampleOneStep(nn.Module): + """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) + + Used in lightweight SR to save parameters. + + Args: + scale (int): + Scale factor. Supported scales: 2^n and 3. + in_channels (int): + Channel number of intermediate features. + out_channels (int): + Channel number of output features. + """ + + def __init__(self, scale, in_channels, out_channels): + super().__init__() + + self.conv = nn.Conv2d(in_channels, (scale**2) * out_channels, 3, 1, 1) + self.pixel_shuffle = nn.PixelShuffle(scale) + + def forward(self, x): + x = self.conv(x) + x = self.pixel_shuffle(x) + + return x + + +class PixelShuffleUpsampler(nn.Module): + def __init__(self, config, num_features): + super().__init__() + self.conv_before_upsample = nn.Conv2d(config.embed_dim, num_features, 3, 1, 1) + self.activation = nn.LeakyReLU(inplace=True) + self.upsample = Upsample(config.upscale, num_features) + self.final_convolution = nn.Conv2d(num_features, config.num_channels_out, 3, 1, 1) + + def forward(self, sequence_output): + x = self.conv_before_upsample(sequence_output) + x = self.activation(x) + x = self.upsample(x) + x = self.final_convolution(x) + + return x + + +class NearestConvUpsampler(nn.Module): + def __init__(self, config, num_features): + super().__init__() + if config.upscale != 4: + raise ValueError("The nearest+conv upsampler only supports an upscale factor of 4 at the moment.") + + self.conv_before_upsample = nn.Conv2d(config.embed_dim, num_features, 3, 1, 1) + self.activation = nn.LeakyReLU(inplace=True) + self.conv_up1 = nn.Conv2d(num_features, num_features, 3, 1, 1) + self.conv_up2 = nn.Conv2d(num_features, num_features, 3, 1, 1) + self.conv_hr = nn.Conv2d(num_features, num_features, 3, 1, 1) + self.final_convolution = nn.Conv2d(num_features, config.num_channels_out, 3, 1, 1) + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + + def forward(self, sequence_output): + sequence_output = self.conv_before_upsample(sequence_output) + sequence_output = self.activation(sequence_output) + sequence_output = self.lrelu( + self.conv_up1(torch.nn.functional.interpolate(sequence_output, scale_factor=2, mode="nearest")) + ) + sequence_output = self.lrelu( + self.conv_up2(torch.nn.functional.interpolate(sequence_output, scale_factor=2, mode="nearest")) + ) + reconstruction = self.final_convolution(self.lrelu(self.conv_hr(sequence_output))) + return reconstruction + + +class PixelShuffleAuxUpsampler(nn.Module): + def __init__(self, config, num_features): + super().__init__() + + self.upscale = config.upscale + self.conv_bicubic = nn.Conv2d(config.num_channels, num_features, 3, 1, 1) + self.conv_before_upsample = nn.Conv2d(config.embed_dim, num_features, 3, 1, 1) + self.activation = nn.LeakyReLU(inplace=True) + self.conv_aux = nn.Conv2d(num_features, config.num_channels, 3, 1, 1) + self.conv_after_aux = nn.Sequential(nn.Conv2d(3, num_features, 3, 1, 1), nn.LeakyReLU(inplace=True)) + self.upsample = Upsample(config.upscale, num_features) + self.final_convolution = nn.Conv2d(num_features, config.num_channels_out, 3, 1, 1) + + def forward(self, sequence_output, bicubic, height, width): + bicubic = self.conv_bicubic(bicubic) + sequence_output = self.conv_before_upsample(sequence_output) + sequence_output = self.activation(sequence_output) + aux = self.conv_aux(sequence_output) + sequence_output = self.conv_after_aux(aux) + sequence_output = ( + self.upsample(sequence_output)[:, :, : height * self.upscale, : width * self.upscale] + + bicubic[:, :, : height * self.upscale, : width * self.upscale] + ) + reconstruction = self.final_convolution(sequence_output) + + return reconstruction, aux + + +@add_start_docstrings( + """ + Swin2SR Model transformer with an upsampler head on top for image super resolution and restoration. + """, + SWIN2SR_START_DOCSTRING, +) +class Swin2SRForImageSuperResolution(Swin2SRPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.swin2sr = Swin2SRModel(config) + self.upsampler = config.upsampler + self.upscale = config.upscale + + # Upsampler + num_features = 64 + if self.upsampler == "pixelshuffle": + self.upsample = PixelShuffleUpsampler(config, num_features) + elif self.upsampler == "pixelshuffle_aux": + self.upsample = PixelShuffleAuxUpsampler(config, num_features) + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR (to save parameters) + self.upsample = UpsampleOneStep(config.upscale, config.embed_dim, config.num_channels_out) + elif self.upsampler == "nearest+conv": + # for real-world SR (less artifacts) + self.upsample = NearestConvUpsampler(config, num_features) + else: + # for image denoising and JPEG compression artifact reduction + self.final_convolution = nn.Conv2d(config.embed_dim, config.num_channels_out, 3, 1, 1) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(SWIN2SR_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=ImageSuperResolutionOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, ImageSuperResolutionOutput]: + r""" + Returns: + + Example: + ```python + >>> import torch + >>> import numpy as np + >>> from PIL import Image + >>> import requests + + >>> from transformers import AutoImageProcessor, Swin2SRForImageSuperResolution + + >>> processor = AutoImageProcessor.from_pretrained("caidas/swin2SR-classical-sr-x2-64") + >>> model = Swin2SRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64") + + >>> url = "https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/butterfly.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + >>> # prepare image for the model + >>> inputs = processor(image, return_tensors="pt") + + >>> # forward pass + >>> with torch.no_grad(): + ... outputs = model(**inputs) + + >>> output = outputs.reconstruction.data.squeeze().float().cpu().clamp_(0, 1).numpy() + >>> output = np.moveaxis(output, source=0, destination=-1) + >>> output = (output * 255.0).round().astype(np.uint8) # float32 to uint8 + >>> # you can visualize `output` with `Image.fromarray` + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + height, width = pixel_values.shape[2:] + + if self.config.upsampler == "pixelshuffle_aux": + bicubic = nn.functional.interpolate( + pixel_values, + size=(height * self.upscale, width * self.upscale), + mode="bicubic", + align_corners=False, + ) + + outputs = self.swin2sr( + pixel_values, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + if self.upsampler in ["pixelshuffle", "pixelshuffledirect", "nearest+conv"]: + reconstruction = self.upsample(sequence_output) + elif self.upsampler == "pixelshuffle_aux": + reconstruction, aux = self.upsample(sequence_output, bicubic, height, width) + aux = aux / self.swin2sr.img_range + self.swin2sr.mean + else: + reconstruction = pixel_values + self.final_convolution(sequence_output) + + reconstruction = reconstruction / self.swin2sr.img_range + self.swin2sr.mean + reconstruction = reconstruction[:, :, : height * self.upscale, : width * self.upscale] + + loss = None + if labels is not None: + raise NotImplementedError("Training is not supported at the moment") + + if not return_dict: + output = (reconstruction,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return ImageSuperResolutionOutput( + loss=loss, + reconstruction=reconstruction, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/swinv2/__pycache__/__init__.cpython-312.pyc 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0000000000000000000000000000000000000000..21deda864c6dd59dd28c3079872f059b2de73d30 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/swinv2/convert_swinv2_timm_to_pytorch.py @@ -0,0 +1,219 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert Swinv2 checkpoints from the timm library.""" + +import argparse +import json +from pathlib import Path + +import requests +import timm +import torch +from huggingface_hub import hf_hub_download +from PIL import Image + +from transformers import AutoImageProcessor, Swinv2Config, Swinv2ForImageClassification + + +def get_swinv2_config(swinv2_name): + config = Swinv2Config() + name_split = swinv2_name.split("_") + + model_size = name_split[1] + if "to" in name_split[3]: + img_size = int(name_split[3][-3:]) + else: + img_size = int(name_split[3]) + if "to" in name_split[2]: + window_size = int(name_split[2][-2:]) + else: + window_size = int(name_split[2][6:]) + + if model_size == "tiny": + embed_dim = 96 + depths = (2, 2, 6, 2) + num_heads = (3, 6, 12, 24) + elif model_size == "small": + embed_dim = 96 + depths = (2, 2, 18, 2) + num_heads = (3, 6, 12, 24) + elif model_size == "base": + embed_dim = 128 + depths = (2, 2, 18, 2) + num_heads = (4, 8, 16, 32) + else: + embed_dim = 192 + depths = (2, 2, 18, 2) + num_heads = (6, 12, 24, 48) + + if "to" in swinv2_name: + config.pretrained_window_sizes = (12, 12, 12, 6) + + if ("22k" in swinv2_name) and ("to" not in swinv2_name): + num_classes = 21841 + repo_id = "huggingface/label-files" + filename = "imagenet-22k-id2label.json" + id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) + id2label = {int(k): v for k, v in id2label.items()} + config.id2label = id2label + config.label2id = {v: k for k, v in id2label.items()} + + else: + num_classes = 1000 + repo_id = "huggingface/label-files" + filename = "imagenet-1k-id2label.json" + id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) + id2label = {int(k): v for k, v in id2label.items()} + config.id2label = id2label + config.label2id = {v: k for k, v in id2label.items()} + + config.image_size = img_size + config.num_labels = num_classes + config.embed_dim = embed_dim + config.depths = depths + config.num_heads = num_heads + config.window_size = window_size + + return config + + +def rename_key(name): + if "patch_embed.proj" in name: + name = name.replace("patch_embed.proj", "embeddings.patch_embeddings.projection") + if "patch_embed.norm" in name: + name = name.replace("patch_embed.norm", "embeddings.norm") + if "layers" in name: + name = "encoder." + name + if "attn.proj" in name: + name = name.replace("attn.proj", "attention.output.dense") + if "attn" in name: + name = name.replace("attn", "attention.self") + if "norm1" in name: + name = name.replace("norm1", "layernorm_before") + if "norm2" in name: + name = name.replace("norm2", "layernorm_after") + if "mlp.fc1" in name: + name = name.replace("mlp.fc1", "intermediate.dense") + if "mlp.fc2" in name: + name = name.replace("mlp.fc2", "output.dense") + if "q_bias" in name: + name = name.replace("q_bias", "query.bias") + if "k_bias" in name: + name = name.replace("k_bias", "key.bias") + if "v_bias" in name: + name = name.replace("v_bias", "value.bias") + if "cpb_mlp" in name: + name = name.replace("cpb_mlp", "continuous_position_bias_mlp") + if name == "norm.weight": + name = "layernorm.weight" + if name == "norm.bias": + name = "layernorm.bias" + + if "head" in name: + name = name.replace("head", "classifier") + else: + name = "swinv2." + name + + return name + + +def convert_state_dict(orig_state_dict, model): + for key in orig_state_dict.copy().keys(): + val = orig_state_dict.pop(key) + + if "mask" in key: + continue + elif "qkv" in key: + key_split = key.split(".") + layer_num = int(key_split[1]) + block_num = int(key_split[3]) + dim = model.swinv2.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size + + if "weight" in key: + orig_state_dict[ + f"swinv2.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.weight" + ] = val[:dim, :] + orig_state_dict[ + f"swinv2.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.weight" + ] = val[dim : dim * 2, :] + orig_state_dict[ + f"swinv2.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.weight" + ] = val[-dim:, :] + else: + orig_state_dict[ + f"swinv2.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.bias" + ] = val[:dim] + orig_state_dict[f"swinv2.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.bias"] = val[ + dim : dim * 2 + ] + orig_state_dict[ + f"swinv2.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.bias" + ] = val[-dim:] + else: + orig_state_dict[rename_key(key)] = val + + return orig_state_dict + + +def convert_swinv2_checkpoint(swinv2_name, pytorch_dump_folder_path): + timm_model = timm.create_model(swinv2_name, pretrained=True) + timm_model.eval() + + config = get_swinv2_config(swinv2_name) + model = Swinv2ForImageClassification(config) + model.eval() + + new_state_dict = convert_state_dict(timm_model.state_dict(), model) + model.load_state_dict(new_state_dict) + + url = "http://images.cocodataset.org/val2017/000000039769.jpg" + + image_processor = AutoImageProcessor.from_pretrained("microsoft/{}".format(swinv2_name.replace("_", "-"))) + image = Image.open(requests.get(url, stream=True).raw) + inputs = image_processor(images=image, return_tensors="pt") + + timm_outs = timm_model(inputs["pixel_values"]) + hf_outs = model(**inputs).logits + + assert torch.allclose(timm_outs, hf_outs, atol=1e-3) + + print(f"Saving model {swinv2_name} to {pytorch_dump_folder_path}") + model.save_pretrained(pytorch_dump_folder_path) + + print(f"Saving image processor to {pytorch_dump_folder_path}") + image_processor.save_pretrained(pytorch_dump_folder_path) + + model.push_to_hub( + repo_path_or_name=Path(pytorch_dump_folder_path, swinv2_name), + organization="nandwalritik", + commit_message="Add model", + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + # Required parameters + parser.add_argument( + "--swinv2_name", + default="swinv2_tiny_patch4_window8_256", + type=str, + help="Name of the Swinv2 timm model you'd like to convert.", + ) + parser.add_argument( + "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." + ) + + args = parser.parse_args() + convert_swinv2_checkpoint(args.swinv2_name, args.pytorch_dump_folder_path) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/switch_transformers/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/switch_transformers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..35816110111092c9f605ba04157593732a8b532a --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/switch_transformers/__init__.py @@ -0,0 +1,80 @@ +# Copyright 2022 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ...utils import ( + OptionalDependencyNotAvailable, + _LazyModule, + is_flax_available, + is_sentencepiece_available, + is_tf_available, + is_tokenizers_available, + is_torch_available, +) + + +_import_structure = { + "configuration_switch_transformers": [ + "SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP", + "SwitchTransformersConfig", + "SwitchTransformersOnnxConfig", + ] +} + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_switch_transformers"] = [ + "SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST", + "SwitchTransformersEncoderModel", + "SwitchTransformersForConditionalGeneration", + "SwitchTransformersModel", + "SwitchTransformersPreTrainedModel", + "SwitchTransformersTop1Router", + "SwitchTransformersSparseMLP", + ] + + +if TYPE_CHECKING: + from .configuration_switch_transformers import ( + SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP, + SwitchTransformersConfig, + SwitchTransformersOnnxConfig, + ) + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_switch_transformers import ( + SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST, + SwitchTransformersEncoderModel, + SwitchTransformersForConditionalGeneration, + SwitchTransformersModel, + SwitchTransformersPreTrainedModel, + SwitchTransformersSparseMLP, + SwitchTransformersTop1Router, + ) + + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/switch_transformers/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/switch_transformers/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..dc7694e1c42cbb5086dab533ee726f7fe7ca7714 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/switch_transformers/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/switch_transformers/__pycache__/configuration_switch_transformers.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/switch_transformers/__pycache__/configuration_switch_transformers.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2eaa732328ff105ca11bf0a4411d0f680ea341e0 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/switch_transformers/__pycache__/configuration_switch_transformers.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/switch_transformers/__pycache__/modeling_switch_transformers.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/switch_transformers/__pycache__/modeling_switch_transformers.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ccb5bb37d56a8d8641545499595fbc37996b1e69 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/switch_transformers/__pycache__/modeling_switch_transformers.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/t5/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/t5/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..dbdbe238ba3376303db04849091312906c96d52f --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/t5/__init__.py @@ -0,0 +1,160 @@ +# Copyright 2020 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ...utils import ( + OptionalDependencyNotAvailable, + _LazyModule, + is_flax_available, + is_sentencepiece_available, + is_tf_available, + is_tokenizers_available, + is_torch_available, +) + + +_import_structure = {"configuration_t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config", "T5OnnxConfig"]} + +try: + if not is_sentencepiece_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["tokenization_t5"] = ["T5Tokenizer"] + +try: + if not is_tokenizers_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["tokenization_t5_fast"] = ["T5TokenizerFast"] + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_t5"] = [ + "T5_PRETRAINED_MODEL_ARCHIVE_LIST", + "T5EncoderModel", + "T5ForConditionalGeneration", + "T5Model", + "T5PreTrainedModel", + "load_tf_weights_in_t5", + "T5ForQuestionAnswering", + "T5ForSequenceClassification", + "T5ForTokenClassification", + ] + +try: + if not is_tf_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_tf_t5"] = [ + "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFT5EncoderModel", + "TFT5ForConditionalGeneration", + "TFT5Model", + "TFT5PreTrainedModel", + ] + +try: + if not is_flax_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_flax_t5"] = [ + "FlaxT5EncoderModel", + "FlaxT5ForConditionalGeneration", + "FlaxT5Model", + "FlaxT5PreTrainedModel", + ] + + +if TYPE_CHECKING: + from .configuration_t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config, T5OnnxConfig + + try: + if not is_sentencepiece_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .tokenization_t5 import T5Tokenizer + + try: + if not is_tokenizers_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .tokenization_t5_fast import T5TokenizerFast + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_t5 import ( + T5_PRETRAINED_MODEL_ARCHIVE_LIST, + T5EncoderModel, + T5ForConditionalGeneration, + T5ForQuestionAnswering, + T5ForSequenceClassification, + T5ForTokenClassification, + T5Model, + T5PreTrainedModel, + load_tf_weights_in_t5, + ) + + try: + if not is_tf_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_tf_t5 import ( + TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST, + TFT5EncoderModel, + TFT5ForConditionalGeneration, + TFT5Model, + TFT5PreTrainedModel, + ) + + try: + if not is_flax_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_flax_t5 import ( + FlaxT5EncoderModel, + FlaxT5ForConditionalGeneration, + FlaxT5Model, + FlaxT5PreTrainedModel, + ) + + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git 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+# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Convert T5X checkpoints from the original repository to JAX/FLAX model.""" + +import argparse + +from t5x import checkpoints + +from transformers import FlaxT5ForConditionalGeneration, T5Config + + +def convert_t5x_checkpoint_to_flax(t5x_checkpoint_path, config_name, flax_dump_folder_path): + config = T5Config.from_pretrained(config_name) + flax_model = FlaxT5ForConditionalGeneration(config=config) + t5x_model = checkpoints.load_t5x_checkpoint(t5x_checkpoint_path) + + split_mlp_wi = "wi_0" in t5x_model["target"]["encoder"]["layers_0"]["mlp"] + + # Encoder + for layer_index in range(config.num_layers): + layer_name = f"layers_{str(layer_index)}" + + # Self-Attention + t5x_attention_key = t5x_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"] + t5x_attention_out = t5x_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"] + t5x_attention_query = t5x_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"] + t5x_attention_value = t5x_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"] + + # Layer Normalization + t5x_attention_layer_norm = t5x_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"] + + if split_mlp_wi: + t5x_mlp_wi_0 = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"] + t5x_mlp_wi_1 = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"] + else: + t5x_mlp_wi = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"] + + t5x_mlp_wo = t5x_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"] + + # Layer Normalization + t5x_mlp_layer_norm = t5x_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"] + + # Assigning + flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["k"][ + "kernel" + ] = t5x_attention_key + flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["o"][ + "kernel" + ] = t5x_attention_out + flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["q"][ + "kernel" + ] = t5x_attention_query + flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["v"][ + "kernel" + ] = t5x_attention_value + + flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["0"]["layer_norm"][ + "weight" + ] = t5x_attention_layer_norm + + if split_mlp_wi: + flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["1"]["DenseReluDense"]["wi_0"][ + "kernel" + ] = t5x_mlp_wi_0 + flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["1"]["DenseReluDense"]["wi_1"][ + "kernel" + ] = t5x_mlp_wi_1 + else: + flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["1"]["DenseReluDense"]["wi"][ + "kernel" + ] = t5x_mlp_wi + + flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["1"]["DenseReluDense"]["wo"][ + "kernel" + ] = t5x_mlp_wo + flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["1"]["layer_norm"][ + "weight" + ] = t5x_mlp_layer_norm + + # Only for layer 0: + t5x_encoder_rel_embedding = t5x_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T + flax_model.params["encoder"]["block"]["0"]["layer"]["0"]["SelfAttention"]["relative_attention_bias"][ + "embedding" + ] = t5x_encoder_rel_embedding + + # Assigning + t5x_encoder_norm = t5x_model["target"]["encoder"]["encoder_norm"]["scale"] + flax_model.params["encoder"]["final_layer_norm"]["weight"] = t5x_encoder_norm + + # Decoder + for layer_index in range(config.num_decoder_layers): + layer_name = f"layers_{str(layer_index)}" + + # Self-Attention + t5x_attention_key = t5x_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"] + t5x_attention_out = t5x_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"] + t5x_attention_query = t5x_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"] + t5x_attention_value = t5x_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"] + + # Layer Normalization + t5x_pre_attention_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][ + "scale" + ] + + # Encoder-Decoder-Attention + t5x_enc_dec_attention_key = t5x_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][ + "kernel" + ] + t5x_enc_dec_attention_out = t5x_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][ + "kernel" + ] + t5x_enc_dec_attention_query = t5x_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][ + "kernel" + ] + t5x_enc_dec_attention_value = t5x_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][ + "kernel" + ] + + # Layer Normalization + t5x_cross_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"] + + # MLP + if split_mlp_wi: + t5x_mlp_wi_0 = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"] + t5x_mlp_wi_1 = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"] + else: + t5x_mlp_wi = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"] + + t5x_mlp_wo = t5x_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"] + + # Layer Normalization + tx5_mlp_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"] + + # Assigning + flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["k"][ + "kernel" + ] = t5x_attention_key + flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["o"][ + "kernel" + ] = t5x_attention_out + flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["q"][ + "kernel" + ] = t5x_attention_query + flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["v"][ + "kernel" + ] = t5x_attention_value + + flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["0"]["layer_norm"][ + "weight" + ] = t5x_pre_attention_layer_norm + + flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["1"]["EncDecAttention"]["k"][ + "kernel" + ] = t5x_enc_dec_attention_key + flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["1"]["EncDecAttention"]["o"][ + "kernel" + ] = t5x_enc_dec_attention_out + flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["1"]["EncDecAttention"]["q"][ + "kernel" + ] = t5x_enc_dec_attention_query + flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["1"]["EncDecAttention"]["v"][ + "kernel" + ] = t5x_enc_dec_attention_value + + flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["1"]["layer_norm"][ + "weight" + ] = t5x_cross_layer_norm + + if split_mlp_wi: + flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["2"]["DenseReluDense"]["wi_0"][ + "kernel" + ] = t5x_mlp_wi_0 + flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["2"]["DenseReluDense"]["wi_1"][ + "kernel" + ] = t5x_mlp_wi_1 + else: + flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["2"]["DenseReluDense"]["wi"][ + "kernel" + ] = t5x_mlp_wi + + flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["2"]["DenseReluDense"]["wo"][ + "kernel" + ] = t5x_mlp_wo + + flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["2"]["layer_norm"][ + "weight" + ] = tx5_mlp_layer_norm + + # Decoder Normalization + tx5_decoder_norm = t5x_model["target"]["decoder"]["decoder_norm"]["scale"] + flax_model.params["decoder"]["final_layer_norm"]["weight"] = tx5_decoder_norm + + # Only for layer 0: + t5x_decoder_rel_embedding = t5x_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T + flax_model.params["decoder"]["block"]["0"]["layer"]["0"]["SelfAttention"]["relative_attention_bias"][ + "embedding" + ] = t5x_decoder_rel_embedding + + # Token Embeddings + tx5_token_embeddings = t5x_model["target"]["token_embedder"]["embedding"] + flax_model.params["shared"]["embedding"] = tx5_token_embeddings + + # LM Head (only in v1.1 checkpoints) + if "logits_dense" in t5x_model["target"]["decoder"]: + flax_model.params["lm_head"]["kernel"] = t5x_model["target"]["decoder"]["logits_dense"]["kernel"] + + flax_model.save_pretrained(flax_dump_folder_path) + print("T5X Model was sucessfully converted!") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + # Required parameters + parser.add_argument( + "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint." + ) + parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.") + parser.add_argument( + "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model." + ) + args = parser.parse_args() + convert_t5x_checkpoint_to_flax(args.t5x_checkpoint_path, args.config_name, args.flax_dump_folder_path) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/t5/tokenization_t5.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/t5/tokenization_t5.py new file mode 100644 index 0000000000000000000000000000000000000000..7292808adc6b56119d264c84b2cb72f0984a2f7a --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/t5/tokenization_t5.py @@ -0,0 +1,449 @@ +# coding=utf-8 +# Copyright 2018 T5 Authors and HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Tokenization class for model T5.""" + + +import os +import re +import warnings +from shutil import copyfile +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple + +import sentencepiece as spm + +from ...convert_slow_tokenizer import import_protobuf +from ...tokenization_utils import PreTrainedTokenizer +from ...tokenization_utils_base import AddedToken + + +if TYPE_CHECKING: + from ...tokenization_utils_base import TextInput +from ...utils import logging + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} + + +# TODO(PVP) - this should be removed in Transformers v5 + +SPIECE_UNDERLINE = "▁" + + +class T5Tokenizer(PreTrainedTokenizer): + """ + Construct a T5 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). + + This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to + this superclass for more information regarding those methods. + + Args: + vocab_file (`str`): + [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that + contains the vocabulary necessary to instantiate a tokenizer. + eos_token (`str`, *optional*, defaults to `""`): + The end of sequence token. + + + + When building a sequence using special tokens, this is not the token that is used for the end of sequence. + The token used is the `sep_token`. + + + + unk_token (`str`, *optional*, defaults to `""`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + pad_token (`str`, *optional*, defaults to `""`): + The token used for padding, for example when batching sequences of different lengths. + extra_ids (`int`, *optional*, defaults to 100): + Add a number of extra ids added to the vocabulary for use as sentinels. These tokens are + accessible as "" where "{%d}" is a number between 0 and extra_ids-1. These tokens can be + retrieved by calling get_sentinel_tokens method and token ids can be by calling get_sentinel_token_ids + method + additional_special_tokens (`List[str]`, *optional*): + Additional special tokens used by the tokenizer. + sp_model_kwargs (`dict`, *optional*): + Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for + SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, + to set: + + - `enable_sampling`: Enable subword regularization. + - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. + + - `nbest_size = {0,1}`: No sampling is performed. + - `nbest_size > 1`: samples from the nbest_size results. + - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) + using forward-filtering-and-backward-sampling algorithm. + + - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for + BPE-dropout. + legacy (`bool`, *optional*): + Whether or not the `legacy` behaviour of the tokenizer should be used. Legacy is before the merge of #24622 + and #25224 which includes fixes to properly handle tokens that appear after special tokens. A simple + example: + + - `legacy=True`: + ```python + >>> from transformers import T5Tokenizer + + >>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base", legacy=True) + >>> tokenizer.encode("Hello .") + [8774, 32099, 3, 5, 1] + ``` + - `legacy=False`: + ```python + >>> from transformers import T5Tokenizer + + >>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base", legacy=False) + >>> tokenizer.encode("Hello .") # the extra space `[3]` is no longer here + [8774, 32099, 5, 1] + ``` + Checkout the [pull request](https://github.com/huggingface/transformers/pull/24565) for more details. + add_prefix_space (`bool`, *optional*, defaults to `False`): + Whether or not to add an initial space to the input. This allows to treat the leading word just as any + other word. + + Attributes: + sp_model (`SentencePieceProcessor`): + The *SentencePiece* processor that is used for every conversion (string, tokens and IDs). + """ + + vocab_files_names = VOCAB_FILES_NAMES + model_input_names = ["input_ids", "attention_mask"] + + def __init__( + self, + vocab_file, + eos_token="", + unk_token="", + pad_token="", + extra_ids=100, + additional_special_tokens=None, + sp_model_kwargs: Optional[Dict[str, Any]] = None, + legacy=None, + add_prefix_space=True, + **kwargs, + ) -> None: + pad_token = AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token + unk_token = AddedToken(unk_token, special=True) if isinstance(unk_token, str) else unk_token + eos_token = AddedToken(eos_token, special=True) if isinstance(eos_token, str) else eos_token + + self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs + + self.vocab_file = vocab_file + self._extra_ids = extra_ids + + self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) + self.sp_model.Load(vocab_file) + + if additional_special_tokens is not None: + extra_tokens = [x for x in additional_special_tokens if "" for i in range(extra_ids)] + elif extra_ids > 0 and extra_ids != len(extra_tokens): + raise ValueError( + f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are" + " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids" + " tokens" + ) + else: + extra_tokens = [f"" for i in range(extra_ids)] + additional_special_tokens = extra_tokens + + # for legacy purpose, we keep this. Will be removed and tests updated. (when `added_tokens_decoder` is not passed as kwargs) + self._added_tokens_decoder = {} + for i in range(len(extra_tokens)): + self._added_tokens_decoder[len(self.sp_model) - 1 + extra_ids - i] = AddedToken( + f"", single_word=False, lstrip=True, rstrip=True, special=True, normalized=False + ) + + if legacy is None: + logger.warning_once( + f"You are using the default legacy behaviour of the {self.__class__}. This is" + " expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you." + " If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it" + " means, and thoroughly read the reason why this was added as explained in" + " https://github.com/huggingface/transformers/pull/24565" + ) + legacy = True + + self.legacy = legacy + self.sp_model = self.get_spm_processor(kwargs.pop("from_slow", False)) + self.vocab_file = vocab_file + self._extra_ids = extra_ids + self.add_prefix_space = add_prefix_space + + super().__init__( + eos_token=eos_token, + unk_token=unk_token, + pad_token=pad_token, + extra_ids=extra_ids, + additional_special_tokens=additional_special_tokens, + sp_model_kwargs=self.sp_model_kwargs, + legacy=legacy, + add_prefix_space=add_prefix_space, + **kwargs, + ) + + # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_spm_processor + def get_spm_processor(self, from_slow=False): + tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs) + if self.legacy or from_slow: # no dependency on protobuf + tokenizer.Load(self.vocab_file) + return tokenizer + + with open(self.vocab_file, "rb") as f: + sp_model = f.read() + model_pb2 = import_protobuf(f"The new behaviour of {self.__class__.__name__} (with `self.legacy = False`)") + model = model_pb2.ModelProto.FromString(sp_model) + normalizer_spec = model_pb2.NormalizerSpec() + normalizer_spec.add_dummy_prefix = False + model.normalizer_spec.MergeFrom(normalizer_spec) + sp_model = model.SerializeToString() + tokenizer.LoadFromSerializedProto(sp_model) + return tokenizer + + @staticmethod + def _eventually_correct_t5_max_length(pretrained_model_name_or_path, max_model_length, init_max_model_length): + if pretrained_model_name_or_path in T5Tokenizer.max_model_input_sizes: + deprecated_max_model_length = T5Tokenizer.max_model_input_sizes[pretrained_model_name_or_path] + if init_max_model_length is not None and init_max_model_length != max_model_length: + return init_max_model_length + elif init_max_model_length is None: + warnings.warn( + "This tokenizer was incorrectly instantiated with a model max length of" + f" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this" + " behavior is kept to avoid breaking backwards compatibility when padding/encoding with" + " `truncation is True`.\n- Be aware that you SHOULD NOT rely on" + f" {pretrained_model_name_or_path} automatically truncating your input to" + f" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences" + f" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with" + " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please" + " instantiate this tokenizer with `model_max_length` set to your preferred value.", + FutureWarning, + ) + + return max_model_length + + @property + def vocab_size(self): + return self.sp_model.get_piece_size() + + def get_vocab(self): + vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} + vocab.update(self.added_tokens_encoder) + return vocab + + def get_special_tokens_mask( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False + ) -> List[int]: + """ + Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding + special tokens using the tokenizer `prepare_for_model` method. + + Args: + token_ids_0 (`List[int]`): + List of IDs. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + already_has_special_tokens (`bool`, *optional*, defaults to `False`): + Whether or not the token list is already formatted with special tokens for the model. + + Returns: + `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. + """ + if already_has_special_tokens: + return super().get_special_tokens_mask( + token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True + ) + + # normal case: some special tokens + if token_ids_1 is None: + return ([0] * len(token_ids_0)) + [1] + return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] + + def get_sentinel_tokens(self): + return list( + set(filter(lambda x: bool(re.search(r"", x)) is not None, self.additional_special_tokens)) + ) + + def get_sentinel_token_ids(self): + return [self.convert_tokens_to_ids(token) for token in self.get_sentinel_tokens()] + + def _add_eos_if_not_present(self, token_ids: List[int]) -> List[int]: + """Do not add eos again if user already added it.""" + if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id: + warnings.warn( + f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated" + " eos tokens being added." + ) + return token_ids + else: + return token_ids + [self.eos_token_id] + + def create_token_type_ids_from_sequences( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make + use of token type ids, therefore a list of zeros is returned. + + Args: + token_ids_0 (`List[int]`): + List of IDs. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of zeros. + """ + eos = [self.eos_token_id] + + if token_ids_1 is None: + return len(token_ids_0 + eos) * [0] + return len(token_ids_0 + eos + token_ids_1 + eos) * [0] + + def build_inputs_with_special_tokens( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and + adding special tokens. A sequence has the following format: + + - single sequence: `X ` + - pair of sequences: `A B ` + + Args: + token_ids_0 (`List[int]`): + List of IDs to which the special tokens will be added. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. + """ + token_ids_0 = self._add_eos_if_not_present(token_ids_0) + if token_ids_1 is None: + return token_ids_0 + else: + token_ids_1 = self._add_eos_if_not_present(token_ids_1) + return token_ids_0 + token_ids_1 + + def __getstate__(self): + state = self.__dict__.copy() + state["sp_model"] = None + return state + + def __setstate__(self, d): + self.__dict__ = d + + # for backward compatibility + if not hasattr(self, "sp_model_kwargs"): + self.sp_model_kwargs = {} + + self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) + self.sp_model.Load(self.vocab_file) + + def tokenize(self, text: "TextInput", **kwargs) -> List[str]: + """ + Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the + first token is special. + """ + if self.legacy or len(text) == 0: + return super().tokenize(text, **kwargs) + + text = text.replace(SPIECE_UNDERLINE, " ") + if self.add_prefix_space: + text = SPIECE_UNDERLINE + text + + tokens = super().tokenize(text, **kwargs) + + if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens: + tokens = tokens[1:] + return tokens + + @property + def unk_token_length(self): + return len(self.sp_model.encode(str(self.unk_token))) + + def _tokenize(self, text, **kwargs): + """ + Returns a tokenized string. + + We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any + SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give + `['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the + `unk_token`. Here is an example with `unk_token = ""` and `unk_token_length = 4`. + `self.tokenizer.sp_model.encode(" Hey", out_type = str)[4:]`. + """ + tokens = self.sp_model.encode(text, out_type=str) + if self.legacy or not text.startswith((SPIECE_UNDERLINE, " ")): + return tokens + + # 1. Encode string + prefix ex: " Hey" + tokens = self.sp_model.encode(self.unk_token + text, out_type=str) + # 2. Remove self.unk_token from ['<','unk','>', '▁Hey'] + return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens + + def _convert_token_to_id(self, token): + """Converts a token (str) in an id using the vocab.""" + return self.sp_model.piece_to_id(token) + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + token = self.sp_model.IdToPiece(index) + return token + + def convert_tokens_to_string(self, tokens): + """Converts a sequence of tokens (string) in a single string.""" + # since we manually add the prefix space, we have to remove it when decoding + if tokens[0].startswith(SPIECE_UNDERLINE) and self.add_prefix_space: + tokens[0] = tokens[0][1:] + + current_sub_tokens = [] + out_string = "" + prev_is_special = False + for token in tokens: + # make sure that special tokens are not decoded using sentencepiece model + if token in self.all_special_tokens: + if not prev_is_special: + out_string += " " + out_string += self.sp_model.decode(current_sub_tokens) + token + prev_is_special = True + current_sub_tokens = [] + else: + current_sub_tokens.append(token) + prev_is_special = False + out_string += self.sp_model.decode(current_sub_tokens) + return out_string.strip() + + def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: + if not os.path.isdir(save_directory): + logger.error(f"Vocabulary path ({save_directory}) should be a directory") + return + out_vocab_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] + ) + + if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): + copyfile(self.vocab_file, out_vocab_file) + elif not os.path.isfile(self.vocab_file): + with open(out_vocab_file, "wb") as fi: + content_spiece_model = self.sp_model.serialized_model_proto() + fi.write(content_spiece_model) + + return (out_vocab_file,) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/table_transformer/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/table_transformer/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7a6bc4b3ea05d86613f4a672a3183b1a34f9e5b4 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/table_transformer/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/table_transformer/__pycache__/convert_table_transformer_to_hf_no_timm.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/table_transformer/__pycache__/convert_table_transformer_to_hf_no_timm.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7a660b3b445c7b82fcea110e628d0ceca7cbf22e Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/table_transformer/__pycache__/convert_table_transformer_to_hf_no_timm.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/table_transformer/configuration_table_transformer.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/table_transformer/configuration_table_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..9a2ff6bbab3b24c7b45f0b2ca8c58af70e560ba3 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/table_transformer/configuration_table_transformer.py @@ -0,0 +1,273 @@ +# coding=utf-8 +# Copyright The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Table Transformer model configuration""" +from collections import OrderedDict +from typing import Mapping + +from packaging import version + +from ...configuration_utils import PretrainedConfig +from ...onnx import OnnxConfig +from ...utils import logging +from ..auto import CONFIG_MAPPING + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class TableTransformerConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`TableTransformerModel`]. It is used to + instantiate a Table Transformer model according to the specified arguments, defining the model architecture. + Instantiating a configuration with the defaults will yield a similar configuration to that of the Table Transformer + [microsoft/table-transformer-detection](https://huggingface.co/microsoft/table-transformer-detection) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + use_timm_backbone (`bool`, *optional*, defaults to `True`): + Whether or not to use the `timm` library for the backbone. If set to `False`, will use the [`AutoBackbone`] + API. + backbone_config (`PretrainedConfig` or `dict`, *optional*): + The configuration of the backbone model. Only used in case `use_timm_backbone` is set to `False` in which + case it will default to `ResNetConfig()`. + num_channels (`int`, *optional*, defaults to 3): + The number of input channels. + num_queries (`int`, *optional*, defaults to 100): + Number of object queries, i.e. detection slots. This is the maximal number of objects + [`TableTransformerModel`] can detect in a single image. For COCO, we recommend 100 queries. + d_model (`int`, *optional*, defaults to 256): + Dimension of the layers. + encoder_layers (`int`, *optional*, defaults to 6): + Number of encoder layers. + decoder_layers (`int`, *optional*, defaults to 6): + Number of decoder layers. + encoder_attention_heads (`int`, *optional*, defaults to 8): + Number of attention heads for each attention layer in the Transformer encoder. + decoder_attention_heads (`int`, *optional*, defaults to 8): + Number of attention heads for each attention layer in the Transformer decoder. + decoder_ffn_dim (`int`, *optional*, defaults to 2048): + Dimension of the "intermediate" (often named feed-forward) layer in decoder. + encoder_ffn_dim (`int`, *optional*, defaults to 2048): + Dimension of the "intermediate" (often named feed-forward) layer in decoder. + activation_function (`str` or `function`, *optional*, defaults to `"relu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + dropout (`float`, *optional*, defaults to 0.1): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + activation_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for activations inside the fully connected layer. + init_std (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + init_xavier_std (`float`, *optional*, defaults to 1): + The scaling factor used for the Xavier initialization gain in the HM Attention map module. + encoder_layerdrop (`float`, *optional*, defaults to 0.0): + The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) + for more details. + decoder_layerdrop (`float`, *optional*, defaults to 0.0): + The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) + for more details. + auxiliary_loss (`bool`, *optional*, defaults to `False`): + Whether auxiliary decoding losses (loss at each decoder layer) are to be used. + position_embedding_type (`str`, *optional*, defaults to `"sine"`): + Type of position embeddings to be used on top of the image features. One of `"sine"` or `"learned"`. + backbone (`str`, *optional*): + Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this + will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone` + is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights. + use_pretrained_backbone (`bool`, *optional*, `True`): + Whether to use pretrained weights for the backbone. + backbone_kwargs (`dict`, *optional*): + Keyword arguments to be passed to AutoBackbone when loading from a checkpoint + e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set. + dilation (`bool`, *optional*, defaults to `False`): + Whether to replace stride with dilation in the last convolutional block (DC5). Only supported when + `use_timm_backbone` = `True`. + class_cost (`float`, *optional*, defaults to 1): + Relative weight of the classification error in the Hungarian matching cost. + bbox_cost (`float`, *optional*, defaults to 5): + Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost. + giou_cost (`float`, *optional*, defaults to 2): + Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost. + mask_loss_coefficient (`float`, *optional*, defaults to 1): + Relative weight of the Focal loss in the panoptic segmentation loss. + dice_loss_coefficient (`float`, *optional*, defaults to 1): + Relative weight of the DICE/F-1 loss in the panoptic segmentation loss. + bbox_loss_coefficient (`float`, *optional*, defaults to 5): + Relative weight of the L1 bounding box loss in the object detection loss. + giou_loss_coefficient (`float`, *optional*, defaults to 2): + Relative weight of the generalized IoU loss in the object detection loss. + eos_coefficient (`float`, *optional*, defaults to 0.1): + Relative classification weight of the 'no-object' class in the object detection loss. + + Examples: + + ```python + >>> from transformers import TableTransformerModel, TableTransformerConfig + + >>> # Initializing a Table Transformer microsoft/table-transformer-detection style configuration + >>> configuration = TableTransformerConfig() + + >>> # Initializing a model from the microsoft/table-transformer-detection style configuration + >>> model = TableTransformerModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "table-transformer" + keys_to_ignore_at_inference = ["past_key_values"] + attribute_map = { + "hidden_size": "d_model", + "num_attention_heads": "encoder_attention_heads", + } + + # Copied from transformers.models.detr.configuration_detr.DetrConfig.__init__ + def __init__( + self, + use_timm_backbone=True, + backbone_config=None, + num_channels=3, + num_queries=100, + encoder_layers=6, + encoder_ffn_dim=2048, + encoder_attention_heads=8, + decoder_layers=6, + decoder_ffn_dim=2048, + decoder_attention_heads=8, + encoder_layerdrop=0.0, + decoder_layerdrop=0.0, + is_encoder_decoder=True, + activation_function="relu", + d_model=256, + dropout=0.1, + attention_dropout=0.0, + activation_dropout=0.0, + init_std=0.02, + init_xavier_std=1.0, + auxiliary_loss=False, + position_embedding_type="sine", + backbone="resnet50", + use_pretrained_backbone=True, + backbone_kwargs=None, + dilation=False, + class_cost=1, + bbox_cost=5, + giou_cost=2, + mask_loss_coefficient=1, + dice_loss_coefficient=1, + bbox_loss_coefficient=5, + giou_loss_coefficient=2, + eos_coefficient=0.1, + **kwargs, + ): + if not use_timm_backbone and use_pretrained_backbone: + raise ValueError( + "Loading pretrained backbone weights from the transformers library is not supported yet. `use_timm_backbone` must be set to `True` when `use_pretrained_backbone=True`" + ) + + if backbone_config is not None and backbone is not None: + raise ValueError("You can't specify both `backbone` and `backbone_config`.") + + if backbone_config is not None and use_timm_backbone: + raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`.") + + if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None: + raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.") + + if not use_timm_backbone: + if backbone_config is None: + logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.") + backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage4"]) + elif isinstance(backbone_config, dict): + backbone_model_type = backbone_config.get("model_type") + config_class = CONFIG_MAPPING[backbone_model_type] + backbone_config = config_class.from_dict(backbone_config) + # set timm attributes to None + dilation, backbone, use_pretrained_backbone = None, None, None + + self.use_timm_backbone = use_timm_backbone + self.backbone_config = backbone_config + self.num_channels = num_channels + self.num_queries = num_queries + self.d_model = d_model + self.encoder_ffn_dim = encoder_ffn_dim + self.encoder_layers = encoder_layers + self.encoder_attention_heads = encoder_attention_heads + self.decoder_ffn_dim = decoder_ffn_dim + self.decoder_layers = decoder_layers + self.decoder_attention_heads = decoder_attention_heads + self.dropout = dropout + self.attention_dropout = attention_dropout + self.activation_dropout = activation_dropout + self.activation_function = activation_function + self.init_std = init_std + self.init_xavier_std = init_xavier_std + self.encoder_layerdrop = encoder_layerdrop + self.decoder_layerdrop = decoder_layerdrop + self.num_hidden_layers = encoder_layers + self.auxiliary_loss = auxiliary_loss + self.position_embedding_type = position_embedding_type + self.backbone = backbone + self.use_pretrained_backbone = use_pretrained_backbone + self.backbone_kwargs = backbone_kwargs + self.dilation = dilation + # Hungarian matcher + self.class_cost = class_cost + self.bbox_cost = bbox_cost + self.giou_cost = giou_cost + # Loss coefficients + self.mask_loss_coefficient = mask_loss_coefficient + self.dice_loss_coefficient = dice_loss_coefficient + self.bbox_loss_coefficient = bbox_loss_coefficient + self.giou_loss_coefficient = giou_loss_coefficient + self.eos_coefficient = eos_coefficient + super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs) + + @property + def num_attention_heads(self) -> int: + return self.encoder_attention_heads + + @property + def hidden_size(self) -> int: + return self.d_model + + +# Copied from transformers.models.detr.configuration_detr.DetrOnnxConfig +class TableTransformerOnnxConfig(OnnxConfig): + torch_onnx_minimum_version = version.parse("1.11") + + @property + def inputs(self) -> Mapping[str, Mapping[int, str]]: + return OrderedDict( + [ + ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), + ("pixel_mask", {0: "batch"}), + ] + ) + + @property + def atol_for_validation(self) -> float: + return 1e-5 + + @property + def default_onnx_opset(self) -> int: + return 12 diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/table_transformer/convert_table_transformer_to_hf_no_timm.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/table_transformer/convert_table_transformer_to_hf_no_timm.py new file mode 100644 index 0000000000000000000000000000000000000000..0a2b7b87fe972a4c79a4c573b52164eb7e01d0ad --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/table_transformer/convert_table_transformer_to_hf_no_timm.py @@ -0,0 +1,435 @@ +# coding=utf-8 +# Copyright 2023 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert Table Transformer checkpoints with native (Transformers) backbone. + +URL: https://github.com/microsoft/table-transformer +""" + + +import argparse +from pathlib import Path + +import torch +from huggingface_hub import hf_hub_download +from PIL import Image +from torchvision.transforms import functional as F + +from transformers import DetrImageProcessor, ResNetConfig, TableTransformerConfig, TableTransformerForObjectDetection +from transformers.utils import logging + + +logging.set_verbosity_info() +logger = logging.get_logger(__name__) + + +def create_rename_keys(config): + # here we list all keys to be renamed (original name on the left, our name on the right) + rename_keys = [] + + # stem + # fmt: off + rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight")) + rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight")) + rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias")) + rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean")) + rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.model.embedder.embedder.normalization.running_var")) + # stages + for stage_idx in range(len(config.backbone_config.depths)): + for layer_idx in range(config.backbone_config.depths[stage_idx]): + rename_keys.append( + ( + f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv1.weight", + f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.0.convolution.weight", + ) + ) + rename_keys.append( + ( + f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn1.weight", + f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.0.normalization.weight", + ) + ) + rename_keys.append( + ( + f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn1.bias", + f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.0.normalization.bias", + ) + ) + rename_keys.append( + ( + f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn1.running_mean", + f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.0.normalization.running_mean", + ) + ) + rename_keys.append( + ( + f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn1.running_var", + f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.0.normalization.running_var", + ) + ) + rename_keys.append( + ( + f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv2.weight", + f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.1.convolution.weight", + ) + ) + rename_keys.append( + ( + f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn2.weight", + f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.1.normalization.weight", + ) + ) + rename_keys.append( + ( + f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn2.bias", + f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.1.normalization.bias", + ) + ) + rename_keys.append( + ( + f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn2.running_mean", + f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.1.normalization.running_mean", + ) + ) + rename_keys.append( + ( + f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn2.running_var", + f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.1.normalization.running_var", + ) + ) + # all ResNet stages except the first one have a downsample as first layer + if stage_idx != 0 and layer_idx == 0: + rename_keys.append( + ( + f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight", + f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight", + ) + ) + rename_keys.append( + ( + f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight", + f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight", + ) + ) + rename_keys.append( + ( + f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias", + f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias", + ) + ) + rename_keys.append( + ( + f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean", + f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean", + ) + ) + rename_keys.append( + ( + # "backbone.conv_encoder.model.encoder.stages.3.layers.0.shortcut.normalization.running_var" + f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var", + f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var", + ) + ) + # fmt: on + + for i in range(config.encoder_layers): + # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms + rename_keys.append( + ( + f"transformer.encoder.layers.{i}.self_attn.out_proj.weight", + f"encoder.layers.{i}.self_attn.out_proj.weight", + ) + ) + rename_keys.append( + (f"transformer.encoder.layers.{i}.self_attn.out_proj.bias", f"encoder.layers.{i}.self_attn.out_proj.bias") + ) + rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"encoder.layers.{i}.fc1.weight")) + rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"encoder.layers.{i}.fc1.bias")) + rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"encoder.layers.{i}.fc2.weight")) + rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"encoder.layers.{i}.fc2.bias")) + rename_keys.append( + (f"transformer.encoder.layers.{i}.norm1.weight", f"encoder.layers.{i}.self_attn_layer_norm.weight") + ) + rename_keys.append( + (f"transformer.encoder.layers.{i}.norm1.bias", f"encoder.layers.{i}.self_attn_layer_norm.bias") + ) + rename_keys.append( + (f"transformer.encoder.layers.{i}.norm2.weight", f"encoder.layers.{i}.final_layer_norm.weight") + ) + rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"encoder.layers.{i}.final_layer_norm.bias")) + # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms + rename_keys.append( + ( + f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", + f"decoder.layers.{i}.self_attn.out_proj.weight", + ) + ) + rename_keys.append( + (f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"decoder.layers.{i}.self_attn.out_proj.bias") + ) + rename_keys.append( + ( + f"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight", + f"decoder.layers.{i}.encoder_attn.out_proj.weight", + ) + ) + rename_keys.append( + ( + f"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias", + f"decoder.layers.{i}.encoder_attn.out_proj.bias", + ) + ) + rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"decoder.layers.{i}.fc1.weight")) + rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"decoder.layers.{i}.fc1.bias")) + rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"decoder.layers.{i}.fc2.weight")) + rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"decoder.layers.{i}.fc2.bias")) + rename_keys.append( + (f"transformer.decoder.layers.{i}.norm1.weight", f"decoder.layers.{i}.self_attn_layer_norm.weight") + ) + rename_keys.append( + (f"transformer.decoder.layers.{i}.norm1.bias", f"decoder.layers.{i}.self_attn_layer_norm.bias") + ) + rename_keys.append( + (f"transformer.decoder.layers.{i}.norm2.weight", f"decoder.layers.{i}.encoder_attn_layer_norm.weight") + ) + rename_keys.append( + (f"transformer.decoder.layers.{i}.norm2.bias", f"decoder.layers.{i}.encoder_attn_layer_norm.bias") + ) + rename_keys.append( + (f"transformer.decoder.layers.{i}.norm3.weight", f"decoder.layers.{i}.final_layer_norm.weight") + ) + rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"decoder.layers.{i}.final_layer_norm.bias")) + + # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads + rename_keys.extend( + [ + ("input_proj.weight", "input_projection.weight"), + ("input_proj.bias", "input_projection.bias"), + ("query_embed.weight", "query_position_embeddings.weight"), + ("transformer.decoder.norm.weight", "decoder.layernorm.weight"), + ("transformer.decoder.norm.bias", "decoder.layernorm.bias"), + ("class_embed.weight", "class_labels_classifier.weight"), + ("class_embed.bias", "class_labels_classifier.bias"), + ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"), + ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"), + ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"), + ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"), + ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"), + ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"), + ("transformer.encoder.norm.weight", "encoder.layernorm.weight"), + ("transformer.encoder.norm.bias", "encoder.layernorm.bias"), + ] + ) + + return rename_keys + + +def rename_key(state_dict, old, new): + val = state_dict.pop(old) + state_dict[new] = val + + +def read_in_q_k_v(state_dict, is_panoptic=False): + prefix = "" + if is_panoptic: + prefix = "detr." + + # first: transformer encoder + for i in range(6): + # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias) + in_proj_weight = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight") + in_proj_bias = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias") + # next, add query, keys and values (in that order) to the state dict + state_dict[f"encoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :] + state_dict[f"encoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256] + state_dict[f"encoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :] + state_dict[f"encoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512] + state_dict[f"encoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :] + state_dict[f"encoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:] + # next: transformer decoder (which is a bit more complex because it also includes cross-attention) + for i in range(6): + # read in weights + bias of input projection layer of self-attention + in_proj_weight = state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight") + in_proj_bias = state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias") + # next, add query, keys and values (in that order) to the state dict + state_dict[f"decoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :] + state_dict[f"decoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256] + state_dict[f"decoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :] + state_dict[f"decoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512] + state_dict[f"decoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :] + state_dict[f"decoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:] + # read in weights + bias of input projection layer of cross-attention + in_proj_weight_cross_attn = state_dict.pop( + f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight" + ) + in_proj_bias_cross_attn = state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias") + # next, add query, keys and values (in that order) of cross-attention to the state dict + state_dict[f"decoder.layers.{i}.encoder_attn.q_proj.weight"] = in_proj_weight_cross_attn[:256, :] + state_dict[f"decoder.layers.{i}.encoder_attn.q_proj.bias"] = in_proj_bias_cross_attn[:256] + state_dict[f"decoder.layers.{i}.encoder_attn.k_proj.weight"] = in_proj_weight_cross_attn[256:512, :] + state_dict[f"decoder.layers.{i}.encoder_attn.k_proj.bias"] = in_proj_bias_cross_attn[256:512] + state_dict[f"decoder.layers.{i}.encoder_attn.v_proj.weight"] = in_proj_weight_cross_attn[-256:, :] + state_dict[f"decoder.layers.{i}.encoder_attn.v_proj.bias"] = in_proj_bias_cross_attn[-256:] + + +def resize(image, checkpoint_url): + width, height = image.size + current_max_size = max(width, height) + target_max_size = 800 if "detection" in checkpoint_url else 1000 + scale = target_max_size / current_max_size + resized_image = image.resize((int(round(scale * width)), int(round(scale * height)))) + + return resized_image + + +def normalize(image): + image = F.to_tensor(image) + image = F.normalize(image, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + return image + + +@torch.no_grad() +def convert_table_transformer_checkpoint(checkpoint_url, pytorch_dump_folder_path, push_to_hub): + """ + Copy/paste/tweak model's weights to our DETR structure. + """ + + logger.info("Converting model...") + + # create HuggingFace model and load state dict + backbone_config = ResNetConfig.from_pretrained( + "microsoft/resnet-18", out_features=["stage1", "stage2", "stage3", "stage4"] + ) + + config = TableTransformerConfig( + backbone_config=backbone_config, + use_timm_backbone=False, + mask_loss_coefficient=1, + dice_loss_coefficient=1, + ce_loss_coefficient=1, + bbox_loss_coefficient=5, + giou_loss_coefficient=2, + eos_coefficient=0.4, + class_cost=1, + bbox_cost=5, + giou_cost=2, + ) + + # load original state dict + state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu") + + # rename keys + for src, dest in create_rename_keys(config): + rename_key(state_dict, src, dest) + # query, key and value matrices need special treatment + read_in_q_k_v(state_dict) + # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them + prefix = "model." + for key in state_dict.copy().keys(): + if not key.startswith("class_labels_classifier") and not key.startswith("bbox_predictor"): + val = state_dict.pop(key) + state_dict[prefix + key] = val + + if "detection" in checkpoint_url: + config.num_queries = 15 + config.num_labels = 2 + id2label = {0: "table", 1: "table rotated"} + config.id2label = id2label + config.label2id = {v: k for k, v in id2label.items()} + else: + config.num_queries = 125 + config.num_labels = 6 + id2label = { + 0: "table", + 1: "table column", + 2: "table row", + 3: "table column header", + 4: "table projected row header", + 5: "table spanning cell", + } + config.id2label = id2label + config.label2id = {v: k for k, v in id2label.items()} + + image_processor = DetrImageProcessor(format="coco_detection", size={"longest_edge": 800}) + model = TableTransformerForObjectDetection(config) + model.load_state_dict(state_dict) + model.eval() + + # verify our conversion + filename = "example_pdf.png" if "detection" in checkpoint_url else "example_table.png" + file_path = hf_hub_download(repo_id="nielsr/example-pdf", repo_type="dataset", filename=filename) + image = Image.open(file_path).convert("RGB") + pixel_values = normalize(resize(image, checkpoint_url)).unsqueeze(0) + + outputs = model(pixel_values) + + if "detection" in checkpoint_url: + expected_shape = (1, 15, 3) + expected_logits = torch.tensor( + [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]] + ) + expected_boxes = torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]) + + else: + expected_shape = (1, 125, 7) + expected_logits = torch.tensor( + [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]] + ) + expected_boxes = torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]]) + + assert outputs.logits.shape == expected_shape + assert torch.allclose(outputs.logits[0, :3, :3], expected_logits, atol=1e-4) + assert torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=1e-4) + print("Looks ok!") + + if pytorch_dump_folder_path is not None: + # Save model and image processor + logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}...") + Path(pytorch_dump_folder_path).mkdir(exist_ok=True) + model.save_pretrained(pytorch_dump_folder_path) + image_processor.save_pretrained(pytorch_dump_folder_path) + + if push_to_hub: + # Push model to HF hub + logger.info("Pushing model to the hub...") + model_name = ( + "microsoft/table-transformer-detection" + if "detection" in checkpoint_url + else "microsoft/table-transformer-structure-recognition" + ) + model.push_to_hub(model_name, revision="no_timm") + image_processor.push_to_hub(model_name, revision="no_timm") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--checkpoint_url", + default="https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth", + type=str, + choices=[ + "https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth", + "https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth", + ], + help="URL of the Table Transformer checkpoint you'd like to convert.", + ) + parser.add_argument( + "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model." + ) + parser.add_argument( + "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." + ) + args = parser.parse_args() + convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/table_transformer/modeling_table_transformer.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/table_transformer/modeling_table_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..8e577a65a5fe0073a196c001f67998c49316dc70 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/table_transformer/modeling_table_transformer.py @@ -0,0 +1,2000 @@ +# coding=utf-8 +# Copyright 2022 Microsoft Research and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch Table Transformer model.""" + + +import math +from dataclasses import dataclass +from typing import Dict, List, Optional, Tuple, Union + +import torch +from torch import Tensor, nn + +from ...activations import ACT2FN +from ...modeling_attn_mask_utils import _prepare_4d_attention_mask +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput +from ...modeling_utils import PreTrainedModel +from ...utils import ( + ModelOutput, + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_accelerate_available, + is_scipy_available, + is_timm_available, + is_vision_available, + logging, + replace_return_docstrings, + requires_backends, +) +from ...utils.backbone_utils import load_backbone +from .configuration_table_transformer import TableTransformerConfig + + +if is_scipy_available(): + from scipy.optimize import linear_sum_assignment + +if is_timm_available(): + from timm import create_model + +if is_vision_available(): + from transformers.image_transforms import center_to_corners_format + +if is_accelerate_available(): + from accelerate import PartialState + from accelerate.utils import reduce + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "TableTransformerConfig" +_CHECKPOINT_FOR_DOC = "microsoft/table-transformer-detection" + + +from ..deprecated._archive_maps import TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +@dataclass +# Copied from transformers.models.detr.modeling_detr.DetrDecoderOutput with DETR->TABLE_TRANSFORMER,Detr->TableTransformer +class TableTransformerDecoderOutput(BaseModelOutputWithCrossAttentions): + """ + Base class for outputs of the TABLE_TRANSFORMER decoder. This class adds one attribute to BaseModelOutputWithCrossAttentions, + namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them + gone through a layernorm. This is useful when training the model with auxiliary decoding losses. + + Args: + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer + plus the initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in + the self-attention heads. + cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, + used to compute the weighted average in the cross-attention heads. + intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, num_queries, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`): + Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a + layernorm. + """ + + intermediate_hidden_states: Optional[torch.FloatTensor] = None + + +@dataclass +# Copied from transformers.models.detr.modeling_detr.DetrModelOutput with DETR->TABLE_TRANSFORMER,Detr->TableTransformer +class TableTransformerModelOutput(Seq2SeqModelOutput): + """ + Base class for outputs of the TABLE_TRANSFORMER encoder-decoder model. This class adds one attribute to Seq2SeqModelOutput, + namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them + gone through a layernorm. This is useful when training the model with auxiliary decoding losses. + + Args: + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each + layer plus the initial embedding outputs. + decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the + weighted average in the self-attention heads. + cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, + used to compute the weighted average in the cross-attention heads. + encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder of the model. + encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each + layer plus the initial embedding outputs. + encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the + weighted average in the self-attention heads. + intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, sequence_length, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`): + Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a + layernorm. + """ + + intermediate_hidden_states: Optional[torch.FloatTensor] = None + + +@dataclass +# Copied from transformers.models.detr.modeling_detr.DetrObjectDetectionOutput with Detr->TableTransformer,DetrImageProcessor->DetrImageProcessor +class TableTransformerObjectDetectionOutput(ModelOutput): + """ + Output type of [`TableTransformerForObjectDetection`]. + + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)): + Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a + bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized + scale-invariant IoU loss. + loss_dict (`Dict`, *optional*): + A dictionary containing the individual losses. Useful for logging. + logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`): + Classification logits (including no-object) for all queries. + pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These + values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding + possible padding). You can use [`~TableTransformerImageProcessor.post_process_object_detection`] to retrieve the + unnormalized bounding boxes. + auxiliary_outputs (`list[Dict]`, *optional*): + Optional, only returned when auxilary losses are activated (i.e. `config.auxiliary_loss` is set to `True`) + and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and + `pred_boxes`) for each decoder layer. + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each + layer plus the initial embedding outputs. + decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the + weighted average in the self-attention heads. + cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, + used to compute the weighted average in the cross-attention heads. + encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder of the model. + encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each + layer plus the initial embedding outputs. + encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the + weighted average in the self-attention heads. + """ + + loss: Optional[torch.FloatTensor] = None + loss_dict: Optional[Dict] = None + logits: torch.FloatTensor = None + pred_boxes: torch.FloatTensor = None + auxiliary_outputs: Optional[List[Dict]] = None + last_hidden_state: Optional[torch.FloatTensor] = None + decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None + decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None + cross_attentions: Optional[Tuple[torch.FloatTensor]] = None + encoder_last_hidden_state: Optional[torch.FloatTensor] = None + encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None + encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None + + +# Copied from transformers.models.detr.modeling_detr.DetrFrozenBatchNorm2d with Detr->TableTransformer +class TableTransformerFrozenBatchNorm2d(nn.Module): + """ + BatchNorm2d where the batch statistics and the affine parameters are fixed. + + Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than + torchvision.models.resnet[18,34,50,101] produce nans. + """ + + def __init__(self, n): + super().__init__() + self.register_buffer("weight", torch.ones(n)) + self.register_buffer("bias", torch.zeros(n)) + self.register_buffer("running_mean", torch.zeros(n)) + self.register_buffer("running_var", torch.ones(n)) + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + num_batches_tracked_key = prefix + "num_batches_tracked" + if num_batches_tracked_key in state_dict: + del state_dict[num_batches_tracked_key] + + super()._load_from_state_dict( + state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ) + + def forward(self, x): + # move reshapes to the beginning + # to make it user-friendly + weight = self.weight.reshape(1, -1, 1, 1) + bias = self.bias.reshape(1, -1, 1, 1) + running_var = self.running_var.reshape(1, -1, 1, 1) + running_mean = self.running_mean.reshape(1, -1, 1, 1) + epsilon = 1e-5 + scale = weight * (running_var + epsilon).rsqrt() + bias = bias - running_mean * scale + return x * scale + bias + + +# Copied from transformers.models.detr.modeling_detr.replace_batch_norm with Detr->TableTransformer +def replace_batch_norm(model): + r""" + Recursively replace all `torch.nn.BatchNorm2d` with `TableTransformerFrozenBatchNorm2d`. + + Args: + model (torch.nn.Module): + input model + """ + for name, module in model.named_children(): + if isinstance(module, nn.BatchNorm2d): + new_module = TableTransformerFrozenBatchNorm2d(module.num_features) + + if not module.weight.device == torch.device("meta"): + new_module.weight.data.copy_(module.weight) + new_module.bias.data.copy_(module.bias) + new_module.running_mean.data.copy_(module.running_mean) + new_module.running_var.data.copy_(module.running_var) + + model._modules[name] = new_module + + if len(list(module.children())) > 0: + replace_batch_norm(module) + + +# Copied from transformers.models.detr.modeling_detr.DetrConvEncoder with Detr->TableTransformer +class TableTransformerConvEncoder(nn.Module): + """ + Convolutional backbone, using either the AutoBackbone API or one from the timm library. + + nn.BatchNorm2d layers are replaced by TableTransformerFrozenBatchNorm2d as defined above. + + """ + + def __init__(self, config): + super().__init__() + + self.config = config + + if config.use_timm_backbone: + requires_backends(self, ["timm"]) + kwargs = {} + if config.dilation: + kwargs["output_stride"] = 16 + backbone = create_model( + config.backbone, + pretrained=config.use_pretrained_backbone, + features_only=True, + out_indices=(1, 2, 3, 4), + in_chans=config.num_channels, + **kwargs, + ) + else: + backbone = load_backbone(config) + + # replace batch norm by frozen batch norm + with torch.no_grad(): + replace_batch_norm(backbone) + self.model = backbone + self.intermediate_channel_sizes = ( + self.model.feature_info.channels() if config.use_timm_backbone else self.model.channels + ) + + backbone_model_type = config.backbone if config.use_timm_backbone else config.backbone_config.model_type + if "resnet" in backbone_model_type: + for name, parameter in self.model.named_parameters(): + if config.use_timm_backbone: + if "layer2" not in name and "layer3" not in name and "layer4" not in name: + parameter.requires_grad_(False) + else: + if "stage.1" not in name and "stage.2" not in name and "stage.3" not in name: + parameter.requires_grad_(False) + + def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor): + # send pixel_values through the model to get list of feature maps + features = self.model(pixel_values) if self.config.use_timm_backbone else self.model(pixel_values).feature_maps + + out = [] + for feature_map in features: + # downsample pixel_mask to match shape of corresponding feature_map + mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0] + out.append((feature_map, mask)) + return out + + +# Copied from transformers.models.detr.modeling_detr.DetrConvModel with Detr->TableTransformer +class TableTransformerConvModel(nn.Module): + """ + This module adds 2D position embeddings to all intermediate feature maps of the convolutional encoder. + """ + + def __init__(self, conv_encoder, position_embedding): + super().__init__() + self.conv_encoder = conv_encoder + self.position_embedding = position_embedding + + def forward(self, pixel_values, pixel_mask): + # send pixel_values and pixel_mask through backbone to get list of (feature_map, pixel_mask) tuples + out = self.conv_encoder(pixel_values, pixel_mask) + pos = [] + for feature_map, mask in out: + # position encoding + pos.append(self.position_embedding(feature_map, mask).to(feature_map.dtype)) + + return out, pos + + +# Copied from transformers.models.detr.modeling_detr.DetrSinePositionEmbedding with Detr->TableTransformer +class TableTransformerSinePositionEmbedding(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one used by the Attention is all you + need paper, generalized to work on images. + """ + + def __init__(self, embedding_dim=64, temperature=10000, normalize=False, scale=None): + super().__init__() + self.embedding_dim = embedding_dim + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, pixel_values, pixel_mask): + if pixel_mask is None: + raise ValueError("No pixel mask provided") + y_embed = pixel_mask.cumsum(1, dtype=torch.float32) + x_embed = pixel_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + y_embed = y_embed / (y_embed[:, -1:, :] + 1e-6) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + 1e-6) * self.scale + + dim_t = torch.arange(self.embedding_dim, dtype=torch.int64, device=pixel_values.device).float() + dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.embedding_dim) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + +# Copied from transformers.models.detr.modeling_detr.DetrLearnedPositionEmbedding with Detr->TableTransformer +class TableTransformerLearnedPositionEmbedding(nn.Module): + """ + This module learns positional embeddings up to a fixed maximum size. + """ + + def __init__(self, embedding_dim=256): + super().__init__() + self.row_embeddings = nn.Embedding(50, embedding_dim) + self.column_embeddings = nn.Embedding(50, embedding_dim) + + def forward(self, pixel_values, pixel_mask=None): + height, width = pixel_values.shape[-2:] + width_values = torch.arange(width, device=pixel_values.device) + height_values = torch.arange(height, device=pixel_values.device) + x_emb = self.column_embeddings(width_values) + y_emb = self.row_embeddings(height_values) + pos = torch.cat([x_emb.unsqueeze(0).repeat(height, 1, 1), y_emb.unsqueeze(1).repeat(1, width, 1)], dim=-1) + pos = pos.permute(2, 0, 1) + pos = pos.unsqueeze(0) + pos = pos.repeat(pixel_values.shape[0], 1, 1, 1) + return pos + + +# Copied from transformers.models.detr.modeling_detr.build_position_encoding with Detr->TableTransformer +def build_position_encoding(config): + n_steps = config.d_model // 2 + if config.position_embedding_type == "sine": + # TODO find a better way of exposing other arguments + position_embedding = TableTransformerSinePositionEmbedding(n_steps, normalize=True) + elif config.position_embedding_type == "learned": + position_embedding = TableTransformerLearnedPositionEmbedding(n_steps) + else: + raise ValueError(f"Not supported {config.position_embedding_type}") + + return position_embedding + + +# Copied from transformers.models.detr.modeling_detr.DetrAttention with DETR->TABLE_TRANSFORMER,Detr->TableTransformer +class TableTransformerAttention(nn.Module): + """ + Multi-headed attention from 'Attention Is All You Need' paper. + + Here, we add position embeddings to the queries and keys (as explained in the TABLE_TRANSFORMER paper). + """ + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + bias: bool = True, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + if self.head_dim * num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int): + return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor], **kwargs): + position_embeddings = kwargs.pop("position_embeddings", None) + + if kwargs: + raise ValueError(f"Unexpected arguments {kwargs.keys()}") + + if position_embeddings is not None and object_queries is not None: + raise ValueError( + "Cannot specify both position_embeddings and object_queries. Please use just object_queries" + ) + + if position_embeddings is not None: + logger.warning_once( + "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead" + ) + object_queries = position_embeddings + + return tensor if object_queries is None else tensor + object_queries + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + object_queries: Optional[torch.Tensor] = None, + key_value_states: Optional[torch.Tensor] = None, + spatial_position_embeddings: Optional[torch.Tensor] = None, + output_attentions: bool = False, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + + position_embeddings = kwargs.pop("position_ebmeddings", None) + key_value_position_embeddings = kwargs.pop("key_value_position_embeddings", None) + + if kwargs: + raise ValueError(f"Unexpected arguments {kwargs.keys()}") + + if position_embeddings is not None and object_queries is not None: + raise ValueError( + "Cannot specify both position_embeddings and object_queries. Please use just object_queries" + ) + + if key_value_position_embeddings is not None and spatial_position_embeddings is not None: + raise ValueError( + "Cannot specify both key_value_position_embeddings and spatial_position_embeddings. Please use just spatial_position_embeddings" + ) + + if position_embeddings is not None: + logger.warning_once( + "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead" + ) + object_queries = position_embeddings + + if key_value_position_embeddings is not None: + logger.warning_once( + "key_value_position_embeddings has been deprecated and will be removed in v4.34. Please use spatial_position_embeddings instead" + ) + spatial_position_embeddings = key_value_position_embeddings + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + batch_size, target_len, embed_dim = hidden_states.size() + + # add position embeddings to the hidden states before projecting to queries and keys + if object_queries is not None: + hidden_states_original = hidden_states + hidden_states = self.with_pos_embed(hidden_states, object_queries) + + # add key-value position embeddings to the key value states + if spatial_position_embeddings is not None: + key_value_states_original = key_value_states + key_value_states = self.with_pos_embed(key_value_states, spatial_position_embeddings) + + # get query proj + query_states = self.q_proj(hidden_states) * self.scaling + # get key, value proj + if is_cross_attention: + # cross_attentions + key_states = self._shape(self.k_proj(key_value_states), -1, batch_size) + value_states = self._shape(self.v_proj(key_value_states_original), -1, batch_size) + else: + # self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, batch_size) + value_states = self._shape(self.v_proj(hidden_states_original), -1, batch_size) + + proj_shape = (batch_size * self.num_heads, -1, self.head_dim) + query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape) + key_states = key_states.view(*proj_shape) + value_states = value_states.view(*proj_shape) + + source_len = key_states.size(1) + + attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) + + if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len): + raise ValueError( + f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (batch_size, 1, target_len, source_len): + raise ValueError( + f"Attention mask should be of size {(batch_size, 1, target_len, source_len)}, but is" + f" {attention_mask.size()}" + ) + attn_weights = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attention_mask + attn_weights = attn_weights.view(batch_size * self.num_heads, target_len, source_len) + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + if output_attentions: + # this operation is a bit awkward, but it's required to + # make sure that attn_weights keeps its gradient. + # In order to do so, attn_weights have to reshaped + # twice and have to be reused in the following + attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len) + else: + attn_weights_reshaped = None + + attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = torch.bmm(attn_probs, value_states) + + if attn_output.size() != (batch_size * self.num_heads, target_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim) + attn_output = attn_output.transpose(1, 2) + attn_output = attn_output.reshape(batch_size, target_len, embed_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights_reshaped + + +class TableTransformerEncoderLayer(nn.Module): + # Copied from transformers.models.detr.modeling_detr.DetrEncoderLayer.__init__ with Detr->TableTransformer + def __init__(self, config: TableTransformerConfig): + super().__init__() + self.embed_dim = config.d_model + self.self_attn = TableTransformerAttention( + embed_dim=self.embed_dim, + num_heads=config.encoder_attention_heads, + dropout=config.attention_dropout, + ) + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) + self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + object_queries: torch.Tensor = None, + output_attentions: bool = False, + ): + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative + values. + object_queries (`torch.FloatTensor`, *optional*): object queries, to be added to hidden_states. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + hidden_states, attn_weights = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + object_queries=object_queries, + output_attentions=output_attentions, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.final_layer_norm(hidden_states) + + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + hidden_states = residual + hidden_states + + if self.training: + if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any(): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +class TableTransformerDecoderLayer(nn.Module): + # Copied from transformers.models.detr.modeling_detr.DetrDecoderLayer.__init__ with Detr->TableTransformer + def __init__(self, config: TableTransformerConfig): + super().__init__() + self.embed_dim = config.d_model + + self.self_attn = TableTransformerAttention( + embed_dim=self.embed_dim, + num_heads=config.decoder_attention_heads, + dropout=config.attention_dropout, + ) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.encoder_attn = TableTransformerAttention( + self.embed_dim, + config.decoder_attention_heads, + dropout=config.attention_dropout, + ) + self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) + self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + object_queries: Optional[torch.Tensor] = None, + query_position_embeddings: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = False, + ): + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative + values. + object_queries (`torch.FloatTensor`, *optional*): + object queries that are added to the queries and keys + in the cross-attention layer. + query_position_embeddings (`torch.FloatTensor`, *optional*): + object queries that are added to the queries and keys + in the self-attention layer. + encoder_hidden_states (`torch.FloatTensor`): + cross attention input to the layer of shape `(batch, seq_len, embed_dim)` + encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size + `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative + values. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights = self.self_attn( + hidden_states=hidden_states, + object_queries=query_position_embeddings, + attention_mask=attention_mask, + output_attentions=output_attentions, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.encoder_attn_layer_norm(hidden_states) + + # Cross-Attention Block + cross_attn_weights = None + if encoder_hidden_states is not None: + hidden_states, cross_attn_weights = self.encoder_attn( + hidden_states=hidden_states, + object_queries=query_position_embeddings, + key_value_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + spatial_position_embeddings=object_queries, + output_attentions=output_attentions, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.final_layer_norm(hidden_states) + + # Fully Connected + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights, cross_attn_weights) + + return outputs + + +# Copied from transformers.models.detr.modeling_detr.DetrClassificationHead with Detr->TableTransformer +class TableTransformerClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, input_dim: int, inner_dim: int, num_classes: int, pooler_dropout: float): + super().__init__() + self.dense = nn.Linear(input_dim, inner_dim) + self.dropout = nn.Dropout(p=pooler_dropout) + self.out_proj = nn.Linear(inner_dim, num_classes) + + def forward(self, hidden_states: torch.Tensor): + hidden_states = self.dropout(hidden_states) + hidden_states = self.dense(hidden_states) + hidden_states = torch.tanh(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.out_proj(hidden_states) + return hidden_states + + +class TableTransformerPreTrainedModel(PreTrainedModel): + config_class = TableTransformerConfig + base_model_prefix = "model" + main_input_name = "pixel_values" + _no_split_modules = [ + r"TableTransformerConvEncoder", + r"TableTransformerEncoderLayer", + r"TableTransformerDecoderLayer", + ] + + def _init_weights(self, module): + std = self.config.init_std + + if isinstance(module, TableTransformerLearnedPositionEmbedding): + nn.init.uniform_(module.row_embeddings.weight) + nn.init.uniform_(module.column_embeddings.weight) + if isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + +TABLE_TRANSFORMER_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`TableTransformerConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +TABLE_TRANSFORMER_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Padding will be ignored by default should you provide it. + + Pixel values can be obtained using [`DetrImageProcessor`]. See [`DetrImageProcessor.__call__`] for details. + + pixel_mask (`torch.FloatTensor` of shape `(batch_size, height, width)`, *optional*): + Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`: + + - 1 for pixels that are real (i.e. **not masked**), + - 0 for pixels that are padding (i.e. **masked**). + + [What are attention masks?](../glossary#attention-mask) + + decoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, num_queries)`, *optional*): + Not used by default. Can be used to mask object queries. + encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): + Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) + `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of + hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you + can choose to directly pass a flattened representation of an image. + decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*): + Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an + embedded representation. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class TableTransformerEncoder(TableTransformerPreTrainedModel): + """ + Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a + [`TableTransformerEncoderLayer`]. + + The encoder updates the flattened feature map through multiple self-attention layers. + + Small tweak for Table Transformer: + + - object_queries are added to the forward pass. + + Args: + config: TableTransformerConfig + """ + + def __init__(self, config: TableTransformerConfig): + super().__init__(config) + + self.dropout = config.dropout + self.layerdrop = config.encoder_layerdrop + + self.layers = nn.ModuleList([TableTransformerEncoderLayer(config) for _ in range(config.encoder_layers)]) + + self.layernorm = nn.LayerNorm(config.d_model) + + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + inputs_embeds=None, + attention_mask=None, + object_queries=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Flattened feature map (output of the backbone + projection layer) that is passed to the encoder. + + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`: + + - 1 for pixel features that are real (i.e. **not masked**), + - 0 for pixel features that are padding (i.e. **masked**). + + [What are attention masks?](../glossary#attention-mask) + + object_queries (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Position embeddings that are added to the queries and keys in each self-attention layer. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + hidden_states = inputs_embeds + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + # expand attention_mask + if attention_mask is not None: + # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len] + attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype) + + encoder_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + for encoder_layer in self.layers: + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + to_drop = False + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: # skip the layer + to_drop = True + + if to_drop: + layer_outputs = (None, None) + else: + # we add object_queries as extra input to the encoder_layer + layer_outputs = encoder_layer( + hidden_states, + attention_mask, + object_queries=object_queries, + output_attentions=output_attentions, + ) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + + hidden_states = self.layernorm(hidden_states) + + if not return_dict: + return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions + ) + + +# Copied from transformers.models.detr.modeling_detr.DetrDecoder with DETR->TABLE_TRANSFORMER,Detr->TableTransformer +class TableTransformerDecoder(TableTransformerPreTrainedModel): + """ + Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TableTransformerDecoderLayer`]. + + The decoder updates the query embeddings through multiple self-attention and cross-attention layers. + + Some small tweaks for TABLE_TRANSFORMER: + + - object_queries and query_position_embeddings are added to the forward pass. + - if self.config.auxiliary_loss is set to True, also returns a stack of activations from all decoding layers. + + Args: + config: TableTransformerConfig + """ + + def __init__(self, config: TableTransformerConfig): + super().__init__(config) + self.dropout = config.dropout + self.layerdrop = config.decoder_layerdrop + + self.layers = nn.ModuleList([TableTransformerDecoderLayer(config) for _ in range(config.decoder_layers)]) + # in TABLE_TRANSFORMER, the decoder uses layernorm after the last decoder layer output + self.layernorm = nn.LayerNorm(config.d_model) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + inputs_embeds=None, + attention_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + object_queries=None, + query_position_embeddings=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + **kwargs, + ): + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + The query embeddings that are passed into the decoder. + + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on certain queries. Mask values selected in `[0, 1]`: + + - 1 for queries that are **not masked**, + - 0 for queries that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention + of the decoder. + encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): + Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected + in `[0, 1]`: + + - 1 for pixels that are real (i.e. **not masked**), + - 0 for pixels that are padding (i.e. **masked**). + + object_queries (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Object queries that are added to the queries and keys in each cross-attention layer. + query_position_embeddings (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + , *optional*): Position embeddings that are added to the values and keys in each self-attention layer. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + position_embeddings = kwargs.pop("position_embeddings", None) + + if kwargs: + raise ValueError(f"Unexpected arguments {kwargs.keys()}") + + if position_embeddings is not None and object_queries is not None: + raise ValueError( + "Cannot specify both position_embeddings and object_queries. Please use just object_queries" + ) + + if position_embeddings is not None: + logger.warning_once( + "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead" + ) + object_queries = position_embeddings + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if inputs_embeds is not None: + hidden_states = inputs_embeds + input_shape = inputs_embeds.size()[:-1] + + combined_attention_mask = None + + if attention_mask is not None and combined_attention_mask is not None: + # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len] + combined_attention_mask = combined_attention_mask + _prepare_4d_attention_mask( + attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1] + ) + + # expand encoder attention mask + if encoder_hidden_states is not None and encoder_attention_mask is not None: + # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len] + encoder_attention_mask = _prepare_4d_attention_mask( + encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1] + ) + + # optional intermediate hidden states + intermediate = () if self.config.auxiliary_loss else None + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None + + for idx, decoder_layer in enumerate(self.layers): + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + if output_hidden_states: + all_hidden_states += (hidden_states,) + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: + continue + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + combined_attention_mask, + encoder_hidden_states, + encoder_attention_mask, + None, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=combined_attention_mask, + object_queries=object_queries, + query_position_embeddings=query_position_embeddings, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + ) + + hidden_states = layer_outputs[0] + + if self.config.auxiliary_loss: + hidden_states = self.layernorm(hidden_states) + intermediate += (hidden_states,) + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + if encoder_hidden_states is not None: + all_cross_attentions += (layer_outputs[2],) + + # finally, apply layernorm + hidden_states = self.layernorm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + # stack intermediate decoder activations + if self.config.auxiliary_loss: + intermediate = torch.stack(intermediate) + + if not return_dict: + return tuple( + v + for v in [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions, intermediate] + if v is not None + ) + return TableTransformerDecoderOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attns, + cross_attentions=all_cross_attentions, + intermediate_hidden_states=intermediate, + ) + + +@add_start_docstrings( + """ + The bare Table Transformer Model (consisting of a backbone and encoder-decoder Transformer) outputting raw + hidden-states without any specific head on top. + """, + TABLE_TRANSFORMER_START_DOCSTRING, +) +class TableTransformerModel(TableTransformerPreTrainedModel): + # Copied from transformers.models.detr.modeling_detr.DetrModel.__init__ with Detr->TableTransformer + def __init__(self, config: TableTransformerConfig): + super().__init__(config) + + # Create backbone + positional encoding + backbone = TableTransformerConvEncoder(config) + object_queries = build_position_encoding(config) + self.backbone = TableTransformerConvModel(backbone, object_queries) + + # Create projection layer + self.input_projection = nn.Conv2d(backbone.intermediate_channel_sizes[-1], config.d_model, kernel_size=1) + + self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model) + + self.encoder = TableTransformerEncoder(config) + self.decoder = TableTransformerDecoder(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_encoder(self): + return self.encoder + + def get_decoder(self): + return self.decoder + + def freeze_backbone(self): + for name, param in self.backbone.conv_encoder.model.named_parameters(): + param.requires_grad_(False) + + def unfreeze_backbone(self): + for name, param in self.backbone.conv_encoder.model.named_parameters(): + param.requires_grad_(True) + + @add_start_docstrings_to_model_forward(TABLE_TRANSFORMER_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=TableTransformerModelOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: torch.FloatTensor, + pixel_mask: Optional[torch.FloatTensor] = None, + decoder_attention_mask: Optional[torch.FloatTensor] = None, + encoder_outputs: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + decoder_inputs_embeds: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.FloatTensor], TableTransformerModelOutput]: + r""" + Returns: + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, TableTransformerModel + >>> from huggingface_hub import hf_hub_download + >>> from PIL import Image + + >>> file_path = hf_hub_download(repo_id="nielsr/example-pdf", repo_type="dataset", filename="example_pdf.png") + >>> image = Image.open(file_path).convert("RGB") + + >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/table-transformer-detection") + >>> model = TableTransformerModel.from_pretrained("microsoft/table-transformer-detection") + + >>> # prepare image for the model + >>> inputs = image_processor(images=image, return_tensors="pt") + + >>> # forward pass + >>> outputs = model(**inputs) + + >>> # the last hidden states are the final query embeddings of the Transformer decoder + >>> # these are of shape (batch_size, num_queries, hidden_size) + >>> last_hidden_states = outputs.last_hidden_state + >>> list(last_hidden_states.shape) + [1, 15, 256] + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + batch_size, num_channels, height, width = pixel_values.shape + device = pixel_values.device + + if pixel_mask is None: + pixel_mask = torch.ones(((batch_size, height, width)), device=device) + + # First, sent pixel_values + pixel_mask through Backbone to obtain the features + # pixel_values should be of shape (batch_size, num_channels, height, width) + # pixel_mask should be of shape (batch_size, height, width) + features, position_embeddings_list = self.backbone(pixel_values, pixel_mask) + + # get final feature map and downsampled mask + feature_map, mask = features[-1] + + if mask is None: + raise ValueError("Backbone does not return downsampled pixel mask") + + # Second, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default) + projected_feature_map = self.input_projection(feature_map) + + # Third, flatten the feature map + object queries of shape NxCxHxW to NxCxHW, and permute it to NxHWxC + # In other words, turn their shape into (batch_size, sequence_length, hidden_size) + flattened_features = projected_feature_map.flatten(2).permute(0, 2, 1) + object_queries = position_embeddings_list[-1].flatten(2).permute(0, 2, 1) + + flattened_mask = mask.flatten(1) + + # Fourth, sent flattened_features + flattened_mask + object queries through encoder + # flattened_features is a Tensor of shape (batch_size, heigth*width, hidden_size) + # flattened_mask is a Tensor of shape (batch_size, heigth*width) + if encoder_outputs is None: + encoder_outputs = self.encoder( + inputs_embeds=flattened_features, + attention_mask=flattened_mask, + object_queries=object_queries, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + # Fifth, sent query embeddings + object queries through the decoder (which is conditioned on the encoder output) + query_position_embeddings = self.query_position_embeddings.weight.unsqueeze(0).repeat(batch_size, 1, 1) + queries = torch.zeros_like(query_position_embeddings) + + # decoder outputs consists of (dec_features, dec_hidden, dec_attn) + decoder_outputs = self.decoder( + inputs_embeds=queries, + attention_mask=None, + object_queries=object_queries, + query_position_embeddings=query_position_embeddings, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=flattened_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + if not return_dict: + return decoder_outputs + encoder_outputs + + return TableTransformerModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + intermediate_hidden_states=decoder_outputs.intermediate_hidden_states, + ) + + +@add_start_docstrings( + """ + Table Transformer Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on + top, for tasks such as COCO detection. + """, + TABLE_TRANSFORMER_START_DOCSTRING, +) +class TableTransformerForObjectDetection(TableTransformerPreTrainedModel): + # Copied from transformers.models.detr.modeling_detr.DetrForObjectDetection.__init__ with Detr->TableTransformer + def __init__(self, config: TableTransformerConfig): + super().__init__(config) + + # DETR encoder-decoder model + self.model = TableTransformerModel(config) + + # Object detection heads + self.class_labels_classifier = nn.Linear( + config.d_model, config.num_labels + 1 + ) # We add one for the "no object" class + self.bbox_predictor = TableTransformerMLPPredictionHead( + input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3 + ) + + # Initialize weights and apply final processing + self.post_init() + + @torch.jit.unused + # Copied from transformers.models.detr.modeling_detr.DetrForObjectDetection._set_aux_loss + def _set_aux_loss(self, outputs_class, outputs_coord): + # this is a workaround to make torchscript happy, as torchscript + # doesn't support dictionary with non-homogeneous values, such + # as a dict having both a Tensor and a list. + return [{"logits": a, "pred_boxes": b} for a, b in zip(outputs_class[:-1], outputs_coord[:-1])] + + @add_start_docstrings_to_model_forward(TABLE_TRANSFORMER_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=TableTransformerObjectDetectionOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: torch.FloatTensor, + pixel_mask: Optional[torch.FloatTensor] = None, + decoder_attention_mask: Optional[torch.FloatTensor] = None, + encoder_outputs: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + decoder_inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[List[Dict]] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.FloatTensor], TableTransformerObjectDetectionOutput]: + r""" + labels (`List[Dict]` of len `(batch_size,)`, *optional*): + Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the + following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch + respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes + in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`. + + Returns: + + Examples: + + ```python + >>> from huggingface_hub import hf_hub_download + >>> from transformers import AutoImageProcessor, TableTransformerForObjectDetection + >>> import torch + >>> from PIL import Image + + >>> file_path = hf_hub_download(repo_id="nielsr/example-pdf", repo_type="dataset", filename="example_pdf.png") + >>> image = Image.open(file_path).convert("RGB") + + >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/table-transformer-detection") + >>> model = TableTransformerForObjectDetection.from_pretrained("microsoft/table-transformer-detection") + + >>> inputs = image_processor(images=image, return_tensors="pt") + >>> outputs = model(**inputs) + + >>> # convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax) + >>> target_sizes = torch.tensor([image.size[::-1]]) + >>> results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[ + ... 0 + ... ] + + >>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): + ... box = [round(i, 2) for i in box.tolist()] + ... print( + ... f"Detected {model.config.id2label[label.item()]} with confidence " + ... f"{round(score.item(), 3)} at location {box}" + ... ) + Detected table with confidence 1.0 at location [202.1, 210.59, 1119.22, 385.09] + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # First, sent images through TABLE_TRANSFORMER base model to obtain encoder + decoder outputs + outputs = self.model( + pixel_values, + pixel_mask=pixel_mask, + decoder_attention_mask=decoder_attention_mask, + encoder_outputs=encoder_outputs, + inputs_embeds=inputs_embeds, + decoder_inputs_embeds=decoder_inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + # class logits + predicted bounding boxes + logits = self.class_labels_classifier(sequence_output) + pred_boxes = self.bbox_predictor(sequence_output).sigmoid() + + loss, loss_dict, auxiliary_outputs = None, None, None + if labels is not None: + # First: create the matcher + matcher = TableTransformerHungarianMatcher( + class_cost=self.config.class_cost, bbox_cost=self.config.bbox_cost, giou_cost=self.config.giou_cost + ) + # Second: create the criterion + losses = ["labels", "boxes", "cardinality"] + criterion = TableTransformerLoss( + matcher=matcher, + num_classes=self.config.num_labels, + eos_coef=self.config.eos_coefficient, + losses=losses, + ) + criterion.to(self.device) + # Third: compute the losses, based on outputs and labels + outputs_loss = {} + outputs_loss["logits"] = logits + outputs_loss["pred_boxes"] = pred_boxes + if self.config.auxiliary_loss: + intermediate = outputs.intermediate_hidden_states if return_dict else outputs[4] + outputs_class = self.class_labels_classifier(intermediate) + outputs_coord = self.bbox_predictor(intermediate).sigmoid() + auxiliary_outputs = self._set_aux_loss(outputs_class, outputs_coord) + outputs_loss["auxiliary_outputs"] = auxiliary_outputs + + loss_dict = criterion(outputs_loss, labels) + # Fourth: compute total loss, as a weighted sum of the various losses + weight_dict = {"loss_ce": 1, "loss_bbox": self.config.bbox_loss_coefficient} + weight_dict["loss_giou"] = self.config.giou_loss_coefficient + if self.config.auxiliary_loss: + aux_weight_dict = {} + for i in range(self.config.decoder_layers - 1): + aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) + weight_dict.update(aux_weight_dict) + loss = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict) + + if not return_dict: + if auxiliary_outputs is not None: + output = (logits, pred_boxes) + auxiliary_outputs + outputs + else: + output = (logits, pred_boxes) + outputs + return ((loss, loss_dict) + output) if loss is not None else output + + return TableTransformerObjectDetectionOutput( + loss=loss, + loss_dict=loss_dict, + logits=logits, + pred_boxes=pred_boxes, + auxiliary_outputs=auxiliary_outputs, + last_hidden_state=outputs.last_hidden_state, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + ) + + +# Copied from transformers.models.detr.modeling_detr.dice_loss +def dice_loss(inputs, targets, num_boxes): + """ + Compute the DICE loss, similar to generalized IOU for masks + + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs (0 for the negative class and 1 for the positive + class). + """ + inputs = inputs.sigmoid() + inputs = inputs.flatten(1) + numerator = 2 * (inputs * targets).sum(1) + denominator = inputs.sum(-1) + targets.sum(-1) + loss = 1 - (numerator + 1) / (denominator + 1) + return loss.sum() / num_boxes + + +# Copied from transformers.models.detr.modeling_detr.sigmoid_focal_loss +def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2): + """ + Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. + + Args: + inputs (`torch.FloatTensor` of arbitrary shape): + The predictions for each example. + targets (`torch.FloatTensor` with the same shape as `inputs`) + A tensor storing the binary classification label for each element in the `inputs` (0 for the negative class + and 1 for the positive class). + alpha (`float`, *optional*, defaults to `0.25`): + Optional weighting factor in the range (0,1) to balance positive vs. negative examples. + gamma (`int`, *optional*, defaults to `2`): + Exponent of the modulating factor (1 - p_t) to balance easy vs hard examples. + + Returns: + Loss tensor + """ + prob = inputs.sigmoid() + ce_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction="none") + # add modulating factor + p_t = prob * targets + (1 - prob) * (1 - targets) + loss = ce_loss * ((1 - p_t) ** gamma) + + if alpha >= 0: + alpha_t = alpha * targets + (1 - alpha) * (1 - targets) + loss = alpha_t * loss + + return loss.mean(1).sum() / num_boxes + + +# Copied from transformers.models.detr.modeling_detr.DetrLoss with Detr->TableTransformer,detr->table_transformer +class TableTransformerLoss(nn.Module): + """ + This class computes the losses for TableTransformerForObjectDetection/TableTransformerForSegmentation. The process happens in two steps: 1) + we compute hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair + of matched ground-truth / prediction (supervise class and box). + + A note on the `num_classes` argument (copied from original repo in table_transformer.py): "the naming of the `num_classes` + parameter of the criterion is somewhat misleading. It indeed corresponds to `max_obj_id` + 1, where `max_obj_id` is + the maximum id for a class in your dataset. For example, COCO has a `max_obj_id` of 90, so we pass `num_classes` to + be 91. As another example, for a dataset that has a single class with `id` 1, you should pass `num_classes` to be 2 + (`max_obj_id` + 1). For more details on this, check the following discussion + https://github.com/facebookresearch/table_transformer/issues/108#issuecomment-650269223" + + + Args: + matcher (`TableTransformerHungarianMatcher`): + Module able to compute a matching between targets and proposals. + num_classes (`int`): + Number of object categories, omitting the special no-object category. + eos_coef (`float`): + Relative classification weight applied to the no-object category. + losses (`List[str]`): + List of all the losses to be applied. See `get_loss` for a list of all available losses. + """ + + def __init__(self, matcher, num_classes, eos_coef, losses): + super().__init__() + self.matcher = matcher + self.num_classes = num_classes + self.eos_coef = eos_coef + self.losses = losses + empty_weight = torch.ones(self.num_classes + 1) + empty_weight[-1] = self.eos_coef + self.register_buffer("empty_weight", empty_weight) + + # removed logging parameter, which was part of the original implementation + def loss_labels(self, outputs, targets, indices, num_boxes): + """ + Classification loss (NLL) targets dicts must contain the key "class_labels" containing a tensor of dim + [nb_target_boxes] + """ + if "logits" not in outputs: + raise KeyError("No logits were found in the outputs") + source_logits = outputs["logits"] + + idx = self._get_source_permutation_idx(indices) + target_classes_o = torch.cat([t["class_labels"][J] for t, (_, J) in zip(targets, indices)]) + target_classes = torch.full( + source_logits.shape[:2], self.num_classes, dtype=torch.int64, device=source_logits.device + ) + target_classes[idx] = target_classes_o + + loss_ce = nn.functional.cross_entropy(source_logits.transpose(1, 2), target_classes, self.empty_weight) + losses = {"loss_ce": loss_ce} + + return losses + + @torch.no_grad() + def loss_cardinality(self, outputs, targets, indices, num_boxes): + """ + Compute the cardinality error, i.e. the absolute error in the number of predicted non-empty boxes. + + This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients. + """ + logits = outputs["logits"] + device = logits.device + target_lengths = torch.as_tensor([len(v["class_labels"]) for v in targets], device=device) + # Count the number of predictions that are NOT "no-object" (which is the last class) + card_pred = (logits.argmax(-1) != logits.shape[-1] - 1).sum(1) + card_err = nn.functional.l1_loss(card_pred.float(), target_lengths.float()) + losses = {"cardinality_error": card_err} + return losses + + def loss_boxes(self, outputs, targets, indices, num_boxes): + """ + Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss. + + Targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]. The target boxes + are expected in format (center_x, center_y, w, h), normalized by the image size. + """ + if "pred_boxes" not in outputs: + raise KeyError("No predicted boxes found in outputs") + idx = self._get_source_permutation_idx(indices) + source_boxes = outputs["pred_boxes"][idx] + target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0) + + loss_bbox = nn.functional.l1_loss(source_boxes, target_boxes, reduction="none") + + losses = {} + losses["loss_bbox"] = loss_bbox.sum() / num_boxes + + loss_giou = 1 - torch.diag( + generalized_box_iou(center_to_corners_format(source_boxes), center_to_corners_format(target_boxes)) + ) + losses["loss_giou"] = loss_giou.sum() / num_boxes + return losses + + def loss_masks(self, outputs, targets, indices, num_boxes): + """ + Compute the losses related to the masks: the focal loss and the dice loss. + + Targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w]. + """ + if "pred_masks" not in outputs: + raise KeyError("No predicted masks found in outputs") + + source_idx = self._get_source_permutation_idx(indices) + target_idx = self._get_target_permutation_idx(indices) + source_masks = outputs["pred_masks"] + source_masks = source_masks[source_idx] + masks = [t["masks"] for t in targets] + # TODO use valid to mask invalid areas due to padding in loss + target_masks, valid = nested_tensor_from_tensor_list(masks).decompose() + target_masks = target_masks.to(source_masks) + target_masks = target_masks[target_idx] + + # upsample predictions to the target size + source_masks = nn.functional.interpolate( + source_masks[:, None], size=target_masks.shape[-2:], mode="bilinear", align_corners=False + ) + source_masks = source_masks[:, 0].flatten(1) + + target_masks = target_masks.flatten(1) + target_masks = target_masks.view(source_masks.shape) + losses = { + "loss_mask": sigmoid_focal_loss(source_masks, target_masks, num_boxes), + "loss_dice": dice_loss(source_masks, target_masks, num_boxes), + } + return losses + + def _get_source_permutation_idx(self, indices): + # permute predictions following indices + batch_idx = torch.cat([torch.full_like(source, i) for i, (source, _) in enumerate(indices)]) + source_idx = torch.cat([source for (source, _) in indices]) + return batch_idx, source_idx + + def _get_target_permutation_idx(self, indices): + # permute targets following indices + batch_idx = torch.cat([torch.full_like(target, i) for i, (_, target) in enumerate(indices)]) + target_idx = torch.cat([target for (_, target) in indices]) + return batch_idx, target_idx + + def get_loss(self, loss, outputs, targets, indices, num_boxes): + loss_map = { + "labels": self.loss_labels, + "cardinality": self.loss_cardinality, + "boxes": self.loss_boxes, + "masks": self.loss_masks, + } + if loss not in loss_map: + raise ValueError(f"Loss {loss} not supported") + return loss_map[loss](outputs, targets, indices, num_boxes) + + def forward(self, outputs, targets): + """ + This performs the loss computation. + + Args: + outputs (`dict`, *optional*): + Dictionary of tensors, see the output specification of the model for the format. + targets (`List[dict]`, *optional*): + List of dicts, such that `len(targets) == batch_size`. The expected keys in each dict depends on the + losses applied, see each loss' doc. + """ + outputs_without_aux = {k: v for k, v in outputs.items() if k != "auxiliary_outputs"} + + # Retrieve the matching between the outputs of the last layer and the targets + indices = self.matcher(outputs_without_aux, targets) + + # Compute the average number of target boxes across all nodes, for normalization purposes + num_boxes = sum(len(t["class_labels"]) for t in targets) + num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device) + world_size = 1 + if is_accelerate_available(): + if PartialState._shared_state != {}: + num_boxes = reduce(num_boxes) + world_size = PartialState().num_processes + num_boxes = torch.clamp(num_boxes / world_size, min=1).item() + + # Compute all the requested losses + losses = {} + for loss in self.losses: + losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes)) + + # In case of auxiliary losses, we repeat this process with the output of each intermediate layer. + if "auxiliary_outputs" in outputs: + for i, auxiliary_outputs in enumerate(outputs["auxiliary_outputs"]): + indices = self.matcher(auxiliary_outputs, targets) + for loss in self.losses: + if loss == "masks": + # Intermediate masks losses are too costly to compute, we ignore them. + continue + l_dict = self.get_loss(loss, auxiliary_outputs, targets, indices, num_boxes) + l_dict = {k + f"_{i}": v for k, v in l_dict.items()} + losses.update(l_dict) + + return losses + + +# Copied from transformers.models.detr.modeling_detr.DetrMLPPredictionHead with Detr->TableTransformer,detr->table_transformer +class TableTransformerMLPPredictionHead(nn.Module): + """ + Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates, + height and width of a bounding box w.r.t. an image. + + Copied from https://github.com/facebookresearch/table_transformer/blob/master/models/table_transformer.py + + """ + + def __init__(self, input_dim, hidden_dim, output_dim, num_layers): + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x) + return x + + +# Copied from transformers.models.detr.modeling_detr.DetrHungarianMatcher with Detr->TableTransformer +class TableTransformerHungarianMatcher(nn.Module): + """ + This class computes an assignment between the targets and the predictions of the network. + + For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more + predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, while the others are + un-matched (and thus treated as non-objects). + + Args: + class_cost: + The relative weight of the classification error in the matching cost. + bbox_cost: + The relative weight of the L1 error of the bounding box coordinates in the matching cost. + giou_cost: + The relative weight of the giou loss of the bounding box in the matching cost. + """ + + def __init__(self, class_cost: float = 1, bbox_cost: float = 1, giou_cost: float = 1): + super().__init__() + requires_backends(self, ["scipy"]) + + self.class_cost = class_cost + self.bbox_cost = bbox_cost + self.giou_cost = giou_cost + if class_cost == 0 and bbox_cost == 0 and giou_cost == 0: + raise ValueError("All costs of the Matcher can't be 0") + + @torch.no_grad() + def forward(self, outputs, targets): + """ + Args: + outputs (`dict`): + A dictionary that contains at least these entries: + * "logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits + * "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates. + targets (`List[dict]`): + A list of targets (len(targets) = batch_size), where each target is a dict containing: + * "class_labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of + ground-truth + objects in the target) containing the class labels + * "boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates. + + Returns: + `List[Tuple]`: A list of size `batch_size`, containing tuples of (index_i, index_j) where: + - index_i is the indices of the selected predictions (in order) + - index_j is the indices of the corresponding selected targets (in order) + For each batch element, it holds: len(index_i) = len(index_j) = min(num_queries, num_target_boxes) + """ + batch_size, num_queries = outputs["logits"].shape[:2] + + # We flatten to compute the cost matrices in a batch + out_prob = outputs["logits"].flatten(0, 1).softmax(-1) # [batch_size * num_queries, num_classes] + out_bbox = outputs["pred_boxes"].flatten(0, 1) # [batch_size * num_queries, 4] + + # Also concat the target labels and boxes + target_ids = torch.cat([v["class_labels"] for v in targets]) + target_bbox = torch.cat([v["boxes"] for v in targets]) + + # Compute the classification cost. Contrary to the loss, we don't use the NLL, + # but approximate it in 1 - proba[target class]. + # The 1 is a constant that doesn't change the matching, it can be ommitted. + class_cost = -out_prob[:, target_ids] + + # Compute the L1 cost between boxes + bbox_cost = torch.cdist(out_bbox, target_bbox, p=1) + + # Compute the giou cost between boxes + giou_cost = -generalized_box_iou(center_to_corners_format(out_bbox), center_to_corners_format(target_bbox)) + + # Final cost matrix + cost_matrix = self.bbox_cost * bbox_cost + self.class_cost * class_cost + self.giou_cost * giou_cost + cost_matrix = cost_matrix.view(batch_size, num_queries, -1).cpu() + + sizes = [len(v["boxes"]) for v in targets] + indices = [linear_sum_assignment(c[i]) for i, c in enumerate(cost_matrix.split(sizes, -1))] + return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices] + + +# Copied from transformers.models.detr.modeling_detr._upcast +def _upcast(t: Tensor) -> Tensor: + # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type + if t.is_floating_point(): + return t if t.dtype in (torch.float32, torch.float64) else t.float() + else: + return t if t.dtype in (torch.int32, torch.int64) else t.int() + + +# Copied from transformers.models.detr.modeling_detr.box_area +def box_area(boxes: Tensor) -> Tensor: + """ + Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates. + + Args: + boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`): + Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1 + < x2` and `0 <= y1 < y2`. + + Returns: + `torch.FloatTensor`: a tensor containing the area for each box. + """ + boxes = _upcast(boxes) + return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) + + +# Copied from transformers.models.detr.modeling_detr.box_iou +def box_iou(boxes1, boxes2): + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] + right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] + + width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2] + inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M] + + union = area1[:, None] + area2 - inter + + iou = inter / union + return iou, union + + +# Copied from transformers.models.detr.modeling_detr.generalized_box_iou +def generalized_box_iou(boxes1, boxes2): + """ + Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format. + + Returns: + `torch.FloatTensor`: a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2) + """ + # degenerate boxes gives inf / nan results + # so do an early check + if not (boxes1[:, 2:] >= boxes1[:, :2]).all(): + raise ValueError(f"boxes1 must be in [x0, y0, x1, y1] (corner) format, but got {boxes1}") + if not (boxes2[:, 2:] >= boxes2[:, :2]).all(): + raise ValueError(f"boxes2 must be in [x0, y0, x1, y1] (corner) format, but got {boxes2}") + iou, union = box_iou(boxes1, boxes2) + + top_left = torch.min(boxes1[:, None, :2], boxes2[:, :2]) + bottom_right = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) + + width_height = (bottom_right - top_left).clamp(min=0) # [N,M,2] + area = width_height[:, :, 0] * width_height[:, :, 1] + + return iou - (area - union) / area + + +# Copied from transformers.models.detr.modeling_detr._max_by_axis +def _max_by_axis(the_list): + # type: (List[List[int]]) -> List[int] + maxes = the_list[0] + for sublist in the_list[1:]: + for index, item in enumerate(sublist): + maxes[index] = max(maxes[index], item) + return maxes + + +# Copied from transformers.models.detr.modeling_detr.NestedTensor +class NestedTensor(object): + def __init__(self, tensors, mask: Optional[Tensor]): + self.tensors = tensors + self.mask = mask + + def to(self, device): + cast_tensor = self.tensors.to(device) + mask = self.mask + if mask is not None: + cast_mask = mask.to(device) + else: + cast_mask = None + return NestedTensor(cast_tensor, cast_mask) + + def decompose(self): + return self.tensors, self.mask + + def __repr__(self): + return str(self.tensors) + + +# Copied from transformers.models.detr.modeling_detr.nested_tensor_from_tensor_list +def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): + if tensor_list[0].ndim == 3: + max_size = _max_by_axis([list(img.shape) for img in tensor_list]) + batch_shape = [len(tensor_list)] + max_size + batch_size, num_channels, height, width = batch_shape + dtype = tensor_list[0].dtype + device = tensor_list[0].device + tensor = torch.zeros(batch_shape, dtype=dtype, device=device) + mask = torch.ones((batch_size, height, width), dtype=torch.bool, device=device) + for img, pad_img, m in zip(tensor_list, tensor, mask): + pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + m[: img.shape[1], : img.shape[2]] = False + else: + raise ValueError("Only 3-dimensional tensors are supported") + return NestedTensor(tensor, mask) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tapas/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tapas/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e1afab325420f7cef5170e549a49f2ead66d322b --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tapas/__init__.py @@ -0,0 +1,95 @@ +# Copyright 2020 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available + + +_import_structure = { + "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"], + "tokenization_tapas": ["TapasTokenizer"], +} + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_tapas"] = [ + "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST", + "TapasForMaskedLM", + "TapasForQuestionAnswering", + "TapasForSequenceClassification", + "TapasModel", + "TapasPreTrainedModel", + "load_tf_weights_in_tapas", + ] +try: + if not is_tf_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_tf_tapas"] = [ + "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFTapasForMaskedLM", + "TFTapasForQuestionAnswering", + "TFTapasForSequenceClassification", + "TFTapasModel", + "TFTapasPreTrainedModel", + ] + + +if TYPE_CHECKING: + from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig + from .tokenization_tapas import TapasTokenizer + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_tapas import ( + TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST, + TapasForMaskedLM, + TapasForQuestionAnswering, + TapasForSequenceClassification, + TapasModel, + TapasPreTrainedModel, + load_tf_weights_in_tapas, + ) + + try: + if not is_tf_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_tf_tapas import ( + TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST, + TFTapasForMaskedLM, + TFTapasForQuestionAnswering, + TFTapasForSequenceClassification, + TFTapasModel, + TFTapasPreTrainedModel, + ) + + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tapas/__pycache__/configuration_tapas.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tapas/__pycache__/configuration_tapas.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1394f537bcf0f57cfc0e4d2291c1f3e73e27967c Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tapas/__pycache__/configuration_tapas.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tapas/__pycache__/convert_tapas_original_tf_checkpoint_to_pytorch.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tapas/__pycache__/convert_tapas_original_tf_checkpoint_to_pytorch.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fb18b1674c9308986a0c45939ad9c8964f1c34d4 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tapas/__pycache__/convert_tapas_original_tf_checkpoint_to_pytorch.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tapas/configuration_tapas.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tapas/configuration_tapas.py new file mode 100644 index 0000000000000000000000000000000000000000..b448afd002206240d7ffd0d8c58d2c5a8c5bee39 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tapas/configuration_tapas.py @@ -0,0 +1,228 @@ +# coding=utf-8 +# Copyright 2020 Google Research and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +TAPAS configuration. Based on the BERT configuration with added parameters. + +Hyperparameters are taken from run_task_main.py and hparam_utils.py of the original implementation. URLS: + +- https://github.com/google-research/tapas/blob/master/tapas/run_task_main.py +- https://github.com/google-research/tapas/blob/master/tapas/utils/hparam_utils.py + +""" + + +from ...configuration_utils import PretrainedConfig +from ..deprecated._archive_maps import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class TapasConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`TapasModel`]. It is used to instantiate a TAPAS + model according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the TAPAS + [google/tapas-base-finetuned-sqa](https://huggingface.co/google/tapas-base-finetuned-sqa) architecture. + + Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Hyperparameters additional to BERT are taken from run_task_main.py and hparam_utils.py of the original + implementation. Original implementation available at https://github.com/google-research/tapas/tree/master. + + Args: + vocab_size (`int`, *optional*, defaults to 30522): + Vocabulary size of the TAPAS model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`TapasModel`]. + hidden_size (`int`, *optional*, defaults to 768): + Dimensionality of the encoder layers and the pooler layer. + num_hidden_layers (`int`, *optional*, defaults to 12): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 12): + Number of attention heads for each attention layer in the Transformer encoder. + intermediate_size (`int`, *optional*, defaults to 3072): + Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. + hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"swish"` and `"gelu_new"` are supported. + hidden_dropout_prob (`float`, *optional*, defaults to 0.1): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): + The dropout ratio for the attention probabilities. + max_position_embeddings (`int`, *optional*, defaults to 1024): + The maximum sequence length that this model might ever be used with. Typically set this to something large + just in case (e.g., 512 or 1024 or 2048). + type_vocab_sizes (`List[int]`, *optional*, defaults to `[3, 256, 256, 2, 256, 256, 10]`): + The vocabulary sizes of the `token_type_ids` passed when calling [`TapasModel`]. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + layer_norm_eps (`float`, *optional*, defaults to 1e-12): + The epsilon used by the layer normalization layers. + positive_label_weight (`float`, *optional*, defaults to 10.0): + Weight for positive labels. + num_aggregation_labels (`int`, *optional*, defaults to 0): + The number of aggregation operators to predict. + aggregation_loss_weight (`float`, *optional*, defaults to 1.0): + Importance weight for the aggregation loss. + use_answer_as_supervision (`bool`, *optional*): + Whether to use the answer as the only supervision for aggregation examples. + answer_loss_importance (`float`, *optional*, defaults to 1.0): + Importance weight for the regression loss. + use_normalized_answer_loss (`bool`, *optional*, defaults to `False`): + Whether to normalize the answer loss by the maximum of the predicted and expected value. + huber_loss_delta (`float`, *optional*): + Delta parameter used to calculate the regression loss. + temperature (`float`, *optional*, defaults to 1.0): + Value used to control (OR change) the skewness of cell logits probabilities. + aggregation_temperature (`float`, *optional*, defaults to 1.0): + Scales aggregation logits to control the skewness of probabilities. + use_gumbel_for_cells (`bool`, *optional*, defaults to `False`): + Whether to apply Gumbel-Softmax to cell selection. + use_gumbel_for_aggregation (`bool`, *optional*, defaults to `False`): + Whether to apply Gumbel-Softmax to aggregation selection. + average_approximation_function (`string`, *optional*, defaults to `"ratio"`): + Method to calculate the expected average of cells in the weak supervision case. One of `"ratio"`, + `"first_order"` or `"second_order"`. + cell_selection_preference (`float`, *optional*): + Preference for cell selection in ambiguous cases. Only applicable in case of weak supervision for + aggregation (WTQ, WikiSQL). If the total mass of the aggregation probabilities (excluding the "NONE" + operator) is higher than this hyperparameter, then aggregation is predicted for an example. + answer_loss_cutoff (`float`, *optional*): + Ignore examples with answer loss larger than cutoff. + max_num_rows (`int`, *optional*, defaults to 64): + Maximum number of rows. + max_num_columns (`int`, *optional*, defaults to 32): + Maximum number of columns. + average_logits_per_cell (`bool`, *optional*, defaults to `False`): + Whether to average logits per cell. + select_one_column (`bool`, *optional*, defaults to `True`): + Whether to constrain the model to only select cells from a single column. + allow_empty_column_selection (`bool`, *optional*, defaults to `False`): + Whether to allow not to select any column. + init_cell_selection_weights_to_zero (`bool`, *optional*, defaults to `False`): + Whether to initialize cell selection weights to 0 so that the initial probabilities are 50%. + reset_position_index_per_cell (`bool`, *optional*, defaults to `True`): + Whether to restart position indexes at every cell (i.e. use relative position embeddings). + disable_per_token_loss (`bool`, *optional*, defaults to `False`): + Whether to disable any (strong or weak) supervision on cells. + aggregation_labels (`Dict[int, label]`, *optional*): + The aggregation labels used to aggregate the results. For example, the WTQ models have the following + aggregation labels: `{0: "NONE", 1: "SUM", 2: "AVERAGE", 3: "COUNT"}` + no_aggregation_label_index (`int`, *optional*): + If the aggregation labels are defined and one of these labels represents "No aggregation", this should be + set to its index. For example, the WTQ models have the "NONE" aggregation label at index 0, so that value + should be set to 0 for these models. + + + Example: + + ```python + >>> from transformers import TapasModel, TapasConfig + + >>> # Initializing a default (SQA) Tapas configuration + >>> configuration = TapasConfig() + >>> # Initializing a model from the configuration + >>> model = TapasModel(configuration) + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "tapas" + + def __init__( + self, + vocab_size=30522, + hidden_size=768, + num_hidden_layers=12, + num_attention_heads=12, + intermediate_size=3072, + hidden_act="gelu", + hidden_dropout_prob=0.1, + attention_probs_dropout_prob=0.1, + max_position_embeddings=1024, + type_vocab_sizes=[3, 256, 256, 2, 256, 256, 10], + initializer_range=0.02, + layer_norm_eps=1e-12, + pad_token_id=0, + positive_label_weight=10.0, + num_aggregation_labels=0, + aggregation_loss_weight=1.0, + use_answer_as_supervision=None, + answer_loss_importance=1.0, + use_normalized_answer_loss=False, + huber_loss_delta=None, + temperature=1.0, + aggregation_temperature=1.0, + use_gumbel_for_cells=False, + use_gumbel_for_aggregation=False, + average_approximation_function="ratio", + cell_selection_preference=None, + answer_loss_cutoff=None, + max_num_rows=64, + max_num_columns=32, + average_logits_per_cell=False, + select_one_column=True, + allow_empty_column_selection=False, + init_cell_selection_weights_to_zero=False, + reset_position_index_per_cell=True, + disable_per_token_loss=False, + aggregation_labels=None, + no_aggregation_label_index=None, + **kwargs, + ): + super().__init__(pad_token_id=pad_token_id, **kwargs) + + # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes) + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.hidden_act = hidden_act + self.intermediate_size = intermediate_size + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.max_position_embeddings = max_position_embeddings + self.type_vocab_sizes = type_vocab_sizes + self.initializer_range = initializer_range + self.layer_norm_eps = layer_norm_eps + + # Fine-tuning task hyperparameters + self.positive_label_weight = positive_label_weight + self.num_aggregation_labels = num_aggregation_labels + self.aggregation_loss_weight = aggregation_loss_weight + self.use_answer_as_supervision = use_answer_as_supervision + self.answer_loss_importance = answer_loss_importance + self.use_normalized_answer_loss = use_normalized_answer_loss + self.huber_loss_delta = huber_loss_delta + self.temperature = temperature + self.aggregation_temperature = aggregation_temperature + self.use_gumbel_for_cells = use_gumbel_for_cells + self.use_gumbel_for_aggregation = use_gumbel_for_aggregation + self.average_approximation_function = average_approximation_function + self.cell_selection_preference = cell_selection_preference + self.answer_loss_cutoff = answer_loss_cutoff + self.max_num_rows = max_num_rows + self.max_num_columns = max_num_columns + self.average_logits_per_cell = average_logits_per_cell + self.select_one_column = select_one_column + self.allow_empty_column_selection = allow_empty_column_selection + self.init_cell_selection_weights_to_zero = init_cell_selection_weights_to_zero + self.reset_position_index_per_cell = reset_position_index_per_cell + self.disable_per_token_loss = disable_per_token_loss + + # Aggregation hyperparameters + self.aggregation_labels = aggregation_labels + self.no_aggregation_label_index = no_aggregation_label_index + + if isinstance(self.aggregation_labels, dict): + self.aggregation_labels = {int(k): v for k, v in aggregation_labels.items()} diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tapas/convert_tapas_original_tf_checkpoint_to_pytorch.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tapas/convert_tapas_original_tf_checkpoint_to_pytorch.py new file mode 100644 index 0000000000000000000000000000000000000000..2772a7f126ef9ad350837e993e264c70e68ae3fb --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tapas/convert_tapas_original_tf_checkpoint_to_pytorch.py @@ -0,0 +1,138 @@ +# coding=utf-8 +# Copyright 2020 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert TAPAS checkpoint.""" + + +import argparse + +from transformers import ( + TapasConfig, + TapasForMaskedLM, + TapasForQuestionAnswering, + TapasForSequenceClassification, + TapasModel, + TapasTokenizer, + load_tf_weights_in_tapas, +) +from transformers.utils import logging + + +logging.set_verbosity_info() + + +def convert_tf_checkpoint_to_pytorch( + task, reset_position_index_per_cell, tf_checkpoint_path, tapas_config_file, pytorch_dump_path +): + # Initialise PyTorch model. + # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of + # TapasConfig to False. + + # initialize configuration from json file + config = TapasConfig.from_json_file(tapas_config_file) + # set absolute/relative position embeddings parameter + config.reset_position_index_per_cell = reset_position_index_per_cell + + # set remaining parameters of TapasConfig as well as the model based on the task + if task == "SQA": + model = TapasForQuestionAnswering(config=config) + elif task == "WTQ": + # run_task_main.py hparams + config.num_aggregation_labels = 4 + config.use_answer_as_supervision = True + # hparam_utils.py hparams + config.answer_loss_cutoff = 0.664694 + config.cell_selection_preference = 0.207951 + config.huber_loss_delta = 0.121194 + config.init_cell_selection_weights_to_zero = True + config.select_one_column = True + config.allow_empty_column_selection = False + config.temperature = 0.0352513 + + model = TapasForQuestionAnswering(config=config) + elif task == "WIKISQL_SUPERVISED": + # run_task_main.py hparams + config.num_aggregation_labels = 4 + config.use_answer_as_supervision = False + # hparam_utils.py hparams + config.answer_loss_cutoff = 36.4519 + config.cell_selection_preference = 0.903421 + config.huber_loss_delta = 222.088 + config.init_cell_selection_weights_to_zero = True + config.select_one_column = True + config.allow_empty_column_selection = True + config.temperature = 0.763141 + + model = TapasForQuestionAnswering(config=config) + elif task == "TABFACT": + model = TapasForSequenceClassification(config=config) + elif task == "MLM": + model = TapasForMaskedLM(config=config) + elif task == "INTERMEDIATE_PRETRAINING": + model = TapasModel(config=config) + else: + raise ValueError(f"Task {task} not supported.") + + print(f"Building PyTorch model from configuration: {config}") + # Load weights from tf checkpoint + load_tf_weights_in_tapas(model, config, tf_checkpoint_path) + + # Save pytorch-model (weights and configuration) + print(f"Save PyTorch model to {pytorch_dump_path}") + model.save_pretrained(pytorch_dump_path) + + # Save tokenizer files + print(f"Save tokenizer files to {pytorch_dump_path}") + tokenizer = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt", model_max_length=512) + tokenizer.save_pretrained(pytorch_dump_path) + + print("Used relative position embeddings:", model.config.reset_position_index_per_cell) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + # Required parameters + parser.add_argument( + "--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA." + ) + parser.add_argument( + "--reset_position_index_per_cell", + default=False, + action="store_true", + help="Whether to use relative position embeddings or not. Defaults to True.", + ) + parser.add_argument( + "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path." + ) + parser.add_argument( + "--tapas_config_file", + default=None, + type=str, + required=True, + help=( + "The config json file corresponding to the pre-trained TAPAS model. \n" + "This specifies the model architecture." + ), + ) + parser.add_argument( + "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model." + ) + args = parser.parse_args() + convert_tf_checkpoint_to_pytorch( + args.task, + args.reset_position_index_per_cell, + args.tf_checkpoint_path, + args.tapas_config_file, + args.pytorch_dump_path, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tapas/modeling_tapas.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tapas/modeling_tapas.py new file mode 100644 index 0000000000000000000000000000000000000000..e2ce847926b38fef2dc8c309fa4ee5525fde5918 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tapas/modeling_tapas.py @@ -0,0 +1,2388 @@ +# coding=utf-8 +# Copyright 2020 Google Research and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch TAPAS model.""" + + +import enum +import math +import os +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ...activations import ACT2FN +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, MaskedLMOutput, SequenceClassifierOutput +from ...modeling_utils import PreTrainedModel +from ...pytorch_utils import ( + apply_chunking_to_forward, + find_pruneable_heads_and_indices, + is_torch_greater_or_equal_than_1_12, + prune_linear_layer, +) +from ...utils import ( + ModelOutput, + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) +from .configuration_tapas import TapasConfig + + +logger = logging.get_logger(__name__) + +if not is_torch_greater_or_equal_than_1_12: + logger.warning( + f"You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use " + "TapasModel. Please upgrade torch." + ) + +_CONFIG_FOR_DOC = "TapasConfig" +_CHECKPOINT_FOR_DOC = "google/tapas-base" + + +from ..deprecated._archive_maps import TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +EPSILON_ZERO_DIVISION = 1e-10 +CLOSE_ENOUGH_TO_LOG_ZERO = -10000.0 + + +@dataclass +class TableQuestionAnsweringOutput(ModelOutput): + """ + Output type of [`TapasForQuestionAnswering`]. + + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` (and possibly `answer`, `aggregation_labels`, `numeric_values` and `numeric_values_scale` are provided)): + Total loss as the sum of the hierarchical cell selection log-likelihood loss and (optionally) the + semi-supervised regression loss and (optionally) supervised loss for aggregations. + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): + Prediction scores of the cell selection head, for every token. + logits_aggregation (`torch.FloatTensor`, *optional*, of shape `(batch_size, num_aggregation_labels)`): + Prediction scores of the aggregation head, for every aggregation operator. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer + plus the initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in + the self-attention heads. + """ + + loss: Optional[torch.FloatTensor] = None + logits: torch.FloatTensor = None + logits_aggregation: torch.FloatTensor = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + + +def load_tf_weights_in_tapas(model, config, tf_checkpoint_path): + """ + Load tf checkpoints in a PyTorch model. This is an adaptation from load_tf_weights_in_bert + + - add cell selection and aggregation heads + - take into account additional token type embedding layers + """ + try: + import re + + import numpy as np + import tensorflow as tf + except ImportError: + logger.error( + "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " + "https://www.tensorflow.org/install/ for installation instructions." + ) + raise + tf_path = os.path.abspath(tf_checkpoint_path) + logger.info(f"Converting TensorFlow checkpoint from {tf_path}") + # Load weights from TF model + init_vars = tf.train.list_variables(tf_path) + names = [] + arrays = [] + for name, shape in init_vars: + logger.info(f"Loading TF weight {name} with shape {shape}") + array = tf.train.load_variable(tf_path, name) + names.append(name) + arrays.append(array) + + for name, array in zip(names, arrays): + name = name.split("/") + # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculate m and v + # which are not required for using pretrained model + if any( + n + in [ + "adam_v", + "adam_m", + "AdamWeightDecayOptimizer", + "AdamWeightDecayOptimizer_1", + "global_step", + "seq_relationship", + ] + for n in name + ): + logger.info(f"Skipping {'/'.join(name)}") + continue + # in case the model is TapasForSequenceClassification, we skip output_bias and output_weights + # since these are not used for classification + if isinstance(model, TapasForSequenceClassification): + if any(n in ["output_bias", "output_weights"] for n in name): + logger.info(f"Skipping {'/'.join(name)}") + continue + # in case the model is TapasModel, we skip output_bias, output_weights, output_bias_cls and output_weights_cls + # since this model does not have MLM and NSP heads + if isinstance(model, TapasModel): + if any(n in ["output_bias", "output_weights", "output_bias_cls", "output_weights_cls"] for n in name): + logger.info(f"Skipping {'/'.join(name)}") + continue + # in case the model is TapasForMaskedLM, we skip the pooler + if isinstance(model, TapasForMaskedLM): + if any(n in ["pooler"] for n in name): + logger.info(f"Skipping {'/'.join(name)}") + continue + # if first scope name starts with "bert", change it to "tapas" + if name[0] == "bert": + name[0] = "tapas" + pointer = model + for m_name in name: + if re.fullmatch(r"[A-Za-z]+_\d+", m_name): + scope_names = re.split(r"_(\d+)", m_name) + else: + scope_names = [m_name] + if scope_names[0] == "kernel" or scope_names[0] == "gamma": + pointer = getattr(pointer, "weight") + elif scope_names[0] == "beta": + pointer = getattr(pointer, "bias") + # cell selection heads + elif scope_names[0] == "output_bias": + if not isinstance(model, TapasForMaskedLM): + pointer = getattr(pointer, "output_bias") + else: + pointer = getattr(pointer, "bias") + elif scope_names[0] == "output_weights": + pointer = getattr(pointer, "output_weights") + elif scope_names[0] == "column_output_bias": + pointer = getattr(pointer, "column_output_bias") + elif scope_names[0] == "column_output_weights": + pointer = getattr(pointer, "column_output_weights") + # aggregation head + elif scope_names[0] == "output_bias_agg": + pointer = getattr(pointer, "aggregation_classifier") + pointer = getattr(pointer, "bias") + elif scope_names[0] == "output_weights_agg": + pointer = getattr(pointer, "aggregation_classifier") + pointer = getattr(pointer, "weight") + # classification head + elif scope_names[0] == "output_bias_cls": + pointer = getattr(pointer, "classifier") + pointer = getattr(pointer, "bias") + elif scope_names[0] == "output_weights_cls": + pointer = getattr(pointer, "classifier") + pointer = getattr(pointer, "weight") + else: + try: + pointer = getattr(pointer, scope_names[0]) + except AttributeError: + logger.info(f"Skipping {'/'.join(name)}") + continue + if len(scope_names) >= 2: + num = int(scope_names[1]) + pointer = pointer[num] + if m_name[-11:] == "_embeddings": + pointer = getattr(pointer, "weight") + elif m_name[-13:] in [f"_embeddings_{i}" for i in range(7)]: + pointer = getattr(pointer, "weight") + elif m_name == "kernel": + array = np.transpose(array) + try: + if pointer.shape != array.shape: + raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched") + except AssertionError as e: + e.args += (pointer.shape, array.shape) + raise + logger.info(f"Initialize PyTorch weight {name}") + # Added a check to see whether the array is a scalar (because bias terms in Tapas checkpoints can be + # scalar => should first be converted to numpy arrays) + if np.isscalar(array): + array = np.array(array) + pointer.data = torch.from_numpy(array) + return model + + +class TapasEmbeddings(nn.Module): + """ + Construct the embeddings from word, position and token_type embeddings. Same as BertEmbeddings but with a number of + additional token type embeddings to encode tabular structure. + """ + + def __init__(self, config): + super().__init__() + # we do not include config.disabled_features and config.disable_position_embeddings from the original implementation + # word embeddings + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + # position embeddings + self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) + # token type embeddings + for i, type_vocab_sizes in enumerate(config.type_vocab_sizes): + name = f"token_type_embeddings_{i}" + setattr(self, name, nn.Embedding(type_vocab_sizes, config.hidden_size)) + + self.number_of_token_type_embeddings = len(config.type_vocab_sizes) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + self.config = config + + def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None): + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + device = input_ids.device if input_ids is not None else inputs_embeds.device + + if position_ids is None: + # create absolute position embeddings + position_ids = torch.arange(seq_length, dtype=torch.long, device=device) + position_ids = position_ids.unsqueeze(0).expand(input_shape) + # when self.config.reset_position_index_per_cell is set to True, create relative position embeddings + if self.config.reset_position_index_per_cell: + # shape (batch_size, seq_len) + col_index = IndexMap(token_type_ids[:, :, 1], self.config.type_vocab_sizes[1], batch_dims=1) + # shape (batch_size, seq_len) + row_index = IndexMap(token_type_ids[:, :, 2], self.config.type_vocab_sizes[2], batch_dims=1) + # shape (batch_size, seq_len) + full_index = ProductIndexMap(col_index, row_index) + # shape (max_rows * max_columns,). First absolute position for every cell + first_position_per_segment = reduce_min(position_ids, full_index)[0] + # ? shape (batch_size, seq_len). First absolute position of the cell for every token + first_position = gather(first_position_per_segment, full_index) + # shape (1, seq_len) + position = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0) + position_ids = torch.min( + torch.as_tensor(self.config.max_position_embeddings - 1, device=device), position - first_position + ) + + if token_type_ids is None: + token_type_ids = torch.zeros( + (input_shape + self.number_of_token_type_embeddings), dtype=torch.long, device=device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + position_embeddings = self.position_embeddings(position_ids) + + embeddings = inputs_embeds + position_embeddings + + for i in range(self.number_of_token_type_embeddings): + name = f"token_type_embeddings_{i}" + embeddings += getattr(self, name)(token_type_ids[:, :, i]) + + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class TapasSelfAttention(nn.Module): + def __init__(self, config): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size {config.hidden_size} is not a multiple of the number of attention " + f"heads {config.num_attention_heads}" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in TapasModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class TapasSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class TapasAttention(nn.Module): + def __init__(self, config): + super().__init__() + self.self = TapasSelfAttention(config) + self.output = TapasSelfOutput(config) + self.pruned_heads = set() + + # Copied from transformers.models.bert.modeling_bert.BertAttention.prune_heads + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + # Copied from transformers.models.bert.modeling_bert.BertAttention.forward + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class TapasIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class TapasOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class TapasLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = TapasAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError(f"{self} should be used as a decoder model if cross attention is added") + self.crossattention = TapasAttention(config) + self.intermediate = TapasIntermediate(config) + self.output = TapasOutput(config) + + # Copied from transformers.models.bert.modeling_bert.BertLayer.forward + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" + " by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + # Copied from transformers.models.bert.modeling_bert.BertLayer.feed_forward_chunk + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +class TapasEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList([TapasLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + ): + all_hidden_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer_module.__call__, + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_values, + output_attentions, + ) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_values, + output_attentions, + ) + hidden_states = layer_outputs[0] + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class TapasPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->Tapas +class TapasPredictionHeadTransform(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if isinstance(config.hidden_act, str): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->Tapas +class TapasLMPredictionHead(nn.Module): + def __init__(self, config): + super().__init__() + self.transform = TapasPredictionHeadTransform(config) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` + self.decoder.bias = self.bias + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + hidden_states = self.decoder(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->Tapas +class TapasOnlyMLMHead(nn.Module): + def __init__(self, config): + super().__init__() + self.predictions = TapasLMPredictionHead(config) + + def forward(self, sequence_output: torch.Tensor) -> torch.Tensor: + prediction_scores = self.predictions(sequence_output) + return prediction_scores + + +class TapasPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = TapasConfig + base_model_prefix = "tapas" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + +TAPAS_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its models (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`TapasConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +TAPAS_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See + [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0}, 7)`, *optional*): + Token indices that encode tabular structure. Indices can be obtained using [`AutoTokenizer`]. See this + class for more info. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. If + `reset_position_index_per_cell` of [`TapasConfig`] is set to `True`, relative position embeddings will be + used. Selected in the range `[0, config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 + indicates the head is **not masked**, - 0 indicates the head is **masked**. + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare Tapas Model transformer outputting raw hidden-states without any specific head on top.", + TAPAS_START_DOCSTRING, +) +class TapasModel(TapasPreTrainedModel): + """ + This class is a small change compared to [`BertModel`], taking into account the additional token type ids. + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in [Attention is + all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, + Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. + + """ + + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = TapasEmbeddings(config) + self.encoder = TapasEncoder(config) + + self.pooler = TapasPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + @add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPooling]: + r""" + Returns: + + Examples: + + ```python + >>> from transformers import AutoTokenizer, TapasModel + >>> import pandas as pd + + >>> tokenizer = AutoTokenizer.from_pretrained("google/tapas-base") + >>> model = TapasModel.from_pretrained("google/tapas-base") + + >>> data = { + ... "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], + ... "Age": ["56", "45", "59"], + ... "Number of movies": ["87", "53", "69"], + ... } + >>> table = pd.DataFrame.from_dict(data) + >>> queries = ["How many movies has George Clooney played in?", "How old is Brad Pitt?"] + + >>> inputs = tokenizer(table=table, queries=queries, padding="max_length", return_tensors="pt") + >>> outputs = model(**inputs) + + >>> last_hidden_states = outputs.last_hidden_state + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + device = input_ids.device if input_ids is not None else inputs_embeds.device + + if attention_mask is None: + attention_mask = torch.ones(input_shape, device=device) + if token_type_ids is None: + token_type_ids = torch.zeros( + (*input_shape, len(self.config.type_vocab_sizes)), dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) + + # If a 2D ou 3D attention mask is provided for the cross-attention + # we need to make broadcastabe to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPooling( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +@add_start_docstrings("""Tapas Model with a `language modeling` head on top.""", TAPAS_START_DOCSTRING) +class TapasForMaskedLM(TapasPreTrainedModel): + _tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"] + config_class = TapasConfig + base_model_prefix = "tapas" + + def __init__(self, config): + super().__init__(config) + + self.tapas = TapasModel(config, add_pooling_layer=False) + self.cls = TapasOnlyMLMHead(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.cls.predictions.decoder + + def set_output_embeddings(self, new_embeddings): + self.cls.predictions.decoder = new_embeddings + + @add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + **kwargs, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + + Returns: + + Examples: + + ```python + >>> from transformers import AutoTokenizer, TapasForMaskedLM + >>> import pandas as pd + + >>> tokenizer = AutoTokenizer.from_pretrained("google/tapas-base") + >>> model = TapasForMaskedLM.from_pretrained("google/tapas-base") + + >>> data = { + ... "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], + ... "Age": ["56", "45", "59"], + ... "Number of movies": ["87", "53", "69"], + ... } + >>> table = pd.DataFrame.from_dict(data) + + >>> inputs = tokenizer( + ... table=table, queries="How many [MASK] has George [MASK] played in?", return_tensors="pt" + ... ) + >>> labels = tokenizer( + ... table=table, queries="How many movies has George Clooney played in?", return_tensors="pt" + ... )["input_ids"] + + >>> outputs = model(**inputs, labels=labels) + >>> logits = outputs.logits + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.tapas( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() # -100 index = padding token + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + """ + Tapas Model with a cell selection head and optional aggregation head on top for question-answering tasks on tables + (linear layers on top of the hidden-states output to compute `logits` and optional `logits_aggregation`), e.g. for + SQA, WTQ or WikiSQL-supervised tasks. + """, + TAPAS_START_DOCSTRING, +) +class TapasForQuestionAnswering(TapasPreTrainedModel): + def __init__(self, config: TapasConfig): + super().__init__(config) + + # base model + self.tapas = TapasModel(config) + + # dropout (only used when training) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # cell selection heads + if config.init_cell_selection_weights_to_zero: + # init_cell_selection_weights_to_zero: Whether the initial weights should be + # set to 0. This ensures that all tokens have the same prior probability. + self.output_weights = nn.Parameter(torch.zeros(config.hidden_size)) + self.column_output_weights = nn.Parameter(torch.zeros(config.hidden_size)) + else: + self.output_weights = nn.Parameter(torch.empty(config.hidden_size)) + nn.init.normal_( + self.output_weights, std=config.initializer_range + ) # here, a truncated normal is used in the original implementation + self.column_output_weights = nn.Parameter(torch.empty(config.hidden_size)) + nn.init.normal_( + self.column_output_weights, std=config.initializer_range + ) # here, a truncated normal is used in the original implementation + self.output_bias = nn.Parameter(torch.zeros([])) + self.column_output_bias = nn.Parameter(torch.zeros([])) + + # aggregation head + if config.num_aggregation_labels > 0: + self.aggregation_classifier = nn.Linear(config.hidden_size, config.num_aggregation_labels) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @replace_return_docstrings(output_type=TableQuestionAnsweringOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + table_mask: Optional[torch.LongTensor] = None, + labels: Optional[torch.LongTensor] = None, + aggregation_labels: Optional[torch.LongTensor] = None, + float_answer: Optional[torch.FloatTensor] = None, + numeric_values: Optional[torch.FloatTensor] = None, + numeric_values_scale: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, TableQuestionAnsweringOutput]: + r""" + table_mask (`torch.LongTensor` of shape `(batch_size, seq_length)`, *optional*): + Mask for the table. Indicates which tokens belong to the table (1). Question tokens, table headers and + padding are 0. + labels (`torch.LongTensor` of shape `(batch_size, seq_length)`, *optional*): + Labels per token for computing the hierarchical cell selection loss. This encodes the positions of the + answer appearing in the table. Can be obtained using [`AutoTokenizer`]. + + - 1 for tokens that are **part of the answer**, + - 0 for tokens that are **not part of the answer**. + + aggregation_labels (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + Aggregation function index for every example in the batch for computing the aggregation loss. Indices + should be in `[0, ..., config.num_aggregation_labels - 1]`. Only required in case of strong supervision for + aggregation (WikiSQL-supervised). + float_answer (`torch.FloatTensor` of shape `(batch_size, )`, *optional*): + Float answer for every example in the batch. Set to *float('nan')* for cell selection questions. Only + required in case of weak supervision (WTQ) to calculate the aggregate mask and regression loss. + numeric_values (`torch.FloatTensor` of shape `(batch_size, seq_length)`, *optional*): + Numeric values of every token, NaN for tokens which are not numeric values. Can be obtained using + [`AutoTokenizer`]. Only required in case of weak supervision for aggregation (WTQ) to calculate the + regression loss. + numeric_values_scale (`torch.FloatTensor` of shape `(batch_size, seq_length)`, *optional*): + Scale of the numeric values of every token. Can be obtained using [`AutoTokenizer`]. Only required in case + of weak supervision for aggregation (WTQ) to calculate the regression loss. + + Returns: + + Examples: + + ```python + >>> from transformers import AutoTokenizer, TapasForQuestionAnswering + >>> import pandas as pd + + >>> tokenizer = AutoTokenizer.from_pretrained("google/tapas-base-finetuned-wtq") + >>> model = TapasForQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq") + + >>> data = { + ... "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], + ... "Age": ["56", "45", "59"], + ... "Number of movies": ["87", "53", "69"], + ... } + >>> table = pd.DataFrame.from_dict(data) + >>> queries = ["How many movies has George Clooney played in?", "How old is Brad Pitt?"] + + >>> inputs = tokenizer(table=table, queries=queries, padding="max_length", return_tensors="pt") + >>> outputs = model(**inputs) + + >>> logits = outputs.logits + >>> logits_aggregation = outputs.logits_aggregation + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.tapas( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + pooled_output = outputs[1] + + sequence_output = self.dropout(sequence_output) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # Construct indices for the table. + if token_type_ids is None: + token_type_ids = torch.zeros( + (*input_shape, len(self.config.type_vocab_sizes)), dtype=torch.long, device=device + ) + + token_types = [ + "segment_ids", + "column_ids", + "row_ids", + "prev_labels", + "column_ranks", + "inv_column_ranks", + "numeric_relations", + ] + + row_ids = token_type_ids[:, :, token_types.index("row_ids")] + column_ids = token_type_ids[:, :, token_types.index("column_ids")] + + row_index = IndexMap( + indices=torch.min(row_ids, torch.as_tensor(self.config.max_num_rows - 1, device=row_ids.device)), + num_segments=self.config.max_num_rows, + batch_dims=1, + ) + col_index = IndexMap( + indices=torch.min(column_ids, torch.as_tensor(self.config.max_num_columns - 1, device=column_ids.device)), + num_segments=self.config.max_num_columns, + batch_dims=1, + ) + cell_index = ProductIndexMap(row_index, col_index) + + # Masks. + input_shape = input_ids.size() if input_ids is not None else inputs_embeds.size()[:-1] + device = input_ids.device if input_ids is not None else inputs_embeds.device + if attention_mask is None: + attention_mask = torch.ones(input_shape, device=device) + # Table cells only, without question tokens and table headers. + if table_mask is None: + table_mask = torch.where(row_ids > 0, torch.ones_like(row_ids), torch.zeros_like(row_ids)) + # torch.FloatTensor[batch_size, seq_length] + input_mask_float = attention_mask.float().to(device) + table_mask_float = table_mask.float().to(device) + # Mask for cells that exist in the table (i.e. that are not padding). + cell_mask, _ = reduce_mean(input_mask_float, cell_index) + + # Compute logits per token. These are used to select individual cells. + logits = compute_token_logits(sequence_output, self.config.temperature, self.output_weights, self.output_bias) + + # Compute logits per column. These are used to select a column. + column_logits = None + if self.config.select_one_column: + column_logits = compute_column_logits( + sequence_output, + self.column_output_weights, + self.column_output_bias, + cell_index, + cell_mask, + self.config.allow_empty_column_selection, + ) + + # Aggregation logits + logits_aggregation = None + if self.config.num_aggregation_labels > 0: + logits_aggregation = self.aggregation_classifier(pooled_output) + + # Total loss calculation + total_loss = 0.0 + calculate_loss = False + if labels is not None: + calculate_loss = True + is_supervised = not self.config.num_aggregation_labels > 0 or not self.config.use_answer_as_supervision + + # Semi-supervised cell selection in case of no aggregation: + # If the answer (the denotation) appears directly in the table we might + # select the answer without applying any aggregation function. There are + # some ambiguous cases, see utils._calculate_aggregate_mask for more info. + # `aggregate_mask` is 1 for examples where we chose to aggregate and 0 + # for examples where we chose to select the answer directly. + # `labels` encodes the positions of the answer appearing in the table. + if is_supervised: + aggregate_mask = None + else: + if float_answer is not None: + assert ( + labels.shape[0] == float_answer.shape[0] + ), "Make sure the answers are a FloatTensor of shape (batch_size,)" + # [batch_size] + aggregate_mask = _calculate_aggregate_mask( + float_answer, + pooled_output, + self.config.cell_selection_preference, + labels, + self.aggregation_classifier, + ) + else: + raise ValueError("You have to specify float answers in order to calculate the aggregate mask") + + # Cell selection log-likelihood + if self.config.average_logits_per_cell: + logits_per_cell, _ = reduce_mean(logits, cell_index) + logits = gather(logits_per_cell, cell_index) + dist_per_token = torch.distributions.Bernoulli(logits=logits) + + # Compute cell selection loss per example. + selection_loss_per_example = None + if not self.config.select_one_column: + weight = torch.where( + labels == 0, + torch.ones_like(labels, dtype=torch.float32), + self.config.positive_label_weight * torch.ones_like(labels, dtype=torch.float32), + ) + selection_loss_per_token = -dist_per_token.log_prob(labels) * weight + selection_loss_per_example = torch.sum(selection_loss_per_token * input_mask_float, dim=1) / ( + torch.sum(input_mask_float, dim=1) + EPSILON_ZERO_DIVISION + ) + else: + selection_loss_per_example, logits = _single_column_cell_selection_loss( + logits, column_logits, labels, cell_index, col_index, cell_mask + ) + dist_per_token = torch.distributions.Bernoulli(logits=logits) + + # Supervised cell selection + if self.config.disable_per_token_loss: + pass + elif is_supervised: + total_loss += torch.mean(selection_loss_per_example) + else: + # For the not supervised case, do not assign loss for cell selection + total_loss += torch.mean(selection_loss_per_example * (1.0 - aggregate_mask)) + + # Semi-supervised regression loss and supervised loss for aggregations + if self.config.num_aggregation_labels > 0: + if is_supervised: + # Note that `aggregate_mask` is None if the setting is supervised. + if aggregation_labels is not None: + assert ( + labels.shape[0] == aggregation_labels.shape[0] + ), "Make sure the aggregation labels are a LongTensor of shape (batch_size,)" + per_example_additional_loss = _calculate_aggregation_loss( + logits_aggregation, + aggregate_mask, + aggregation_labels, + self.config.use_answer_as_supervision, + self.config.num_aggregation_labels, + self.config.aggregation_loss_weight, + ) + else: + raise ValueError( + "You have to specify aggregation labels in order to calculate the aggregation loss" + ) + else: + # Set aggregation labels to zeros + aggregation_labels = torch.zeros(labels.shape[0], dtype=torch.long, device=labels.device) + per_example_additional_loss = _calculate_aggregation_loss( + logits_aggregation, + aggregate_mask, + aggregation_labels, + self.config.use_answer_as_supervision, + self.config.num_aggregation_labels, + self.config.aggregation_loss_weight, + ) + + if self.config.use_answer_as_supervision: + if numeric_values is not None and numeric_values_scale is not None: + assert numeric_values.shape == numeric_values_scale.shape + # Add regression loss for numeric answers which require aggregation. + answer_loss, large_answer_loss_mask = _calculate_regression_loss( + float_answer, + aggregate_mask, + dist_per_token, + numeric_values, + numeric_values_scale, + table_mask_float, + logits_aggregation, + self.config, + ) + per_example_additional_loss += answer_loss + # Zero loss for examples with answer_loss > cutoff. + per_example_additional_loss *= large_answer_loss_mask + else: + raise ValueError( + "You have to specify numeric values and numeric values scale in order to calculate the" + " regression loss" + ) + + total_loss += torch.mean(per_example_additional_loss) + + else: + # if no label ids are provided, set them to zeros in order to properly compute logits + labels = torch.zeros_like(logits) + _, logits = _single_column_cell_selection_loss( + logits, column_logits, labels, cell_index, col_index, cell_mask + ) + if not return_dict: + output = (logits, logits_aggregation) + outputs[2:] + return ((total_loss,) + output) if calculate_loss else output + + return TableQuestionAnsweringOutput( + loss=total_loss if calculate_loss else None, + logits=logits, + logits_aggregation=logits_aggregation, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + """ + Tapas Model with a sequence classification head on top (a linear layer on top of the pooled output), e.g. for table + entailment tasks, such as TabFact (Chen et al., 2020). + """, + TAPAS_START_DOCSTRING, +) +class TapasForSequenceClassification(TapasPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + + self.tapas = TapasModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Note: this is called + "classification_class_index" in the original implementation. + + Returns: + + Examples: + + ```python + >>> from transformers import AutoTokenizer, TapasForSequenceClassification + >>> import torch + >>> import pandas as pd + + >>> tokenizer = AutoTokenizer.from_pretrained("google/tapas-base-finetuned-tabfact") + >>> model = TapasForSequenceClassification.from_pretrained("google/tapas-base-finetuned-tabfact") + + >>> data = { + ... "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], + ... "Age": ["56", "45", "59"], + ... "Number of movies": ["87", "53", "69"], + ... } + >>> table = pd.DataFrame.from_dict(data) + >>> queries = [ + ... "There is only one actor who is 45 years old", + ... "There are 3 actors which played in more than 60 movies", + ... ] + + >>> inputs = tokenizer(table=table, queries=queries, padding="max_length", return_tensors="pt") + >>> labels = torch.tensor([1, 0]) # 1 means entailed, 0 means refuted + + >>> outputs = model(**inputs, labels=labels) + >>> loss = outputs.loss + >>> logits = outputs.logits + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.tapas( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + pooled_output = outputs[1] + + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +""" TAPAS utilities.""" + + +class AverageApproximationFunction(str, enum.Enum): + RATIO = "ratio" + FIRST_ORDER = "first_order" + SECOND_ORDER = "second_order" + + +# Beginning of everything related to segmented tensors + + +class IndexMap(object): + """Index grouping entries within a tensor.""" + + def __init__(self, indices, num_segments, batch_dims=0): + """ + Creates an index + + Args: + indices (`torch.LongTensor`, same shape as a *values* Tensor to which the indices refer): + Tensor containing the indices. + num_segments (`torch.LongTensor`): + Scalar tensor, the number of segments. All elements in a batched segmented tensor must have the same + number of segments (although many segments can be empty). + batch_dims (`int`, *optional*, defaults to 0): + The number of batch dimensions. The first *batch_dims* dimensions of a SegmentedTensor are treated as + batch dimensions. Segments in different batch elements are always distinct even if they have the same + index. + """ + self.indices = torch.as_tensor(indices) + self.num_segments = torch.as_tensor(num_segments, device=indices.device) + self.batch_dims = batch_dims + + def batch_shape(self): + return self.indices.size()[: self.batch_dims] # returns a torch.Size object + + +class ProductIndexMap(IndexMap): + """The product of two indices.""" + + def __init__(self, outer_index, inner_index): + """ + Combines indices i and j into pairs (i, j). The result is an index where each segment (i, j) is the + intersection of segments i and j. For example if the inputs represent table cells indexed by respectively rows + and columns the output will be a table indexed by (row, column) pairs, i.e. by cell. The implementation + combines indices {0, .., n - 1} and {0, .., m - 1} into {0, .., nm - 1}. The output has *num_segments* equal to + *outer_index.num_segments* * *inner_index.num_segments* + + Args: + outer_index (`IndexMap`): + IndexMap. + inner_index (`IndexMap`): + IndexMap, must have the same shape as *outer_index*. + """ + if outer_index.batch_dims != inner_index.batch_dims: + raise ValueError("outer_index.batch_dims and inner_index.batch_dims must be the same.") + + super().__init__( + indices=(inner_index.indices + outer_index.indices * inner_index.num_segments), + num_segments=inner_index.num_segments * outer_index.num_segments, + batch_dims=inner_index.batch_dims, + ) + self.outer_index = outer_index + self.inner_index = inner_index + + def project_outer(self, index): + """Projects an index with the same index set onto the outer components.""" + indices = torch.div(index.indices, self.inner_index.num_segments, rounding_mode="floor").type(torch.long) + return IndexMap(indices=indices, num_segments=self.outer_index.num_segments, batch_dims=index.batch_dims) + + def project_inner(self, index): + """Projects an index with the same index set onto the inner components.""" + return IndexMap( + indices=torch.fmod(index.indices, self.inner_index.num_segments) + .type(torch.float) + .floor() + .type(torch.long), + num_segments=self.inner_index.num_segments, + batch_dims=index.batch_dims, + ) + + +def gather(values, index, name="segmented_gather"): + """ + Gathers from *values* using the index map. For each element in the domain of the index map this operation looks up + a value for that index in *values*. Two elements from the same segment always get assigned the same value. + + Args: + values (`torch.Tensor` of shape (B1, ..., Bn, num_segments, V1, ...)): + Tensor with segment values. + index (`IndexMap` of shape (B1, ..., Bn, I1, ..., Ik)): + IndexMap. + name (`str`, *optional*, defaults to 'segmented_gather'): + Name for the operation. Currently not used + + Returns: + `tuple(torch.Tensor)`: Tensor of shape (B1, ..., Bn, I1, ..., Ik, V1, ...) with the gathered values. + """ + indices = index.indices + # first, check whether the indices of the index represent scalar values (i.e. not vectorized) + if len(values.shape[index.batch_dims :]) < 2: + return torch.gather( + values, + index.batch_dims, + indices.view( + values.size()[0], -1 + ), # torch.gather expects index to have the same number of dimensions as values + ).view(indices.size()) + else: + # this means we have a vectorized version + # we have to adjust the index + indices = indices.unsqueeze(-1).expand(values.shape) + return torch.gather(values, index.batch_dims, indices) + + +def flatten(index, name="segmented_flatten"): + """ + Flattens a batched index map (which is typically of shape batch_size, seq_length) to a 1d index map. This operation + relabels the segments to keep batch elements distinct. The k-th batch element will have indices shifted by + *num_segments* * (k - 1). The result is a tensor with *num_segments* multiplied by the number of elements in the + batch. + + Args: + index (`IndexMap`): + IndexMap to flatten. + name (`str`, *optional*, defaults to 'segmented_flatten'): + Name for the operation. Currently not used + + Returns: + (`IndexMap`): The flattened IndexMap. + """ + # first, get batch_size as scalar tensor + batch_size = torch.prod(torch.tensor(list(index.batch_shape()))) + # next, create offset as 1-D tensor of length batch_size, + # and multiply element-wise by num segments (to offset different elements in the batch) e.g. if batch size is 2: [0, 64] + offset = torch.arange(start=0, end=batch_size, device=index.num_segments.device) * index.num_segments + offset = offset.view(index.batch_shape()) + for _ in range(index.batch_dims, len(index.indices.size())): # typically range(1,2) + offset = offset.unsqueeze(-1) + + indices = offset + index.indices + return IndexMap(indices=indices.view(-1), num_segments=index.num_segments * batch_size, batch_dims=0) + + +def range_index_map(batch_shape, num_segments, name="range_index_map"): + """ + Constructs an index map equal to range(num_segments). + + Args: + batch_shape (`torch.Size`): + Batch shape + num_segments (`int`): + Number of segments + name (`str`, *optional*, defaults to 'range_index_map'): + Name for the operation. Currently not used + + Returns: + (`IndexMap`): IndexMap of shape batch_shape with elements equal to range(num_segments). + """ + batch_shape = torch.as_tensor( + batch_shape, dtype=torch.long + ) # create a rank 1 tensor vector containing batch_shape (e.g. [2]) + assert len(batch_shape.size()) == 1 + num_segments = torch.as_tensor(num_segments) # create a rank 0 tensor (scalar) containing num_segments (e.g. 64) + assert len(num_segments.size()) == 0 + + indices = torch.arange( + start=0, end=num_segments, device=num_segments.device + ) # create a rank 1 vector with num_segments elements + new_tensor = torch.cat( + [torch.ones_like(batch_shape, dtype=torch.long, device=num_segments.device), num_segments.unsqueeze(dim=0)], + dim=0, + ) + # new_tensor is just a vector of [1 64] for example (assuming only 1 batch dimension) + new_shape = [int(x) for x in new_tensor.tolist()] + indices = indices.view(new_shape) + + multiples = torch.cat([batch_shape, torch.as_tensor([1])], dim=0) + indices = indices.repeat(multiples.tolist()) + # equivalent (in Numpy:) + # indices = torch.as_tensor(np.tile(indices.numpy(), multiples.tolist())) + + return IndexMap(indices=indices, num_segments=num_segments, batch_dims=list(batch_shape.size())[0]) + + +def _segment_reduce(values, index, segment_reduce_fn, name): + """ + Applies a segment reduction segment-wise. + + Args: + values (`torch.Tensor`): + Tensor with segment values. + index (`IndexMap`): + IndexMap. + segment_reduce_fn (`str`): + Name for the reduce operation. One of "sum", "mean", "max" or "min". + name (`str`): + Name for the operation. Currently not used + + Returns: + (`IndexMap`): IndexMap of shape batch_shape with elements equal to range(num_segments). + """ + # Flatten the batch dimensions, as segments ops (scatter) do not support batching. + # However if `values` has extra dimensions to the right keep them + # unflattened. Segmented ops support vector-valued operations. + flat_index = flatten(index) + vector_shape = values.size()[len(index.indices.size()) :] # torch.Size object + flattened_shape = torch.cat( + [torch.as_tensor([-1], dtype=torch.long), torch.as_tensor(vector_shape, dtype=torch.long)], dim=0 + ) + # changed "view" by "reshape" in the following line + flat_values = values.reshape(flattened_shape.tolist()) + + out = torch.zeros(int(flat_index.num_segments), dtype=torch.float, device=flat_values.device) + segment_means = out.scatter_reduce( + dim=0, index=flat_index.indices.long(), src=flat_values.float(), reduce=segment_reduce_fn, include_self=False + ) + + # Unflatten the values. + new_shape = torch.cat( + [ + torch.as_tensor(index.batch_shape(), dtype=torch.long), + torch.as_tensor([index.num_segments], dtype=torch.long), + torch.as_tensor(vector_shape, dtype=torch.long), + ], + dim=0, + ) + + output_values = segment_means.clone().view(new_shape.tolist()).to(values.dtype) + output_index = range_index_map(index.batch_shape(), index.num_segments) + return output_values, output_index + + +def reduce_sum(values, index, name="segmented_reduce_sum"): + """ + Sums a tensor over its segments. + + Outputs 0 for empty segments. + + This operations computes the sum over segments, with support for: + + - Batching using the first dimensions [B1, B2, ..., Bn]. Each element in a batch can have different indices. + - Vectorization using the last dimension [V1, V2, ...]. If they are present, the output will be a sum of + vectors rather than scalars. Only the middle dimensions [I1, ..., Ik] are reduced by the operation. + + Args: + values (`torch.Tensor` of shape [B1, B2, ..., Bn, I1, .., Ik, V1, V2, ..]): + Tensor containing the values of which the sum must be taken segment-wise. + index (`IndexMap`, indices are of shape [B1, B2, ..., Bn, I1, .., Ik].): + Index defining the segments. + name (`str`, *optional*, defaults to 'segmented_reduce_sum'): + Name for the operation. Currently not used + + Returns: + output_values (`torch.Tensor`of shape [B1, B2, ..., Bn, num_segments, V1, V2, ..]): Tensor containing the + output values. output_index (`IndexMap`): IndexMap with shape [B1, B2, ..., Bn, num_segments]. . + """ + return _segment_reduce(values, index, "sum", name) + + +def reduce_mean(values, index, name="segmented_reduce_mean"): + """ + Averages a tensor over its segments. + + Outputs 0 for empty segments. + + This operations computes the mean over segments, with support for: + + - Batching using the first dimensions [B1, B2, ..., Bn]. Each element in a batch can have different indices. + - Vectorization using the last dimension [V1, V2, ...]. If they are present, the output will be a mean of + vectors rather than scalars. + + Only the middle dimensions [I1, ..., Ik] are reduced by the operation. + + Args: + values (`torch.Tensor` of shape [B1, B2, ..., Bn, I1, .., Ik, V1, V2, ..]): + Tensor containing the values of which the mean must be taken segment-wise. + index (`IndexMap`, indices are of shape [B1, B2, ..., Bn, I1, .., Ik].): + Index defining the segments. + name (`str`, *optional*, defaults to 'segmented_reduce_sum'): + Name for the operation. Currently not used + + Returns: + output_values (`torch.Tensor`of shape [B1, B2, ..., Bn, num_segments, V1, V2, ..]): Tensor containing the + output values. output_index (`IndexMap`): IndexMap with shape [B1, B2, ..., Bn, num_segments]. + """ + return _segment_reduce(values, index, "mean", name) + + +def reduce_max(values, index, name="segmented_reduce_max"): + """ + Computes the maximum over segments. + + This operation computes the maximum over segments, with support for: + + - Batching using the first dimensions [B1, B2, ..., Bn]. Each element in a batch can have different indices. + - Vectorization using the last dimension [V1, V2, ...]. If they are present, the output will be an element-wise + maximum of vectors rather than scalars. + + Only the middle dimensions [I1, ..., Ik] are reduced by the operation. + + Args: + values (`torch.Tensor` of shape [B1, B2, ..., Bn, I1, .., Ik, V1, V2, ..]): + Tensor containing the values of which the max must be taken segment-wise. + index (`IndexMap`, indices are of shape [B1, B2, ..., Bn, I1, .., Ik].): + Index defining the segments. + name (`str`, *optional*, defaults to 'segmented_reduce_sum'): + Name for the operation. Currently not used + + Returns: + output_values (`torch.Tensor`of shape [B1, B2, ..., Bn, num_segments, V1, V2, ..]): Tensor containing the + output values. output_index (`IndexMap`): IndexMap with shape [B1, B2, ..., Bn, num_segments]. + """ + return _segment_reduce(values, index, "amax", name) + + +def reduce_min(values, index, name="segmented_reduce_min"): + """ + Computes the minimum over segments. + + This operations computes the minimum over segments, with support for: + + - Batching using the first dimensions [B1, B2, ..., Bn]. Each element in a batch can have different indices. + - Vectorization using the last dimension [V1, V2, ...]. If they are present, the output will be an element-wise + minimum of vectors rather than scalars. + + Only the middle dimensions [I1, ..., Ik] are reduced by the operation. + + Args: + values (`torch.Tensor` of shape [B1, B2, ..., Bn, I1, .., Ik, V1, V2, ..]): + Tensor containing the values of which the min must be taken segment-wise. + index (`IndexMap`, indices are of shape [B1, B2, ..., Bn, I1, .., Ik].): + Index defining the segments. + name (`str`, *optional*, defaults to 'segmented_reduce_sum'): + Name for the operation. Currently not used + + Returns: + output_values (`torch.Tensor`of shape [B1, B2, ..., Bn, num_segments, V1, V2, ..]): Tensor containing the + output values. output_index (`IndexMap`): IndexMap with shape [B1, B2, ..., Bn, num_segments]. + """ + return _segment_reduce(values, index, "amin", name) + + +# End of everything related to segmented tensors + + +def compute_column_logits( + sequence_output, column_output_weights, column_output_bias, cell_index, cell_mask, allow_empty_column_selection +): + """ + Computes the column logits. + + Args: + sequence_output (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Also known as last_hidden_state. Sequence of hidden-states at the output of the last layer of the model. + column_output_weights (`torch.FloatTensor` of shape `(hidden_size)`): + Weights of the linear layer for column selection. + column_output_bias (`torch.FloatTensor` of shape `()`): + Bias of the linear layer for column selection. + cell_index (`ProductIndexMap`): + Index that groups tokens into cells. + cell_mask (`torch.FloatTensor` of shape `(batch_size, max_num_rows * max_num_cols)`): + Mask for cells that exist in the table (i.e. that are not padding). + allow_empty_column_selection (`bool`): + Whether to allow not to select any column + + Returns: + column_logits (`torch.FloatTensor`of shape `(batch_size, max_num_cols)`): Tensor containing the column logits + for every example in the batch. + """ + + # First, compute the token logits (batch_size, seq_len) - without temperature + token_logits = torch.einsum("bsj,j->bs", sequence_output, column_output_weights) + column_output_bias + + # Next, average the logits per cell (batch_size, max_num_cols*max_num_rows) + cell_logits, cell_logits_index = reduce_mean(token_logits, cell_index) + + # Finally, average the logits per column (batch_size, max_num_cols) + column_index = cell_index.project_inner(cell_logits_index) + column_logits, out_index = reduce_sum(cell_logits * cell_mask, column_index) + + cell_count, _ = reduce_sum(cell_mask, column_index) + column_logits /= cell_count + EPSILON_ZERO_DIVISION + + # Mask columns that do not appear in the example. + is_padding = torch.logical_and(cell_count < 0.5, ~torch.eq(out_index.indices, 0)) + column_logits += CLOSE_ENOUGH_TO_LOG_ZERO * torch.as_tensor( + is_padding, dtype=torch.float32, device=is_padding.device + ) + + if not allow_empty_column_selection: + column_logits += CLOSE_ENOUGH_TO_LOG_ZERO * torch.as_tensor( + torch.eq(out_index.indices, 0), dtype=torch.float32, device=out_index.indices.device + ) + + return column_logits + + +def _single_column_cell_selection_loss(token_logits, column_logits, labels, cell_index, col_index, cell_mask): + """ + Computes the loss for cell selection constrained to a single column. The loss is a hierarchical log-likelihood. The + model first predicts a column and then selects cells within that column (conditioned on the column). Cells outside + the selected column are never selected. + + Args: + token_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): + Tensor containing the logits per token. + column_logits (`torch.FloatTensor` of shape `(batch_size, max_num_cols)`): + Tensor containing the logits per column. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Labels per token. + cell_index (`ProductIndexMap`): + Index that groups tokens into cells. + col_index (`IndexMap`): + Index that groups tokens into columns. + cell_mask (`torch.FloatTensor` of shape `(batch_size, max_num_rows * max_num_cols)`): + Mask for cells that exist in the table (i.e. that are not padding). + + Returns: + selection_loss_per_example (`torch.FloatTensor` of shape `(batch_size,)`): Loss for each example. logits + (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): New logits which are only allowed to select + cells in a single column. Logits outside of the most likely column according to *column_logits* will be set to + a very low value (such that the probabilities are 0). + """ + # Part 1: column loss + + # First find the column we should select. We use the column with maximum number of selected cells. + labels_per_column, _ = reduce_sum(torch.as_tensor(labels, dtype=torch.float32, device=labels.device), col_index) + # shape of labels_per_column is (batch_size, max_num_cols). It contains the number of label ids for every column, for every example + column_label = torch.argmax(labels_per_column, dim=-1) # shape (batch_size,) + # Check if there are no selected cells in the column. In that case the model + # should predict the special column id 0, which means "select nothing". + no_cell_selected = torch.eq( + torch.max(labels_per_column, dim=-1)[0], 0 + ) # no_cell_selected is of shape (batch_size,) and equals True + # if an example of the batch has no cells selected (i.e. if there are no labels set to 1 for that example) + column_label = torch.where( + no_cell_selected.view(column_label.size()), torch.zeros_like(column_label), column_label + ) + + column_dist = torch.distributions.Categorical(logits=column_logits) # shape (batch_size, max_num_cols) + column_loss_per_example = -column_dist.log_prob(column_label) + + # Part 2: cell loss + + # Reduce the labels and logits to per-cell from per-token. + # logits_per_cell: shape (batch_size, max_num_rows*max_num_cols) i.e. (batch_size, 64*32) + logits_per_cell, _ = reduce_mean(token_logits, cell_index) + # labels_per_cell: shape (batch_size, 64*32), indicating whether each cell should be selected (1) or not (0) + labels_per_cell, labels_index = reduce_max( + torch.as_tensor(labels, dtype=torch.long, device=labels.device), cell_index + ) + + # Mask for the selected column. + # column_id_for_cells: shape (batch_size, 64*32), indicating to which column each cell belongs + column_id_for_cells = cell_index.project_inner(labels_index).indices + # column_mask: shape (batch_size, 64*32), equal to 1 if cell belongs to column to be selected + column_mask = torch.as_tensor( + torch.eq(column_id_for_cells, torch.unsqueeze(column_label, dim=-1)), + dtype=torch.float32, + device=cell_mask.device, + ) + + # Compute the log-likelihood for cells, but only for the selected column. + cell_dist = torch.distributions.Bernoulli(logits=logits_per_cell) # shape (batch_size, 64*32) + cell_log_prob = cell_dist.log_prob(labels_per_cell.type(torch.float32)) # shape(batch_size, 64*32) + + cell_loss = -torch.sum(cell_log_prob * column_mask * cell_mask, dim=1) + + # We need to normalize the loss by the number of cells in the column. + cell_loss /= torch.sum(column_mask * cell_mask, dim=1) + EPSILON_ZERO_DIVISION + + selection_loss_per_example = column_loss_per_example + selection_loss_per_example += torch.where( + no_cell_selected.view(selection_loss_per_example.size()), + torch.zeros_like(selection_loss_per_example), + cell_loss, + ) + + # Set the probs outside the selected column (selected by the *model*) + # to 0. This ensures backwards compatibility with models that select + # cells from multiple columns. + selected_column_id = torch.as_tensor( + torch.argmax(column_logits, dim=-1), dtype=torch.long, device=column_logits.device + ) # shape (batch_size,) + + # selected_column_mask: shape (batch_size, 64*32), equal to 1 if cell belongs to column selected by the model + selected_column_mask = torch.as_tensor( + torch.eq(column_id_for_cells, torch.unsqueeze(selected_column_id, dim=-1)), + dtype=torch.float32, + device=selected_column_id.device, + ) + + # Never select cells with the special column id 0. + selected_column_mask = torch.where( + torch.eq(column_id_for_cells, 0).view(selected_column_mask.size()), + torch.zeros_like(selected_column_mask), + selected_column_mask, + ) + new_logits_per_cell = logits_per_cell + CLOSE_ENOUGH_TO_LOG_ZERO * (1.0 - cell_mask * selected_column_mask) + logits = gather(new_logits_per_cell, cell_index) + + return selection_loss_per_example, logits + + +def compute_token_logits(sequence_output, temperature, output_weights, output_bias): + """ + Computes logits per token + + Args: + sequence_output (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Also known as last_hidden_state. Sequence of hidden-states at the output of the last layer of the model. + temperature (`float`): + Temperature for the Bernoulli distribution. + output_weights (`torch.FloatTensor` of shape `(hidden_size,)`): + Weights of the linear layer for cell selection. + output_bias (`torch.FloatTensor` of shape `()`): + Bias of the linear layer for cell selection + + Returns: + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Logits per token. + """ + logits = (torch.einsum("bsj,j->bs", sequence_output, output_weights) + output_bias) / temperature + + return logits + + +def _calculate_aggregate_mask(answer, pooled_output, cell_selection_preference, labels, aggregation_classifier): + """ + Finds examples where the model should select cells with no aggregation. + + Returns a mask that determines for which examples should the model select answers directly from the table, without + any aggregation function. If the answer is a piece of text the case is unambiguous as aggregation functions only + apply to numbers. If the answer is a number but does not appear in the table then we must use some aggregation + case. The ambiguous case is when the answer is a number that also appears in the table. In this case we use the + aggregation function probabilities predicted by the model to decide whether to select or aggregate. The threshold + for this is a hyperparameter *cell_selection_preference* + + Args: + answer (`torch.FloatTensor` of shape `(batch_size, )`): + Answer for every example in the batch. Nan if there is no scalar answer. + pooled_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`): + Output of the pooler (BertPooler) on top of the encoder layer. + cell_selection_preference (`float`): + Preference for cell selection in ambiguous cases. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Labels per token. aggregation_classifier (`torch.nn.Linear`): Aggregation head + + Returns: + aggregate_mask (`torch.FloatTensor` of shape `(batch_size,)`): A mask set to 1 for examples that should use + aggregation functions. + """ + # torch.FloatTensor(batch_size,) + aggregate_mask_init = torch.logical_not(torch.isnan(answer)).type(torch.FloatTensor).to(answer.device) + logits_aggregation = aggregation_classifier(pooled_output) + dist_aggregation = torch.distributions.categorical.Categorical(logits=logits_aggregation) + # Index 0 corresponds to "no aggregation". + aggregation_ops_total_mass = torch.sum(dist_aggregation.probs[:, 1:], dim=1) + + # Cell selection examples according to current model. + is_pred_cell_selection = aggregation_ops_total_mass <= cell_selection_preference + + # Examples with non-empty cell selection supervision. + is_cell_supervision_available = torch.sum(labels, dim=1) > 0 + + # torch.where is not equivalent to tf.where (in tensorflow 1) + # hence the added .view on the condition to match the shape of the first tensor + aggregate_mask = torch.where( + torch.logical_and(is_pred_cell_selection, is_cell_supervision_available).view(aggregate_mask_init.size()), + torch.zeros_like(aggregate_mask_init, dtype=torch.float32), + aggregate_mask_init, + ) + + aggregate_mask = aggregate_mask.detach() + + return aggregate_mask + + +def _calculate_aggregation_loss_known( + logits_aggregation, aggregate_mask, aggregation_labels, use_answer_as_supervision, num_aggregation_labels +): + """ + Calculates aggregation loss when its type is known during training. + + In the weakly supervised setting, the only known information is that for cell selection examples, "no aggregation" + should be predicted. For other examples (those that require aggregation), no loss is accumulated. In the setting + where aggregation type is always known, standard cross entropy loss is accumulated for all examples + + Args: + logits_aggregation (`torch.FloatTensor` of shape `(batch_size, num_aggregation_labels)`): + Logits per aggregation operation. + aggregate_mask (`torch.FloatTensor` of shape `(batch_size, )`): + A mask set to 1 for examples that should use aggregation functions. + aggregation_labels (`torch.LongTensor` of shape `(batch_size, )`): + Aggregation function id for every example in the batch. + use_answer_as_supervision (`bool`, *optional*): + Whether to use the answer as the only supervision for aggregation examples. + num_aggregation_labels (`int`, *optional*, defaults to 0): + The number of aggregation operators to predict. + + Returns: + aggregation_loss_known (`torch.FloatTensor` of shape `(batch_size,)`): Aggregation loss (when its type is known + during training) per example. + """ + if use_answer_as_supervision: + # Prepare "no aggregation" targets for cell selection examples. + target_aggregation = torch.zeros_like(aggregate_mask, dtype=torch.long) + else: + # Use aggregation supervision as the target. + target_aggregation = aggregation_labels + + one_hot_labels = nn.functional.one_hot(target_aggregation, num_classes=num_aggregation_labels).type(torch.float32) + log_probs = nn.functional.log_softmax(logits_aggregation, dim=-1) + + # torch.FloatTensor[batch_size] + per_example_aggregation_intermediate = -torch.sum(one_hot_labels * log_probs, dim=-1) + if use_answer_as_supervision: + # Accumulate loss only for examples requiring cell selection + # (no aggregation). + return per_example_aggregation_intermediate * (1 - aggregate_mask) + else: + return per_example_aggregation_intermediate + + +def _calculate_aggregation_loss_unknown(logits_aggregation, aggregate_mask): + """ + Calculates aggregation loss in the case of answer supervision. + + Args: + logits_aggregation (`torch.FloatTensor` of shape `(batch_size, num_aggregation_labels)`): + Logits per aggregation operation. + aggregate_mask (`torch.FloatTensor` of shape `(batch_size, )`): + A mask set to 1 for examples that should use aggregation functions + + Returns: + aggregation_loss_unknown (`torch.FloatTensor` of shape `(batch_size,)`): Aggregation loss (in case of answer + supervision) per example. + """ + dist_aggregation = torch.distributions.categorical.Categorical(logits=logits_aggregation) + # Index 0 corresponds to "no aggregation". + aggregation_ops_total_mass = torch.sum(dist_aggregation.probs[:, 1:], dim=1) + # Predict some aggregation in case of an answer that needs aggregation. + # This increases the probability of all aggregation functions, in a way + # similar to MML, but without considering whether the function gives the + # correct answer. + return -torch.log(aggregation_ops_total_mass) * aggregate_mask + + +def _calculate_aggregation_loss( + logits_aggregation, + aggregate_mask, + aggregation_labels, + use_answer_as_supervision, + num_aggregation_labels, + aggregation_loss_weight, +): + """ + Calculates the aggregation loss per example. + + Args: + logits_aggregation (`torch.FloatTensor` of shape `(batch_size, num_aggregation_labels)`): + Logits per aggregation operation. + aggregate_mask (`torch.FloatTensor` of shape `(batch_size, )`): + A mask set to 1 for examples that should use aggregation functions. + aggregation_labels (`torch.LongTensor` of shape `(batch_size, )`): + Aggregation function id for every example in the batch. + use_answer_as_supervision (`bool`, *optional*): + Whether to use the answer as the only supervision for aggregation examples. + num_aggregation_labels (`int`, *optional*, defaults to 0): + The number of aggregation operators to predict. + aggregation_loss_weight (`float`, *optional*, defaults to 1.0): + Importance weight for the aggregation loss. + + Returns: + aggregation_loss (`torch.FloatTensor` of shape `(batch_size,)`): Aggregation loss per example. + """ + per_example_aggregation_loss = _calculate_aggregation_loss_known( + logits_aggregation, aggregate_mask, aggregation_labels, use_answer_as_supervision, num_aggregation_labels + ) + + if use_answer_as_supervision: + # Add aggregation loss for numeric answers that need aggregation. + per_example_aggregation_loss += _calculate_aggregation_loss_unknown(logits_aggregation, aggregate_mask) + return aggregation_loss_weight * per_example_aggregation_loss + + +def _calculate_expected_result( + dist_per_cell, numeric_values, numeric_values_scale, input_mask_float, logits_aggregation, config +): + """ + Calculates the expected result given cell and aggregation probabilities. + + Args: + dist_per_cell (`torch.distributions.Bernoulli`): + Cell selection distribution for each cell. + numeric_values (`torch.FloatTensor` of shape `(batch_size, seq_length)`): + Numeric values of every token. Nan for tokens which are not numeric values. + numeric_values_scale (`torch.FloatTensor` of shape `(batch_size, seq_length)`): + Scale of the numeric values of every token. + input_mask_float (`torch.FloatTensor` of shape `(batch_size, seq_length)`): + Mask for the table, without question tokens and table headers. + logits_aggregation (`torch.FloatTensor` of shape `(batch_size, num_aggregation_labels)`): + Logits per aggregation operation. + config ([`TapasConfig`]): + Model configuration class with all the hyperparameters of the model + + Returns: + expected_result (`torch.FloatTensor` of shape `(batch_size,)`): The expected result per example. + """ + if config.use_gumbel_for_cells: + gumbel_dist = torch.distributions.RelaxedBernoulli( + # The token logits where already divided by the temperature and used for + # computing cell selection errors so we need to multiply it again here + temperature=config.temperature, + logits=dist_per_cell.logits * config.temperature, + ) + scaled_probability_per_cell = gumbel_dist.sample() + else: + scaled_probability_per_cell = dist_per_cell.probs + + # [batch_size, seq_length] + scaled_probability_per_cell = (scaled_probability_per_cell / numeric_values_scale) * input_mask_float + count_result = torch.sum(scaled_probability_per_cell, dim=1) + numeric_values_masked = torch.where( + torch.isnan(numeric_values), torch.zeros_like(numeric_values), numeric_values + ) # Mask non-numeric table values to zero. + sum_result = torch.sum(scaled_probability_per_cell * numeric_values_masked, dim=1) + avg_approximation = config.average_approximation_function + if avg_approximation == AverageApproximationFunction.RATIO: + average_result = sum_result / (count_result + EPSILON_ZERO_DIVISION) + elif avg_approximation == AverageApproximationFunction.FIRST_ORDER: + # The sum of all probabilities except that correspond to other cells + # Ex here stands for expectation, more explicitly the expectation of the sum of N-1 Bernoulli random variables plus + # the constant 1, which is computed as adding all N expected values and subtracting the extra one. It corresponds to X_c + # in Appendix D of the original TAPAS paper which is trying to approximate the average of a random set. + ex = torch.sum(scaled_probability_per_cell, dim=1, keepdim=True) - scaled_probability_per_cell + 1 + average_result = torch.sum(numeric_values_masked * scaled_probability_per_cell / ex, dim=1) + elif avg_approximation == AverageApproximationFunction.SECOND_ORDER: + # The sum of all probabilities except that correspond to other cells + ex = torch.sum(scaled_probability_per_cell, dim=1, keepdim=True) - scaled_probability_per_cell + 1 + pointwise_var = scaled_probability_per_cell * (1 - scaled_probability_per_cell) + var = torch.sum(pointwise_var, dim=1, keepdim=True) - pointwise_var + + multiplier = (var / torch.square(ex) + 1) / ex + average_result = torch.sum(numeric_values_masked * scaled_probability_per_cell * multiplier, dim=1) + else: + raise ValueError(f"Invalid average_approximation_function: {config.average_approximation_function}") + + if config.use_gumbel_for_aggregation: + gumbel_dist = torch.distributions.RelaxedOneHotCategorical( + config.aggregation_temperature, logits=logits_aggregation[:, 1:] + ) + # [batch_size, num_aggregation_labels - 1] + aggregation_op_only_probs = gumbel_dist.sample() + else: + # [batch_size, num_aggregation_labels - 1] + aggregation_op_only_probs = nn.functional.softmax( + logits_aggregation[:, 1:] / config.aggregation_temperature, dim=-1 + ) + + all_results = torch.cat( + [ + torch.unsqueeze(sum_result, dim=1), + torch.unsqueeze(average_result, dim=1), + torch.unsqueeze(count_result, dim=1), + ], + dim=1, + ) + + expected_result = torch.sum(all_results * aggregation_op_only_probs, dim=1) + return expected_result + + +# PyTorch does not currently support Huber loss with custom delta so we define it ourself +def huber_loss(input, target, delta: float = 1.0): + errors = torch.abs(input - target) # shape (batch_size,) + return torch.where(errors < delta, 0.5 * errors**2, errors * delta - (0.5 * delta**2)) + + +def _calculate_regression_loss( + answer, + aggregate_mask, + dist_per_cell, + numeric_values, + numeric_values_scale, + input_mask_float, + logits_aggregation, + config, +): + """ + Calculates the regression loss per example. + + Args: + answer (`torch.FloatTensor` of shape `(batch_size,)`): + Answer for every example in the batch. Nan if there is no scalar answer. + aggregate_mask (`torch.FloatTensor` of shape `(batch_size,)`): + A mask set to 1 for examples that should use aggregation functions. + dist_per_cell (`torch.distributions.Bernoulli`): + Cell selection distribution for each cell. + numeric_values (`torch.FloatTensor` of shape `(batch_size, seq_length)`): + Numeric values of every token. Nan for tokens which are not numeric values. + numeric_values_scale (`torch.FloatTensor` of shape `(batch_size, seq_length)`): + Scale of the numeric values of every token. + input_mask_float (`torch.FloatTensor` of shape `(batch_size, seq_length)`): + Mask for the table, without question tokens and table headers. + logits_aggregation (`torch.FloatTensor` of shape `(batch_size, num_aggregation_labels)`): + Logits per aggregation operation. + config ([`TapasConfig`]): + Model configuration class with all the parameters of the model + + Returns: + per_example_answer_loss_scaled (`torch.FloatTensor` of shape `(batch_size,)`): Scales answer loss for each + example in the batch. large_answer_loss_mask (`torch.FloatTensor` of shape `(batch_size,)`): A mask which is 1 + for examples for which their answer loss is larger than the answer_loss_cutoff. + """ + # float32 (batch_size,) + expected_result = _calculate_expected_result( + dist_per_cell, numeric_values, numeric_values_scale, input_mask_float, logits_aggregation, config + ) + + # float32 (batch_size,) + answer_masked = torch.where(torch.isnan(answer), torch.zeros_like(answer), answer) + + if config.use_normalized_answer_loss: + normalizer = (torch.max(torch.abs(expected_result), torch.abs(answer_masked)) + EPSILON_ZERO_DIVISION).detach() + + normalized_answer_masked = answer_masked / normalizer + normalized_expected_result = expected_result / normalizer + per_example_answer_loss = huber_loss( + normalized_expected_result * aggregate_mask, normalized_answer_masked * aggregate_mask + ) + else: + per_example_answer_loss = huber_loss( + expected_result * aggregate_mask, answer_masked * aggregate_mask, delta=config.huber_loss_delta + ) + + if config.answer_loss_cutoff is None: + large_answer_loss_mask = torch.ones_like(per_example_answer_loss, dtype=torch.float32) + + else: + large_answer_loss_mask = torch.where( + per_example_answer_loss > config.answer_loss_cutoff, + torch.zeros_like(per_example_answer_loss, 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a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/timesformer/modeling_timesformer.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/timesformer/modeling_timesformer.py new file mode 100644 index 0000000000000000000000000000000000000000..337447250842ee0cb0c83e66041d64e1ff04880d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/timesformer/modeling_timesformer.py @@ -0,0 +1,816 @@ +# coding=utf-8 +# Copyright 2022 Meta and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch TimeSformer model.""" + + +import collections +from typing import Optional, Tuple, Union + +import torch +import torch.nn.functional +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ...activations import ACT2FN +from ...modeling_outputs import BaseModelOutput, ImageClassifierOutput +from ...modeling_utils import PreTrainedModel +from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings +from .configuration_timesformer import TimesformerConfig + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "TimesformerConfig" +_CHECKPOINT_FOR_DOC = "facebook/timesformer" + + +from ..deprecated._archive_maps import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +# Adapted from https://github.com/facebookresearch/TimeSformer/blob/a5ef29a7b7264baff199a30b3306ac27de901133/timesformer/models/vit.py#L155 +class TimesformerPatchEmbeddings(nn.Module): + """Image to Patch Embedding""" + + def __init__(self, config): + super().__init__() + + image_size = config.image_size + patch_size = config.patch_size + + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + + num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) + self.image_size = image_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.projection = nn.Conv2d(config.num_channels, config.hidden_size, kernel_size=patch_size, stride=patch_size) + + def forward(self, pixel_values): + batch_size, num_frames, num_channels, height, width = pixel_values.shape + pixel_values = pixel_values.reshape(batch_size * num_frames, num_channels, height, width) + + embeddings = self.projection(pixel_values) + patch_width = embeddings.size(-1) + embeddings = embeddings.flatten(2).transpose(1, 2) + return embeddings, num_frames, patch_width + + +class TimesformerEmbeddings(nn.Module): + """ + Construct the patch and position embeddings. + """ + + def __init__(self, config): + super().__init__() + + embed_dim = config.hidden_size + num_frames = config.num_frames + drop_rate = config.hidden_dropout_prob + attention_type = config.attention_type + + self.attention_type = attention_type + self.patch_embeddings = TimesformerPatchEmbeddings(config) + self.num_patches = self.patch_embeddings.num_patches + + # Positional Embeddings + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.position_embeddings = nn.Parameter(torch.zeros(1, self.num_patches + 1, embed_dim)) + self.pos_drop = nn.Dropout(p=drop_rate) + if attention_type != "space_only": + self.time_embeddings = nn.Parameter(torch.zeros(1, num_frames, embed_dim)) + self.time_drop = nn.Dropout(p=drop_rate) + + def forward(self, pixel_values): + batch_size = pixel_values.shape[0] + + # create patch embeddings + embeddings, num_frames, patch_width = self.patch_embeddings(pixel_values) + + cls_tokens = self.cls_token.expand(embeddings.size(0), -1, -1) + embeddings = torch.cat((cls_tokens, embeddings), dim=1) + + # resizing the positional embeddings in case they don't match the input at inference + if embeddings.size(1) != self.position_embeddings.size(1): + position_embeddings = self.position_embeddings + cls_pos_embed = position_embeddings[0, 0, :].unsqueeze(0).unsqueeze(1) + other_pos_embed = position_embeddings[0, 1:, :].unsqueeze(0).transpose(1, 2) + patch_num = int(other_pos_embed.size(2) ** 0.5) + patch_height = embeddings.size(1) // patch_width + other_pos_embed = other_pos_embed.reshape(1, embeddings.size(2), patch_num, patch_num) + new_pos_embed = nn.functional.interpolate( + other_pos_embed, size=(patch_height, patch_width), mode="nearest" + ) + new_pos_embed = new_pos_embed.flatten(2) + new_pos_embed = new_pos_embed.transpose(1, 2) + new_pos_embed = torch.cat((cls_pos_embed, new_pos_embed), 1) + embeddings = embeddings + new_pos_embed + else: + embeddings = embeddings + self.position_embeddings + embeddings = self.pos_drop(embeddings) + + # Time Embeddings + if self.attention_type != "space_only": + cls_tokens = embeddings[:batch_size, 0, :].unsqueeze(1) + embeddings = embeddings[:, 1:] + _, patch_height, patch_width = embeddings.shape + embeddings = ( + embeddings.reshape(batch_size, num_frames, patch_height, patch_width) + .permute(0, 2, 1, 3) + .reshape(batch_size * patch_height, num_frames, patch_width) + ) + # Resizing time embeddings in case they don't match + if num_frames != self.time_embeddings.size(1): + time_embeddings = self.time_embeddings.transpose(1, 2) + new_time_embeddings = nn.functional.interpolate(time_embeddings, size=(num_frames), mode="nearest") + new_time_embeddings = new_time_embeddings.transpose(1, 2) + embeddings = embeddings + new_time_embeddings + else: + embeddings = embeddings + self.time_embeddings + embeddings = self.time_drop(embeddings) + embeddings = embeddings.view(batch_size, patch_height, num_frames, patch_width).reshape( + batch_size, patch_height * num_frames, patch_width + ) + embeddings = torch.cat((cls_tokens, embeddings), dim=1) + + return embeddings + + +# Copied from transformers.models.beit.modeling_beit.drop_path +def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor: + """ + Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks, + however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the + layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the + argument. + """ + if drop_prob == 0.0 or not training: + return input + keep_prob = 1 - drop_prob + shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device) + random_tensor.floor_() # binarize + output = input.div(keep_prob) * random_tensor + return output + + +# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->TimeSformer +class TimeSformerDropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob: Optional[float] = None) -> None: + super().__init__() + self.drop_prob = drop_prob + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + return drop_path(hidden_states, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return "p={}".format(self.drop_prob) + + +# Adapted from https://github.com/facebookresearch/TimeSformer/blob/a5ef29a7b7264baff199a30b3306ac27de901133/timesformer/models/vit.py#L57 +class TimesformerSelfAttention(nn.Module): + def __init__(self, config: TimesformerConfig): + super().__init__() + + num_heads = config.num_attention_heads + qkv_bias = config.qkv_bias + attention_dropout_prob = config.attention_probs_dropout_prob + + self.num_heads = num_heads + head_dim = config.hidden_size // num_heads + self.scale = head_dim**-0.5 + self.qkv = nn.Linear(config.hidden_size, config.hidden_size * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attention_dropout_prob) + + def forward(self, hidden_states, output_attentions: bool = False): + batch_size, hidden_size, num_channels = hidden_states.shape + qkv = ( + self.qkv(hidden_states) + .reshape(batch_size, hidden_size, 3, self.num_heads, num_channels // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) + query, key, value = qkv[0], qkv[1], qkv[2] + + attention_probs = (query @ key.transpose(-2, -1)) * self.scale + attention_probs = attention_probs.softmax(dim=-1) + attention_probs = self.attn_drop(attention_probs) + + context_layer = (attention_probs @ value).transpose(1, 2).reshape(batch_size, hidden_size, num_channels) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +class TimesformerSelfOutput(nn.Module): + """ + The residual connection is defined in TimesformerLayer instead of here (as is the case with other models), due to + the layernorm applied before each block. + """ + + def __init__(self, config: TimesformerConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + + return hidden_states + + +class TimeSformerAttention(nn.Module): + def __init__(self, config: TimesformerConfig) -> None: + super().__init__() + self.attention = TimesformerSelfAttention(config) + self.output = TimesformerSelfOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + output_attentions: bool = False, + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + self_outputs = self.attention(hidden_states, output_attentions) + + attention_output = self.output(self_outputs[0]) + + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +# Adapted from https://github.com/facebookresearch/TimeSformer/blob/a5ef29a7b7264baff199a30b3306ac27de901133/timesformer/models/vit.py#L39 +class TimesformerIntermediate(nn.Module): + def __init__(self, config: TimesformerConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + hidden_states = self.dropout(hidden_states) + + return hidden_states + + +class TimesformerOutput(nn.Module): + def __init__(self, config: TimesformerConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + + return hidden_states + + +# Adapted from https://github.com/facebookresearch/TimeSformer/blob/a5ef29a7b7264baff199a30b3306ac27de901133/timesformer/models/vit.py#L89 +class TimesformerLayer(nn.Module): + def __init__(self, config: TimesformerConfig, layer_index: int) -> None: + super().__init__() + + attention_type = config.attention_type + + drop_path_rates = [ + x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers) + ] # stochastic depth decay rule + drop_path_rate = drop_path_rates[layer_index] + + self.drop_path = TimeSformerDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity() + self.attention = TimeSformerAttention(config) + self.intermediate = TimesformerIntermediate(config) + self.output = TimesformerOutput(config) + self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.config = config + self.attention_type = attention_type + if attention_type not in ["divided_space_time", "space_only", "joint_space_time"]: + raise ValueError("Unknown attention type: {}".format(attention_type)) + + # Temporal Attention Parameters + if self.attention_type == "divided_space_time": + self.temporal_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.temporal_attention = TimeSformerAttention(config) + self.temporal_dense = nn.Linear(config.hidden_size, config.hidden_size) + + def forward(self, hidden_states: torch.Tensor, output_attentions: bool = False): + num_frames = self.config.num_frames + num_patch_width = self.config.image_size // self.config.patch_size + batch_size = hidden_states.shape[0] + num_spatial_tokens = (hidden_states.size(1) - 1) // num_frames + num_patch_height = num_spatial_tokens // num_patch_width + + if self.attention_type in ["space_only", "joint_space_time"]: + self_attention_outputs = self.attention( + self.layernorm_before(hidden_states), output_attentions=output_attentions + ) + attention_output = self_attention_outputs[0] + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + hidden_states = hidden_states + self.drop_path(attention_output) + + layer_output = self.layernorm_after(hidden_states) + layer_output = self.intermediate(layer_output) + layer_output = self.output(layer_output) + layer_output = hidden_states + self.drop_path(layer_output) + + outputs = (layer_output,) + outputs + + return outputs + + elif self.attention_type == "divided_space_time": + # Temporal + temporal_embedding = hidden_states[:, 1:, :] + temporal_embedding = temporal_embedding.reshape( + batch_size, num_patch_height, num_patch_width, num_frames, temporal_embedding.shape[2] + ).reshape(batch_size * num_patch_height * num_patch_width, num_frames, temporal_embedding.shape[2]) + + temporal_attention_outputs = self.temporal_attention( + self.temporal_layernorm(temporal_embedding), + ) + attention_output = temporal_attention_outputs[0] + + residual_temporal = self.drop_path(attention_output) + + residual_temporal = residual_temporal.reshape( + batch_size, num_patch_height, num_patch_width, num_frames, residual_temporal.shape[2] + ).reshape(batch_size, num_patch_height * num_patch_width * num_frames, residual_temporal.shape[2]) + residual_temporal = self.temporal_dense(residual_temporal) + temporal_embedding = hidden_states[:, 1:, :] + residual_temporal + + # Spatial + init_cls_token = hidden_states[:, 0, :].unsqueeze(1) + cls_token = init_cls_token.repeat(1, num_frames, 1) + cls_token = cls_token.reshape(batch_size * num_frames, 1, cls_token.shape[2]) + spatial_embedding = temporal_embedding + spatial_embedding = ( + spatial_embedding.reshape( + batch_size, num_patch_height, num_patch_width, num_frames, spatial_embedding.shape[2] + ) + .permute(0, 3, 1, 2, 4) + .reshape(batch_size * num_frames, num_patch_height * num_patch_width, spatial_embedding.shape[2]) + ) + spatial_embedding = torch.cat((cls_token, spatial_embedding), 1) + + spatial_attention_outputs = self.attention( + self.layernorm_before(spatial_embedding), output_attentions=output_attentions + ) + attention_output = spatial_attention_outputs[0] + outputs = spatial_attention_outputs[1:] # add self attentions if we output attention weights + + residual_spatial = self.drop_path(attention_output) + + # Taking care of CLS token + cls_token = residual_spatial[:, 0, :] + cls_token = cls_token.reshape(batch_size, num_frames, cls_token.shape[1]) + cls_token = torch.mean(cls_token, 1, True) # averaging for every frame + residual_spatial = residual_spatial[:, 1:, :] + residual_spatial = ( + residual_spatial.reshape( + batch_size, num_frames, num_patch_height, num_patch_width, residual_spatial.shape[2] + ) + .permute(0, 2, 3, 1, 4) + .reshape(batch_size, num_patch_height * num_patch_width * num_frames, residual_spatial.shape[2]) + ) + residual = residual_spatial + hidden_states = temporal_embedding + + # Mlp + hidden_states = torch.cat((init_cls_token, hidden_states), 1) + torch.cat((cls_token, residual), 1) + layer_output = self.layernorm_after(hidden_states) + layer_output = self.intermediate(layer_output) + layer_output = self.output(layer_output) + layer_output = hidden_states + self.drop_path(layer_output) + + outputs = (layer_output,) + outputs + + return outputs + + +class TimesformerEncoder(nn.Module): + def __init__(self, config: TimesformerConfig) -> None: + super().__init__() + self.config = config + self.layer = nn.ModuleList([TimesformerLayer(config, ind) for ind in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ) -> Union[tuple, BaseModelOutput]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer_module.__call__, + hidden_states, + output_attentions, + ) + else: + layer_outputs = layer_module(hidden_states, output_attentions) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +class TimesformerPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = TimesformerConfig + base_model_prefix = "timesformer" + main_input_name = "pixel_values" + supports_gradient_checkpointing = True + + def _init_weights(self, module): + if isinstance(module, (nn.Linear, nn.Conv2d)): + nn.init.trunc_normal_(module.weight, std=self.config.initializer_range) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + elif isinstance(module, nn.LayerNorm): + nn.init.constant_(module.bias, 0) + nn.init.constant_(module.weight, 1.0) + elif isinstance(module, TimesformerEmbeddings): + nn.init.trunc_normal_(module.cls_token, std=self.config.initializer_range) + nn.init.trunc_normal_(module.position_embeddings, std=self.config.initializer_range) + module.patch_embeddings.apply(self._init_weights) + + +TIMESFORMER_START_DOCSTRING = r""" + This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it + as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and + behavior. + + Parameters: + config ([`TimesformerConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +TIMESFORMER_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See + [`VideoMAEImageProcessor.preprocess`] for details. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare TimeSformer Model transformer outputting raw hidden-states without any specific head on top.", + TIMESFORMER_START_DOCSTRING, +) +class TimesformerModel(TimesformerPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.config = config + + self.embeddings = TimesformerEmbeddings(config) + self.encoder = TimesformerEncoder(config) + + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.patch_embeddings + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + @add_start_docstrings_to_model_forward(TIMESFORMER_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: torch.FloatTensor, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.FloatTensor], BaseModelOutput]: + r""" + Returns: + + Examples: + + ```python + >>> import av + >>> import numpy as np + + >>> from transformers import AutoImageProcessor, TimesformerModel + >>> from huggingface_hub import hf_hub_download + + >>> np.random.seed(0) + + + >>> def read_video_pyav(container, indices): + ... ''' + ... Decode the video with PyAV decoder. + ... Args: + ... container (`av.container.input.InputContainer`): PyAV container. + ... indices (`List[int]`): List of frame indices to decode. + ... Returns: + ... result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3). + ... ''' + ... frames = [] + ... container.seek(0) + ... start_index = indices[0] + ... end_index = indices[-1] + ... for i, frame in enumerate(container.decode(video=0)): + ... if i > end_index: + ... break + ... if i >= start_index and i in indices: + ... frames.append(frame) + ... return np.stack([x.to_ndarray(format="rgb24") for x in frames]) + + + >>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len): + ... ''' + ... Sample a given number of frame indices from the video. + ... Args: + ... clip_len (`int`): Total number of frames to sample. + ... frame_sample_rate (`int`): Sample every n-th frame. + ... seg_len (`int`): Maximum allowed index of sample's last frame. + ... Returns: + ... indices (`List[int]`): List of sampled frame indices + ... ''' + ... converted_len = int(clip_len * frame_sample_rate) + ... end_idx = np.random.randint(converted_len, seg_len) + ... start_idx = end_idx - converted_len + ... indices = np.linspace(start_idx, end_idx, num=clip_len) + ... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64) + ... return indices + + + >>> # video clip consists of 300 frames (10 seconds at 30 FPS) + >>> file_path = hf_hub_download( + ... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset" + ... ) + >>> container = av.open(file_path) + + >>> # sample 8 frames + >>> indices = sample_frame_indices(clip_len=8, frame_sample_rate=4, seg_len=container.streams.video[0].frames) + >>> video = read_video_pyav(container, indices) + + >>> image_processor = AutoImageProcessor.from_pretrained("MCG-NJU/videomae-base") + >>> model = TimesformerModel.from_pretrained("facebook/timesformer-base-finetuned-k400") + + >>> # prepare video for the model + >>> inputs = image_processor(list(video), return_tensors="pt") + + >>> # forward pass + >>> outputs = model(**inputs) + >>> last_hidden_states = outputs.last_hidden_state + >>> list(last_hidden_states.shape) + [1, 1569, 768] + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + embedding_output = self.embeddings(pixel_values) + + encoder_outputs = self.encoder( + embedding_output, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + if self.layernorm is not None: + sequence_output = self.layernorm(sequence_output) + + if not return_dict: + return (sequence_output,) + encoder_outputs[1:] + + return BaseModelOutput( + last_hidden_state=sequence_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +@add_start_docstrings( + """TimeSformer Model transformer with a video classification head on top (a linear layer on top of the final hidden state +of the [CLS] token) e.g. for ImageNet.""", + TIMESFORMER_START_DOCSTRING, +) +class TimesformerForVideoClassification(TimesformerPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.num_labels = config.num_labels + self.timesformer = TimesformerModel(config) + + # Classifier head + self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity() + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(TIMESFORMER_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=ImageClassifierOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, ImageClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + + Returns: + + Examples: + + ```python + >>> import av + >>> import torch + >>> import numpy as np + + >>> from transformers import AutoImageProcessor, TimesformerForVideoClassification + >>> from huggingface_hub import hf_hub_download + + >>> np.random.seed(0) + + + >>> def read_video_pyav(container, indices): + ... ''' + ... Decode the video with PyAV decoder. + ... Args: + ... container (`av.container.input.InputContainer`): PyAV container. + ... indices (`List[int]`): List of frame indices to decode. + ... Returns: + ... result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3). + ... ''' + ... frames = [] + ... container.seek(0) + ... start_index = indices[0] + ... end_index = indices[-1] + ... for i, frame in enumerate(container.decode(video=0)): + ... if i > end_index: + ... break + ... if i >= start_index and i in indices: + ... frames.append(frame) + ... return np.stack([x.to_ndarray(format="rgb24") for x in frames]) + + + >>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len): + ... ''' + ... Sample a given number of frame indices from the video. + ... Args: + ... clip_len (`int`): Total number of frames to sample. + ... frame_sample_rate (`int`): Sample every n-th frame. + ... seg_len (`int`): Maximum allowed index of sample's last frame. + ... Returns: + ... indices (`List[int]`): List of sampled frame indices + ... ''' + ... converted_len = int(clip_len * frame_sample_rate) + ... end_idx = np.random.randint(converted_len, seg_len) + ... start_idx = end_idx - converted_len + ... indices = np.linspace(start_idx, end_idx, num=clip_len) + ... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64) + ... return indices + + + >>> # video clip consists of 300 frames (10 seconds at 30 FPS) + >>> file_path = hf_hub_download( + ... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset" + ... ) + >>> container = av.open(file_path) + + >>> # sample 8 frames + >>> indices = sample_frame_indices(clip_len=8, frame_sample_rate=1, seg_len=container.streams.video[0].frames) + >>> video = read_video_pyav(container, indices) + + >>> image_processor = AutoImageProcessor.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics") + >>> model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400") + + >>> inputs = image_processor(list(video), return_tensors="pt") + + >>> with torch.no_grad(): + ... outputs = model(**inputs) + ... logits = outputs.logits + + >>> # model predicts one of the 400 Kinetics-400 classes + >>> predicted_label = logits.argmax(-1).item() + >>> print(model.config.id2label[predicted_label]) + eating spaghetti + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.timesformer( + pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0][:, 0] + + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return ImageClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/timm_backbone/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/timm_backbone/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..774f49232514e0c71d6669ad1405c38925e9d54b Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/timm_backbone/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/timm_backbone/__pycache__/configuration_timm_backbone.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/timm_backbone/__pycache__/configuration_timm_backbone.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d401613f83f4a25505b1a59a6aca5d2b82e21248 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/timm_backbone/__pycache__/configuration_timm_backbone.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/timm_backbone/__pycache__/modeling_timm_backbone.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/timm_backbone/__pycache__/modeling_timm_backbone.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6659d6b476950d69bc36e7e4507537d1156e4e7f Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/timm_backbone/__pycache__/modeling_timm_backbone.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/timm_backbone/configuration_timm_backbone.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/timm_backbone/configuration_timm_backbone.py new file mode 100644 index 0000000000000000000000000000000000000000..0f2f1b0b6c31348f2f25382029b700694a18d257 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/timm_backbone/configuration_timm_backbone.py @@ -0,0 +1,83 @@ +# coding=utf-8 +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" Configuration for Backbone models""" + +from ...configuration_utils import PretrainedConfig +from ...utils import logging + + +logger = logging.get_logger(__name__) + + +class TimmBackboneConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration for a timm backbone [`TimmBackbone`]. + + It is used to instantiate a timm backbone model according to the specified arguments, defining the model. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + backbone (`str`, *optional*): + The timm checkpoint to load. + num_channels (`int`, *optional*, defaults to 3): + The number of input channels. + features_only (`bool`, *optional*, defaults to `True`): + Whether to output only the features or also the logits. + use_pretrained_backbone (`bool`, *optional*, defaults to `True`): + Whether to use a pretrained backbone. + out_indices (`List[int]`, *optional*): + If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how + many stages the model has). Will default to the last stage if unset. + freeze_batch_norm_2d (`bool`, *optional*, defaults to `False`): + Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. + + Example: + ```python + >>> from transformers import TimmBackboneConfig, TimmBackbone + + >>> # Initializing a timm backbone + >>> configuration = TimmBackboneConfig("resnet50") + + >>> # Initializing a model from the configuration + >>> model = TimmBackbone(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "timm_backbone" + + def __init__( + self, + backbone=None, + num_channels=3, + features_only=True, + use_pretrained_backbone=True, + out_indices=None, + freeze_batch_norm_2d=False, + **kwargs, + ): + super().__init__(**kwargs) + self.backbone = backbone + self.num_channels = num_channels + self.features_only = features_only + self.use_pretrained_backbone = use_pretrained_backbone + self.use_timm_backbone = True + self.out_indices = out_indices if out_indices is not None else (-1,) + self.freeze_batch_norm_2d = freeze_batch_norm_2d diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/trocr/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/trocr/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..08400fc916ec21c52ace1428079fd206345d42b9 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/trocr/__init__.py @@ -0,0 +1,59 @@ +# Copyright 2021 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import ( + OptionalDependencyNotAvailable, + _LazyModule, + is_sentencepiece_available, + is_speech_available, + is_torch_available, +) + + +_import_structure = { + "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"], + "processing_trocr": ["TrOCRProcessor"], +} + + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_trocr"] = [ + "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST", + "TrOCRForCausalLM", + "TrOCRPreTrainedModel", + ] + + +if TYPE_CHECKING: + from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig + from .processing_trocr import TrOCRProcessor + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/trocr/__pycache__/__init__.cpython-312.pyc 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0000000000000000000000000000000000000000..bf20d91c3fbabd8311db08e1569ad66928489907 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/trocr/__pycache__/processing_trocr.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/trocr/modeling_trocr.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/trocr/modeling_trocr.py new file mode 100644 index 0000000000000000000000000000000000000000..72ead7143ad4928bb93d24e52d7c7b936ce1c02d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/trocr/modeling_trocr.py @@ -0,0 +1,967 @@ +# coding=utf-8 +# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch TrOCR decoder model (based on RoBERTa).""" + + +import copy +import math +from typing import Optional, Tuple, Union + +import torch +from torch import nn +from torch.nn import CrossEntropyLoss + +from ...activations import ACT2FN +from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask +from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions +from ...modeling_utils import PreTrainedModel +from ...utils import add_start_docstrings, logging, replace_return_docstrings +from .configuration_trocr import TrOCRConfig + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "TrOCRConfig" +_CHECKPOINT_FOR_DOC = "microsoft/trocr-base-handwritten" + + +from ..deprecated._archive_maps import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +# Copied from transformers.models.bart.modeling_bart.BartLearnedPositionalEmbedding with Bart->TrOCR +class TrOCRLearnedPositionalEmbedding(nn.Embedding): + """ + This module learns positional embeddings up to a fixed maximum size. + """ + + def __init__(self, num_embeddings: int, embedding_dim: int): + # TrOCR is set up so that if padding_idx is specified then offset the embedding ids by 2 + # and adjust num_embeddings appropriately. Other models don't have this hack + self.offset = 2 + super().__init__(num_embeddings + self.offset, embedding_dim) + + def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0): + """`input_ids' shape is expected to be [bsz x seqlen].""" + + bsz, seq_len = input_ids.shape[:2] + positions = torch.arange( + past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device + ).expand(bsz, -1) + + return super().forward(positions + self.offset) + + +class TrOCRSinusoidalPositionalEmbedding(nn.Module): + """This module produces sinusoidal positional embeddings of any length.""" + + def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None): + super().__init__() + self.offset = 2 + self.embedding_dim = embedding_dim + self.padding_idx = padding_idx + self.weights = self.get_embedding(num_positions, embedding_dim, padding_idx) + self.register_buffer("_float_tensor", torch.FloatTensor(1)) + + @staticmethod + def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None): + """ + Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the + description in Section 3.5 of "Attention Is All You Need". + """ + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb) + emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0) + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1) + if embedding_dim % 2 == 1: + # zero pad + emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) + if padding_idx is not None: + emb[padding_idx, :] = 0 + + return emb.to(torch.get_default_dtype()) + + @torch.no_grad() + def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0): + bsz, seq_len = input_ids.size() + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = self.create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length).to( + input_ids.device + ) + + # expand embeddings if needed + max_pos = self.padding_idx + 1 + seq_len + if self.weights is None or max_pos > self.weights.size(0): + # recompute/expand embeddings if needed + self.weights = self.get_embedding(max_pos, self.embedding_dim, self.padding_idx) + self.weights = self.weights.to(self._float_tensor) + + x = self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, -1).detach() + + return x + + def create_position_ids_from_input_ids( + self, input_ids: torch.Tensor, padding_idx: int, past_key_values_length: Optional[int] = 0 + ): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding + symbols are ignored. This is modified from fairseq's `utils.make_positions`. + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask + return incremental_indices.long() + padding_idx + + +class TrOCRAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper.""" + + def __init__( + self, + config, + embed_dim: int, + num_heads: int, + kdim: int = None, + vdim: int = None, + dropout: float = 0.0, + is_decoder: bool = False, + bias: bool = True, + is_cross_attention: bool = False, + ): + super().__init__() + self.embed_dim = embed_dim + self.kdim = kdim if kdim is not None else embed_dim + self.vdim = vdim if vdim is not None else embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + if not (self.head_dim * num_heads == self.embed_dim): + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + + self.k_proj = nn.Linear(self.kdim, embed_dim, bias=bias) + self.v_proj = nn.Linear(self.vdim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + bsz, tgt_len, embed_dim = hidden_states.size() + + # get query proj + query_states = self.q_proj(hidden_states) * self.scaling + # get key, value proj + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_states = past_key_value[0] + value_states = past_key_value[1] + elif is_cross_attention: + # cross_attentions + key_states = self._shape(self.k_proj(key_value_states), -1, bsz) + value_states = self._shape(self.v_proj(key_value_states), -1, bsz) + elif past_key_value is not None: + # reuse k, v, self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + else: + # self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_states, value_states) + + proj_shape = (bsz * self.num_heads, -1, self.head_dim) + query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) + key_states = key_states.view(*proj_shape) + value_states = value_states.view(*proj_shape) + + src_len = key_states.size(1) + attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) + + if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, tgt_len, src_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + if layer_head_mask is not None: + if layer_head_mask.size() != (self.num_heads,): + raise ValueError( + f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" + f" {layer_head_mask.size()}" + ) + attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + if output_attentions: + # this operation is a bit awkward, but it's required to + # make sure that attn_weights keeps its gradient. + # In order to do so, attn_weights have to be reshaped + # twice and have to be reused in the following + attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) + else: + attn_weights_reshaped = None + + attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = torch.bmm(attn_probs, value_states) + + if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) + attn_output = attn_output.transpose(1, 2) + attn_output = attn_output.reshape(bsz, tgt_len, embed_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights_reshaped, past_key_value + + +class TrOCRDecoderLayer(nn.Module): + def __init__(self, config: TrOCRConfig): + super().__init__() + self.embed_dim = config.hidden_size + + self.self_attn = TrOCRAttention( + config, + embed_dim=self.embed_dim, + num_heads=config.decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + ) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + + if config.is_decoder: + self.encoder_attn = TrOCRAttention( + config, + embed_dim=self.embed_dim, + num_heads=config.decoder_attention_heads, + kdim=config.cross_attention_hidden_size, + vdim=config.cross_attention_hidden_size, + dropout=config.attention_dropout, + is_decoder=True, + is_cross_attention=True, + ) + self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) + + self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) + self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + cross_attn_layer_head_mask: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = True, + ): + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + encoder_hidden_states (`torch.FloatTensor`): + cross attention input to the layer of shape `(batch, seq_len, embed_dim)` + encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size + `(encoder_attention_heads,)`. + cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of + size *(decoder_attention_heads,)*. + past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + + # Self Attention + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None + # add present self-attn cache to positions 1,2 of present_key_value tuple + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + past_key_value=self_attn_past_key_value, + attention_mask=attention_mask, + layer_head_mask=layer_head_mask, + output_attentions=output_attentions, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Cross-Attention Block + cross_attn_present_key_value = None + cross_attn_weights = None + + if encoder_hidden_states is not None: + residual = hidden_states + + # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple + cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None + hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( + hidden_states=hidden_states, + key_value_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + layer_head_mask=cross_attn_layer_head_mask, + past_key_value=cross_attn_past_key_value, + output_attentions=output_attentions, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.encoder_attn_layer_norm(hidden_states) + + # add cross-attn to positions 3,4 of present_key_value tuple + present_key_value = present_key_value + cross_attn_present_key_value + + # Fully Connected + residual = hidden_states + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.final_layer_norm(hidden_states) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights, cross_attn_weights) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +class TrOCRPreTrainedModel(PreTrainedModel): + config_class = TrOCRConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + + def _init_weights(self, module): + std = self.config.init_std + if isinstance(module, (nn.Linear, nn.Conv1d)): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + +TROCR_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`TrOCRConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +class TrOCRDecoder(TrOCRPreTrainedModel): + """ + Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TrOCRDecoderLayer`] + + Args: + config: TrOCRConfig + """ + + def __init__(self, config: TrOCRConfig): + super().__init__(config) + self.dropout = config.dropout + self.layerdrop = config.decoder_layerdrop + self.padding_idx = config.pad_token_id + self.embed_scale = math.sqrt(config.hidden_size) if config.scale_embedding else 1.0 + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + + if config.use_learned_position_embeddings: + self.embed_positions = TrOCRLearnedPositionalEmbedding(config.max_position_embeddings, config.hidden_size) + else: + self.embed_positions = TrOCRSinusoidalPositionalEmbedding( + config.max_position_embeddings + self.padding_idx + 1, + config.hidden_size, + self.padding_idx, + ) + + if config.layernorm_embedding: + self.layernorm_embedding = nn.LayerNorm(config.hidden_size) + else: + self.layernorm_embedding = None + + self.layers = nn.ModuleList([TrOCRDecoderLayer(config) for _ in range(config.decoder_layers)]) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + def forward( + self, + input_ids=None, + attention_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + head_mask=None, + cross_attn_head_mask=None, + past_key_values=None, + inputs_embeds=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you + provide it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention + of the decoder. + encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): + Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values + selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules in encoder to avoid performing cross-attention + on hidden heads. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of + shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the + cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those + that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of + all `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + input = input_ids + input_ids = input_ids.view(-1, input.shape[-1]) + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + input = inputs_embeds[:, :, -1] + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + # past_key_values_length + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale + + if self.config.use_learned_position_embeddings: + embed_pos = self.embed_positions(input, past_key_values_length=past_key_values_length) + else: + embed_pos = self.embed_positions(input_ids, past_key_values_length=past_key_values_length) + + hidden_states = inputs_embeds + embed_pos + + if self.layernorm_embedding is not None: + hidden_states = self.layernorm_embedding(hidden_states) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + input_shape = input.shape + + attention_mask = _prepare_4d_causal_attention_mask( + attention_mask, input_shape, inputs_embeds, past_key_values_length + ) + + # expand encoder attention mask + if encoder_hidden_states is not None and encoder_attention_mask is not None: + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + encoder_attention_mask = _prepare_4d_attention_mask( + encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1] + ) + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache = True` is incompatible with gradient checkpointing. Setting `use_cache = False`..." + ) + use_cache = False + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None + next_decoder_cache = () if use_cache else None + + # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired + for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]): + if attn_mask is not None: + if attn_mask.size()[0] != (len(self.layers)): + raise ValueError( + f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for" + f" {head_mask.size()[0]}." + ) + for idx, decoder_layer in enumerate(self.layers): + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + if output_hidden_states: + all_hidden_states += (hidden_states,) + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: + continue + + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + attention_mask, + encoder_hidden_states, + encoder_attention_mask, + head_mask[idx] if head_mask is not None else None, + cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None, + None, + output_attentions, + use_cache, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + layer_head_mask=(head_mask[idx] if head_mask is not None else None), + cross_attn_layer_head_mask=( + cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None + ), + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache += (layer_outputs[3 if output_attentions else 1],) + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + if encoder_hidden_states is not None: + all_cross_attentions += (layer_outputs[2],) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + if not return_dict: + return tuple( + v + for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + cross_attentions=all_cross_attentions, + ) + + +@add_start_docstrings( + "The TrOCR Model with a language modeling head. Can be used for summarization.", + TROCR_START_DOCSTRING, +) +class TrOCRDecoderWrapper(TrOCRPreTrainedModel): + """ + This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is + used in combination with the [`EncoderDecoderModel`] framework. + """ + + def __init__(self, config): + super().__init__(config) + self.decoder = TrOCRDecoder(config) + + def forward(self, *args, **kwargs): + return self.decoder(*args, **kwargs) + + +@add_start_docstrings( + "The TrOCR Decoder with a language modeling head. Can be used as the decoder part of [`EncoderDecoderModel`] and" + " [`VisionEncoderDecoder`].", + TROCR_START_DOCSTRING, +) +class TrOCRForCausalLM(TrOCRPreTrainedModel): + _tied_weights_keys = ["output_projection.weight"] + + def __init__(self, config): + config = copy.deepcopy(config) + config.is_decoder = True + config.is_encoder_decoder = False + super().__init__(config) + self.model = TrOCRDecoderWrapper(config) + + self.output_projection = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.decoder.embed_tokens + + def set_input_embeddings(self, value): + self.model.decoder.embed_tokens = value + + def get_output_embeddings(self): + return self.output_projection + + def set_output_embeddings(self, new_embeddings): + self.output_projection = new_embeddings + + def set_decoder(self, decoder): + self.model.decoder = decoder + + def get_decoder(self): + return self.model.decoder + + @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.Tensor] = None, + cross_attn_head_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]: + r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you + provide it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention + if the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used + in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of + shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional + tensors are only required when the model is used as a decoder in a Sequence to Sequence model. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the + cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those + that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of + all `decoder_input_ids` of shape `(batch_size, sequence_length)`. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + + Returns: + + Example: + + ```python + >>> from transformers import ( + ... TrOCRConfig, + ... TrOCRProcessor, + ... TrOCRForCausalLM, + ... ViTConfig, + ... ViTModel, + ... VisionEncoderDecoderModel, + ... ) + >>> import requests + >>> from PIL import Image + + >>> # TrOCR is a decoder model and should be used within a VisionEncoderDecoderModel + >>> # init vision2text model with random weights + >>> encoder = ViTModel(ViTConfig()) + >>> decoder = TrOCRForCausalLM(TrOCRConfig()) + >>> model = VisionEncoderDecoderModel(encoder=encoder, decoder=decoder) + + >>> # If you want to start from the pretrained model, load the checkpoint with `VisionEncoderDecoderModel` + >>> processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") + >>> model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten") + + >>> # load image from the IAM dataset + >>> url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB") + >>> pixel_values = processor(image, return_tensors="pt").pixel_values + >>> text = "industry, ' Mr. Brown commented icily. ' Let us have a" + + >>> # training + >>> model.config.decoder_start_token_id = processor.tokenizer.eos_token_id + >>> model.config.pad_token_id = processor.tokenizer.pad_token_id + >>> model.config.vocab_size = model.config.decoder.vocab_size + + >>> labels = processor.tokenizer(text, return_tensors="pt").input_ids + >>> outputs = model(pixel_values, labels=labels) + >>> loss = outputs.loss + >>> round(loss.item(), 2) + 5.30 + + >>> # inference + >>> generated_ids = model.generate(pixel_values) + >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] + >>> generated_text + 'industry, " Mr. Brown commented icily. " Let us have a' + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model.decoder( + input_ids=input_ids, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + head_mask=head_mask, + cross_attn_head_mask=cross_attn_head_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + logits = self.output_projection(outputs[0]) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, **kwargs + ): + # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly + if attention_mask is None: + attention_mask = input_ids.new_ones(input_ids.shape) + + if past_key_values: + past_length = past_key_values[0][0].shape[2] + + # Some generation methods already pass only the last input ID + if input_ids.shape[1] > past_length: + remove_prefix_length = past_length + else: + # Default to old behavior: keep only final ID + remove_prefix_length = input_ids.shape[1] - 1 + + input_ids = input_ids[:, remove_prefix_length:] + # first step, decoder_cached_states are empty + return { + "input_ids": input_ids, # encoder_outputs is defined. input_ids not needed + "attention_mask": attention_mask, + "past_key_values": past_key_values, + "use_cache": use_cache, + } + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += ( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), + ) + return reordered_past diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tvlt/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tvlt/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..86c0f7c1c0b99d1bfaff6d2b644d7b7c7b67441a --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tvlt/__init__.py @@ -0,0 +1,88 @@ +# flake8: noqa +# There's no way to ignore "F401 '...' imported but unused" warnings in this +# module, but to preserve other warnings. So, don't check this module at all. + +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import ( + OptionalDependencyNotAvailable, + _LazyModule, + is_torch_available, + is_vision_available, +) + + +_import_structure = { + "configuration_tvlt": ["TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP", "TvltConfig"], + "feature_extraction_tvlt": ["TvltFeatureExtractor"], + "processing_tvlt": ["TvltProcessor"], +} + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_tvlt"] = [ + "TVLT_PRETRAINED_MODEL_ARCHIVE_LIST", + "TvltModel", + "TvltForPreTraining", + "TvltForAudioVisualClassification", + "TvltPreTrainedModel", + ] + +try: + if not is_vision_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["image_processing_tvlt"] = ["TvltImageProcessor"] + + +if TYPE_CHECKING: + from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig + from .processing_tvlt import TvltProcessor + from .feature_extraction_tvlt import TvltFeatureExtractor + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_tvlt import ( + TVLT_PRETRAINED_MODEL_ARCHIVE_LIST, + TvltForAudioVisualClassification, + TvltForPreTraining, + TvltModel, + TvltPreTrainedModel, + ) + + try: 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a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tvp/configuration_tvp.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tvp/configuration_tvp.py new file mode 100644 index 0000000000000000000000000000000000000000..85b7ac6a41cbccccbfb1fb62becbcac2f4f2b63e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tvp/configuration_tvp.py @@ -0,0 +1,201 @@ +# coding=utf-8 +# Copyright 2023 The Intel AIA Team Authors, and HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License=, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing=, software +# distributed under the License is distributed on an "AS IS" BASIS=, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND=, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" TVP model configuration""" + +import copy + +from ...configuration_utils import PretrainedConfig +from ...utils import logging +from ..auto import CONFIG_MAPPING + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import TVP_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class TvpConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`TvpModel`]. It is used to instantiate an Tvp + model according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the Tvp + [Intel/tvp-base](https://huggingface.co/Intel/tvp-base) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + backbone_config (`PretrainedConfig` or `dict`, *optional*): + The configuration of the backbone model. + backbone (`str`, *optional*): + Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this + will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone` + is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights. + use_pretrained_backbone (`bool`, *optional*, defaults to `False`): + Whether to use pretrained weights for the backbone. + use_timm_backbone (`bool`, *optional*, defaults to `False`): + Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers + library. + backbone_kwargs (`dict`, *optional*): + Keyword arguments to be passed to AutoBackbone when loading from a checkpoint + e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set. + distance_loss_weight (`float`, *optional*, defaults to 1.0): + The weight of distance loss. + duration_loss_weight (`float`, *optional*, defaults to 0.1): + The weight of duration loss. + visual_prompter_type (`str`, *optional*, defaults to `"framepad"`): + Visual prompt type. The type of padding. Framepad means padding on each frame. Should be one of "framepad" + or "framedownpad" + visual_prompter_apply (`str`, *optional*, defaults to `"replace"`): + The way of applying visual prompt. Replace means use the value of prompt to change the original value in + visual inputs. Should be one of "replace", or "add", or "remove". + visual_prompt_size (`int`, *optional*, defaults to 96): + The size of visual prompt. + max_img_size (`int`, *optional*, defaults to 448): + The maximum size of frame. + num_frames (`int`, *optional*, defaults to 48): + The number of frames extracted from a video. + vocab_size (`int`, *optional*, defaults to 30522): + Vocabulary size of the Tvp text model. Defines the number of different tokens that can be represented by + the `inputs_ids` passed when calling [`TvpModel`]. + hidden_size (`int`, *optional*, defaults to 768): + Dimensionality of the encoder layers. + intermediate_size (`int`, *optional*, defaults to 3072): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + num_hidden_layers (`int`, *optional*, defaults to 12): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 12): + Number of attention heads for each attention layer in the Transformer encoder. + max_position_embeddings (`int`, *optional*, defaults to 512): + The maximum sequence length that this model might ever be used with. Typically set this to something large + just in case (e.g., 512 or 1024 or 2048). + max_grid_col_position_embeddings (`int`, *optional*, defaults to 100): + The largest number of horizontal patches from a video frame. + max_grid_row_position_embeddings (`int`, *optional*, defaults to 100): + The largest number of vertical patches from a video frame. + hidden_dropout_prob (`float`, *optional*, defaults to 0.1): + The dropout probability of hidden layers. + hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported. + layer_norm_eps (`float`, *optional*, defaults to 1e-12): + The epsilon used by the layer normalization layers. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): + The dropout probability of attention layers. + """ + + model_type = "tvp" + + def __init__( + self, + backbone_config=None, + backbone=None, + use_pretrained_backbone=False, + use_timm_backbone=False, + backbone_kwargs=None, + distance_loss_weight=1.0, + duration_loss_weight=0.1, + visual_prompter_type="framepad", + visual_prompter_apply="replace", + visual_prompt_size=96, + max_img_size=448, + num_frames=48, + vocab_size=30522, + hidden_size=768, + intermediate_size=3072, + num_hidden_layers=12, + num_attention_heads=12, + max_position_embeddings=512, + max_grid_col_position_embeddings=100, + max_grid_row_position_embeddings=100, + hidden_dropout_prob=0.1, + hidden_act="gelu", + layer_norm_eps=1e-12, + initializer_range=0.02, + attention_probs_dropout_prob=0.1, + **kwargs, + ): + super().__init__(**kwargs) + if use_pretrained_backbone: + raise ValueError("Pretrained backbones are not supported yet.") + + if backbone_config is not None and backbone is not None: + raise ValueError("You can't specify both `backbone` and `backbone_config`.") + + if backbone_config is None and backbone is None: + logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.") + backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage4"]) + elif isinstance(backbone_config, dict): + backbone_model_type = backbone_config.get("model_type") + config_class = CONFIG_MAPPING[backbone_model_type] + backbone_config = config_class.from_dict(backbone_config) + + if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None: + raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.") + + self.backbone_config = backbone_config + self.backbone = backbone + self.use_pretrained_backbone = use_pretrained_backbone + self.use_timm_backbone = use_timm_backbone + self.backbone_kwargs = backbone_kwargs + self.distance_loss_weight = distance_loss_weight + self.duration_loss_weight = duration_loss_weight + self.visual_prompter_type = visual_prompter_type + self.visual_prompter_apply = visual_prompter_apply + self.visual_prompt_size = visual_prompt_size + self.max_img_size = max_img_size + self.num_frames = num_frames + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.max_position_embeddings = max_position_embeddings + self.max_grid_col_position_embeddings = max_grid_col_position_embeddings + self.max_grid_row_position_embeddings = max_grid_row_position_embeddings + self.layer_norm_eps = layer_norm_eps + self.hidden_dropout_prob = hidden_dropout_prob + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.attention_probs_dropout_prob = attention_probs_dropout_prob + + @classmethod + def from_backbone_config(cls, backbone_config: PretrainedConfig, **kwargs): + """Instantiate a [`TvpConfig`] (or a derived class) from a pre-trained backbone model configuration. + + Args: + backbone_config ([`PretrainedConfig`]): + The backbone configuration. + Returns: + [`TvpConfig`]: An instance of a configuration object + """ + return cls(backbone_config=backbone_config, **kwargs) + + def to_dict(self): + """ + Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. + + Returns: + `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, + """ + output = copy.deepcopy(self.__dict__) + if output["backbone_config"] is not None: + output["backbone_config"] = self.backbone_config.to_dict() + output["model_type"] = self.__class__.model_type + return output diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tvp/image_processing_tvp.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tvp/image_processing_tvp.py new file mode 100644 index 0000000000000000000000000000000000000000..18600ee5fbe7f3c029c60c4b15df1defe43cf6aa --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tvp/image_processing_tvp.py @@ -0,0 +1,502 @@ +# coding=utf-8 +# Copyright 2023 The Intel AIA Team Authors, and HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License=, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing=, software +# distributed under the License is distributed on an "AS IS" BASIS=, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND=, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for TVP.""" + +from typing import Dict, Iterable, List, Optional, Tuple, Union + +import numpy as np + +from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict +from ...image_transforms import ( + PaddingMode, + flip_channel_order, + pad, + resize, + to_channel_dimension_format, +) +from ...image_utils import ( + IMAGENET_STANDARD_MEAN, + IMAGENET_STANDARD_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + get_image_size, + is_valid_image, + to_numpy_array, + valid_images, + validate_kwargs, + validate_preprocess_arguments, +) +from ...utils import TensorType, is_vision_available, logging + + +if is_vision_available(): + import PIL + + +logger = logging.get_logger(__name__) + + +# Copied from transformers.models.vivit.image_processing_vivit.make_batched +def make_batched(videos) -> List[List[ImageInput]]: + if isinstance(videos, (list, tuple)) and isinstance(videos[0], (list, tuple)) and is_valid_image(videos[0][0]): + return videos + + elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]): + return [videos] + + elif is_valid_image(videos): + return [[videos]] + + raise ValueError(f"Could not make batched video from {videos}") + + +def get_resize_output_image_size( + input_image: np.ndarray, + max_size: int = 448, + input_data_format: Optional[Union[str, ChannelDimension]] = None, +) -> Tuple[int, int]: + height, width = get_image_size(input_image, input_data_format) + if height >= width: + ratio = width * 1.0 / height + new_height = max_size + new_width = new_height * ratio + else: + ratio = height * 1.0 / width + new_width = max_size + new_height = new_width * ratio + size = (int(new_height), int(new_width)) + + return size + + +class TvpImageProcessor(BaseImageProcessor): + r""" + Constructs a Tvp image processor. + + Args: + do_resize (`bool`, *optional*, defaults to `True`): + Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the + `do_resize` parameter in the `preprocess` method. + size (`Dict[str, int]` *optional*, defaults to `{"longest_edge": 448}`): + Size of the output image after resizing. The longest edge of the image will be resized to + `size["longest_edge"]` while maintaining the aspect ratio of the original image. Can be overriden by + `size` in the `preprocess` method. + resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): + Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the + `preprocess` method. + do_center_crop (`bool`, *optional*, defaults to `True`): + Whether to center crop the image to the specified `crop_size`. Can be overridden by the `do_center_crop` + parameter in the `preprocess` method. + crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 448, "width": 448}`): + Size of the image after applying the center crop. Can be overridden by the `crop_size` parameter in the + `preprocess` method. + do_rescale (`bool`, *optional*, defaults to `True`): + Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` + parameter in the `preprocess` method. + rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): + Defines the scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter + in the `preprocess` method. + do_pad (`bool`, *optional*, defaults to `True`): + Whether to pad the image. Can be overridden by the `do_pad` parameter in the `preprocess` method. + pad_size (`Dict[str, int]`, *optional*, defaults to `{"height": 448, "width": 448}`): + Size of the image after applying the padding. Can be overridden by the `pad_size` parameter in the + `preprocess` method. + constant_values (`Union[float, Iterable[float]]`, *optional*, defaults to 0): + The fill value to use when padding the image. + pad_mode (`PaddingMode`, *optional*, defaults to `PaddingMode.CONSTANT`): + Use what kind of mode in padding. + do_normalize (`bool`, *optional*, defaults to `True`): + Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` + method. + do_flip_channel_order (`bool`, *optional*, defaults to `True`): + Whether to flip the color channels from RGB to BGR. Can be overridden by the `do_flip_channel_order` + parameter in the `preprocess` method. + image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): + Mean to use if normalizing the image. This is a float or list of floats the length of the number of + channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. + image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): + Standard deviation to use if normalizing the image. This is a float or list of floats the length of the + number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. + """ + + model_input_names = ["pixel_values"] + + def __init__( + self, + do_resize: bool = True, + size: Dict[str, int] = None, + resample: PILImageResampling = PILImageResampling.BILINEAR, + do_center_crop: bool = True, + crop_size: Dict[str, int] = None, + do_rescale: bool = True, + rescale_factor: Union[int, float] = 1 / 255, + do_pad: bool = True, + pad_size: Dict[str, int] = None, + constant_values: Union[float, Iterable[float]] = 0, + pad_mode: PaddingMode = PaddingMode.CONSTANT, + do_normalize: bool = True, + do_flip_channel_order: bool = True, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + **kwargs, + ) -> None: + super().__init__(**kwargs) + size = size if size is not None else {"longest_edge": 448} + crop_size = crop_size if crop_size is not None else {"height": 448, "width": 448} + pad_size = pad_size if pad_size is not None else {"height": 448, "width": 448} + + self.do_resize = do_resize + self.size = size + self.do_center_crop = do_center_crop + self.crop_size = crop_size + self.resample = resample + self.do_rescale = do_rescale + self.rescale_factor = rescale_factor + self.do_pad = do_pad + self.pad_size = pad_size + self.constant_values = constant_values + self.pad_mode = pad_mode + self.do_normalize = do_normalize + self.do_flip_channel_order = do_flip_channel_order + self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN + self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD + self._valid_processor_keys = [ + "videos", + "do_resize", + "size", + "resample", + "do_center_crop", + "crop_size", + "do_rescale", + "rescale_factor", + "do_pad", + "pad_size", + "constant_values", + "pad_mode", + "do_normalize", + "do_flip_channel_order", + "image_mean", + "image_std", + "return_tensors", + "data_format", + "input_data_format", + ] + + def resize( + self, + image: np.ndarray, + size: Dict[str, int], + resample: PILImageResampling = PILImageResampling.BILINEAR, + data_format: Optional[Union[str, ChannelDimension]] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ) -> np.ndarray: + """ + Resize an image. + + Args: + image (`np.ndarray`): + Image to resize. + size (`Dict[str, int]`): + Size of the output image. If `size` is of the form `{"height": h, "width": w}`, the output image will + have the size `(h, w)`. If `size` is of the form `{"longest_edge": s}`, the output image will have its + longest edge of length `s` while keeping the aspect ratio of the original image. + resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`): + Resampling filter to use when resiizing the image. + data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format of the image. If not provided, it will be the same as the input image. + input_data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format of the input image. If not provided, it will be inferred. + """ + size = get_size_dict(size, default_to_square=False) + if "height" in size and "width" in size: + output_size = (size["height"], size["width"]) + elif "longest_edge" in size: + output_size = get_resize_output_image_size(image, size["longest_edge"], input_data_format) + else: + raise ValueError(f"Size must have 'height' and 'width' or 'longest_edge' as keys. Got {size.keys()}") + + return resize( + image, + size=output_size, + resample=resample, + data_format=data_format, + input_data_format=input_data_format, + **kwargs, + ) + + def pad_image( + self, + image: np.ndarray, + pad_size: Dict[str, int] = None, + constant_values: Union[float, Iterable[float]] = 0, + pad_mode: PaddingMode = PaddingMode.CONSTANT, + data_format: Optional[Union[str, ChannelDimension]] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ): + """ + Pad an image with zeros to the given size. + + Args: + image (`np.ndarray`): + Image to pad. + pad_size (`Dict[str, int]`) + Size of the output image with pad. + constant_values (`Union[float, Iterable[float]]`) + The fill value to use when padding the image. + pad_mode (`PaddingMode`) + The pad mode, default to PaddingMode.CONSTANT + data_format (`ChannelDimension` or `str`, *optional*) + The channel dimension format of the image. If not provided, it will be the same as the input image. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format of the input image. If not provided, it will be inferred. + """ + height, width = get_image_size(image, channel_dim=input_data_format) + max_height = pad_size.get("height", height) + max_width = pad_size.get("width", width) + + pad_right, pad_bottom = max_width - width, max_height - height + if pad_right < 0 or pad_bottom < 0: + raise ValueError("The padding size must be greater than image size") + + padding = ((0, pad_bottom), (0, pad_right)) + padded_image = pad( + image, + padding, + mode=pad_mode, + constant_values=constant_values, + data_format=data_format, + input_data_format=input_data_format, + ) + + return padded_image + + def _preprocess_image( + self, + image: ImageInput, + do_resize: bool = None, + size: Dict[str, int] = None, + resample: PILImageResampling = None, + do_center_crop: bool = None, + crop_size: Dict[str, int] = None, + do_rescale: bool = None, + rescale_factor: float = None, + do_pad: bool = True, + pad_size: Dict[str, int] = None, + constant_values: Union[float, Iterable[float]] = None, + pad_mode: PaddingMode = None, + do_normalize: bool = None, + do_flip_channel_order: bool = None, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ) -> np.ndarray: + """Preprocesses a single image.""" + + validate_preprocess_arguments( + do_rescale=do_rescale, + rescale_factor=rescale_factor, + do_normalize=do_normalize, + image_mean=image_mean, + image_std=image_std, + do_pad=do_pad, + size_divisibility=pad_size, # here the pad() method simply requires the pad_size argument. + do_center_crop=do_center_crop, + crop_size=crop_size, + do_resize=do_resize, + size=size, + resample=resample, + ) + + # All transformations expect numpy arrays. + image = to_numpy_array(image) + + if do_resize: + image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format) + + if do_center_crop: + image = self.center_crop(image, size=crop_size, input_data_format=input_data_format) + + if do_rescale: + image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) + + if do_normalize: + image = self.normalize( + image=image.astype(np.float32), mean=image_mean, std=image_std, input_data_format=input_data_format + ) + + if do_pad: + image = self.pad_image( + image=image, + pad_size=pad_size, + constant_values=constant_values, + pad_mode=pad_mode, + input_data_format=input_data_format, + ) + + # the pretrained checkpoints assume images are BGR, not RGB + if do_flip_channel_order: + image = flip_channel_order(image=image, input_data_format=input_data_format) + + image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) + + return image + + def preprocess( + self, + videos: Union[ImageInput, List[ImageInput], List[List[ImageInput]]], + do_resize: bool = None, + size: Dict[str, int] = None, + resample: PILImageResampling = None, + do_center_crop: bool = None, + crop_size: Dict[str, int] = None, + do_rescale: bool = None, + rescale_factor: float = None, + do_pad: bool = None, + pad_size: Dict[str, int] = None, + constant_values: Union[float, Iterable[float]] = None, + pad_mode: PaddingMode = None, + do_normalize: bool = None, + do_flip_channel_order: bool = None, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + return_tensors: Optional[Union[str, TensorType]] = None, + data_format: ChannelDimension = ChannelDimension.FIRST, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ) -> PIL.Image.Image: + """ + Preprocess an image or batch of images. + + Args: + videos (`ImageInput` or `List[ImageInput]` or `List[List[ImageInput]]`): + Frames to preprocess. + do_resize (`bool`, *optional*, defaults to `self.do_resize`): + Whether to resize the image. + size (`Dict[str, int]`, *optional*, defaults to `self.size`): + Size of the image after applying resize. + resample (`PILImageResampling`, *optional*, defaults to `self.resample`): + Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only + has an effect if `do_resize` is set to `True`. + do_center_crop (`bool`, *optional*, defaults to `self.do_centre_crop`): + Whether to centre crop the image. + crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`): + Size of the image after applying the centre crop. + do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): + Whether to rescale the image values between [0 - 1]. + rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): + Rescale factor to rescale the image by if `do_rescale` is set to `True`. + do_pad (`bool`, *optional*, defaults to `True`): + Whether to pad the image. Can be overridden by the `do_pad` parameter in the `preprocess` method. + pad_size (`Dict[str, int]`, *optional*, defaults to `{"height": 448, "width": 448}`): + Size of the image after applying the padding. Can be overridden by the `pad_size` parameter in the + `preprocess` method. + constant_values (`Union[float, Iterable[float]]`, *optional*, defaults to 0): + The fill value to use when padding the image. + pad_mode (`PaddingMode`, *optional*, defaults to "PaddingMode.CONSTANT"): + Use what kind of mode in padding. + do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): + Whether to normalize the image. + do_flip_channel_order (`bool`, *optional*, defaults to `self.do_flip_channel_order`): + Whether to flip the channel order of the image. + image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): + Image mean. + image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): + Image standard deviation. + return_tensors (`str` or `TensorType`, *optional*): + The type of tensors to return. Can be one of: + - Unset: Return a list of `np.ndarray`. + - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. + - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. + - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. + - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. + data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): + The channel dimension format for the output image. Can be one of: + - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - Unset: Use the inferred channel dimension format of the input image. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format for the input image. If unset, the channel dimension format is inferred + from the input image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. + """ + do_resize = do_resize if do_resize is not None else self.do_resize + resample = resample if resample is not None else self.resample + do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop + do_rescale = do_rescale if do_rescale is not None else self.do_rescale + rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor + do_pad = do_pad if do_pad is not None else self.do_pad + pad_size = pad_size if pad_size is not None else self.pad_size + constant_values = constant_values if constant_values is not None else self.constant_values + pad_mode = pad_mode if pad_mode else self.pad_mode + do_normalize = do_normalize if do_normalize is not None else self.do_normalize + do_flip_channel_order = ( + do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order + ) + image_mean = image_mean if image_mean is not None else self.image_mean + image_std = image_std if image_std is not None else self.image_std + + size = size if size is not None else self.size + size = get_size_dict(size, default_to_square=False) + crop_size = crop_size if crop_size is not None else self.crop_size + crop_size = get_size_dict(crop_size, param_name="crop_size") + + validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys) + + if not valid_images(videos): + raise ValueError( + "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " + "torch.Tensor, tf.Tensor or jax.ndarray." + ) + + videos = make_batched(videos) + + videos = [ + np.array( + [ + self._preprocess_image( + image=img, + do_resize=do_resize, + size=size, + resample=resample, + do_center_crop=do_center_crop, + crop_size=crop_size, + do_rescale=do_rescale, + rescale_factor=rescale_factor, + do_pad=do_pad, + pad_size=pad_size, + constant_values=constant_values, + pad_mode=pad_mode, + do_normalize=do_normalize, + do_flip_channel_order=do_flip_channel_order, + image_mean=image_mean, + image_std=image_std, + data_format=data_format, + input_data_format=input_data_format, + ) + for img in video + ] + ) + for video in videos + ] + + data = {"pixel_values": videos} + return BatchFeature(data=data, tensor_type=return_tensors) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tvp/processing_tvp.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tvp/processing_tvp.py new file mode 100644 index 0000000000000000000000000000000000000000..4e27399ab8053fdd62c42edacc2c2d1dd8586920 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/tvp/processing_tvp.py @@ -0,0 +1,154 @@ +# coding=utf-8 +# Copyright 2023 The Intel AIA Team Authors, and HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License=, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing=, software +# distributed under the License is distributed on an "AS IS" BASIS=, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND=, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Processor class for TVP. +""" + + +from ...processing_utils import ProcessorMixin +from ...tokenization_utils_base import BatchEncoding + + +class TvpProcessor(ProcessorMixin): + r""" + Constructs an TVP processor which wraps a TVP image processor and a Bert tokenizer into a single processor. + + [`TvpProcessor`] offers all the functionalities of [`TvpImageProcessor`] and [`BertTokenizerFast`]. See the + [`~TvpProcessor.__call__`] and [`~TvpProcessor.decode`] for more information. + + Args: + image_processor ([`TvpImageProcessor`], *optional*): + The image processor is a required input. + tokenizer ([`BertTokenizerFast`], *optional*): + The tokenizer is a required input. + """ + + attributes = ["image_processor", "tokenizer"] + image_processor_class = "TvpImageProcessor" + tokenizer_class = ("BertTokenizer", "BertTokenizerFast") + + def __init__(self, image_processor=None, tokenizer=None, **kwargs): + if image_processor is None: + raise ValueError("You need to specify an `image_processor`.") + if tokenizer is None: + raise ValueError("You need to specify a `tokenizer`.") + + super().__init__(image_processor, tokenizer) + + def __call__(self, text=None, videos=None, return_tensors=None, **kwargs): + """ + Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` + and `kwargs` arguments to BertTokenizerFast's [`~BertTokenizerFast.__call__`] if `text` is not `None` to encode + the text. To prepare the image(s), this method forwards the `videos` and `kwargs` arguments to + TvpImageProcessor's [`~TvpImageProcessor.__call__`] if `videos` is not `None`. Please refer to the doctsring of + the above two methods for more information. + + Args: + text (`str`, `List[str]`, `List[List[str]]`): + The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings + (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set + `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). + videos (`List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`, `List[List[PIL.Image.Image]]`, `List[List[np.ndarrray]]`,: + `List[List[torch.Tensor]]`): The video or batch of videos to be prepared. Each video should be a list + of frames, which can be either PIL images or NumPy arrays. In case of NumPy arrays/PyTorch tensors, + each frame should be of shape (H, W, C), where H and W are frame height and width, and C is a number of + channels. + + return_tensors (`str` or [`~utils.TensorType`], *optional*): + If set, will return tensors of a particular framework. Acceptable values are: + + - `'tf'`: Return TensorFlow `tf.constant` objects. + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return NumPy `np.ndarray` objects. + - `'jax'`: Return JAX `jnp.ndarray` objects. + + Returns: + [`BatchEncoding`]: A [`BatchEncoding`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not + `None`). + - **pixel_values** -- Pixel values to be fed to a model. Returned when `videos` is not `None`. + """ + + max_text_length = kwargs.pop("max_text_length", None) + + if text is None and videos is None: + raise ValueError("You have to specify either text or videos. Both cannot be none.") + + encoding = {} + if text is not None: + textual_input = self.tokenizer.batch_encode_plus( + text, + truncation=True, + padding="max_length", + max_length=max_text_length, + pad_to_max_length=True, + return_tensors=return_tensors, + return_token_type_ids=False, + **kwargs, + ) + encoding.update(textual_input) + + if videos is not None: + image_features = self.image_processor(videos, return_tensors=return_tensors, **kwargs) + encoding.update(image_features) + + return BatchEncoding(data=encoding, tensor_type=return_tensors) + + def batch_decode(self, *args, **kwargs): + """ + This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please + refer to the docstring of this method for more information. + """ + return self.tokenizer.batch_decode(*args, **kwargs) + + def decode(self, *args, **kwargs): + """ + This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to + the docstring of this method for more information. + """ + return self.tokenizer.decode(*args, **kwargs) + + def post_process_video_grounding(self, logits, video_durations): + """ + Compute the time of the video. + + Args: + logits (`torch.Tensor`): + The logits output of TvpForVideoGrounding. + video_durations (`float`): + The video's duration. + + Returns: + start (`float`): + The start time of the video. + end (`float`): + The end time of the video. + """ + start, end = ( + round(logits.tolist()[0][0] * video_durations, 1), + round(logits.tolist()[0][1] * video_durations, 1), + ) + + return start, end + + @property + # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names + def model_input_names(self): + tokenizer_input_names = self.tokenizer.model_input_names + image_processor_input_names = self.image_processor.model_input_names + return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/udop/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/udop/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5066fde6af1d15b9f30189c5caac5fd545f9a6e5 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/udop/__init__.py @@ -0,0 +1,98 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ...utils import ( + OptionalDependencyNotAvailable, + _LazyModule, + is_sentencepiece_available, + is_tokenizers_available, + is_torch_available, +) + + +_import_structure = { + "configuration_udop": ["UDOP_PRETRAINED_CONFIG_ARCHIVE_MAP", "UdopConfig"], + "processing_udop": ["UdopProcessor"], +} + +try: + if not is_sentencepiece_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["tokenization_udop"] = ["UdopTokenizer"] + +try: + if not is_tokenizers_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["tokenization_udop_fast"] = ["UdopTokenizerFast"] + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_udop"] = [ + "UDOP_PRETRAINED_MODEL_ARCHIVE_LIST", + "UdopForConditionalGeneration", + "UdopPreTrainedModel", + "UdopModel", + "UdopEncoderModel", + ] + +if TYPE_CHECKING: + from .configuration_udop import UDOP_PRETRAINED_CONFIG_ARCHIVE_MAP, UdopConfig + from .processing_udop import UdopProcessor + + try: + if not is_sentencepiece_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .tokenization_udop import UdopTokenizer + + try: + if not is_tokenizers_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .tokenization_udop_fast import UdopTokenizerFast + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_udop import ( + UDOP_PRETRAINED_MODEL_ARCHIVE_LIST, + UdopEncoderModel, + UdopForConditionalGeneration, + UdopModel, + UdopPreTrainedModel, + ) + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/udop/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/udop/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 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0000000000000000000000000000000000000000..5f4e6a5dad5f213c484e21309a48522ef9ab128b Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/udop/__pycache__/tokenization_udop_fast.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/udop/configuration_udop.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/udop/configuration_udop.py new file mode 100644 index 0000000000000000000000000000000000000000..ba124d0aa15e6d306432cc440c1c8cedff3ddfcb --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/udop/configuration_udop.py @@ -0,0 +1,161 @@ +# coding=utf-8 +# Copyright 2024 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" UDOP model configuration""" + + +from ...configuration_utils import PretrainedConfig +from ...utils import logging + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import UDOP_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class UdopConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`UdopForConditionalGeneration`]. It is used to + instantiate a UDOP model according to the specified arguments, defining the model architecture. Instantiating a + configuration with the defaults will yield a similar configuration to that of the UDOP + [microsoft/udop-large](https://huggingface.co/microsoft/udop-large) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Arguments: + vocab_size (`int`, *optional*, defaults to 33201): + Vocabulary size of the UDOP model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`UdopForConditionalGeneration`]. + d_model (`int`, *optional*, defaults to 1024): + Size of the encoder layers and the pooler layer. + d_kv (`int`, *optional*, defaults to 64): + Size of the key, query, value projections per attention head. The `inner_dim` of the projection layer will + be defined as `num_heads * d_kv`. + d_ff (`int`, *optional*, defaults to 4096): + Size of the intermediate feed forward layer in each `UdopBlock`. + num_layers (`int`, *optional*, defaults to 24): + Number of hidden layers in the Transformer encoder and decoder. + num_decoder_layers (`int`, *optional*): + Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set. + num_heads (`int`, *optional*, defaults to 16): + Number of attention heads for each attention layer in the Transformer encoder and decoder. + relative_attention_num_buckets (`int`, *optional*, defaults to 32): + The number of buckets to use for each attention layer. + relative_attention_max_distance (`int`, *optional*, defaults to 128): + The maximum distance of the longer sequences for the bucket separation. + relative_bias_args (`List[dict]`, *optional*, defaults to `[{'type': '1d'}, {'type': 'horizontal'}, {'type': 'vertical'}]`): + A list of dictionaries containing the arguments for the relative bias layers. + dropout_rate (`float`, *optional*, defaults to 0.1): + The ratio for all dropout layers. + layer_norm_epsilon (`float`, *optional*, defaults to 1e-06): + The epsilon used by the layer normalization layers. + initializer_factor (`float`, *optional*, defaults to 1.0): + A factor for initializing all weight matrices (should be kept to 1, used internally for initialization + testing). + feed_forward_proj (`string`, *optional*, defaults to `"relu"`): + Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. Udopv1.1 uses the + `"gated-gelu"` feed forward projection. Original Udop uses `"relu"`. + is_encoder_decoder (`bool`, *optional*, defaults to `True`): + Whether the model should behave as an encoder/decoder or not. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). + pad_token_id (`int`, *optional*, defaults to 0): + The id of the padding token in the vocabulary. + eos_token_id (`int`, *optional*, defaults to 1): + The id of the end-of-sequence token in the vocabulary. + max_2d_position_embeddings (`int`, *optional*, defaults to 1024): + The maximum absolute position embeddings for relative position encoding. + image_size (`int`, *optional*, defaults to 224): + The size of the input images. + patch_size (`int`, *optional*, defaults to 16): + The patch size used by the vision encoder. + num_channels (`int`, *optional*, defaults to 3): + The number of channels in the input images. + """ + + model_type = "udop" + keys_to_ignore_at_inference = ["past_key_values"] + attribute_map = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"} + + def __init__( + self, + vocab_size=33201, + d_model=1024, + d_kv=64, + d_ff=4096, + num_layers=24, + num_decoder_layers=None, + num_heads=16, + relative_attention_num_buckets=32, + relative_attention_max_distance=128, + relative_bias_args=[{"type": "1d"}, {"type": "horizontal"}, {"type": "vertical"}], + dropout_rate=0.1, + layer_norm_epsilon=1e-6, + initializer_factor=1.0, + feed_forward_proj="relu", + is_encoder_decoder=True, + use_cache=True, + pad_token_id=0, + eos_token_id=1, + max_2d_position_embeddings=1024, + image_size=224, + patch_size=16, + num_channels=3, + **kwargs, + ): + self.vocab_size = vocab_size + self.d_model = d_model + self.d_kv = d_kv + self.d_ff = d_ff + self.num_layers = num_layers + self.num_decoder_layers = ( + num_decoder_layers if num_decoder_layers is not None else self.num_layers + ) # default = symmetry + self.num_heads = num_heads + self.relative_attention_num_buckets = relative_attention_num_buckets + self.relative_attention_max_distance = relative_attention_max_distance + self.dropout_rate = dropout_rate + self.layer_norm_epsilon = layer_norm_epsilon + self.initializer_factor = initializer_factor + self.feed_forward_proj = feed_forward_proj + self.use_cache = use_cache + + # UDOP attributes + self.max_2d_position_embeddings = max_2d_position_embeddings + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + if not isinstance(relative_bias_args, list): + raise ValueError("`relative_bias_args` should be a list of dictionaries.") + self.relative_bias_args = relative_bias_args + + act_info = self.feed_forward_proj.split("-") + self.dense_act_fn = act_info[-1] + self.is_gated_act = act_info[0] == "gated" + + if len(act_info) > 1 and act_info[0] != "gated" or len(act_info) > 2: + raise ValueError( + f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer." + "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. " + "'gated-gelu' or 'relu'" + ) + + super().__init__( + pad_token_id=pad_token_id, + eos_token_id=eos_token_id, + is_encoder_decoder=is_encoder_decoder, + **kwargs, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/udop/convert_udop_to_hf.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/udop/convert_udop_to_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..f9cf07f1286bf1507290a663a2793550da892a3a --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/udop/convert_udop_to_hf.py @@ -0,0 +1,213 @@ +# coding=utf-8 +# Copyright 2024 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert UDOP checkpoints from the original repository. URL: https://github.com/microsoft/i-Code/tree/main/i-Code-Doc""" + + +import argparse + +import torch +from huggingface_hub import hf_hub_download +from PIL import Image +from torchvision import transforms as T + +from transformers import ( + LayoutLMv3ImageProcessor, + UdopConfig, + UdopForConditionalGeneration, + UdopProcessor, + UdopTokenizer, +) +from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD + + +def original_transform(image, image_size=224): + transform = T.Compose( + [ + T.Resize([image_size, image_size]), + T.ToTensor(), + T.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD), + ] + ) + + image = transform(image) + return image + + +def get_image(): + filepath = hf_hub_download( + repo_id="hf-internal-testing/fixtures_docvqa", filename="document_2.png", repo_type="dataset" + ) + image = Image.open(filepath).convert("RGB") + + return image + + +def prepare_dummy_inputs(tokenizer, image_processor): + prompt = "Question answering. What is the name of the company?" + prompt = "Question answering. In which year is the report made?" + prompt_ids = tokenizer.encode(prompt, add_special_tokens=False) + + image = get_image() + # words, boxes = apply_tesseract(image, lang=None) + # fmt: off + words = ['7', 'ITC', 'Limited', 'REPORT', 'AND', 'ACCOUNTS', '2013', 'ITC’s', 'Brands:', 'An', 'Asset', 'for', 'the', 'Nation', 'The', 'consumer', 'needs', 'and', 'aspirations', 'they', 'fulfil,', 'the', 'benefit', 'they', 'generate', 'for', 'millions', 'across', 'ITC’s', 'value', 'chains,', 'the', 'future-ready', 'capabilities', 'that', 'support', 'them,', 'and', 'the', 'value', 'that', 'they', 'create', 'for', 'the', 'country,', 'have', 'made', 'ITC’s', 'brands', 'national', 'assets,', 'adding', 'to', 'India’s', 'competitiveness.', 'It', 'is', 'ITC’s', 'aspiration', 'to', 'be', 'the', 'No', '1', 'FMCG', 'player', 'in', 'the', 'country,', 'driven', 'by', 'its', 'new', 'FMCG', 'businesses.', 'A', 'recent', 'Nielsen', 'report', 'has', 'highlighted', 'that', "ITC's", 'new', 'FMCG', 'businesses', 'are', 'the', 'fastest', 'growing', 'among', 'the', 'top', 'consumer', 'goods', 'companies', 'operating', 'in', 'India.', 'ITC', 'takes', 'justifiable', 'pride', 'that,', 'along', 'with', 'generating', 'economic', 'value,', 'these', 'celebrated', 'Indian', 'brands', 'also', 'drive', 'the', 'creation', 'of', 'larger', 'societal', 'capital', 'through', 'the', 'virtuous', 'cycle', 'of', 'sustainable', 'and', 'inclusive', 'growth.', 'DI', 'WILLS', '*', ';', 'LOVE', 'DELIGHTFULLY', 'SOFT', 'SKIN?', 'aia', 'Ans', 'Source:', 'https://www.industrydocuments.ucsf.edu/docs/snbx0223'] + boxes = [[0, 45, 67, 80], [72, 56, 109, 67], [116, 56, 189, 67], [198, 59, 253, 66], [257, 59, 285, 66], [289, 59, 365, 66], [372, 59, 407, 66], [74, 136, 161, 158], [175, 137, 306, 158], [318, 137, 363, 158], [374, 137, 472, 158], [483, 136, 529, 158], [540, 137, 593, 158], [608, 137, 717, 158], [73, 194, 100, 203], [106, 196, 177, 203], [183, 194, 227, 203], [233, 194, 259, 203], [265, 194, 344, 205], [74, 211, 104, 222], [109, 210, 141, 221], [147, 211, 169, 220], [175, 210, 223, 220], [229, 211, 259, 222], [265, 211, 329, 222], [334, 210, 352, 220], [74, 227, 127, 236], [133, 229, 180, 236], [187, 227, 221, 236], [226, 227, 264, 236], [270, 227, 320, 237], [327, 227, 349, 236], [74, 243, 161, 254], [166, 243, 249, 254], [254, 243, 281, 252], [286, 244, 342, 254], [74, 260, 112, 270], [119, 260, 145, 269], [151, 260, 174, 269], [179, 260, 217, 269], [222, 260, 249, 269], [254, 260, 285, 271], [290, 260, 335, 269], [340, 259, 359, 269], [74, 276, 95, 284], [101, 276, 156, 287], [164, 276, 198, 284], [203, 276, 244, 284], [251, 275, 285, 284], [291, 276, 340, 284], [74, 292, 129, 301], [135, 292, 185, 302], [192, 292, 242, 303], [248, 292, 261, 301], [267, 292, 312, 301], [74, 308, 195, 319], [75, 335, 82, 344], [88, 335, 98, 344], [105, 335, 138, 344], [144, 335, 214, 346], [220, 336, 233, 344], [239, 335, 256, 344], [262, 335, 283, 344], [290, 335, 309, 344], [316, 335, 320, 344], [74, 351, 119, 360], [126, 352, 170, 362], [176, 352, 186, 360], [192, 352, 214, 360], [220, 352, 276, 362], [282, 352, 326, 360], [333, 352, 349, 362], [74, 368, 89, 377], [95, 370, 124, 377], [129, 367, 175, 377], [181, 368, 266, 377], [272, 368, 283, 376], [289, 368, 333, 377], [74, 384, 126, 393], [134, 385, 175, 395], [181, 384, 206, 393], [212, 384, 292, 395], [298, 384, 325, 393], [330, 384, 366, 393], [74, 403, 103, 409], [109, 400, 154, 409], [161, 401, 241, 409], [247, 403, 269, 409], [275, 401, 296, 409], [302, 400, 349, 409], [74, 417, 131, 428], [137, 419, 186, 428], [192, 417, 214, 426], [219, 417, 242, 428], [248, 419, 319, 426], [74, 433, 119, 444], [125, 433, 204, 444], [210, 433, 278, 444], [285, 433, 295, 441], [302, 433, 340, 442], [75, 449, 98, 458], [104, 449, 142, 458], [146, 449, 215, 460], [221, 449, 258, 460], [263, 449, 293, 459], [300, 449, 339, 460], [74, 466, 101, 474], [108, 466, 185, 476], [191, 466, 261, 474], [267, 466, 309, 476], [315, 466, 354, 474], [74, 482, 151, 491], [158, 482, 201, 491], [208, 482, 258, 491], [263, 482, 292, 491], [298, 482, 333, 491], [338, 482, 360, 491], [74, 498, 131, 507], [137, 498, 150, 507], [156, 498, 197, 509], [202, 498, 257, 507], [263, 498, 310, 509], [74, 515, 128, 525], [134, 515, 156, 523], [161, 515, 218, 523], [223, 515, 261, 525], [267, 514, 280, 523], [74, 531, 156, 540], [162, 531, 188, 540], [195, 531, 257, 540], [263, 531, 315, 542], [871, 199, 878, 202], [883, 199, 908, 202], [894, 251, 904, 257], [841, 268, 841, 270], [784, 373, 811, 378], [816, 373, 896, 378], [784, 381, 811, 387], [815, 381, 847, 387], [645, 908, 670, 915], [692, 908, 712, 915], [220, 984, 285, 993], [293, 983, 779, 996]] + # fmt: on + text_list = [] + bbox_list = [] + for text, box in zip(words, boxes): + if text == "": + continue + sub_tokens = tokenizer.tokenize(text) + for sub_token in sub_tokens: + text_list.append(sub_token) + bbox_list.append(box) + + input_ids = tokenizer.convert_tokens_to_ids(text_list) + + input_ids = prompt_ids + input_ids + bbox = [[0, 0, 0, 0]] * len(prompt_ids) + bbox_list + + pixel_values = image_processor(image, return_tensors="pt").pixel_values + original_pixel_values = original_transform(image, image_size=image_processor.size["height"]).unsqueeze(0) + # verify pixel values + assert torch.allclose(original_pixel_values, pixel_values) + print("Pixel values are ok!") + + return torch.tensor(input_ids).unsqueeze(0), torch.tensor(bbox).unsqueeze(0).float(), pixel_values + + +def convert_udop_checkpoint(model_name, pytorch_dump_folder_path=None, push_to_hub=False): + # model_name to checkpoint_path + name_to_checkpoint_path = { + "udop-large": "/Users/nielsrogge/Documents/UDOP/udop-unimodel-large-224/pytorch_model.bin", + "udop-large-512": "/Users/nielsrogge/Documents/UDOP/udop-unimodel-large-512/pytorch_model.bin", + "udop-large-512-300k": "/Users/nielsrogge/Documents/UDOP/udop-unimodel-large-512-300k-steps/pytorch_model.bin", + } + + # load original state dict + checkpoint_path = name_to_checkpoint_path[model_name] + state_dict = torch.load(checkpoint_path, map_location="cpu") + + print("Checkpoint path:", checkpoint_path) + + # create HF model + image_size = 512 if "512" in model_name else 224 + config = UdopConfig(decoder_start_token_id=0, image_size=image_size) + model = UdopForConditionalGeneration(config) + model.eval() + + # rename keys + state_dict = {k.replace("cell2dembedding", "cell_2d_embedding"): v for k, v in state_dict.items()} + + # load weights + missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False) + print("Missing keys:", missing_keys) + print("Unexpected keys:", unexpected_keys) + assert missing_keys == ["encoder.embed_patches.proj.weight", "encoder.embed_patches.proj.bias"] + assert unexpected_keys == ["pos_embed"] + + # prepare dummy inputs + tokenizer = UdopTokenizer.from_pretrained("t5-base", legacy=True) + size = {"height": image_size, "width": image_size} + image_processor = LayoutLMv3ImageProcessor( + image_mean=IMAGENET_DEFAULT_MEAN, image_std=IMAGENET_DEFAULT_STD, size=size + ) + processor = UdopProcessor(image_processor=image_processor, tokenizer=tokenizer) + input_ids, bbox, image = prepare_dummy_inputs(tokenizer, image_processor) + prompt = "Question answering. In which year is the report made?" + encoding = processor(images=get_image(), text=prompt, return_tensors="pt") + + input_ids = encoding.input_ids + try: + EXPECTED_INPUT_IDS = torch.tensor([[11860, 18243, 5, 86, 84, 215, 19, 8, 934, 263, 58, 1, 489, 27, 3838, 7363, 4083, 14536, 3430, 5686, 5911, 17161, 134, 2038, 27, 3838, 22, 7, 4688, 7, 10, 389, 18202, 21, 8, 11046, 37, 3733, 523, 11, 38, 2388, 1628, 3, 13133, 23334, 6, 8, 1656, 79, 3806, 21, 4040, 640, 27, 3838, 22, 7, 701, 16534, 6, 8, 3, 76, 2693, 18, 23015, 5644, 24, 380, 3, 6015, 6, 11, 8, 701, 24, 79, 482, 21, 3, 88, 684, 6, 43, 263, 27, 3838, 22, 7, 3635, 1157, 4089, 6, 2651, 12, 1547, 22, 7, 3265, 655, 5, 19, 27, 3838, 22, 7, 38, 2388, 257, 12, 36, 8, 465, 209, 13409, 12150, 1959, 16, 8, 684, 6, 6737, 57, 165, 126, 13409, 12150, 1623, 5, 71, 1100, 30298, 934, 65, 12566, 24, 27, 3838, 31, 7, 126, 13409, 12150, 1623, 33, 8, 10391, 1710, 859, 8, 420, 3733, 4968, 688, 2699, 16, 1547, 5, 27, 3838, 1217, 131, 99, 23, 179, 6064, 24, 6, 590, 28, 3, 11600, 1456, 701, 6, 175, 9443, 2557, 3635, 92, 1262, 8, 3409, 13, 2186, 3, 27908, 1784, 190, 8, 3, 5771, 17, 13281, 4005, 13, 5086, 11, 13066, 1170, 5, 10826, 16309, 134, 3, 2, 276, 26, 3, 55, 391, 13570, 5, 10315, 309, 3577, 19114, 371, 4254, 5121, 5055, 6245, 3, 10047, 3162, 58, 3, 9, 61, 1713, 2703, 476, 667, 25158, 301, 6058, 6038, 476, 3765, 9149, 10, 4893, 1303, 1986, 5, 13580, 7, 8224, 28244, 7, 5, 76, 75, 7, 89, 5, 15, 1259, 87, 7171, 7, 87, 7, 29, 115, 226, 4305, 2773, 1]]) # fmt: skip + torch.testing.assert_close(EXPECTED_INPUT_IDS, input_ids) + bbox = encoding.bbox.float() + pixel_values = encoding.pixel_values + except Exception: + print("Input_ids don't match, preparing dummy inputs") + input_ids, bbox, pixel_values = prepare_dummy_inputs(tokenizer, image_processor) + + # Verify single forward pass + print("Testing single forward pass..") + with torch.no_grad(): + decoder_input_ids = torch.tensor([[101]]) + outputs = model(input_ids=input_ids, bbox=bbox, pixel_values=pixel_values, decoder_input_ids=decoder_input_ids) + print("Shape of logits:", outputs.logits.shape) + print("First values of logits:", outputs.logits[0, :3, :3]) + + # tensor([[-18.5262, 1.5087, -15.7051]]) on linux + # tensor([[-19.4976, 0.8515, -17.1873]]) on mac + try: + assert torch.allclose(outputs.logits[0, :3, :3], torch.tensor([[-18.5262, 1.5087, -15.7051]]), atol=1e-4) + print("Looks ok!") + except Exception: + print("logits don't match let's try to generate") + + # Verify autoregressive decoding + print("Testing generation...") + model_kwargs = {"bbox": bbox, "pixel_values": pixel_values} + outputs = model.generate(input_ids=input_ids, **model_kwargs, max_new_tokens=20) + + print("Generated:", tokenizer.batch_decode(outputs, skip_special_tokens=True)) + + # autoregressive decoding with original input data + print("Testing generation with original inputs...") + filepath = hf_hub_download(repo_id="nielsr/test-image", filename="input_ids_udop.pt", repo_type="dataset") + input_ids = torch.load(filepath) + filepath = hf_hub_download(repo_id="nielsr/test-image", filename="bbox_udop.pt", repo_type="dataset") + bbox = torch.load(filepath) + pixel_values_filename = "pixel_values_udop_512.pt" if "512" in model_name else "pixel_values_udop_224.pt" + filepath = hf_hub_download(repo_id="nielsr/test-image", filename=pixel_values_filename, repo_type="dataset") + pixel_values = torch.load(filepath) + + print("Decoded input ids:", tokenizer.decode(input_ids[0], skip_special_tokens=True)) + print("Bbox shape:", bbox.shape) + + model_kwargs = {"bbox": bbox, "pixel_values": pixel_values} + outputs = model.generate(input_ids=input_ids, **model_kwargs, max_new_tokens=20) + generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] + print("Generated:", generated_text) + + if pytorch_dump_folder_path is not None: + model.save_pretrained(pytorch_dump_folder_path) + tokenizer.save_pretrained(pytorch_dump_folder_path) + + if push_to_hub: + model.push_to_hub(f"microsoft/{model_name}") + processor.push_to_hub(f"microsoft/{model_name}") + # BIG note here: to save the fast tokenizer files in the repo on the hub, you need to do the following: + # see https://discuss.huggingface.co/t/convert-slow-xlmrobertatokenizer-to-fast-one/20876 + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + # Required parameters + parser.add_argument( + "--model_name", + default="udop-large", + type=str, + choices=["udop-large", "udop-large-512", "udop-large-512-300k"], + help=("Name of the UDOP model you'd like to convert."), + ) + parser.add_argument( + "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." + ) + parser.add_argument( + "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." + ) + + args = parser.parse_args() + convert_udop_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/udop/tokenization_udop.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/udop/tokenization_udop.py new file mode 100644 index 0000000000000000000000000000000000000000..c3b270bc55a8bf55592757069b67fab074eaec89 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/udop/tokenization_udop.py @@ -0,0 +1,1476 @@ +# coding=utf-8 +# Copyright 2024 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License +""" Tokenization classes for UDOP model.""" + + +import os +import re +import warnings +from shutil import copyfile +from typing import Any, Dict, List, Optional, Tuple, Union + +import sentencepiece as spm + +from ...tokenization_utils import PreTrainedTokenizer +from ...tokenization_utils_base import ( + AddedToken, + BatchEncoding, + EncodedInput, + PreTokenizedInput, + TextInput, + TextInputPair, + TruncationStrategy, +) +from ...utils import PaddingStrategy, TensorType, add_end_docstrings, logging + + +logger = logging.get_logger(__name__) + + +SPIECE_UNDERLINE = "▁" + + +UDOP_ENCODE_KWARGS_DOCSTRING = r""" + add_special_tokens (`bool`, *optional*, defaults to `True`): + Whether or not to encode the sequences with the special tokens relative to their model. + padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`): + Activates and controls padding. Accepts the following values: + + - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single + sequence if provided). + - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum + acceptable input length for the model if that argument is not provided. + - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different + lengths). + truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`): + Activates and controls truncation. Accepts the following values: + + - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or + to the maximum acceptable input length for the model if that argument is not provided. This will + truncate token by token, removing a token from the longest sequence in the pair if a pair of + sequences (or a batch of pairs) is provided. + - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the + maximum acceptable input length for the model if that argument is not provided. This will only + truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided. + - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the + maximum acceptable input length for the model if that argument is not provided. This will only + truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided. + - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths + greater than the model maximum admissible input size). + max_length (`int`, *optional*): + Controls the maximum length to use by one of the truncation/padding parameters. + + If left unset or set to `None`, this will use the predefined model maximum length if a maximum length + is required by one of the truncation/padding parameters. If the model has no specific maximum input + length (like XLNet) truncation/padding to a maximum length will be deactivated. + stride (`int`, *optional*, defaults to 0): + If set to a number along with `max_length`, the overflowing tokens returned when + `return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence + returned to provide some overlap between truncated and overflowing sequences. The value of this + argument defines the number of overlapping tokens. + pad_to_multiple_of (`int`, *optional*): + If set will pad the sequence to a multiple of the provided value. This is especially useful to enable + the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta). + return_tensors (`str` or [`~file_utils.TensorType`], *optional*): + If set, will return tensors instead of list of python integers. Acceptable values are: + + - `'tf'`: Return TensorFlow `tf.constant` objects. + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return Numpy `np.ndarray` objects. + return_token_type_ids (`bool`, *optional*): + Whether to return token type IDs. If left to the default, will return the token type IDs according to + the specific tokenizer's default, defined by the `return_outputs` attribute. + + [What are token type IDs?](../glossary#token-type-ids) + return_attention_mask (`bool`, *optional*): + Whether to return the attention mask. If left to the default, will return the attention mask according + to the specific tokenizer's default, defined by the `return_outputs` attribute. + + [What are attention masks?](../glossary#attention-mask) + return_overflowing_tokens (`bool`, *optional*, defaults to `False`): + Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch + of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead + of returning overflowing tokens. + return_special_tokens_mask (`bool`, *optional*, defaults to `False`): + Whether or not to return special tokens mask information. + return_offsets_mapping (`bool`, *optional*, defaults to `False`): + Whether or not to return `(char_start, char_end)` for each token. + + This is only available on fast tokenizers inheriting from [`PreTrainedTokenizerFast`], if using + Python's tokenizer, this method will raise `NotImplementedError`. + return_length (`bool`, *optional*, defaults to `False`): + Whether or not to return the lengths of the encoded inputs. + verbose (`bool`, *optional*, defaults to `True`): + Whether or not to print more information and warnings. + **kwargs: passed to the `self.tokenize()` method + + Return: + [`BatchEncoding`]: A [`BatchEncoding`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. + + [What are input IDs?](../glossary#input-ids) + + - **bbox** -- List of bounding boxes to be fed to a model. + + - **token_type_ids** -- List of token type ids to be fed to a model (when `return_token_type_ids=True` or + if *"token_type_ids"* is in `self.model_input_names`). + + [What are token type IDs?](../glossary#token-type-ids) + + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`). + + [What are attention masks?](../glossary#attention-mask) + + - **labels** -- List of labels to be fed to a model. (when `word_labels` is specified). + - **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and + `return_overflowing_tokens=True`). + - **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and + `return_overflowing_tokens=True`). + - **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying + regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`). + - **length** -- The length of the inputs (when `return_length=True`). +""" + +VOCAB_FILES_NAMES = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"} + +PRETRAINED_VOCAB_FILES_MAP = { + "vocab_file": { + "microsoft/udop-large": "https://huggingface.co/microsoft/udop-large/resolve/main/spiece.model", + }, + "tokenizer_file": { + "microsoft/udop-large": "https://huggingface.co/microsoft/udop-large/resolve/main/tokenizer.json", + }, +} + + +class UdopTokenizer(PreTrainedTokenizer): + """ + Adapted from [`LayoutXLMTokenizer`] and [`T5Tokenizer`]. Based on + [SentencePiece](https://github.com/google/sentencepiece). + + This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to + this superclass for more information regarding those methods. + + Args: + vocab_file (`str`): + Path to the vocabulary file. + + eos_token (`str`, *optional*, defaults to `""`): + The end of sequence token. + + + + When building a sequence using special tokens, this is not the token that is used for the end of sequence. + The token used is the `sep_token`. + + + + unk_token (`str`, *optional*, defaults to `""`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + + sep_token (`str`, *optional*, defaults to `""`): + The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for + sequence classification or for a text and a question for question answering. It is also used as the last + token of a sequence built with special tokens. + + pad_token (`str`, *optional*, defaults to `""`): + The token used for padding, for example when batching sequences of different lengths. + sep_token_box (`List[int]`, *optional*, defaults to `[1000, 1000, 1000, 1000]`): + The bounding box to use for the special [SEP] token. + pad_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`): + The bounding box to use for the special [PAD] token. + pad_token_label (`int`, *optional*, defaults to -100): + The label to use for padding tokens. Defaults to -100, which is the `ignore_index` of PyTorch's + CrossEntropyLoss. + only_label_first_subword (`bool`, *optional*, defaults to `True`): + Whether or not to only label the first subword, in case word labels are provided. + additional_special_tokens (`List[str]`, *optional*, defaults to `["NOTUSED", "NOTUSED"]`): + Additional special tokens used by the tokenizer. + + sp_model_kwargs (`dict`, *optional*): + Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for + SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, + to set: + + - `enable_sampling`: Enable subword regularization. + - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. + + - `nbest_size = {0,1}`: No sampling is performed. + - `nbest_size > 1`: samples from the nbest_size results. + - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) + using forward-filtering-and-backward-sampling algorithm. + + - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for + BPE-dropout. + legacy (`bool`, *optional*, defaults to `True`): + Whether or not the `legacy` behaviour of the tokenizer should be used. Legacy is before the merge of #24622 + which includes fixes to properly handle tokens that appear after special tokens. A simple example: + - `legacy=True`: + ```python + >>> from transformers import T5Tokenizer + + >>> tokenizer = T5Tokenizer.from_pretrained("t5-base", legacy=True) + >>> tokenizer.encode("Hello .") + [8774, 32099, 3, 5, 1] + ``` + - `legacy=False`: + ```python + >>> from transformers import T5Tokenizer + + >>> tokenizer = T5Tokenizer.from_pretrained("t5-base", legacy=False) + >>> tokenizer.encode("Hello .") # the extra space `[3]` is no longer here + [8774, 32099, 5, 1] + ``` + Checkout the pull request and the issue [here](https://github.com/huggingface/transformers/pull/24565) for + more details. + add_prefix_space (`bool`, *optional*, defaults to `True`): + Whether or not to add an initial space to the input. This allows to treat the leading word just as any + other word. + + + Attributes: + sp_model (`SentencePieceProcessor`): + The *SentencePiece* processor that is used for every conversion (string, tokens and IDs). + """ + + vocab_files_names = VOCAB_FILES_NAMES + pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP + model_input_names = ["input_ids", "attention_mask"] + + def __init__( + self, + vocab_file, + eos_token="", + unk_token="", + sep_token="", + pad_token="", + sep_token_box=[1000, 1000, 1000, 1000], + pad_token_box=[0, 0, 0, 0], + pad_token_label=-100, + only_label_first_subword=True, + additional_special_tokens=None, + sp_model_kwargs: Optional[Dict[str, Any]] = None, + legacy=True, + add_prefix_space=True, + **kwargs, + ) -> None: + eos_token = AddedToken(eos_token, special=True) if isinstance(eos_token, str) else eos_token + unk_token = AddedToken(unk_token, special=True) if isinstance(unk_token, str) else unk_token + sep_token = AddedToken(sep_token, special=True) if isinstance(sep_token, str) else sep_token + pad_token = AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token + + self.legacy = legacy + self.add_prefix_space = add_prefix_space + self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs + + self.vocab_file = vocab_file + + self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) + self.sp_model.Load(vocab_file) + + # additional properties + self.sep_token_box = sep_token_box + self.pad_token_box = pad_token_box + self.pad_token_label = pad_token_label + self.only_label_first_subword = only_label_first_subword + + super().__init__( + eos_token=eos_token, + unk_token=unk_token, + sep_token=sep_token, + pad_token=pad_token, + sep_token_box=sep_token_box, + pad_token_box=pad_token_box, + pad_token_label=pad_token_label, + only_label_first_subword=only_label_first_subword, + additional_special_tokens=additional_special_tokens, + sp_model_kwargs=self.sp_model_kwargs, + legacy=legacy, + add_prefix_space=add_prefix_space, + **kwargs, + ) + + @property + def vocab_size(self): + return len(self.sp_model) + + # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_vocab + def get_vocab(self): + vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} + vocab.update(self.added_tokens_encoder) + return vocab + + # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_special_tokens_mask + def get_special_tokens_mask( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False + ) -> List[int]: + """ + Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding + special tokens using the tokenizer `prepare_for_model` method. + + Args: + token_ids_0 (`List[int]`): + List of IDs. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + already_has_special_tokens (`bool`, *optional*, defaults to `False`): + Whether or not the token list is already formatted with special tokens for the model. + + Returns: + `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. + """ + if already_has_special_tokens: + return super().get_special_tokens_mask( + token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True + ) + + # normal case: some special tokens + if token_ids_1 is None: + return ([0] * len(token_ids_0)) + [1] + return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] + + # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_sentinel_tokens + def get_sentinel_tokens(self): + return list( + set(filter(lambda x: bool(re.search(r"", x)) is not None, self.additional_special_tokens)) + ) + + # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_sentinel_token_ids + def get_sentinel_token_ids(self): + return [self.convert_tokens_to_ids(token) for token in self.get_sentinel_tokens()] + + # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._add_eos_if_not_present + def _add_eos_if_not_present(self, token_ids: List[int]) -> List[int]: + """Do not add eos again if user already added it.""" + if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id: + warnings.warn( + f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated" + " eos tokens being added." + ) + return token_ids + else: + return token_ids + [self.eos_token_id] + + # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.create_token_type_ids_from_sequences + def create_token_type_ids_from_sequences( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make + use of token type ids, therefore a list of zeros is returned. + + Args: + token_ids_0 (`List[int]`): + List of IDs. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of zeros. + """ + eos = [self.eos_token_id] + + if token_ids_1 is None: + return len(token_ids_0 + eos) * [0] + return len(token_ids_0 + eos + token_ids_1 + eos) * [0] + + # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.build_inputs_with_special_tokens + def build_inputs_with_special_tokens( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and + adding special tokens. A sequence has the following format: + + - single sequence: `X ` + - pair of sequences: `A B ` + + Args: + token_ids_0 (`List[int]`): + List of IDs to which the special tokens will be added. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. + """ + token_ids_0 = self._add_eos_if_not_present(token_ids_0) + if token_ids_1 is None: + return token_ids_0 + else: + token_ids_1 = self._add_eos_if_not_present(token_ids_1) + return token_ids_0 + token_ids_1 + + # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.__getstate__ + def __getstate__(self): + state = self.__dict__.copy() + state["sp_model"] = None + return state + + def __setstate__(self, d): + self.__dict__ = d + self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) + self.sp_model.Load(self.vocab_file) + + # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.tokenize + def tokenize(self, text: "TextInput", **kwargs) -> List[str]: + """ + Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the + first token is special. + """ + if self.legacy or len(text) == 0: + return super().tokenize(text, **kwargs) + + text = text.replace(SPIECE_UNDERLINE, " ") + if self.add_prefix_space: + text = SPIECE_UNDERLINE + text + + tokens = super().tokenize(text, **kwargs) + + if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens: + tokens = tokens[1:] + return tokens + + # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._tokenize + def _tokenize(self, text, **kwargs): + """ + Returns a tokenized string. + + We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any + SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give + `['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the + `unk_token`. Here is an example with `unk_token = ""` and `unk_token_length = 4`. + `self.tokenizer.sp_model.encode(" Hey", out_type = str)[4:]`. + """ + tokens = self.sp_model.encode(text, out_type=str) + if self.legacy or not text.startswith((SPIECE_UNDERLINE, " ")): + return tokens + + # 1. Encode string + prefix ex: " Hey" + tokens = self.sp_model.encode(self.unk_token + text, out_type=str) + # 2. Remove self.unk_token from ['<','unk','>', '▁Hey'] + return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens + + def _convert_token_to_id(self, token): + """Converts a token (str) in an id using the vocab.""" + return self.sp_model.piece_to_id(token) + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + return self.sp_model.IdToPiece(index) + + # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.convert_tokens_to_string + def convert_tokens_to_string(self, tokens): + """Converts a sequence of tokens (string) in a single string.""" + # since we manually add the prefix space, we have to remove it when decoding + if tokens[0].startswith(SPIECE_UNDERLINE) and self.add_prefix_space: + tokens[0] = tokens[0][1:] + + current_sub_tokens = [] + out_string = "" + prev_is_special = False + for token in tokens: + # make sure that special tokens are not decoded using sentencepiece model + if token in self.all_special_tokens: + if not prev_is_special: + out_string += " " + out_string += self.sp_model.decode(current_sub_tokens) + token + prev_is_special = True + current_sub_tokens = [] + else: + current_sub_tokens.append(token) + prev_is_special = False + out_string += self.sp_model.decode(current_sub_tokens) + return out_string.strip() + + # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.save_vocabulary + def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: + if not os.path.isdir(save_directory): + logger.error(f"Vocabulary path ({save_directory}) should be a directory") + return + out_vocab_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] + ) + + if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): + copyfile(self.vocab_file, out_vocab_file) + elif not os.path.isfile(self.vocab_file): + with open(out_vocab_file, "wb") as fi: + content_spiece_model = self.sp_model.serialized_model_proto() + fi.write(content_spiece_model) + + return (out_vocab_file,) + + @add_end_docstrings(UDOP_ENCODE_KWARGS_DOCSTRING) + def __call__( + self, + text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, + text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None, + boxes: Union[List[List[int]], List[List[List[int]]]] = None, + word_labels: Optional[Union[List[int], List[List[int]]]] = None, + text_target: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, + text_pair_target: Optional[ + Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] + ] = None, + **kwargs, + ) -> BatchEncoding: + if text is None and text_target is None: + raise ValueError("You need to specify either `text` or `text_target`.") + if text is not None: + # The context manager will send the inputs as normal texts and not text_target, but we shouldn't change the + # input mode in this case. + if not self._in_target_context_manager: + self._switch_to_input_mode() + encodings = self.call_boxes(text=text, text_pair=text_pair, boxes=boxes, word_labels=word_labels, **kwargs) + if text_target is not None: + self._switch_to_target_mode() + target_encodings = self._call_one(text=text_target, text_pair=text_pair_target, **kwargs) + # Leave back tokenizer in input mode + self._switch_to_input_mode() + + if text_target is None: + return encodings + elif text is None: + return target_encodings + else: + encodings["labels"] = target_encodings["input_ids"] + return encodings + + def call_boxes( + self, + text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]], + text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None, + boxes: Union[List[List[int]], List[List[List[int]]]] = None, + word_labels: Optional[Union[List[int], List[List[int]]]] = None, + add_special_tokens: bool = True, + padding: Union[bool, str, PaddingStrategy] = False, + truncation: Union[bool, str, TruncationStrategy] = None, + max_length: Optional[int] = None, + stride: int = 0, + pad_to_multiple_of: Optional[int] = None, + return_tensors: Optional[Union[str, TensorType]] = None, + return_token_type_ids: Optional[bool] = None, + return_attention_mask: Optional[bool] = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_offsets_mapping: bool = False, + return_length: bool = False, + verbose: bool = True, + **kwargs, + ) -> BatchEncoding: + """ + Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of + sequences with word-level normalized bounding boxes and optional labels. + + Args: + text (`str`, `List[str]`, `List[List[str]]`): + The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings + (words of a single example or questions of a batch of examples) or a list of list of strings (batch of + words). + text_pair (`List[str]`, `List[List[str]]`): + The sequence or batch of sequences to be encoded. Each sequence should be a list of strings + (pretokenized string). + boxes (`List[List[int]]`, `List[List[List[int]]]`): + Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale. + word_labels (`List[int]`, `List[List[int]]`, *optional*): + Word-level integer labels (for token classification tasks such as FUNSD, CORD). + """ + + # Input type checking for clearer error + def _is_valid_text_input(t): + if isinstance(t, str): + # Strings are fine + return True + elif isinstance(t, (list, tuple)): + # List are fine as long as they are... + if len(t) == 0: + # ... empty + return True + elif isinstance(t[0], str): + # ... list of strings + return True + elif isinstance(t[0], (list, tuple)): + # ... list with an empty list or with a list of strings + return len(t[0]) == 0 or isinstance(t[0][0], str) + else: + return False + else: + return False + + if text_pair is not None: + # in case text + text_pair are provided, text = questions, text_pair = words + if not _is_valid_text_input(text): + raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ") + if not isinstance(text_pair, (list, tuple)): + raise ValueError( + "words must of type `List[str]` (single pretokenized example), " + "or `List[List[str]]` (batch of pretokenized examples)." + ) + else: + # in case only text is provided => must be words + if not isinstance(text, (list, tuple)): + raise ValueError( + "Words must of type `List[str]` (single pretokenized example), " + "or `List[List[str]]` (batch of pretokenized examples)." + ) + + if text_pair is not None: + is_batched = isinstance(text, (list, tuple)) + else: + is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple)) + + words = text if text_pair is None else text_pair + if boxes is None: + raise ValueError("You must provide corresponding bounding boxes") + if is_batched: + if len(words) != len(boxes): + raise ValueError("You must provide words and boxes for an equal amount of examples") + for words_example, boxes_example in zip(words, boxes): + if len(words_example) != len(boxes_example): + raise ValueError("You must provide as many words as there are bounding boxes") + else: + if len(words) != len(boxes): + raise ValueError("You must provide as many words as there are bounding boxes") + + if is_batched: + if text_pair is not None and len(text) != len(text_pair): + raise ValueError( + f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:" + f" {len(text_pair)}." + ) + batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text + is_pair = bool(text_pair is not None) + return self.batch_encode_plus_boxes( + batch_text_or_text_pairs=batch_text_or_text_pairs, + is_pair=is_pair, + boxes=boxes, + word_labels=word_labels, + add_special_tokens=add_special_tokens, + padding=padding, + truncation=truncation, + max_length=max_length, + stride=stride, + pad_to_multiple_of=pad_to_multiple_of, + return_tensors=return_tensors, + return_token_type_ids=return_token_type_ids, + return_attention_mask=return_attention_mask, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_offsets_mapping=return_offsets_mapping, + return_length=return_length, + verbose=verbose, + **kwargs, + ) + else: + return self.encode_plus_boxes( + text=text, + text_pair=text_pair, + boxes=boxes, + word_labels=word_labels, + add_special_tokens=add_special_tokens, + padding=padding, + truncation=truncation, + max_length=max_length, + stride=stride, + pad_to_multiple_of=pad_to_multiple_of, + return_tensors=return_tensors, + return_token_type_ids=return_token_type_ids, + return_attention_mask=return_attention_mask, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_offsets_mapping=return_offsets_mapping, + return_length=return_length, + verbose=verbose, + **kwargs, + ) + + def batch_encode_plus_boxes( + self, + batch_text_or_text_pairs: Union[ + List[TextInput], + List[TextInputPair], + List[PreTokenizedInput], + ], + is_pair: bool = None, + boxes: Optional[List[List[List[int]]]] = None, + word_labels: Optional[List[List[int]]] = None, + add_special_tokens: bool = True, + padding: Union[bool, str, PaddingStrategy] = False, + truncation: Union[bool, str, TruncationStrategy] = None, + max_length: Optional[int] = None, + stride: int = 0, + is_split_into_words: bool = False, + pad_to_multiple_of: Optional[int] = None, + return_tensors: Optional[Union[str, TensorType]] = None, + return_token_type_ids: Optional[bool] = None, + return_attention_mask: Optional[bool] = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_offsets_mapping: bool = False, + return_length: bool = False, + verbose: bool = True, + **kwargs, + ) -> BatchEncoding: + """ + Tokenize and prepare for the model a list of sequences or a list of pairs of sequences. + + Args: + batch_text_or_text_pairs (`List[str]`, `List[Tuple[str, str]]`, `List[List[str]]`, `List[Tuple[List[str], List[str]]]`, and for not-fast tokenizers, also `List[List[int]]`, `List[Tuple[List[int], List[int]]]`): + Batch of sequences or pair of sequences to be encoded. This can be a list of + string/string-sequences/int-sequences or a list of pair of string/string-sequences/int-sequence (see + details in `encode_plus`). + """ + + # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' + padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( + padding=padding, + truncation=truncation, + max_length=max_length, + pad_to_multiple_of=pad_to_multiple_of, + verbose=verbose, + **kwargs, + ) + + return self._batch_encode_plus_boxes( + batch_text_or_text_pairs=batch_text_or_text_pairs, + is_pair=is_pair, + boxes=boxes, + word_labels=word_labels, + add_special_tokens=add_special_tokens, + padding_strategy=padding_strategy, + truncation_strategy=truncation_strategy, + max_length=max_length, + stride=stride, + is_split_into_words=is_split_into_words, + pad_to_multiple_of=pad_to_multiple_of, + return_tensors=return_tensors, + return_token_type_ids=return_token_type_ids, + return_attention_mask=return_attention_mask, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_offsets_mapping=return_offsets_mapping, + return_length=return_length, + verbose=verbose, + **kwargs, + ) + + def encode_boxes( + self, + text: Union[TextInput, PreTokenizedInput, EncodedInput], + text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None, + boxes: Optional[List[List[int]]] = None, + word_labels: Optional[List[List[int]]] = None, + add_special_tokens: bool = True, + padding: Union[bool, str, PaddingStrategy] = False, + truncation: Union[bool, str, TruncationStrategy] = None, + max_length: Optional[int] = None, + stride: int = 0, + return_tensors: Optional[Union[str, TensorType]] = None, + **kwargs, + ) -> List[int]: + """ + Args: + Converts a string to a sequence of ids (integer), using the tokenizer and vocabulary. Same as doing + `self.convert_tokens_to_ids(self.tokenize(text))`. + text (`str`, `List[str]` or `List[int]`): + The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the + `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids` + method). + text_pair (`str`, `List[str]` or `List[int]`, *optional*): + Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using + the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids` + method). + """ + encoded_inputs = self.encode_plus_boxes( + text, + text_pair=text_pair, + boxes=boxes, + word_labels=word_labels, + add_special_tokens=add_special_tokens, + padding=padding, + truncation=truncation, + max_length=max_length, + stride=stride, + return_tensors=return_tensors, + **kwargs, + ) + + return encoded_inputs["input_ids"] + + def encode_plus_boxes( + self, + text: Union[TextInput, PreTokenizedInput], + text_pair: Optional[PreTokenizedInput] = None, + boxes: Optional[List[List[int]]] = None, + word_labels: Optional[List[List[int]]] = None, + add_special_tokens: bool = True, + padding: Union[bool, str, PaddingStrategy] = False, + truncation: Union[bool, str, TruncationStrategy] = None, + max_length: Optional[int] = None, + stride: int = 0, + is_split_into_words: bool = False, + pad_to_multiple_of: Optional[int] = None, + return_tensors: Optional[Union[str, TensorType]] = None, + return_token_type_ids: Optional[bool] = None, + return_attention_mask: Optional[bool] = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_offsets_mapping: bool = False, + return_length: bool = False, + verbose: bool = True, + **kwargs, + ) -> BatchEncoding: + """ + Tokenize and prepare for the model a sequence or a pair of sequences. + + + + This method is deprecated, `__call__` should be used instead. + + + + Args: + text (`str`, `List[str]` or `List[int]` (the latter only for not-fast tokenizers)): + The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the + `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids` + method). + text_pair (`str`, `List[str]` or `List[int]`, *optional*): + Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using + the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids` + method). + """ + + # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' + padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( + padding=padding, + truncation=truncation, + max_length=max_length, + pad_to_multiple_of=pad_to_multiple_of, + verbose=verbose, + **kwargs, + ) + + return self._encode_plus_boxes( + text=text, + text_pair=text_pair, + boxes=boxes, + word_labels=word_labels, + add_special_tokens=add_special_tokens, + padding_strategy=padding_strategy, + truncation_strategy=truncation_strategy, + max_length=max_length, + stride=stride, + is_split_into_words=is_split_into_words, + pad_to_multiple_of=pad_to_multiple_of, + return_tensors=return_tensors, + return_token_type_ids=return_token_type_ids, + return_attention_mask=return_attention_mask, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_offsets_mapping=return_offsets_mapping, + return_length=return_length, + verbose=verbose, + **kwargs, + ) + + def _batch_encode_plus_boxes( + self, + batch_text_or_text_pairs: Union[ + List[TextInput], + List[TextInputPair], + List[PreTokenizedInput], + ], + is_pair: bool = None, + boxes: Optional[List[List[List[int]]]] = None, + word_labels: Optional[List[List[int]]] = None, + add_special_tokens: bool = True, + padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, + truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, + max_length: Optional[int] = None, + stride: int = 0, + pad_to_multiple_of: Optional[int] = None, + return_tensors: Optional[Union[str, TensorType]] = None, + return_token_type_ids: Optional[bool] = None, + return_attention_mask: Optional[bool] = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_offsets_mapping: bool = False, + return_length: bool = False, + verbose: bool = True, + **kwargs, + ) -> BatchEncoding: + if return_offsets_mapping: + raise NotImplementedError( + "return_offset_mapping is not available when using Python tokenizers. " + "To use this feature, change your tokenizer to one deriving from " + "transformers.PreTrainedTokenizerFast." + ) + + batch_outputs = self._batch_prepare_for_model_boxes( + batch_text_or_text_pairs=batch_text_or_text_pairs, + is_pair=is_pair, + boxes=boxes, + word_labels=word_labels, + add_special_tokens=add_special_tokens, + padding_strategy=padding_strategy, + truncation_strategy=truncation_strategy, + max_length=max_length, + stride=stride, + pad_to_multiple_of=pad_to_multiple_of, + return_attention_mask=return_attention_mask, + return_token_type_ids=return_token_type_ids, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_length=return_length, + return_tensors=return_tensors, + verbose=verbose, + ) + + return BatchEncoding(batch_outputs) + + @add_end_docstrings(UDOP_ENCODE_KWARGS_DOCSTRING) + def _batch_prepare_for_model_boxes( + self, + batch_text_or_text_pairs, + is_pair: bool = None, + boxes: Optional[List[List[int]]] = None, + word_labels: Optional[List[List[int]]] = None, + add_special_tokens: bool = True, + padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, + truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, + max_length: Optional[int] = None, + stride: int = 0, + pad_to_multiple_of: Optional[int] = None, + return_tensors: Optional[str] = None, + return_token_type_ids: Optional[bool] = None, + return_attention_mask: Optional[bool] = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_length: bool = False, + verbose: bool = True, + ) -> BatchEncoding: + """ + Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It + adds special tokens, truncates sequences if overflowing while taking into account the special tokens and + manages a moving window (with user defined stride) for overflowing tokens + + Args: + batch_ids_pairs: list of tokenized input ids or input ids pairs + """ + + batch_outputs = {} + for idx, example in enumerate(zip(batch_text_or_text_pairs, boxes)): + batch_text_or_text_pair, boxes_example = example + outputs = self.prepare_for_model_boxes( + batch_text_or_text_pair[0] if is_pair else batch_text_or_text_pair, + batch_text_or_text_pair[1] if is_pair else None, + boxes_example, + word_labels=word_labels[idx] if word_labels is not None else None, + add_special_tokens=add_special_tokens, + padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward + truncation=truncation_strategy.value, + max_length=max_length, + stride=stride, + pad_to_multiple_of=None, # we pad in batch afterward + return_attention_mask=False, # we pad in batch afterward + return_token_type_ids=return_token_type_ids, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_length=return_length, + return_tensors=None, # We convert the whole batch to tensors at the end + prepend_batch_axis=False, + verbose=verbose, + ) + + for key, value in outputs.items(): + if key not in batch_outputs: + batch_outputs[key] = [] + batch_outputs[key].append(value) + + batch_outputs = self.pad( + batch_outputs, + padding=padding_strategy.value, + max_length=max_length, + pad_to_multiple_of=pad_to_multiple_of, + return_attention_mask=return_attention_mask, + ) + + batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors) + + return batch_outputs + + def _encode_plus_boxes( + self, + text: Union[TextInput, PreTokenizedInput], + text_pair: Optional[PreTokenizedInput] = None, + boxes: Optional[List[List[int]]] = None, + word_labels: Optional[List[int]] = None, + add_special_tokens: bool = True, + padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, + truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, + max_length: Optional[int] = None, + stride: int = 0, + pad_to_multiple_of: Optional[int] = None, + return_tensors: Optional[Union[str, TensorType]] = None, + return_token_type_ids: Optional[bool] = None, + return_attention_mask: Optional[bool] = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_offsets_mapping: bool = False, + return_length: bool = False, + verbose: bool = True, + **kwargs, + ) -> BatchEncoding: + if return_offsets_mapping: + raise NotImplementedError( + "return_offset_mapping is not available when using Python tokenizers. " + "To use this feature, change your tokenizer to one deriving from " + "transformers.PreTrainedTokenizerFast. " + "More information on available tokenizers at " + "https://github.com/huggingface/transformers/pull/2674" + ) + + return self.prepare_for_model_boxes( + text=text, + text_pair=text_pair, + boxes=boxes, + word_labels=word_labels, + add_special_tokens=add_special_tokens, + padding=padding_strategy.value, + truncation=truncation_strategy.value, + max_length=max_length, + stride=stride, + pad_to_multiple_of=pad_to_multiple_of, + return_tensors=return_tensors, + prepend_batch_axis=True, + return_attention_mask=return_attention_mask, + return_token_type_ids=return_token_type_ids, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_length=return_length, + verbose=verbose, + ) + + @add_end_docstrings(UDOP_ENCODE_KWARGS_DOCSTRING) + def prepare_for_model_boxes( + self, + text: Union[TextInput, PreTokenizedInput], + text_pair: Optional[PreTokenizedInput] = None, + boxes: Optional[List[List[int]]] = None, + word_labels: Optional[List[int]] = None, + add_special_tokens: bool = True, + padding: Union[bool, str, PaddingStrategy] = False, + truncation: Union[bool, str, TruncationStrategy] = None, + max_length: Optional[int] = None, + stride: int = 0, + pad_to_multiple_of: Optional[int] = None, + return_tensors: Optional[Union[str, TensorType]] = None, + return_token_type_ids: Optional[bool] = None, + return_attention_mask: Optional[bool] = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_offsets_mapping: bool = False, + return_length: bool = False, + verbose: bool = True, + prepend_batch_axis: bool = False, + **kwargs, + ) -> BatchEncoding: + """ + Prepares a sequence or a pair of sequences so that it can be used by the model. It adds special tokens, + truncates sequences if overflowing while taking into account the special tokens and manages a moving window + (with user defined stride) for overflowing tokens. + + Word-level `boxes` are turned into token-level `bbox`. If provided, word-level `word_labels` are turned into + token-level `labels`. The word label is used for the first token of the word, while remaining tokens are + labeled with -100, such that they will be ignored by the loss function. + + Args: + text (`str`, `List[str]`, `List[List[str]]`): + The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings. + text_pair (`List[str]` or `List[int]`, *optional*): + Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a + list of list of strings (words of a batch of examples). + """ + + # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' + padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( + padding=padding, + truncation=truncation, + max_length=max_length, + pad_to_multiple_of=pad_to_multiple_of, + verbose=verbose, + **kwargs, + ) + + tokens = [] + pair_tokens = [] + token_boxes = [] + pair_token_boxes = [] + labels = [] + + if text_pair is None: + if word_labels is None: + # CASE 1: document image classification (training + inference) + CASE 2: token classification (inference) + for word, box in zip(text, boxes): + if len(word) < 1: # skip empty words + continue + word_tokens = self.tokenize(word) + tokens.extend(word_tokens) + token_boxes.extend([box] * len(word_tokens)) + else: + # CASE 2: token classification (training) + for word, box, label in zip(text, boxes, word_labels): + if len(word) < 1: # skip empty words + continue + word_tokens = self.tokenize(word) + tokens.extend(word_tokens) + token_boxes.extend([box] * len(word_tokens)) + if self.only_label_first_subword: + # Use the real label id for the first token of the word, and padding ids for the remaining tokens + labels.extend([label] + [self.pad_token_label] * (len(word_tokens) - 1)) + else: + labels.extend([label] * len(word_tokens)) + else: + # CASE 3: document visual question answering (inference) + # text = question + # text_pair = words + tokens = self.tokenize(text) + token_boxes = [self.pad_token_box for _ in range(len(tokens))] + + for word, box in zip(text_pair, boxes): + if len(word) < 1: # skip empty words + continue + word_tokens = self.tokenize(word) + pair_tokens.extend(word_tokens) + pair_token_boxes.extend([box] * len(word_tokens)) + + # Create ids + pair_ids + ids = self.convert_tokens_to_ids(tokens) + pair_ids = self.convert_tokens_to_ids(pair_tokens) if pair_tokens else None + + # Compute the total size of the returned encodings + pair = bool(pair_ids is not None) + len_ids = len(ids) + len_pair_ids = len(pair_ids) if pair else 0 + total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0) + + # Truncation: Handle max sequence length + overflowing_tokens = [] + overflowing_token_boxes = [] + overflowing_labels = [] + if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length: + ( + ids, + token_boxes, + pair_ids, + pair_token_boxes, + labels, + overflowing_tokens, + overflowing_token_boxes, + overflowing_labels, + ) = self.truncate_sequences( + ids, + token_boxes, + pair_ids=pair_ids, + pair_token_boxes=pair_token_boxes, + labels=labels, + num_tokens_to_remove=total_len - max_length, + truncation_strategy=truncation_strategy, + stride=stride, + ) + + if return_token_type_ids and not add_special_tokens: + raise ValueError( + "Asking to return token_type_ids while setting add_special_tokens to False " + "results in an undefined behavior. Please set add_special_tokens to True or " + "set return_token_type_ids to None." + ) + + # Load from model defaults + if return_token_type_ids is None: + return_token_type_ids = "token_type_ids" in self.model_input_names + if return_attention_mask is None: + return_attention_mask = "attention_mask" in self.model_input_names + + encoded_inputs = {} + + if return_overflowing_tokens: + encoded_inputs["overflowing_tokens"] = overflowing_tokens + encoded_inputs["overflowing_token_boxes"] = overflowing_token_boxes + encoded_inputs["overflowing_labels"] = overflowing_labels + encoded_inputs["num_truncated_tokens"] = total_len - max_length + + # Add special tokens + if add_special_tokens: + sequence = self.build_inputs_with_special_tokens(ids, pair_ids) + token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids) + token_boxes = token_boxes + [self.sep_token_box] + if pair_token_boxes: + pair_token_boxes = pair_token_boxes + [self.sep_token_box] + if labels: + labels = labels + [self.pad_token_label] + else: + sequence = ids + pair_ids if pair else ids + token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else []) + + # Build output dictionary + encoded_inputs["input_ids"] = sequence + encoded_inputs["bbox"] = token_boxes + pair_token_boxes + if return_token_type_ids: + encoded_inputs["token_type_ids"] = token_type_ids + if return_special_tokens_mask: + if add_special_tokens: + encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids) + else: + encoded_inputs["special_tokens_mask"] = [0] * len(sequence) + + if labels: + encoded_inputs["labels"] = labels + + # Check lengths + self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose) + + # Padding + if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask: + encoded_inputs = self.pad( + encoded_inputs, + max_length=max_length, + padding=padding_strategy.value, + pad_to_multiple_of=pad_to_multiple_of, + return_attention_mask=return_attention_mask, + ) + + if return_length: + encoded_inputs["length"] = len(encoded_inputs["input_ids"]) + + batch_outputs = BatchEncoding( + encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis + ) + + return batch_outputs + + # Copied from transformers.models.layoutxlm.tokenization_layoutxlm.LayoutXLMTokenizer.truncate_sequences + def truncate_sequences( + self, + ids: List[int], + token_boxes: List[List[int]], + pair_ids: Optional[List[int]] = None, + pair_token_boxes: Optional[List[List[int]]] = None, + labels: Optional[List[int]] = None, + num_tokens_to_remove: int = 0, + truncation_strategy: Union[str, TruncationStrategy] = "longest_first", + stride: int = 0, + ) -> Tuple[List[int], List[int], List[int]]: + """ + Truncates a sequence pair in-place following the strategy. + + Args: + ids (`List[int]`): + Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and + `convert_tokens_to_ids` methods. + token_boxes (`List[List[int]]`): + Bounding boxes of the first sequence. + pair_ids (`List[int]`, *optional*): + Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize` + and `convert_tokens_to_ids` methods. + pair_token_boxes (`List[List[int]]`, *optional*): + Bounding boxes of the second sequence. + labels (`List[int]`, *optional*): + Labels of the first sequence (for token classification tasks). + num_tokens_to_remove (`int`, *optional*, defaults to 0): + Number of tokens to remove using the truncation strategy. + truncation_strategy (`str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`): + The strategy to follow for truncation. Can be: + + - `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the + maximum acceptable input length for the model if that argument is not provided. This will truncate + token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a + batch of pairs) is provided. + - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the + maximum acceptable input length for the model if that argument is not provided. This will only + truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided. + - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the + maximum acceptable input length for the model if that argument is not provided. This will only + truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided. + - `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater + than the model maximum admissible input size). + stride (`int`, *optional*, defaults to 0): + If set to a positive number, the overflowing tokens returned will contain some tokens from the main + sequence returned. The value of this argument defines the number of additional tokens. + + Returns: + `Tuple[List[int], List[int], List[int]]`: The truncated `ids`, the truncated `pair_ids` and the list of + overflowing tokens. + """ + if num_tokens_to_remove <= 0: + return ids, token_boxes, pair_ids, pair_token_boxes, labels, [], [], [] + + if not isinstance(truncation_strategy, TruncationStrategy): + truncation_strategy = TruncationStrategy(truncation_strategy) + + overflowing_tokens = [] + overflowing_token_boxes = [] + overflowing_labels = [] + if truncation_strategy == TruncationStrategy.LONGEST_FIRST: + for _ in range(num_tokens_to_remove): + if pair_ids is None or len(ids) > len(pair_ids): + if not overflowing_tokens: + window_len = min(len(ids), stride + 1) + else: + window_len = 1 + overflowing_tokens.extend(ids[-window_len:]) + overflowing_token_boxes.extend(token_boxes[-window_len:]) + overflowing_labels.extend(labels[-window_len:]) + ids = ids[:-1] + token_boxes = token_boxes[:-1] + labels = labels[:-1] + else: + if not overflowing_tokens: + window_len = min(len(pair_ids), stride + 1) + else: + window_len = 1 + overflowing_tokens.extend(pair_ids[-window_len:]) + overflowing_token_boxes.extend(pair_token_boxes[-window_len:]) + pair_ids = pair_ids[:-1] + pair_token_boxes = pair_token_boxes[:-1] + elif truncation_strategy == TruncationStrategy.ONLY_FIRST: + if len(ids) > num_tokens_to_remove: + window_len = min(len(ids), stride + num_tokens_to_remove) + overflowing_tokens = ids[-window_len:] + overflowing_token_boxes = token_boxes[-window_len:] + overflowing_labels = labels[-window_len:] + ids = ids[:-num_tokens_to_remove] + token_boxes = token_boxes[:-num_tokens_to_remove] + labels = labels[:-num_tokens_to_remove] + else: + logger.error( + f"We need to remove {num_tokens_to_remove} to truncate the input " + f"but the first sequence has a length {len(ids)}. " + f"Please select another truncation strategy than {truncation_strategy}, " + "for instance 'longest_first' or 'only_second'." + ) + elif truncation_strategy == TruncationStrategy.ONLY_SECOND and pair_ids is not None: + if len(pair_ids) > num_tokens_to_remove: + window_len = min(len(pair_ids), stride + num_tokens_to_remove) + overflowing_tokens = pair_ids[-window_len:] + overflowing_token_boxes = pair_token_boxes[-window_len:] + pair_ids = pair_ids[:-num_tokens_to_remove] + pair_token_boxes = pair_token_boxes[:-num_tokens_to_remove] + else: + logger.error( + f"We need to remove {num_tokens_to_remove} to truncate the input " + f"but the second sequence has a length {len(pair_ids)}. " + f"Please select another truncation strategy than {truncation_strategy}, " + "for instance 'longest_first' or 'only_first'." + ) + + return ( + ids, + token_boxes, + pair_ids, + pair_token_boxes, + labels, + overflowing_tokens, + overflowing_token_boxes, + overflowing_labels, + ) + + # Copied from transformers.models.layoutxlm.tokenization_layoutxlm.LayoutXLMTokenizer._pad + def _pad( + self, + encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], + max_length: Optional[int] = None, + padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, + pad_to_multiple_of: Optional[int] = None, + return_attention_mask: Optional[bool] = None, + ) -> dict: + """ + Pad encoded inputs (on left/right and up to predefined length or max length in the batch) + + Args: + encoded_inputs: + Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). + max_length: maximum length of the returned list and optionally padding length (see below). + Will truncate by taking into account the special tokens. + padding_strategy: PaddingStrategy to use for padding. + + - PaddingStrategy.LONGEST Pad to the longest sequence in the batch + - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) + - PaddingStrategy.DO_NOT_PAD: Do not pad + The tokenizer padding sides are defined in self.padding_side: + + - 'left': pads on the left of the sequences + - 'right': pads on the right of the sequences + pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. + This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability + `>= 7.5` (Volta). + return_attention_mask: + (optional) Set to False to avoid returning attention mask (default: set to model specifics) + """ + # Load from model defaults + if return_attention_mask is None: + return_attention_mask = "attention_mask" in self.model_input_names + + required_input = encoded_inputs[self.model_input_names[0]] + + if padding_strategy == PaddingStrategy.LONGEST: + max_length = len(required_input) + + if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): + max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of + + needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length + + # Initialize attention mask if not present. + if return_attention_mask and "attention_mask" not in encoded_inputs: + encoded_inputs["attention_mask"] = [1] * len(required_input) + + if needs_to_be_padded: + difference = max_length - len(required_input) + if self.padding_side == "right": + if return_attention_mask: + encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference + if "token_type_ids" in encoded_inputs: + encoded_inputs["token_type_ids"] = ( + encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference + ) + if "bbox" in encoded_inputs: + encoded_inputs["bbox"] = encoded_inputs["bbox"] + [self.pad_token_box] * difference + if "labels" in encoded_inputs: + encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference + if "special_tokens_mask" in encoded_inputs: + encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference + encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference + elif self.padding_side == "left": + if return_attention_mask: + encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"] + if "token_type_ids" in encoded_inputs: + encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[ + "token_type_ids" + ] + if "bbox" in encoded_inputs: + encoded_inputs["bbox"] = [self.pad_token_box] * difference + encoded_inputs["bbox"] + if "labels" in encoded_inputs: + encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"] + if "special_tokens_mask" in encoded_inputs: + encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"] + encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input + else: + raise ValueError("Invalid padding strategy:" + str(self.padding_side)) + + return encoded_inputs diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/udop/tokenization_udop_fast.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/udop/tokenization_udop_fast.py new file mode 100644 index 0000000000000000000000000000000000000000..cce527a80537d9ed5c509e23d63e50deaea85001 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/udop/tokenization_udop_fast.py @@ -0,0 +1,1017 @@ +# coding=utf-8 +# Copyright 2024 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License +""" Tokenization classes for UDOP model.""" + + +import os +from shutil import copyfile +from typing import Dict, List, Optional, Tuple, Union + +from ...tokenization_utils_base import ( + BatchEncoding, + EncodedInput, + PreTokenizedInput, + TextInput, + TextInputPair, + TruncationStrategy, +) +from ...tokenization_utils_fast import PreTrainedTokenizerFast +from ...utils import PaddingStrategy, TensorType, add_end_docstrings, is_sentencepiece_available, logging + + +if is_sentencepiece_available(): + from .tokenization_udop import UdopTokenizer +else: + UdopTokenizer = None + + +VOCAB_FILES_NAMES = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"} + +PRETRAINED_VOCAB_FILES_MAP = { + "vocab_file": { + "microsoft/udop-large": "https://huggingface.co/microsoft/udop-large/resolve/main/spiece.model", + }, + "tokenizer_file": { + "microsoft/udop-large": "https://huggingface.co/microsoft/udop-large/resolve/main/tokenizer.json", + }, +} + +logger = logging.get_logger(__name__) + +UDOP_ENCODE_KWARGS_DOCSTRING = r""" + add_special_tokens (`bool`, *optional*, defaults to `True`): + Whether or not to encode the sequences with the special tokens relative to their model. + padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`): + Activates and controls padding. Accepts the following values: + + - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single + sequence if provided). + - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum + acceptable input length for the model if that argument is not provided. + - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different + lengths). + truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`): + Activates and controls truncation. Accepts the following values: + + - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or + to the maximum acceptable input length for the model if that argument is not provided. This will + truncate token by token, removing a token from the longest sequence in the pair if a pair of + sequences (or a batch of pairs) is provided. + - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the + maximum acceptable input length for the model if that argument is not provided. This will only + truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided. + - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the + maximum acceptable input length for the model if that argument is not provided. This will only + truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided. + - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths + greater than the model maximum admissible input size). + max_length (`int`, *optional*): + Controls the maximum length to use by one of the truncation/padding parameters. + + If left unset or set to `None`, this will use the predefined model maximum length if a maximum length + is required by one of the truncation/padding parameters. If the model has no specific maximum input + length (like XLNet) truncation/padding to a maximum length will be deactivated. + stride (`int`, *optional*, defaults to 0): + If set to a number along with `max_length`, the overflowing tokens returned when + `return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence + returned to provide some overlap between truncated and overflowing sequences. The value of this + argument defines the number of overlapping tokens. + pad_to_multiple_of (`int`, *optional*): + If set will pad the sequence to a multiple of the provided value. This is especially useful to enable + the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta). + return_tensors (`str` or [`~file_utils.TensorType`], *optional*): + If set, will return tensors instead of list of python integers. Acceptable values are: + + - `'tf'`: Return TensorFlow `tf.constant` objects. + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return Numpy `np.ndarray` objects. + return_token_type_ids (`bool`, *optional*): + Whether to return token type IDs. If left to the default, will return the token type IDs according to + the specific tokenizer's default, defined by the `return_outputs` attribute. + + [What are token type IDs?](../glossary#token-type-ids) + return_attention_mask (`bool`, *optional*): + Whether to return the attention mask. If left to the default, will return the attention mask according + to the specific tokenizer's default, defined by the `return_outputs` attribute. + + [What are attention masks?](../glossary#attention-mask) + return_overflowing_tokens (`bool`, *optional*, defaults to `False`): + Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch + of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead + of returning overflowing tokens. + return_special_tokens_mask (`bool`, *optional*, defaults to `False`): + Whether or not to return special tokens mask information. + return_offsets_mapping (`bool`, *optional*, defaults to `False`): + Whether or not to return `(char_start, char_end)` for each token. + + This is only available on fast tokenizers inheriting from [`PreTrainedTokenizerFast`], if using + Python's tokenizer, this method will raise `NotImplementedError`. + return_length (`bool`, *optional*, defaults to `False`): + Whether or not to return the lengths of the encoded inputs. + verbose (`bool`, *optional*, defaults to `True`): + Whether or not to print more information and warnings. + **kwargs: passed to the `self.tokenize()` method + + Return: + [`BatchEncoding`]: A [`BatchEncoding`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. + + [What are input IDs?](../glossary#input-ids) + + - **bbox** -- List of bounding boxes to be fed to a model. + + - **token_type_ids** -- List of token type ids to be fed to a model (when `return_token_type_ids=True` or + if *"token_type_ids"* is in `self.model_input_names`). + + [What are token type IDs?](../glossary#token-type-ids) + + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`). + + [What are attention masks?](../glossary#attention-mask) + + - **labels** -- List of labels to be fed to a model. (when `word_labels` is specified). + - **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and + `return_overflowing_tokens=True`). + - **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and + `return_overflowing_tokens=True`). + - **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying + regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`). + - **length** -- The length of the inputs (when `return_length=True`). +""" + + +class UdopTokenizerFast(PreTrainedTokenizerFast): + """ + Construct a "fast" UDOP tokenizer (backed by HuggingFace's *tokenizers* library). Adapted from + [`LayoutXLMTokenizer`] and [`T5Tokenizer`]. Based on + [BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models). + + This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should + refer to this superclass for more information regarding those methods. + + Args: + vocab_file (`str`, *optional*): + Path to the vocabulary file. + + tokenizer_file (`str`, *optional*): + Path to the tokenizer file. + eos_token (`str`, *optional*, defaults to `""`): + The end of sequence token. + + + + When building a sequence using special tokens, this is not the token that is used for the end of sequence. + The token used is the `sep_token`. + + + + sep_token (`str`, *optional*, defaults to `""`): + The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for + sequence classification or for a text and a question for question answering. It is also used as the last + token of a sequence built with special tokens. + unk_token (`str`, *optional*, defaults to `""`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + pad_token (`str`, *optional*, defaults to `""`): + The token used for padding, for example when batching sequences of different lengths. + sep_token_box (`List[int]`, *optional*, defaults to `[1000, 1000, 1000, 1000]`): + The bounding box to use for the special [SEP] token. + pad_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`): + The bounding box to use for the special [PAD] token. + pad_token_label (`int`, *optional*, defaults to -100): + The label to use for padding tokens. Defaults to -100, which is the `ignore_index` of PyTorch's + CrossEntropyLoss. + only_label_first_subword (`bool`, *optional*, defaults to `True`): + Whether or not to only label the first subword, in case word labels are provided. + additional_special_tokens (`List[str]`, *optional*, defaults to `["NOTUSED", "NOTUSED"]`): + Additional special tokens used by the tokenizer. + """ + + vocab_files_names = VOCAB_FILES_NAMES + pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP + model_input_names = ["input_ids", "attention_mask"] + slow_tokenizer_class = UdopTokenizer + + def __init__( + self, + vocab_file=None, + tokenizer_file=None, + eos_token="", + sep_token="", + unk_token="", + pad_token="", + sep_token_box=[1000, 1000, 1000, 1000], + pad_token_box=[0, 0, 0, 0], + pad_token_label=-100, + only_label_first_subword=True, + additional_special_tokens=None, + **kwargs, + ): + super().__init__( + vocab_file, + tokenizer_file=tokenizer_file, + eos_token=eos_token, + sep_token=sep_token, + unk_token=unk_token, + pad_token=pad_token, + sep_token_box=sep_token_box, + pad_token_box=pad_token_box, + pad_token_label=pad_token_label, + only_label_first_subword=only_label_first_subword, + additional_special_tokens=additional_special_tokens, + **kwargs, + ) + + self.vocab_file = vocab_file + + # additional properties + self.sep_token_box = sep_token_box + self.pad_token_box = pad_token_box + self.pad_token_label = pad_token_label + self.only_label_first_subword = only_label_first_subword + + @property + def can_save_slow_tokenizer(self) -> bool: + return os.path.isfile(self.vocab_file) if self.vocab_file else False + + @add_end_docstrings(UDOP_ENCODE_KWARGS_DOCSTRING) + def __call__( + self, + text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, + text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None, + boxes: Union[List[List[int]], List[List[List[int]]]] = None, + word_labels: Optional[Union[List[int], List[List[int]]]] = None, + text_target: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, + text_pair_target: Optional[ + Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] + ] = None, + **kwargs, + ) -> BatchEncoding: + if text is None and text_target is None: + raise ValueError("You need to specify either `text` or `text_target`.") + if text is not None: + # The context manager will send the inputs as normal texts and not text_target, but we shouldn't change the + # input mode in this case. + if not self._in_target_context_manager: + self._switch_to_input_mode() + encodings = self.call_boxes(text=text, text_pair=text_pair, boxes=boxes, word_labels=word_labels, **kwargs) + if text_target is not None: + self._switch_to_target_mode() + target_encodings = self._call_one(text=text_target, text_pair=text_pair_target, **kwargs) + # Leave back tokenizer in input mode + self._switch_to_input_mode() + + if text_target is None: + return encodings + elif text is None: + return target_encodings + else: + encodings["labels"] = target_encodings["input_ids"] + return encodings + + @add_end_docstrings(UDOP_ENCODE_KWARGS_DOCSTRING) + def call_boxes( + self, + text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]], + text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None, + boxes: Union[List[List[int]], List[List[List[int]]]] = None, + word_labels: Optional[Union[List[int], List[List[int]]]] = None, + add_special_tokens: bool = True, + padding: Union[bool, str, PaddingStrategy] = False, + truncation: Union[bool, str, TruncationStrategy] = None, + max_length: Optional[int] = None, + stride: int = 0, + pad_to_multiple_of: Optional[int] = None, + return_tensors: Optional[Union[str, TensorType]] = None, + return_token_type_ids: Optional[bool] = None, + return_attention_mask: Optional[bool] = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_offsets_mapping: bool = False, + return_length: bool = False, + verbose: bool = True, + **kwargs, + ) -> BatchEncoding: + """ + Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of + sequences with word-level normalized bounding boxes and optional labels. + + Args: + text (`str`, `List[str]`, `List[List[str]]`): + The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings + (words of a single example or questions of a batch of examples) or a list of list of strings (batch of + words). + text_pair (`List[str]`, `List[List[str]]`): + The sequence or batch of sequences to be encoded. Each sequence should be a list of strings + (pretokenized string). + boxes (`List[List[int]]`, `List[List[List[int]]]`): + Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale. + word_labels (`List[int]`, `List[List[int]]`, *optional*): + Word-level integer labels (for token classification tasks such as FUNSD, CORD). + """ + + # Input type checking for clearer error + def _is_valid_text_input(t): + if isinstance(t, str): + # Strings are fine + return True + elif isinstance(t, (list, tuple)): + # List are fine as long as they are... + if len(t) == 0: + # ... empty + return True + elif isinstance(t[0], str): + # ... list of strings + return True + elif isinstance(t[0], (list, tuple)): + # ... list with an empty list or with a list of strings + return len(t[0]) == 0 or isinstance(t[0][0], str) + else: + return False + else: + return False + + if text_pair is not None: + # in case text + text_pair are provided, text = questions, text_pair = words + if not _is_valid_text_input(text): + raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ") + if not isinstance(text_pair, (list, tuple)): + raise ValueError( + "words must of type `List[str]` (single pretokenized example), " + "or `List[List[str]]` (batch of pretokenized examples)." + ) + else: + # in case only text is provided => must be words + if not isinstance(text, (list, tuple)): + raise ValueError( + "Words must of type `List[str]` (single pretokenized example), " + "or `List[List[str]]` (batch of pretokenized examples)." + ) + + if text_pair is not None: + is_batched = isinstance(text, (list, tuple)) + else: + is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple)) + + words = text if text_pair is None else text_pair + if boxes is None: + raise ValueError("You must provide corresponding bounding boxes") + if is_batched: + if len(words) != len(boxes): + raise ValueError("You must provide words and boxes for an equal amount of examples") + for words_example, boxes_example in zip(words, boxes): + if len(words_example) != len(boxes_example): + raise ValueError("You must provide as many words as there are bounding boxes") + else: + if len(words) != len(boxes): + raise ValueError("You must provide as many words as there are bounding boxes") + + if is_batched: + if text_pair is not None and len(text) != len(text_pair): + raise ValueError( + f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:" + f" {len(text_pair)}." + ) + batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text + is_pair = bool(text_pair is not None) + return self.batch_encode_plus_boxes( + batch_text_or_text_pairs=batch_text_or_text_pairs, + is_pair=is_pair, + boxes=boxes, + word_labels=word_labels, + add_special_tokens=add_special_tokens, + padding=padding, + truncation=truncation, + max_length=max_length, + stride=stride, + pad_to_multiple_of=pad_to_multiple_of, + return_tensors=return_tensors, + return_token_type_ids=return_token_type_ids, + return_attention_mask=return_attention_mask, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_offsets_mapping=return_offsets_mapping, + return_length=return_length, + verbose=verbose, + **kwargs, + ) + else: + return self.encode_plus_boxes( + text=text, + text_pair=text_pair, + boxes=boxes, + word_labels=word_labels, + add_special_tokens=add_special_tokens, + padding=padding, + truncation=truncation, + max_length=max_length, + stride=stride, + pad_to_multiple_of=pad_to_multiple_of, + return_tensors=return_tensors, + return_token_type_ids=return_token_type_ids, + return_attention_mask=return_attention_mask, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_offsets_mapping=return_offsets_mapping, + return_length=return_length, + verbose=verbose, + **kwargs, + ) + + # Copied from transformers.models.layoutxlm.tokenization_layoutxlm_fast.LayoutXLMTokenizerFast.tokenize + def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> List[str]: + batched_input = [(text, pair)] if pair else [text] + encodings = self._tokenizer.encode_batch( + batched_input, add_special_tokens=add_special_tokens, is_pretokenized=False, **kwargs + ) + + return encodings[0].tokens + + def batch_encode_plus_boxes( + self, + batch_text_or_text_pairs: Union[ + List[TextInput], + List[TextInputPair], + List[PreTokenizedInput], + ], + is_pair: bool = None, + boxes: Optional[List[List[List[int]]]] = None, + word_labels: Optional[List[List[int]]] = None, + add_special_tokens: bool = True, + padding: Union[bool, str, PaddingStrategy] = False, + truncation: Union[bool, str, TruncationStrategy] = None, + max_length: Optional[int] = None, + stride: int = 0, + is_split_into_words: bool = False, + pad_to_multiple_of: Optional[int] = None, + return_tensors: Optional[Union[str, TensorType]] = None, + return_token_type_ids: Optional[bool] = None, + return_attention_mask: Optional[bool] = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_offsets_mapping: bool = False, + return_length: bool = False, + verbose: bool = True, + **kwargs, + ) -> BatchEncoding: + """ + Tokenize and prepare for the model a list of sequences or a list of pairs of sequences. + + + + This method is deprecated, `__call__` should be used instead. + + + + Args: + batch_text_or_text_pairs (`List[str]`, `List[Tuple[str, str]]`, `List[List[str]]`, `List[Tuple[List[str], List[str]]]`, and for not-fast tokenizers, also `List[List[int]]`, `List[Tuple[List[int], List[int]]]`): + Batch of sequences or pair of sequences to be encoded. This can be a list of + string/string-sequences/int-sequences or a list of pair of string/string-sequences/int-sequence (see + details in `encode_plus`). + """ + + # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' + padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( + padding=padding, + truncation=truncation, + max_length=max_length, + pad_to_multiple_of=pad_to_multiple_of, + verbose=verbose, + **kwargs, + ) + + return self._batch_encode_plus_boxes( + batch_text_or_text_pairs=batch_text_or_text_pairs, + is_pair=is_pair, + boxes=boxes, + word_labels=word_labels, + add_special_tokens=add_special_tokens, + padding_strategy=padding_strategy, + truncation_strategy=truncation_strategy, + max_length=max_length, + stride=stride, + is_split_into_words=is_split_into_words, + pad_to_multiple_of=pad_to_multiple_of, + return_tensors=return_tensors, + return_token_type_ids=return_token_type_ids, + return_attention_mask=return_attention_mask, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_offsets_mapping=return_offsets_mapping, + return_length=return_length, + verbose=verbose, + **kwargs, + ) + + def _batch_encode_plus_boxes( + self, + batch_text_or_text_pairs: Union[ + List[TextInput], + List[TextInputPair], + List[PreTokenizedInput], + ], + is_pair: bool = None, + boxes: Optional[List[List[List[int]]]] = None, + word_labels: Optional[List[List[int]]] = None, + add_special_tokens: bool = True, + padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, + truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, + max_length: Optional[int] = None, + stride: int = 0, + pad_to_multiple_of: Optional[int] = None, + return_tensors: Optional[str] = None, + return_token_type_ids: Optional[bool] = None, + return_attention_mask: Optional[bool] = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_offsets_mapping: bool = False, + return_length: bool = False, + verbose: bool = True, + **kwargs, + ) -> BatchEncoding: + if not isinstance(batch_text_or_text_pairs, list): + raise TypeError(f"batch_text_or_text_pairs has to be a list (got {type(batch_text_or_text_pairs)})") + + # Set the truncation and padding strategy and restore the initial configuration + self.set_truncation_and_padding( + padding_strategy=padding_strategy, + truncation_strategy=truncation_strategy, + max_length=max_length, + stride=stride, + pad_to_multiple_of=pad_to_multiple_of, + ) + + if is_pair: + batch_text_or_text_pairs = [(text.split(), text_pair) for text, text_pair in batch_text_or_text_pairs] + + encodings = self._tokenizer.encode_batch( + batch_text_or_text_pairs, + add_special_tokens=add_special_tokens, + is_pretokenized=True, # we set this to True as LayoutLMv2 always expects pretokenized inputs + ) + + # Convert encoding to dict + # `Tokens` has type: Tuple[ + # List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]], + # List[EncodingFast] + # ] + # with nested dimensions corresponding to batch, overflows, sequence length + tokens_and_encodings = [ + self._convert_encoding( + encoding=encoding, + return_token_type_ids=return_token_type_ids, + return_attention_mask=return_attention_mask, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_offsets_mapping=True + if word_labels is not None + else return_offsets_mapping, # we use offsets to create the labels + return_length=return_length, + verbose=verbose, + ) + for encoding in encodings + ] + + # Convert the output to have dict[list] from list[dict] and remove the additional overflows dimension + # From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length) + # (we say ~ because the number of overflow varies with the example in the batch) + # + # To match each overflowing sample with the original sample in the batch + # we add an overflow_to_sample_mapping array (see below) + sanitized_tokens = {} + for key in tokens_and_encodings[0][0].keys(): + stack = [e for item, _ in tokens_and_encodings for e in item[key]] + sanitized_tokens[key] = stack + sanitized_encodings = [e for _, item in tokens_and_encodings for e in item] + + # If returning overflowing tokens, we need to return a mapping + # from the batch idx to the original sample + if return_overflowing_tokens: + overflow_to_sample_mapping = [] + for i, (toks, _) in enumerate(tokens_and_encodings): + overflow_to_sample_mapping += [i] * len(toks["input_ids"]) + sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping + + for input_ids in sanitized_tokens["input_ids"]: + self._eventual_warn_about_too_long_sequence(input_ids, max_length, verbose) + + # create the token boxes + token_boxes = [] + for batch_index in range(len(sanitized_tokens["input_ids"])): + if return_overflowing_tokens: + original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index] + else: + original_index = batch_index + token_boxes_example = [] + for id, sequence_id, word_id in zip( + sanitized_tokens["input_ids"][batch_index], + sanitized_encodings[batch_index].sequence_ids, + sanitized_encodings[batch_index].word_ids, + ): + if word_id is not None: + if is_pair and sequence_id == 0: + token_boxes_example.append(self.pad_token_box) + else: + token_boxes_example.append(boxes[original_index][word_id]) + else: + if id == self.sep_token_id: + token_boxes_example.append(self.sep_token_box) + elif id == self.pad_token_id: + token_boxes_example.append(self.pad_token_box) + else: + raise ValueError("Id not recognized") + token_boxes.append(token_boxes_example) + + sanitized_tokens["bbox"] = token_boxes + + # optionally, create the labels + if word_labels is not None: + labels = [] + for batch_index in range(len(sanitized_tokens["input_ids"])): + if return_overflowing_tokens: + original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index] + else: + original_index = batch_index + labels_example = [] + previous_token_empty = False + for id, offset, word_id in zip( + sanitized_tokens["input_ids"][batch_index], + sanitized_tokens["offset_mapping"][batch_index], + sanitized_encodings[batch_index].word_ids, + ): + if word_id is not None: + if self.only_label_first_subword: + if offset[0] == 0 and not previous_token_empty: + # Use the real label id for the first token of the word, and padding ids for the remaining tokens + labels_example.append(word_labels[original_index][word_id]) + else: + labels_example.append(self.pad_token_label) + else: + labels_example.append(word_labels[original_index][word_id]) + if self.decode(id) == "": + previous_token_empty = True + else: + previous_token_empty = False + else: + labels_example.append(self.pad_token_label) + labels.append(labels_example) + + sanitized_tokens["labels"] = labels + # finally, remove offsets if the user didn't want them + if not return_offsets_mapping: + del sanitized_tokens["offset_mapping"] + + return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors) + + def _encode_plus_boxes( + self, + text: Union[TextInput, PreTokenizedInput], + text_pair: Optional[PreTokenizedInput] = None, + boxes: Optional[List[List[int]]] = None, + word_labels: Optional[List[int]] = None, + add_special_tokens: bool = True, + padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, + truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, + max_length: Optional[int] = None, + stride: int = 0, + pad_to_multiple_of: Optional[int] = None, + return_tensors: Optional[bool] = None, + return_token_type_ids: Optional[bool] = None, + return_attention_mask: Optional[bool] = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_offsets_mapping: bool = False, + return_length: bool = False, + verbose: bool = True, + **kwargs, + ) -> BatchEncoding: + # make it a batched input + # 2 options: + # 1) only text, in case text must be a list of str + # 2) text + text_pair, in which case text = str and text_pair a list of str + batched_input = [(text, text_pair)] if text_pair else [text] + batched_boxes = [boxes] + batched_word_labels = [word_labels] if word_labels is not None else None + batched_output = self._batch_encode_plus_boxes( + batched_input, + is_pair=bool(text_pair is not None), + boxes=batched_boxes, + word_labels=batched_word_labels, + add_special_tokens=add_special_tokens, + padding_strategy=padding_strategy, + truncation_strategy=truncation_strategy, + max_length=max_length, + stride=stride, + pad_to_multiple_of=pad_to_multiple_of, + return_tensors=return_tensors, + return_token_type_ids=return_token_type_ids, + return_attention_mask=return_attention_mask, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_offsets_mapping=return_offsets_mapping, + return_length=return_length, + verbose=verbose, + **kwargs, + ) + + # Return tensor is None, then we can remove the leading batch axis + # Overflowing tokens are returned as a batch of output so we keep them in this case + if return_tensors is None and not return_overflowing_tokens: + batched_output = BatchEncoding( + { + key: value[0] if len(value) > 0 and isinstance(value[0], list) else value + for key, value in batched_output.items() + }, + batched_output.encodings, + ) + + self._eventual_warn_about_too_long_sequence(batched_output["input_ids"], max_length, verbose) + + return batched_output + + def encode_boxes( + self, + text: Union[TextInput, PreTokenizedInput, EncodedInput], + text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None, + boxes: Optional[List[List[int]]] = None, + word_labels: Optional[List[List[int]]] = None, + add_special_tokens: bool = True, + padding: Union[bool, str, PaddingStrategy] = False, + truncation: Union[bool, str, TruncationStrategy] = None, + max_length: Optional[int] = None, + stride: int = 0, + return_tensors: Optional[Union[str, TensorType]] = None, + **kwargs, + ) -> List[int]: + """ + Args: + Converts a string to a sequence of ids (integer), using the tokenizer and vocabulary. Same as doing + `self.convert_tokens_to_ids(self.tokenize(text))`. + text (`str`, `List[str]` or `List[int]`): + The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the + `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids` + method). + text_pair (`str`, `List[str]` or `List[int]`, *optional*): + Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using + the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids` + method). + """ + encoded_inputs = self.encode_plus_boxes( + text, + text_pair=text_pair, + boxes=boxes, + word_labels=word_labels, + add_special_tokens=add_special_tokens, + padding=padding, + truncation=truncation, + max_length=max_length, + stride=stride, + return_tensors=return_tensors, + **kwargs, + ) + + return encoded_inputs["input_ids"] + + def encode_plus_boxes( + self, + text: Union[TextInput, PreTokenizedInput], + text_pair: Optional[PreTokenizedInput] = None, + boxes: Optional[List[List[int]]] = None, + word_labels: Optional[List[List[int]]] = None, + add_special_tokens: bool = True, + padding: Union[bool, str, PaddingStrategy] = False, + truncation: Union[bool, str, TruncationStrategy] = None, + max_length: Optional[int] = None, + stride: int = 0, + is_split_into_words: bool = False, + pad_to_multiple_of: Optional[int] = None, + return_tensors: Optional[Union[str, TensorType]] = None, + return_token_type_ids: Optional[bool] = None, + return_attention_mask: Optional[bool] = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_offsets_mapping: bool = False, + return_length: bool = False, + verbose: bool = True, + **kwargs, + ) -> BatchEncoding: + """ + Tokenize and prepare for the model a sequence or a pair of sequences. + + + + This method is deprecated, `__call__` should be used instead. + + + + Args: + text (`str`, `List[str]` or `List[int]` (the latter only for not-fast tokenizers)): + The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the + `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids` + method). + text_pair (`str`, `List[str]` or `List[int]`, *optional*): + Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using + the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids` + method). + """ + + # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' + padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( + padding=padding, + truncation=truncation, + max_length=max_length, + pad_to_multiple_of=pad_to_multiple_of, + verbose=verbose, + **kwargs, + ) + + return self._encode_plus_boxes( + text=text, + text_pair=text_pair, + boxes=boxes, + word_labels=word_labels, + add_special_tokens=add_special_tokens, + padding_strategy=padding_strategy, + truncation_strategy=truncation_strategy, + max_length=max_length, + stride=stride, + is_split_into_words=is_split_into_words, + pad_to_multiple_of=pad_to_multiple_of, + return_tensors=return_tensors, + return_token_type_ids=return_token_type_ids, + return_attention_mask=return_attention_mask, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_offsets_mapping=return_offsets_mapping, + return_length=return_length, + verbose=verbose, + **kwargs, + ) + + # Copied from transformers.models.layoutxlm.tokenization_layoutxlm_fast.LayoutXLMTokenizerFast._pad + def _pad( + self, + encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], + max_length: Optional[int] = None, + padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, + pad_to_multiple_of: Optional[int] = None, + return_attention_mask: Optional[bool] = None, + ) -> dict: + """ + Pad encoded inputs (on left/right and up to predefined length or max length in the batch) + + Args: + encoded_inputs: + Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). + max_length: maximum length of the returned list and optionally padding length (see below). + Will truncate by taking into account the special tokens. + padding_strategy: PaddingStrategy to use for padding. + + - PaddingStrategy.LONGEST Pad to the longest sequence in the batch + - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) + - PaddingStrategy.DO_NOT_PAD: Do not pad + The tokenizer padding sides are defined in self.padding_side: + + - 'left': pads on the left of the sequences + - 'right': pads on the right of the sequences + pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. + This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability + `>= 7.5` (Volta). + return_attention_mask: + (optional) Set to False to avoid returning attention mask (default: set to model specifics) + """ + # Load from model defaults + if return_attention_mask is None: + return_attention_mask = "attention_mask" in self.model_input_names + + required_input = encoded_inputs[self.model_input_names[0]] + + if padding_strategy == PaddingStrategy.LONGEST: + max_length = len(required_input) + + if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): + max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of + + needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length + + # Initialize attention mask if not present. + if return_attention_mask and "attention_mask" not in encoded_inputs: + encoded_inputs["attention_mask"] = [1] * len(required_input) + + if needs_to_be_padded: + difference = max_length - len(required_input) + if self.padding_side == "right": + if return_attention_mask: + encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference + if "token_type_ids" in encoded_inputs: + encoded_inputs["token_type_ids"] = ( + encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference + ) + if "bbox" in encoded_inputs: + encoded_inputs["bbox"] = encoded_inputs["bbox"] + [self.pad_token_box] * difference + if "labels" in encoded_inputs: + encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference + if "special_tokens_mask" in encoded_inputs: + encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference + encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference + elif self.padding_side == "left": + if return_attention_mask: + encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"] + if "token_type_ids" in encoded_inputs: + encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[ + "token_type_ids" + ] + if "bbox" in encoded_inputs: + encoded_inputs["bbox"] = [self.pad_token_box] * difference + encoded_inputs["bbox"] + if "labels" in encoded_inputs: + encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"] + if "special_tokens_mask" in encoded_inputs: + encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"] + encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input + else: + raise ValueError("Invalid padding strategy:" + str(self.padding_side)) + + return encoded_inputs + + def build_inputs_with_special_tokens( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and + adding special tokens. An XLM-RoBERTa sequence has the following format: + + - single sequence: ` X ` + - pair of sequences: ` A B ` + + Args: + token_ids_0 (`List[int]`): + List of IDs to which the special tokens will be added. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. + """ + + if token_ids_1 is None: + return token_ids_0 + [self.sep_token_id] + sep = [self.sep_token_id] + return token_ids_0 + sep + token_ids_1 + sep + + def create_token_type_ids_from_sequences( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-RoBERTa does + not make use of token type ids, therefore a list of zeros is returned. + + Args: + token_ids_0 (`List[int]`): + List of IDs. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of zeros. + + """ + + sep = [self.sep_token_id] + + if token_ids_1 is None: + return len(token_ids_0 + sep) * [0] + return len(token_ids_0 + sep + token_ids_1 + sep) * [0] + + # Copied from transformers.models.layoutxlm.tokenization_layoutxlm_fast.LayoutXLMTokenizerFast.save_vocabulary + def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: + if not self.can_save_slow_tokenizer: + raise ValueError( + "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow " + "tokenizer." + ) + + if not os.path.isdir(save_directory): + logger.error(f"Vocabulary path ({save_directory}) should be a directory.") + return + out_vocab_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] + ) + + if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file): + copyfile(self.vocab_file, out_vocab_file) + + return (out_vocab_file,) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/umt5/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/umt5/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e68ae4cb3737cf7fce75b980e4fc19e6ba93361d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/umt5/__init__.py @@ -0,0 +1,60 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available + + +_import_structure = {"configuration_umt5": ["UMT5Config", "UMT5OnnxConfig"]} + + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_umt5"] = [ + "UMT5EncoderModel", + "UMT5ForConditionalGeneration", + "UMT5ForQuestionAnswering", + "UMT5ForSequenceClassification", + "UMT5ForTokenClassification", + "UMT5Model", + "UMT5PreTrainedModel", + ] + +if TYPE_CHECKING: + from .configuration_umt5 import UMT5Config, UMT5OnnxConfig + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_umt5 import ( + UMT5EncoderModel, + UMT5ForConditionalGeneration, + UMT5ForQuestionAnswering, + UMT5ForSequenceClassification, + UMT5ForTokenClassification, + UMT5Model, + UMT5PreTrainedModel, + ) +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/umt5/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/umt5/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..81e33315608ac95c86e2483f2c745e2ff554e6c1 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/umt5/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/umt5/__pycache__/configuration_umt5.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/umt5/__pycache__/configuration_umt5.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..048022d255885761e6fbcf353e79900787a59a48 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/umt5/__pycache__/configuration_umt5.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/umt5/__pycache__/convert_umt5_checkpoint_to_pytorch.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/umt5/__pycache__/convert_umt5_checkpoint_to_pytorch.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..081cb5d281d957a642b14b70e49bc6f65244c34b Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/umt5/__pycache__/convert_umt5_checkpoint_to_pytorch.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/umt5/__pycache__/modeling_umt5.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/umt5/__pycache__/modeling_umt5.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4d3bd40f61ea1dcaff75053db331b697acb8fbbd Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/umt5/__pycache__/modeling_umt5.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/unispeech/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/unispeech/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2800fa17076e6ea069eb943c558678e7cf4c61b5 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/unispeech/__init__.py @@ -0,0 +1,63 @@ +# Copyright 2021 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import ( + OptionalDependencyNotAvailable, + _LazyModule, + is_flax_available, + is_tf_available, + is_torch_available, +) + + +_import_structure = {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]} + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_unispeech"] = [ + "UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST", + "UniSpeechForCTC", + "UniSpeechForPreTraining", + "UniSpeechForSequenceClassification", + "UniSpeechModel", + "UniSpeechPreTrainedModel", + ] + +if TYPE_CHECKING: + from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_unispeech import ( + UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST, + UniSpeechForCTC, + UniSpeechForPreTraining, + UniSpeechForSequenceClassification, + UniSpeechModel, + UniSpeechPreTrainedModel, + ) + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git 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a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/unispeech/configuration_unispeech.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/unispeech/configuration_unispeech.py new file mode 100644 index 0000000000000000000000000000000000000000..25a003ae9f5f9a3f9c4716ccbf330b1a827a1db9 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/unispeech/configuration_unispeech.py @@ -0,0 +1,309 @@ +# coding=utf-8 +# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" UniSpeech model configuration""" + +import functools +import operator + +from ...configuration_utils import PretrainedConfig +from ...utils import logging + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class UniSpeechConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`UniSpeechModel`]. It is used to instantiate an + UniSpeech model according to the specified arguments, defining the model architecture. Instantiating a + configuration with the defaults will yield a similar configuration to that of the UniSpeech + [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 32): + Vocabulary size of the UniSpeech model. Defines the number of different tokens that can be represented by + the `inputs_ids` passed when calling [`UniSpeechModel`]. Vocabulary size of the model. Defines the + different tokens that can be represented by the *inputs_ids* passed to the forward method of + [`UniSpeechModel`]. + hidden_size (`int`, *optional*, defaults to 768): + Dimensionality of the encoder layers and the pooler layer. + num_hidden_layers (`int`, *optional*, defaults to 12): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 12): + Number of attention heads for each attention layer in the Transformer encoder. + intermediate_size (`int`, *optional*, defaults to 3072): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"` and `"gelu_new"` are supported. + hidden_dropout (`float`, *optional*, defaults to 0.1): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + activation_dropout (`float`, *optional*, defaults to 0.1): + The dropout ratio for activations inside the fully connected layer. + attention_dropout (`float`, *optional*, defaults to 0.1): + The dropout ratio for the attention probabilities. + feat_proj_dropout (`float`, *optional*, defaults to 0.0): + The dropout probability for output of the feature encoder. + feat_quantizer_dropout (`float`, *optional*, defaults to 0.0): + The dropout probability for the output of the feature encoder that's used by the quantizer. + final_dropout (`float`, *optional*, defaults to 0.1): + The dropout probability for the final projection layer of [`UniSpeechForCTC`]. + layerdrop (`float`, *optional*, defaults to 0.1): + The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more + details. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + layer_norm_eps (`float`, *optional*, defaults to 1e-05): + The epsilon used by the layer normalization layers. + feat_extract_norm (`str`, *optional*, defaults to `"group"`): + The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group + normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D + convolutional layers. + feat_extract_activation (`str, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the 1D convolutional layers of the feature + extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. + conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`): + A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the + feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers. + conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`): + A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length + of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*. + conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 2, 2)`): + A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The + length of *conv_kernel* defines the number of convolutional layers and has to match the length of + *conv_dim*. + conv_bias (`bool`, *optional*, defaults to `False`): + Whether the 1D convolutional layers have a bias. + num_conv_pos_embeddings (`int`, *optional*, defaults to 128): + Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional + embeddings layer. + num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16): + Number of groups of 1D convolutional positional embeddings layer. + do_stable_layer_norm (`bool`, *optional*, defaults to `False`): + Whether to apply *stable* layer norm architecture of the Transformer encoder. `do_stable_layer_norm is + True` corresponds to applying layer norm before the attention layer, whereas `do_stable_layer_norm is + False` corresponds to applying layer norm after the attention layer. + apply_spec_augment (`bool`, *optional*, defaults to `True`): + Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see + [SpecAugment: A Simple Data Augmentation Method for Automatic Speech + Recognition](https://arxiv.org/abs/1904.08779). + mask_time_prob (`float`, *optional*, defaults to 0.05): + Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking + procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If + reasoning from the propability of each feature vector to be chosen as the start of the vector span to be + masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the + actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`. + mask_time_length (`int`, *optional*, defaults to 10): + Length of vector span along the time axis. + mask_time_min_masks (`int`, *optional*, defaults to 2): + The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step, + irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length < + mask_time_min_masks'' + mask_feature_prob (`float`, *optional*, defaults to 0.0): + Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The + masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over + the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector + span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap + may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is + True`. + mask_feature_length (`int`, *optional*, defaults to 10): + Length of vector span along the feature axis. + mask_feature_min_masks (`int`, *optional*, defaults to 0): + The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time + step, irrespectively of `mask_feature_prob`. Only relevant if + ''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks'' + num_codevectors_per_group (`int`, *optional*, defaults to 320): + Number of entries in each quantization codebook (group). + num_codevector_groups (`int`, *optional*, defaults to 2): + Number of codevector groups for product codevector quantization. + contrastive_logits_temperature (`float`, *optional*, defaults to 0.1): + The temperature *kappa* in the contrastive loss. + num_negatives (`int`, *optional*, defaults to 100): + Number of negative samples for the contrastive loss. + codevector_dim (`int`, *optional*, defaults to 256): + Dimensionality of the quantized feature vectors. + proj_codevector_dim (`int`, *optional*, defaults to 256): + Dimensionality of the final projection of both the quantized and the transformer features. + diversity_loss_weight (`int`, *optional*, defaults to 0.1): + The weight of the codebook diversity loss component. + ctc_loss_reduction (`str`, *optional*, defaults to `"mean"`): + Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an + instance of [`UniSpeechForCTC`]. + ctc_zero_infinity (`bool`, *optional*, defaults to `False`): + Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly + occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance + of [`UniSpeechForCTC`]. + use_weighted_layer_sum (`bool`, *optional*, defaults to `False`): + Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an + instance of [`UniSpeechForSequenceClassification`]. + classifier_proj_size (`int`, *optional*, defaults to 256): + Dimensionality of the projection before token mean-pooling for classification. + num_ctc_classes (`int`, *optional*, defaults to 80): + Specifies the number of classes (phoneme tokens and blank token) for phoneme-level CTC loss. Only relevant + when using an instance of [`UniSpeechForPreTraining`]. + pad_token_id (`int`, *optional*, defaults to 0): + The id of the padding token. + bos_token_id (`int`, *optional*, defaults to 1): + The id of the "beginning-of-sequence" token. + eos_token_id (`int`, *optional*, defaults to 2): + The id of the "end-of-sequence" token. + replace_prob (`float`, *optional*, defaults to 0.5): + Propability that transformer feature is replaced by quantized feature for pretraining. + + Example: + + ```python + >>> from transformers import UniSpeechConfig, UniSpeechModel + + >>> # Initializing a UniSpeech facebook/unispeech-base-960h style configuration + >>> configuration = UniSpeechConfig() + + >>> # Initializing a model (with random weights) from the facebook/unispeech-base-960h style configuration + >>> model = UniSpeechModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "unispeech" + + def __init__( + self, + vocab_size=32, + hidden_size=768, + num_hidden_layers=12, + num_attention_heads=12, + intermediate_size=3072, + hidden_act="gelu", + hidden_dropout=0.1, + activation_dropout=0.1, + attention_dropout=0.1, + feat_proj_dropout=0.0, + feat_quantizer_dropout=0.0, + final_dropout=0.1, + layerdrop=0.1, + initializer_range=0.02, + layer_norm_eps=1e-5, + feat_extract_norm="group", + feat_extract_activation="gelu", + conv_dim=(512, 512, 512, 512, 512, 512, 512), + conv_stride=(5, 2, 2, 2, 2, 2, 2), + conv_kernel=(10, 3, 3, 3, 3, 2, 2), + conv_bias=False, + num_conv_pos_embeddings=128, + num_conv_pos_embedding_groups=16, + do_stable_layer_norm=False, + apply_spec_augment=True, + mask_time_prob=0.05, + mask_time_length=10, + mask_time_min_masks=2, + mask_feature_prob=0.0, + mask_feature_length=10, + mask_feature_min_masks=0, + num_codevectors_per_group=320, + num_codevector_groups=2, + contrastive_logits_temperature=0.1, + num_negatives=100, + codevector_dim=256, + proj_codevector_dim=256, + diversity_loss_weight=0.1, + ctc_loss_reduction="mean", + ctc_zero_infinity=False, + use_weighted_layer_sum=False, + classifier_proj_size=256, + num_ctc_classes=80, + pad_token_id=0, + bos_token_id=1, + eos_token_id=2, + replace_prob=0.5, + **kwargs, + ): + super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id) + self.hidden_size = hidden_size + self.feat_extract_norm = feat_extract_norm + self.feat_extract_activation = feat_extract_activation + self.conv_dim = list(conv_dim) + self.conv_stride = list(conv_stride) + self.conv_kernel = list(conv_kernel) + self.conv_bias = conv_bias + self.num_conv_pos_embeddings = num_conv_pos_embeddings + self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups + self.num_feat_extract_layers = len(self.conv_dim) + self.num_hidden_layers = num_hidden_layers + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.num_attention_heads = num_attention_heads + self.hidden_dropout = hidden_dropout + self.attention_dropout = attention_dropout + self.activation_dropout = activation_dropout + self.feat_proj_dropout = feat_proj_dropout + self.final_dropout = final_dropout + self.layerdrop = layerdrop + self.layer_norm_eps = layer_norm_eps + self.initializer_range = initializer_range + self.num_ctc_classes = num_ctc_classes + self.vocab_size = vocab_size + self.do_stable_layer_norm = do_stable_layer_norm + self.use_weighted_layer_sum = use_weighted_layer_sum + self.classifier_proj_size = classifier_proj_size + + if ( + (len(self.conv_stride) != self.num_feat_extract_layers) + or (len(self.conv_kernel) != self.num_feat_extract_layers) + or (len(self.conv_dim) != self.num_feat_extract_layers) + ): + raise ValueError( + "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` ==" + " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) =" + f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`," + f" `len(config.conv_kernel) = {len(self.conv_kernel)}`." + ) + + # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 + self.apply_spec_augment = apply_spec_augment + self.mask_time_prob = mask_time_prob + self.mask_time_length = mask_time_length + self.mask_time_min_masks = mask_time_min_masks + self.mask_feature_prob = mask_feature_prob + self.mask_feature_length = mask_feature_length + self.mask_feature_min_masks = mask_feature_min_masks + + # parameters for pretraining with codevector quantized representations + self.num_codevectors_per_group = num_codevectors_per_group + self.num_codevector_groups = num_codevector_groups + self.contrastive_logits_temperature = contrastive_logits_temperature + self.feat_quantizer_dropout = feat_quantizer_dropout + self.num_negatives = num_negatives + self.codevector_dim = codevector_dim + self.proj_codevector_dim = proj_codevector_dim + self.diversity_loss_weight = diversity_loss_weight + + # ctc loss + self.ctc_loss_reduction = ctc_loss_reduction + self.ctc_zero_infinity = ctc_zero_infinity + + # pretraining loss + self.replace_prob = replace_prob + + @property + def inputs_to_logits_ratio(self): + return functools.reduce(operator.mul, self.conv_stride, 1) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/unispeech/modeling_unispeech.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/unispeech/modeling_unispeech.py new file mode 100644 index 0000000000000000000000000000000000000000..473bc7d4ff12e41f985c77ecd948061382e53101 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/unispeech/modeling_unispeech.py @@ -0,0 +1,1637 @@ +# coding=utf-8 +# Copyright 2021 The Fairseq Authors and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch UniSpeech model.""" + +import math +import warnings +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import numpy as np +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss + +from ...activations import ACT2FN +from ...integrations.deepspeed import is_deepspeed_zero3_enabled +from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput, Wav2Vec2BaseModelOutput +from ...modeling_utils import PreTrainedModel +from ...utils import ( + ModelOutput, + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) +from .configuration_unispeech import UniSpeechConfig + + +logger = logging.get_logger(__name__) + + +_HIDDEN_STATES_START_POSITION = 2 + +# General docstring +_CONFIG_FOR_DOC = "UniSpeechConfig" + +# Base docstring +_CHECKPOINT_FOR_DOC = "patrickvonplaten/unispeech-large-1500h-cv-timit" +_EXPECTED_OUTPUT_SHAPE = [1, 292, 1024] + +# CTC docstring +_CTC_EXPECTED_OUTPUT = "'mister quilter is the apposl of the midle classes and weare glad to welcom his gosepl'" +_CTC_EXPECTED_LOSS = 17.17 + + +from ..deprecated._archive_maps import UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +@dataclass +class UniSpeechForPreTrainingOutput(ModelOutput): + """ + Output type of [`UniSpeechForPreTrainingOutput`], with potential hidden states and attentions. + + Args: + loss (*optional*, returned when model is in train mode, `torch.FloatTensor` of shape `(1,)`): + Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the [official + paper](https://arxiv.org/pdf/2006.11477.pdf) . (classification) loss. + projected_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`): + Hidden-states of the model projected to *config.proj_codevector_dim* that can be used to predict the masked + projected quantized states. + projected_quantized_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`): + Quantized extracted feature vectors projected to *config.proj_codevector_dim* representing the positive + target vectors for contrastive loss. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + """ + + loss: Optional[torch.FloatTensor] = None + projected_states: torch.FloatTensor = None + projected_quantized_states: torch.FloatTensor = None + codevector_perplexity: torch.FloatTensor = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2._compute_mask_indices +def _compute_mask_indices( + shape: Tuple[int, int], + mask_prob: float, + mask_length: int, + attention_mask: Optional[torch.LongTensor] = None, + min_masks: int = 0, +) -> np.ndarray: + """ + Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for + ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on + CPU as part of the preprocessing during training. + + Args: + shape: The shape for which to compute masks. This should be of a tuple of size 2 where + the first element is the batch size and the second element is the length of the axis to span. + mask_prob: The percentage of the whole axis (between 0 and 1) which will be masked. The number of + independently generated mask spans of length `mask_length` is computed by + `mask_prob*shape[1]/mask_length`. Note that due to overlaps, `mask_prob` is an upper bound and the + actual percentage will be smaller. + mask_length: size of the mask + min_masks: minimum number of masked spans + attention_mask: A (right-padded) attention mask which independently shortens the feature axis of + each batch dimension. + """ + batch_size, sequence_length = shape + + if mask_length < 1: + raise ValueError("`mask_length` has to be bigger than 0.") + + if mask_length > sequence_length: + raise ValueError( + f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length}" + f" and `sequence_length`: {sequence_length}`" + ) + + # epsilon is used for probabilistic rounding + epsilon = np.random.rand(1).item() + + def compute_num_masked_span(input_length): + """Given input length, compute how many spans should be masked""" + num_masked_span = int(mask_prob * input_length / mask_length + epsilon) + num_masked_span = max(num_masked_span, min_masks) + + # make sure num masked span <= sequence_length + if num_masked_span * mask_length > sequence_length: + num_masked_span = sequence_length // mask_length + + # make sure num_masked span is also <= input_length - (mask_length - 1) + if input_length - (mask_length - 1) < num_masked_span: + num_masked_span = max(input_length - (mask_length - 1), 0) + + return num_masked_span + + # compute number of masked spans in batch + input_lengths = ( + attention_mask.sum(-1).detach().tolist() + if attention_mask is not None + else [sequence_length for _ in range(batch_size)] + ) + + # SpecAugment mask to fill + spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool) + spec_aug_mask_idxs = [] + + max_num_masked_span = compute_num_masked_span(sequence_length) + + if max_num_masked_span == 0: + return spec_aug_mask + + for input_length in input_lengths: + # compute num of masked spans for this input + num_masked_span = compute_num_masked_span(input_length) + + # get random indices to mask + spec_aug_mask_idx = np.random.choice( + np.arange(input_length - (mask_length - 1)), num_masked_span, replace=False + ) + + # pick first sampled index that will serve as a dummy index to pad vector + # to ensure same dimension for all batches due to probabilistic rounding + # Picking first sample just pads those vectors twice. + if len(spec_aug_mask_idx) == 0: + # this case can only happen if `input_length` is strictly smaller then + # `sequence_length` in which case the last token has to be a padding + # token which we can use as a dummy mask id + dummy_mask_idx = sequence_length - 1 + else: + dummy_mask_idx = spec_aug_mask_idx[0] + + spec_aug_mask_idx = np.concatenate( + [spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx] + ) + spec_aug_mask_idxs.append(spec_aug_mask_idx) + + spec_aug_mask_idxs = np.array(spec_aug_mask_idxs) + + # expand masked indices to masked spans + spec_aug_mask_idxs = np.broadcast_to( + spec_aug_mask_idxs[:, :, None], (batch_size, max_num_masked_span, mask_length) + ) + spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, max_num_masked_span * mask_length) + + # add offset to the starting indexes so that indexes now create a span + offsets = np.arange(mask_length)[None, None, :] + offsets = np.broadcast_to(offsets, (batch_size, max_num_masked_span, mask_length)).reshape( + batch_size, max_num_masked_span * mask_length + ) + spec_aug_mask_idxs = spec_aug_mask_idxs + offsets + + # ensure that we cannot have indices larger than sequence_length + if spec_aug_mask_idxs.max() > sequence_length - 1: + spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1 + + # scatter indices to mask + np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1) + + return spec_aug_mask + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2NoLayerNormConvLayer with Wav2Vec2->UniSpeech +class UniSpeechNoLayerNormConvLayer(nn.Module): + def __init__(self, config, layer_id=0): + super().__init__() + self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1 + self.out_conv_dim = config.conv_dim[layer_id] + + self.conv = nn.Conv1d( + self.in_conv_dim, + self.out_conv_dim, + kernel_size=config.conv_kernel[layer_id], + stride=config.conv_stride[layer_id], + bias=config.conv_bias, + ) + self.activation = ACT2FN[config.feat_extract_activation] + + def forward(self, hidden_states): + hidden_states = self.conv(hidden_states) + hidden_states = self.activation(hidden_states) + return hidden_states + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2LayerNormConvLayer with Wav2Vec2->UniSpeech +class UniSpeechLayerNormConvLayer(nn.Module): + def __init__(self, config, layer_id=0): + super().__init__() + self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1 + self.out_conv_dim = config.conv_dim[layer_id] + + self.conv = nn.Conv1d( + self.in_conv_dim, + self.out_conv_dim, + kernel_size=config.conv_kernel[layer_id], + stride=config.conv_stride[layer_id], + bias=config.conv_bias, + ) + self.layer_norm = nn.LayerNorm(self.out_conv_dim, elementwise_affine=True) + self.activation = ACT2FN[config.feat_extract_activation] + + def forward(self, hidden_states): + hidden_states = self.conv(hidden_states) + + hidden_states = hidden_states.transpose(-2, -1) + hidden_states = self.layer_norm(hidden_states) + hidden_states = hidden_states.transpose(-2, -1) + + hidden_states = self.activation(hidden_states) + return hidden_states + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2GroupNormConvLayer with Wav2Vec2->UniSpeech +class UniSpeechGroupNormConvLayer(nn.Module): + def __init__(self, config, layer_id=0): + super().__init__() + self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1 + self.out_conv_dim = config.conv_dim[layer_id] + + self.conv = nn.Conv1d( + self.in_conv_dim, + self.out_conv_dim, + kernel_size=config.conv_kernel[layer_id], + stride=config.conv_stride[layer_id], + bias=config.conv_bias, + ) + self.activation = ACT2FN[config.feat_extract_activation] + + self.layer_norm = nn.GroupNorm(num_groups=self.out_conv_dim, num_channels=self.out_conv_dim, affine=True) + + def forward(self, hidden_states): + hidden_states = self.conv(hidden_states) + hidden_states = self.layer_norm(hidden_states) + hidden_states = self.activation(hidden_states) + return hidden_states + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2PositionalConvEmbedding with Wav2Vec2->UniSpeech +class UniSpeechPositionalConvEmbedding(nn.Module): + def __init__(self, config): + super().__init__() + self.conv = nn.Conv1d( + config.hidden_size, + config.hidden_size, + kernel_size=config.num_conv_pos_embeddings, + padding=config.num_conv_pos_embeddings // 2, + groups=config.num_conv_pos_embedding_groups, + ) + + weight_norm = nn.utils.weight_norm + if hasattr(nn.utils.parametrizations, "weight_norm"): + weight_norm = nn.utils.parametrizations.weight_norm + + if is_deepspeed_zero3_enabled(): + import deepspeed + + with deepspeed.zero.GatheredParameters(self.conv.weight, modifier_rank=0): + self.conv = weight_norm(self.conv, name="weight", dim=2) + deepspeed.zero.register_external_parameter(self, self.conv.weight_v) + deepspeed.zero.register_external_parameter(self, self.conv.weight_g) + else: + self.conv = weight_norm(self.conv, name="weight", dim=2) + + self.padding = UniSpeechSamePadLayer(config.num_conv_pos_embeddings) + self.activation = ACT2FN[config.feat_extract_activation] + + def forward(self, hidden_states): + hidden_states = hidden_states.transpose(1, 2) + + hidden_states = self.conv(hidden_states) + hidden_states = self.padding(hidden_states) + hidden_states = self.activation(hidden_states) + + hidden_states = hidden_states.transpose(1, 2) + return hidden_states + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2SamePadLayer with Wav2Vec2->UniSpeech +class UniSpeechSamePadLayer(nn.Module): + def __init__(self, num_conv_pos_embeddings): + super().__init__() + self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0 + + def forward(self, hidden_states): + if self.num_pad_remove > 0: + hidden_states = hidden_states[:, :, : -self.num_pad_remove] + return hidden_states + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeatureEncoder with Wav2Vec2->UniSpeech +class UniSpeechFeatureEncoder(nn.Module): + """Construct the features from raw audio waveform""" + + def __init__(self, config): + super().__init__() + + if config.feat_extract_norm == "group": + conv_layers = [UniSpeechGroupNormConvLayer(config, layer_id=0)] + [ + UniSpeechNoLayerNormConvLayer(config, layer_id=i + 1) + for i in range(config.num_feat_extract_layers - 1) + ] + elif config.feat_extract_norm == "layer": + conv_layers = [ + UniSpeechLayerNormConvLayer(config, layer_id=i) for i in range(config.num_feat_extract_layers) + ] + else: + raise ValueError( + f"`config.feat_extract_norm` is {config.feat_extract_norm}, but has to be one of ['group', 'layer']" + ) + self.conv_layers = nn.ModuleList(conv_layers) + self.gradient_checkpointing = False + self._requires_grad = True + + def _freeze_parameters(self): + for param in self.parameters(): + param.requires_grad = False + self._requires_grad = False + + def forward(self, input_values): + hidden_states = input_values[:, None] + + # make sure hidden_states require grad for gradient_checkpointing + if self._requires_grad and self.training: + hidden_states.requires_grad = True + + for conv_layer in self.conv_layers: + if self._requires_grad and self.gradient_checkpointing and self.training: + hidden_states = self._gradient_checkpointing_func( + conv_layer.__call__, + hidden_states, + ) + else: + hidden_states = conv_layer(hidden_states) + + return hidden_states + + +class UniSpeechFeatureExtractor(UniSpeechFeatureEncoder): + def __init__(self, config): + super().__init__(config) + warnings.warn( + f"The class `{self.__class__.__name__}` has been depreciated " + "and will be removed in Transformers v5. " + f"Use `{self.__class__.__bases__[0].__name__}` instead.", + FutureWarning, + ) + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeatureProjection with Wav2Vec2->UniSpeech +class UniSpeechFeatureProjection(nn.Module): + def __init__(self, config): + super().__init__() + self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.layer_norm_eps) + self.projection = nn.Linear(config.conv_dim[-1], config.hidden_size) + self.dropout = nn.Dropout(config.feat_proj_dropout) + + def forward(self, hidden_states): + # non-projected hidden states are needed for quantization + norm_hidden_states = self.layer_norm(hidden_states) + hidden_states = self.projection(norm_hidden_states) + hidden_states = self.dropout(hidden_states) + return hidden_states, norm_hidden_states + + +# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->UniSpeech +class UniSpeechAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + is_decoder: bool = False, + bias: bool = True, + is_causal: bool = False, + config: Optional[UniSpeechConfig] = None, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + self.config = config + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + self.is_causal = is_causal + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + bsz, tgt_len, _ = hidden_states.size() + + # get query proj + query_states = self.q_proj(hidden_states) * self.scaling + # get key, value proj + # `past_key_value[0].shape[2] == key_value_states.shape[1]` + # is checking that the `sequence_length` of the `past_key_value` is the same as + # the provided `key_value_states` to support prefix tuning + if ( + is_cross_attention + and past_key_value is not None + and past_key_value[0].shape[2] == key_value_states.shape[1] + ): + # reuse k,v, cross_attentions + key_states = past_key_value[0] + value_states = past_key_value[1] + elif is_cross_attention: + # cross_attentions + key_states = self._shape(self.k_proj(key_value_states), -1, bsz) + value_states = self._shape(self.v_proj(key_value_states), -1, bsz) + elif past_key_value is not None: + # reuse k, v, self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + else: + # self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_states, value_states) + + proj_shape = (bsz * self.num_heads, -1, self.head_dim) + query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) + key_states = key_states.reshape(*proj_shape) + value_states = value_states.reshape(*proj_shape) + + src_len = key_states.size(1) + attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) + + if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, tgt_len, src_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + if layer_head_mask is not None: + if layer_head_mask.size() != (self.num_heads,): + raise ValueError( + f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" + f" {layer_head_mask.size()}" + ) + attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + if output_attentions: + # this operation is a bit awkward, but it's required to + # make sure that attn_weights keeps its gradient. + # In order to do so, attn_weights have to be reshaped + # twice and have to be reused in the following + attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) + else: + attn_weights_reshaped = None + + attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = torch.bmm(attn_probs, value_states) + + if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) + attn_output = attn_output.transpose(1, 2) + + # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be + # partitioned across GPUs when using tensor-parallelism. + attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights_reshaped, past_key_value + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeedForward with Wav2Vec2->UniSpeech +class UniSpeechFeedForward(nn.Module): + def __init__(self, config): + super().__init__() + self.intermediate_dropout = nn.Dropout(config.activation_dropout) + + self.intermediate_dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + self.output_dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.output_dropout = nn.Dropout(config.hidden_dropout) + + def forward(self, hidden_states): + hidden_states = self.intermediate_dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + hidden_states = self.intermediate_dropout(hidden_states) + + hidden_states = self.output_dense(hidden_states) + hidden_states = self.output_dropout(hidden_states) + return hidden_states + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2EncoderLayer with Wav2Vec2->UniSpeech +class UniSpeechEncoderLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.attention = UniSpeechAttention( + embed_dim=config.hidden_size, + num_heads=config.num_attention_heads, + dropout=config.attention_dropout, + is_decoder=False, + ) + self.dropout = nn.Dropout(config.hidden_dropout) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.feed_forward = UniSpeechFeedForward(config) + self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states, attention_mask=None, output_attentions=False): + attn_residual = hidden_states + hidden_states, attn_weights, _ = self.attention( + hidden_states, attention_mask=attention_mask, output_attentions=output_attentions + ) + hidden_states = self.dropout(hidden_states) + hidden_states = attn_residual + hidden_states + + hidden_states = self.layer_norm(hidden_states) + hidden_states = hidden_states + self.feed_forward(hidden_states) + hidden_states = self.final_layer_norm(hidden_states) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2AttnAdapterLayer with Wav2Vec2->UniSpeech +class UniSpeechAttnAdapterLayer(nn.Module): + def __init__(self, config): + """ + Implements adapter modules directly with 3D tensor weight as parameters and without using ModuleList to speed + up training throughput. + """ + super().__init__() + self.input_dim = config.adapter_attn_dim + self.hidden_dim = config.hidden_size + + self.norm = nn.LayerNorm(self.hidden_dim) + self.linear_1 = nn.Linear(self.hidden_dim, self.input_dim) + self.act_fn = nn.ReLU() + self.linear_2 = nn.Linear(self.input_dim, self.hidden_dim) + + def forward(self, hidden_states: torch.FloatTensor): + hidden_states = self.norm(hidden_states) + + hidden_states = self.linear_1(hidden_states) + hidden_states = self.act_fn(hidden_states) + hidden_states = self.linear_2(hidden_states) + + return hidden_states + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2EncoderLayerStableLayerNorm with Wav2Vec2->UniSpeech +class UniSpeechEncoderLayerStableLayerNorm(nn.Module): + def __init__(self, config): + super().__init__() + self.attention = UniSpeechAttention( + embed_dim=config.hidden_size, + num_heads=config.num_attention_heads, + dropout=config.attention_dropout, + is_decoder=False, + ) + self.dropout = nn.Dropout(config.hidden_dropout) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.feed_forward = UniSpeechFeedForward(config) + self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + if getattr(config, "adapter_attn_dim", None) is not None: + self.adapter_layer = UniSpeechAttnAdapterLayer(config) + else: + self.adapter_layer = None + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ): + attn_residual = hidden_states + hidden_states = self.layer_norm(hidden_states) + hidden_states, attn_weights, _ = self.attention( + hidden_states, attention_mask=attention_mask, output_attentions=output_attentions + ) + hidden_states = self.dropout(hidden_states) + hidden_states = attn_residual + hidden_states + hidden_states = hidden_states + self.feed_forward(self.final_layer_norm(hidden_states)) + + if self.adapter_layer is not None: + hidden_states = hidden_states + self.adapter_layer(hidden_states) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Encoder with Wav2Vec2->UniSpeech +class UniSpeechEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.pos_conv_embed = UniSpeechPositionalConvEmbedding(config) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout) + self.layers = nn.ModuleList([UniSpeechEncoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.tensor, + attention_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + if attention_mask is not None: + # make sure padded tokens output 0 + expand_attention_mask = attention_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2]) + hidden_states[~expand_attention_mask] = 0 + + # extend attention_mask + attention_mask = 1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype) + attention_mask = attention_mask * torch.finfo(hidden_states.dtype).min + attention_mask = attention_mask.expand( + attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1] + ) + + position_embeddings = self.pos_conv_embed(hidden_states) + hidden_states = hidden_states + position_embeddings + hidden_states = self.layer_norm(hidden_states) + hidden_states = self.dropout(hidden_states) + + deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled() + + for layer in self.layers: + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + dropout_probability = torch.rand([]) + + skip_the_layer = True if self.training and (dropout_probability < self.config.layerdrop) else False + if not skip_the_layer or deepspeed_zero3_is_enabled: + # under deepspeed zero3 all gpus must run in sync + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer.__call__, + hidden_states, + attention_mask, + output_attentions, + ) + else: + layer_outputs = layer( + hidden_states, attention_mask=attention_mask, output_attentions=output_attentions + ) + hidden_states = layer_outputs[0] + + if skip_the_layer: + layer_outputs = (None, None) + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2EncoderStableLayerNorm with Wav2Vec2->UniSpeech +class UniSpeechEncoderStableLayerNorm(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.pos_conv_embed = UniSpeechPositionalConvEmbedding(config) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout) + self.layers = nn.ModuleList( + [UniSpeechEncoderLayerStableLayerNorm(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + if attention_mask is not None: + # make sure padded tokens are not attended to + expand_attention_mask = attention_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2]) + hidden_states[~expand_attention_mask] = 0 + + # extend attention_mask + attention_mask = 1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype) + attention_mask = attention_mask * torch.finfo(hidden_states.dtype).min + attention_mask = attention_mask.expand( + attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1] + ) + + position_embeddings = self.pos_conv_embed(hidden_states) + hidden_states = hidden_states + position_embeddings + hidden_states = self.dropout(hidden_states) + + deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled() + + for layer in self.layers: + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + dropout_probability = torch.rand([]) + + skip_the_layer = True if self.training and (dropout_probability < self.config.layerdrop) else False + if not skip_the_layer or deepspeed_zero3_is_enabled: + # under deepspeed zero3 all gpus must run in sync + # XXX: could optimize this like synced_gpus in generate_utils but not sure if it's worth the code complication + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer.__call__, + hidden_states, + attention_mask, + output_attentions, + ) + else: + layer_outputs = layer( + hidden_states, attention_mask=attention_mask, output_attentions=output_attentions + ) + hidden_states = layer_outputs[0] + + if skip_the_layer: + layer_outputs = (None, None) + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + hidden_states = self.layer_norm(hidden_states) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +class UniSpeechGumbelVectorQuantizer(nn.Module): + """ + Vector quantization using gumbel softmax. See [CATEGORICAL REPARAMETERIZATION WITH + GUMBEL-SOFTMAX](https://arxiv.org/pdf/1611.01144.pdf) for more information. + """ + + def __init__(self, config): + super().__init__() + self.num_groups = config.num_codevector_groups + self.num_vars = config.num_codevectors_per_group + + if config.codevector_dim % self.num_groups != 0: + raise ValueError( + f"`config.codevector_dim {config.codevector_dim} must be divisible by `config.num_codevector_groups`" + f" {self.num_groups} for concatenation" + ) + + # storage for codebook variables (codewords) + self.codevectors = nn.Parameter( + torch.FloatTensor(1, self.num_groups * self.num_vars, config.codevector_dim // self.num_groups) + ) + self.weight_proj = nn.Linear(config.conv_dim[-1], self.num_groups * self.num_vars) + + # can be decayed for training + self.temperature = 2 + + @staticmethod + def _compute_perplexity(probs): + marginal_probs = probs.mean(dim=0) + perplexity = torch.exp(-torch.sum(marginal_probs * torch.log(marginal_probs + 1e-7), dim=-1)).sum() + return perplexity + + def forward(self, hidden_states): + batch_size, sequence_length, hidden_size = hidden_states.shape + + # project to codevector dim + hidden_states = self.weight_proj(hidden_states) + hidden_states = hidden_states.view(batch_size * sequence_length * self.num_groups, -1) + + if self.training: + # sample code vector probs via gumbel in differentiateable way + codevector_probs = nn.functional.gumbel_softmax( + hidden_states.float(), tau=self.temperature, hard=True + ).type_as(hidden_states) + + # compute perplexity + codevector_soft_dist = torch.softmax( + hidden_states.view(batch_size * sequence_length, self.num_groups, -1).float(), dim=-1 + ) + perplexity = self._compute_perplexity(codevector_soft_dist) + else: + # take argmax in non-differentiable way + # comptute hard codevector distribution (one hot) + codevector_idx = hidden_states.argmax(dim=-1) + codevector_probs = hidden_states.new_zeros(*hidden_states.shape).scatter_( + -1, codevector_idx.view(-1, 1), 1.0 + ) + codevector_probs = codevector_probs.view(batch_size * sequence_length, self.num_groups, -1) + + perplexity = self._compute_perplexity(codevector_probs) + + codevector_probs = codevector_probs.view(batch_size * sequence_length, -1) + # use probs to retrieve codevectors + codevectors_per_group = codevector_probs.unsqueeze(-1) * self.codevectors + codevectors = codevectors_per_group.view(batch_size * sequence_length, self.num_groups, self.num_vars, -1) + codevectors = codevectors.sum(-2).view(batch_size, sequence_length, -1) + + return codevectors, perplexity + + +class UniSpeechPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = UniSpeechConfig + base_model_prefix = "unispeech" + main_input_name = "input_values" + supports_gradient_checkpointing = True + + def _init_weights(self, module): + """Initialize the weights""" + # gumbel softmax requires special init + if isinstance(module, UniSpeechGumbelVectorQuantizer): + module.weight_proj.weight.data.normal_(mean=0.0, std=1) + module.weight_proj.bias.data.zero_() + nn.init.uniform_(module.codevectors) + elif isinstance(module, UniSpeechPositionalConvEmbedding): + nn.init.normal_( + module.conv.weight, + mean=0, + std=2 * math.sqrt(1 / (module.conv.kernel_size[0] * module.conv.in_channels)), + ) + nn.init.constant_(module.conv.bias, 0) + elif isinstance(module, UniSpeechFeatureProjection): + k = math.sqrt(1 / module.projection.in_features) + nn.init.uniform_(module.projection.weight, a=-k, b=k) + nn.init.uniform_(module.projection.bias, a=-k, b=k) + elif isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + elif isinstance(module, nn.Conv1d): + nn.init.kaiming_normal_(module.weight) + + if module.bias is not None: + k = math.sqrt(module.groups / (module.in_channels * module.kernel_size[0])) + nn.init.uniform_(module.bias, a=-k, b=k) + + def _get_feat_extract_output_lengths(self, input_lengths: Union[torch.LongTensor, int]): + """ + Computes the output length of the convolutional layers + """ + + def _conv_out_length(input_length, kernel_size, stride): + # 1D convolutional layer output length formula taken + # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html + return torch.div(input_length - kernel_size, stride, rounding_mode="floor") + 1 + + for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): + input_lengths = _conv_out_length(input_lengths, kernel_size, stride) + + return input_lengths + + def _get_feature_vector_attention_mask(self, feature_vector_length: int, attention_mask: torch.LongTensor): + # Effectively attention_mask.sum(-1), but not inplace to be able to run + # on inference mode. + non_padded_lengths = attention_mask.cumsum(dim=-1)[:, -1] + output_lengths = self._get_feat_extract_output_lengths(non_padded_lengths).to(torch.long) + batch_size = attention_mask.shape[0] + + attention_mask = torch.zeros( + (batch_size, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device + ) + # these two operations makes sure that all values before the output lengths idxs are attended to + attention_mask[(torch.arange(attention_mask.shape[0], device=attention_mask.device), output_lengths - 1)] = 1 + attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool() + return attention_mask + + +UNISPEECH_START_DOCSTRING = r""" + UniSpeech was proposed in [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled + Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, + Michael Zeng, Xuedong Huang. + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving etc.). + + This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use + it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and + behavior. + + Parameters: + config ([`UniSpeechConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +UNISPEECH_INPUTS_DOCSTRING = r""" + Args: + input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): + Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file + into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install + soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and + conversion into a tensor of type `torch.FloatTensor`. See [`Wav2Vec2Processor.__call__`] for details. + attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0, + 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + + + `attention_mask` should only be passed if the corresponding processor has `config.return_attention_mask == + True`. For all models whose processor has `config.return_attention_mask == False`, `attention_mask` should + **not** be passed to avoid degraded performance when doing batched inference. For such models + `input_values` should simply be padded with 0 and passed without `attention_mask`. Be aware that these + models also yield slightly different results depending on whether `input_values` is padded or not. + + + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare UniSpeech Model transformer outputting raw hidden-states without any specific head on top.", + UNISPEECH_START_DOCSTRING, +) +class UniSpeechModel(UniSpeechPreTrainedModel): + def __init__(self, config: UniSpeechConfig): + super().__init__(config) + self.config = config + self.feature_extractor = UniSpeechFeatureEncoder(config) + self.feature_projection = UniSpeechFeatureProjection(config) + + if config.mask_time_prob > 0.0 or config.mask_feature_prob > 0.0: + self.masked_spec_embed = nn.Parameter(torch.FloatTensor(config.hidden_size).uniform_()) + + if config.do_stable_layer_norm: + self.encoder = UniSpeechEncoderStableLayerNorm(config) + else: + self.encoder = UniSpeechEncoder(config) + + # Initialize weights and apply final processing + self.post_init() + + # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Model._mask_hidden_states + def _mask_hidden_states( + self, + hidden_states: torch.FloatTensor, + mask_time_indices: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.LongTensor] = None, + ): + """ + Masks extracted features along time axis and/or along feature axis according to + [SpecAugment](https://arxiv.org/abs/1904.08779). + """ + + # `config.apply_spec_augment` can set masking to False + if not getattr(self.config, "apply_spec_augment", True): + return hidden_states + + # generate indices & apply SpecAugment along time axis + batch_size, sequence_length, hidden_size = hidden_states.size() + + if mask_time_indices is not None: + # apply SpecAugment along time axis with given mask_time_indices + hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype) + elif self.config.mask_time_prob > 0 and self.training: + mask_time_indices = _compute_mask_indices( + (batch_size, sequence_length), + mask_prob=self.config.mask_time_prob, + mask_length=self.config.mask_time_length, + attention_mask=attention_mask, + min_masks=self.config.mask_time_min_masks, + ) + mask_time_indices = torch.tensor(mask_time_indices, device=hidden_states.device, dtype=torch.bool) + hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype) + + if self.config.mask_feature_prob > 0 and self.training: + # generate indices & apply SpecAugment along feature axis + mask_feature_indices = _compute_mask_indices( + (batch_size, hidden_size), + mask_prob=self.config.mask_feature_prob, + mask_length=self.config.mask_feature_length, + min_masks=self.config.mask_feature_min_masks, + ) + mask_feature_indices = torch.tensor(mask_feature_indices, device=hidden_states.device, dtype=torch.bool) + mask_feature_indices = mask_feature_indices[:, None].expand(-1, sequence_length, -1) + hidden_states[mask_feature_indices] = 0 + + return hidden_states + + @add_start_docstrings_to_model_forward(UNISPEECH_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=Wav2Vec2BaseModelOutput, + config_class=_CONFIG_FOR_DOC, + modality="audio", + expected_output=_EXPECTED_OUTPUT_SHAPE, + ) + def forward( + self, + input_values: Optional[torch.Tensor], + attention_mask: Optional[torch.Tensor] = None, + mask_time_indices: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, Wav2Vec2BaseModelOutput]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + extract_features = self.feature_extractor(input_values) + extract_features = extract_features.transpose(1, 2) + + if attention_mask is not None: + # compute reduced attention_mask corresponding to feature vectors + attention_mask = self._get_feature_vector_attention_mask(extract_features.shape[1], attention_mask) + + hidden_states, extract_features = self.feature_projection(extract_features) + hidden_states = self._mask_hidden_states( + hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask + ) + + encoder_outputs = self.encoder( + hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = encoder_outputs[0] + + if not return_dict: + return (hidden_states, extract_features) + encoder_outputs[1:] + + return Wav2Vec2BaseModelOutput( + last_hidden_state=hidden_states, + extract_features=extract_features, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +@add_start_docstrings( + """UniSpeech Model with a vector-quantization module and ctc loss for pre-training.""", UNISPEECH_START_DOCSTRING +) +class UniSpeechForPreTraining(UniSpeechPreTrainedModel): + def __init__(self, config: UniSpeechConfig): + super().__init__(config) + self.unispeech = UniSpeechModel(config) + self.dropout_features = nn.Dropout(config.feat_quantizer_dropout) + + self.quantizer = UniSpeechGumbelVectorQuantizer(config) + self.project_q = nn.Linear(config.codevector_dim, config.proj_codevector_dim) + self.project_hid = nn.Linear(config.proj_codevector_dim, config.hidden_size) + + self.ctc_proj = nn.Linear(config.hidden_size, config.num_ctc_classes) + self.dropout = nn.Dropout(config.final_dropout) + + # Initialize weights and apply final processing + self.post_init() + + def set_gumbel_temperature(self, temperature: int): + """ + Set the Gumbel softmax temperature to a given value. Only necessary for training + """ + self.quantizer.temperature = temperature + + def freeze_feature_extractor(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameters will + not be updated during training. + """ + warnings.warn( + "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. " + "Please use the equivalent `freeze_feature_encoder` method instead.", + FutureWarning, + ) + self.freeze_feature_encoder() + + def freeze_feature_encoder(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameter will + not be updated during training. + """ + self.unispeech.feature_extractor._freeze_parameters() + + @staticmethod + def compute_contrastive_logits( + target_features: torch.FloatTensor, + negative_features: torch.FloatTensor, + predicted_features: torch.FloatTensor, + temperature: int = 1, + ): + """ + Compute logits for contrastive loss based using cosine similarity as the distance measure between + `[positive_feature, negative_features]` and `[predicted_features]`. Additionally, temperature can be applied. + """ + target_features = torch.cat([target_features, negative_features], dim=0) + + logits = torch.cosine_similarity(predicted_features.float(), target_features.float(), dim=-1) + logits = logits.type_as(target_features) + + # apply temperature + logits = logits / temperature + return logits + + @add_start_docstrings_to_model_forward(UNISPEECH_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=UniSpeechForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_values: Optional[torch.Tensor], + attention_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, UniSpeechForPreTrainingOutput]: + r""" + mask_time_indices (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict + masked extracted features in *config.proj_codevector_dim* space. + sampled_negative_indices (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_negatives)`, *optional*): + Indices indicating which quantized target vectors are used as negative sampled vectors in contrastive loss. + Required input for pre-training. + + Returns: + + Example: + + ```python + >>> import torch + >>> from transformers import AutoFeatureExtractor, UniSpeechForPreTraining + + >>> feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/unispeech-large-1500h-cv") + >>> model = UniSpeechForPreTraining.from_pretrained("microsoft/unispeech-large-1500h-cv") + >>> # TODO: Add full pretraining example + ```""" + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.unispeech( + input_values, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + transformer_features = outputs[0] + + # quantize all (unmasked) extracted features and project to final vq dim + extract_features = self.dropout_features(outputs[1]) + quantized_features, codevector_perplexity = self.quantizer(extract_features) + + # project quantized features twice + quantized_features = self.project_q(quantized_features) + quantized_features = self.project_hid(quantized_features) + + prob_replace_matrix = torch.empty(transformer_features.size(0), transformer_features.size(1)).fill_( + self.config.replace_prob + ) + prob_replace_matrix = prob_replace_matrix.transpose(0, 1) + sampled_replace_matrix = torch.bernoulli(prob_replace_matrix).bool().to(transformer_features.device) + sampled_replace_matrix = sampled_replace_matrix.transpose(0, 1) + sampled_replace_matrix = sampled_replace_matrix.unsqueeze(-1) + logits = transformer_features.masked_fill(sampled_replace_matrix, 0.0) + ( + quantized_features.masked_fill(~sampled_replace_matrix, 0.0) + ) + + # project to ctc units + logits = self.dropout(logits) + logits = self.ctc_proj(logits) + + # TODO(PVP) - add negative sampling & loss computation + loss = None + if not return_dict: + if loss is not None: + return (loss, transformer_features, quantized_features, codevector_perplexity) + outputs[2:] + return (transformer_features, quantized_features, codevector_perplexity) + outputs[2:] + + return UniSpeechForPreTrainingOutput( + loss=loss, + projected_states=transformer_features, + projected_quantized_states=quantized_features, + codevector_perplexity=codevector_perplexity, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + """UniSpeech Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).""", + UNISPEECH_START_DOCSTRING, + """ + target_lang (`str`, *optional*): + Language id of adapter weights. Adapter weights are stored in the format adapter..safetensors or + adapter..bin. Only relevant when using an instance of [`UniSpeechForCTC`] with adapters. Uses 'eng' + by default. + """, +) +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC with Wav2Vec2->UniSpeech, wav2vec2->unispeech, WAV_2_VEC_2->UNISPEECH +class UniSpeechForCTC(UniSpeechPreTrainedModel): + def __init__(self, config, target_lang: Optional[str] = None): + super().__init__(config) + + self.unispeech = UniSpeechModel(config) + self.dropout = nn.Dropout(config.final_dropout) + + self.target_lang = target_lang + + if config.vocab_size is None: + raise ValueError( + f"You are trying to instantiate {self.__class__} with a configuration that " + "does not define the vocabulary size of the language model head. Please " + "instantiate the model as follows: `UniSpeechForCTC.from_pretrained(..., vocab_size=vocab_size)`. " + "or define `vocab_size` of your model's configuration." + ) + output_hidden_size = ( + config.output_hidden_size if hasattr(config, "add_adapter") and config.add_adapter else config.hidden_size + ) + self.lm_head = nn.Linear(output_hidden_size, config.vocab_size) + + # Initialize weights and apply final processing + self.post_init() + + def tie_weights(self): + """ + This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when + passing `target_lang=...` to `from_pretrained(...)`. + + This method is **not** supposed to be called by the user and is prone to be changed in the future. + """ + + # Note that `tie_weights` is usually used to tie input and output embedding weights. The method is re-purposed to + # correctly load adapter layers for UniSpeech so that we do not have to introduce a new API to + # [`PreTrainedModel`]. While slightly hacky, UniSpeech never has to tie input and output embeddings, so that it is + # ok to repurpose this function here. + target_lang = self.target_lang + + if target_lang is not None and getattr(self.config, "adapter_attn_dim", None) is None: + raise ValueError(f"Cannot pass `target_lang`: {target_lang} if `config.adapter_attn_dim` is not defined.") + elif target_lang is None and getattr(self.config, "adapter_attn_dim", None) is not None: + logger.info("By default `target_lang` is set to 'eng'.") + elif target_lang is not None: + self.load_adapter(target_lang, force_load=True) + + def freeze_feature_extractor(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameter will + not be updated during training. + """ + warnings.warn( + "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. " + "Please use the equivalent `freeze_feature_encoder` method instead.", + FutureWarning, + ) + self.freeze_feature_encoder() + + def freeze_feature_encoder(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameter will + not be updated during training. + """ + self.unispeech.feature_extractor._freeze_parameters() + + def freeze_base_model(self): + """ + Calling this function will disable the gradient computation for the base model so that its parameters will not + be updated during training. Only the classification head will be updated. + """ + for param in self.unispeech.parameters(): + param.requires_grad = False + + @add_start_docstrings_to_model_forward(UNISPEECH_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=CausalLMOutput, + config_class=_CONFIG_FOR_DOC, + expected_output=_CTC_EXPECTED_OUTPUT, + expected_loss=_CTC_EXPECTED_LOSS, + ) + def forward( + self, + input_values: Optional[torch.Tensor], + attention_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + labels: Optional[torch.Tensor] = None, + ) -> Union[Tuple, CausalLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*): + Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to + the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`. + All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., + config.vocab_size - 1]`. + """ + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.unispeech( + input_values, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + hidden_states = self.dropout(hidden_states) + + logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + if labels.max() >= self.config.vocab_size: + raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}") + + # retrieve loss input_lengths from attention_mask + attention_mask = ( + attention_mask if attention_mask is not None else torch.ones_like(input_values, dtype=torch.long) + ) + input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long) + + # assuming that padded tokens are filled with -100 + # when not being attended to + labels_mask = labels >= 0 + target_lengths = labels_mask.sum(-1) + flattened_targets = labels.masked_select(labels_mask) + + # ctc_loss doesn't support fp16 + log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1) + + with torch.backends.cudnn.flags(enabled=False): + loss = nn.functional.ctc_loss( + log_probs, + flattened_targets, + input_lengths, + target_lengths, + blank=self.config.pad_token_id, + reduction=self.config.ctc_loss_reduction, + zero_infinity=self.config.ctc_zero_infinity, + ) + + if not return_dict: + output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutput( + loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions + ) + + +@add_start_docstrings( + """ + UniSpeech Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like + SUPERB Keyword Spotting. + """, + UNISPEECH_START_DOCSTRING, +) +class UniSpeechForSequenceClassification(UniSpeechPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + if hasattr(config, "add_adapter") and config.add_adapter: + raise ValueError( + "Sequence classification does not support the use of UniSpeech adapters (config.add_adapter=True)" + ) + self.unispeech = UniSpeechModel(config) + num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings + if config.use_weighted_layer_sum: + self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers) + self.projector = nn.Linear(config.hidden_size, config.classifier_proj_size) + self.classifier = nn.Linear(config.classifier_proj_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_feature_extractor + def freeze_feature_extractor(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameters will + not be updated during training. + """ + warnings.warn( + "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. " + "Please use the equivalent `freeze_feature_encoder` method instead.", + FutureWarning, + ) + self.freeze_feature_encoder() + + # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_feature_encoder with wav2vec2->unispeech + def freeze_feature_encoder(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameter will + not be updated during training. + """ + self.unispeech.feature_extractor._freeze_parameters() + + # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_base_model with wav2vec2->unispeech + def freeze_base_model(self): + """ + Calling this function will disable the gradient computation for the base model so that its parameters will not + be updated during training. Only the classification head will be updated. + """ + for param in self.unispeech.parameters(): + param.requires_grad = False + + @add_start_docstrings_to_model_forward(UNISPEECH_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=SequenceClassifierOutput, + config_class=_CONFIG_FOR_DOC, + modality="audio", + ) + # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.forward with Wav2Vec2->UniSpeech, wav2vec2->unispeech + def forward( + self, + input_values: Optional[torch.Tensor], + attention_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + labels: Optional[torch.Tensor] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states + + outputs = self.unispeech( + input_values, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + if self.config.use_weighted_layer_sum: + hidden_states = outputs[_HIDDEN_STATES_START_POSITION] + hidden_states = torch.stack(hidden_states, dim=1) + norm_weights = nn.functional.softmax(self.layer_weights, dim=-1) + hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1) + else: + hidden_states = outputs[0] + + hidden_states = self.projector(hidden_states) + if attention_mask is None: + pooled_output = hidden_states.mean(dim=1) + else: + padding_mask = self._get_feature_vector_attention_mask(hidden_states.shape[1], attention_mask) + hidden_states[~padding_mask] = 0.0 + pooled_output = hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1) + + logits = self.classifier(pooled_output) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/unispeech_sat/__pycache__/__init__.cpython-312.pyc 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b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/unispeech_sat/configuration_unispeech_sat.py @@ -0,0 +1,327 @@ +# coding=utf-8 +# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" UniSpeechSat model configuration""" + +import functools +import operator + +from ...configuration_utils import PretrainedConfig +from ...utils import logging + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class UniSpeechSatConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`UniSpeechSatModel`]. It is used to instantiate an + UniSpeechSat model according to the specified arguments, defining the model architecture. Instantiating a + configuration with the defaults will yield a similar configuration to that of the UniSpeechSat + [microsoft/unispeech-sat-base-100h-libri-ft](https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft) + architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 32): + Vocabulary size of the UniSpeechSat model. Defines the number of different tokens that can be represented + by the `inputs_ids` passed when calling [`UniSpeechSatModel`]. Vocabulary size of the model. Defines the + different tokens that can be represented by the *inputs_ids* passed to the forward method of + [`UniSpeechSatModel`]. + hidden_size (`int`, *optional*, defaults to 768): + Dimensionality of the encoder layers and the pooler layer. + num_hidden_layers (`int`, *optional*, defaults to 12): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 12): + Number of attention heads for each attention layer in the Transformer encoder. + intermediate_size (`int`, *optional*, defaults to 3072): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"` and `"gelu_new"` are supported. + hidden_dropout (`float`, *optional*, defaults to 0.1): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + activation_dropout (`float`, *optional*, defaults to 0.1): + The dropout ratio for activations inside the fully connected layer. + attention_dropout (`float`, *optional*, defaults to 0.1): + The dropout ratio for the attention probabilities. + feat_proj_dropout (`float`, *optional*, defaults to 0.0): + The dropout probability for output of the feature encoder. + feat_quantizer_dropout (`float`, *optional*, defaults to 0.0): + The dropout probability for the output of the feature encoder that's used by the quantizer. + final_dropout (`float`, *optional*, defaults to 0.1): + The dropout probability for the final projection layer of [`UniSpeechSatForCTC`]. + layerdrop (`float`, *optional*, defaults to 0.1): + The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more + details. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + layer_norm_eps (`float`, *optional*, defaults to 1e-05): + The epsilon used by the layer normalization layers. + feat_extract_norm (`str`, *optional*, defaults to `"group"`): + The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group + normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D + convolutional layers. + feat_extract_activation (`str, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the 1D convolutional layers of the feature + extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. + conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`): + A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the + feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers. + conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`): + A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length + of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*. + conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 2, 2)`): + A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The + length of *conv_kernel* defines the number of convolutional layers and has to match the length of + *conv_dim*. + conv_bias (`bool`, *optional*, defaults to `False`): + Whether the 1D convolutional layers have a bias. + num_conv_pos_embeddings (`int`, *optional*, defaults to 128): + Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional + embeddings layer. + num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16): + Number of groups of 1D convolutional positional embeddings layer. + do_stable_layer_norm (`bool`, *optional*, defaults to `False`): + Whether to apply *stable* layer norm architecture of the Transformer encoder. `do_stable_layer_norm is + True` corresponds to applying layer norm before the attention layer, whereas `do_stable_layer_norm is + False` corresponds to applying layer norm after the attention layer. + apply_spec_augment (`bool`, *optional*, defaults to `True`): + Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see + [SpecAugment: A Simple Data Augmentation Method for Automatic Speech + Recognition](https://arxiv.org/abs/1904.08779). + mask_time_prob (`float`, *optional*, defaults to 0.05): + Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking + procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If + reasoning from the propability of each feature vector to be chosen as the start of the vector span to be + masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the + actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`. + mask_time_length (`int`, *optional*, defaults to 10): + Length of vector span along the time axis. + mask_time_min_masks (`int`, *optional*, defaults to 2): + The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step, + irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length < + mask_time_min_masks'' + mask_feature_prob (`float`, *optional*, defaults to 0.0): + Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The + masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over + the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector + span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap + may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is + True`. + mask_feature_length (`int`, *optional*, defaults to 10): + Length of vector span along the feature axis. + mask_feature_min_masks (`int`, *optional*, defaults to 0): + The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time + step, irrespectively of `mask_feature_prob`. Only relevant if + ''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks'' + num_codevectors_per_group (`int`, *optional*, defaults to 320): + Number of entries in each quantization codebook (group). + num_codevector_groups (`int`, *optional*, defaults to 2): + Number of codevector groups for product codevector quantization. + contrastive_logits_temperature (`float`, *optional*, defaults to 0.1): + The temperature *kappa* in the contrastive loss. + num_negatives (`int`, *optional*, defaults to 100): + Number of negative samples for the contrastive loss. + codevector_dim (`int`, *optional*, defaults to 256): + Dimensionality of the quantized feature vectors. + proj_codevector_dim (`int`, *optional*, defaults to 256): + Dimensionality of the final projection of both the quantized and the transformer features. + diversity_loss_weight (`int`, *optional*, defaults to 0.1): + The weight of the codebook diversity loss component. + ctc_loss_reduction (`str`, *optional*, defaults to `"mean"`): + Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an + instance of [`UniSpeechSatForCTC`]. + ctc_zero_infinity (`bool`, *optional*, defaults to `False`): + Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly + occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance + of [`UniSpeechSatForCTC`]. + use_weighted_layer_sum (`bool`, *optional*, defaults to `False`): + Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an + instance of [`UniSpeechSatForSequenceClassification`]. + classifier_proj_size (`int`, *optional*, defaults to 256): + Dimensionality of the projection before token mean-pooling for classification. + tdnn_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 1500)`): + A tuple of integers defining the number of output channels of each 1D convolutional layer in the *TDNN* + module of the *XVector* model. The length of *tdnn_dim* defines the number of *TDNN* layers. + tdnn_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 3, 3, 1, 1)`): + A tuple of integers defining the kernel size of each 1D convolutional layer in the *TDNN* module of the + *XVector* model. The length of *tdnn_kernel* has to match the length of *tdnn_dim*. + tdnn_dilation (`Tuple[int]` or `List[int]`, *optional*, defaults to `(1, 2, 3, 1, 1)`): + A tuple of integers defining the dilation factor of each 1D convolutional layer in *TDNN* module of the + *XVector* model. The length of *tdnn_dilation* has to match the length of *tdnn_dim*. + xvector_output_dim (`int`, *optional*, defaults to 512): + Dimensionality of the *XVector* embedding vectors. + pad_token_id (`int`, *optional*, defaults to 0): + The id of the padding token. + bos_token_id (`int`, *optional*, defaults to 1): + The id of the "beginning-of-sequence" token. + eos_token_id (`int`, *optional*, defaults to 2): + The id of the "end-of-sequence" token. + num_clusters (`int`, *optional*, defaults to 504): + Number of clusters for weak labeling. Only relevant when using an instance of + [`UniSpeechSatForPreTraining`]. + + Example: + + ```python + >>> from transformers import UniSpeechSatModel, UniSpeechSatConfig + + >>> # Initializing a UniSpeechSat microsoft/unispeech-sat-base-100h-libri-ft style configuration + >>> configuration = UniSpeechSatConfig() + + >>> # Initializing a model from the microsoft/unispeech-sat-base-100h-libri-ft style configuration + >>> model = UniSpeechSatModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "unispeech-sat" + + def __init__( + self, + vocab_size=32, + hidden_size=768, + num_hidden_layers=12, + num_attention_heads=12, + intermediate_size=3072, + hidden_act="gelu", + hidden_dropout=0.1, + activation_dropout=0.1, + attention_dropout=0.1, + feat_proj_dropout=0.0, + feat_quantizer_dropout=0.0, + final_dropout=0.1, + layerdrop=0.1, + initializer_range=0.02, + layer_norm_eps=1e-5, + feat_extract_norm="group", + feat_extract_activation="gelu", + conv_dim=(512, 512, 512, 512, 512, 512, 512), + conv_stride=(5, 2, 2, 2, 2, 2, 2), + conv_kernel=(10, 3, 3, 3, 3, 2, 2), + conv_bias=False, + num_conv_pos_embeddings=128, + num_conv_pos_embedding_groups=16, + do_stable_layer_norm=False, + apply_spec_augment=True, + mask_time_prob=0.05, + mask_time_length=10, + mask_time_min_masks=2, + mask_feature_prob=0.0, + mask_feature_length=10, + mask_feature_min_masks=0, + num_codevectors_per_group=320, + num_codevector_groups=2, + contrastive_logits_temperature=0.1, + num_negatives=100, + codevector_dim=256, + proj_codevector_dim=256, + diversity_loss_weight=0.1, + ctc_loss_reduction="mean", + ctc_zero_infinity=False, + use_weighted_layer_sum=False, + classifier_proj_size=256, + tdnn_dim=(512, 512, 512, 512, 1500), + tdnn_kernel=(5, 3, 3, 1, 1), + tdnn_dilation=(1, 2, 3, 1, 1), + xvector_output_dim=512, + pad_token_id=0, + bos_token_id=1, + eos_token_id=2, + num_clusters=504, + **kwargs, + ): + super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id) + self.hidden_size = hidden_size + self.feat_extract_norm = feat_extract_norm + self.feat_extract_activation = feat_extract_activation + self.conv_dim = list(conv_dim) + self.conv_stride = list(conv_stride) + self.conv_kernel = list(conv_kernel) + self.conv_bias = conv_bias + self.num_conv_pos_embeddings = num_conv_pos_embeddings + self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups + self.num_feat_extract_layers = len(self.conv_dim) + self.num_hidden_layers = num_hidden_layers + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.num_attention_heads = num_attention_heads + self.hidden_dropout = hidden_dropout + self.attention_dropout = attention_dropout + self.activation_dropout = activation_dropout + self.feat_proj_dropout = feat_proj_dropout + self.final_dropout = final_dropout + self.layerdrop = layerdrop + self.layer_norm_eps = layer_norm_eps + self.initializer_range = initializer_range + self.vocab_size = vocab_size + self.num_clusters = num_clusters + self.do_stable_layer_norm = do_stable_layer_norm + self.use_weighted_layer_sum = use_weighted_layer_sum + + if ( + (len(self.conv_stride) != self.num_feat_extract_layers) + or (len(self.conv_kernel) != self.num_feat_extract_layers) + or (len(self.conv_dim) != self.num_feat_extract_layers) + ): + raise ValueError( + "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` ==" + " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) =" + f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`," + f" `len(config.conv_kernel) = {len(self.conv_kernel)}`." + ) + + # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 + self.apply_spec_augment = apply_spec_augment + self.mask_time_prob = mask_time_prob + self.mask_time_length = mask_time_length + self.mask_time_min_masks = mask_time_min_masks + self.mask_feature_prob = mask_feature_prob + self.mask_feature_length = mask_feature_length + self.mask_feature_min_masks = mask_feature_min_masks + + # parameters for pretraining with codevector quantized representations + self.num_codevectors_per_group = num_codevectors_per_group + self.num_codevector_groups = num_codevector_groups + self.contrastive_logits_temperature = contrastive_logits_temperature + self.feat_quantizer_dropout = feat_quantizer_dropout + self.num_negatives = num_negatives + self.codevector_dim = codevector_dim + self.proj_codevector_dim = proj_codevector_dim + self.diversity_loss_weight = diversity_loss_weight + + # ctc loss + self.ctc_loss_reduction = ctc_loss_reduction + self.ctc_zero_infinity = ctc_zero_infinity + + # SequenceClassification-specific parameter. Feel free to ignore for other classes. + self.classifier_proj_size = classifier_proj_size + + # XVector-specific parameters. Feel free to ignore for other classes. + self.tdnn_dim = list(tdnn_dim) + self.tdnn_kernel = list(tdnn_kernel) + self.tdnn_dilation = list(tdnn_dilation) + self.xvector_output_dim = xvector_output_dim + + @property + def inputs_to_logits_ratio(self): + return functools.reduce(operator.mul, self.conv_stride, 1) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/unispeech_sat/convert_unispeech_original_s3prl_checkpoint_to_pytorch.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/unispeech_sat/convert_unispeech_original_s3prl_checkpoint_to_pytorch.py new file mode 100644 index 0000000000000000000000000000000000000000..56c9d52e185d25bbe0f58ca951419d848eead9de --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/unispeech_sat/convert_unispeech_original_s3prl_checkpoint_to_pytorch.py @@ -0,0 +1,110 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert Hubert checkpoint.""" + + +import argparse + +import torch + +from transformers import ( + UniSpeechSatConfig, + UniSpeechSatForAudioFrameClassification, + UniSpeechSatForSequenceClassification, + UniSpeechSatForXVector, + Wav2Vec2FeatureExtractor, + logging, +) + + +logging.set_verbosity_info() +logger = logging.get_logger(__name__) + + +def convert_classification(base_model_name, hf_config, downstream_dict): + model = UniSpeechSatForSequenceClassification.from_pretrained(base_model_name, config=hf_config) + model.projector.weight.data = downstream_dict["projector.weight"] + model.projector.bias.data = downstream_dict["projector.bias"] + model.classifier.weight.data = downstream_dict["model.post_net.linear.weight"] + model.classifier.bias.data = downstream_dict["model.post_net.linear.bias"] + return model + + +def convert_diarization(base_model_name, hf_config, downstream_dict): + model = UniSpeechSatForAudioFrameClassification.from_pretrained(base_model_name, config=hf_config) + model.classifier.weight.data = downstream_dict["model.linear.weight"] + model.classifier.bias.data = downstream_dict["model.linear.bias"] + return model + + +def convert_xvector(base_model_name, hf_config, downstream_dict): + model = UniSpeechSatForXVector.from_pretrained(base_model_name, config=hf_config) + model.projector.weight.data = downstream_dict["connector.weight"] + model.projector.bias.data = downstream_dict["connector.bias"] + for i, kernel_size in enumerate(hf_config.tdnn_kernel): + model.tdnn[i].kernel.weight.data = downstream_dict[ + f"model.framelevel_feature_extractor.module.{i}.kernel.weight" + ] + model.tdnn[i].kernel.bias.data = downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"] + + model.feature_extractor.weight.data = downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"] + model.feature_extractor.bias.data = downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"] + model.classifier.weight.data = downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"] + model.classifier.bias.data = downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"] + model.objective.weight.data = downstream_dict["objective.W"] + return model + + +@torch.no_grad() +def convert_s3prl_checkpoint(base_model_name, config_path, checkpoint_path, model_dump_path): + """ + Copy/paste/tweak model's weights to transformers design. + """ + checkpoint = torch.load(checkpoint_path, map_location="cpu") + + downstream_dict = checkpoint["Downstream"] + + hf_config = UniSpeechSatConfig.from_pretrained(config_path) + hf_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( + base_model_name, return_attention_mask=True, do_normalize=False + ) + + arch = hf_config.architectures[0] + if arch.endswith("ForSequenceClassification"): + hf_model = convert_classification(base_model_name, hf_config, downstream_dict) + elif arch.endswith("ForAudioFrameClassification"): + hf_model = convert_diarization(base_model_name, hf_config, downstream_dict) + elif arch.endswith("ForXVector"): + hf_model = convert_xvector(base_model_name, hf_config, downstream_dict) + else: + raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}") + + if hf_config.use_weighted_layer_sum: + hf_model.layer_weights.data = checkpoint["Featurizer"]["weights"] + + hf_feature_extractor.save_pretrained(model_dump_path) + hf_model.save_pretrained(model_dump_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model." + ) + parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.") + parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.") + parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.") + args = parser.parse_args() + convert_s3prl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/unispeech_sat/convert_unispeech_sat_original_pytorch_checkpoint_to_pytorch.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/unispeech_sat/convert_unispeech_sat_original_pytorch_checkpoint_to_pytorch.py new file mode 100644 index 0000000000000000000000000000000000000000..93750b64cc3a2db5b0b162a5496ecda4e36746e0 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/unispeech_sat/convert_unispeech_sat_original_pytorch_checkpoint_to_pytorch.py @@ -0,0 +1,225 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert UniSpeechSat checkpoint.""" + + +import argparse + +import fairseq +import torch + +from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging + + +logging.set_verbosity_info() +logger = logging.get_logger(__name__) + +MAPPING = { + "post_extract_proj": "feature_projection.projection", + "encoder.pos_conv.0": "encoder.pos_conv_embed.conv", + "self_attn.k_proj": "encoder.layers.*.attention.k_proj", + "self_attn.v_proj": "encoder.layers.*.attention.v_proj", + "self_attn.q_proj": "encoder.layers.*.attention.q_proj", + "self_attn.out_proj": "encoder.layers.*.attention.out_proj", + "self_attn_layer_norm": "encoder.layers.*.layer_norm", + "fc1": "encoder.layers.*.feed_forward.intermediate_dense", + "fc2": "encoder.layers.*.feed_forward.output_dense", + "final_layer_norm": "encoder.layers.*.final_layer_norm", + "encoder.layer_norm": "encoder.layer_norm", + "encoder.layer_norm_for_extract": "layer_norm_for_extract", + "w2v_model.layer_norm": "feature_projection.layer_norm", + "quantizer.weight_proj": "quantizer.weight_proj", + "quantizer.vars": "quantizer.codevectors", + "project_q": "project_q", + "final_proj": "project_hid", + "w2v_encoder.proj": "lm_head", + "label_embs_concat": "label_embeddings_concat", + "mask_emb": "masked_spec_embed", + "spk_proj": "speaker_proj", +} +TOP_LEVEL_KEYS = [ + "lm_head", + "quantizer.weight_proj", + "quantizer.codevectors", + "project_q", + "project_hid", + "label_embeddings_concat", + "speaker_proj", + "layer_norm_for_extract", +] + + +def set_recursively(hf_pointer, key, value, full_name, weight_type): + for attribute in key.split("."): + hf_pointer = getattr(hf_pointer, attribute) + + if weight_type is not None: + hf_shape = getattr(hf_pointer, weight_type).shape + else: + hf_shape = hf_pointer.shape + + if hf_shape != value.shape: + raise ValueError( + f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be" + f" {value.shape} for {full_name}" + ) + + if weight_type == "weight": + hf_pointer.weight.data = value + elif weight_type == "weight_g": + hf_pointer.weight_g.data = value + elif weight_type == "weight_v": + hf_pointer.weight_v.data = value + elif weight_type == "bias": + hf_pointer.bias.data = value + else: + hf_pointer.data = value + + logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.") + + +def recursively_load_weights(fairseq_model, hf_model): + unused_weights = [] + fairseq_dict = fairseq_model.state_dict() + + feature_extractor = hf_model.unispeech_sat.feature_extractor + + for name, value in fairseq_dict.items(): + is_used = False + if "conv_layers" in name: + load_conv_layer( + name, + value, + feature_extractor, + unused_weights, + hf_model.config.feat_extract_norm == "group", + ) + is_used = True + else: + for key, mapped_key in MAPPING.items(): + mapped_key = "unispeech_sat." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key + if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]: + if "layer_norm_for_extract" in name and (".".join(name.split(".")[:-1]) != key): + # special case since naming is very similar + continue + is_used = True + if "*" in mapped_key: + layer_index = name.split(key)[0].split(".")[-2] + mapped_key = mapped_key.replace("*", layer_index) + if "weight_g" in name: + weight_type = "weight_g" + elif "weight_v" in name: + weight_type = "weight_v" + elif "bias" in name: + weight_type = "bias" + elif "weight" in name: + # TODO: don't match quantizer.weight_proj + weight_type = "weight" + else: + weight_type = None + set_recursively(hf_model, mapped_key, value, name, weight_type) + continue + if not is_used: + unused_weights.append(name) + + logger.warning(f"Unused weights: {unused_weights}") + + +def load_conv_layer(full_name, value, feature_extractor, unused_weights, use_group_norm): + name = full_name.split("conv_layers.")[-1] + items = name.split(".") + layer_id = int(items[0]) + type_id = int(items[1]) + + if type_id == 0: + if "bias" in name: + if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape: + raise ValueError( + f"{full_name} has size {value.shape}, but" + f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found." + ) + feature_extractor.conv_layers[layer_id].conv.bias.data = value + logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.") + elif "weight" in name: + if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape: + raise ValueError( + f"{full_name} has size {value.shape}, but" + f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found." + ) + feature_extractor.conv_layers[layer_id].conv.weight.data = value + logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.") + elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): + if "bias" in name: + if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape: + raise ValueError( + f"{full_name} has size {value.shape}, but" + f" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found." + ) + feature_extractor.conv_layers[layer_id].layer_norm.bias.data = value + logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.") + elif "weight" in name: + if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape: + raise ValueError( + f"{full_name} has size {value.shape}, but" + f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found." + ) + feature_extractor.conv_layers[layer_id].layer_norm.weight.data = value + logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.") + else: + unused_weights.append(full_name) + + +@torch.no_grad() +def convert_unispeech_sat_checkpoint( + checkpoint_path, pytorch_dump_folder_path, config_path=None, dict_path=None, is_finetuned=True +): + """ + Copy/paste/tweak model's weights to transformers design. + """ + if config_path is not None: + config = UniSpeechSatConfig.from_pretrained(config_path) + else: + config = UniSpeechSatConfig() + + dict_path = "" + + if is_finetuned: + hf_wav2vec = UniSpeechSatForCTC(config) + else: + hf_wav2vec = UniSpeechSatForPreTraining(config) + + model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task( + [checkpoint_path], arg_overrides={"data": "/".join(dict_path.split("/")[:-1])} + ) + model = model[0].eval() + + recursively_load_weights(model, hf_wav2vec) + + hf_wav2vec.save_pretrained(pytorch_dump_folder_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") + parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint") + parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model") + parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert") + parser.add_argument( + "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not" + ) + args = parser.parse_args() + convert_unispeech_sat_checkpoint( + args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/unispeech_sat/modeling_unispeech_sat.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/unispeech_sat/modeling_unispeech_sat.py new file mode 100644 index 0000000000000000000000000000000000000000..f38da0d47f5c3d5589206d595c79bde48b90e288 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/unispeech_sat/modeling_unispeech_sat.py @@ -0,0 +1,1969 @@ +# coding=utf-8 +# Copyright 2021 The Fairseq Authors and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch UniSpeechSat model.""" + +import math +import warnings +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import numpy as np +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss + +from ...activations import ACT2FN +from ...integrations.deepspeed import is_deepspeed_zero3_enabled +from ...modeling_outputs import ( + BaseModelOutput, + CausalLMOutput, + SequenceClassifierOutput, + TokenClassifierOutput, + Wav2Vec2BaseModelOutput, + XVectorOutput, +) +from ...modeling_utils import PreTrainedModel +from ...utils import ( + ModelOutput, + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_peft_available, + logging, + replace_return_docstrings, +) +from .configuration_unispeech_sat import UniSpeechSatConfig + + +logger = logging.get_logger(__name__) + + +_HIDDEN_STATES_START_POSITION = 2 + +# General docstring +_CONFIG_FOR_DOC = "UniSpeechSatConfig" + +# Base docstring +_CHECKPOINT_FOR_DOC = "microsoft/unispeech-sat-base-100h-libri-ft" +_EXPECTED_OUTPUT_SHAPE = [1, 292, 768] + +# CTC docstring +_CTC_EXPECTED_OUTPUT = "'MISTER QUILDER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL'" +_CTC_EXPECTED_LOSS = 39.88 + +# Frame class docstring +_FRAME_CLASS_CHECKPOINT = "microsoft/unispeech-sat-base-plus-sd" +_FRAME_EXPECTED_OUTPUT = [0, 0] + +# Speaker Verification docstring +_XVECTOR_CHECKPOINT = "microsoft/unispeech-sat-base-plus-sv" +_XVECTOR_EXPECTED_OUTPUT = 0.97 + + +from ..deprecated._archive_maps import UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +@dataclass +class UniSpeechSatForPreTrainingOutput(ModelOutput): + """ + Output type of [`UniSpeechSatForPreTrainingOutput`], with potential hidden states and attentions. + + Args: + loss (*optional*, returned when model is in train mode, `torch.FloatTensor` of shape `(1,)`): + Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the [official + paper](https://arxiv.org/pdf/2006.11477.pdf) . (classification) loss. + projected_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`): + Hidden-states of the model projected to *config.proj_codevector_dim* that can be used to predict the masked + projected quantized states. + projected_quantized_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`): + Quantized extracted feature vectors projected to *config.proj_codevector_dim* representing the positive + target vectors for contrastive loss. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + """ + + loss: Optional[torch.FloatTensor] = None + logits: torch.FloatTensor = None + projected_states: torch.FloatTensor = None + projected_quantized_states: torch.FloatTensor = None + codevector_perplexity: torch.FloatTensor = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2._compute_mask_indices +def _compute_mask_indices( + shape: Tuple[int, int], + mask_prob: float, + mask_length: int, + attention_mask: Optional[torch.LongTensor] = None, + min_masks: int = 0, +) -> np.ndarray: + """ + Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for + ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on + CPU as part of the preprocessing during training. + + Args: + shape: The shape for which to compute masks. This should be of a tuple of size 2 where + the first element is the batch size and the second element is the length of the axis to span. + mask_prob: The percentage of the whole axis (between 0 and 1) which will be masked. The number of + independently generated mask spans of length `mask_length` is computed by + `mask_prob*shape[1]/mask_length`. Note that due to overlaps, `mask_prob` is an upper bound and the + actual percentage will be smaller. + mask_length: size of the mask + min_masks: minimum number of masked spans + attention_mask: A (right-padded) attention mask which independently shortens the feature axis of + each batch dimension. + """ + batch_size, sequence_length = shape + + if mask_length < 1: + raise ValueError("`mask_length` has to be bigger than 0.") + + if mask_length > sequence_length: + raise ValueError( + f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length}" + f" and `sequence_length`: {sequence_length}`" + ) + + # epsilon is used for probabilistic rounding + epsilon = np.random.rand(1).item() + + def compute_num_masked_span(input_length): + """Given input length, compute how many spans should be masked""" + num_masked_span = int(mask_prob * input_length / mask_length + epsilon) + num_masked_span = max(num_masked_span, min_masks) + + # make sure num masked span <= sequence_length + if num_masked_span * mask_length > sequence_length: + num_masked_span = sequence_length // mask_length + + # make sure num_masked span is also <= input_length - (mask_length - 1) + if input_length - (mask_length - 1) < num_masked_span: + num_masked_span = max(input_length - (mask_length - 1), 0) + + return num_masked_span + + # compute number of masked spans in batch + input_lengths = ( + attention_mask.sum(-1).detach().tolist() + if attention_mask is not None + else [sequence_length for _ in range(batch_size)] + ) + + # SpecAugment mask to fill + spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool) + spec_aug_mask_idxs = [] + + max_num_masked_span = compute_num_masked_span(sequence_length) + + if max_num_masked_span == 0: + return spec_aug_mask + + for input_length in input_lengths: + # compute num of masked spans for this input + num_masked_span = compute_num_masked_span(input_length) + + # get random indices to mask + spec_aug_mask_idx = np.random.choice( + np.arange(input_length - (mask_length - 1)), num_masked_span, replace=False + ) + + # pick first sampled index that will serve as a dummy index to pad vector + # to ensure same dimension for all batches due to probabilistic rounding + # Picking first sample just pads those vectors twice. + if len(spec_aug_mask_idx) == 0: + # this case can only happen if `input_length` is strictly smaller then + # `sequence_length` in which case the last token has to be a padding + # token which we can use as a dummy mask id + dummy_mask_idx = sequence_length - 1 + else: + dummy_mask_idx = spec_aug_mask_idx[0] + + spec_aug_mask_idx = np.concatenate( + [spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx] + ) + spec_aug_mask_idxs.append(spec_aug_mask_idx) + + spec_aug_mask_idxs = np.array(spec_aug_mask_idxs) + + # expand masked indices to masked spans + spec_aug_mask_idxs = np.broadcast_to( + spec_aug_mask_idxs[:, :, None], (batch_size, max_num_masked_span, mask_length) + ) + spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, max_num_masked_span * mask_length) + + # add offset to the starting indexes so that indexes now create a span + offsets = np.arange(mask_length)[None, None, :] + offsets = np.broadcast_to(offsets, (batch_size, max_num_masked_span, mask_length)).reshape( + batch_size, max_num_masked_span * mask_length + ) + spec_aug_mask_idxs = spec_aug_mask_idxs + offsets + + # ensure that we cannot have indices larger than sequence_length + if spec_aug_mask_idxs.max() > sequence_length - 1: + spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1 + + # scatter indices to mask + np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1) + + return spec_aug_mask + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2NoLayerNormConvLayer with Wav2Vec2->UniSpeechSat +class UniSpeechSatNoLayerNormConvLayer(nn.Module): + def __init__(self, config, layer_id=0): + super().__init__() + self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1 + self.out_conv_dim = config.conv_dim[layer_id] + + self.conv = nn.Conv1d( + self.in_conv_dim, + self.out_conv_dim, + kernel_size=config.conv_kernel[layer_id], + stride=config.conv_stride[layer_id], + bias=config.conv_bias, + ) + self.activation = ACT2FN[config.feat_extract_activation] + + def forward(self, hidden_states): + hidden_states = self.conv(hidden_states) + hidden_states = self.activation(hidden_states) + return hidden_states + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2LayerNormConvLayer with Wav2Vec2->UniSpeechSat +class UniSpeechSatLayerNormConvLayer(nn.Module): + def __init__(self, config, layer_id=0): + super().__init__() + self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1 + self.out_conv_dim = config.conv_dim[layer_id] + + self.conv = nn.Conv1d( + self.in_conv_dim, + self.out_conv_dim, + kernel_size=config.conv_kernel[layer_id], + stride=config.conv_stride[layer_id], + bias=config.conv_bias, + ) + self.layer_norm = nn.LayerNorm(self.out_conv_dim, elementwise_affine=True) + self.activation = ACT2FN[config.feat_extract_activation] + + def forward(self, hidden_states): + hidden_states = self.conv(hidden_states) + + hidden_states = hidden_states.transpose(-2, -1) + hidden_states = self.layer_norm(hidden_states) + hidden_states = hidden_states.transpose(-2, -1) + + hidden_states = self.activation(hidden_states) + return hidden_states + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2GroupNormConvLayer with Wav2Vec2->UniSpeechSat +class UniSpeechSatGroupNormConvLayer(nn.Module): + def __init__(self, config, layer_id=0): + super().__init__() + self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1 + self.out_conv_dim = config.conv_dim[layer_id] + + self.conv = nn.Conv1d( + self.in_conv_dim, + self.out_conv_dim, + kernel_size=config.conv_kernel[layer_id], + stride=config.conv_stride[layer_id], + bias=config.conv_bias, + ) + self.activation = ACT2FN[config.feat_extract_activation] + + self.layer_norm = nn.GroupNorm(num_groups=self.out_conv_dim, num_channels=self.out_conv_dim, affine=True) + + def forward(self, hidden_states): + hidden_states = self.conv(hidden_states) + hidden_states = self.layer_norm(hidden_states) + hidden_states = self.activation(hidden_states) + return hidden_states + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2PositionalConvEmbedding with Wav2Vec2->UniSpeechSat +class UniSpeechSatPositionalConvEmbedding(nn.Module): + def __init__(self, config): + super().__init__() + self.conv = nn.Conv1d( + config.hidden_size, + config.hidden_size, + kernel_size=config.num_conv_pos_embeddings, + padding=config.num_conv_pos_embeddings // 2, + groups=config.num_conv_pos_embedding_groups, + ) + + weight_norm = nn.utils.weight_norm + if hasattr(nn.utils.parametrizations, "weight_norm"): + weight_norm = nn.utils.parametrizations.weight_norm + + if is_deepspeed_zero3_enabled(): + import deepspeed + + with deepspeed.zero.GatheredParameters(self.conv.weight, modifier_rank=0): + self.conv = weight_norm(self.conv, name="weight", dim=2) + deepspeed.zero.register_external_parameter(self, self.conv.weight_v) + deepspeed.zero.register_external_parameter(self, self.conv.weight_g) + else: + self.conv = weight_norm(self.conv, name="weight", dim=2) + + self.padding = UniSpeechSatSamePadLayer(config.num_conv_pos_embeddings) + self.activation = ACT2FN[config.feat_extract_activation] + + def forward(self, hidden_states): + hidden_states = hidden_states.transpose(1, 2) + + hidden_states = self.conv(hidden_states) + hidden_states = self.padding(hidden_states) + hidden_states = self.activation(hidden_states) + + hidden_states = hidden_states.transpose(1, 2) + return hidden_states + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2SamePadLayer with Wav2Vec2->UniSpeechSat +class UniSpeechSatSamePadLayer(nn.Module): + def __init__(self, num_conv_pos_embeddings): + super().__init__() + self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0 + + def forward(self, hidden_states): + if self.num_pad_remove > 0: + hidden_states = hidden_states[:, :, : -self.num_pad_remove] + return hidden_states + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeatureEncoder with Wav2Vec2->UniSpeechSat +class UniSpeechSatFeatureEncoder(nn.Module): + """Construct the features from raw audio waveform""" + + def __init__(self, config): + super().__init__() + + if config.feat_extract_norm == "group": + conv_layers = [UniSpeechSatGroupNormConvLayer(config, layer_id=0)] + [ + UniSpeechSatNoLayerNormConvLayer(config, layer_id=i + 1) + for i in range(config.num_feat_extract_layers - 1) + ] + elif config.feat_extract_norm == "layer": + conv_layers = [ + UniSpeechSatLayerNormConvLayer(config, layer_id=i) for i in range(config.num_feat_extract_layers) + ] + else: + raise ValueError( + f"`config.feat_extract_norm` is {config.feat_extract_norm}, but has to be one of ['group', 'layer']" + ) + self.conv_layers = nn.ModuleList(conv_layers) + self.gradient_checkpointing = False + self._requires_grad = True + + def _freeze_parameters(self): + for param in self.parameters(): + param.requires_grad = False + self._requires_grad = False + + def forward(self, input_values): + hidden_states = input_values[:, None] + + # make sure hidden_states require grad for gradient_checkpointing + if self._requires_grad and self.training: + hidden_states.requires_grad = True + + for conv_layer in self.conv_layers: + if self._requires_grad and self.gradient_checkpointing and self.training: + hidden_states = self._gradient_checkpointing_func( + conv_layer.__call__, + hidden_states, + ) + else: + hidden_states = conv_layer(hidden_states) + + return hidden_states + + +class UniSpeechSatFeatureExtractor(UniSpeechSatFeatureEncoder): + def __init__(self, config): + super().__init__(config) + warnings.warn( + f"The class `{self.__class__.__name__}` has been depreciated " + "and will be removed in Transformers v5. " + f"Use `{self.__class__.__bases__[0].__name__}` instead.", + FutureWarning, + ) + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeatureProjection with Wav2Vec2->UniSpeechSat +class UniSpeechSatFeatureProjection(nn.Module): + def __init__(self, config): + super().__init__() + self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.layer_norm_eps) + self.projection = nn.Linear(config.conv_dim[-1], config.hidden_size) + self.dropout = nn.Dropout(config.feat_proj_dropout) + + def forward(self, hidden_states): + # non-projected hidden states are needed for quantization + norm_hidden_states = self.layer_norm(hidden_states) + hidden_states = self.projection(norm_hidden_states) + hidden_states = self.dropout(hidden_states) + return hidden_states, norm_hidden_states + + +# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->UniSpeechSat +class UniSpeechSatAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + is_decoder: bool = False, + bias: bool = True, + is_causal: bool = False, + config: Optional[UniSpeechSatConfig] = None, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + self.config = config + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + self.is_causal = is_causal + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + bsz, tgt_len, _ = hidden_states.size() + + # get query proj + query_states = self.q_proj(hidden_states) * self.scaling + # get key, value proj + # `past_key_value[0].shape[2] == key_value_states.shape[1]` + # is checking that the `sequence_length` of the `past_key_value` is the same as + # the provided `key_value_states` to support prefix tuning + if ( + is_cross_attention + and past_key_value is not None + and past_key_value[0].shape[2] == key_value_states.shape[1] + ): + # reuse k,v, cross_attentions + key_states = past_key_value[0] + value_states = past_key_value[1] + elif is_cross_attention: + # cross_attentions + key_states = self._shape(self.k_proj(key_value_states), -1, bsz) + value_states = self._shape(self.v_proj(key_value_states), -1, bsz) + elif past_key_value is not None: + # reuse k, v, self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + else: + # self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_states, value_states) + + proj_shape = (bsz * self.num_heads, -1, self.head_dim) + query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) + key_states = key_states.reshape(*proj_shape) + value_states = value_states.reshape(*proj_shape) + + src_len = key_states.size(1) + attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) + + if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, tgt_len, src_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + if layer_head_mask is not None: + if layer_head_mask.size() != (self.num_heads,): + raise ValueError( + f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" + f" {layer_head_mask.size()}" + ) + attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + if output_attentions: + # this operation is a bit awkward, but it's required to + # make sure that attn_weights keeps its gradient. + # In order to do so, attn_weights have to be reshaped + # twice and have to be reused in the following + attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) + else: + attn_weights_reshaped = None + + attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = torch.bmm(attn_probs, value_states) + + if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) + attn_output = attn_output.transpose(1, 2) + + # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be + # partitioned across GPUs when using tensor-parallelism. + attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights_reshaped, past_key_value + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeedForward with Wav2Vec2->UniSpeechSat +class UniSpeechSatFeedForward(nn.Module): + def __init__(self, config): + super().__init__() + self.intermediate_dropout = nn.Dropout(config.activation_dropout) + + self.intermediate_dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + self.output_dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.output_dropout = nn.Dropout(config.hidden_dropout) + + def forward(self, hidden_states): + hidden_states = self.intermediate_dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + hidden_states = self.intermediate_dropout(hidden_states) + + hidden_states = self.output_dense(hidden_states) + hidden_states = self.output_dropout(hidden_states) + return hidden_states + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2EncoderLayer with Wav2Vec2->UniSpeechSat +class UniSpeechSatEncoderLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.attention = UniSpeechSatAttention( + embed_dim=config.hidden_size, + num_heads=config.num_attention_heads, + dropout=config.attention_dropout, + is_decoder=False, + ) + self.dropout = nn.Dropout(config.hidden_dropout) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.feed_forward = UniSpeechSatFeedForward(config) + self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states, attention_mask=None, output_attentions=False): + attn_residual = hidden_states + hidden_states, attn_weights, _ = self.attention( + hidden_states, attention_mask=attention_mask, output_attentions=output_attentions + ) + hidden_states = self.dropout(hidden_states) + hidden_states = attn_residual + hidden_states + + hidden_states = self.layer_norm(hidden_states) + hidden_states = hidden_states + self.feed_forward(hidden_states) + hidden_states = self.final_layer_norm(hidden_states) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2AttnAdapterLayer with Wav2Vec2->UniSpeechSat +class UniSpeechSatAttnAdapterLayer(nn.Module): + def __init__(self, config): + """ + Implements adapter modules directly with 3D tensor weight as parameters and without using ModuleList to speed + up training throughput. + """ + super().__init__() + self.input_dim = config.adapter_attn_dim + self.hidden_dim = config.hidden_size + + self.norm = nn.LayerNorm(self.hidden_dim) + self.linear_1 = nn.Linear(self.hidden_dim, self.input_dim) + self.act_fn = nn.ReLU() + self.linear_2 = nn.Linear(self.input_dim, self.hidden_dim) + + def forward(self, hidden_states: torch.FloatTensor): + hidden_states = self.norm(hidden_states) + + hidden_states = self.linear_1(hidden_states) + hidden_states = self.act_fn(hidden_states) + hidden_states = self.linear_2(hidden_states) + + return hidden_states + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2EncoderLayerStableLayerNorm with Wav2Vec2->UniSpeechSat +class UniSpeechSatEncoderLayerStableLayerNorm(nn.Module): + def __init__(self, config): + super().__init__() + self.attention = UniSpeechSatAttention( + embed_dim=config.hidden_size, + num_heads=config.num_attention_heads, + dropout=config.attention_dropout, + is_decoder=False, + ) + self.dropout = nn.Dropout(config.hidden_dropout) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.feed_forward = UniSpeechSatFeedForward(config) + self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + if getattr(config, "adapter_attn_dim", None) is not None: + self.adapter_layer = UniSpeechSatAttnAdapterLayer(config) + else: + self.adapter_layer = None + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ): + attn_residual = hidden_states + hidden_states = self.layer_norm(hidden_states) + hidden_states, attn_weights, _ = self.attention( + hidden_states, attention_mask=attention_mask, output_attentions=output_attentions + ) + hidden_states = self.dropout(hidden_states) + hidden_states = attn_residual + hidden_states + hidden_states = hidden_states + self.feed_forward(self.final_layer_norm(hidden_states)) + + if self.adapter_layer is not None: + hidden_states = hidden_states + self.adapter_layer(hidden_states) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Encoder with Wav2Vec2->UniSpeechSat +class UniSpeechSatEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.pos_conv_embed = UniSpeechSatPositionalConvEmbedding(config) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout) + self.layers = nn.ModuleList([UniSpeechSatEncoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.tensor, + attention_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + if attention_mask is not None: + # make sure padded tokens output 0 + expand_attention_mask = attention_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2]) + hidden_states[~expand_attention_mask] = 0 + + # extend attention_mask + attention_mask = 1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype) + attention_mask = attention_mask * torch.finfo(hidden_states.dtype).min + attention_mask = attention_mask.expand( + attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1] + ) + + position_embeddings = self.pos_conv_embed(hidden_states) + hidden_states = hidden_states + position_embeddings + hidden_states = self.layer_norm(hidden_states) + hidden_states = self.dropout(hidden_states) + + deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled() + + for layer in self.layers: + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + dropout_probability = torch.rand([]) + + skip_the_layer = True if self.training and (dropout_probability < self.config.layerdrop) else False + if not skip_the_layer or deepspeed_zero3_is_enabled: + # under deepspeed zero3 all gpus must run in sync + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer.__call__, + hidden_states, + attention_mask, + output_attentions, + ) + else: + layer_outputs = layer( + hidden_states, attention_mask=attention_mask, output_attentions=output_attentions + ) + hidden_states = layer_outputs[0] + + if skip_the_layer: + layer_outputs = (None, None) + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2EncoderStableLayerNorm with Wav2Vec2->UniSpeechSat +class UniSpeechSatEncoderStableLayerNorm(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.pos_conv_embed = UniSpeechSatPositionalConvEmbedding(config) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout) + self.layers = nn.ModuleList( + [UniSpeechSatEncoderLayerStableLayerNorm(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + if attention_mask is not None: + # make sure padded tokens are not attended to + expand_attention_mask = attention_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2]) + hidden_states[~expand_attention_mask] = 0 + + # extend attention_mask + attention_mask = 1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype) + attention_mask = attention_mask * torch.finfo(hidden_states.dtype).min + attention_mask = attention_mask.expand( + attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1] + ) + + position_embeddings = self.pos_conv_embed(hidden_states) + hidden_states = hidden_states + position_embeddings + hidden_states = self.dropout(hidden_states) + + deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled() + + for layer in self.layers: + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + dropout_probability = torch.rand([]) + + skip_the_layer = True if self.training and (dropout_probability < self.config.layerdrop) else False + if not skip_the_layer or deepspeed_zero3_is_enabled: + # under deepspeed zero3 all gpus must run in sync + # XXX: could optimize this like synced_gpus in generate_utils but not sure if it's worth the code complication + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer.__call__, + hidden_states, + attention_mask, + output_attentions, + ) + else: + layer_outputs = layer( + hidden_states, attention_mask=attention_mask, output_attentions=output_attentions + ) + hidden_states = layer_outputs[0] + + if skip_the_layer: + layer_outputs = (None, None) + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + hidden_states = self.layer_norm(hidden_states) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +class UniSpeechSatGumbelVectorQuantizer(nn.Module): + """ + Vector quantization using gumbel softmax. See [CATEGORICAL REPARAMETERIZATION WITH + GUMBEL-SOFTMAX](https://arxiv.org/pdf/1611.01144.pdf) for more information. + """ + + def __init__(self, config): + super().__init__() + self.num_groups = config.num_codevector_groups + self.num_vars = config.num_codevectors_per_group + + if config.codevector_dim % self.num_groups != 0: + raise ValueError( + f"`config.codevector_dim {config.codevector_dim} must be divisible by `config.num_codevector_groups`" + f" {self.num_groups} for concatenation" + ) + + # storage for codebook variables (codewords) + self.codevectors = nn.Parameter( + torch.FloatTensor(1, self.num_groups * self.num_vars, config.codevector_dim // self.num_groups) + ) + self.weight_proj = nn.Linear(config.hidden_size, self.num_groups * self.num_vars) + + # can be decayed for training + self.temperature = 2 + + @staticmethod + def _compute_perplexity(probs, mask=None): + marginal_probs = probs.mean(dim=0) + perplexity = torch.exp(-torch.sum(marginal_probs * torch.log(marginal_probs + 1e-7), dim=-1)).sum() + return perplexity + + def forward(self, hidden_states): + batch_size, sequence_length, hidden_size = hidden_states.shape + + # project to codevector dim + hidden_states = self.weight_proj(hidden_states) + hidden_states = hidden_states.view(batch_size * sequence_length * self.num_groups, -1) + + if self.training: + # sample code vector probs via gumbel in differentiateable way + codevector_probs = nn.functional.gumbel_softmax( + hidden_states.float(), tau=self.temperature, hard=True + ).type_as(hidden_states) + + # compute perplexity + codevector_soft_dist = torch.softmax( + hidden_states.view(batch_size * sequence_length, self.num_groups, -1).float(), dim=-1 + ) + perplexity = self._compute_perplexity(codevector_soft_dist) + else: + # take argmax in non-differentiable way + # comptute hard codevector distribution (one hot) + codevector_idx = hidden_states.argmax(dim=-1) + codevector_probs = hidden_states.new_zeros(*hidden_states.shape).scatter_( + -1, codevector_idx.view(-1, 1), 1.0 + ) + codevector_probs = codevector_probs.view(batch_size * sequence_length, self.num_groups, -1) + + perplexity = self._compute_perplexity(codevector_probs) + + codevector_probs = codevector_probs.view(batch_size * sequence_length, -1) + # use probs to retrieve codevectors + codevectors_per_group = codevector_probs.unsqueeze(-1) * self.codevectors + codevectors = codevectors_per_group.view(batch_size * sequence_length, self.num_groups, self.num_vars, -1) + codevectors = codevectors.sum(-2).view(batch_size, sequence_length, -1) + + return codevectors, perplexity + + +class UniSpeechSatPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = UniSpeechSatConfig + base_model_prefix = "unispeech_sat" + main_input_name = "input_values" + supports_gradient_checkpointing = True + + def _init_weights(self, module): + """Initialize the weights""" + # gumbel softmax requires special init + if isinstance(module, UniSpeechSatGumbelVectorQuantizer): + module.weight_proj.weight.data.normal_(mean=0.0, std=1) + module.weight_proj.bias.data.zero_() + nn.init.uniform_(module.codevectors) + elif isinstance(module, UniSpeechSatPositionalConvEmbedding): + nn.init.normal_( + module.conv.weight, + mean=0, + std=2 * math.sqrt(1 / (module.conv.kernel_size[0] * module.conv.in_channels)), + ) + nn.init.constant_(module.conv.bias, 0) + elif isinstance(module, UniSpeechSatFeatureProjection): + k = math.sqrt(1 / module.projection.in_features) + nn.init.uniform_(module.projection.weight, a=-k, b=k) + nn.init.uniform_(module.projection.bias, a=-k, b=k) + elif isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + elif isinstance(module, nn.Conv1d): + nn.init.kaiming_normal_(module.weight) + + if module.bias is not None: + k = math.sqrt(module.groups / (module.in_channels * module.kernel_size[0])) + nn.init.uniform_(module.bias, a=-k, b=k) + + def _get_feat_extract_output_lengths(self, input_lengths: Union[torch.LongTensor, int]): + """ + Computes the output length of the convolutional layers + """ + + def _conv_out_length(input_length, kernel_size, stride): + # 1D convolutional layer output length formula taken + # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html + return torch.div(input_length - kernel_size, stride, rounding_mode="floor") + 1 + + for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): + input_lengths = _conv_out_length(input_lengths, kernel_size, stride) + + return input_lengths + + def _get_feature_vector_attention_mask(self, feature_vector_length: int, attention_mask: torch.LongTensor): + # Effectively attention_mask.sum(-1), but not inplace to be able to run + # on inference mode. + non_padded_lengths = attention_mask.cumsum(dim=-1)[:, -1] + output_lengths = self._get_feat_extract_output_lengths(non_padded_lengths).to(torch.long) + batch_size = attention_mask.shape[0] + + attention_mask = torch.zeros( + (batch_size, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device + ) + # these two operations makes sure that all values before the output lengths idxs are attended to + attention_mask[(torch.arange(attention_mask.shape[0], device=attention_mask.device), output_lengths - 1)] = 1 + attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool() + return attention_mask + + +UNISPEECH_SAT_START_DOCSTRING = r""" + UniSpeechSat was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech + Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael + Auli. + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving etc.). + + This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use + it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and + behavior. + + Parameters: + config ([`UniSpeechSatConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +UNISPEECH_SAT_INPUTS_DOCSTRING = r""" + Args: + input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): + Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file + into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install + soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and + conversion into a tensor of type `torch.FloatTensor`. See [`Wav2Vec2Processor.__call__`] for details. + attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0, + 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + + + `attention_mask` should only be passed if the corresponding processor has `config.return_attention_mask == + True`. For all models whose processor has `config.return_attention_mask == False`, such as + [microsoft/unispeech-sat-base-100h-libri-ft](https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft), + `attention_mask` should **not** be passed to avoid degraded performance when doing batched inference. For + such models `input_values` should simply be padded with 0 and passed without `attention_mask`. Be aware + that these models also yield slightly different results depending on whether `input_values` is padded or + not. + + + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare UniSpeechSat Model transformer outputting raw hidden-states without any specific head on top.", + UNISPEECH_SAT_START_DOCSTRING, +) +class UniSpeechSatModel(UniSpeechSatPreTrainedModel): + def __init__(self, config: UniSpeechSatConfig): + super().__init__(config) + self.config = config + self.feature_extractor = UniSpeechSatFeatureEncoder(config) + self.feature_projection = UniSpeechSatFeatureProjection(config) + + self.masked_spec_embed = nn.Parameter(torch.FloatTensor(config.hidden_size).uniform_()) + + if config.do_stable_layer_norm: + self.encoder = UniSpeechSatEncoderStableLayerNorm(config) + else: + self.encoder = UniSpeechSatEncoder(config) + + # Initialize weights and apply final processing + self.post_init() + + # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Model._mask_hidden_states + def _mask_hidden_states( + self, + hidden_states: torch.FloatTensor, + mask_time_indices: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.LongTensor] = None, + ): + """ + Masks extracted features along time axis and/or along feature axis according to + [SpecAugment](https://arxiv.org/abs/1904.08779). + """ + + # `config.apply_spec_augment` can set masking to False + if not getattr(self.config, "apply_spec_augment", True): + return hidden_states + + # generate indices & apply SpecAugment along time axis + batch_size, sequence_length, hidden_size = hidden_states.size() + + if mask_time_indices is not None: + # apply SpecAugment along time axis with given mask_time_indices + hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype) + elif self.config.mask_time_prob > 0 and self.training: + mask_time_indices = _compute_mask_indices( + (batch_size, sequence_length), + mask_prob=self.config.mask_time_prob, + mask_length=self.config.mask_time_length, + attention_mask=attention_mask, + min_masks=self.config.mask_time_min_masks, + ) + mask_time_indices = torch.tensor(mask_time_indices, device=hidden_states.device, dtype=torch.bool) + hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype) + + if self.config.mask_feature_prob > 0 and self.training: + # generate indices & apply SpecAugment along feature axis + mask_feature_indices = _compute_mask_indices( + (batch_size, hidden_size), + mask_prob=self.config.mask_feature_prob, + mask_length=self.config.mask_feature_length, + min_masks=self.config.mask_feature_min_masks, + ) + mask_feature_indices = torch.tensor(mask_feature_indices, device=hidden_states.device, dtype=torch.bool) + mask_feature_indices = mask_feature_indices[:, None].expand(-1, sequence_length, -1) + hidden_states[mask_feature_indices] = 0 + + return hidden_states + + @add_start_docstrings_to_model_forward(UNISPEECH_SAT_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=Wav2Vec2BaseModelOutput, + config_class=_CONFIG_FOR_DOC, + modality="audio", + expected_output=_EXPECTED_OUTPUT_SHAPE, + ) + def forward( + self, + input_values: Optional[torch.Tensor], + attention_mask: Optional[torch.Tensor] = None, + mask_time_indices: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, Wav2Vec2BaseModelOutput]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + extract_features = self.feature_extractor(input_values) + extract_features = extract_features.transpose(1, 2) + + if attention_mask is not None: + # compute reduced attention_mask corresponding to feature vectors + attention_mask = self._get_feature_vector_attention_mask(extract_features.shape[1], attention_mask) + + hidden_states, extract_features = self.feature_projection(extract_features) + hidden_states = self._mask_hidden_states( + hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask + ) + + encoder_outputs = self.encoder( + hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = encoder_outputs[0] + + if not return_dict: + return (hidden_states, extract_features) + encoder_outputs[1:] + + return Wav2Vec2BaseModelOutput( + last_hidden_state=hidden_states, + extract_features=extract_features, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +@add_start_docstrings("""UniSpeechSat Model with a quantizer and `VQ` head on top.""", UNISPEECH_SAT_START_DOCSTRING) +class UniSpeechSatForPreTraining(UniSpeechSatPreTrainedModel): + def __init__(self, config: UniSpeechSatConfig): + super().__init__(config) + self.unispeech_sat = UniSpeechSatModel(config) + self.dropout_features = nn.Dropout(config.feat_quantizer_dropout) + + self.quantizer = UniSpeechSatGumbelVectorQuantizer(config) + self.project_q = nn.Linear(config.codevector_dim, config.proj_codevector_dim) + self.project_hid = nn.Linear(config.hidden_size, config.proj_codevector_dim) + + self.dropout = nn.Dropout(config.final_dropout) + + self.speaker_proj = nn.Linear(config.hidden_size, config.codevector_dim) + self.label_embeddings_concat = nn.Parameter(torch.FloatTensor(config.num_clusters, config.codevector_dim)) + self.label_embeddings_concat.data.zero_() + + self.layer_norm_for_extract = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + if self.config.do_stable_layer_norm: + self.layer_norm_for_extract.requires_grad = False + + # Initialize weights and apply final processing + self.post_init() + + def set_gumbel_temperature(self, temperature: int): + """ + Set the Gumbel softmax temperature to a given value. Only necessary for training + """ + self.quantizer.temperature = temperature + + def freeze_feature_extractor(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameters will + not be updated during training. + """ + warnings.warn( + "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. " + "Please use the equivalent `freeze_feature_encoder` method instead.", + FutureWarning, + ) + self.freeze_feature_encoder() + + def freeze_feature_encoder(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameter will + not be updated during training. + """ + self.wav2vec2.feature_extractor._freeze_parameters() + + @staticmethod + def compute_contrastive_logits( + target_features: torch.FloatTensor, + negative_features: torch.FloatTensor, + predicted_features: torch.FloatTensor, + temperature: int = 1, + ): + """ + Compute logits for contrastive loss based using cosine similarity as the distance measure between + `[positive_feature, negative_features]` and `[predicted_features]`. Additionally, temperature can be applied. + """ + target_features = torch.cat([target_features, negative_features], dim=0) + + logits = torch.cosine_similarity(predicted_features.float(), target_features.float(), dim=-1) + logits = logits.type_as(target_features) + + # apply temperature + logits = logits / temperature + return logits + + @add_start_docstrings_to_model_forward(UNISPEECH_SAT_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=UniSpeechSatForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_values: Optional[torch.Tensor], + attention_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, UniSpeechSatForPreTrainingOutput]: + r""" + Returns: + + Example: + + ```python + >>> import torch + >>> from transformers import AutoFeatureExtractor, UniSpeechSatForPreTraining + >>> from transformers.models.unispeech_sat.modeling_unispeech_sat import _compute_mask_indices + + >>> feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/unispeech-sat-base") + >>> model = UniSpeechSatForPreTraining.from_pretrained("microsoft/unispeech-sat-base") + >>> # TODO: Add full pretraining example + ```""" + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.unispeech_sat( + input_values, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + transformer_features = outputs[0] + + # quantize all (unmasked) extracted features and project to final vq dim + extract_features = self.dropout_features(outputs[1]) + + # TODO(PVP) - add pretraining logic and add to tests + logits = extract_features + loss = quantized_features = codevector_perplexity = None + + # layer normalization (has no effect when `config.do_stable_layer_norm == False`) + # extract_features = self.layer_norm_for_extract(extract_features) + # quantized_features, codevector_perplexity = self.quantizer(extract_features) + # + # project quantized features twice + # quantized_features = self.project_q(quantized_features) + # quantized_features = self.project_hid(quantized_features) + # + # loss = None + # logits = quantized_features + if not return_dict: + if loss is not None: + return (loss, logits, transformer_features, quantized_features, codevector_perplexity) + outputs[2:] + return (logits, transformer_features, quantized_features, codevector_perplexity) + outputs[2:] + + return UniSpeechSatForPreTrainingOutput( + loss=loss, + logits=logits, + projected_states=transformer_features, + projected_quantized_states=quantized_features, + codevector_perplexity=codevector_perplexity, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + """UniSpeechSat Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).""", + UNISPEECH_SAT_START_DOCSTRING, + """ + target_lang (`str`, *optional*): + Language id of adapter weights. Adapter weights are stored in the format adapter..safetensors or + adapter..bin. Only relevant when using an instance of [`UniSpeechSatForCTC`] with adapters. Uses + 'eng' by default. + """, +) +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC with Wav2Vec2->UniSpeechSat, wav2vec2->unispeech_sat, WAV_2_VEC_2->UNISPEECH_SAT +class UniSpeechSatForCTC(UniSpeechSatPreTrainedModel): + def __init__(self, config, target_lang: Optional[str] = None): + super().__init__(config) + + self.unispeech_sat = UniSpeechSatModel(config) + self.dropout = nn.Dropout(config.final_dropout) + + self.target_lang = target_lang + + if config.vocab_size is None: + raise ValueError( + f"You are trying to instantiate {self.__class__} with a configuration that " + "does not define the vocabulary size of the language model head. Please " + "instantiate the model as follows: `UniSpeechSatForCTC.from_pretrained(..., vocab_size=vocab_size)`. " + "or define `vocab_size` of your model's configuration." + ) + output_hidden_size = ( + config.output_hidden_size if hasattr(config, "add_adapter") and config.add_adapter else config.hidden_size + ) + self.lm_head = nn.Linear(output_hidden_size, config.vocab_size) + + # Initialize weights and apply final processing + self.post_init() + + def tie_weights(self): + """ + This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when + passing `target_lang=...` to `from_pretrained(...)`. + + This method is **not** supposed to be called by the user and is prone to be changed in the future. + """ + + # Note that `tie_weights` is usually used to tie input and output embedding weights. The method is re-purposed to + # correctly load adapter layers for UniSpeechSat so that we do not have to introduce a new API to + # [`PreTrainedModel`]. While slightly hacky, UniSpeechSat never has to tie input and output embeddings, so that it is + # ok to repurpose this function here. + target_lang = self.target_lang + + if target_lang is not None and getattr(self.config, "adapter_attn_dim", None) is None: + raise ValueError(f"Cannot pass `target_lang`: {target_lang} if `config.adapter_attn_dim` is not defined.") + elif target_lang is None and getattr(self.config, "adapter_attn_dim", None) is not None: + logger.info("By default `target_lang` is set to 'eng'.") + elif target_lang is not None: + self.load_adapter(target_lang, force_load=True) + + def freeze_feature_extractor(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameter will + not be updated during training. + """ + warnings.warn( + "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. " + "Please use the equivalent `freeze_feature_encoder` method instead.", + FutureWarning, + ) + self.freeze_feature_encoder() + + def freeze_feature_encoder(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameter will + not be updated during training. + """ + self.unispeech_sat.feature_extractor._freeze_parameters() + + def freeze_base_model(self): + """ + Calling this function will disable the gradient computation for the base model so that its parameters will not + be updated during training. Only the classification head will be updated. + """ + for param in self.unispeech_sat.parameters(): + param.requires_grad = False + + @add_start_docstrings_to_model_forward(UNISPEECH_SAT_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=CausalLMOutput, + config_class=_CONFIG_FOR_DOC, + expected_output=_CTC_EXPECTED_OUTPUT, + expected_loss=_CTC_EXPECTED_LOSS, + ) + def forward( + self, + input_values: Optional[torch.Tensor], + attention_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + labels: Optional[torch.Tensor] = None, + ) -> Union[Tuple, CausalLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*): + Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to + the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`. + All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., + config.vocab_size - 1]`. + """ + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.unispeech_sat( + input_values, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + hidden_states = self.dropout(hidden_states) + + logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + if labels.max() >= self.config.vocab_size: + raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}") + + # retrieve loss input_lengths from attention_mask + attention_mask = ( + attention_mask if attention_mask is not None else torch.ones_like(input_values, dtype=torch.long) + ) + input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long) + + # assuming that padded tokens are filled with -100 + # when not being attended to + labels_mask = labels >= 0 + target_lengths = labels_mask.sum(-1) + flattened_targets = labels.masked_select(labels_mask) + + # ctc_loss doesn't support fp16 + log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1) + + with torch.backends.cudnn.flags(enabled=False): + loss = nn.functional.ctc_loss( + log_probs, + flattened_targets, + input_lengths, + target_lengths, + blank=self.config.pad_token_id, + reduction=self.config.ctc_loss_reduction, + zero_infinity=self.config.ctc_zero_infinity, + ) + + if not return_dict: + output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutput( + loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions + ) + + +@add_start_docstrings( + """ + UniSpeechSat Model with a sequence classification head on top (a linear layer over the pooled output) for tasks + like SUPERB Keyword Spotting. + """, + UNISPEECH_SAT_START_DOCSTRING, +) +class UniSpeechSatForSequenceClassification(UniSpeechSatPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + if hasattr(config, "add_adapter") and config.add_adapter: + raise ValueError( + "Sequence classification does not support the use of UniSpeechSat adapters (config.add_adapter=True)" + ) + self.unispeech_sat = UniSpeechSatModel(config) + num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings + if config.use_weighted_layer_sum: + self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers) + self.projector = nn.Linear(config.hidden_size, config.classifier_proj_size) + self.classifier = nn.Linear(config.classifier_proj_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_feature_extractor + def freeze_feature_extractor(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameters will + not be updated during training. + """ + warnings.warn( + "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. " + "Please use the equivalent `freeze_feature_encoder` method instead.", + FutureWarning, + ) + self.freeze_feature_encoder() + + # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_feature_encoder with wav2vec2->unispeech_sat + def freeze_feature_encoder(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameter will + not be updated during training. + """ + self.unispeech_sat.feature_extractor._freeze_parameters() + + # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_base_model with wav2vec2->unispeech_sat + def freeze_base_model(self): + """ + Calling this function will disable the gradient computation for the base model so that its parameters will not + be updated during training. Only the classification head will be updated. + """ + for param in self.unispeech_sat.parameters(): + param.requires_grad = False + + @add_start_docstrings_to_model_forward(UNISPEECH_SAT_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=SequenceClassifierOutput, + config_class=_CONFIG_FOR_DOC, + modality="audio", + ) + # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.forward with Wav2Vec2->UniSpeechSat, wav2vec2->unispeech_sat + def forward( + self, + input_values: Optional[torch.Tensor], + attention_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + labels: Optional[torch.Tensor] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states + + outputs = self.unispeech_sat( + input_values, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + if self.config.use_weighted_layer_sum: + hidden_states = outputs[_HIDDEN_STATES_START_POSITION] + hidden_states = torch.stack(hidden_states, dim=1) + norm_weights = nn.functional.softmax(self.layer_weights, dim=-1) + hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1) + else: + hidden_states = outputs[0] + + hidden_states = self.projector(hidden_states) + if attention_mask is None: + pooled_output = hidden_states.mean(dim=1) + else: + padding_mask = self._get_feature_vector_attention_mask(hidden_states.shape[1], attention_mask) + hidden_states[~padding_mask] = 0.0 + pooled_output = hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1) + + logits = self.classifier(pooled_output) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + """ + UniSpeech-SAT Model with a frame classification head on top for tasks like Speaker Diarization. + """, + UNISPEECH_SAT_START_DOCSTRING, +) +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForAudioFrameClassification with Wav2Vec2->UniSpeechSat, wav2vec2->unispeech_sat, WAV_2_VEC_2->UNISPEECH_SAT +class UniSpeechSatForAudioFrameClassification(UniSpeechSatPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + if hasattr(config, "add_adapter") and config.add_adapter: + raise ValueError( + "Audio frame classification does not support the use of UniSpeechSat adapters (config.add_adapter=True)" + ) + self.unispeech_sat = UniSpeechSatModel(config) + num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings + if config.use_weighted_layer_sum: + self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + self.num_labels = config.num_labels + + self.init_weights() + + def freeze_feature_extractor(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameter will + not be updated during training. + """ + warnings.warn( + "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. " + "Please use the equivalent `freeze_feature_encoder` method instead.", + FutureWarning, + ) + self.freeze_feature_encoder() + + def freeze_feature_encoder(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameter will + not be updated during training. + """ + self.unispeech_sat.feature_extractor._freeze_parameters() + + def freeze_base_model(self): + """ + Calling this function will disable the gradient computation for the base model so that its parameters will not + be updated during training. Only the classification head will be updated. + """ + for param in self.unispeech_sat.parameters(): + param.requires_grad = False + + @add_start_docstrings_to_model_forward(UNISPEECH_SAT_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_FRAME_CLASS_CHECKPOINT, + output_type=TokenClassifierOutput, + config_class=_CONFIG_FOR_DOC, + modality="audio", + expected_output=_FRAME_EXPECTED_OUTPUT, + ) + def forward( + self, + input_values: Optional[torch.Tensor], + attention_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, TokenClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states + + outputs = self.unispeech_sat( + input_values, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + if self.config.use_weighted_layer_sum: + hidden_states = outputs[_HIDDEN_STATES_START_POSITION] + hidden_states = torch.stack(hidden_states, dim=1) + norm_weights = nn.functional.softmax(self.layer_weights, dim=-1) + hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1) + else: + hidden_states = outputs[0] + + logits = self.classifier(hidden_states) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), torch.argmax(labels.view(-1, self.num_labels), axis=1)) + + if not return_dict: + output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:] + return output + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.AMSoftmaxLoss +class AMSoftmaxLoss(nn.Module): + def __init__(self, input_dim, num_labels, scale=30.0, margin=0.4): + super(AMSoftmaxLoss, self).__init__() + self.scale = scale + self.margin = margin + self.num_labels = num_labels + self.weight = nn.Parameter(torch.randn(input_dim, num_labels), requires_grad=True) + self.loss = nn.CrossEntropyLoss() + + def forward(self, hidden_states, labels): + labels = labels.flatten() + weight = nn.functional.normalize(self.weight, dim=0) + hidden_states = nn.functional.normalize(hidden_states, dim=1) + cos_theta = torch.mm(hidden_states, weight) + psi = cos_theta - self.margin + + onehot = nn.functional.one_hot(labels, self.num_labels) + logits = self.scale * torch.where(onehot.bool(), psi, cos_theta) + loss = self.loss(logits, labels) + + return loss + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.TDNNLayer +class TDNNLayer(nn.Module): + def __init__(self, config, layer_id=0): + super().__init__() + self.in_conv_dim = config.tdnn_dim[layer_id - 1] if layer_id > 0 else config.tdnn_dim[layer_id] + self.out_conv_dim = config.tdnn_dim[layer_id] + self.kernel_size = config.tdnn_kernel[layer_id] + self.dilation = config.tdnn_dilation[layer_id] + + self.kernel = nn.Linear(self.in_conv_dim * self.kernel_size, self.out_conv_dim) + self.activation = nn.ReLU() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + if is_peft_available(): + from peft.tuners.lora import LoraLayer + + if isinstance(self.kernel, LoraLayer): + warnings.warn( + "Detected LoRA on TDNNLayer. LoRA weights won't be applied due to optimization. " + "You should exclude TDNNLayer from LoRA's target modules.", + ) + + # for backward compatibility, we keep nn.Linear but call F.conv1d for speed up + hidden_states = hidden_states.transpose(1, 2) + weight = self.kernel.weight.view(self.out_conv_dim, self.kernel_size, self.in_conv_dim).transpose(1, 2) + hidden_states = nn.functional.conv1d(hidden_states, weight, self.kernel.bias, dilation=self.dilation) + hidden_states = hidden_states.transpose(1, 2) + + hidden_states = self.activation(hidden_states) + return hidden_states + + +@add_start_docstrings( + """ + UniSpeech-SAT Model with an XVector feature extraction head on top for tasks like Speaker Verification. + """, + UNISPEECH_SAT_START_DOCSTRING, +) +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForXVector with Wav2Vec2->UniSpeechSat, wav2vec2->unispeech_sat, WAV_2_VEC_2->UNISPEECH_SAT +class UniSpeechSatForXVector(UniSpeechSatPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.unispeech_sat = UniSpeechSatModel(config) + num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings + if config.use_weighted_layer_sum: + self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers) + self.projector = nn.Linear(config.hidden_size, config.tdnn_dim[0]) + + tdnn_layers = [TDNNLayer(config, i) for i in range(len(config.tdnn_dim))] + self.tdnn = nn.ModuleList(tdnn_layers) + + self.feature_extractor = nn.Linear(config.tdnn_dim[-1] * 2, config.xvector_output_dim) + self.classifier = nn.Linear(config.xvector_output_dim, config.xvector_output_dim) + + self.objective = AMSoftmaxLoss(config.xvector_output_dim, config.num_labels) + + self.init_weights() + + def freeze_feature_extractor(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameter will + not be updated during training. + """ + warnings.warn( + "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. " + "Please use the equivalent `freeze_feature_encoder` method instead.", + FutureWarning, + ) + self.freeze_feature_encoder() + + def freeze_feature_encoder(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameter will + not be updated during training. + """ + self.unispeech_sat.feature_extractor._freeze_parameters() + + def freeze_base_model(self): + """ + Calling this function will disable the gradient computation for the base model so that its parameters will not + be updated during training. Only the classification head will be updated. + """ + for param in self.unispeech_sat.parameters(): + param.requires_grad = False + + def _get_tdnn_output_lengths(self, input_lengths: Union[torch.LongTensor, int]): + """ + Computes the output length of the TDNN layers + """ + + def _conv_out_length(input_length, kernel_size, stride): + # 1D convolutional layer output length formula taken + # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html + return (input_length - kernel_size) // stride + 1 + + for kernel_size in self.config.tdnn_kernel: + input_lengths = _conv_out_length(input_lengths, kernel_size, 1) + + return input_lengths + + @add_start_docstrings_to_model_forward(UNISPEECH_SAT_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_XVECTOR_CHECKPOINT, + output_type=XVectorOutput, + config_class=_CONFIG_FOR_DOC, + modality="audio", + expected_output=_XVECTOR_EXPECTED_OUTPUT, + ) + def forward( + self, + input_values: Optional[torch.Tensor], + attention_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + labels: Optional[torch.Tensor] = None, + ) -> Union[Tuple, XVectorOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states + + outputs = self.unispeech_sat( + input_values, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + if self.config.use_weighted_layer_sum: + hidden_states = outputs[_HIDDEN_STATES_START_POSITION] + hidden_states = torch.stack(hidden_states, dim=1) + norm_weights = nn.functional.softmax(self.layer_weights, dim=-1) + hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1) + else: + hidden_states = outputs[0] + + hidden_states = self.projector(hidden_states) + + for tdnn_layer in self.tdnn: + hidden_states = tdnn_layer(hidden_states) + + # Statistic Pooling + if attention_mask is None: + mean_features = hidden_states.mean(dim=1) + std_features = hidden_states.std(dim=1) + else: + feat_extract_output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(dim=1)) + tdnn_output_lengths = self._get_tdnn_output_lengths(feat_extract_output_lengths) + mean_features = [] + std_features = [] + for i, length in enumerate(tdnn_output_lengths): + mean_features.append(hidden_states[i, :length].mean(dim=0)) + std_features.append(hidden_states[i, :length].std(dim=0)) + mean_features = torch.stack(mean_features) + std_features = torch.stack(std_features) + statistic_pooling = torch.cat([mean_features, std_features], dim=-1) + + output_embeddings = self.feature_extractor(statistic_pooling) + logits = self.classifier(output_embeddings) + + loss = None + if labels is not None: + loss = self.objective(logits, labels) + + if not return_dict: + output = (logits, output_embeddings) + outputs[_HIDDEN_STATES_START_POSITION:] + return ((loss,) + output) if loss is not None else output + + return XVectorOutput( + loss=loss, + logits=logits, + embeddings=output_embeddings, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/univnet/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/univnet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..afb03ee9894b0ebe58c9ec864e3eccb32719b993 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/univnet/__init__.py @@ -0,0 +1,65 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ...utils import ( + OptionalDependencyNotAvailable, + _LazyModule, + is_torch_available, +) + + +_import_structure = { + "configuration_univnet": [ + "UNIVNET_PRETRAINED_CONFIG_ARCHIVE_MAP", + "UnivNetConfig", + ], + "feature_extraction_univnet": ["UnivNetFeatureExtractor"], +} + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_univnet"] = [ + "UNIVNET_PRETRAINED_MODEL_ARCHIVE_LIST", + "UnivNetModel", + ] + + +if TYPE_CHECKING: + from .configuration_univnet import ( + UNIVNET_PRETRAINED_CONFIG_ARCHIVE_MAP, + UnivNetConfig, + ) + from .feature_extraction_univnet import UnivNetFeatureExtractor + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_univnet import ( + UNIVNET_PRETRAINED_MODEL_ARCHIVE_LIST, + UnivNetModel, + ) + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/univnet/__pycache__/__init__.cpython-312.pyc 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All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse + +import torch + +from transformers import UnivNetConfig, UnivNetModel, logging + + +logging.set_verbosity_info() +logger = logging.get_logger("transformers.models.univnet") + + +def get_kernel_predictor_key_mapping(config: UnivNetConfig, old_prefix: str = "", new_prefix: str = ""): + mapping = {} + # Initial conv layer + mapping[f"{old_prefix}.input_conv.0.weight_g"] = f"{new_prefix}.input_conv.weight_g" + mapping[f"{old_prefix}.input_conv.0.weight_v"] = f"{new_prefix}.input_conv.weight_v" + mapping[f"{old_prefix}.input_conv.0.bias"] = f"{new_prefix}.input_conv.bias" + + # Kernel predictor resnet blocks + for i in range(config.kernel_predictor_num_blocks): + mapping[f"{old_prefix}.residual_convs.{i}.1.weight_g"] = f"{new_prefix}.resblocks.{i}.conv1.weight_g" + mapping[f"{old_prefix}.residual_convs.{i}.1.weight_v"] = f"{new_prefix}.resblocks.{i}.conv1.weight_v" + mapping[f"{old_prefix}.residual_convs.{i}.1.bias"] = f"{new_prefix}.resblocks.{i}.conv1.bias" + + mapping[f"{old_prefix}.residual_convs.{i}.3.weight_g"] = f"{new_prefix}.resblocks.{i}.conv2.weight_g" + mapping[f"{old_prefix}.residual_convs.{i}.3.weight_v"] = f"{new_prefix}.resblocks.{i}.conv2.weight_v" + mapping[f"{old_prefix}.residual_convs.{i}.3.bias"] = f"{new_prefix}.resblocks.{i}.conv2.bias" + + # Kernel output conv + mapping[f"{old_prefix}.kernel_conv.weight_g"] = f"{new_prefix}.kernel_conv.weight_g" + mapping[f"{old_prefix}.kernel_conv.weight_v"] = f"{new_prefix}.kernel_conv.weight_v" + mapping[f"{old_prefix}.kernel_conv.bias"] = f"{new_prefix}.kernel_conv.bias" + + # Bias output conv + mapping[f"{old_prefix}.bias_conv.weight_g"] = f"{new_prefix}.bias_conv.weight_g" + mapping[f"{old_prefix}.bias_conv.weight_v"] = f"{new_prefix}.bias_conv.weight_v" + mapping[f"{old_prefix}.bias_conv.bias"] = f"{new_prefix}.bias_conv.bias" + + return mapping + + +def get_key_mapping(config: UnivNetConfig): + mapping = {} + + # NOTE: inital conv layer keys are the same + + # LVC Residual blocks + for i in range(len(config.resblock_stride_sizes)): + # LVCBlock initial convt layer + mapping[f"res_stack.{i}.convt_pre.1.weight_g"] = f"resblocks.{i}.convt_pre.weight_g" + mapping[f"res_stack.{i}.convt_pre.1.weight_v"] = f"resblocks.{i}.convt_pre.weight_v" + mapping[f"res_stack.{i}.convt_pre.1.bias"] = f"resblocks.{i}.convt_pre.bias" + + # Kernel predictor + kernel_predictor_mapping = get_kernel_predictor_key_mapping( + config, old_prefix=f"res_stack.{i}.kernel_predictor", new_prefix=f"resblocks.{i}.kernel_predictor" + ) + mapping.update(kernel_predictor_mapping) + + # LVC Residual blocks + for j in range(len(config.resblock_dilation_sizes[i])): + mapping[f"res_stack.{i}.conv_blocks.{j}.1.weight_g"] = f"resblocks.{i}.resblocks.{j}.conv.weight_g" + mapping[f"res_stack.{i}.conv_blocks.{j}.1.weight_v"] = f"resblocks.{i}.resblocks.{j}.conv.weight_v" + mapping[f"res_stack.{i}.conv_blocks.{j}.1.bias"] = f"resblocks.{i}.resblocks.{j}.conv.bias" + + # Output conv layer + mapping["conv_post.1.weight_g"] = "conv_post.weight_g" + mapping["conv_post.1.weight_v"] = "conv_post.weight_v" + mapping["conv_post.1.bias"] = "conv_post.bias" + + return mapping + + +def rename_state_dict(state_dict, keys_to_modify, keys_to_remove): + model_state_dict = {} + for key, value in state_dict.items(): + if key in keys_to_remove: + continue + + if key in keys_to_modify: + new_key = keys_to_modify[key] + model_state_dict[new_key] = value + else: + model_state_dict[key] = value + return model_state_dict + + +def convert_univnet_checkpoint( + checkpoint_path, + pytorch_dump_folder_path, + config_path=None, + repo_id=None, + safe_serialization=False, +): + model_state_dict_base = torch.load(checkpoint_path, map_location="cpu") + # Get the generator's state dict + state_dict = model_state_dict_base["model_g"] + + if config_path is not None: + config = UnivNetConfig.from_pretrained(config_path) + else: + config = UnivNetConfig() + + keys_to_modify = get_key_mapping(config) + keys_to_remove = set() + hf_state_dict = rename_state_dict(state_dict, keys_to_modify, keys_to_remove) + + model = UnivNetModel(config) + # Apply weight norm since the original checkpoint has weight norm applied + model.apply_weight_norm() + model.load_state_dict(hf_state_dict) + # Remove weight norm in preparation for inference + model.remove_weight_norm() + + model.save_pretrained(pytorch_dump_folder_path, safe_serialization=safe_serialization) + + if repo_id: + print("Pushing to the hub...") + model.push_to_hub(repo_id) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint") + parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert") + parser.add_argument( + "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model." + ) + parser.add_argument( + "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub." + ) + parser.add_argument( + "--safe_serialization", action="store_true", help="Whether to save the model using `safetensors`." + ) + + args = parser.parse_args() + + convert_univnet_checkpoint( + args.checkpoint_path, + args.pytorch_dump_folder_path, + args.config_path, + args.push_to_hub, + args.safe_serialization, + ) + + +if __name__ == "__main__": + main() diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/univnet/feature_extraction_univnet.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/univnet/feature_extraction_univnet.py new file mode 100644 index 0000000000000000000000000000000000000000..067aacc3d8c8ca51336680ee7afe8a9fec677fd7 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/univnet/feature_extraction_univnet.py @@ -0,0 +1,456 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Feature extractor class for UnivNetModel.""" + +from typing import Any, Dict, List, Optional, Union + +import numpy as np + +from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function +from ...feature_extraction_sequence_utils import SequenceFeatureExtractor +from ...feature_extraction_utils import BatchFeature +from ...utils import PaddingStrategy, TensorType, logging + + +logger = logging.get_logger(__name__) + + +class UnivNetFeatureExtractor(SequenceFeatureExtractor): + r""" + Constructs a UnivNet feature extractor. + + This class extracts log-mel-filter bank features from raw speech using the short time Fourier Transform (STFT). The + STFT implementation follows that of TacoTron 2 and Hifi-GAN. + + This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains + most of the main methods. Users should refer to this superclass for more information regarding those methods. + + Args: + feature_size (`int`, *optional*, defaults to 1): + The feature dimension of the extracted features. + sampling_rate (`int`, *optional*, defaults to 24000): + The sampling rate at which the audio files should be digitalized expressed in hertz (Hz). + padding_value (`float`, *optional*, defaults to 0.0): + The value to pad with when applying the padding strategy defined by the `padding` argument to + [`UnivNetFeatureExtractor.__call__`]. Should correspond to audio silence. The `pad_end` argument to + `__call__` will also use this padding value. + do_normalize (`bool`, *optional*, defaults to `False`): + Whether to perform Tacotron 2 normalization on the input. Normalizing can help to significantly improve the + performance for some models. + num_mel_bins (`int`, *optional*, defaults to 100): + The number of mel-frequency bins in the extracted spectrogram features. This should match + `UnivNetModel.config.num_mel_bins`. + hop_length (`int`, *optional*, defaults to 256): + The direct number of samples between sliding windows. Otherwise referred to as "shift" in many papers. Note + that this is different from other audio feature extractors such as [`SpeechT5FeatureExtractor`] which take + the `hop_length` in ms. + win_length (`int`, *optional*, defaults to 1024): + The direct number of samples for each sliding window. Note that this is different from other audio feature + extractors such as [`SpeechT5FeatureExtractor`] which take the `win_length` in ms. + win_function (`str`, *optional*, defaults to `"hann_window"`): + Name for the window function used for windowing, must be accessible via `torch.{win_function}` + filter_length (`int`, *optional*, defaults to 1024): + The number of FFT components to use. If `None`, this is determined using + `transformers.audio_utils.optimal_fft_length`. + max_length_s (`int`, *optional*, defaults to 10): + The maximum input lenght of the model in seconds. This is used to pad the audio. + fmin (`float`, *optional*, defaults to 0.0): + Minimum mel frequency in Hz. + fmax (`float`, *optional*): + Maximum mel frequency in Hz. If not set, defaults to `sampling_rate / 2`. + mel_floor (`float`, *optional*, defaults to 1e-09): + Minimum value of mel frequency banks. Note that the way [`UnivNetFeatureExtractor`] uses `mel_floor` is + different than in [`transformers.audio_utils.spectrogram`]. + center (`bool`, *optional*, defaults to `False`): + Whether to pad the waveform so that frame `t` is centered around time `t * hop_length`. If `False`, frame + `t` will start at time `t * hop_length`. + compression_factor (`float`, *optional*, defaults to 1.0): + The multiplicative compression factor for dynamic range compression during spectral normalization. + compression_clip_val (`float`, *optional*, defaults to 1e-05): + The clip value applied to the waveform before applying dynamic range compression during spectral + normalization. + normalize_min (`float`, *optional*, defaults to -11.512925148010254): + The min value used for Tacotron 2-style linear normalization. The default is the original value from the + Tacotron 2 implementation. + normalize_max (`float`, *optional*, defaults to 2.3143386840820312): + The max value used for Tacotron 2-style linear normalization. The default is the original value from the + Tacotron 2 implementation. + model_in_channels (`int`, *optional*, defaults to 64): + The number of input channels to the [`UnivNetModel`] model. This should match + `UnivNetModel.config.model_in_channels`. + pad_end_length (`int`, *optional*, defaults to 10): + If padding the end of each waveform, the number of spectrogram frames worth of samples to append. The + number of appended samples will be `pad_end_length * hop_length`. + return_attention_mask (`bool`, *optional*, defaults to `True`): + Whether or not [`~UnivNetFeatureExtractor.__call__`] should return `attention_mask`. + """ + + model_input_names = ["input_features", "noise_sequence", "padding_mask"] + + def __init__( + self, + feature_size: int = 1, + sampling_rate: int = 24000, + padding_value: float = 0.0, + do_normalize: bool = False, + num_mel_bins: int = 100, + hop_length: int = 256, + win_length: int = 1024, + win_function: str = "hann_window", + filter_length: Optional[int] = 1024, + max_length_s: int = 10, + fmin: float = 0.0, + fmax: Optional[float] = None, + mel_floor: float = 1e-9, + center: bool = False, + compression_factor: float = 1.0, + compression_clip_val: float = 1e-5, + normalize_min: float = -11.512925148010254, + normalize_max: float = 2.3143386840820312, + model_in_channels: int = 64, + pad_end_length: int = 10, + return_attention_mask=True, + **kwargs, + ): + super().__init__( + feature_size=feature_size, + sampling_rate=sampling_rate, + padding_value=padding_value, + return_attention_mask=return_attention_mask, + **kwargs, + ) + + self.do_normalize = do_normalize + + self.num_mel_bins = num_mel_bins + self.hop_length = hop_length + self.win_length = win_length + self.win_function = win_function + self.filter_length = filter_length + self.fmin = fmin + if fmax is None: + # Follows the librosa.filters.mel implementation + fmax = float(sampling_rate) / 2 + self.fmax = fmax + self.mel_floor = mel_floor + + self.max_length_s = max_length_s + self.num_max_samples = max_length_s * sampling_rate + + if self.filter_length is None: + self.n_fft = optimal_fft_length(self.win_length) + else: + self.n_fft = self.filter_length + self.n_freqs = (self.n_fft // 2) + 1 + + self.window = window_function(window_length=self.win_length, name=self.win_function, periodic=True) + + self.mel_filters = mel_filter_bank( + num_frequency_bins=self.n_freqs, + num_mel_filters=self.num_mel_bins, + min_frequency=self.fmin, + max_frequency=self.fmax, + sampling_rate=self.sampling_rate, + norm="slaney", + mel_scale="slaney", + ) + + self.center = center + self.compression_factor = compression_factor + self.compression_clip_val = compression_clip_val + self.normalize_min = normalize_min + self.normalize_max = normalize_max + self.model_in_channels = model_in_channels + self.pad_end_length = pad_end_length + + def normalize(self, spectrogram): + return 2 * ((spectrogram - self.normalize_min) / (self.normalize_max - self.normalize_min)) - 1 + + def denormalize(self, spectrogram): + return self.normalize_min + (self.normalize_max - self.normalize_min) * ((spectrogram + 1) / 2) + + def mel_spectrogram(self, waveform: np.ndarray) -> np.ndarray: + """ + Calculates log MEL spectrograms from a batch of waveforms. Note that the input waveform(s) will be padded by + `int(self.n_fft - self.hop_length) / 2` on both sides using the `reflect` padding mode. + + Args: + waveform (`np.ndarray` of shape `(length,)`): + The input waveform. This must be a single real-valued, mono waveform. + + Returns: + `numpy.ndarray`: Array containing a log-mel spectrogram of shape `(num_frames, num_mel_bins)`. + """ + # Do custom padding based on the official MelGAN and Hifi-GAN implementations + # See https://github.com/maum-ai/univnet/blob/9bb2b54838bb6d7ce767131cc7b8b61198bc7558/utils/stft.py#L84-L86 + waveform = np.pad( + waveform, + (int((self.n_fft - self.hop_length) / 2), int((self.n_fft - self.hop_length) / 2)), + mode="reflect", + ) + + # Get the complex spectrogram. + # Note: waveform must be unbatched currently due to the implementation of spectrogram(...). + complex_spectrogram = spectrogram( + waveform, + window=self.window, + frame_length=self.n_fft, + hop_length=self.hop_length, + fft_length=self.n_fft, + power=None, + center=self.center, + mel_filters=None, + mel_floor=None, + ) + + # Apply the MEL filter bank and MEL floor manually since UnivNet uses a slightly different implementation + amplitude_spectrogram = np.sqrt( + np.real(complex_spectrogram) ** 2 + np.imag(complex_spectrogram) ** 2 + self.mel_floor + ) + mel_spectrogram = np.matmul(self.mel_filters.T, amplitude_spectrogram) + + # Perform spectral normalization to get the log mel spectrogram. + log_mel_spectrogram = np.log( + np.clip(mel_spectrogram, a_min=self.compression_clip_val, a_max=None) * self.compression_factor + ) + + # Return spectrogram with num_mel_bins last + return log_mel_spectrogram.T + + def generate_noise( + self, + noise_length: int, + generator: Optional[np.random.Generator] = None, + ) -> np.ndarray: + """ + Generates a random noise sequence of standard Gaussian noise for use in the `noise_sequence` argument of + [`UnivNetModel.forward`]. + + Args: + spectrogram_length (`int`): + The length (dim 0) of the generated noise. + model_in_channels (`int`, *optional*, defaults to `None`): + The number of features (dim 1) of the generated noise. This should correspond to the + `model_in_channels` of the [`UnivNetGan`] model. If not set, this will default to + `self.config.model_in_channels`. + generator (`numpy.random.Generator`, *optional*, defaults to `None`) + An optional `numpy.random.Generator` random number generator to control noise generation. If not set, a + new generator with fresh entropy will be created. + + Returns: + `numpy.ndarray`: Array containing random standard Gaussian noise of shape `(noise_length, + model_in_channels)`. + """ + if generator is None: + generator = np.random.default_rng() + + noise_shape = (noise_length, self.model_in_channels) + noise = generator.standard_normal(noise_shape, dtype=np.float32) + + return noise + + def batch_decode(self, waveforms, waveform_lengths=None) -> List[np.ndarray]: + r""" + Removes padding from generated audio after running [`UnivNetModel.forward`]. This returns a ragged list of 1D + audio waveform arrays and not a single tensor/array because in general the waveforms will have different + lengths after removing padding. + + Args: + waveforms (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): + The batched output waveforms from the [`UnivNetModel`]. + waveform_lengths (`torch.FloatTensor` of shape `(batch_size,)`, *optional*): + The batched lengths of each waveform before padding. + + Returns: + `List[np.ndarray]`: A ragged list of 1D waveform arrays with padding removed. + """ + # Collapse the batched waveform tensor to a list of 1D audio waveforms + waveforms = [waveform.detach().clone().cpu().numpy() for waveform in waveforms] + + if waveform_lengths is not None: + waveforms = [waveform[: waveform_lengths[i]] for i, waveform in enumerate(waveforms)] + + return waveforms + + def __call__( + self, + raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]], + sampling_rate: Optional[int] = None, + padding: Union[bool, str, PaddingStrategy] = True, + max_length: Optional[int] = None, + truncation: bool = True, + pad_to_multiple_of: Optional[int] = None, + return_noise: bool = True, + generator: Optional[np.random.Generator] = None, + pad_end: bool = False, + pad_length: Optional[int] = None, + do_normalize: Optional[str] = None, + return_attention_mask: Optional[bool] = None, + return_tensors: Optional[Union[str, TensorType]] = None, + ) -> BatchFeature: + """ + Main method to featurize and prepare for the model one or several sequence(s). + + Args: + raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`): + The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float + values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not + stereo, i.e. single float per timestep. + sampling_rate (`int`, *optional*): + The sampling rate at which the `raw_speech` input was sampled. It is strongly recommended to pass + `sampling_rate` at the forward call to prevent silent errors and allow automatic speech recognition + pipeline. + padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`): + Select a strategy to pad the input `raw_speech` waveforms (according to the model's padding side and + padding index) among: + + - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single + sequence if provided). + - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum + acceptable input length for the model if that argument is not provided. + - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different + lengths). + + If `pad_end = True`, that padding will occur before the `padding` strategy is applied. + max_length (`int`, *optional*): + Maximum length of the returned list and optionally padding length (see above). + truncation (`bool`, *optional*, defaults to `True`): + Activates truncation to cut input sequences longer than `max_length` to `max_length`. + pad_to_multiple_of (`int`, *optional*): + If set will pad the sequence to a multiple of the provided value. + + This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability + `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128. + return_noise (`bool`, *optional*, defaults to `True`): + Whether to generate and return a noise waveform for use in [`UnivNetModel.forward`]. + generator (`numpy.random.Generator`, *optional*, defaults to `None`): + An optional `numpy.random.Generator` random number generator to use when generating noise. + pad_end (`bool`, *optional*, defaults to `False`): + Whether to pad the end of each waveform with silence. This can help reduce artifacts at the end of the + generated audio sample; see https://github.com/seungwonpark/melgan/issues/8 for more details. This + padding will be done before the padding strategy specified in `padding` is performed. + pad_length (`int`, *optional*, defaults to `None`): + If padding the end of each waveform, the length of the padding in spectrogram frames. If not set, this + will default to `self.config.pad_end_length`. + do_normalize (`bool`, *optional*): + Whether to perform Tacotron 2 normalization on the input. Normalizing can help to significantly improve + the performance for some models. If not set, this will default to `self.config.do_normalize`. + return_attention_mask (`bool`, *optional*): + Whether to return the attention mask. If left to the default, will return the attention mask according + to the specific feature_extractor's default. + + [What are attention masks?](../glossary#attention-mask) + + return_tensors (`str` or [`~utils.TensorType`], *optional*): + If set, will return tensors instead of list of python integers. Acceptable values are: + + - `'tf'`: Return TensorFlow `tf.constant` objects. + - `'pt'`: Return PyTorch `torch.np.array` objects. + - `'np'`: Return Numpy `np.ndarray` objects. + """ + do_normalize = do_normalize if do_normalize is not None else self.do_normalize + + if sampling_rate is not None: + if sampling_rate != self.sampling_rate: + raise ValueError( + f"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a" + f" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input" + f" was sampled with {self.sampling_rate} and not {sampling_rate}." + ) + else: + logger.warning( + "It is strongly recommended to pass the `sampling_rate` argument to this function. " + "Failing to do so can result in silent errors that might be hard to debug." + ) + + is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1 + if is_batched_numpy and len(raw_speech.shape) > 2: + raise ValueError(f"Only mono-channel audio is supported for input to {self}") + is_batched = is_batched_numpy or ( + isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list))) + ) + + if is_batched: + raw_speech = [np.asarray(speech, dtype=np.float32) for speech in raw_speech] + elif not is_batched and not isinstance(raw_speech, np.ndarray): + raw_speech = np.asarray(raw_speech, dtype=np.float32) + elif isinstance(raw_speech, np.ndarray) and raw_speech.dtype is np.dtype(np.float64): + raw_speech = raw_speech.astype(np.float32) + + # always return batch + if not is_batched: + raw_speech = [np.asarray(raw_speech, dtype=np.float32)] + + # Pad end to reduce artifacts + if pad_end: + pad_length = pad_length if pad_length is not None else self.pad_end_length + raw_speech = [ + np.pad(waveform, (0, pad_length * self.hop_length), constant_values=self.padding_value) + for waveform in raw_speech + ] + + batched_speech = BatchFeature({"input_features": raw_speech}) + + padded_inputs = self.pad( + batched_speech, + padding=padding, + max_length=max_length if max_length is not None else self.num_max_samples, + truncation=truncation, + pad_to_multiple_of=pad_to_multiple_of, + return_attention_mask=return_attention_mask, + ) + + # make sure list is in array format + # input_features = padded_inputs.get("input_features").transpose(2, 0, 1) + input_features = padded_inputs.get("input_features") + + mel_spectrograms = [self.mel_spectrogram(waveform) for waveform in input_features] + + if isinstance(input_features[0], List): + batched_speech["input_features"] = [np.asarray(mel, dtype=np.float32) for mel in mel_spectrograms] + else: + batched_speech["input_features"] = [mel.astype(np.float32) for mel in mel_spectrograms] + + # convert attention_mask to correct format + attention_mask = padded_inputs.get("attention_mask") + if attention_mask is not None: + batched_speech["padding_mask"] = [np.asarray(array, dtype=np.int32) for array in attention_mask] + + if return_noise: + noise = [ + self.generate_noise(spectrogram.shape[0], generator) + for spectrogram in batched_speech["input_features"] + ] + batched_speech["noise_sequence"] = noise + + if do_normalize: + batched_speech["input_features"] = [ + self.normalize(spectrogram) for spectrogram in batched_speech["input_features"] + ] + + if return_tensors is not None: + batched_speech = batched_speech.convert_to_tensors(return_tensors) + + return batched_speech + + def to_dict(self) -> Dict[str, Any]: + output = super().to_dict() + + # Don't serialize these as they are derived from the other properties. + names = ["window", "mel_filters", "n_fft", "n_freqs", "num_max_samples"] + for name in names: + if name in output: + del output[name] + + return output diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/univnet/modeling_univnet.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/univnet/modeling_univnet.py new file mode 100644 index 0000000000000000000000000000000000000000..c2551d7265319635b970089a143da5e98253121f --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/univnet/modeling_univnet.py @@ -0,0 +1,634 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch UnivNetModel model.""" + +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn + +from ...modeling_utils import ModelOutput, PreTrainedModel +from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings +from .configuration_univnet import UnivNetConfig + + +logger = logging.get_logger(__name__) + +# General docstring +_CONFIG_FOR_DOC = "UnivNetConfig" + +_CHECKPOINT_FOR_DOC = "dg845/univnet-dev" + + +from ..deprecated._archive_maps import UNIVNET_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +@dataclass +class UnivNetModelOutput(ModelOutput): + """ + Output class for the [`UnivNetModel`], which includes the generated audio waveforms and the original unpadded + lengths of those waveforms (so that the padding can be removed by [`UnivNetModel.batch_decode`]). + + Args: + waveforms (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): + Batched 1D (mono-channel) output audio waveforms. + waveform_lengths (`torch.FloatTensor` of shape `(batch_size,)`): + The batched length in samples of each unpadded waveform in `waveforms`. + """ + + waveforms: torch.FloatTensor = None + waveform_lengths: torch.FloatTensor = None + + +class UnivNetKernelPredictorResidualBlock(nn.Module): + """ + Implementation of the residual block for the kernel predictor network inside each location variable convolution + block (LVCBlock). + + Parameters: + config: (`UnivNetConfig`): + Config for the `UnivNetModel` model. + """ + + def __init__( + self, + config: UnivNetConfig, + ): + super().__init__() + self.channels = config.model_in_channels + self.kernel_size = config.kernel_predictor_conv_size + self.dropout_prob = config.kernel_predictor_dropout + self.leaky_relu_slope = config.leaky_relu_slope + + padding = (self.kernel_size - 1) // 2 + + self.dropout = nn.Dropout(self.dropout_prob) + self.conv1 = nn.Conv1d(self.channels, self.channels, self.kernel_size, padding=padding, bias=True) + self.conv2 = nn.Conv1d(self.channels, self.channels, self.kernel_size, padding=padding, bias=True) + + def forward(self, hidden_states: torch.FloatTensor): + # hidden_states should have shape (batch_size, channels, seq_length) + residual = hidden_states + hidden_states = self.dropout(hidden_states) + hidden_states = self.conv1(hidden_states) + hidden_states = nn.functional.leaky_relu(hidden_states, self.leaky_relu_slope) + hidden_states = self.conv2(hidden_states) + hidden_states = nn.functional.leaky_relu(hidden_states, self.leaky_relu_slope) + return hidden_states + residual + + def apply_weight_norm(self): + nn.utils.weight_norm(self.conv1) + nn.utils.weight_norm(self.conv2) + + def remove_weight_norm(self): + nn.utils.remove_weight_norm(self.conv1) + nn.utils.remove_weight_norm(self.conv2) + + +class UnivNetKernelPredictor(nn.Module): + """ + Implementation of the kernel predictor network which supplies the kernel and bias for the location variable + convolutional layers (LVCs) in each UnivNet LVCBlock. + + Based on the KernelPredictor implementation in + [maum-ai/univnet](https://github.com/maum-ai/univnet/blob/9bb2b54838bb6d7ce767131cc7b8b61198bc7558/model/lvcnet.py#L7). + + Parameters: + config: (`UnivNetConfig`): + Config for the `UnivNetModel` model. + conv_kernel_size (`int`, *optional*, defaults to 3): + The kernel size for the location variable convolutional layer kernels (convolutional weight tensor). + conv_layers (`int`, *optional*, defaults to 4): + The number of location variable convolutional layers to output kernels and biases for. + """ + + def __init__( + self, + config: UnivNetConfig, + conv_kernel_size: int = 3, + conv_layers: int = 4, + ): + super().__init__() + + self.conv_in_channels = config.model_hidden_channels + self.conv_out_channels = 2 * config.model_hidden_channels + self.conv_kernel_size = conv_kernel_size + self.conv_layers = conv_layers + + self.kernel_channels = ( + self.conv_in_channels * self.conv_out_channels * self.conv_kernel_size * self.conv_layers + ) + self.bias_channels = self.conv_out_channels * self.conv_layers + + self.resnet_in_channels = config.num_mel_bins + self.resnet_hidden_channels = config.kernel_predictor_hidden_channels + self.resnet_kernel_size = config.kernel_predictor_conv_size + self.num_blocks = config.kernel_predictor_num_blocks + + self.leaky_relu_slope = config.leaky_relu_slope + + padding = (self.resnet_kernel_size - 1) // 2 + + self.input_conv = nn.Conv1d(self.resnet_in_channels, self.resnet_hidden_channels, 5, padding=2, bias=True) + + self.resblocks = nn.ModuleList([UnivNetKernelPredictorResidualBlock(config) for _ in range(self.num_blocks)]) + + self.kernel_conv = nn.Conv1d( + self.resnet_hidden_channels, self.kernel_channels, self.resnet_kernel_size, padding=padding, bias=True + ) + self.bias_conv = nn.Conv1d( + self.resnet_hidden_channels, self.bias_channels, self.resnet_kernel_size, padding=padding, bias=True + ) + + def forward(self, spectrogram: torch.FloatTensor): + """ + Maps a conditioning log-mel spectrogram to a tensor of convolutional kernels and biases, for use in location + variable convolutional layers. Note that the input spectrogram should have shape (batch_size, input_channels, + seq_length). + + Args: + spectrogram (`torch.FloatTensor` of shape `(batch_size, input_channels, seq_length)`): + Tensor containing the log-mel spectrograms. + + Returns: + Tuple[`torch.FloatTensor, `torch.FloatTensor`]: tuple of tensors where the first element is the tensor of + location variable convolution kernels of shape `(batch_size, self.conv_layers, self.conv_in_channels, + self.conv_out_channels, self.conv_kernel_size, seq_length)` and the second element is the tensor of + location variable convolution biases of shape `(batch_size, self.conv_layers. self.conv_out_channels, + seq_length)`. + """ + batch_size, _, seq_length = spectrogram.shape + + hidden_states = self.input_conv(spectrogram) + hidden_states = nn.functional.leaky_relu(hidden_states, self.leaky_relu_slope) + + for resblock in self.resblocks: + hidden_states = resblock(hidden_states) + + kernel_hidden_states = self.kernel_conv(hidden_states) + bias_hidden_states = self.bias_conv(hidden_states) + + # Reshape kernels and biases to appropriate shape + kernels = kernel_hidden_states.view( + batch_size, + self.conv_layers, + self.conv_in_channels, + self.conv_out_channels, + self.conv_kernel_size, + seq_length, + ).contiguous() + biases = bias_hidden_states.view( + batch_size, + self.conv_layers, + self.conv_out_channels, + seq_length, + ).contiguous() + + return kernels, biases + + def apply_weight_norm(self): + nn.utils.weight_norm(self.input_conv) + for layer in self.resblocks: + layer.apply_weight_norm() + nn.utils.weight_norm(self.kernel_conv) + nn.utils.weight_norm(self.bias_conv) + + def remove_weight_norm(self): + nn.utils.remove_weight_norm(self.input_conv) + for layer in self.resblocks: + layer.remove_weight_norm() + nn.utils.remove_weight_norm(self.kernel_conv) + nn.utils.remove_weight_norm(self.bias_conv) + + +class UnivNetLvcResidualBlock(nn.Module): + """ + Implementation of the location variable convolution (LVC) residual block for the UnivNet residual network. + + Parameters: + config: (`UnivNetConfig`): + Config for the `UnivNetModel` model. + kernel_size (`int`): + The kernel size for the dilated 1D convolutional layer. + dilation (`int`): + The dilation for the dilated 1D convolutional layer. + """ + + def __init__( + self, + config: UnivNetConfig, + kernel_size: int, + dilation: int, + ): + super().__init__() + self.hidden_channels = config.model_hidden_channels + self.kernel_size = kernel_size + self.dilation = dilation + self.leaky_relu_slope = config.leaky_relu_slope + + padding = self.dilation * (self.kernel_size - 1) // 2 + + self.conv = nn.Conv1d( + self.hidden_channels, + self.hidden_channels, + self.kernel_size, + padding=padding, + dilation=self.dilation, + ) + + def forward(self, hidden_states, kernel, bias, hop_size=256): + residual = hidden_states + hidden_states = nn.functional.leaky_relu(hidden_states, self.leaky_relu_slope) + hidden_states = self.conv(hidden_states) + hidden_states = nn.functional.leaky_relu(hidden_states, self.leaky_relu_slope) + hidden_states = self.location_variable_convolution(hidden_states, kernel, bias, hop_size=hop_size) + # Gated activation unit + hidden_states = torch.sigmoid(hidden_states[:, : self.hidden_channels, :]) * torch.tanh( + hidden_states[:, self.hidden_channels :, :] + ) + # Skip connection + hidden_states = residual + hidden_states + + return hidden_states + + # Based on https://github.com/maum-ai/univnet/blob/9bb2b54838bb6d7ce767131cc7b8b61198bc7558/model/lvcnet.py#L171 + def location_variable_convolution( + self, + hidden_states: torch.FloatTensor, + kernel: torch.FloatTensor, + bias: torch.FloatTensor, + dilation: int = 1, + hop_size: int = 256, + ): + """ + Performs location-variable convolution operation on the input sequence (hidden_states) using the local + convolution kernel. This was introduced in [LVCNet: Efficient Condition-Dependent Modeling Network for Waveform + Generation](https://arxiv.org/abs/2102.10815) by Zhen Zheng, Jianzong Wang, Ning Cheng, and Jing Xiao. + + Time: 414 μs ± 309 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each), test on NVIDIA V100. + + Args: + hidden_states (`torch.FloatTensor` of shape `(batch_size, in_channels, in_length)`): + The input sequence of shape (batch, in_channels, in_length). + kernel (`torch.FloatTensor` of shape `(batch_size, in_channels, out_channels, kernel_size, kernel_length)`): + The local convolution kernel of shape (batch, in_channels, out_channels, kernel_size, kernel_length). + bias (`torch.FloatTensor` of shape `(batch_size, out_channels, kernel_length)`): + The bias for the local convolution of shape (batch, out_channels, kernel_length). + dilation (`int`, *optional*, defaults to 1): + The dilation of convolution. + hop_size (`int`, *optional*, defaults to 256): + The hop_size of the conditioning sequence. + Returns: + `torch.FloatTensor`: the output sequence after performing local convolution with shape (batch_size, + out_channels, in_length). + """ + batch, _, in_length = hidden_states.shape + batch, _, out_channels, kernel_size, kernel_length = kernel.shape + if in_length != (kernel_length * hop_size): + raise ValueError( + f"Dim 2 of `hidden_states` should be {kernel_length * hop_size}) but got {in_length}. Please check" + " `hidden_states` or `kernel` and `hop_size` to make sure they are correct." + ) + + padding = dilation * int((kernel_size - 1) / 2) + + # (batch, in_channels, in_length + 2*padding) + hidden_states = nn.functional.pad(hidden_states, (padding, padding), "constant", 0) + # (batch, in_channels, kernel_length, hop_size + 2*padding) + hidden_states = hidden_states.unfold(2, hop_size + 2 * padding, hop_size) + + if hop_size < dilation: + hidden_states = nn.functional.pad(hidden_states, (0, dilation), "constant", 0) + # (batch, in_channels, kernel_length, (hop_size + 2*padding)/dilation, dilation) + hidden_states = hidden_states.unfold(3, dilation, dilation) + hidden_states = hidden_states[:, :, :, :, :hop_size] + # (batch, in_channels, kernel_length, dilation, (hop_size + 2*padding)/dilation) + hidden_states = hidden_states.transpose(3, 4) + # (batch, in_channels, kernel_length, dilation, _, kernel_size) + hidden_states = hidden_states.unfold(4, kernel_size, 1) + + # Apply local convolution kernel to hidden_states. + output_hidden_states = torch.einsum("bildsk,biokl->bolsd", hidden_states, kernel) + + output_hidden_states = output_hidden_states.to(memory_format=torch.channels_last_3d) + bias = bias.unsqueeze(-1).unsqueeze(-1).to(memory_format=torch.channels_last_3d) + output_hidden_states = output_hidden_states + bias + output_hidden_states = output_hidden_states.contiguous().view(batch, out_channels, -1) + + return output_hidden_states + + def apply_weight_norm(self): + nn.utils.weight_norm(self.conv) + + def remove_weight_norm(self): + nn.utils.remove_weight_norm(self.conv) + + +class UnivNetLvcBlock(nn.Module): + """ + Implementation of the location variable convolution (LVC) residual block of the UnivNet residual block. Includes a + `UnivNetKernelPredictor` inside to predict the kernels and biases of the LVC layers. + + Based on LVCBlock in + [maum-ai/univnet](https://github.com/maum-ai/univnet/blob/9bb2b54838bb6d7ce767131cc7b8b61198bc7558/model/lvcnet.py#L98) + + Parameters: + config (`UnivNetConfig`): + Config for the `UnivNetModel` model. + layer_id (`int`): + An integer corresponding to the index of the current LVC resnet block layer. This should be between 0 and + `len(config.resblock_stride_sizes) - 1)` inclusive. + lvc_hop_size (`int`, *optional*, defaults to 256): + The hop size for the location variable convolutional layers. + """ + + def __init__( + self, + config: UnivNetConfig, + layer_id: int, + lvc_hop_size: int = 256, + ): + super().__init__() + self.hidden_channels = config.model_hidden_channels + self.kernel_size = config.resblock_kernel_sizes[layer_id] + self.stride = config.resblock_stride_sizes[layer_id] + self.dilations = config.resblock_dilation_sizes[layer_id] + self.cond_hop_length = lvc_hop_size + self.leaky_relu_slope = config.leaky_relu_slope + self.num_blocks = len(self.dilations) + + self.convt_pre = nn.ConvTranspose1d( + self.hidden_channels, + self.hidden_channels, + 2 * self.stride, + stride=self.stride, + padding=self.stride // 2 + self.stride % 2, + output_padding=self.stride % 2, + ) + + self.kernel_predictor = UnivNetKernelPredictor(config, self.kernel_size, self.num_blocks) + + self.resblocks = nn.ModuleList( + [UnivNetLvcResidualBlock(config, self.kernel_size, self.dilations[i]) for i in range(self.num_blocks)] + ) + + def forward(self, hidden_states: torch.FloatTensor, spectrogram: torch.FloatTensor): + # hidden_states: (batch_size, hidden_channels, seq_length) + # spectrogram: (batch_size, cond_channels, cond_length) + hidden_states = nn.functional.leaky_relu(hidden_states, self.leaky_relu_slope) + hidden_states = self.convt_pre(hidden_states) + + kernels, biases = self.kernel_predictor(spectrogram) + + for i, resblock in enumerate(self.resblocks): + kernel = kernels[:, i, :, :, :, :] + bias = biases[:, i, :, :] + hidden_states = resblock(hidden_states, kernel, bias, hop_size=self.cond_hop_length) + + return hidden_states + + def apply_weight_norm(self): + nn.utils.weight_norm(self.convt_pre) + self.kernel_predictor.apply_weight_norm() + for layer in self.resblocks: + layer.apply_weight_norm() + + def remove_weight_norm(self): + nn.utils.remove_weight_norm(self.convt_pre) + self.kernel_predictor.remove_weight_norm() + for layer in self.resblocks: + layer.remove_weight_norm() + + +UNIVNET_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`UnivNetConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +UNIVNET_INPUTS_DOCSTRING = r""" + Converts a noise waveform and a conditioning spectrogram to a speech waveform. Passing a batch of log-mel + spectrograms returns a batch of speech waveforms. Passing a single, un-batched log-mel spectrogram returns a + single, un-batched speech waveform. + + Args: + input_features (`torch.FloatTensor`): + Tensor containing the log-mel spectrograms. Can be batched and of shape `(batch_size, sequence_length, + config.num_mel_channels)`, or un-batched and of shape `(sequence_length, config.num_mel_channels)`. + noise_sequence (`torch.FloatTensor`, *optional*): + Tensor containing a noise sequence of standard Gaussian noise. Can be batched and of shape `(batch_size, + sequence_length, config.model_in_channels)`, or un-batched and of shape (sequence_length, + config.model_in_channels)`. If not supplied, will be randomly generated. + padding_mask (`torch.BoolTensor`, *optional*): + Mask indicating which parts of each sequence are padded. Mask values are selected in `[0, 1]`: + + - 1 for tokens that are **not masked** + - 0 for tokens that are **masked** + + The mask can be batched and of shape `(batch_size, sequence_length)` or un-batched and of shape + `(sequence_length,)`. + generator (`torch.Generator`, *optional*): + A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation + deterministic. + return_dict: + Whether to return a [`~utils.ModelOutput`] subclass instead of a plain tuple. +""" + + +@add_start_docstrings( + """UnivNet GAN vocoder.""", + UNIVNET_START_DOCSTRING, +) +class UnivNetModel(PreTrainedModel): + config_class = UnivNetConfig + main_input_name = "input_features" + + def __init__(self, config: UnivNetConfig): + super().__init__(config) + + self.num_kernels = len(config.resblock_kernel_sizes) + self.leaky_relu_slope = config.leaky_relu_slope + + self.conv_pre = nn.Conv1d( + config.model_in_channels, + config.model_hidden_channels, + kernel_size=7, + stride=1, + padding=3, + padding_mode="reflect", + ) + + # Initialize location-variable convolution ResNet Blocks. + num_layers = len(config.resblock_stride_sizes) + hop_length = 1 + hop_lengths = [] + for stride in config.resblock_stride_sizes: + hop_length = hop_length * stride + hop_lengths.append(hop_length) + + self.resblocks = nn.ModuleList( + [ + UnivNetLvcBlock( + config, + layer_id=i, + lvc_hop_size=hop_lengths[i], + ) + for i in range(num_layers) + ] + ) + + self.conv_post = nn.Conv1d(config.model_hidden_channels, 1, 7, padding=3, padding_mode="reflect") + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(UNIVNET_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=UnivNetModelOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_features: torch.FloatTensor, + noise_sequence: Optional[torch.FloatTensor] = None, + padding_mask: Optional[torch.FloatTensor] = None, + generator: Optional[torch.Generator] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.FloatTensor], UnivNetModelOutput]: + r""" + Returns: + + Example: + + ```python + >>> from transformers import UnivNetFeatureExtractor, UnivNetModel + >>> from datasets import load_dataset, Audio + + >>> model = UnivNetModel.from_pretrained("dg845/univnet-dev") + >>> feature_extractor = UnivNetFeatureExtractor.from_pretrained("dg845/univnet-dev") + + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + >>> # Resample the audio to the feature extractor's sampling rate. + >>> ds = ds.cast_column("audio", Audio(sampling_rate=feature_extractor.sampling_rate)) + >>> inputs = feature_extractor( + ... ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="pt" + ... ) + >>> audio = model(**inputs).waveforms + >>> list(audio.shape) + [1, 140288] + ``` + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # Resolve batch sizes for noise_sequence and spectrogram + spectrogram_batched = input_features.dim() == 3 + if not spectrogram_batched: + input_features = input_features.unsqueeze(0) + spectrogram_batch_size, spectrogram_length, _ = input_features.shape + + if noise_sequence is not None: + noise_sequence_batched = noise_sequence.dim() == 3 + if not noise_sequence_batched: + noise_sequence = noise_sequence.unsqueeze(0) + else: + # Randomly generate noise_sequence + noise_sequence_shape = (spectrogram_batch_size, spectrogram_length, self.config.model_in_channels) + noise_sequence = torch.randn( + noise_sequence_shape, generator=generator, dtype=input_features.dtype, device=input_features.device + ) + noise_sequence_batch_size = noise_sequence.shape[0] + + if spectrogram_batch_size > 1 and noise_sequence_batch_size == 1: + # Repeat noise_sequence spectrogram_batch_size times + noise_sequence = noise_sequence.repeat(spectrogram_batch_size, 1, 1) + elif noise_sequence_batch_size > 1 and spectrogram_batch_size == 1: + # Repeat spectrogram noise_sequence_batch_size times + input_features = input_features.repeat(noise_sequence_batch_size, 1, 1) + + if noise_sequence_batch_size != spectrogram_batch_size: + raise ValueError( + f"The batch size of `noise_sequence` is {noise_sequence_batch_size} and the batch size of" + f" `input_features` is {spectrogram_batch_size}, but the two are expected to be equal." + ) + + if padding_mask is not None: + if padding_mask.dim() == 1: + padding_mask = padding_mask.unsqueeze(0) + padding_mask_batch_size = padding_mask.shape[0] + if padding_mask_batch_size != spectrogram_batch_size: + raise ValueError( + f"The batch size of `padding_mask` is {padding_mask_batch_size} and the batch size of" + f" `input_features` is {spectrogram_batch_size}, but the two are expected to be equal." + ) + + # Change shapes to have channels before sequence lengths + hidden_states = noise_sequence.transpose(2, 1) + input_features = input_features.transpose(2, 1) + + hidden_states = self.conv_pre(hidden_states) + + for resblock in self.resblocks: + hidden_states = resblock(hidden_states, input_features) + + hidden_states = nn.functional.leaky_relu(hidden_states, self.leaky_relu_slope) + hidden_states = self.conv_post(hidden_states) + hidden_states = torch.tanh(hidden_states) + + # Remove sequence length dimension since this collapses to 1 + # NOTE: keep waveforms batched even if there's only one + waveform = hidden_states.squeeze(1) + + # Get sequence lengths for UnivNetFeatureExtractor.batch_decode. + waveform_lengths = None + if padding_mask is not None: + # Padding is always contiguous and added on the right + waveform_lengths = torch.sum(padding_mask, dim=1) + + if not return_dict: + outputs = (waveform, waveform_lengths) + return outputs + + return UnivNetModelOutput( + waveforms=waveform, + waveform_lengths=waveform_lengths, + ) + + def _init_weights(self, module): + """Initialize the weights.""" + if isinstance(module, (nn.Linear, nn.Conv1d, nn.ConvTranspose1d)): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + + def apply_weight_norm(self): + nn.utils.weight_norm(self.conv_pre) + for layer in self.resblocks: + layer.apply_weight_norm() + nn.utils.weight_norm(self.conv_post) + + def remove_weight_norm(self): + nn.utils.remove_weight_norm(self.conv_pre) + for layer in self.resblocks: + layer.remove_weight_norm() + nn.utils.remove_weight_norm(self.conv_post) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/upernet/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/upernet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3954fe4594dad04c3908a447f36dd02a1dea8c7c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/upernet/__init__.py @@ -0,0 +1,50 @@ +# Copyright 2022 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available + + +_import_structure = { + "configuration_upernet": ["UperNetConfig"], +} + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_upernet"] = [ + "UperNetForSemanticSegmentation", + "UperNetPreTrainedModel", + ] + + +if TYPE_CHECKING: + from .configuration_upernet import UperNetConfig + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel + + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/upernet/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/upernet/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9643ad8e3d0208763e33daaf0c9d25dc477e7257 Binary files /dev/null and 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b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/upernet/configuration_upernet.py new file mode 100644 index 0000000000000000000000000000000000000000..609818c80d17b728322d52586ab1cfedf687f8e0 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/upernet/configuration_upernet.py @@ -0,0 +1,138 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" UperNet model configuration""" + + +from ...configuration_utils import PretrainedConfig +from ...utils import logging +from ..auto.configuration_auto import CONFIG_MAPPING + + +logger = logging.get_logger(__name__) + + +class UperNetConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of an [`UperNetForSemanticSegmentation`]. It is used to + instantiate an UperNet model according to the specified arguments, defining the model architecture. Instantiating a + configuration with the defaults will yield a similar configuration to that of the UperNet + [openmmlab/upernet-convnext-tiny](https://huggingface.co/openmmlab/upernet-convnext-tiny) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + backbone_config (`PretrainedConfig` or `dict`, *optional*, defaults to `ResNetConfig()`): + The configuration of the backbone model. + backbone (`str`, *optional*): + Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this + will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone` + is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights. + use_pretrained_backbone (`bool`, *optional*, `False`): + Whether to use pretrained weights for the backbone. + use_timm_backbone (`bool`, *optional*, `False`): + Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers + library. + backbone_kwargs (`dict`, *optional*): + Keyword arguments to be passed to AutoBackbone when loading from a checkpoint + e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set. + hidden_size (`int`, *optional*, defaults to 512): + The number of hidden units in the convolutional layers. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + pool_scales (`Tuple[int]`, *optional*, defaults to `[1, 2, 3, 6]`): + Pooling scales used in Pooling Pyramid Module applied on the last feature map. + use_auxiliary_head (`bool`, *optional*, defaults to `True`): + Whether to use an auxiliary head during training. + auxiliary_loss_weight (`float`, *optional*, defaults to 0.4): + Weight of the cross-entropy loss of the auxiliary head. + auxiliary_channels (`int`, *optional*, defaults to 256): + Number of channels to use in the auxiliary head. + auxiliary_num_convs (`int`, *optional*, defaults to 1): + Number of convolutional layers to use in the auxiliary head. + auxiliary_concat_input (`bool`, *optional*, defaults to `False`): + Whether to concatenate the output of the auxiliary head with the input before the classification layer. + loss_ignore_index (`int`, *optional*, defaults to 255): + The index that is ignored by the loss function. + + Examples: + + ```python + >>> from transformers import UperNetConfig, UperNetForSemanticSegmentation + + >>> # Initializing a configuration + >>> configuration = UperNetConfig() + + >>> # Initializing a model (with random weights) from the configuration + >>> model = UperNetForSemanticSegmentation(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "upernet" + + def __init__( + self, + backbone_config=None, + backbone=None, + use_pretrained_backbone=False, + use_timm_backbone=False, + backbone_kwargs=None, + hidden_size=512, + initializer_range=0.02, + pool_scales=[1, 2, 3, 6], + use_auxiliary_head=True, + auxiliary_loss_weight=0.4, + auxiliary_in_channels=384, + auxiliary_channels=256, + auxiliary_num_convs=1, + auxiliary_concat_input=False, + loss_ignore_index=255, + **kwargs, + ): + super().__init__(**kwargs) + if use_pretrained_backbone: + raise ValueError("Pretrained backbones are not supported yet.") + + if backbone_config is not None and backbone is not None: + raise ValueError("You can't specify both `backbone` and `backbone_config`.") + + if backbone_config is None and backbone is None: + logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.") + backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"]) + elif isinstance(backbone_config, dict): + backbone_model_type = backbone_config.get("model_type") + config_class = CONFIG_MAPPING[backbone_model_type] + backbone_config = config_class.from_dict(backbone_config) + + if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None: + raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.") + + self.backbone_config = backbone_config + self.backbone = backbone + self.use_pretrained_backbone = use_pretrained_backbone + self.use_timm_backbone = use_timm_backbone + self.backbone_kwargs = backbone_kwargs + self.hidden_size = hidden_size + self.initializer_range = initializer_range + self.pool_scales = pool_scales + self.use_auxiliary_head = use_auxiliary_head + self.auxiliary_loss_weight = auxiliary_loss_weight + self.auxiliary_in_channels = auxiliary_in_channels + self.auxiliary_channels = auxiliary_channels + self.auxiliary_num_convs = auxiliary_num_convs + self.auxiliary_concat_input = auxiliary_concat_input + self.loss_ignore_index = loss_ignore_index diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/upernet/convert_convnext_upernet_to_pytorch.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/upernet/convert_convnext_upernet_to_pytorch.py new file mode 100644 index 0000000000000000000000000000000000000000..eeb3ab5fc9938171099a47feef23c4694d8b5169 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/upernet/convert_convnext_upernet_to_pytorch.py @@ -0,0 +1,214 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert ConvNext + UperNet checkpoints from mmsegmentation.""" + +import argparse +import json + +import requests +import torch +from huggingface_hub import hf_hub_download +from PIL import Image + +from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation + + +def get_upernet_config(model_name): + auxiliary_in_channels = 384 + if "tiny" in model_name: + depths = [3, 3, 9, 3] + hidden_sizes = [96, 192, 384, 768] + if "small" in model_name: + depths = [3, 3, 27, 3] + hidden_sizes = [96, 192, 384, 768] + if "base" in model_name: + depths = [3, 3, 27, 3] + hidden_sizes = [128, 256, 512, 1024] + auxiliary_in_channels = 512 + if "large" in model_name: + depths = [3, 3, 27, 3] + hidden_sizes = [192, 384, 768, 1536] + auxiliary_in_channels = 768 + if "xlarge" in model_name: + depths = [3, 3, 27, 3] + hidden_sizes = [256, 512, 1024, 2048] + auxiliary_in_channels = 1024 + + # set label information + num_labels = 150 + repo_id = "huggingface/label-files" + filename = "ade20k-id2label.json" + id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) + id2label = {int(k): v for k, v in id2label.items()} + label2id = {v: k for k, v in id2label.items()} + + backbone_config = ConvNextConfig( + depths=depths, hidden_sizes=hidden_sizes, out_features=["stage1", "stage2", "stage3", "stage4"] + ) + config = UperNetConfig( + backbone_config=backbone_config, + auxiliary_in_channels=auxiliary_in_channels, + num_labels=num_labels, + id2label=id2label, + label2id=label2id, + ) + + return config + + +# here we list all keys to be renamed (original name on the left, our name on the right) +def create_rename_keys(config): + rename_keys = [] + + # fmt: off + # stem + rename_keys.append(("backbone.downsample_layers.0.0.weight", "backbone.embeddings.patch_embeddings.weight")) + rename_keys.append(("backbone.downsample_layers.0.0.bias", "backbone.embeddings.patch_embeddings.bias")) + rename_keys.append(("backbone.downsample_layers.0.1.weight", "backbone.embeddings.layernorm.weight")) + rename_keys.append(("backbone.downsample_layers.0.1.bias", "backbone.embeddings.layernorm.bias")) + # stages + for i in range(len(config.backbone_config.depths)): + for j in range(config.backbone_config.depths[i]): + rename_keys.append((f"backbone.stages.{i}.{j}.gamma", f"backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter")) + rename_keys.append((f"backbone.stages.{i}.{j}.depthwise_conv.weight", f"backbone.encoder.stages.{i}.layers.{j}.dwconv.weight")) + rename_keys.append((f"backbone.stages.{i}.{j}.depthwise_conv.bias", f"backbone.encoder.stages.{i}.layers.{j}.dwconv.bias")) + rename_keys.append((f"backbone.stages.{i}.{j}.norm.weight", f"backbone.encoder.stages.{i}.layers.{j}.layernorm.weight")) + rename_keys.append((f"backbone.stages.{i}.{j}.norm.bias", f"backbone.encoder.stages.{i}.layers.{j}.layernorm.bias")) + rename_keys.append((f"backbone.stages.{i}.{j}.pointwise_conv1.weight", f"backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight")) + rename_keys.append((f"backbone.stages.{i}.{j}.pointwise_conv1.bias", f"backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias")) + rename_keys.append((f"backbone.stages.{i}.{j}.pointwise_conv2.weight", f"backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight")) + rename_keys.append((f"backbone.stages.{i}.{j}.pointwise_conv2.bias", f"backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias")) + if i > 0: + rename_keys.append((f"backbone.downsample_layers.{i}.0.weight", f"backbone.encoder.stages.{i}.downsampling_layer.0.weight")) + rename_keys.append((f"backbone.downsample_layers.{i}.0.bias", f"backbone.encoder.stages.{i}.downsampling_layer.0.bias")) + rename_keys.append((f"backbone.downsample_layers.{i}.1.weight", f"backbone.encoder.stages.{i}.downsampling_layer.1.weight")) + rename_keys.append((f"backbone.downsample_layers.{i}.1.bias", f"backbone.encoder.stages.{i}.downsampling_layer.1.bias")) + + rename_keys.append((f"backbone.norm{i}.weight", f"backbone.hidden_states_norms.stage{i+1}.weight")) + rename_keys.append((f"backbone.norm{i}.bias", f"backbone.hidden_states_norms.stage{i+1}.bias")) + + # decode head + rename_keys.extend( + [ + ("decode_head.conv_seg.weight", "decode_head.classifier.weight"), + ("decode_head.conv_seg.bias", "decode_head.classifier.bias"), + ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"), + ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"), + ] + ) + # fmt: on + + return rename_keys + + +def rename_key(dct, old, new): + val = dct.pop(old) + dct[new] = val + + +def convert_upernet_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub): + model_name_to_url = { + "upernet-convnext-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth", + "upernet-convnext-small": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth", + "upernet-convnext-base": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth", + "upernet-convnext-large": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth", + "upernet-convnext-xlarge": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth", + } + checkpoint_url = model_name_to_url[model_name] + state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")["state_dict"] + + config = get_upernet_config(model_name) + model = UperNetForSemanticSegmentation(config) + model.eval() + + # replace "bn" => "batch_norm" + for key in state_dict.copy().keys(): + val = state_dict.pop(key) + if "bn" in key: + key = key.replace("bn", "batch_norm") + state_dict[key] = val + + # rename keys + rename_keys = create_rename_keys(config) + for src, dest in rename_keys: + rename_key(state_dict, src, dest) + + model.load_state_dict(state_dict) + + # verify on image + url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg" + image = Image.open(requests.get(url, stream=True).raw).convert("RGB") + + processor = SegformerImageProcessor() + pixel_values = processor(image, return_tensors="pt").pixel_values + + with torch.no_grad(): + outputs = model(pixel_values) + + if model_name == "upernet-convnext-tiny": + expected_slice = torch.tensor( + [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]] + ) + elif model_name == "upernet-convnext-small": + expected_slice = torch.tensor( + [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]] + ) + elif model_name == "upernet-convnext-base": + expected_slice = torch.tensor( + [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]] + ) + elif model_name == "upernet-convnext-large": + expected_slice = torch.tensor( + [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]] + ) + elif model_name == "upernet-convnext-xlarge": + expected_slice = torch.tensor( + [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]] + ) + print("Logits:", outputs.logits[0, 0, :3, :3]) + assert torch.allclose(outputs.logits[0, 0, :3, :3], expected_slice, atol=1e-4) + print("Looks ok!") + + if pytorch_dump_folder_path is not None: + print(f"Saving model {model_name} to {pytorch_dump_folder_path}") + model.save_pretrained(pytorch_dump_folder_path) + print(f"Saving processor to {pytorch_dump_folder_path}") + processor.save_pretrained(pytorch_dump_folder_path) + + if push_to_hub: + print(f"Pushing model and processor for {model_name} to hub") + model.push_to_hub(f"openmmlab/{model_name}") + processor.push_to_hub(f"openmmlab/{model_name}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + # Required parameters + parser.add_argument( + "--model_name", + default="upernet-convnext-tiny", + type=str, + choices=[f"upernet-convnext-{size}" for size in ["tiny", "small", "base", "large", "xlarge"]], + help="Name of the ConvNext UperNet model you'd like to convert.", + ) + parser.add_argument( + "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." + ) + parser.add_argument( + "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." + ) + + args = parser.parse_args() + convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/upernet/convert_swin_upernet_to_pytorch.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/upernet/convert_swin_upernet_to_pytorch.py new file mode 100644 index 0000000000000000000000000000000000000000..9580af7c46a50c26c25fe5a9f2728188fbd0193e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/upernet/convert_swin_upernet_to_pytorch.py @@ -0,0 +1,297 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert Swin Transformer + UperNet checkpoints from mmsegmentation. + +URL: https://github.com/open-mmlab/mmsegmentation/tree/master/configs/swin +""" + +import argparse +import json + +import requests +import torch +from huggingface_hub import hf_hub_download +from PIL import Image + +from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation + + +def get_upernet_config(model_name): + auxiliary_in_channels = 384 + window_size = 7 + if "tiny" in model_name: + embed_dim = 96 + depths = (2, 2, 6, 2) + num_heads = (3, 6, 12, 24) + elif "small" in model_name: + embed_dim = 96 + depths = (2, 2, 18, 2) + num_heads = (3, 6, 12, 24) + elif "base" in model_name: + embed_dim = 128 + depths = (2, 2, 18, 2) + num_heads = (4, 8, 16, 32) + window_size = 12 + auxiliary_in_channels = 512 + elif "large" in model_name: + embed_dim = 192 + depths = (2, 2, 18, 2) + num_heads = (6, 12, 24, 48) + window_size = 12 + auxiliary_in_channels = 768 + + # set label information + num_labels = 150 + repo_id = "huggingface/label-files" + filename = "ade20k-id2label.json" + id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) + id2label = {int(k): v for k, v in id2label.items()} + label2id = {v: k for k, v in id2label.items()} + + backbone_config = SwinConfig( + embed_dim=embed_dim, + depths=depths, + num_heads=num_heads, + window_size=window_size, + out_features=["stage1", "stage2", "stage3", "stage4"], + ) + config = UperNetConfig( + backbone_config=backbone_config, + auxiliary_in_channels=auxiliary_in_channels, + num_labels=num_labels, + id2label=id2label, + label2id=label2id, + ) + + return config + + +# here we list all keys to be renamed (original name on the left, our name on the right) +def create_rename_keys(config): + rename_keys = [] + + # fmt: off + # stem + rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight")) + rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias")) + rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight")) + rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.embeddings.norm.bias")) + # stages + for i in range(len(config.backbone_config.depths)): + for j in range(config.backbone_config.depths[i]): + rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")) + rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")) + rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")) + rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")) + rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")) + rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")) + rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")) + rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")) + rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")) + rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")) + rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight")) + rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias")) + + if i < 3: + rename_keys.append((f"backbone.stages.{i}.downsample.reduction.weight", f"backbone.encoder.layers.{i}.downsample.reduction.weight")) + rename_keys.append((f"backbone.stages.{i}.downsample.norm.weight", f"backbone.encoder.layers.{i}.downsample.norm.weight")) + rename_keys.append((f"backbone.stages.{i}.downsample.norm.bias", f"backbone.encoder.layers.{i}.downsample.norm.bias")) + rename_keys.append((f"backbone.norm{i}.weight", f"backbone.hidden_states_norms.stage{i+1}.weight")) + rename_keys.append((f"backbone.norm{i}.bias", f"backbone.hidden_states_norms.stage{i+1}.bias")) + + # decode head + rename_keys.extend( + [ + ("decode_head.conv_seg.weight", "decode_head.classifier.weight"), + ("decode_head.conv_seg.bias", "decode_head.classifier.bias"), + ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"), + ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"), + ] + ) + # fmt: on + + return rename_keys + + +def rename_key(dct, old, new): + val = dct.pop(old) + dct[new] = val + + +# we split up the matrix of each encoder layer into queries, keys and values +def read_in_q_k_v(state_dict, backbone_config): + num_features = [int(backbone_config.embed_dim * 2**i) for i in range(len(backbone_config.depths))] + for i in range(len(backbone_config.depths)): + dim = num_features[i] + for j in range(backbone_config.depths[i]): + # fmt: off + # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias) + in_proj_weight = state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight") + in_proj_bias = state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias") + # next, add query, keys and values (in that order) to the state dict + state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.query.weight"] = in_proj_weight[:dim, :] + state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.query.bias"] = in_proj_bias[: dim] + state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.key.weight"] = in_proj_weight[ + dim : dim * 2, : + ] + state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.key.bias"] = in_proj_bias[ + dim : dim * 2 + ] + state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.value.weight"] = in_proj_weight[ + -dim :, : + ] + state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.value.bias"] = in_proj_bias[-dim :] + # fmt: on + + +def correct_unfold_reduction_order(x): + out_channel, in_channel = x.shape + x = x.reshape(out_channel, 4, in_channel // 4) + x = x[:, [0, 2, 1, 3], :].transpose(1, 2).reshape(out_channel, in_channel) + return x + + +def reverse_correct_unfold_reduction_order(x): + out_channel, in_channel = x.shape + x = x.reshape(out_channel, in_channel // 4, 4) + x = x[:, :, [0, 2, 1, 3]].transpose(1, 2).reshape(out_channel, in_channel) + + return x + + +def correct_unfold_norm_order(x): + in_channel = x.shape[0] + x = x.reshape(4, in_channel // 4) + x = x[[0, 2, 1, 3], :].transpose(0, 1).reshape(in_channel) + return x + + +# there was an incompatibility with this version, due to a new implementation of their downsampling operation using nn.Unfold. +# was resolved as seen here: +# https://github.com/open-mmlab/mmdetection/blob/31c84958f54287a8be2b99cbf87a6dcf12e57753/mmdet/models/utils/ckpt_convert.py#L96. +def reverse_correct_unfold_norm_order(x): + in_channel = x.shape[0] + x = x.reshape(in_channel // 4, 4) + x = x[:, [0, 2, 1, 3]].transpose(0, 1).reshape(in_channel) + return x + + +def convert_upernet_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub): + model_name_to_url = { + "upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth", + "upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth", + "upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth", + "upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth", + } + checkpoint_url = model_name_to_url[model_name] + state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu", file_name=model_name)[ + "state_dict" + ] + + for name, param in state_dict.items(): + print(name, param.shape) + + config = get_upernet_config(model_name) + model = UperNetForSemanticSegmentation(config) + model.eval() + + # replace "bn" => "batch_norm" + for key in state_dict.copy().keys(): + val = state_dict.pop(key) + if "bn" in key: + key = key.replace("bn", "batch_norm") + state_dict[key] = val + + # rename keys + rename_keys = create_rename_keys(config) + for src, dest in rename_keys: + rename_key(state_dict, src, dest) + read_in_q_k_v(state_dict, config.backbone_config) + + # fix downsample parameters + for key, value in state_dict.items(): + if "downsample" in key: + if "reduction" in key: + state_dict[key] = reverse_correct_unfold_reduction_order(value) + if "norm" in key: + state_dict[key] = reverse_correct_unfold_norm_order(value) + + model.load_state_dict(state_dict) + + # verify on image + url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg" + image = Image.open(requests.get(url, stream=True).raw).convert("RGB") + + processor = SegformerImageProcessor() + pixel_values = processor(image, return_tensors="pt").pixel_values + + with torch.no_grad(): + outputs = model(pixel_values) + logits = outputs.logits + + print(logits.shape) + print("First values of logits:", logits[0, 0, :3, :3]) + # assert values + if model_name == "upernet-swin-tiny": + expected_slice = torch.tensor( + [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]] + ) + elif model_name == "upernet-swin-small": + expected_slice = torch.tensor( + [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]] + ) + elif model_name == "upernet-swin-base": + expected_slice = torch.tensor( + [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]] + ) + elif model_name == "upernet-swin-large": + expected_slice = torch.tensor( + [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]] + ) + print("Logits:", outputs.logits[0, 0, :3, :3]) + assert torch.allclose(outputs.logits[0, 0, :3, :3], expected_slice, atol=1e-4) + print("Looks ok!") + + if pytorch_dump_folder_path is not None: + print(f"Saving model {model_name} to {pytorch_dump_folder_path}") + model.save_pretrained(pytorch_dump_folder_path) + print(f"Saving processor to {pytorch_dump_folder_path}") + processor.save_pretrained(pytorch_dump_folder_path) + + if push_to_hub: + print(f"Pushing model and processor for {model_name} to hub") + model.push_to_hub(f"openmmlab/{model_name}") + processor.push_to_hub(f"openmmlab/{model_name}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + # Required parameters + parser.add_argument( + "--model_name", + default="upernet-swin-tiny", + type=str, + choices=[f"upernet-swin-{size}" for size in ["tiny", "small", "base", "large"]], + help="Name of the Swin + UperNet model you'd like to convert.", + ) + parser.add_argument( + "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." + ) + parser.add_argument( + "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." + ) + + args = parser.parse_args() + convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/upernet/modeling_upernet.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/upernet/modeling_upernet.py new file mode 100644 index 0000000000000000000000000000000000000000..2d5b4443e35df3ddbf75744b6198915ae724bb6f --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/upernet/modeling_upernet.py @@ -0,0 +1,440 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch UperNet model. Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.""" + +from typing import List, Optional, Tuple, Union + +import torch +from torch import nn +from torch.nn import CrossEntropyLoss + +from ...modeling_outputs import SemanticSegmenterOutput +from ...modeling_utils import PreTrainedModel +from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings +from ...utils.backbone_utils import load_backbone +from .configuration_upernet import UperNetConfig + + +# General docstring +_CONFIG_FOR_DOC = "UperNetConfig" + + +class UperNetConvModule(nn.Module): + """ + A convolutional block that bundles conv/norm/activation layers. This block simplifies the usage of convolution + layers, which are commonly used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU). + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: Union[int, Tuple[int, int]], + padding: Union[int, Tuple[int, int], str] = 0, + bias: bool = False, + dilation: Union[int, Tuple[int, int]] = 1, + ) -> None: + super().__init__() + self.conv = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + padding=padding, + bias=bias, + dilation=dilation, + ) + self.batch_norm = nn.BatchNorm2d(out_channels) + self.activation = nn.ReLU() + + def forward(self, input: torch.Tensor) -> torch.Tensor: + output = self.conv(input) + output = self.batch_norm(output) + output = self.activation(output) + + return output + + +class UperNetPyramidPoolingBlock(nn.Module): + def __init__(self, pool_scale: int, in_channels: int, channels: int) -> None: + super().__init__() + self.layers = [ + nn.AdaptiveAvgPool2d(pool_scale), + UperNetConvModule(in_channels, channels, kernel_size=1), + ] + for i, layer in enumerate(self.layers): + self.add_module(str(i), layer) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + hidden_state = input + for layer in self.layers: + hidden_state = layer(hidden_state) + return hidden_state + + +class UperNetPyramidPoolingModule(nn.Module): + """ + Pyramid Pooling Module (PPM) used in PSPNet. + + Args: + pool_scales (`Tuple[int]`): + Pooling scales used in Pooling Pyramid Module. + in_channels (`int`): + Input channels. + channels (`int`): + Channels after modules, before conv_seg. + align_corners (`bool`): + align_corners argument of F.interpolate. + """ + + def __init__(self, pool_scales: Tuple[int, ...], in_channels: int, channels: int, align_corners: bool) -> None: + super().__init__() + self.pool_scales = pool_scales + self.align_corners = align_corners + self.in_channels = in_channels + self.channels = channels + self.blocks = [] + for i, pool_scale in enumerate(pool_scales): + block = UperNetPyramidPoolingBlock(pool_scale=pool_scale, in_channels=in_channels, channels=channels) + self.blocks.append(block) + self.add_module(str(i), block) + + def forward(self, x: torch.Tensor) -> List[torch.Tensor]: + ppm_outs = [] + for ppm in self.blocks: + ppm_out = ppm(x) + upsampled_ppm_out = nn.functional.interpolate( + ppm_out, size=x.size()[2:], mode="bilinear", align_corners=self.align_corners + ) + ppm_outs.append(upsampled_ppm_out) + return ppm_outs + + +class UperNetHead(nn.Module): + """ + Unified Perceptual Parsing for Scene Understanding. This head is the implementation of + [UPerNet](https://arxiv.org/abs/1807.10221). + """ + + def __init__(self, config, in_channels): + super().__init__() + + self.config = config + self.pool_scales = config.pool_scales # e.g. (1, 2, 3, 6) + self.in_channels = in_channels + self.channels = config.hidden_size + self.align_corners = False + self.classifier = nn.Conv2d(self.channels, config.num_labels, kernel_size=1) + + # PSP Module + self.psp_modules = UperNetPyramidPoolingModule( + self.pool_scales, + self.in_channels[-1], + self.channels, + align_corners=self.align_corners, + ) + self.bottleneck = UperNetConvModule( + self.in_channels[-1] + len(self.pool_scales) * self.channels, + self.channels, + kernel_size=3, + padding=1, + ) + # FPN Module + self.lateral_convs = nn.ModuleList() + self.fpn_convs = nn.ModuleList() + for in_channels in self.in_channels[:-1]: # skip the top layer + l_conv = UperNetConvModule(in_channels, self.channels, kernel_size=1) + fpn_conv = UperNetConvModule(self.channels, self.channels, kernel_size=3, padding=1) + self.lateral_convs.append(l_conv) + self.fpn_convs.append(fpn_conv) + + self.fpn_bottleneck = UperNetConvModule( + len(self.in_channels) * self.channels, + self.channels, + kernel_size=3, + padding=1, + ) + + def init_weights(self): + self.apply(self._init_weights) + + def _init_weights(self, module): + if isinstance(module, nn.Conv2d): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + + def psp_forward(self, inputs): + x = inputs[-1] + psp_outs = [x] + psp_outs.extend(self.psp_modules(x)) + psp_outs = torch.cat(psp_outs, dim=1) + output = self.bottleneck(psp_outs) + + return output + + def forward(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor: + # build laterals + laterals = [lateral_conv(encoder_hidden_states[i]) for i, lateral_conv in enumerate(self.lateral_convs)] + + laterals.append(self.psp_forward(encoder_hidden_states)) + + # build top-down path + used_backbone_levels = len(laterals) + for i in range(used_backbone_levels - 1, 0, -1): + prev_shape = laterals[i - 1].shape[2:] + laterals[i - 1] = laterals[i - 1] + nn.functional.interpolate( + laterals[i], size=prev_shape, mode="bilinear", align_corners=self.align_corners + ) + + # build outputs + fpn_outs = [self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels - 1)] + # append psp feature + fpn_outs.append(laterals[-1]) + + for i in range(used_backbone_levels - 1, 0, -1): + fpn_outs[i] = nn.functional.interpolate( + fpn_outs[i], size=fpn_outs[0].shape[2:], mode="bilinear", align_corners=self.align_corners + ) + fpn_outs = torch.cat(fpn_outs, dim=1) + output = self.fpn_bottleneck(fpn_outs) + output = self.classifier(output) + + return output + + +class UperNetFCNHead(nn.Module): + """ + Fully Convolution Networks for Semantic Segmentation. This head is the implementation of + [FCNNet](https://arxiv.org/abs/1411.4038>). + + Args: + config: + Configuration. + in_channels (int): + Number of input channels. + kernel_size (int): + The kernel size for convs in the head. Default: 3. + dilation (int): + The dilation rate for convs in the head. Default: 1. + """ + + def __init__( + self, config, in_index: int = 2, kernel_size: int = 3, dilation: Union[int, Tuple[int, int]] = 1 + ) -> None: + super().__init__() + + self.config = config + self.in_channels = config.auxiliary_in_channels + self.channels = config.auxiliary_channels + self.num_convs = config.auxiliary_num_convs + self.concat_input = config.auxiliary_concat_input + self.in_index = in_index + + conv_padding = (kernel_size // 2) * dilation + convs = [] + convs.append( + UperNetConvModule( + self.in_channels, self.channels, kernel_size=kernel_size, padding=conv_padding, dilation=dilation + ) + ) + for i in range(self.num_convs - 1): + convs.append( + UperNetConvModule( + self.channels, self.channels, kernel_size=kernel_size, padding=conv_padding, dilation=dilation + ) + ) + if self.num_convs == 0: + self.convs = nn.Identity() + else: + self.convs = nn.Sequential(*convs) + if self.concat_input: + self.conv_cat = UperNetConvModule( + self.in_channels + self.channels, self.channels, kernel_size=kernel_size, padding=kernel_size // 2 + ) + + self.classifier = nn.Conv2d(self.channels, config.num_labels, kernel_size=1) + + def init_weights(self): + self.apply(self._init_weights) + + def _init_weights(self, module): + if isinstance(module, nn.Conv2d): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + + def forward(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor: + # just take the relevant feature maps + hidden_states = encoder_hidden_states[self.in_index] + output = self.convs(hidden_states) + if self.concat_input: + output = self.conv_cat(torch.cat([hidden_states, output], dim=1)) + output = self.classifier(output) + return output + + +class UperNetPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = UperNetConfig + main_input_name = "pixel_values" + + def _init_weights(self, module): + if isinstance(module, UperNetPreTrainedModel): + module.backbone.init_weights() + module.decode_head.init_weights() + if module.auxiliary_head is not None: + module.auxiliary_head.init_weights() + + def init_weights(self): + """Initialize the weights""" + self.backbone.init_weights() + self.decode_head.init_weights() + if self.auxiliary_head is not None: + self.auxiliary_head.init_weights() + + +UPERNET_START_DOCSTRING = r""" + Parameters: + This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use + it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and + behavior. + config ([`UperNetConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +UPERNET_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using + [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See + `attentions` under returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under + returned tensors for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + """UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.""", + UPERNET_START_DOCSTRING, +) +class UperNetForSemanticSegmentation(UperNetPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.backbone = load_backbone(config) + + # Semantic segmentation head(s) + self.decode_head = UperNetHead(config, in_channels=self.backbone.channels) + self.auxiliary_head = UperNetFCNHead(config) if config.use_auxiliary_head else None + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @replace_return_docstrings(output_type=SemanticSegmenterOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + labels: Optional[torch.Tensor] = None, + return_dict: Optional[bool] = None, + ) -> Union[tuple, SemanticSegmenterOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): + Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy). + + Returns: + + Examples: + ```python + >>> from transformers import AutoImageProcessor, UperNetForSemanticSegmentation + >>> from PIL import Image + >>> from huggingface_hub import hf_hub_download + + >>> image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-tiny") + >>> model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-tiny") + + >>> filepath = hf_hub_download( + ... repo_id="hf-internal-testing/fixtures_ade20k", filename="ADE_val_00000001.jpg", repo_type="dataset" + ... ) + >>> image = Image.open(filepath).convert("RGB") + + >>> inputs = image_processor(images=image, return_tensors="pt") + + >>> outputs = model(**inputs) + + >>> logits = outputs.logits # shape (batch_size, num_labels, height, width) + >>> list(logits.shape) + [1, 150, 512, 512] + ```""" + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + + outputs = self.backbone.forward_with_filtered_kwargs( + pixel_values, output_hidden_states=output_hidden_states, output_attentions=output_attentions + ) + features = outputs.feature_maps + + logits = self.decode_head(features) + logits = nn.functional.interpolate(logits, size=pixel_values.shape[2:], mode="bilinear", align_corners=False) + + auxiliary_logits = None + if self.auxiliary_head is not None: + auxiliary_logits = self.auxiliary_head(features) + auxiliary_logits = nn.functional.interpolate( + auxiliary_logits, size=pixel_values.shape[2:], mode="bilinear", align_corners=False + ) + + loss = None + if labels is not None: + if self.config.num_labels == 1: + raise ValueError("The number of labels should be greater than one") + else: + # compute weighted loss + loss_fct = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index) + loss = loss_fct(logits, labels) + if auxiliary_logits is not None: + auxiliary_loss = loss_fct(auxiliary_logits, labels) + loss += self.config.auxiliary_loss_weight * auxiliary_loss + + if not return_dict: + if output_hidden_states: + output = (logits,) + outputs[1:] + else: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SemanticSegmenterOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/videomae/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/videomae/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..663b6d41aba605b98e97509cd7dbc4b0acf001f7 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/videomae/__init__.py @@ -0,0 +1,75 @@ +# Copyright 2022 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available + + +_import_structure = { + "configuration_videomae": ["VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP", "VideoMAEConfig"], +} + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_videomae"] = [ + "VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST", + "VideoMAEForPreTraining", + "VideoMAEModel", + "VideoMAEPreTrainedModel", + "VideoMAEForVideoClassification", + ] + +try: + if not is_vision_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["feature_extraction_videomae"] = ["VideoMAEFeatureExtractor"] + _import_structure["image_processing_videomae"] = ["VideoMAEImageProcessor"] + +if TYPE_CHECKING: + from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_videomae import ( + VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST, + VideoMAEForPreTraining, + VideoMAEForVideoClassification, + VideoMAEModel, + VideoMAEPreTrainedModel, + ) + + try: + if not is_vision_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .feature_extraction_videomae import VideoMAEFeatureExtractor + from .image_processing_videomae import VideoMAEImageProcessor + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/videomae/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/videomae/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..89b89da578f346047a4f22123945a5ef7fa7bcb3 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/videomae/__pycache__/__init__.cpython-312.pyc differ diff --git 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All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" VideoMAE model configuration""" + +from ...configuration_utils import PretrainedConfig +from ...utils import logging + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class VideoMAEConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`VideoMAEModel`]. It is used to instantiate a + VideoMAE model according to the specified arguments, defining the model architecture. Instantiating a configuration + with the defaults will yield a similar configuration to that of the VideoMAE + [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + image_size (`int`, *optional*, defaults to 224): + The size (resolution) of each image. + patch_size (`int`, *optional*, defaults to 16): + The size (resolution) of each patch. + num_channels (`int`, *optional*, defaults to 3): + The number of input channels. + num_frames (`int`, *optional*, defaults to 16): + The number of frames in each video. + tubelet_size (`int`, *optional*, defaults to 2): + The number of tubelets. + hidden_size (`int`, *optional*, defaults to 768): + Dimensionality of the encoder layers and the pooler layer. + num_hidden_layers (`int`, *optional*, defaults to 12): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 12): + Number of attention heads for each attention layer in the Transformer encoder. + intermediate_size (`int`, *optional*, defaults to 3072): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"` and `"gelu_new"` are supported. + hidden_dropout_prob (`float`, *optional*, defaults to 0.0): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + layer_norm_eps (`float`, *optional*, defaults to 1e-12): + The epsilon used by the layer normalization layers. + qkv_bias (`bool`, *optional*, defaults to `True`): + Whether to add a bias to the queries, keys and values. + use_mean_pooling (`bool`, *optional*, defaults to `True`): + Whether to mean pool the final hidden states instead of using the final hidden state of the [CLS] token. + decoder_num_attention_heads (`int`, *optional*, defaults to 6): + Number of attention heads for each attention layer in the decoder. + decoder_hidden_size (`int`, *optional*, defaults to 384): + Dimensionality of the decoder. + decoder_num_hidden_layers (`int`, *optional*, defaults to 4): + Number of hidden layers in the decoder. + decoder_intermediate_size (`int`, *optional*, defaults to 1536): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the decoder. + norm_pix_loss (`bool`, *optional*, defaults to `True`): + Whether to normalize the target patch pixels. + + Example: + + ```python + >>> from transformers import VideoMAEConfig, VideoMAEModel + + >>> # Initializing a VideoMAE videomae-base style configuration + >>> configuration = VideoMAEConfig() + + >>> # Randomly initializing a model from the configuration + >>> model = VideoMAEModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "videomae" + + def __init__( + self, + image_size=224, + patch_size=16, + num_channels=3, + num_frames=16, + tubelet_size=2, + hidden_size=768, + num_hidden_layers=12, + num_attention_heads=12, + intermediate_size=3072, + hidden_act="gelu", + hidden_dropout_prob=0.0, + attention_probs_dropout_prob=0.0, + initializer_range=0.02, + layer_norm_eps=1e-12, + qkv_bias=True, + use_mean_pooling=True, + decoder_num_attention_heads=6, + decoder_hidden_size=384, + decoder_num_hidden_layers=4, + decoder_intermediate_size=1536, + norm_pix_loss=True, + **kwargs, + ): + super().__init__(**kwargs) + + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_frames = num_frames + self.tubelet_size = tubelet_size + + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.initializer_range = initializer_range + self.layer_norm_eps = layer_norm_eps + self.qkv_bias = qkv_bias + self.use_mean_pooling = use_mean_pooling + + self.decoder_num_attention_heads = decoder_num_attention_heads + self.decoder_hidden_size = decoder_hidden_size + self.decoder_num_hidden_layers = decoder_num_hidden_layers + self.decoder_intermediate_size = decoder_intermediate_size + self.norm_pix_loss = norm_pix_loss diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/videomae/feature_extraction_videomae.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/videomae/feature_extraction_videomae.py new file mode 100644 index 0000000000000000000000000000000000000000..4a90d10c9c55e83711a20e29a494782b6b8415f9 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/videomae/feature_extraction_videomae.py @@ -0,0 +1,33 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Feature extractor class for VideoMAE.""" + +import warnings + +from ...utils import logging +from .image_processing_videomae import VideoMAEImageProcessor + + +logger = logging.get_logger(__name__) + + +class VideoMAEFeatureExtractor(VideoMAEImageProcessor): + def __init__(self, *args, **kwargs) -> None: + warnings.warn( + "The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers." + " Please use VideoMAEImageProcessor instead.", + FutureWarning, + ) + super().__init__(*args, **kwargs) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/videomae/image_processing_videomae.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/videomae/image_processing_videomae.py new file mode 100644 index 0000000000000000000000000000000000000000..6563d69c6503ea6bc9777854073c84b9981c79d6 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/videomae/image_processing_videomae.py @@ -0,0 +1,364 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for VideoMAE.""" + +from typing import Dict, List, Optional, Union + +import numpy as np + +from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict +from ...image_transforms import ( + get_resize_output_image_size, + resize, + to_channel_dimension_format, +) +from ...image_utils import ( + IMAGENET_STANDARD_MEAN, + IMAGENET_STANDARD_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + infer_channel_dimension_format, + is_scaled_image, + is_valid_image, + to_numpy_array, + valid_images, + validate_kwargs, + validate_preprocess_arguments, +) +from ...utils import TensorType, is_vision_available, logging + + +if is_vision_available(): + import PIL + + +logger = logging.get_logger(__name__) + + +def make_batched(videos) -> List[List[ImageInput]]: + if isinstance(videos, (list, tuple)) and isinstance(videos[0], (list, tuple)) and is_valid_image(videos[0][0]): + return videos + + elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]): + return [videos] + + elif is_valid_image(videos): + return [[videos]] + + raise ValueError(f"Could not make batched video from {videos}") + + +class VideoMAEImageProcessor(BaseImageProcessor): + r""" + Constructs a VideoMAE image processor. + + Args: + do_resize (`bool`, *optional*, defaults to `True`): + Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the + `do_resize` parameter in the `preprocess` method. + size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`): + Size of the output image after resizing. The shortest edge of the image will be resized to + `size["shortest_edge"]` while maintaining the aspect ratio of the original image. Can be overriden by + `size` in the `preprocess` method. + resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): + Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the + `preprocess` method. + do_center_crop (`bool`, *optional*, defaults to `True`): + Whether to center crop the image to the specified `crop_size`. Can be overridden by the `do_center_crop` + parameter in the `preprocess` method. + crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`): + Size of the image after applying the center crop. Can be overridden by the `crop_size` parameter in the + `preprocess` method. + do_rescale (`bool`, *optional*, defaults to `True`): + Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` + parameter in the `preprocess` method. + rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): + Defines the scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter + in the `preprocess` method. + do_normalize (`bool`, *optional*, defaults to `True`): + Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` + method. + image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): + Mean to use if normalizing the image. This is a float or list of floats the length of the number of + channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. + image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): + Standard deviation to use if normalizing the image. This is a float or list of floats the length of the + number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. + """ + + model_input_names = ["pixel_values"] + + def __init__( + self, + do_resize: bool = True, + size: Dict[str, int] = None, + resample: PILImageResampling = PILImageResampling.BILINEAR, + do_center_crop: bool = True, + crop_size: Dict[str, int] = None, + do_rescale: bool = True, + rescale_factor: Union[int, float] = 1 / 255, + do_normalize: bool = True, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + **kwargs, + ) -> None: + super().__init__(**kwargs) + size = size if size is not None else {"shortest_edge": 224} + size = get_size_dict(size, default_to_square=False) + crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224} + crop_size = get_size_dict(crop_size, param_name="crop_size") + + self.do_resize = do_resize + self.size = size + self.do_center_crop = do_center_crop + self.crop_size = crop_size + self.resample = resample + self.do_rescale = do_rescale + self.rescale_factor = rescale_factor + self.do_normalize = do_normalize + self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN + self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD + self._valid_processor_keys = [ + "videos", + "do_resize", + "size", + "resample", + "do_center_crop", + "crop_size", + "do_rescale", + "rescale_factor", + "do_normalize", + "image_mean", + "image_std", + "return_tensors", + "data_format", + "input_data_format", + ] + + def resize( + self, + image: np.ndarray, + size: Dict[str, int], + resample: PILImageResampling = PILImageResampling.BILINEAR, + data_format: Optional[Union[str, ChannelDimension]] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ) -> np.ndarray: + """ + Resize an image. + + Args: + image (`np.ndarray`): + Image to resize. + size (`Dict[str, int]`): + Size of the output image. If `size` is of the form `{"height": h, "width": w}`, the output image will + have the size `(h, w)`. If `size` is of the form `{"shortest_edge": s}`, the output image will have its + shortest edge of length `s` while keeping the aspect ratio of the original image. + resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`): + Resampling filter to use when resiizing the image. + data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format of the image. If not provided, it will be the same as the input image. + input_data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format of the input image. If not provided, it will be inferred. + """ + size = get_size_dict(size, default_to_square=False) + if "shortest_edge" in size: + output_size = get_resize_output_image_size( + image, size["shortest_edge"], default_to_square=False, input_data_format=input_data_format + ) + elif "height" in size and "width" in size: + output_size = (size["height"], size["width"]) + else: + raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}") + return resize( + image, + size=output_size, + resample=resample, + data_format=data_format, + input_data_format=input_data_format, + **kwargs, + ) + + def _preprocess_image( + self, + image: ImageInput, + do_resize: bool = None, + size: Dict[str, int] = None, + resample: PILImageResampling = None, + do_center_crop: bool = None, + crop_size: Dict[str, int] = None, + do_rescale: bool = None, + rescale_factor: float = None, + do_normalize: bool = None, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + ) -> np.ndarray: + """Preprocesses a single image.""" + validate_preprocess_arguments( + do_rescale=do_rescale, + rescale_factor=rescale_factor, + do_normalize=do_normalize, + image_mean=image_mean, + image_std=image_std, + do_center_crop=do_center_crop, + crop_size=crop_size, + do_resize=do_resize, + size=size, + resample=resample, + ) + + # All transformations expect numpy arrays. + image = to_numpy_array(image) + + if is_scaled_image(image) and do_rescale: + logger.warning_once( + "It looks like you are trying to rescale already rescaled images. If the input" + " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." + ) + + if input_data_format is None: + input_data_format = infer_channel_dimension_format(image) + + if do_resize: + image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format) + + if do_center_crop: + image = self.center_crop(image, size=crop_size, input_data_format=input_data_format) + + if do_rescale: + image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) + + if do_normalize: + image = self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format) + + image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) + return image + + def preprocess( + self, + videos: ImageInput, + do_resize: bool = None, + size: Dict[str, int] = None, + resample: PILImageResampling = None, + do_center_crop: bool = None, + crop_size: Dict[str, int] = None, + do_rescale: bool = None, + rescale_factor: float = None, + do_normalize: bool = None, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + return_tensors: Optional[Union[str, TensorType]] = None, + data_format: ChannelDimension = ChannelDimension.FIRST, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ) -> PIL.Image.Image: + """ + Preprocess an image or batch of images. + + Args: + images (`ImageInput`): + Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If + passing in images with pixel values between 0 and 1, set `do_rescale=False`. + do_resize (`bool`, *optional*, defaults to `self.do_resize`): + Whether to resize the image. + size (`Dict[str, int]`, *optional*, defaults to `self.size`): + Size of the image after applying resize. + resample (`PILImageResampling`, *optional*, defaults to `self.resample`): + Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only + has an effect if `do_resize` is set to `True`. + do_center_crop (`bool`, *optional*, defaults to `self.do_centre_crop`): + Whether to centre crop the image. + crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`): + Size of the image after applying the centre crop. + do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): + Whether to rescale the image values between [0 - 1]. + rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): + Rescale factor to rescale the image by if `do_rescale` is set to `True`. + do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): + Whether to normalize the image. + image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): + Image mean. + image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): + Image standard deviation. + return_tensors (`str` or `TensorType`, *optional*): + The type of tensors to return. Can be one of: + - Unset: Return a list of `np.ndarray`. + - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. + - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. + - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. + - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. + data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): + The channel dimension format for the output image. Can be one of: + - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - Unset: Use the inferred channel dimension format of the input image. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format for the input image. If unset, the channel dimension format is inferred + from the input image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. + """ + do_resize = do_resize if do_resize is not None else self.do_resize + resample = resample if resample is not None else self.resample + do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop + do_rescale = do_rescale if do_rescale is not None else self.do_rescale + rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor + do_normalize = do_normalize if do_normalize is not None else self.do_normalize + image_mean = image_mean if image_mean is not None else self.image_mean + image_std = image_std if image_std is not None else self.image_std + + size = size if size is not None else self.size + size = get_size_dict(size, default_to_square=False) + crop_size = crop_size if crop_size is not None else self.crop_size + crop_size = get_size_dict(crop_size, param_name="crop_size") + + validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys) + + if not valid_images(videos): + raise ValueError( + "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " + "torch.Tensor, tf.Tensor or jax.ndarray." + ) + + videos = make_batched(videos) + + videos = [ + [ + self._preprocess_image( + image=img, + do_resize=do_resize, + size=size, + resample=resample, + do_center_crop=do_center_crop, + crop_size=crop_size, + do_rescale=do_rescale, + rescale_factor=rescale_factor, + do_normalize=do_normalize, + image_mean=image_mean, + image_std=image_std, + data_format=data_format, + input_data_format=input_data_format, + ) + for img in video + ] + for video in videos + ] + + data = {"pixel_values": videos} + return BatchFeature(data=data, tensor_type=return_tensors) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/videomae/modeling_videomae.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/videomae/modeling_videomae.py new file mode 100644 index 0000000000000000000000000000000000000000..6beb18bb77ce0af998d27d999693214ce36e2b8f --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/videomae/modeling_videomae.py @@ -0,0 +1,1095 @@ +# coding=utf-8 +# Copyright 2022 Multimedia Computing Group, Nanjing University and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch VideoMAE (masked autoencoder) model.""" + + +import collections.abc +import math +from copy import deepcopy +from dataclasses import dataclass +from typing import Optional, Set, Tuple, Union + +import numpy as np +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ...activations import ACT2FN +from ...modeling_outputs import BaseModelOutput, ImageClassifierOutput +from ...modeling_utils import PreTrainedModel +from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer +from ...utils import ( + ModelOutput, + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) +from ...utils.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD +from .configuration_videomae import VideoMAEConfig + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "VideoMAEConfig" +_CHECKPOINT_FOR_DOC = "MCG-NJU/videomae-base" + + +from ..deprecated._archive_maps import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +@dataclass +class VideoMAEDecoderOutput(ModelOutput): + """ + Class for VideoMAEDecoder's outputs, with potential hidden states and attentions. + + Args: + logits (`torch.FloatTensor` of shape `(batch_size, patch_size ** 2 * num_channels)`): + Pixel reconstruction logits. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer + plus the initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in + the self-attention heads. + """ + + logits: torch.FloatTensor = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + + +@dataclass +class VideoMAEForPreTrainingOutput(ModelOutput): + """ + Class for VideoMAEForPreTraining's outputs, with potential hidden states and attentions. + + Args: + loss (`torch.FloatTensor` of shape `(1,)`): + Pixel reconstruction loss. + logits (`torch.FloatTensor` of shape `(batch_size, patch_size ** 2 * num_channels)`): + Pixel reconstruction logits. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer + plus the initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in + the self-attention heads. + """ + + loss: Optional[torch.FloatTensor] = None + logits: torch.FloatTensor = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + + +# sin-cos position encoding +# https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/master/transformer/Models.py#L31 +def get_sinusoid_encoding_table(n_position, d_hid): + """Sinusoid position encoding table""" + + # TODO: make it with torch instead of numpy + def get_position_angle_vec(position): + return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] + + sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)]) + sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i + sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 + + return torch.FloatTensor(sinusoid_table).unsqueeze(0) + + +class VideoMAEEmbeddings(nn.Module): + """ + Construct the patch and position embeddings. + + """ + + def __init__(self, config): + super().__init__() + + self.patch_embeddings = VideoMAEPatchEmbeddings(config) + self.num_patches = self.patch_embeddings.num_patches + # fixed sin-cos embedding + self.position_embeddings = get_sinusoid_encoding_table(self.num_patches, config.hidden_size) + self.config = config + + def forward(self, pixel_values, bool_masked_pos): + # create patch embeddings + embeddings = self.patch_embeddings(pixel_values) + + # add position embeddings + embeddings = embeddings + self.position_embeddings.type_as(embeddings).to(embeddings.device).clone().detach() + + # only keep visible patches + # ~bool_masked_pos means visible + if bool_masked_pos is not None: + batch_size, _, num_channels = embeddings.shape + embeddings = embeddings[~bool_masked_pos] + embeddings = embeddings.reshape(batch_size, -1, num_channels) + + return embeddings + + +class VideoMAEPatchEmbeddings(nn.Module): + """ + Video to Patch Embedding. This module turns a batch of videos of shape (batch_size, num_frames, num_channels, + height, width) into a tensor of shape (batch_size, seq_len, hidden_size) to be consumed by a Transformer encoder. + + The seq_len (the number of patches) equals (number of frames // tubelet_size) * (height // patch_size) * (width // + patch_size). + + """ + + def __init__(self, config): + super().__init__() + + image_size = config.image_size + patch_size = config.patch_size + num_channels = config.num_channels + hidden_size = config.hidden_size + num_frames = config.num_frames + tubelet_size = config.tubelet_size + + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + self.image_size = image_size + self.patch_size = patch_size + self.tubelet_size = int(tubelet_size) + num_patches = ( + (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) * (num_frames // self.tubelet_size) + ) + self.num_channels = num_channels + self.num_patches = num_patches + self.projection = nn.Conv3d( + in_channels=num_channels, + out_channels=hidden_size, + kernel_size=(self.tubelet_size, patch_size[0], patch_size[1]), + stride=(self.tubelet_size, patch_size[0], patch_size[1]), + ) + + def forward(self, pixel_values): + batch_size, num_frames, num_channels, height, width = pixel_values.shape + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + ) + if height != self.image_size[0] or width != self.image_size[1]: + raise ValueError( + f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})." + ) + # permute to (batch_size, num_channels, num_frames, height, width) + pixel_values = pixel_values.permute(0, 2, 1, 3, 4) + embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2) + return embeddings + + +class VideoMAESelfAttention(nn.Module): + def __init__(self, config: VideoMAEConfig) -> None: + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " + f"heads {config.num_attention_heads}." + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=False) + self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=False) + self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=False) + + if config.qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(self.all_head_size)) + self.v_bias = nn.Parameter(torch.zeros(self.all_head_size)) + else: + self.q_bias = None + self.v_bias = None + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + k_bias = torch.zeros_like(self.v_bias, requires_grad=False) if self.q_bias is not None else None + keys = nn.functional.linear(input=hidden_states, weight=self.key.weight, bias=k_bias) + values = nn.functional.linear(input=hidden_states, weight=self.value.weight, bias=self.v_bias) + queries = nn.functional.linear(input=hidden_states, weight=self.query.weight, bias=self.q_bias) + + key_layer = self.transpose_for_scores(keys) + value_layer = self.transpose_for_scores(values) + query_layer = self.transpose_for_scores(queries) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->VideoMAE +class VideoMAESelfOutput(nn.Module): + """ + The residual connection is defined in VideoMAELayer instead of here (as is the case with other models), due to the + layernorm applied before each block. + """ + + def __init__(self, config: VideoMAEConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + + return hidden_states + + +# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->VideoMAE +class VideoMAEAttention(nn.Module): + def __init__(self, config: VideoMAEConfig) -> None: + super().__init__() + self.attention = VideoMAESelfAttention(config) + self.output = VideoMAESelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads: Set[int]) -> None: + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.attention.query = prune_linear_layer(self.attention.query, index) + self.attention.key = prune_linear_layer(self.attention.key, index) + self.attention.value = prune_linear_layer(self.attention.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) + self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + self_outputs = self.attention(hidden_states, head_mask, output_attentions) + + attention_output = self.output(self_outputs[0], hidden_states) + + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +# Copied from transformers.models.vit.modeling_vit.ViTIntermediate ViT->VideoMAE +class VideoMAEIntermediate(nn.Module): + def __init__(self, config: VideoMAEConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + + return hidden_states + + +# Copied from transformers.models.vit.modeling_vit.ViTOutput ViT->VideoMAE +class VideoMAEOutput(nn.Module): + def __init__(self, config: VideoMAEConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + + hidden_states = hidden_states + input_tensor + + return hidden_states + + +# Copied from transformers.models.vit.modeling_vit.ViTLayer with ViT->VideoMAE +class VideoMAELayer(nn.Module): + """This corresponds to the Block class in the timm implementation.""" + + def __init__(self, config: VideoMAEConfig) -> None: + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = VideoMAEAttention(config) + self.intermediate = VideoMAEIntermediate(config) + self.output = VideoMAEOutput(config) + self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + self_attention_outputs = self.attention( + self.layernorm_before(hidden_states), # in VideoMAE, layernorm is applied before self-attention + head_mask, + output_attentions=output_attentions, + ) + attention_output = self_attention_outputs[0] + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + # first residual connection + hidden_states = attention_output + hidden_states + + # in VideoMAE, layernorm is also applied after self-attention + layer_output = self.layernorm_after(hidden_states) + layer_output = self.intermediate(layer_output) + + # second residual connection is done here + layer_output = self.output(layer_output, hidden_states) + + outputs = (layer_output,) + outputs + + return outputs + + +# Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->VideoMAE +class VideoMAEEncoder(nn.Module): + def __init__(self, config: VideoMAEConfig) -> None: + super().__init__() + self.config = config + self.layer = nn.ModuleList([VideoMAELayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ) -> Union[tuple, BaseModelOutput]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer_module.__call__, + hidden_states, + layer_head_mask, + output_attentions, + ) + else: + layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +class VideoMAEPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = VideoMAEConfig + base_model_prefix = "videomae" + main_input_name = "pixel_values" + supports_gradient_checkpointing = True + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, (nn.Linear, nn.Conv3d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + +VIDEOMAE_START_DOCSTRING = r""" + This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it + as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and + behavior. + + Parameters: + config ([`VideoMAEConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +VIDEOMAE_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See + [`VideoMAEImageProcessor.__call__`] for details. + + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare VideoMAE Model transformer outputting raw hidden-states without any specific head on top.", + VIDEOMAE_START_DOCSTRING, +) +class VideoMAEModel(VideoMAEPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.config = config + + self.embeddings = VideoMAEEmbeddings(config) + self.encoder = VideoMAEEncoder(config) + + if config.use_mean_pooling: + self.layernorm = None + else: + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.patch_embeddings + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + @add_start_docstrings_to_model_forward(VIDEOMAE_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: torch.FloatTensor, + bool_masked_pos: Optional[torch.BoolTensor] = None, + head_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutput]: + r""" + bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*): + Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). Each video in the + batch must have the same number of masked patches. If `None`, then all patches are considered. Sequence + length is `(num_frames // tubelet_size) * (image_size // patch_size) ** 2`. + + Returns: + + Examples: + + ```python + >>> import av + >>> import numpy as np + + >>> from transformers import AutoImageProcessor, VideoMAEModel + >>> from huggingface_hub import hf_hub_download + + >>> np.random.seed(0) + + + >>> def read_video_pyav(container, indices): + ... ''' + ... Decode the video with PyAV decoder. + ... Args: + ... container (`av.container.input.InputContainer`): PyAV container. + ... indices (`List[int]`): List of frame indices to decode. + ... Returns: + ... result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3). + ... ''' + ... frames = [] + ... container.seek(0) + ... start_index = indices[0] + ... end_index = indices[-1] + ... for i, frame in enumerate(container.decode(video=0)): + ... if i > end_index: + ... break + ... if i >= start_index and i in indices: + ... frames.append(frame) + ... return np.stack([x.to_ndarray(format="rgb24") for x in frames]) + + + >>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len): + ... ''' + ... Sample a given number of frame indices from the video. + ... Args: + ... clip_len (`int`): Total number of frames to sample. + ... frame_sample_rate (`int`): Sample every n-th frame. + ... seg_len (`int`): Maximum allowed index of sample's last frame. + ... Returns: + ... indices (`List[int]`): List of sampled frame indices + ... ''' + ... converted_len = int(clip_len * frame_sample_rate) + ... end_idx = np.random.randint(converted_len, seg_len) + ... start_idx = end_idx - converted_len + ... indices = np.linspace(start_idx, end_idx, num=clip_len) + ... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64) + ... return indices + + + >>> # video clip consists of 300 frames (10 seconds at 30 FPS) + >>> file_path = hf_hub_download( + ... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset" + ... ) + >>> container = av.open(file_path) + + >>> # sample 16 frames + >>> indices = sample_frame_indices(clip_len=16, frame_sample_rate=1, seg_len=container.streams.video[0].frames) + >>> video = read_video_pyav(container, indices) + + >>> image_processor = AutoImageProcessor.from_pretrained("MCG-NJU/videomae-base") + >>> model = VideoMAEModel.from_pretrained("MCG-NJU/videomae-base") + + >>> # prepare video for the model + >>> inputs = image_processor(list(video), return_tensors="pt") + + >>> # forward pass + >>> outputs = model(**inputs) + >>> last_hidden_states = outputs.last_hidden_state + >>> list(last_hidden_states.shape) + [1, 1568, 768] + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings(pixel_values, bool_masked_pos) + + encoder_outputs = self.encoder( + embedding_output, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + if self.layernorm is not None: + sequence_output = self.layernorm(sequence_output) + + if not return_dict: + return (sequence_output,) + encoder_outputs[1:] + + return BaseModelOutput( + last_hidden_state=sequence_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +class VideoMAEDecoder(nn.Module): + def __init__(self, config, num_patches): + super().__init__() + + decoder_num_labels = config.num_channels * config.tubelet_size * config.patch_size**2 + + decoder_config = deepcopy(config) + decoder_config.hidden_size = config.decoder_hidden_size + decoder_config.num_hidden_layers = config.decoder_num_hidden_layers + decoder_config.num_attention_heads = config.decoder_num_attention_heads + decoder_config.intermediate_size = config.decoder_intermediate_size + self.decoder_layers = nn.ModuleList( + [VideoMAELayer(decoder_config) for _ in range(config.decoder_num_hidden_layers)] + ) + + self.norm = nn.LayerNorm(config.decoder_hidden_size) + self.head = ( + nn.Linear(config.decoder_hidden_size, decoder_num_labels) if decoder_num_labels > 0 else nn.Identity() + ) + + self.gradient_checkpointing = False + self.config = config + + def forward( + self, + hidden_states, + return_token_num, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + ): + # apply Transformer layers (blocks) + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + for i, layer_module in enumerate(self.decoder_layers): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer_module.__call__, + hidden_states, + None, + output_attentions, + ) + else: + layer_outputs = layer_module(hidden_states, head_mask=None, output_attentions=output_attentions) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if return_token_num > 0: + hidden_states = hidden_states[:, -return_token_num:] + + # predictor projection + hidden_states = self.norm(hidden_states) + logits = self.head(hidden_states) + + if not return_dict: + return tuple(v for v in [logits, all_hidden_states, all_self_attentions] if v is not None) + return VideoMAEDecoderOutput(logits=logits, hidden_states=all_hidden_states, attentions=all_self_attentions) + + +@add_start_docstrings( + "The VideoMAE Model transformer with the decoder on top for self-supervised pre-training.", + VIDEOMAE_START_DOCSTRING, +) +class VideoMAEForPreTraining(VideoMAEPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.config = config + + self.videomae = VideoMAEModel(config) + + self.encoder_to_decoder = nn.Linear(config.hidden_size, config.decoder_hidden_size, bias=False) + self.mask_token = nn.Parameter(torch.zeros(1, 1, config.decoder_hidden_size)) + self.position_embeddings = get_sinusoid_encoding_table( + self.videomae.embeddings.num_patches, config.decoder_hidden_size + ) + + self.decoder = VideoMAEDecoder(config, num_patches=self.videomae.embeddings.num_patches) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(VIDEOMAE_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=VideoMAEForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: torch.FloatTensor, + bool_masked_pos: torch.BoolTensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[tuple, VideoMAEForPreTrainingOutput]: + r""" + bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, sequence_length)`): + Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). Each video in the + batch must have the same number of masked patches. Sequence length is `(num_frames // tubelet_size) * + (image_size // patch_size) ** 2`. + + Returns: + + Examples: + ```python + >>> from transformers import AutoImageProcessor, VideoMAEForPreTraining + >>> import numpy as np + >>> import torch + + >>> num_frames = 16 + >>> video = list(np.random.randint(0, 256, (num_frames, 3, 224, 224))) + + >>> image_processor = AutoImageProcessor.from_pretrained("MCG-NJU/videomae-base") + >>> model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base") + + >>> pixel_values = image_processor(video, return_tensors="pt").pixel_values + + >>> num_patches_per_frame = (model.config.image_size // model.config.patch_size) ** 2 + >>> seq_length = (num_frames // model.config.tubelet_size) * num_patches_per_frame + >>> bool_masked_pos = torch.randint(0, 2, (1, seq_length)).bool() + + >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos) + >>> loss = outputs.loss + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.videomae( + pixel_values, + bool_masked_pos=bool_masked_pos, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + sequence_output = self.encoder_to_decoder( + sequence_output + ) # [batch_size, num_visible_patches, decoder_hidden_size] + batch_size, seq_len, num_channels = sequence_output.shape + + # we don't unshuffle the correct visible token order, but shuffle the position embeddings accordingly. + if bool_masked_pos is None: + raise ValueError("One must provided a boolean mask ") + expanded_position_embeddings = self.position_embeddings.expand(batch_size, -1, -1).type_as(pixel_values) + expanded_position_embeddings = expanded_position_embeddings.to(pixel_values.device).clone().detach() + pos_emb_visible = expanded_position_embeddings[~bool_masked_pos].reshape(batch_size, -1, num_channels) + pos_emb_mask = expanded_position_embeddings[bool_masked_pos].reshape(batch_size, -1, num_channels) + + # [batch_size, num_patches, decoder_hidden_size] + x_full = torch.cat([sequence_output + pos_emb_visible, self.mask_token + pos_emb_mask], dim=1) + + # [batch_size, num_masked_patches, num_channels * patch_size * patch_size] + decoder_outputs = self.decoder(x_full, pos_emb_mask.shape[1]) + logits = decoder_outputs.logits + + loss = None + with torch.no_grad(): + # calculate the labels to be predicted + if self.config.num_channels != 3: + # Can't unnormalize with default means/stds + frames = pixel_values + else: + # first, unnormalize the frames + device = pixel_values.device + dtype = pixel_values.dtype + mean = torch.as_tensor(IMAGENET_DEFAULT_MEAN).to(device=device, dtype=dtype)[None, None, :, None, None] + std = torch.as_tensor(IMAGENET_DEFAULT_STD).to(device=device, dtype=dtype)[None, None, :, None, None] + frames = pixel_values * std + mean # in [0, 1] + + batch_size, time, num_channels, height, width = frames.shape + tubelet_size, patch_size = self.config.tubelet_size, self.config.patch_size + if self.config.norm_pix_loss: + # step 1: split up dimensions (time by tubelet_size, height by patch_size, width by patch_size) + frames = frames.view( + batch_size, + time // tubelet_size, + tubelet_size, + num_channels, + height // patch_size, + patch_size, + width // patch_size, + patch_size, + ) + # step 2: move dimensions to concatenate: + frames = frames.permute(0, 1, 4, 6, 2, 5, 7, 3).contiguous() + # step 3: concatenate: + frames = frames.view( + batch_size, + time // tubelet_size * height // patch_size * width // patch_size, + tubelet_size * patch_size * patch_size, + num_channels, + ) + # step 4: normalize. The authors find that the mean is about 0.48 and standard deviation is about 0.08. + frames_norm = (frames - frames.mean(dim=-2, keepdim=True)) / ( + frames.var(dim=-2, unbiased=True, keepdim=True).sqrt() + 1e-6 + ) + # step 5: reshape to (batch_size, T//ts * H//ps * W//ps, ts * ps * ps * C) + videos_patch = frames_norm.view( + batch_size, + time // tubelet_size * height // patch_size * width // patch_size, + tubelet_size * patch_size * patch_size * num_channels, + ) + else: + if self.config.num_channels != 3: + raise ValueError( + "Can't unnormalize non-RGB images. Consider setting config.norm_pix_loss to False." + ) + # step 1: split up dimensions (time by tubelet_size, height by patch_size, width by patch_size) + frames = frames.view( + batch_size, + time // tubelet_size, + tubelet_size, + num_channels, + height // patch_size, + patch_size, + width // patch_size, + patch_size, + ) + # step 2: move dimensions to concatenate: (batch_size, T//ts, H//ps, W//ps, ts, ps, ps, C) + frames = frames.permute(0, 1, 4, 6, 2, 5, 7, 3).contiguous() + # step 3: concatenate + videos_patch = frames.view( + batch_size, + time // tubelet_size * height // patch_size * width // patch_size, + tubelet_size * patch_size * patch_size * num_channels, + ) + + batch_size, _, num_channels = videos_patch.shape + labels = videos_patch[bool_masked_pos].reshape(batch_size, -1, num_channels) + + loss_fct = MSELoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return VideoMAEForPreTrainingOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + """VideoMAE Model transformer with a video classification head on top (a linear layer on top of the average pooled hidden + states of all tokens) e.g. for ImageNet.""", + VIDEOMAE_START_DOCSTRING, +) +class VideoMAEForVideoClassification(VideoMAEPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.num_labels = config.num_labels + self.videomae = VideoMAEModel(config) + + # Classifier head + self.fc_norm = nn.LayerNorm(config.hidden_size) if config.use_mean_pooling else None + self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity() + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(VIDEOMAE_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=ImageClassifierOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, ImageClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + + Returns: + + Examples: + + ```python + >>> import av + >>> import torch + >>> import numpy as np + + >>> from transformers import AutoImageProcessor, VideoMAEForVideoClassification + >>> from huggingface_hub import hf_hub_download + + >>> np.random.seed(0) + + + >>> def read_video_pyav(container, indices): + ... ''' + ... Decode the video with PyAV decoder. + ... Args: + ... container (`av.container.input.InputContainer`): PyAV container. + ... indices (`List[int]`): List of frame indices to decode. + ... Returns: + ... result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3). + ... ''' + ... frames = [] + ... container.seek(0) + ... start_index = indices[0] + ... end_index = indices[-1] + ... for i, frame in enumerate(container.decode(video=0)): + ... if i > end_index: + ... break + ... if i >= start_index and i in indices: + ... frames.append(frame) + ... return np.stack([x.to_ndarray(format="rgb24") for x in frames]) + + + >>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len): + ... ''' + ... Sample a given number of frame indices from the video. + ... Args: + ... clip_len (`int`): Total number of frames to sample. + ... frame_sample_rate (`int`): Sample every n-th frame. + ... seg_len (`int`): Maximum allowed index of sample's last frame. + ... Returns: + ... indices (`List[int]`): List of sampled frame indices + ... ''' + ... converted_len = int(clip_len * frame_sample_rate) + ... end_idx = np.random.randint(converted_len, seg_len) + ... start_idx = end_idx - converted_len + ... indices = np.linspace(start_idx, end_idx, num=clip_len) + ... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64) + ... return indices + + + >>> # video clip consists of 300 frames (10 seconds at 30 FPS) + >>> file_path = hf_hub_download( + ... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset" + ... ) + >>> container = av.open(file_path) + + >>> # sample 16 frames + >>> indices = sample_frame_indices(clip_len=16, frame_sample_rate=1, seg_len=container.streams.video[0].frames) + >>> video = read_video_pyav(container, indices) + + >>> image_processor = AutoImageProcessor.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics") + >>> model = VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics") + + >>> inputs = image_processor(list(video), return_tensors="pt") + + >>> with torch.no_grad(): + ... outputs = model(**inputs) + ... logits = outputs.logits + + >>> # model predicts one of the 400 Kinetics-400 classes + >>> predicted_label = logits.argmax(-1).item() + >>> print(model.config.id2label[predicted_label]) + eating spaghetti + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.videomae( + pixel_values, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + if self.fc_norm is not None: + sequence_output = self.fc_norm(sequence_output.mean(1)) + else: + sequence_output = sequence_output[:, 0] + + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return ImageClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vilt/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vilt/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6d5afba10dacfcdd5691c42b4d56b0aeed92d78b --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vilt/__init__.py @@ -0,0 +1,85 @@ +# Copyright 2022 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available + + +_import_structure = {"configuration_vilt": ["VILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViltConfig"]} + +try: + if not is_vision_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["feature_extraction_vilt"] = ["ViltFeatureExtractor"] + _import_structure["image_processing_vilt"] = ["ViltImageProcessor"] + _import_structure["processing_vilt"] = ["ViltProcessor"] + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_vilt"] = [ + "VILT_PRETRAINED_MODEL_ARCHIVE_LIST", + "ViltForImageAndTextRetrieval", + "ViltForImagesAndTextClassification", + "ViltForTokenClassification", + "ViltForMaskedLM", + "ViltForQuestionAnswering", + "ViltLayer", + "ViltModel", + "ViltPreTrainedModel", + ] + + +if TYPE_CHECKING: + from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig + + try: + if not is_vision_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .feature_extraction_vilt import ViltFeatureExtractor + from .image_processing_vilt import ViltImageProcessor + from .processing_vilt import ViltProcessor + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_vilt import ( + VILT_PRETRAINED_MODEL_ARCHIVE_LIST, + ViltForImageAndTextRetrieval, + ViltForImagesAndTextClassification, + ViltForMaskedLM, + ViltForQuestionAnswering, + ViltForTokenClassification, + ViltLayer, + ViltModel, + ViltPreTrainedModel, + ) + + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vilt/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vilt/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5d982fed2149a8e6a27273ebb218c88e4c36901b Binary files 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All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" VilT model configuration""" + +from ...configuration_utils import PretrainedConfig +from ...utils import logging + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class ViltConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`ViLTModel`]. It is used to instantiate an ViLT + model according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the ViLT + [dandelin/vilt-b32-mlm](https://huggingface.co/dandelin/vilt-b32-mlm) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + vocab_size (`int`, *optional*, defaults to 30522): + Vocabulary size of the text part of the model. Defines the number of different tokens that can be + represented by the `inputs_ids` passed when calling [`ViltModel`]. + type_vocab_size (`int`, *optional*, defaults to 2): + The vocabulary size of the `token_type_ids` passed when calling [`ViltModel`]. This is used when encoding + text. + modality_type_vocab_size (`int`, *optional*, defaults to 2): + The vocabulary size of the modalities passed when calling [`ViltModel`]. This is used after concatening the + embeddings of the text and image modalities. + max_position_embeddings (`int`, *optional*, defaults to 40): + The maximum sequence length that this model might ever be used with. + hidden_size (`int`, *optional*, defaults to 768): + Dimensionality of the encoder layers and the pooler layer. + num_hidden_layers (`int`, *optional*, defaults to 12): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 12): + Number of attention heads for each attention layer in the Transformer encoder. + intermediate_size (`int`, *optional*, defaults to 3072): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"` and `"gelu_new"` are supported. + hidden_dropout_prob (`float`, *optional*, defaults to 0.0): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + layer_norm_eps (`float`, *optional*, defaults to 1e-12): + The epsilon used by the layer normalization layers. + image_size (`int`, *optional*, defaults to 384): + The size (resolution) of each image. + patch_size (`int`, *optional*, defaults to 32): + The size (resolution) of each patch. + num_channels (`int`, *optional*, defaults to 3): + The number of input channels. + qkv_bias (`bool`, *optional*, defaults to `True`): + Whether to add a bias to the queries, keys and values. + max_image_length (`int`, *optional*, defaults to -1): + The maximum number of patches to take as input for the Transformer encoder. If set to a positive integer, + the encoder will sample `max_image_length` patches at maximum. If set to -1, will not be taken into + account. + num_images (`int`, *optional*, defaults to -1): + The number of images to use for natural language visual reasoning. If set to a positive integer, will be + used by [`ViltForImagesAndTextClassification`] for defining the classifier head. + + Example: + + ```python + >>> from transformers import ViLTModel, ViLTConfig + + >>> # Initializing a ViLT dandelin/vilt-b32-mlm style configuration + >>> configuration = ViLTConfig() + + >>> # Initializing a model from the dandelin/vilt-b32-mlm style configuration + >>> model = ViLTModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "vilt" + + def __init__( + self, + vocab_size=30522, + type_vocab_size=2, + modality_type_vocab_size=2, + max_position_embeddings=40, + hidden_size=768, + num_hidden_layers=12, + num_attention_heads=12, + intermediate_size=3072, + hidden_act="gelu", + hidden_dropout_prob=0.0, + attention_probs_dropout_prob=0.0, + initializer_range=0.02, + layer_norm_eps=1e-12, + image_size=384, + patch_size=32, + num_channels=3, + qkv_bias=True, + max_image_length=-1, + tie_word_embeddings=False, + num_images=-1, + **kwargs, + ): + super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) + + self.vocab_size = vocab_size + self.type_vocab_size = type_vocab_size + self.modality_type_vocab_size = modality_type_vocab_size + self.max_position_embeddings = max_position_embeddings + + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.initializer_range = initializer_range + self.layer_norm_eps = layer_norm_eps + + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.qkv_bias = qkv_bias + self.max_image_length = max_image_length + self.num_images = num_images diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vilt/convert_vilt_original_to_pytorch.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vilt/convert_vilt_original_to_pytorch.py new file mode 100644 index 0000000000000000000000000000000000000000..e597d0d7e778b7e0fff61e5c1eec83996170b2e1 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vilt/convert_vilt_original_to_pytorch.py @@ -0,0 +1,300 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert ViLT checkpoints from the original Github repository.""" + + +import argparse +import json +from pathlib import Path + +import requests +import torch +from huggingface_hub import hf_hub_download +from PIL import Image + +from transformers import ( + BertTokenizer, + ViltConfig, + ViltForImageAndTextRetrieval, + ViltForImagesAndTextClassification, + ViltForMaskedLM, + ViltForQuestionAnswering, + ViltImageProcessor, + ViltProcessor, +) +from transformers.utils import logging + + +logging.set_verbosity_info() +logger = logging.get_logger(__name__) + + +# here we list all keys to be renamed (original name on the left, our name on the right) +def create_rename_keys(config, vqa_model=False, nlvr_model=False, irtr_model=False): + rename_keys = [] + for i in range(config.num_hidden_layers): + # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms + rename_keys.append((f"transformer.blocks.{i}.norm1.weight", f"vilt.encoder.layer.{i}.layernorm_before.weight")) + rename_keys.append((f"transformer.blocks.{i}.norm1.bias", f"vilt.encoder.layer.{i}.layernorm_before.bias")) + rename_keys.append( + (f"transformer.blocks.{i}.attn.proj.weight", f"vilt.encoder.layer.{i}.attention.output.dense.weight") + ) + rename_keys.append( + (f"transformer.blocks.{i}.attn.proj.bias", f"vilt.encoder.layer.{i}.attention.output.dense.bias") + ) + rename_keys.append((f"transformer.blocks.{i}.norm2.weight", f"vilt.encoder.layer.{i}.layernorm_after.weight")) + rename_keys.append((f"transformer.blocks.{i}.norm2.bias", f"vilt.encoder.layer.{i}.layernorm_after.bias")) + rename_keys.append( + (f"transformer.blocks.{i}.mlp.fc1.weight", f"vilt.encoder.layer.{i}.intermediate.dense.weight") + ) + rename_keys.append((f"transformer.blocks.{i}.mlp.fc1.bias", f"vilt.encoder.layer.{i}.intermediate.dense.bias")) + rename_keys.append((f"transformer.blocks.{i}.mlp.fc2.weight", f"vilt.encoder.layer.{i}.output.dense.weight")) + rename_keys.append((f"transformer.blocks.{i}.mlp.fc2.bias", f"vilt.encoder.layer.{i}.output.dense.bias")) + + # embeddings + rename_keys.extend( + [ + # text embeddings + ("text_embeddings.word_embeddings.weight", "vilt.embeddings.text_embeddings.word_embeddings.weight"), + ( + "text_embeddings.position_embeddings.weight", + "vilt.embeddings.text_embeddings.position_embeddings.weight", + ), + ("text_embeddings.position_ids", "vilt.embeddings.text_embeddings.position_ids"), + ( + "text_embeddings.token_type_embeddings.weight", + "vilt.embeddings.text_embeddings.token_type_embeddings.weight", + ), + ("text_embeddings.LayerNorm.weight", "vilt.embeddings.text_embeddings.LayerNorm.weight"), + ("text_embeddings.LayerNorm.bias", "vilt.embeddings.text_embeddings.LayerNorm.bias"), + # patch embeddings + ("transformer.cls_token", "vilt.embeddings.cls_token"), + ("transformer.patch_embed.proj.weight", "vilt.embeddings.patch_embeddings.projection.weight"), + ("transformer.patch_embed.proj.bias", "vilt.embeddings.patch_embeddings.projection.bias"), + ("transformer.pos_embed", "vilt.embeddings.position_embeddings"), + # token type embeddings + ("token_type_embeddings.weight", "vilt.embeddings.token_type_embeddings.weight"), + ] + ) + + # final layernorm + pooler + rename_keys.extend( + [ + ("transformer.norm.weight", "vilt.layernorm.weight"), + ("transformer.norm.bias", "vilt.layernorm.bias"), + ("pooler.dense.weight", "vilt.pooler.dense.weight"), + ("pooler.dense.bias", "vilt.pooler.dense.bias"), + ] + ) + + # classifier head(s) + if vqa_model: + # classification head + rename_keys.extend( + [ + ("vqa_classifier.0.weight", "classifier.0.weight"), + ("vqa_classifier.0.bias", "classifier.0.bias"), + ("vqa_classifier.1.weight", "classifier.1.weight"), + ("vqa_classifier.1.bias", "classifier.1.bias"), + ("vqa_classifier.3.weight", "classifier.3.weight"), + ("vqa_classifier.3.bias", "classifier.3.bias"), + ] + ) + elif nlvr_model: + # classification head + rename_keys.extend( + [ + ("nlvr2_classifier.0.weight", "classifier.0.weight"), + ("nlvr2_classifier.0.bias", "classifier.0.bias"), + ("nlvr2_classifier.1.weight", "classifier.1.weight"), + ("nlvr2_classifier.1.bias", "classifier.1.bias"), + ("nlvr2_classifier.3.weight", "classifier.3.weight"), + ("nlvr2_classifier.3.bias", "classifier.3.bias"), + ] + ) + else: + pass + + return rename_keys + + +# we split up the matrix of each encoder layer into queries, keys and values +def read_in_q_k_v(state_dict, config): + for i in range(config.num_hidden_layers): + prefix = "vilt." + # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) + in_proj_weight = state_dict.pop(f"transformer.blocks.{i}.attn.qkv.weight") + in_proj_bias = state_dict.pop(f"transformer.blocks.{i}.attn.qkv.bias") + # next, add query, keys and values (in that order) to the state dict + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[ + : config.hidden_size, : + ] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.bias"] = in_proj_bias[: config.hidden_size] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[ + config.hidden_size : config.hidden_size * 2, : + ] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.bias"] = in_proj_bias[ + config.hidden_size : config.hidden_size * 2 + ] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[ + -config.hidden_size :, : + ] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.bias"] = in_proj_bias[-config.hidden_size :] + + +def remove_classification_head_(state_dict): + ignore_keys = ["head.weight", "head.bias"] + for k in ignore_keys: + state_dict.pop(k, None) + + +def rename_key(dct, old, new): + val = dct.pop(old) + dct[new] = val + + +@torch.no_grad() +def convert_vilt_checkpoint(checkpoint_url, pytorch_dump_folder_path): + """ + Copy/paste/tweak model's weights to our ViLT structure. + """ + + # define configuration and initialize HuggingFace model + config = ViltConfig(image_size=384, patch_size=32, tie_word_embeddings=False) + mlm_model = False + vqa_model = False + nlvr_model = False + irtr_model = False + if "vqa" in checkpoint_url: + vqa_model = True + config.num_labels = 3129 + repo_id = "huggingface/label-files" + filename = "vqa2-id2label.json" + id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) + id2label = {int(k): v for k, v in id2label.items()} + config.id2label = id2label + config.label2id = {v: k for k, v in id2label.items()} + model = ViltForQuestionAnswering(config) + elif "nlvr" in checkpoint_url: + nlvr_model = True + config.num_labels = 2 + config.id2label = {0: "False", 1: "True"} + config.label2id = {v: k for k, v in config.id2label.items()} + config.modality_type_vocab_size = 3 + model = ViltForImagesAndTextClassification(config) + elif "irtr" in checkpoint_url: + irtr_model = True + model = ViltForImageAndTextRetrieval(config) + elif "mlm_itm" in checkpoint_url: + mlm_model = True + model = ViltForMaskedLM(config) + else: + raise ValueError("Unknown model type") + + # load state_dict of original model, remove and rename some keys + state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")["state_dict"] + rename_keys = create_rename_keys(config, vqa_model, nlvr_model, irtr_model) + for src, dest in rename_keys: + rename_key(state_dict, src, dest) + read_in_q_k_v(state_dict, config) + if mlm_model or irtr_model: + ignore_keys = ["itm_score.fc.weight", "itm_score.fc.bias"] + for k in ignore_keys: + state_dict.pop(k, None) + + # load state dict into HuggingFace model + model.eval() + if mlm_model: + missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False) + assert missing_keys == ["mlm_score.decoder.bias"] + else: + model.load_state_dict(state_dict) + + # Define processor + image_processor = ViltImageProcessor(size=384) + tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased") + processor = ViltProcessor(image_processor, tokenizer) + + # Forward pass on example inputs (image + text) + if nlvr_model: + image1 = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg", stream=True).raw) + image2 = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg", stream=True).raw) + text = ( + "The left image contains twice the number of dogs as the right image, and at least two dogs in total are" + " standing." + ) + encoding_1 = processor(image1, text, return_tensors="pt") + encoding_2 = processor(image2, text, return_tensors="pt") + outputs = model( + input_ids=encoding_1.input_ids, + pixel_values=encoding_1.pixel_values, + pixel_values_2=encoding_2.pixel_values, + ) + else: + image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw) + if mlm_model: + text = "a bunch of [MASK] laying on a [MASK]." + else: + text = "How many cats are there?" + encoding = processor(image, text, return_tensors="pt") + outputs = model(**encoding) + + # Verify outputs + if mlm_model: + expected_shape = torch.Size([1, 11, 30522]) + expected_slice = torch.tensor([-12.5061, -12.5123, -12.5174]) + assert outputs.logits.shape == expected_shape + assert torch.allclose(outputs.logits[0, 0, :3], expected_slice, atol=1e-4) + + # verify masked token prediction equals "cats" + predicted_id = outputs.logits[0, 4, :].argmax(-1).item() + assert tokenizer.decode([predicted_id]) == "cats" + elif vqa_model: + expected_shape = torch.Size([1, 3129]) + expected_slice = torch.tensor([-15.9495, -18.1472, -10.3041]) + assert torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4) + assert outputs.logits.shape == expected_shape + assert torch.allclose(outputs.logits[0, 0, :3], expected_slice, atol=1e-4) + + # verify vqa prediction equals "2" + predicted_idx = outputs.logits.argmax(-1).item() + assert model.config.id2label[predicted_idx] == "2" + elif nlvr_model: + expected_shape = torch.Size([1, 2]) + expected_slice = torch.tensor([-2.8721, 2.1291]) + assert torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4) + assert outputs.logits.shape == expected_shape + + Path(pytorch_dump_folder_path).mkdir(exist_ok=True) + print(f"Saving model and processor to {pytorch_dump_folder_path}") + model.save_pretrained(pytorch_dump_folder_path) + processor.save_pretrained(pytorch_dump_folder_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + # Required parameters + parser.add_argument( + "--checkpoint_url", + default="https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt", + type=str, + help="URL of the checkpoint you'd like to convert.", + ) + parser.add_argument( + "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." + ) + + args = parser.parse_args() + convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vilt/feature_extraction_vilt.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vilt/feature_extraction_vilt.py new file mode 100644 index 0000000000000000000000000000000000000000..5091946bf94334dae16408346e707cf2fcaffaa4 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vilt/feature_extraction_vilt.py @@ -0,0 +1,33 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Feature extractor class for ViLT.""" + +import warnings + +from ...utils import logging +from .image_processing_vilt import ViltImageProcessor + + +logger = logging.get_logger(__name__) + + +class ViltFeatureExtractor(ViltImageProcessor): + def __init__(self, *args, **kwargs) -> None: + warnings.warn( + "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please" + " use ViltImageProcessor instead.", + FutureWarning, + ) + super().__init__(*args, **kwargs) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vilt/image_processing_vilt.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vilt/image_processing_vilt.py new file mode 100644 index 0000000000000000000000000000000000000000..42e5b3f439d6aab9d9d9fc1349df6f0cf947f28c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vilt/image_processing_vilt.py @@ -0,0 +1,505 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for Vilt.""" + +from typing import Any, Dict, Iterable, List, Optional, Tuple, Union + +import numpy as np + +from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict +from ...image_transforms import PaddingMode, pad, resize, to_channel_dimension_format +from ...image_utils import ( + IMAGENET_STANDARD_MEAN, + IMAGENET_STANDARD_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + get_image_size, + infer_channel_dimension_format, + is_scaled_image, + make_list_of_images, + to_numpy_array, + valid_images, + validate_kwargs, + validate_preprocess_arguments, +) +from ...utils import TensorType, is_vision_available, logging + + +if is_vision_available(): + import PIL + + +logger = logging.get_logger(__name__) + + +def max_across_indices(values: Iterable[Any]) -> List[Any]: + """ + Return the maximum value across all indices of an iterable of values. + """ + return [max(values_i) for values_i in zip(*values)] + + +def make_pixel_mask( + image: np.ndarray, output_size: Tuple[int, int], input_data_format: Optional[Union[str, ChannelDimension]] = None +) -> np.ndarray: + """ + Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding. + + Args: + image (`np.ndarray`): + Image to make the pixel mask for. + output_size (`Tuple[int, int]`): + Output size of the mask. + """ + input_height, input_width = get_image_size(image, channel_dim=input_data_format) + mask = np.zeros(output_size, dtype=np.int64) + mask[:input_height, :input_width] = 1 + return mask + + +def get_max_height_width( + images: List[np.ndarray], input_data_format: Optional[Union[str, ChannelDimension]] = None +) -> List[int]: + """ + Get the maximum height and width across all images in a batch. + """ + if input_data_format is None: + input_data_format = infer_channel_dimension_format(images[0]) + + if input_data_format == ChannelDimension.FIRST: + _, max_height, max_width = max_across_indices([img.shape for img in images]) + elif input_data_format == ChannelDimension.LAST: + max_height, max_width, _ = max_across_indices([img.shape for img in images]) + else: + raise ValueError(f"Invalid channel dimension format: {input_data_format}") + return (max_height, max_width) + + +def get_resize_output_image_size( + input_image: np.ndarray, + shorter: int = 800, + longer: int = 1333, + size_divisor: int = 32, + input_data_format: Optional[Union[str, ChannelDimension]] = None, +) -> Tuple[int, int]: + input_height, input_width = get_image_size(input_image, input_data_format) + min_size, max_size = shorter, longer + + scale = min_size / min(input_height, input_width) + + if input_height < input_width: + new_height = min_size + new_width = scale * input_width + else: + new_height = scale * input_height + new_width = min_size + + if max(new_height, new_width) > max_size: + scale = max_size / max(new_height, new_width) + new_height = scale * new_height + new_width = scale * new_width + + new_height, new_width = int(new_height + 0.5), int(new_width + 0.5) + new_height = new_height // size_divisor * size_divisor + new_width = new_width // size_divisor * size_divisor + + return new_height, new_width + + +class ViltImageProcessor(BaseImageProcessor): + r""" + Constructs a ViLT image processor. + + Args: + do_resize (`bool`, *optional*, defaults to `True`): + Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the + `do_resize` parameter in the `preprocess` method. + size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 384}`): + Resize the shorter side of the input to `size["shortest_edge"]`. The longer side will be limited to under + `int((1333 / 800) * size["shortest_edge"])` while preserving the aspect ratio. Only has an effect if + `do_resize` is set to `True`. Can be overridden by the `size` parameter in the `preprocess` method. + size_divisor (`int`, *optional*, defaults to 32): + The size by which to make sure both the height and width can be divided. Only has an effect if `do_resize` + is set to `True`. Can be overridden by the `size_divisor` parameter in the `preprocess` method. + resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`): + Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be + overridden by the `resample` parameter in the `preprocess` method. + do_rescale (`bool`, *optional*, defaults to `True`): + Wwhether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the + `do_rescale` parameter in the `preprocess` method. + rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): + Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be + overridden by the `rescale_factor` parameter in the `preprocess` method. + do_normalize (`bool`, *optional*, defaults to `True`): + Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` + method. Can be overridden by the `do_normalize` parameter in the `preprocess` method. + image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): + Mean to use if normalizing the image. This is a float or list of floats the length of the number of + channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be + overridden by the `image_mean` parameter in the `preprocess` method. + image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): + Standard deviation to use if normalizing the image. This is a float or list of floats the length of the + number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. + Can be overridden by the `image_std` parameter in the `preprocess` method. + do_pad (`bool`, *optional*, defaults to `True`): + Whether to pad the image to the `(max_height, max_width)` of the images in the batch. Can be overridden by + the `do_pad` parameter in the `preprocess` method. + """ + + model_input_names = ["pixel_values"] + + def __init__( + self, + do_resize: bool = True, + size: Dict[str, int] = None, + size_divisor: int = 32, + resample: PILImageResampling = PILImageResampling.BICUBIC, + do_rescale: bool = True, + rescale_factor: Union[int, float] = 1 / 255, + do_normalize: bool = True, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + do_pad: bool = True, + **kwargs, + ) -> None: + if "pad_and_return_pixel_mask" in kwargs: + do_pad = kwargs.pop("pad_and_return_pixel_mask") + + super().__init__(**kwargs) + size = size if size is not None else {"shortest_edge": 384} + size = get_size_dict(size, default_to_square=False) + + self.do_resize = do_resize + self.size = size + self.size_divisor = size_divisor + self.resample = resample + self.do_rescale = do_rescale + self.rescale_factor = rescale_factor + self.do_normalize = do_normalize + self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN + self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD + self.do_pad = do_pad + self._valid_processor_keys = [ + "images", + "do_resize", + "size", + "size_divisor", + "resample", + "do_rescale", + "rescale_factor", + "do_normalize", + "image_mean", + "image_std", + "do_pad", + "return_tensors", + "data_format", + "input_data_format", + ] + + @classmethod + def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs): + """ + Overrides the `from_dict` method from the base class to make sure `reduce_labels` is updated if image processor + is created using from_dict and kwargs e.g. `ViltImageProcessor.from_pretrained(checkpoint, + pad_and_return_pixel_mask=False)` + """ + image_processor_dict = image_processor_dict.copy() + if "pad_and_return_pixel_mask" in kwargs: + image_processor_dict["pad_and_return_pixel_mask"] = kwargs.pop("pad_and_return_pixel_mask") + return super().from_dict(image_processor_dict, **kwargs) + + def resize( + self, + image: np.ndarray, + size: Dict[str, int], + size_divisor: int = 32, + resample: PILImageResampling = PILImageResampling.BICUBIC, + data_format: Optional[Union[str, ChannelDimension]] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ) -> np.ndarray: + """ + Resize an image. + + Resizes the shorter side of the image to `size["shortest_edge"]` while preserving the aspect ratio. If the + longer side is larger than the max size `(int(`size["shortest_edge"]` * 1333 / 800))`, the longer side is then + resized to the max size while preserving the aspect ratio. + + Args: + image (`np.ndarray`): + Image to resize. + size (`Dict[str, int]`): + Controls the size of the output image. Should be of the form `{"shortest_edge": int}`. + size_divisor (`int`, defaults to 32): + The image is resized to a size that is a multiple of this value. + resample (`PILImageResampling` filter, *optional*, defaults to `PILImageResampling.BICUBIC`): + Resampling filter to use when resiizing the image. + data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format of the image. If not provided, it will be the same as the input image. + input_data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format of the input image. If not provided, it will be inferred. + """ + size = get_size_dict(size, default_to_square=False) + if "shortest_edge" not in size: + raise ValueError(f"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}") + shorter = size["shortest_edge"] + longer = int(1333 / 800 * shorter) + output_size = get_resize_output_image_size( + image, shorter=shorter, longer=longer, size_divisor=size_divisor, input_data_format=input_data_format + ) + return resize( + image, + size=output_size, + resample=resample, + data_format=data_format, + input_data_format=input_data_format, + **kwargs, + ) + + def _pad_image( + self, + image: np.ndarray, + output_size: Tuple[int, int], + constant_values: Union[float, Iterable[float]] = 0, + data_format: Optional[ChannelDimension] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + ) -> np.ndarray: + """ + Pad an image with zeros to the given size. + """ + input_height, input_width = get_image_size(image, channel_dim=input_data_format) + output_height, output_width = output_size + + pad_bottom = output_height - input_height + pad_right = output_width - input_width + padding = ((0, pad_bottom), (0, pad_right)) + padded_image = pad( + image, + padding, + mode=PaddingMode.CONSTANT, + constant_values=constant_values, + data_format=data_format, + input_data_format=input_data_format, + ) + return padded_image + + def pad( + self, + images: List[np.ndarray], + constant_values: Union[float, Iterable[float]] = 0, + return_pixel_mask: bool = True, + return_tensors: Optional[Union[str, TensorType]] = None, + data_format: Optional[ChannelDimension] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + ) -> BatchFeature: + """ + Pads a batch of images to the bottom and right of the image with zeros to the size of largest height and width + in the batch and optionally returns their corresponding pixel mask. + + Args: + image (`np.ndarray`): + Image to pad. + constant_values (`float` or `Iterable[float]`, *optional*): + The value to use for the padding if `mode` is `"constant"`. + return_pixel_mask (`bool`, *optional*, defaults to `True`): + Whether to return a pixel mask. + return_tensors (`str` or `TensorType`, *optional*): + The type of tensors to return. Can be one of: + - Unset: Return a list of `np.ndarray`. + - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. + - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. + - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. + - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. + data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format of the image. If not provided, it will be the same as the input image. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format of the input image. If not provided, it will be inferred. + """ + pad_size = get_max_height_width(images, input_data_format=input_data_format) + + padded_images = [ + self._pad_image( + image, + pad_size, + constant_values=constant_values, + data_format=data_format, + input_data_format=input_data_format, + ) + for image in images + ] + data = {"pixel_values": padded_images} + + if return_pixel_mask: + masks = [ + make_pixel_mask(image=image, output_size=pad_size, input_data_format=input_data_format) + for image in images + ] + data["pixel_mask"] = masks + + return BatchFeature(data=data, tensor_type=return_tensors) + + def preprocess( + self, + images: ImageInput, + do_resize: Optional[bool] = None, + size: Optional[Dict[str, int]] = None, + size_divisor: Optional[int] = None, + resample: PILImageResampling = None, + do_rescale: Optional[bool] = None, + rescale_factor: Optional[float] = None, + do_normalize: Optional[bool] = None, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + do_pad: Optional[bool] = None, + return_tensors: Optional[Union[str, TensorType]] = None, + data_format: ChannelDimension = ChannelDimension.FIRST, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ) -> PIL.Image.Image: + """ + Preprocess an image or batch of images. + + Args: + images (`ImageInput`): + Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If + passing in images with pixel values between 0 and 1, set `do_rescale=False`. + do_resize (`bool`, *optional*, defaults to `self.do_resize`): + Whether to resize the image. + size (`Dict[str, int]`, *optional*, defaults to `self.size`): + Controls the size of the image after `resize`. The shortest edge of the image is resized to + `size["shortest_edge"]` whilst preserving the aspect ratio. If the longest edge of this resized image + is > `int(size["shortest_edge"] * (1333 / 800))`, then the image is resized again to make the longest + edge equal to `int(size["shortest_edge"] * (1333 / 800))`. + size_divisor (`int`, *optional*, defaults to `self.size_divisor`): + The image is resized to a size that is a multiple of this value. + resample (`PILImageResampling`, *optional*, defaults to `self.resample`): + Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. + do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): + Whether to rescale the image values between [0 - 1]. + rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): + Rescale factor to rescale the image by if `do_rescale` is set to `True`. + do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): + Whether to normalize the image. + image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): + Image mean to normalize the image by if `do_normalize` is set to `True`. + image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): + Image standard deviation to normalize the image by if `do_normalize` is set to `True`. + do_pad (`bool`, *optional*, defaults to `self.do_pad`): + Whether to pad the image to the (max_height, max_width) in the batch. If `True`, a pixel mask is also + created and returned. + return_tensors (`str` or `TensorType`, *optional*): + The type of tensors to return. Can be one of: + - Unset: Return a list of `np.ndarray`. + - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. + - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. + - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. + - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. + data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): + The channel dimension format for the output image. Can be one of: + - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `ChannelDimension.LAST`: image in (height, width, num_channels) format. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format for the input image. If unset, the channel dimension format is inferred + from the input image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. + """ + do_resize = do_resize if do_resize is not None else self.do_resize + size_divisor = size_divisor if size_divisor is not None else self.size_divisor + resample = resample if resample is not None else self.resample + do_rescale = do_rescale if do_rescale is not None else self.do_rescale + rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor + do_normalize = do_normalize if do_normalize is not None else self.do_normalize + image_mean = image_mean if image_mean is not None else self.image_mean + image_std = image_std if image_std is not None else self.image_std + do_pad = do_pad if do_pad is not None else self.do_pad + + size = size if size is not None else self.size + size = get_size_dict(size, default_to_square=False) + + images = make_list_of_images(images) + + validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys) + + if not valid_images(images): + raise ValueError( + "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " + "torch.Tensor, tf.Tensor or jax.ndarray." + ) + + # Here the pad() method does not require any additional argument as it takes the maximum of (height, width). + # Hence, it does not need to be passed to a validate_preprocess_arguments() method. + validate_preprocess_arguments( + do_rescale=do_rescale, + rescale_factor=rescale_factor, + do_normalize=do_normalize, + image_mean=image_mean, + image_std=image_std, + do_resize=do_resize, + size=size, + resample=resample, + ) + + # All transformations expect numpy arrays. + images = [to_numpy_array(image) for image in images] + + if is_scaled_image(images[0]) and do_rescale: + logger.warning_once( + "It looks like you are trying to rescale already rescaled images. If the input" + " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." + ) + + if input_data_format is None: + # We assume that all images have the same channel dimension format. + input_data_format = infer_channel_dimension_format(images[0]) + + if do_resize: + images = [ + self.resize( + image=image, + size=size, + size_divisor=size_divisor, + resample=resample, + input_data_format=input_data_format, + ) + for image in images + ] + + if do_rescale: + images = [ + self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) + for image in images + ] + + if do_normalize: + images = [ + self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format) + for image in images + ] + + images = [ + to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images + ] + + if do_pad: + encoded_outputs = self.pad( + images, return_pixel_mask=True, return_tensors=return_tensors, input_data_format=data_format + ) + else: + encoded_outputs = BatchFeature(data={"pixel_values": images}, tensor_type=return_tensors) + + return encoded_outputs diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vilt/modeling_vilt.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vilt/modeling_vilt.py new file mode 100644 index 0000000000000000000000000000000000000000..5545b881bd670a724041814ddc85a225311c00ad --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vilt/modeling_vilt.py @@ -0,0 +1,1488 @@ +# coding=utf-8 +# Copyright 2022 NAVER AI Labs and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch ViLT model.""" + +import collections.abc +import math +from dataclasses import dataclass +from typing import List, Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss + +from ...activations import ACT2FN +from ...modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPooling, + MaskedLMOutput, + ModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from ...modeling_utils import PreTrainedModel +from ...pytorch_utils import ( + find_pruneable_heads_and_indices, + meshgrid, + prune_linear_layer, +) +from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings +from .configuration_vilt import ViltConfig + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "ViltConfig" +_CHECKPOINT_FOR_DOC = "dandelin/vilt-b32-mlm" + + +from ..deprecated._archive_maps import VILT_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +@dataclass +class ViltForImagesAndTextClassificationOutput(ModelOutput): + """ + Class for outputs of [`ViltForImagesAndTextClassification`]. + + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Classification (or regression if config.num_labels==1) loss. + logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): + Classification (or regression if config.num_labels==1) scores (before SoftMax). + hidden_states (`List[tuple(torch.FloatTensor)]`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + List of tuples of `torch.FloatTensor` (one for each image-text pair, each tuple containing the output of + the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + attentions (`List[tuple(torch.FloatTensor)]`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + List of tuples of `torch.FloatTensor` (one for each image-text pair, each tuple containing the attention + weights of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the + attention softmax, used to compute the weighted average in the self-attention heads. + """ + + loss: Optional[torch.FloatTensor] = None + logits: torch.FloatTensor = None + hidden_states: Optional[List[Tuple[torch.FloatTensor]]] = None + attentions: Optional[List[Tuple[torch.FloatTensor]]] = None + + +class ViltEmbeddings(nn.Module): + """ + Construct the text and patch embeddings. + + Text embeddings are equivalent to BERT embeddings. + + Patch embeddings are equivalent to ViT embeddings. + """ + + def __init__(self, config): + super().__init__() + + # text embeddings + self.text_embeddings = TextEmbeddings(config) + # patch embeddings + self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) + self.patch_embeddings = ViltPatchEmbeddings(config) + num_patches = self.patch_embeddings.num_patches + self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size)) + # modality type (text/patch) embeddings + self.token_type_embeddings = nn.Embedding(config.modality_type_vocab_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.config = config + + def visual_embed(self, pixel_values, pixel_mask, max_image_length=200): + _, _, ph, pw = self.patch_embeddings.projection.weight.shape + + x = self.patch_embeddings(pixel_values) + x_mask = pixel_mask[:, None, :, :].float() + x_mask = nn.functional.interpolate(x_mask, size=(x.shape[2], x.shape[3])).long() + x_h = x_mask[:, 0].sum(dim=1)[:, 0] + x_w = x_mask[:, 0].sum(dim=2)[:, 0] + + batch_size, num_channels, height, width = x.shape + patch_dim = self.config.image_size // self.config.patch_size + spatial_pos = self.position_embeddings[:, 1:, :].transpose(1, 2).view(1, num_channels, patch_dim, patch_dim) + pos_embed = torch.cat( + [ + nn.functional.pad( + nn.functional.interpolate( + spatial_pos, + size=(h, w), + mode="bilinear", + align_corners=True, + ), + (0, width - w, 0, height - h), + ) + for h, w in zip(x_h, x_w) + ], + dim=0, + ) + + pos_embed = pos_embed.flatten(2).transpose(1, 2) + x = x.flatten(2).transpose(1, 2) + # Set `device` here, otherwise `patch_index` will always be on `CPU` and will fail near the end for torch>=1.13 + patch_index = torch.stack( + meshgrid(torch.arange(x_mask.shape[-2]), torch.arange(x_mask.shape[-1]), indexing="ij"), dim=-1 + ).to(device=x_mask.device) + patch_index = patch_index[None, None, :, :, :] + patch_index = patch_index.expand(x_mask.shape[0], x_mask.shape[1], -1, -1, -1) + patch_index = patch_index.flatten(1, 3) + x_mask = x_mask.flatten(1) + + if max_image_length < 0 or max_image_length is None or not isinstance(max_image_length, int): + # suppose aug is 800 x 1333, then, maximum effective res is 800 x 1333 (if one side gets bigger, the other will be constrained and be shrinked) + # (800 // self.patch_size) * (1333 // self.patch_size) is the maximum number of patches that single image can get. + # if self.patch_size = 32, 25 * 41 = 1025 + # if res is 384 x 640, 12 * 20 = 240 + effective_resolution = x_h * x_w + max_image_length = effective_resolution.max() + else: + effective_resolution = x_h * x_w + max_image_length = min(effective_resolution.max(), max_image_length) + + valid_idx = x_mask.nonzero(as_tuple=False) + non_valid_idx = (1 - x_mask).nonzero(as_tuple=False) + unique_rows = valid_idx[:, 0].unique() + valid_row_idx = [valid_idx[valid_idx[:, 0] == u] for u in unique_rows] + non_valid_row_idx = [non_valid_idx[non_valid_idx[:, 0] == u] for u in unique_rows] + + valid_nums = [v.size(0) for v in valid_row_idx] + non_valid_nums = [v.size(0) for v in non_valid_row_idx] + pad_nums = [max_image_length - v for v in valid_nums] + + select = [] + for i, (v, nv, p) in enumerate(zip(valid_nums, non_valid_nums, pad_nums)): + if p <= 0: + valid_choice = torch.multinomial(torch.ones(v).float(), max_image_length) + select.append(valid_row_idx[i][valid_choice]) + else: + pad_choice = torch.multinomial(torch.ones(nv).float(), p, replacement=True) + select.append(torch.cat([valid_row_idx[i], non_valid_row_idx[i][pad_choice]], dim=0)) + + select = torch.cat(select, dim=0) + x = x[select[:, 0], select[:, 1]].view(batch_size, -1, num_channels) + x_mask = x_mask[select[:, 0], select[:, 1]].view(batch_size, -1) + # `patch_index` should be on the same device as `select` (for torch>=1.13), which is ensured at definition time. + patch_index = patch_index[select[:, 0], select[:, 1]].view(batch_size, -1, 2) + pos_embed = pos_embed[select[:, 0], select[:, 1]].view(batch_size, -1, num_channels) + + cls_tokens = self.cls_token.expand(batch_size, -1, -1) + x = torch.cat((cls_tokens, x), dim=1) + pos_embed = torch.cat( + (self.position_embeddings[:, 0, :][:, None, :].expand(batch_size, -1, -1), pos_embed), dim=1 + ) + x = x + pos_embed + x = self.dropout(x) + + x_mask = torch.cat([torch.ones(x_mask.shape[0], 1).to(x_mask), x_mask], dim=1) + + return x, x_mask, (patch_index, (height, width)) + + def forward( + self, + input_ids, + attention_mask, + token_type_ids, + pixel_values, + pixel_mask, + inputs_embeds, + image_embeds, + image_token_type_idx=1, + ): + # PART 1: text embeddings + text_embeds = self.text_embeddings( + input_ids=input_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds + ) + + # PART 2: patch embeddings (with interpolated position encodings) + if image_embeds is None: + image_embeds, image_masks, patch_index = self.visual_embed( + pixel_values, pixel_mask, max_image_length=self.config.max_image_length + ) + else: + image_masks = pixel_mask.flatten(1) + + # PART 3: add modality type embeddings + # 0 indicates text, 1 indicates image, 2 is optionally used when a second image is provided (NLVR2) + if image_token_type_idx is None: + image_token_type_idx = 1 + text_embeds = text_embeds + self.token_type_embeddings( + torch.zeros_like(attention_mask, dtype=torch.long, device=text_embeds.device) + ) + image_embeds = image_embeds + self.token_type_embeddings( + torch.full_like(image_masks, image_token_type_idx, dtype=torch.long, device=text_embeds.device) + ) + + # PART 4: concatenate + embeddings = torch.cat([text_embeds, image_embeds], dim=1) + masks = torch.cat([attention_mask, image_masks], dim=1) + + return embeddings, masks + + +class TextEmbeddings(nn.Module): + """Construct the embeddings from word, position and token_type embeddings.""" + + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) + self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False + ) + self.register_buffer( + "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False + ) + + def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None): + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + if position_ids is None: + position_ids = self.position_ids[:, :seq_length] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class ViltPatchEmbeddings(nn.Module): + """ + Image to Patch Embedding. + """ + + def __init__(self, config): + super().__init__() + image_size, patch_size = config.image_size, config.patch_size + num_channels, hidden_size = config.num_channels, config.hidden_size + + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_patches = num_patches + + self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) + + def forward(self, pixel_values): + batch_size, num_channels, height, width = pixel_values.shape + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + ) + target_dtype = self.projection.weight.dtype + x = self.projection(pixel_values.to(dtype=target_dtype)) + return x + + +class ViltSelfAttention(nn.Module): + def __init__(self, config): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " + f"heads {config.num_attention_heads}." + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False): + mixed_query_layer = self.query(hidden_states) + + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + query_layer = self.transpose_for_scores(mixed_query_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->Vilt +class ViltSelfOutput(nn.Module): + """ + The residual connection is defined in ViltLayer instead of here (as is the case with other models), due to the + layernorm applied before each block. + """ + + def __init__(self, config: ViltConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + + return hidden_states + + +class ViltAttention(nn.Module): + def __init__(self, config): + super().__init__() + self.attention = ViltSelfAttention(config) + self.output = ViltSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.attention.query = prune_linear_layer(self.attention.query, index) + self.attention.key = prune_linear_layer(self.attention.key, index) + self.attention.value = prune_linear_layer(self.attention.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) + self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False): + self_outputs = self.attention(hidden_states, attention_mask, head_mask, output_attentions) + + attention_output = self.output(self_outputs[0], hidden_states) + + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +# Copied from transformers.models.vit.modeling_vit.ViTIntermediate with ViT->Vilt +class ViltIntermediate(nn.Module): + def __init__(self, config: ViltConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + + return hidden_states + + +# Copied from transformers.models.vit.modeling_vit.ViTOutput with ViT->Vilt +class ViltOutput(nn.Module): + def __init__(self, config: ViltConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + + hidden_states = hidden_states + input_tensor + + return hidden_states + + +class ViltLayer(nn.Module): + """This corresponds to the Block class in the timm implementation.""" + + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = ViltAttention(config) + self.intermediate = ViltIntermediate(config) + self.output = ViltOutput(config) + self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False): + self_attention_outputs = self.attention( + self.layernorm_before(hidden_states), # in ViLT, layernorm is applied before self-attention + attention_mask, + head_mask, + output_attentions=output_attentions, + ) + attention_output = self_attention_outputs[0] + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + # first residual connection + hidden_states = attention_output + hidden_states.to(attention_output.device) + + # in ViLT, layernorm is also applied after self-attention + layer_output = self.layernorm_after(hidden_states) + layer_output = self.intermediate(layer_output) + + # second residual connection is done here + layer_output = self.output(layer_output, hidden_states) + + outputs = (layer_output,) + outputs + + return outputs + + +class ViltEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList([ViltLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer_module.__call__, + hidden_states, + attention_mask, + layer_head_mask, + output_attentions, + ) + else: + layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +class ViltPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = ViltConfig + base_model_prefix = "vilt" + supports_gradient_checkpointing = True + _no_split_modules = ["ViltEmbeddings", "ViltSelfAttention"] + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, (nn.Linear, nn.Conv2d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + +VILT_START_DOCSTRING = r""" + This model is a PyTorch `torch.nn.Module `_ subclass. Use + it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and + behavior. + + Parameters: + config ([`ViltConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +VILT_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See + [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input + IDs?](../glossary#input-ids) + + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + [What are attention masks?](../glossary#attention-mask) + + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + [What are token type IDs?](../glossary#token-type-ids) + + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See + [`ViltImageProcessor.__call__`] for details. + + pixel_mask (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): + Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`: + + - 1 for pixels that are real (i.e. **not masked**), + - 0 for pixels that are padding (i.e. **masked**). + `What are attention masks? <../glossary.html#attention-mask>`__ + + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + + image_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`, *optional*): + Optionally, instead of passing `pixel_values`, you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `pixel_values` into patch embeddings. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + +VILT_IMAGES_AND_TEXT_CLASSIFICATION_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See + [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input + IDs?](../glossary#input-ids) + + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + [What are attention masks?](../glossary#attention-mask) + + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + [What are token type IDs?](../glossary#token-type-ids) + + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_images, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See + [`ViltImageProcessor.__call__`] for details. + + pixel_mask (`torch.LongTensor` of shape `(batch_size, num_images, height, width)`, *optional*): + Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`: + + - 1 for pixels that are real (i.e. **not masked**), + - 0 for pixels that are padding (i.e. **masked**). + `What are attention masks? <../glossary.html#attention-mask>`__ + + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + + image_embeds (`torch.FloatTensor` of shape `(batch_size, num_images, num_patches, hidden_size)`, *optional*): + Optionally, instead of passing `pixel_values`, you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `pixel_values` into patch embeddings. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare ViLT Model transformer outputting raw hidden-states without any specific head on top.", + VILT_START_DOCSTRING, +) +class ViltModel(ViltPreTrainedModel): + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = ViltEmbeddings(config) + self.encoder = ViltEncoder(config) + + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.pooler = ViltPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.text_embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.text_embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + @add_start_docstrings_to_model_forward(VILT_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + pixel_values: Optional[torch.FloatTensor] = None, + pixel_mask: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + image_embeds: Optional[torch.FloatTensor] = None, + image_token_type_idx: Optional[int] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[BaseModelOutputWithPooling, Tuple[torch.FloatTensor]]: + r""" + Returns: + + Examples: + + ```python + >>> from transformers import ViltProcessor, ViltModel + >>> from PIL import Image + >>> import requests + + >>> # prepare image and text + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + >>> text = "hello world" + + >>> processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-mlm") + >>> model = ViltModel.from_pretrained("dandelin/vilt-b32-mlm") + + >>> inputs = processor(image, text, return_tensors="pt") + >>> outputs = model(**inputs) + >>> last_hidden_states = outputs.last_hidden_state + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + text_batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + if attention_mask is None: + attention_mask = torch.ones(((text_batch_size, seq_length)), device=device) + + if pixel_values is not None and image_embeds is not None: + raise ValueError("You cannot specify both pixel_values and image_embeds at the same time") + elif pixel_values is None and image_embeds is None: + raise ValueError("You have to specify either pixel_values or image_embeds") + + image_batch_size = pixel_values.shape[0] if pixel_values is not None else image_embeds.shape[0] + if image_batch_size != text_batch_size: + raise ValueError("The text inputs and image inputs need to have the same batch size") + if pixel_mask is None: + pixel_mask = torch.ones((image_batch_size, self.config.image_size, self.config.image_size), device=device) + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output, attention_mask = self.embeddings( + input_ids, + attention_mask, + token_type_ids, + pixel_values, + pixel_mask, + inputs_embeds, + image_embeds, + image_token_type_idx=image_token_type_idx, + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) + + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + sequence_output = self.layernorm(sequence_output) + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPooling( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +class ViltPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +@add_start_docstrings( + """ + ViLT Model with a language modeling head on top as done during pretraining. + """, + VILT_START_DOCSTRING, +) +class ViltForMaskedLM(ViltPreTrainedModel): + _tied_weights_keys = ["mlm_score.decoder.weight", "mlm_score.decoder.bias"] + + def __init__(self, config): + super().__init__(config) + + self.vilt = ViltModel(config) + self.mlm_score = ViltMLMHead(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.mlm_score.decoder + + def set_output_embeddings(self, new_embeddings): + self.mlm_score.decoder = new_embeddings + + @add_start_docstrings_to_model_forward(VILT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + pixel_values: Optional[torch.FloatTensor] = None, + pixel_mask: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + image_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[MaskedLMOutput, Tuple[torch.FloatTensor]]: + r""" + labels (*torch.LongTensor* of shape *(batch_size, sequence_length)*, *optional*): + Labels for computing the masked language modeling loss. Indices should be in *[-100, 0, ..., + config.vocab_size]* (see *input_ids* docstring) Tokens with indices set to *-100* are ignored (masked), the + loss is only computed for the tokens with labels in *[0, ..., config.vocab_size]* + + Returns: + + Examples: + + ```python + >>> from transformers import ViltProcessor, ViltForMaskedLM + >>> import requests + >>> from PIL import Image + >>> import re + >>> import torch + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + >>> text = "a bunch of [MASK] laying on a [MASK]." + + >>> processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-mlm") + >>> model = ViltForMaskedLM.from_pretrained("dandelin/vilt-b32-mlm") + + >>> # prepare inputs + >>> encoding = processor(image, text, return_tensors="pt") + + >>> # forward pass + >>> outputs = model(**encoding) + + >>> tl = len(re.findall("\[MASK\]", text)) + >>> inferred_token = [text] + + >>> # gradually fill in the MASK tokens, one by one + >>> with torch.no_grad(): + ... for i in range(tl): + ... encoded = processor.tokenizer(inferred_token) + ... input_ids = torch.tensor(encoded.input_ids) + ... encoded = encoded["input_ids"][0][1:-1] + ... outputs = model(input_ids=input_ids, pixel_values=encoding.pixel_values) + ... mlm_logits = outputs.logits[0] # shape (seq_len, vocab_size) + ... # only take into account text features (minus CLS and SEP token) + ... mlm_logits = mlm_logits[1 : input_ids.shape[1] - 1, :] + ... mlm_values, mlm_ids = mlm_logits.softmax(dim=-1).max(dim=-1) + ... # only take into account text + ... mlm_values[torch.tensor(encoded) != 103] = 0 + ... select = mlm_values.argmax().item() + ... encoded[select] = mlm_ids[select].item() + ... inferred_token = [processor.decode(encoded)] + + >>> selected_token = "" + >>> encoded = processor.tokenizer(inferred_token) + >>> output = processor.decode(encoded.input_ids[0], skip_special_tokens=True) + >>> print(output) + a bunch of cats laying on a couch. + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.vilt( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + pixel_values=pixel_values, + pixel_mask=pixel_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + image_embeds=image_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output, pooled_output = outputs[:2] + # split up final hidden states into text and image features + text_seq_len = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] + text_features, _ = (sequence_output[:, :text_seq_len], sequence_output[:, text_seq_len:]) + + mlm_logits = self.mlm_score(text_features) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() # -100 index = padding token + # move labels to correct device to enable PP + labels = labels.to(mlm_logits.device) + masked_lm_loss = loss_fct(mlm_logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (mlm_logits,) + outputs[2:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=mlm_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class ViltPredictionHeadTransform(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if isinstance(config.hidden_act, str): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +class ViltMLMHead(nn.Module): + def __init__(self, config, weight=None): + super().__init__() + self.config = config + self.transform = ViltPredictionHeadTransform(config) + self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + if weight is not None: + self.decoder.weight = weight + + # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` + self.decoder.bias = self.bias + + def forward(self, x): + x = self.transform(x) + x = self.decoder(x) + return x + + +@add_start_docstrings( + """ + Vilt Model transformer with a classifier head on top (a linear layer on top of the final hidden state of the [CLS] + token) for visual question answering, e.g. for VQAv2. + """, + VILT_START_DOCSTRING, +) +class ViltForQuestionAnswering(ViltPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.num_labels = config.num_labels + self.vilt = ViltModel(config) + + # Classifier head + self.classifier = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size * 2), + nn.LayerNorm(config.hidden_size * 2), + nn.GELU(), + nn.Linear(config.hidden_size * 2, config.num_labels), + ) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(VILT_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + pixel_values: Optional[torch.FloatTensor] = None, + pixel_mask: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + image_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[SequenceClassifierOutput, Tuple[torch.FloatTensor]]: + r""" + labels (`torch.FloatTensor` of shape `(batch_size, num_labels)`, *optional*): + Labels for computing the visual question answering loss. This tensor must be either a one-hot encoding of + all answers that are applicable for a given example in the batch, or a soft encoding indicating which + answers are applicable, where 1.0 is the highest score. + + Returns: + + Examples: + + ```python + >>> from transformers import ViltProcessor, ViltForQuestionAnswering + >>> import requests + >>> from PIL import Image + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + >>> text = "How many cats are there?" + + >>> processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa") + >>> model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa") + + >>> # prepare inputs + >>> encoding = processor(image, text, return_tensors="pt") + + >>> # forward pass + >>> outputs = model(**encoding) + >>> logits = outputs.logits + >>> idx = logits.argmax(-1).item() + >>> print("Predicted answer:", model.config.id2label[idx]) + Predicted answer: 2 + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.vilt( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + pixel_values=pixel_values, + pixel_mask=pixel_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + image_embeds=image_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + pooler_output = outputs.pooler_output if return_dict else outputs[1] + + logits = self.classifier(pooler_output) + + loss = None + if labels is not None: + # move labels to correct device to enable PP + labels = labels.to(logits.device) + loss = nn.functional.binary_cross_entropy_with_logits(logits, labels) * labels.shape[1] + # see https://github.com/jnhwkim/ban-vqa/blob/master/train.py#L19 + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + """ + Vilt Model transformer with a classifier head on top (a linear layer on top of the final hidden state of the [CLS] + token) for image-to-text or text-to-image retrieval, e.g. MSCOCO and F30K. + """, + VILT_START_DOCSTRING, +) +class ViltForImageAndTextRetrieval(ViltPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.vilt = ViltModel(config) + + # Classifier head + self.rank_output = nn.Linear(config.hidden_size, 1) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(VILT_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + pixel_values: Optional[torch.FloatTensor] = None, + pixel_mask: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + image_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[SequenceClassifierOutput, Tuple[torch.FloatTensor]]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels are currently not supported. + + Returns: + + Examples: + + ```python + >>> from transformers import ViltProcessor, ViltForImageAndTextRetrieval + >>> import requests + >>> from PIL import Image + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + >>> texts = ["An image of two cats chilling on a couch", "A football player scoring a goal"] + + >>> processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-coco") + >>> model = ViltForImageAndTextRetrieval.from_pretrained("dandelin/vilt-b32-finetuned-coco") + + >>> # forward pass + >>> scores = dict() + >>> for text in texts: + ... # prepare inputs + ... encoding = processor(image, text, return_tensors="pt") + ... outputs = model(**encoding) + ... scores[text] = outputs.logits[0, :].item() + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.vilt( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + pixel_values=pixel_values, + pixel_mask=pixel_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + image_embeds=image_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + pooler_output = outputs.pooler_output if return_dict else outputs[1] + + logits = self.rank_output(pooler_output) + + loss = None + if labels is not None: + # move labels to correct device to enable PP + labels = labels.to(logits.device) + raise NotImplementedError("Training is not yet supported.") + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + """ + Vilt Model transformer with a classifier head on top for natural language visual reasoning, e.g. NLVR2. + """, + VILT_IMAGES_AND_TEXT_CLASSIFICATION_INPUTS_DOCSTRING, +) +class ViltForImagesAndTextClassification(ViltPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.num_labels = config.num_labels + self.vilt = ViltModel(config) + + # Classifier head + num_images = config.num_images + self.classifier = nn.Sequential( + nn.Linear(config.hidden_size * num_images, config.hidden_size * num_images), + nn.LayerNorm(config.hidden_size * num_images), + nn.GELU(), + nn.Linear(config.hidden_size * num_images, config.num_labels), + ) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(VILT_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=ViltForImagesAndTextClassificationOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + pixel_values: Optional[torch.FloatTensor] = None, + pixel_mask: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + image_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[ViltForImagesAndTextClassificationOutput, Tuple[torch.FloatTensor]]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Binary classification labels. + + Returns: + + Examples: + + ```python + >>> from transformers import ViltProcessor, ViltForImagesAndTextClassification + >>> import requests + >>> from PIL import Image + + >>> image1 = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg", stream=True).raw) + >>> image2 = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_1.jpg", stream=True).raw) + >>> text = "The left image contains twice the number of dogs as the right image." + + >>> processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-nlvr2") + >>> model = ViltForImagesAndTextClassification.from_pretrained("dandelin/vilt-b32-finetuned-nlvr2") + + >>> # prepare inputs + >>> encoding = processor([image1, image2], text, return_tensors="pt") + + >>> # forward pass + >>> outputs = model(input_ids=encoding.input_ids, pixel_values=encoding.pixel_values.unsqueeze(0)) + >>> logits = outputs.logits + >>> idx = logits.argmax(-1).item() + >>> print("Predicted answer:", model.config.id2label[idx]) + Predicted answer: True + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if pixel_values is not None and pixel_values.ndim == 4: + # add dummy num_images dimension + pixel_values = pixel_values.unsqueeze(1) + + if image_embeds is not None and image_embeds.ndim == 3: + # add dummy num_images dimension + image_embeds = image_embeds.unsqueeze(1) + + num_images = pixel_values.shape[1] if pixel_values is not None else None + if num_images is None: + num_images = image_embeds.shape[1] if image_embeds is not None else None + if num_images != self.config.num_images: + raise ValueError( + "Make sure to match the number of images in the model with the number of images in the input." + ) + pooler_outputs = [] + hidden_states = [] if output_hidden_states else None + attentions = [] if output_attentions else None + for i in range(num_images): + # forward every image through the model + outputs = self.vilt( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + pixel_values=pixel_values[:, i, :, :, :] if pixel_values is not None else None, + pixel_mask=pixel_mask[:, i, :, :] if pixel_mask is not None else None, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + image_embeds=image_embeds[:, i, :, :] if image_embeds is not None else None, + image_token_type_idx=i + 1, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + pooler_output = outputs.pooler_output if return_dict else outputs[1] + pooler_outputs.append(pooler_output) + if output_hidden_states: + hidden_states.append(outputs.hidden_states) + if output_attentions: + attentions.append(outputs.attentions) + + pooled_output = torch.cat(pooler_outputs, dim=-1) + logits = self.classifier(pooled_output) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + # move labels to correct device to enable PP + labels = labels.to(logits.device) + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + if not return_dict: + output = (logits, hidden_states, attentions) + return ((loss,) + output) if loss is not None else output + + return ViltForImagesAndTextClassificationOutput( + loss=loss, + logits=logits, + hidden_states=hidden_states, + attentions=attentions, + ) + + +@add_start_docstrings( + """ + ViLT Model with a token classification head on top (a linear layer on top of the final hidden-states of the text + tokens) e.g. for Named-Entity-Recognition (NER) tasks. + """, + VILT_START_DOCSTRING, +) +class ViltForTokenClassification(ViltPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.num_labels = config.num_labels + self.vilt = ViltModel(config, add_pooling_layer=False) + + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(VILT_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + pixel_values: Optional[torch.FloatTensor] = None, + pixel_mask: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + image_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[TokenClassifierOutput, Tuple[torch.FloatTensor]]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, text_sequence_length)`, *optional*): + Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. + + Returns: + """ + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.vilt( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + pixel_values=pixel_values, + pixel_mask=pixel_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + image_embeds=image_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + text_input_size = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] + + sequence_output = self.dropout(sequence_output) + logits = self.classifier(sequence_output[:, :text_input_size]) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + # move labels to correct device to enable PP + labels = labels.to(logits.device) + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vilt/processing_vilt.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vilt/processing_vilt.py new file mode 100644 index 0000000000000000000000000000000000000000..0ccb884ea00c9d1b9df3322281083ddf166e5dc9 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vilt/processing_vilt.py @@ -0,0 +1,148 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Processor class for ViLT. +""" + +import warnings +from typing import List, Optional, Union + +from ...processing_utils import ProcessorMixin +from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy +from ...utils import TensorType + + +class ViltProcessor(ProcessorMixin): + r""" + Constructs a ViLT processor which wraps a BERT tokenizer and ViLT image processor into a single processor. + + [`ViltProcessor`] offers all the functionalities of [`ViltImageProcessor`] and [`BertTokenizerFast`]. See the + docstring of [`~ViltProcessor.__call__`] and [`~ViltProcessor.decode`] for more information. + + Args: + image_processor (`ViltImageProcessor`, *optional*): + An instance of [`ViltImageProcessor`]. The image processor is a required input. + tokenizer (`BertTokenizerFast`, *optional*): + An instance of ['BertTokenizerFast`]. The tokenizer is a required input. + """ + + attributes = ["image_processor", "tokenizer"] + image_processor_class = "ViltImageProcessor" + tokenizer_class = ("BertTokenizer", "BertTokenizerFast") + + def __init__(self, image_processor=None, tokenizer=None, **kwargs): + feature_extractor = None + if "feature_extractor" in kwargs: + warnings.warn( + "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`" + " instead.", + FutureWarning, + ) + feature_extractor = kwargs.pop("feature_extractor") + + image_processor = image_processor if image_processor is not None else feature_extractor + if image_processor is None: + raise ValueError("You need to specify an `image_processor`.") + if tokenizer is None: + raise ValueError("You need to specify a `tokenizer`.") + + super().__init__(image_processor, tokenizer) + self.current_processor = self.image_processor + + def __call__( + self, + images, + text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, + add_special_tokens: bool = True, + padding: Union[bool, str, PaddingStrategy] = False, + truncation: Union[bool, str, TruncationStrategy] = None, + max_length: Optional[int] = None, + stride: int = 0, + pad_to_multiple_of: Optional[int] = None, + return_token_type_ids: Optional[bool] = None, + return_attention_mask: Optional[bool] = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_offsets_mapping: bool = False, + return_length: bool = False, + verbose: bool = True, + return_tensors: Optional[Union[str, TensorType]] = None, + **kwargs, + ) -> BatchEncoding: + """ + This method uses [`ViltImageProcessor.__call__`] method to prepare image(s) for the model, and + [`BertTokenizerFast.__call__`] to prepare text for the model. + + Please refer to the docstring of the above two methods for more information. + """ + encoding = self.tokenizer( + text=text, + add_special_tokens=add_special_tokens, + padding=padding, + truncation=truncation, + max_length=max_length, + stride=stride, + pad_to_multiple_of=pad_to_multiple_of, + return_token_type_ids=return_token_type_ids, + return_attention_mask=return_attention_mask, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_offsets_mapping=return_offsets_mapping, + return_length=return_length, + verbose=verbose, + return_tensors=return_tensors, + **kwargs, + ) + # add pixel_values + pixel_mask + encoding_image_processor = self.image_processor(images, return_tensors=return_tensors) + encoding.update(encoding_image_processor) + + return encoding + + def batch_decode(self, *args, **kwargs): + """ + This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please + refer to the docstring of this method for more information. + """ + return self.tokenizer.batch_decode(*args, **kwargs) + + def decode(self, *args, **kwargs): + """ + This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to + the docstring of this method for more information. + """ + return self.tokenizer.decode(*args, **kwargs) + + @property + def model_input_names(self): + tokenizer_input_names = self.tokenizer.model_input_names + image_processor_input_names = self.image_processor.model_input_names + return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) + + @property + def feature_extractor_class(self): + warnings.warn( + "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.", + FutureWarning, + ) + return self.image_processor_class + + @property + def feature_extractor(self): + warnings.warn( + "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.", + FutureWarning, + ) + return self.image_processor diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2853605ba2d275073624c1b189ee5247eba695ca --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/__init__.py @@ -0,0 +1,54 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available + + +_import_structure = {"configuration_vipllava": ["VIPLLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP", "VipLlavaConfig"]} + + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_vipllava"] = [ + "VIPLLAVA_PRETRAINED_MODEL_ARCHIVE_LIST", + "VipLlavaForConditionalGeneration", + "VipLlavaPreTrainedModel", + ] + + +if TYPE_CHECKING: + from .configuration_vipllava import VIPLLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP, VipLlavaConfig + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_vipllava import ( + VIPLLAVA_PRETRAINED_MODEL_ARCHIVE_LIST, + VipLlavaForConditionalGeneration, + VipLlavaPreTrainedModel, + ) + + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f7ec953356f6d7d555732d71a1eca92fc21ea666 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/__pycache__/configuration_vipllava.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/__pycache__/configuration_vipllava.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a56af688d6926aa3c2c616f8da7c2b06fca072ed Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/__pycache__/configuration_vipllava.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/__pycache__/convert_vipllava_weights_to_hf.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/__pycache__/convert_vipllava_weights_to_hf.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..87840d86b7240ac1602c73b5ce87399165cee862 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/__pycache__/convert_vipllava_weights_to_hf.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/__pycache__/modeling_vipllava.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/__pycache__/modeling_vipllava.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2c00bfbad814e239f5db35f2cd64005a76775163 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/__pycache__/modeling_vipllava.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/configuration_vipllava.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/configuration_vipllava.py new file mode 100644 index 0000000000000000000000000000000000000000..d57f4179492ea20996ec5d340f53b870273a14c2 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/configuration_vipllava.py @@ -0,0 +1,144 @@ +# coding=utf-8 +# Copyright 2023 Microsoft Research & University of Wisconsin-Madison and the HuggingFace Inc. team. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" VipLlava model configuration""" + +import warnings + +from ...configuration_utils import PretrainedConfig +from ...utils import logging +from ..auto import CONFIG_MAPPING + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import VIPLLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class VipLlavaConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`VipLlavaForConditionalGeneration`]. It is used to instantiate an + VipLlava model according to the specified arguments, defining the model architecture. Instantiating a configuration + with the defaults will yield a similar configuration to that of the VipLlava-9B. + + e.g. [ybelkada/vip-llava-7b-hf](https://huggingface.co/ybelkada/vip-llava-7b-hf) + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + vision_config (`VipLlavaVisionConfig`, *optional*): + Custom vision config or dict + text_config (`Union[AutoConfig, dict]`, *optional*): + The config object of the text backbone. Can be any of `LlamaConfig` or `MistralConfig`. + ignore_index (`int`, *optional*, defaults to -100): + The ignore index for the loss function. + image_token_index (`int`, *optional*, defaults to 32000): + The image token index to encode the image prompt. + projector_hidden_act (`str`, *optional*, defaults to `"gelu"`): + The activation function used by the multimodal projector. + projector_layernorm_eps (`float`, *optional*, defaults to 1e-05): + The layer norm epsilon of the projector layernorm + vision_feature_layers (`List[int]`, *optional*, defaults to `[-2, -5, -8, -11, 6]`): + The list of layers to select the vision features from. + + Example: + + ```python + >>> from transformers import VipLlavaForConditionalGeneration, VipLlavaConfig, CLIPVisionConfig, LlamaConfig + + >>> # Initializing a CLIP-vision config + >>> vision_config = CLIPVisionConfig() + + >>> # Initializing a Llama config + >>> text_config = LlamaConfig() + + >>> # Initializing a VipLlava vipllava-7b style configuration + >>> configuration = VipLlavaConfig(vision_config, text_config) + + >>> # Initializing a model from the vipllava-7b style configuration + >>> model = VipLlavaForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "vipllava" + is_composition = False + + def __init__( + self, + vision_config=None, + text_config=None, + ignore_index=-100, + image_token_index=32000, + projector_hidden_act="gelu", + projector_layernorm_eps=1e-5, + vision_feature_layers=[-2, -5, -8, -11, 6], + **kwargs, + ): + self.ignore_index = ignore_index + self.image_token_index = image_token_index + self.projector_hidden_act = projector_hidden_act + self.projector_layernorm_eps = projector_layernorm_eps + self.vision_feature_layers = vision_feature_layers + + if "vocab_size" in kwargs: + warnings.warn( + "The `vocab_size` argument is deprecated and will be removed in v4.42, since it can be inferred from the `text_config`. Passing this argument has no effect", + FutureWarning, + ) + + self.vision_config = vision_config + + if isinstance(self.vision_config, dict): + vision_config["model_type"] = ( + vision_config["model_type"] if "model_type" in vision_config else "clip_vision_model" + ) + self.vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config) + elif vision_config is None: + self.vision_config = CONFIG_MAPPING["clip_vision_model"]( + intermediate_size=4096, + hidden_size=1024, + patch_size=14, + image_size=336, + num_hidden_layers=24, + num_attention_heads=16, + vocab_size=32000, + projection_dim=768, + ) + + if isinstance(text_config, dict): + text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "llama" + text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config) + elif text_config is None: + text_config = CONFIG_MAPPING["llama"]() + + self.text_config = text_config + self._vocab_size = self.text_config.vocab_size + + super().__init__(**kwargs) + + @property + def vocab_size(self): + warnings.warn( + "The `vocab_size` attribute is deprecated and will be removed in v4.42, Please use `text_config.vocab_size` instead.", + FutureWarning, + ) + return self._vocab_size + + def to_dict(self): + output = super().to_dict() + output.pop("_vocab_size", None) + return output diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/convert_vipllava_weights_to_hf.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/convert_vipllava_weights_to_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2914cfdfcd4b421c5c020852364034037fcc9558 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/convert_vipllava_weights_to_hf.py @@ -0,0 +1,132 @@ +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse + +import torch +from huggingface_hub import hf_hub_download + +from transformers import ( + AddedToken, + AutoConfig, + AutoTokenizer, + CLIPImageProcessor, + LlavaProcessor, + VipLlavaConfig, + VipLlavaForConditionalGeneration, +) + + +KEYS_TO_MODIFY_MAPPING = { + "model.vision_tower.": "", + "model.mm_projector": "multi_modal_projector", + "model": "model.model", + "vision_model.model": "vision_model", + "lm_head": "language_model.lm_head", + "model.model": "language_model.model", + "multi_modal_projector.0": "multi_modal_projector.linear_1", + "multi_modal_projector.2": "multi_modal_projector.linear_2", + "final_linear.0": "linear_1", + "final_linear.2": "linear_2", + "multi_modal_projector.clip_layernorm": "multi_modal_projector.projector_layernorm", +} + + +# Copied from transformers.models.llava.convert_llava_weights_to_hf.convert_state_dict_to_hf +def convert_state_dict_to_hf(state_dict): + new_state_dict = {} + for key, value in state_dict.items(): + if key.endswith(".inv_freq"): + continue + for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items(): + if key_to_modify in key: + key = key.replace(key_to_modify, new_key) + new_state_dict[key] = value + return new_state_dict + + +def convert_vipllava_llama_to_hf(text_model_id, vision_model_id, output_hub_path, old_state_dict_id): + torch.set_default_dtype(torch.float16) + text_config = AutoConfig.from_pretrained(text_model_id) + + tokenizer = AutoTokenizer.from_pretrained(text_model_id) + tokenizer.add_tokens(AddedToken("", special=True, normalized=False), special_tokens=True) + tokenizer.add_special_tokens({"pad_token": ""}) + + image_processor = CLIPImageProcessor.from_pretrained(vision_model_id) + + processor = LlavaProcessor(tokenizer=tokenizer, image_processor=image_processor) + + config = VipLlavaConfig(text_config=text_config) + config.pad_token_id = 32001 + + with torch.device("meta"): + model = VipLlavaForConditionalGeneration(config) + + # Pad to 64 for performance reasons + pad_shape = 64 + + state_dict_path = hf_hub_download(old_state_dict_id, "model_state_dict_7b.bin") + + state_dict = torch.load(state_dict_path, map_location="cpu") + state_dict = convert_state_dict_to_hf(state_dict) + + model.load_state_dict(state_dict, strict=True, assign=True) + + pre_expansion_embeddings = model.language_model.model.embed_tokens.weight.data + mu = torch.mean(pre_expansion_embeddings, dim=0).float() + n = pre_expansion_embeddings.size()[0] + sigma = ((pre_expansion_embeddings - mu).T @ (pre_expansion_embeddings - mu)) / n + dist = torch.distributions.multivariate_normal.MultivariateNormal(mu, covariance_matrix=1e-5 * sigma) + + # We add an image token so we resize the model + model.resize_token_embeddings(config.text_config.vocab_size + 2, pad_shape) + model.language_model.model.embed_tokens.weight.data[32000:] = torch.stack( + tuple((dist.sample() for _ in range(model.language_model.model.embed_tokens.weight.data[32000:].shape[0]))), + dim=0, + ) + model.language_model.lm_head.weight.data[32000:] = torch.stack( + tuple((dist.sample() for _ in range(model.language_model.lm_head.weight.data[32000:].shape[0]))), + dim=0, + ) + + model.push_to_hub(output_hub_path) + processor.push_to_hub(output_hub_path) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--text_model_id", + help="Hub location of the text model", + ) + parser.add_argument( + "--vision_model_id", + help="Hub location of the vision model", + ) + parser.add_argument( + "--output_hub_path", + help="Location on the hub of the converted model", + ) + parser.add_argument( + "--old_state_dict_id", + help="Location on the hub of the raw state dict of the original model. The filename needs to be `model_state_dict.bin`", + ) + args = parser.parse_args() + convert_vipllava_llama_to_hf( + args.text_model_id, args.vision_model_id, args.output_hub_path, args.old_state_dict_id + ) + + +if __name__ == "__main__": + main() diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/modeling_vipllava.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/modeling_vipllava.py new file mode 100644 index 0000000000000000000000000000000000000000..1b20353410c895d64a859e26f0cd838a45141c26 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vipllava/modeling_vipllava.py @@ -0,0 +1,566 @@ +# coding=utf-8 +# Copyright 2023 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch VipLlava model.""" +from dataclasses import dataclass +from typing import List, Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn + +from ... import PreTrainedModel +from ...activations import ACT2FN +from ...cache_utils import Cache +from ...modeling_outputs import ModelOutput +from ...utils import ( + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) +from ..auto import AutoModel, AutoModelForCausalLM +from .configuration_vipllava import VipLlavaConfig + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "VipLlavaConfig" + + +from ..deprecated._archive_maps import VIPLLAVA_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +@dataclass +# Copied from transformers.models.idefics.modeling_idefics.IdeficsCausalLMOutputWithPast with Idefics->VipLlava +class VipLlavaCausalLMOutputWithPast(ModelOutput): + """ + Base class for VipLlava causal language model (or autoregressive) outputs. + + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + image_hidden_states (`tuple(torch.FloatTensor)`, *optional*): + Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images, + sequence_length, hidden_size)`. + + image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver + """ + + loss: Optional[torch.FloatTensor] = None + logits: torch.FloatTensor = None + past_key_values: Optional[List[torch.FloatTensor]] = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None + + +class VipLlavaMultiModalProjector(nn.Module): + def __init__(self, config: VipLlavaConfig): + super().__init__() + self.projector_layernorm = nn.LayerNorm( + len(config.vision_feature_layers) * config.vision_config.hidden_size, eps=config.projector_layernorm_eps + ) + + self.linear_1 = nn.Linear( + len(config.vision_feature_layers) * config.vision_config.hidden_size, + config.text_config.hidden_size, + bias=True, + ) + self.act = ACT2FN[config.projector_hidden_act] + self.linear_2 = nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size, bias=True) + + def forward(self, hidden_states): + hidden_states = self.projector_layernorm(hidden_states) + hidden_states = self.linear_1(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.linear_2(hidden_states) + return hidden_states + + +VIPLLAVA_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`VipLlavaConfig`] or [`VipLlavaVisionConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare VipLlava Model outputting raw hidden-states without any specific head on top.", + VIPLLAVA_START_DOCSTRING, +) +# Copied from transformers.models.llava.modeling_llava.LlavaPreTrainedModel with Llava->VipLlava,llava->vipllava +class VipLlavaPreTrainedModel(PreTrainedModel): + config_class = VipLlavaConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["VipLlavaVisionAttention"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn_2 = True + + def _init_weights(self, module): + # important: this ported version of VipLlava isn't meant for training from scratch - only + # inference and fine-tuning - so the proper init weights code has been removed - the original codebase + # https://github.com/haotian-liu/LLaVA/tree/main/vipllava should serve for that purpose + std = ( + self.config.initializer_range + if hasattr(self.config, "initializer_range") + else self.config.text_config.initializer_range + ) + + if hasattr(module, "class_embedding"): + module.class_embedding.data.normal_(mean=0.0, std=std) + + if isinstance(module, (nn.Linear, nn.Conv2d)): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + @property + def _supports_sdpa(self): + """ + Retrieve language_model's attribute to check whether the model supports + SDPA or not. + """ + return self.language_model._supports_sdpa + + +VIPLLAVA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)): + The tensors corresponding to the input images. Pixel values can be obtained using + [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details ([]`LlavaProcessor`] uses + [`CLIPImageProcessor`] for processing images). + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape + `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + """The VIPLLAVA model which consists of a vision backbone and a language model.""", + VIPLLAVA_START_DOCSTRING, +) +# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration with LLAVA->VIPLLAVA,Llava->VipLlava +class VipLlavaForConditionalGeneration(VipLlavaPreTrainedModel): + def __init__(self, config: VipLlavaConfig): + super().__init__(config) + self.vision_tower = AutoModel.from_config(config.vision_config) + + self.multi_modal_projector = VipLlavaMultiModalProjector(config) + self.vocab_size = config.text_config.vocab_size + self.language_model = AutoModelForCausalLM.from_config( + config.text_config, attn_implementation=config._attn_implementation + ) + self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1 + self.post_init() + + def get_input_embeddings(self): + return self.language_model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.language_model.set_input_embeddings(value) + + def get_output_embeddings(self): + return self.language_model.get_output_embeddings() + + def set_output_embeddings(self, new_embeddings): + self.language_model.set_output_embeddings(new_embeddings) + + def set_decoder(self, decoder): + self.language_model.set_decoder(decoder) + + def get_decoder(self): + return self.language_model.get_decoder() + + def tie_weights(self): + return self.language_model.tie_weights() + + def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding: + model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of) + # update vocab size + self.config.text_config.vocab_size = model_embeds.num_embeddings + self.vocab_size = model_embeds.num_embeddings + return model_embeds + + def _merge_input_ids_with_image_features(self, image_features, inputs_embeds, input_ids, attention_mask, labels): + num_images, num_image_patches, embed_dim = image_features.shape + batch_size, sequence_length = input_ids.shape + left_padding = not torch.sum(input_ids[:, -1] == torch.tensor(self.pad_token_id)) + # 1. Create a mask to know where special image tokens are + special_image_token_mask = input_ids == self.config.image_token_index + num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1) + # Compute the maximum embed dimension + max_embed_dim = (num_special_image_tokens.max() * (num_image_patches - 1)) + sequence_length + batch_indices, non_image_indices = torch.where(input_ids != self.config.image_token_index) + + # 2. Compute the positions where text should be written + # Calculate new positions for text tokens in merged image-text sequence. + # `special_image_token_mask` identifies image tokens. Each image token will be replaced by `nb_text_tokens_per_images - 1` text tokens. + # `torch.cumsum` computes how each image token shifts subsequent text token positions. + # - 1 to adjust for zero-based indexing, as `cumsum` inherently increases indices by one. + new_token_positions = torch.cumsum((special_image_token_mask * (num_image_patches - 1) + 1), -1) - 1 + nb_image_pad = max_embed_dim - 1 - new_token_positions[:, -1] + if left_padding: + new_token_positions += nb_image_pad[:, None] # offset for left padding + text_to_overwrite = new_token_positions[batch_indices, non_image_indices] + + # 3. Create the full embedding, already padded to the maximum position + final_embedding = torch.zeros( + batch_size, max_embed_dim, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device + ) + final_attention_mask = torch.zeros( + batch_size, max_embed_dim, dtype=attention_mask.dtype, device=inputs_embeds.device + ) + if labels is not None: + final_labels = torch.full( + (batch_size, max_embed_dim), self.config.ignore_index, dtype=input_ids.dtype, device=input_ids.device + ) + # In case the Vision model or the Language model has been offloaded to CPU, we need to manually + # set the corresponding tensors into their correct target device. + target_device = inputs_embeds.device + batch_indices, non_image_indices, text_to_overwrite = ( + batch_indices.to(target_device), + non_image_indices.to(target_device), + text_to_overwrite.to(target_device), + ) + attention_mask = attention_mask.to(target_device) + + # 4. Fill the embeddings based on the mask. If we have ["hey" "", "how", "are"] + # we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features + final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_image_indices] + final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_image_indices] + if labels is not None: + final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_image_indices] + + # 5. Fill the embeddings corresponding to the images. Anything that is still zeros needs filling + image_to_overwrite = torch.all(final_embedding == 0, dim=-1) + image_to_overwrite &= image_to_overwrite.cumsum(-1) - 1 >= nb_image_pad[:, None].to(target_device) + + if image_to_overwrite.sum() != image_features.shape[:-1].numel(): + raise ValueError( + f"The input provided to the model are wrong. The number of image tokens is {torch.sum(special_image_token_mask)} while" + f" the number of image given to the model is {num_images}. This prevents correct indexing and breaks batch generation." + ) + + final_embedding[image_to_overwrite] = image_features.contiguous().reshape(-1, embed_dim).to(target_device) + final_attention_mask |= image_to_overwrite + position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1) + + # 6. Mask out the embedding at padding positions, as we later use the past_key_value value to determine the non-attended tokens. + batch_indices, pad_indices = torch.where(input_ids == self.pad_token_id) + indices_to_mask = new_token_positions[batch_indices, pad_indices] + + final_embedding[batch_indices, indices_to_mask] = 0 + + if labels is None: + final_labels = None + + return final_embedding, final_attention_mask, final_labels, position_ids + + @add_start_docstrings_to_model_forward(VIPLLAVA_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=VipLlavaCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + # Ignore copy + def forward( + self, + input_ids: torch.LongTensor = None, + pixel_values: torch.FloatTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + vision_feature_layers: Optional[List[int]] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, VipLlavaCausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> import torch + >>> from PIL import Image + >>> import requests + >>> from transformers import AutoProcessor, VipLlavaForConditionalGeneration + + >>> model = VipLlavaForConditionalGeneration.from_pretrained("llava-hf/vip-llava-7b-hf", device_map="auto", torch_dtype=torch.float16) + >>> processor = AutoProcessor.from_pretrained("llava-hf/vip-llava-7b-hf") + + >>> prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.###Human: \n{}###Assistant:" + >>> question = "Can you please describe this image?" + >>> prompt = prompt.format(question) + >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/compel-neg.png" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> inputs = processor(text=text, images=image, return_tensors="pt").to(0, torch.float16) + + >>> # Generate + >>> generate_ids = model.generate(**inputs, max_new_tokens=20) + >>> processor.decode(generate_ids[0][len(inputs["input_ids"][0]):], skip_special_tokens=True) + The image features a brown and white cat sitting on a green surface, with a red ball in its + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + vision_feature_layers = ( + vision_feature_layers if vision_feature_layers is not None else self.config.vision_feature_layers + ) + + if inputs_embeds is None: + # 1. Extra the input embeddings + inputs_embeds = self.get_input_embeddings()(input_ids) + + # 2. Merge text and images + if pixel_values is not None and input_ids.shape[1] != 1: + image_outputs = self.vision_tower(pixel_values, output_hidden_states=True) + # For VIP-llava, the image features are computed this way + # We select the features from index 1: for the layers -2, -5, -8, -11 and 6 + image_features = [image_outputs.hidden_states[index][:, 1:] for index in vision_feature_layers] + image_features = torch.cat(image_features, dim=-1) + + image_features = self.multi_modal_projector(image_features) + inputs_embeds, attention_mask, labels, position_ids = self._merge_input_ids_with_image_features( + image_features, inputs_embeds, input_ids, attention_mask, labels + ) + if labels is None: + labels = torch.full_like(attention_mask, self.config.ignore_index).to(torch.long) + else: + # In case input_ids.shape[1] == 1 & pixel_values==None & past_key_values != None, we are in the case of + # generation with cache + if past_key_values is not None and pixel_values is not None and input_ids.shape[1] == 1: + # Retrieve the first layer to inspect the logits and mask out the hidden states + # that are set to 0 + first_layer_past_key_value = past_key_values[0][0][:, :, :, 0] + + # Sum all dimensions of head_dim (-1) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941 + batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0) + + target_length = input_ids.shape[1] + past_length = first_layer_past_key_value.shape[-1] + + extended_attention_mask = torch.ones( + (attention_mask.shape[0], past_length), + dtype=attention_mask.dtype, + device=attention_mask.device, + ) + + # Filter out only the tokens that can be un-attended, this can happen + # in the case one uses Llava + Fused modules where the cache on the + # first iteration is already big enough, or if one passes custom cache + valid_indices = non_attended_tokens < extended_attention_mask.size(-1) + new_batch_index = batch_index[valid_indices] + new_non_attended_tokens = non_attended_tokens[valid_indices] + + # Zero-out the places where we don't need to attend + extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0 + + attention_mask = torch.cat((extended_attention_mask, attention_mask[:, -target_length:]), dim=1) + position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1 + + outputs = self.language_model( + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + logits = outputs[0] + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + if attention_mask is not None: + shift_attention_mask = attention_mask[..., 1:] + shift_logits = logits[..., :-1, :][shift_attention_mask.to(logits.device) != 0].contiguous() + shift_labels = labels[..., 1:][shift_attention_mask.to(labels.device) != 0].contiguous() + else: + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct( + shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device) + ) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return VipLlavaCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, inputs_embeds=None, pixel_values=None, attention_mask=None, **kwargs + ): + if past_key_values is not None: + if isinstance(past_key_values, Cache): + cache_length = past_key_values.get_seq_length() + past_length = past_key_values.seen_tokens + else: + cache_length = past_length = past_key_values[0][0].shape[2] + + # Keep only the unprocessed tokens: + # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where + # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as + # input) + if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: + input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] + # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard + # input_ids based on the past_length. + elif past_length < input_ids.shape[1]: + input_ids = input_ids[:, past_length:] + # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. + elif self.config.image_token_index in input_ids: + input_ids = input_ids[:, input_ids.shape[1] - 1 :] + # If the cache has seen more tokens than it can hold, then the cache has a size limit. Let's discard the + # older attention values, as their corresponding values are not part of the input. + if cache_length < past_length and attention_mask is not None: + attention_mask = attention_mask[:, -(cache_length + input_ids.shape[1]) :] + + position_ids = kwargs.get("position_ids", None) + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -input_ids.shape[1] :] + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + "pixel_values": pixel_values, + } + ) + return model_inputs + + def _reorder_cache(self, *args, **kwargs): + return self.language_model._reorder_cache(*args, **kwargs) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_encoder_decoder/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_encoder_decoder/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b0fe3bdc82a9a53c444ad25e8f749451f85f839b --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_encoder_decoder/__init__.py @@ -0,0 +1,84 @@ +# Copyright 2021 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ...utils import ( + OptionalDependencyNotAvailable, + _LazyModule, + is_flax_available, + is_tf_available, + is_torch_available, +) + + +_import_structure = { + "configuration_vision_encoder_decoder": ["VisionEncoderDecoderConfig", "VisionEncoderDecoderOnnxConfig"] +} + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_vision_encoder_decoder"] = ["VisionEncoderDecoderModel"] + +try: + if not is_tf_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_tf_vision_encoder_decoder"] = ["TFVisionEncoderDecoderModel"] + +try: + if not is_flax_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_flax_vision_encoder_decoder"] = ["FlaxVisionEncoderDecoderModel"] + +if TYPE_CHECKING: + from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel + + try: + if not is_tf_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel + + try: + if not is_flax_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_encoder_decoder/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_encoder_decoder/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bcccb241179e2a497e96522b7b3ecf63ccbffebf Binary files /dev/null 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Copyright 2021 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Classes to support Vision-Encoder-Text-Decoder architectures""" + + +import os +from typing import Optional, Tuple, Union + +import flax.linen as nn +import jax +import jax.numpy as jnp +from flax.core.frozen_dict import FrozenDict, freeze, unfreeze +from flax.traverse_util import flatten_dict, unflatten_dict +from jax import lax +from jax.random import PRNGKey + +from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutputWithCrossAttentions, FlaxSeq2SeqLMOutput +from ...modeling_flax_utils import FlaxPreTrainedModel +from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings +from ..auto.configuration_auto import AutoConfig +from ..auto.modeling_flax_auto import FlaxAutoModel, FlaxAutoModelForCausalLM +from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "VisionEncoderDecoderConfig" + +VISION_ENCODER_DECODER_START_DOCSTRING = r""" + This class can be used to initialize an image-to-text-sequence model with any pretrained vision autoencoding model + as the encoder and any pretrained text autoregressive model as the decoder. The encoder is loaded via + [`~AutoModel.from_pretrained`] function and the decoder is loaded via [`~AutoModelForCausalLM.from_pretrained`] + function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream + generative task, like image captioning. + + The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation + tasks was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation + Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi + Zhou, Wei Li, Peter J. Liu. + + Additionally, in [TrOCR: Transformer-based Optical Character Recognition with Pre-trained + Models](https://arxiv.org/abs/2109.10282) it is shown how leveraging large pretrained vision models for optical + character recognition (OCR) yields a significant performance improvement. + + After such a Vision-Encoder-Text-Decoder model has been trained/fine-tuned, it can be saved/loaded just like any + other models (see the examples for more information). + + This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a Flax Linen + [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a + regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. + + Parameters: + config ([`VisionEncoderDecoderConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. + dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): + The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and + `jax.numpy.bfloat16` (on TPUs). + + This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If + specified all the computation will be performed with the given `dtype`. + + **Note that this only specifies the dtype of the computation and does not influence the dtype of model + parameters.** + + If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and + [`~FlaxPreTrainedModel.to_bf16`]. +""" + +VISION_ENCODER_DECODER_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using the vision model's image processor. For example, using + [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`] for details. + decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + decoder_position_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the + range `[0, config.decoder.max_position_embeddings - 1]`. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + If set to `True`, the model will return a [`~utils.FlaxSeq2SeqLMOutput`] instead of a plain tuple. +""" + +VISION_ENCODER_DECODER_ENCODE_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using the vision model's image processor. For example, using + [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`] for details. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + If set to `True`, the model will return a [`~utils.FlaxBaseModelOutput`] instead of a plain tuple. +""" + +VISION_ENCODER_DECODER_DECODE_INPUTS_DOCSTRING = r""" + Args: + decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + For sequence to sequence training, `decoder_input_ids` should be provided. If no `decoder_input_ids` is + provided, the model will create this tensor by shifting the `input_ids` to the right for denoising + pre-training. + encoder_outputs (`tuple(tuple(jnp.ndarray)`): + Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) + `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of + hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. + decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + decoder_position_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the + range `[0, config.decoder.max_position_embeddings - 1]`. + past_key_values (`Dict[str, jnp.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`): + Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast + auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + If set to `True`, the model will return a [`~utils.FlaxCausalLMOutputWithCrossAttentions`] instead of a + plain tuple. +""" + + +class FlaxVisionEncoderDecoderModule(nn.Module): + config: VisionEncoderDecoderConfig + dtype: jnp.dtype = jnp.float32 + + def setup(self): + encoder_config = self.config.encoder + decoder_config = self.config.decoder + + # Copied from `modeling_hybrid_clip.py` with modifications. + from ...models.auto.modeling_flax_auto import FLAX_MODEL_FOR_CAUSAL_LM_MAPPING, FLAX_MODEL_MAPPING + + encoder_module = FLAX_MODEL_MAPPING[encoder_config.__class__].module_class + decoder_module = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING[decoder_config.__class__].module_class + + self.encoder = encoder_module(encoder_config, dtype=self.dtype) + self.decoder = decoder_module(decoder_config, dtype=self.dtype) + + # encoder outputs might need to be projected to different dimension for decoder + if ( + self.encoder.config.hidden_size != self.decoder.config.hidden_size + and self.decoder.config.cross_attention_hidden_size is None + ): + self.enc_to_dec_proj = nn.Dense( + self.decoder.config.hidden_size, + kernel_init=jax.nn.initializers.normal(self.decoder.config.initializer_range), + dtype=self.dtype, + ) + else: + self.enc_to_dec_proj = None + + def _get_encoder_module(self): + return self.encoder + + def _get_projection_module(self): + return self.enc_to_dec_proj + + def _get_decoder_module(self): + return self.decoder + + def __call__( + self, + pixel_values, + decoder_input_ids, + decoder_attention_mask, + decoder_position_ids, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + deterministic: bool = True, + ): + encoder_outputs = self.encoder( + pixel_values=pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + deterministic=deterministic, + ) + + encoder_hidden_states = encoder_outputs[0] + + # optionally project encoder_hidden_states + if self.enc_to_dec_proj is not None: + encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states) + + # The advantage of explicitly setting this is TPU XLA compiler knows as soon as possible what shape this + # variable has and can better optimize. Also passing `None` can lead to some problems when jitting the model. + # In Flax/JAX, we only want to pass `None` for non-tensor function inputs. For all tensor function inputs, we + # should always pass a tensor and not `None`. + batch_size, sequence_length = encoder_hidden_states.shape[:2] + encoder_attention_mask = jnp.ones((batch_size, sequence_length)) + + decoder_outputs = self.decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + position_ids=decoder_position_ids, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + deterministic=deterministic, + ) + + if not return_dict: + return decoder_outputs + encoder_outputs + + return FlaxSeq2SeqLMOutput( + logits=decoder_outputs.logits, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + +@add_start_docstrings(VISION_ENCODER_DECODER_START_DOCSTRING) +class FlaxVisionEncoderDecoderModel(FlaxPreTrainedModel): + r""" + [`FlaxVisionEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture + with the module (flax.nn.Module) of one of the base vision model classes of the library as encoder module and + another one as decoder module when created with the :meth*~transformers.FlaxAutoModel.from_pretrained* class method + for the encoder and :meth*~transformers.FlaxAutoModelForCausalLM.from_pretrained* class method for the decoder. + """ + + config_class = VisionEncoderDecoderConfig + base_model_prefix = "vision_encoder_decoder" + main_input_name = "pixel_values" + module_class = FlaxVisionEncoderDecoderModule + + def __init__( + self, + config: VisionEncoderDecoderConfig, + input_shape: Optional[Tuple] = None, + seed: int = 0, + dtype: jnp.dtype = jnp.float32, + _do_init: bool = True, + **kwargs, + ): + if not _do_init: + raise ValueError( + "`FlaxVisionEncoderDecoderModel` cannot be created without initializing, `_do_init` must be `True`." + ) + + if input_shape is None: + num_channels = getattr(config.encoder, "num_channels", 3) + input_shape = ( + (1, config.encoder.image_size, config.encoder.image_size, num_channels), + (1, 1), + ) + + if config.decoder.cross_attention_hidden_size is not None: + if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size: + raise ValueError( + "If `cross_attention_hidden_size` is specified in the decoder's configuration, it has to be equal" + f" to the encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for" + f" `config.decoder.cross_attention_hidden_size` and {config.encoder.hidden_size} for" + " `config.encoder.hidden_size`." + ) + + module = self.module_class(config=config, dtype=dtype, **kwargs) + super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) + + def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: + encoder_input_shape, decoder_input_shape = input_shape + + # init input tensors + pixel_values = jnp.zeros(encoder_input_shape, dtype=self.dtype) + decoder_input_ids = jnp.zeros(decoder_input_shape, dtype="i4") + decoder_attention_mask = jnp.ones_like(decoder_input_ids) + + batch_size, _, _, _ = pixel_values.shape + decoder_batch_size, decoder_sequence_length = decoder_input_ids.shape + if not decoder_batch_size == batch_size: + raise ValueError( + f"The inputs of encoder and decoder should have the same batch size, but got {batch_size} for encoder " + f"and {decoder_batch_size} for decoder." + ) + decoder_position_ids = jnp.broadcast_to( + jnp.arange(decoder_sequence_length)[None, :], (decoder_batch_size, decoder_sequence_length) + ) + + params_rng, dropout_rng = jax.random.split(rng) + rngs = {"params": params_rng, "dropout": dropout_rng} + + random_params = self.module.init( + rngs, + pixel_values, + decoder_input_ids, + decoder_attention_mask, + decoder_position_ids, + )["params"] + + if params is not None: + random_params = flatten_dict(unfreeze(random_params)) + params = flatten_dict(unfreeze(params)) + for missing_key in self._missing_keys: + params[missing_key] = random_params[missing_key] + self._missing_keys = set() + return freeze(unflatten_dict(params)) + else: + return random_params + + def init_cache(self, batch_size, max_length, encoder_outputs): + r""" + Args: + batch_size (`int`): + batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. + max_length (`int`): + maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized + cache. + encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`): + `encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: + `attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) + is a sequence of hidden-states at the output of the last layer of the encoder. Used in the + cross-attention of the decoder. + """ + # init input variables to retrieve cache + decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4") + decoder_attention_mask = jnp.ones_like(decoder_input_ids) + decoder_position_ids = jnp.broadcast_to( + jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape + ) + + def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): + decoder_module = module._get_decoder_module() + return decoder_module( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + position_ids=decoder_position_ids, + **kwargs, + ) + + init_variables = self.module.init( + jax.random.PRNGKey(0), + decoder_input_ids=decoder_input_ids, + decoder_attention_mask=decoder_attention_mask, + decoder_position_ids=decoder_position_ids, + encoder_hidden_states=encoder_outputs[0], + init_cache=True, + method=_decoder_forward, # we only need to call the decoder to init the cache + ) + return unfreeze(init_variables["cache"]) + + @add_start_docstrings(VISION_ENCODER_DECODER_ENCODE_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=_CONFIG_FOR_DOC) + def encode( + self, + pixel_values: jnp.ndarray, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + train: bool = False, + params: dict = None, + dropout_rng: PRNGKey = None, + ): + r""" + Returns: + + Example: + + ```python + >>> from transformers import AutoImageProcessor, FlaxVisionEncoderDecoderModel + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k") + + >>> # initialize a vit-gpt2 from pretrained ViT and GPT2 models. Note that the cross-attention layers will be randomly initialized + >>> model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained( + ... "google/vit-base-patch16-224-in21k", "openai-community/gpt2" + ... ) + + >>> pixel_values = image_processor(images=image, return_tensors="np").pixel_values + >>> encoder_outputs = model.encode(pixel_values) + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + # `FlaxViTModel` expects channel first format, but `FlaxViTModule` expects channel last format. + # Currently, we assume this holds for all Flax vision models, and perform a transpose here. + pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) + + # Handle any PRNG if needed + rngs = {} + if dropout_rng is not None: + rngs["dropout"] = dropout_rng + + def _encoder_forward(module, pixel_values, **kwargs): + encode_module = module._get_encoder_module() + return encode_module(pixel_values, **kwargs) + + outputs = self.module.apply( + {"params": params or self.params}, + pixel_values=jnp.array(pixel_values, dtype=self.dtype), + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + deterministic=not train, + rngs=rngs, + method=_encoder_forward, + ) + + if return_dict: + outputs = FlaxBaseModelOutput( + last_hidden_state=outputs.last_hidden_state, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + return outputs + + @add_start_docstrings(VISION_ENCODER_DECODER_DECODE_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) + def decode( + self, + decoder_input_ids, + encoder_outputs, + decoder_attention_mask: Optional[jnp.ndarray] = None, + decoder_position_ids: Optional[jnp.ndarray] = None, + past_key_values: dict = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + train: bool = False, + params: dict = None, + dropout_rng: PRNGKey = None, + ): + r""" + Returns: + + Example: + + ```python + >>> from transformers import AutoImageProcessor, FlaxVisionEncoderDecoderModel + >>> import jax.numpy as jnp + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k") + + >>> # initialize a vit-gpt2 from pretrained ViT and GPT2 models. Note that the cross-attention layers will be randomly initialized + >>> model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained( + ... "google/vit-base-patch16-224-in21k", "openai-community/gpt2" + ... ) + + >>> pixel_values = image_processor(images=image, return_tensors="np").pixel_values + >>> encoder_outputs = model.encode(pixel_values) + + >>> decoder_start_token_id = model.config.decoder.bos_token_id + >>> decoder_input_ids = jnp.ones((pixel_values.shape[0], 1), dtype="i4") * decoder_start_token_id + + >>> outputs = model.decode(decoder_input_ids, encoder_outputs) + >>> logits = outputs.logits + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + encoder_hidden_states = encoder_outputs[0] + + batch_size, sequence_length = encoder_hidden_states.shape[:2] + encoder_attention_mask = jnp.ones((batch_size, sequence_length)) + + batch_size, sequence_length = decoder_input_ids.shape + if decoder_attention_mask is None: + decoder_attention_mask = jnp.ones((batch_size, sequence_length)) + + if decoder_position_ids is None: + if past_key_values is not None: + raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.") + + decoder_position_ids = jnp.broadcast_to( + jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) + ) + + # Handle any PRNG if needed + rngs = {} + if dropout_rng is not None: + rngs["dropout"] = dropout_rng + + inputs = {"params": params or self.params} + + # if past_key_values are passed then cache is already initialized a private flag init_cache has to be + # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that + # it can be changed by FlaxBartAttention module + if past_key_values: + inputs["cache"] = past_key_values + mutable = ["cache"] + else: + mutable = False + + def _decoder_forward( + module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, encoder_hidden_states, **kwargs + ): + projection_module = module._get_projection_module() + decoder_module = module._get_decoder_module() + + # optionally project encoder_hidden_states + if projection_module is not None: + encoder_hidden_states = projection_module(encoder_hidden_states) + + return decoder_module( + decoder_input_ids, + decoder_attention_mask, + decoder_position_ids, + encoder_hidden_states, + **kwargs, + ) + + outputs = self.module.apply( + inputs, + decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), + decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), + decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"), + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + deterministic=not train, + rngs=rngs, + mutable=mutable, + method=_decoder_forward, + ) + + # add updated cache to model output + if past_key_values is not None and return_dict: + outputs, past = outputs + outputs["past_key_values"] = unfreeze(past["cache"]) + return outputs + elif past_key_values is not None and not return_dict: + outputs, past = outputs + outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] + + return outputs + + @add_start_docstrings_to_model_forward(VISION_ENCODER_DECODER_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) + def __call__( + self, + pixel_values: jnp.ndarray, + decoder_input_ids: Optional[jnp.ndarray] = None, + decoder_attention_mask: Optional[jnp.ndarray] = None, + decoder_position_ids: Optional[jnp.ndarray] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + train: bool = False, + params: dict = None, + dropout_rng: PRNGKey = None, + ): + r""" + Returns: + + Examples: + + ```python + >>> from transformers import FlaxVisionEncoderDecoderModel, AutoImageProcessor, AutoTokenizer + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k") + + >>> # load output tokenizer + >>> tokenizer_output = AutoTokenizer.from_pretrained("openai-community/gpt2") + + >>> # initialize a vit-gpt2 from pretrained ViT and GPT2 models. Note that the cross-attention layers will be randomly initialized + >>> model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained( + ... "google/vit-base-patch16-224-in21k", "openai-community/gpt2" + ... ) + + >>> pixel_values = image_processor(images=image, return_tensors="np").pixel_values + + >>> # use GPT2's eos_token as the pad as well as eos token + >>> model.config.eos_token_id = model.config.decoder.eos_token_id + >>> model.config.pad_token_id = model.config.eos_token_id + + >>> # generation + >>> sequences = model.generate(pixel_values, num_beams=4, max_length=12).sequences + + >>> captions = tokenizer_output.batch_decode(sequences, skip_special_tokens=True) + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + # prepare encoder inputs + + # `FlaxViTModel` expects channel first format, but `FlaxViTModule` expects channel last format. + # Currently, we assume this holds for all Flax vision models, and perform a transpose here. + pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) + + # prepare decoder inputs + if decoder_input_ids is None: + raise ValueError("`decoder_input_ids` can't be `None`.") + if decoder_attention_mask is None: + decoder_attention_mask = jnp.ones_like(decoder_input_ids) + if decoder_position_ids is None: + batch_size, sequence_length = decoder_input_ids.shape + decoder_position_ids = jnp.broadcast_to( + jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) + ) + + # Handle any PRNG if needed + rngs = {"dropout": dropout_rng} if dropout_rng is not None else {} + + return self.module.apply( + {"params": params or self.params}, + pixel_values=jnp.array(pixel_values, dtype=self.dtype), + decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), + decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), + decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + deterministic=not train, + rngs=rngs, + ) + + def prepare_inputs_for_generation( + self, + decoder_input_ids, + max_length, + decoder_attention_mask: Optional[jax.Array] = None, + encoder_outputs=None, + **kwargs, + ): + # initializing the cache + batch_size, seq_length = decoder_input_ids.shape + + past_key_values = self.init_cache(batch_size, max_length, encoder_outputs) + # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. + # But since the decoder uses a causal mask, those positions are masked anyways. + # Thus we can create a single static attention_mask here, which is more efficient for compilation + extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") + if decoder_attention_mask is not None: + decoder_position_ids = decoder_attention_mask.cumsum(axis=-1) - 1 + extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0)) + else: + decoder_position_ids = jnp.broadcast_to( + jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length) + ) + + return { + "past_key_values": past_key_values, + "encoder_outputs": encoder_outputs, + "decoder_attention_mask": extended_attention_mask, + "decoder_position_ids": decoder_position_ids, + } + + def update_inputs_for_generation(self, model_outputs, model_kwargs): + model_kwargs["past_key_values"] = model_outputs.past_key_values + model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1 + return model_kwargs + + @classmethod + def from_encoder_decoder_pretrained( + cls, + encoder_pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None, + decoder_pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None, + *model_args, + **kwargs, + ) -> FlaxPreTrainedModel: + r""" + Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model + checkpoints. + + Params: + encoder_pretrained_model_name_or_path (`Union[str, os.PathLike]`, *optional*): + Information necessary to initiate the encoder. Can be either: + + - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. An + example is `google/vit-base-patch16-224-in21k`. + - A path to a *directory* containing model weights saved using + [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. + + decoder_pretrained_model_name_or_path (`Union[str, os.PathLike]`, *optional*, defaults to `None`): + Information necessary to initiate the decoder. Can be either: + + - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. + - A path to a *directory* containing model weights saved using + [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. + + model_args (remaining positional arguments, *optional*): + All remaning positional arguments will be passed to the underlying model's `__init__` method. + + kwargs (remaining dictionary of keyword arguments, *optional*): + Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., + `output_attentions=True`). + + - To update the encoder configuration, use the prefix *encoder_* for each configuration parameter. + - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter. + - To update the parent model configuration, do not use a prefix for each configuration parameter. + + Behaves differently depending on whether a `config` is provided or automatically loaded. + + Example: + + ```python + >>> from transformers import FlaxVisionEncoderDecoderModel + + >>> # initialize a vit-gpt2 from a pretrained ViT and a pretrained GPT2 model. Note that the cross-attention layers will be randomly initialized + >>> model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained( + ... "google/vit-base-patch16-224-in21k", "openai-community/gpt2" + ... ) + >>> # saving model after fine-tuning + >>> model.save_pretrained("./vit-gpt2") + >>> # load fine-tuned model + >>> model = FlaxVisionEncoderDecoderModel.from_pretrained("./vit-gpt2") + ```""" + + kwargs_encoder = { + argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_") + } + + kwargs_decoder = { + argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_") + } + + # remove encoder, decoder kwargs from kwargs + for key in kwargs_encoder.keys(): + del kwargs["encoder_" + key] + for key in kwargs_decoder.keys(): + del kwargs["decoder_" + key] + + # Load and initialize the encoder and decoder + # The distinction between encoder and decoder at the model level is made + # by the value of the flag `is_decoder` that we need to set correctly. + encoder = kwargs_encoder.pop("model", None) + if encoder is None: + if encoder_pretrained_model_name_or_path is None: + raise ValueError( + "If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has " + "to be defined." + ) + + if "config" not in kwargs_encoder: + encoder_config = AutoConfig.from_pretrained(encoder_pretrained_model_name_or_path) + if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True: + logger.info( + f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model " + "from a decoder model. Cross-attention and casual mask are disabled." + ) + encoder_config.is_decoder = False + encoder_config.add_cross_attention = False + + kwargs_encoder["config"] = encoder_config + + encoder = FlaxAutoModel.from_pretrained( + encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder + ) + + decoder = kwargs_decoder.pop("model", None) + if decoder is None: + if decoder_pretrained_model_name_or_path is None: + raise ValueError( + "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has " + "to be defined." + ) + + if "config" not in kwargs_decoder: + decoder_config = AutoConfig.from_pretrained(decoder_pretrained_model_name_or_path) + if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False: + logger.info( + f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention" + f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if" + f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers." + ) + decoder_config.is_decoder = True + decoder_config.add_cross_attention = True + + kwargs_decoder["config"] = decoder_config + + if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False: + logger.warning( + f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. " + f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, " + "make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` " + "passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a " + "`decoder_config` to `.from_encoder_decoder_pretrained(...)`" + ) + + decoder = FlaxAutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder) + + # instantiate config with corresponding kwargs + dtype = kwargs.pop("dtype", jnp.float32) + config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs) + + # init model + model = cls(config, dtype=dtype) + model.params["encoder"] = encoder.params + model.params["decoder"] = decoder.params + + return model diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_encoder_decoder/modeling_tf_vision_encoder_decoder.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_encoder_decoder/modeling_tf_vision_encoder_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..75ff2dbd82e48bd126b789bbc3f58ad8fbbf14e4 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_encoder_decoder/modeling_tf_vision_encoder_decoder.py @@ -0,0 +1,698 @@ +# coding=utf-8 +# Copyright 2022 HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Classes to support TF Vision-Encoder-Text-Decoder architectures""" + + +from __future__ import annotations + +import re +import warnings +from typing import Optional, Tuple, Union + +import numpy as np +import tensorflow as tf + +from ...configuration_utils import PretrainedConfig +from ...modeling_tf_outputs import TFBaseModelOutput, TFSeq2SeqLMOutput +from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFPreTrainedModel, get_initializer, keras, unpack_inputs +from ...tf_utils import shape_list +from ...utils import ( + ModelOutput, + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) +from ..auto.configuration_auto import AutoConfig +from ..auto.modeling_tf_auto import TFAutoModel, TFAutoModelForCausalLM +from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "VisionEncoderDecoderConfig" + +DEPRECATION_WARNING = ( + "Version v4.17.0 introduces a better way to train encoder-decoder models by computing the loss inside the" + " encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if" + " fine-tuning a model trained with versions anterior to 4.17.0. The decoder_input_ids are now created based on the" + " labels, no need to pass them yourself anymore." +) + +VISION_ENCODER_DECODER_START_DOCSTRING = r""" + This class can be used to initialize an image-to-text-sequence model with any pretrained vision autoencoding model + as the encoder and any pretrained text autoregressive model as the decoder. The encoder is loaded via + [`~TFAutoModel.from_pretrained`] function and the decoder is loaded via [`~TFAutoModelForCausalLM.from_pretrained`] + function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream + generative task, like image captioning. + + The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation + tasks was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation + Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi + Zhou, Wei Li, Peter J. Liu. + + Additionally, in [TrOCR: Transformer-based Optical Character Recognition with Pre-trained + Models](https://arxiv.org/abs/2109.10282) it is shown how leveraging large pretrained vision models for optical + character recognition (OCR) yields a significant performance improvement. + + After such a Vision-Encoder-Text-Decoder model has been trained/fine-tuned, it can be saved/loaded just like any + other models (see the examples for more information). + + This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it + as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and + behavior. + + Parameters: + config ([`VisionEncoderDecoderConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. +""" + +VISION_ENCODER_DECODER_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using the vision's model's image processor. For example, using + [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`] for details. + decoder_input_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + Provide for sequence to sequence training to the decoder. Indices can be obtained using + [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for + details. + decoder_attention_mask (`np.ndarray` or `tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + encoder_outputs (`tuple(tuple(tf.Tensor)`, *optional*): + This tuple must consist of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) + `last_hidden_state` (`tf.Tensor` of shape `({0}, hidden_size)`) is a tensor of hidden-states at the output + of the last layer of the encoder. Used in the cross-attention of the decoder. + past_key_values (`tuple(tuple(tf.Tensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `({0})`. + decoder_inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded + representation. This is useful if you want more control over how to convert `decoder_input_ids` indices + into associated vectors than the model's internal embedding lookup matrix. + labels (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*): + Labels for computing the masked language modeling loss for the decoder. Indices should be in `[-100, 0, + ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + If set to `True`, the model will return a [`~utils.Seq2SeqLMOutput`] instead of a plain tuple. + training (`bool`, *optional*, defaults to `False`): + Whether or not to use the model in training mode (some modules like dropout modules have different + behaviors between training and evaluation). + kwargs (*optional*): Remaining dictionary of keyword arguments. Keyword arguments come in two flavors: + + - Without a prefix which will be input as `**encoder_kwargs` for the encoder forward function. + - With a *decoder_* prefix which will be input as `**decoder_kwargs` for the decoder forward function. +""" + + +# Copied from transformers.models.encoder_decoder.modeling_tf_encoder_decoder.shift_tokens_right +def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int): + if pad_token_id is None: + raise ValueError("Make sure to set the pad_token_id attribute of the model's configuration.") + pad_token_id = tf.cast(pad_token_id, input_ids.dtype) + + if decoder_start_token_id is None: + raise ValueError("Make sure to set the decoder_start_token_id attribute of the model's configuration.") + decoder_start_token_id = tf.cast(decoder_start_token_id, input_ids.dtype) + + start_tokens = tf.fill((shape_list(input_ids)[0], 1), decoder_start_token_id) + shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1) + # replace possible -100 values in labels by `pad_token_id` + shifted_input_ids = tf.where( + shifted_input_ids == -100, tf.fill(shape_list(shifted_input_ids), pad_token_id), shifted_input_ids + ) + + # "Verify that `labels` has only positive values and -100" + assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=input_ids.dtype)) + + # Make sure the assertion op is called by wrapping the result in an identity no-op + with tf.control_dependencies([assert_gte0]): + shifted_input_ids = tf.identity(shifted_input_ids) + + return shifted_input_ids + + +@add_start_docstrings(VISION_ENCODER_DECODER_START_DOCSTRING) +class TFVisionEncoderDecoderModel(TFPreTrainedModel, TFCausalLanguageModelingLoss): + r""" + [`TFVisionEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture + with one of the base vision model classes of the library as encoder and another one of the base model classes as + decoder when created with the [`~TFAutoModel.from_pretrained`] class method for the encoder and + [`~TFAutoModelForCausalLM.from_pretrained`] class method for the decoder. + """ + + config_class = VisionEncoderDecoderConfig + base_model_prefix = "vision_encoder_decoder" + load_weight_prefix = "tf_vision_encoder_decoder_model" + main_input_name = "pixel_values" + + def __init__( + self, + config: Optional[PretrainedConfig] = None, + encoder: Optional[TFPreTrainedModel] = None, + decoder: Optional[TFPreTrainedModel] = None, + ): + if config is None and (encoder is None or decoder is None): + raise ValueError("Either a configuration or an encoder and a decoder has to be provided.") + if config is None: + config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config) + else: + if not isinstance(config, self.config_class): + raise ValueError(f"config: {config} has to be of type {self.config_class}") + + if config.decoder.cross_attention_hidden_size is not None: + if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size: + raise ValueError( + "If `cross_attention_hidden_size` is specified in the decoder's configuration, it has to be equal" + f" to the encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for" + f" `config.decoder.cross_attention_hidden_size` and {config.encoder.hidden_size} for" + " `config.encoder.hidden_size`." + ) + + # initialize with config + super().__init__(config) + + if encoder is None: + encoder = TFAutoModel.from_config(config.encoder, name="encoder") + + if decoder is None: + decoder = TFAutoModelForCausalLM.from_config(config.decoder, name="decoder") + + self.encoder = encoder + self.decoder = decoder + + if self.encoder.config.to_dict() != self.config.encoder.to_dict(): + logger.warning( + f"Config of the encoder: {self.encoder.__class__} is overwritten by shared encoder config:" + f" {self.config.encoder}" + ) + if self.decoder.config.to_dict() != self.config.decoder.to_dict(): + logger.warning( + f"Config of the decoder: {self.decoder.__class__} is overwritten by shared decoder config:" + f" {self.config.decoder}" + ) + + # make sure that the individual model's config refers to the shared config + # so that the updates to the config will be synced + self.encoder.config = self.config.encoder + self.decoder.config = self.config.decoder + + # encoder outputs might need to be projected to different dimension for decoder + if ( + self.encoder.config.hidden_size != self.decoder.config.hidden_size + and self.decoder.config.cross_attention_hidden_size is None + ): + self.enc_to_dec_proj = keras.layers.Dense( + units=self.decoder.config.hidden_size, + kernel_initializer=get_initializer(config.encoder.initializer_range), + name="enc_to_dec_proj", + ) + + if self.encoder.get_output_embeddings() is not None: + raise ValueError( + f"The encoder {self.encoder} should not have a LM Head. Please use a model without LM Head" + ) + + @property + def input_signature(self): + vision_config = self.config.encoder + if hasattr(vision_config, "vision_config"): + vision_config = vision_config.vision_config + if hasattr(vision_config, "image_size"): + image_size = vision_config.image_size + else: + image_size = vision_config.input_size + return { + "pixel_values": tf.TensorSpec( + shape=( + None, + vision_config.num_channels, + image_size, + image_size, + ), + dtype=tf.float32, + ), + "decoder_input_ids": tf.TensorSpec(shape=(None, None), dtype=tf.int32, name="decoder_input_ids"), + } + + def get_encoder(self): + return self.encoder + + def get_decoder(self): + return self.decoder + + def get_input_embeddings(self): + return self.encoder.get_input_embeddings() + + def get_output_embeddings(self): + return self.decoder.get_output_embeddings() + + def set_output_embeddings(self, new_embeddings): + return self.decoder.set_output_embeddings(new_embeddings) + + def tf_to_pt_weight_rename(self, tf_weight): + # Matt: The TF and PT weights don't align because our TF base classes have an extra layer compared to PT models + # (the main model stem is in the MainLayer class). If we remove that layer, then weight names sync up as normal. + # However, the name of that extra layer is the name of the MainLayer in the base model. We make the assumption + # here that the config model_type is the same as the name of the MainLayer. I don't know of anywhere that's + # not the case, and I wasn't sure how else to go from the config to the correct MainLayer name! + + # This override is only needed in the case where we're crossloading weights from PT. However, since weights are + # often safetensors now, we don't know if we're going to be crossloading until we sniff the weights file. + # Therefore, we specify tf_to_pt_weight_rename anyway, and let the super method figure out if it needs it + # or not. + encoder_model_type = self.config.encoder.model_type + if "encoder" in tf_weight and "decoder" not in tf_weight: + return (re.sub(rf"encoder\.{encoder_model_type}\.", "encoder.", tf_weight),) + else: + return (tf_weight,) + + @classmethod + def from_encoder_decoder_pretrained( + cls, + encoder_pretrained_model_name_or_path: str = None, + decoder_pretrained_model_name_or_path: str = None, + *model_args, + **kwargs, + ) -> TFPreTrainedModel: + r""" + Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model + checkpoints. + + + Params: + encoder_pretrained_model_name_or_path (`str`, *optional*): + Information necessary to initiate the encoder. Can be either: + + - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. An + example is `google/vit-base-patch16-224-in21k`. + - A path to a *directory* containing model weights saved using + [`~TFPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. + - A path or url to a *pytorch index checkpoint file* (e.g, `./pt_model/`). In this case, + `encoder_from_pt` should be set to `True`. + + decoder_pretrained_model_name_or_path (`str`, *optional*, defaults to *None*): + Information necessary to initiate the decoder. Can be either: + + - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. + - A path to a *directory* containing model weights saved using + [`~TFPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. + - A path or url to a *pytorch checkpoint file* (e.g, `./pt_model/`). In this case, + `decoder_from_pt` should be set to `True`. + + model_args (remaining positional arguments, *optional*): + All remaning positional arguments will be passed to the underlying model's `__init__` method. + + kwargs (remaining dictionary of keyword arguments, *optional*): + Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., + `output_attentions=True`). + + - To update the encoder configuration, use the prefix *encoder_* for each configuration parameter. + - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter. + - To update the parent model configuration, do not use a prefix for each configuration parameter. + + Behaves differently depending on whether a `config` is provided or automatically loaded. + + Example: + + ```python + >>> from transformers import TFVisionEncoderDecoderModel + + >>> # initialize a vit-bert from a pretrained ViT and a pretrained BERT model. Note that the cross-attention layers will be randomly initialized + >>> model = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained( + ... "google/vit-base-patch16-224-in21k", "google-bert/bert-base-uncased" + ... ) + >>> # saving model after fine-tuning + >>> model.save_pretrained("./vit-bert") + >>> # load fine-tuned model + >>> model = TFVisionEncoderDecoderModel.from_pretrained("./vit-bert") + ```""" + + kwargs_encoder = { + argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_") + } + + kwargs_decoder = { + argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_") + } + + # remove encoder, decoder kwargs from kwargs + for key in kwargs_encoder.keys(): + del kwargs["encoder_" + key] + for key in kwargs_decoder.keys(): + del kwargs["decoder_" + key] + + # Load and initialize the encoder and decoder + # The distinction between encoder and decoder at the model level is made + # by the value of the flag `is_decoder` that we need to set correctly. + encoder = kwargs_encoder.pop("model", None) + if encoder is None: + if encoder_pretrained_model_name_or_path is None: + raise ValueError( + "If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has " + "to be defined." + ) + + if "config" not in kwargs_encoder: + encoder_config = AutoConfig.from_pretrained(encoder_pretrained_model_name_or_path) + if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True: + logger.info( + f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model " + "from a decoder model. Cross-attention and casual mask are disabled." + ) + encoder_config.is_decoder = False + encoder_config.add_cross_attention = False + + kwargs_encoder["config"] = encoder_config + + kwargs_encoder["name"] = "encoder" + kwargs_encoder["load_weight_prefix"] = cls.load_weight_prefix + encoder = TFAutoModel.from_pretrained(encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder) + + decoder = kwargs_decoder.pop("model", None) + if decoder is None: + if decoder_pretrained_model_name_or_path is None: + raise ValueError( + "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has " + "to be defined." + ) + + if "config" not in kwargs_decoder: + decoder_config = AutoConfig.from_pretrained(decoder_pretrained_model_name_or_path) + if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False: + logger.info( + f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention" + f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if" + f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers." + ) + decoder_config.is_decoder = True + decoder_config.add_cross_attention = True + + kwargs_decoder["config"] = decoder_config + + if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False: + logger.warning( + f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. " + f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, " + "make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` " + "passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a " + "`decoder_config` to `.from_encoder_decoder_pretrained(...)`" + ) + + kwargs_decoder["name"] = "decoder" + kwargs_decoder["load_weight_prefix"] = cls.load_weight_prefix + decoder = TFAutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder) + + # Make sure these 2 `keras.Model` have fixed names so `from_pretrained` could load model weights correctly. + if encoder.name != "encoder": + raise ValueError("encoder model must be created with the name `encoder`.") + if decoder.name != "decoder": + raise ValueError("decoder model must be created with the name `decoder`.") + + # instantiate config with corresponding kwargs + config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs) + return cls(encoder=encoder, decoder=decoder, config=config) + + @unpack_inputs + @add_start_docstrings_to_model_forward( + VISION_ENCODER_DECODER_INPUTS_DOCSTRING.format("batch_size, sequence_length") + ) + @replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) + def call( + self, + pixel_values: np.ndarray | tf.Tensor | None = None, + decoder_input_ids: np.ndarray | tf.Tensor | None = None, + decoder_attention_mask: np.ndarray | tf.Tensor | None = None, + encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None, + past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, + decoder_inputs_embeds: np.ndarray | tf.Tensor | None = None, + labels: np.ndarray | tf.Tensor | None = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + training: bool = False, + **kwargs, + ) -> Union[TFSeq2SeqLMOutput, Tuple[tf.Tensor]]: + r""" + Returns: + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, AutoTokenizer, TFVisionEncoderDecoderModel + >>> from PIL import Image + >>> import requests + + >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k") + >>> decoder_tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2") + + >>> # initialize a bert2gpt2 from a pretrained BERT and GPT2 models. Note that the cross-attention layers will be randomly initialized + >>> model = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained( + ... "google/vit-base-patch16-224-in21k", "openai-community/gpt2" + ... ) + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> img = Image.open(requests.get(url, stream=True).raw) + + >>> # forward + >>> pixel_values = image_processor(images=img, return_tensors="tf").pixel_values # Batch size 1 + >>> decoder_input_ids = decoder_tokenizer("Linda Davis", return_tensors="tf").input_ids # Batch size 1 + >>> outputs = model(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids) + + >>> # training + >>> outputs = model(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids, labels=decoder_input_ids) + >>> loss, logits = outputs.loss, outputs.logits + + >>> # save and load from pretrained + >>> model.save_pretrained("vit-gpt2") + >>> model = TFVisionEncoderDecoderModel.from_pretrained("vit-gpt2") + + >>> # generation + >>> generated = model.generate(pixel_values, decoder_start_token_id=model.config.decoder.bos_token_id) + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")} + + kwargs_decoder = { + argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_") + } + + # Let the user be responsible for the expected format. + if encoder_outputs is not None: + if return_dict and not isinstance(encoder_outputs, ModelOutput): + raise ValueError( + "If `return_dict=True` and `encoder_outputs` is provided, it should be an instance of " + f"`ModelOutput`. Got an instance {type(encoder_outputs)} for `encoder_outputs`." + ) + + if encoder_outputs is None: + encoder_inputs = { + "input_ids": pixel_values, + "output_attentions": output_attentions, + "output_hidden_states": output_hidden_states, + "return_dict": return_dict, + "training": training, + } + + # Add arguments to encoder from `kwargs_encoder` + encoder_inputs.update(kwargs_encoder) + + if "input_ids" in encoder_inputs: + encoder_inputs["pixel_values"] = encoder_inputs.pop("input_ids") + + if encoder_inputs["pixel_values"] is None: + raise ValueError("You have to specify pixel_values") + + # Handle the case where the inputs are passed as a single dict which contains `labels`. + # The `labels` shouldn't be passed to `self.encoder` below, because it is a based model without this + # parameter (otherwise, an error occurs when `input_processing` is called inside `self.encoder.call()`). + if "labels" in encoder_inputs: + labels = encoder_inputs.pop("labels") + + # handle the init case where `dummy_inputs` returns a dict containing `decoder_input_ids`. + if "decoder_input_ids" in encoder_inputs: + decoder_input_ids = encoder_inputs.pop("decoder_input_ids") + # handle the init case where `dummy_inputs` returns a dict containing `decoder_input_ids`. + if "decoder_attention_mask" in encoder_inputs: + decoder_attention_mask = encoder_inputs.pop("decoder_attention_mask") + + encoder_outputs = self.encoder(**encoder_inputs) + + encoder_hidden_states = encoder_outputs[0] + + # optionally project encoder_hidden_states + if ( + self.encoder.config.hidden_size != self.decoder.config.hidden_size + and self.decoder.config.cross_attention_hidden_size is None + ): + encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states) + + if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None): + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + batch_size, sequence_length = shape_list(encoder_hidden_states)[:2] + encoder_attention_mask = tf.ones(shape=(batch_size, sequence_length), dtype=tf.int32) + + decoder_inputs = { + "input_ids": decoder_input_ids, + "attention_mask": decoder_attention_mask, + "encoder_hidden_states": encoder_hidden_states, + "encoder_attention_mask": encoder_attention_mask, + "inputs_embeds": decoder_inputs_embeds, + "output_attentions": output_attentions, + "output_hidden_states": output_hidden_states, + "use_cache": use_cache, + "past_key_values": past_key_values, + "return_dict": return_dict, + "training": training, + } + + # Add arguments to decoder from `kwargs_decoder` + decoder_inputs.update(kwargs_decoder) + + decoder_outputs = self.decoder(**decoder_inputs) + + logits = decoder_outputs[0] + + # Compute loss independent from decoder (as some shift the logits inside them) + loss = None + if labels is not None: + warnings.warn(DEPRECATION_WARNING, FutureWarning) + loss = self.hf_compute_loss(labels, logits) + + if not return_dict: + past_key_values = None + if use_cache: + past_key_values = decoder_outputs[1] + # The starting index of the remaining elements in `decoder_outputs` + start_index = sum([1 if x is not None else 0 for x in (loss, logits, past_key_values)]) + + if not isinstance(encoder_outputs, tuple): + encoder_outputs = encoder_outputs.to_tuple() + output = (loss, logits, past_key_values) + decoder_outputs[start_index:] + encoder_outputs + output = tuple([x for x in output if x is not None]) + return output + + return TFSeq2SeqLMOutput( + loss=loss, + logits=decoder_outputs.logits, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + def serving_output(self, output): + pkv = tf.tuple(output.past_key_values)[1] if self.config.decoder.use_cache else None + dec_hs = ( + tf.convert_to_tensor(output.decoder_hidden_states) if self.config.decoder.output_hidden_states else None + ) + dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.decoder.output_attentions else None + enc_hs = ( + tf.convert_to_tensor(output.encoder_hidden_states) if self.config.encoder.output_hidden_states else None + ) + enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.encoder.output_attentions else None + cross_attns = ( + tf.convert_to_tensor(output.cross_attentions) + if self.config.decoder.output_attentions and output.cross_attentions is not None + else None + ) + + return TFSeq2SeqLMOutput( + logits=output.logits, + past_key_values=pkv, + decoder_hidden_states=dec_hs, + decoder_attentions=dec_attns, + encoder_last_hidden_state=output.encoder_last_hidden_state, + encoder_hidden_states=enc_hs, + encoder_attentions=enc_attns, + cross_attentions=cross_attns, + ) + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs + ): + decoder_inputs = self.decoder.prepare_inputs_for_generation(input_ids, past_key_values=past_key_values) + decoder_attention_mask = decoder_inputs["attention_mask"] if "attention_mask" in decoder_inputs else None + past_key_values = decoder_inputs.get("past_key_values") + input_dict = { + "pixel_values": None, # needs to be passed to make Keras.layer.__call__ happy + "attention_mask": attention_mask, + "decoder_attention_mask": decoder_attention_mask, + "decoder_input_ids": decoder_inputs["input_ids"], + # TODO (joao): the `TFBaseModelOutput` wrapper should not be needed after the generate refactor is complete + "encoder_outputs": TFBaseModelOutput(last_hidden_state=encoder_outputs[0]), + "past_key_values": past_key_values, + "use_cache": use_cache, + } + return input_dict + + def prepare_decoder_input_ids_from_labels(self, labels: tf.Tensor): + return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id) + + def resize_token_embeddings(self, *args, **kwargs): + raise NotImplementedError( + "Resizing the embedding layers via the TFVisionEncoderDecoderModel directly is not supported. " + "Please use the respective methods of the wrapped objects (model.decoder.resize_token_embeddings(...))" + ) + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "enc_to_dec_proj", None) is not None: + with tf.name_scope(self.enc_to_dec_proj.name): + self.enc_to_dec_proj.build([None, None, self.encoder.config.hidden_size]) + if getattr(self, "encoder", None) is not None: + with tf.name_scope(self.encoder.name): + self.encoder.build(None) + if getattr(self, "decoder", None) is not None: + with tf.name_scope(self.decoder.name): + self.decoder.build(None) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..0bdf76044153b164cdcfdb76be181df19463cdaf --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py @@ -0,0 +1,668 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Classes to support Vision-Encoder-Text-Decoder architectures""" + + +import gc +import os +import tempfile +from typing import Optional, Tuple, Union + +import torch +from torch import nn +from torch.nn import CrossEntropyLoss + +from ...configuration_utils import PretrainedConfig +from ...modeling_outputs import BaseModelOutput, Seq2SeqLMOutput +from ...modeling_utils import PreTrainedModel +from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings +from ..auto.configuration_auto import AutoConfig +from ..auto.modeling_auto import AutoModel, AutoModelForCausalLM +from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig + + +# Copied from transformers.models.encoder_decoder.modeling_encoder_decoder.shift_tokens_right +def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): + """ + Shift input ids one token to the right. + """ + shifted_input_ids = input_ids.new_zeros(input_ids.shape) + shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() + if decoder_start_token_id is None: + raise ValueError("Make sure to set the decoder_start_token_id attribute of the model's configuration.") + shifted_input_ids[:, 0] = decoder_start_token_id + + if pad_token_id is None: + raise ValueError("Make sure to set the pad_token_id attribute of the model's configuration.") + # replace possible -100 values in labels by `pad_token_id` + shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) + + return shifted_input_ids + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "VisionEncoderDecoderConfig" + +VISION_ENCODER_DECODER_START_DOCSTRING = r""" + This class can be used to initialize an image-to-text-sequence model with any pretrained vision autoencoding model + as the encoder and any pretrained text autoregressive model as the decoder. The encoder is loaded via + [`~AutoModel.from_pretrained`] function and the decoder is loaded via [`~AutoModelForCausalLM.from_pretrained`] + function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream + generative task, like image captioning. + + The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation + tasks was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation + Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi + Zhou, Wei Li, Peter J. Liu. + + Additionally, in [TrOCR: Transformer-based Optical Character Recognition with Pre-trained + Models](https://arxiv.org/abs/2109.10282) it is shown how leveraging large pretrained vision models for optical + character recognition (OCR) yields a significant performance improvement. + + After such a Vision-Encoder-Text-Decoder model has been trained/fine-tuned, it can be saved/loaded just like any + other models (see the examples for more information). + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`VisionEncoderDecoderConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +VISION_ENCODER_DECODER_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using an image processor (e.g. if you use ViT as the encoder, + you should use [`AutoImageProcessor`]). See [`ViTImageProcessor.__call__`] for details. + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + For training, `decoder_input_ids` are automatically created by the model by shifting the `labels` to the + right, replacing -100 by the `pad_token_id` and prepending them with the `decoder_start_token_id`. + decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + encoder_outputs (`tuple(torch.FloatTensor)`, *optional*): + This tuple must consist of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) + `last_hidden_state` (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) is a tensor + of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the + decoder. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded + representation. This is useful if you want more control over how to convert `decoder_input_ids` indices + into associated vectors than the model's internal embedding lookup matrix. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss for the decoder. Indices should be in `[-100, 0, + ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + If set to `True`, the model will return a [`~utils.Seq2SeqLMOutput`] instead of a plain tuple. + kwargs (*optional*): Remaining dictionary of keyword arguments. Keyword arguments come in two flavors: + + - Without a prefix which will be input as `**encoder_kwargs` for the encoder forward function. + - With a *decoder_* prefix which will be input as `**decoder_kwargs` for the decoder forward function. +""" + + +@add_start_docstrings(VISION_ENCODER_DECODER_START_DOCSTRING) +class VisionEncoderDecoderModel(PreTrainedModel): + r""" + [`VisionEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture with + one of the base vision model classes of the library as encoder and another one as decoder when created with the + :meth*~transformers.AutoModel.from_pretrained* class method for the encoder and + :meth*~transformers.AutoModelForCausalLM.from_pretrained* class method for the decoder. + """ + + config_class = VisionEncoderDecoderConfig + base_model_prefix = "vision_encoder_decoder" + main_input_name = "pixel_values" + supports_gradient_checkpointing = True + + def __init__( + self, + config: Optional[PretrainedConfig] = None, + encoder: Optional[PreTrainedModel] = None, + decoder: Optional[PreTrainedModel] = None, + ): + if config is None and (encoder is None or decoder is None): + raise ValueError("Either a configuration or an encoder and a decoder has to be provided.") + if config is None: + config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config) + else: + if not isinstance(config, self.config_class): + raise ValueError(f"Config: {config} has to be of type {self.config_class}") + + if config.decoder.cross_attention_hidden_size is not None: + if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size: + raise ValueError( + "If `cross_attention_hidden_size` is specified in the decoder's configuration, it has to be equal" + f" to the encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for" + f" `config.decoder.cross_attention_hidden_size` and {config.encoder.hidden_size} for" + " `config.encoder.hidden_size`." + ) + + # initialize with config + # make sure input & output embeddings is not tied + config.tie_word_embeddings = False + super().__init__(config) + + if encoder is None: + encoder = AutoModel.from_config(config.encoder) + + if decoder is None: + decoder = AutoModelForCausalLM.from_config(config.decoder) + + self.encoder = encoder + self.decoder = decoder + + if self.encoder.config.to_dict() != self.config.encoder.to_dict(): + logger.warning( + f"Config of the encoder: {self.encoder.__class__} is overwritten by shared encoder config:" + f" {self.config.encoder}" + ) + if self.decoder.config.to_dict() != self.config.decoder.to_dict(): + logger.warning( + f"Config of the decoder: {self.decoder.__class__} is overwritten by shared decoder config:" + f" {self.config.decoder}" + ) + + # make sure that the individual model's config refers to the shared config + # so that the updates to the config will be synced + self.encoder.config = self.config.encoder + self.decoder.config = self.config.decoder + + # encoder outputs might need to be projected to different dimension for decoder + if ( + self.encoder.config.hidden_size != self.decoder.config.hidden_size + and self.decoder.config.cross_attention_hidden_size is None + ): + self.enc_to_dec_proj = nn.Linear(self.encoder.config.hidden_size, self.decoder.config.hidden_size) + + if self.encoder.get_output_embeddings() is not None: + raise ValueError( + f"The encoder {self.encoder} should not have a LM Head. Please use a model without LM Head" + ) + + def get_encoder(self): + return self.encoder + + def get_decoder(self): + return self.decoder + + def get_output_embeddings(self): + return self.decoder.get_output_embeddings() + + def set_output_embeddings(self, new_embeddings): + return self.decoder.set_output_embeddings(new_embeddings) + + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): + r""" + Example: + + ```python + >>> from transformers import VisionEncoderDecoderModel, AutoImageProcessor, AutoTokenizer + >>> from PIL import Image + >>> import requests + + >>> image_processor = AutoImageProcessor.from_pretrained("ydshieh/vit-gpt2-coco-en") + >>> decoder_tokenizer = AutoTokenizer.from_pretrained("ydshieh/vit-gpt2-coco-en") + >>> model = VisionEncoderDecoderModel.from_pretrained("ydshieh/vit-gpt2-coco-en") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> img = Image.open(requests.get(url, stream=True).raw) + >>> pixel_values = image_processor(images=img, return_tensors="pt").pixel_values # Batch size 1 + + >>> output_ids = model.generate( + ... pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True + ... ).sequences + + >>> preds = decoder_tokenizer.batch_decode(output_ids, skip_special_tokens=True) + >>> preds = [pred.strip() for pred in preds] + + >>> assert preds == ["a cat laying on top of a couch next to another cat"] + ```""" + + from_tf = kwargs.pop("from_tf", False) + if from_tf: + from transformers import TFVisionEncoderDecoderModel + + # a workaround to load from tensorflow checkpoint + # Using `_tf_model` won't work, because the weight names in the encoder/decoder of `_tf_model` get + # extended before saving those components. For example, The name of `_tf_model.encoder.vit` is + # `[top model name]/encoder/vit`, but the name of `tf_model.encoder.vit` is `[top model name]/vit`. The + # [top model name] is handled (stripped) by the conversion method, and the former case gets extra `encoder`, + # which should not occur when we want to save the components alone. + # There was a (very) ugly potential fix, which wasn't integrated to `transformers`: see + # https://github.com/huggingface/transformers/pull/13222/commits/dbb3c9de76eee235791d2064094654637c99f36d#r697304245 + # (the change in `src/transformers/modeling_tf_utils.py`) + _tf_model = TFVisionEncoderDecoderModel.from_pretrained( + pretrained_model_name_or_path, *model_args, **kwargs + ) + config = _tf_model.config + + # Using `tf_model` instead + encoder = _tf_model.encoder.__class__(_tf_model.config.encoder) + decoder = _tf_model.decoder.__class__(_tf_model.config.decoder) + # Make sure models are built + encoder(encoder.dummy_inputs) + decoder(decoder.dummy_inputs) + + # Get the variable correspondence between `_tf_model` and `encoder` and `decoder` + encoder_variables = {} + for v in encoder.trainable_variables + encoder.non_trainable_variables: + encoder_variables["/".join(v.name.split("/")[1:])] = v + decoder_variables = {} + for v in decoder.trainable_variables + decoder.non_trainable_variables: + decoder_variables["/".join(v.name.split("/")[1:])] = v + + _encoder_variables = {} + for v in _tf_model.encoder.trainable_variables + _tf_model.encoder.non_trainable_variables: + _encoder_variables["/".join(v.name.split("/")[2:])] = v + _decoder_variables = {} + for v in _tf_model.decoder.trainable_variables + _tf_model.decoder.non_trainable_variables: + _decoder_variables["/".join(v.name.split("/")[2:])] = v + + # assign weight values to `encoder` and `decoder` from `_tf_model` + for name, v in encoder_variables.items(): + v.assign(_encoder_variables[name]) + for name, v in decoder_variables.items(): + v.assign(_decoder_variables[name]) + + tf_model = TFVisionEncoderDecoderModel(encoder=encoder, decoder=decoder) + + # Deal with `enc_to_dec_proj` + if hasattr(_tf_model, "enc_to_dec_proj"): + tf_model(tf_model.dummy_inputs) + tf_model.enc_to_dec_proj.kernel.assign(_tf_model.enc_to_dec_proj.kernel) + tf_model.enc_to_dec_proj.bias.assign(_tf_model.enc_to_dec_proj.bias) + + with tempfile.TemporaryDirectory() as tmpdirname: + encoder_dir = os.path.join(tmpdirname, "encoder") + decoder_dir = os.path.join(tmpdirname, "decoder") + tf_model.encoder.save_pretrained(encoder_dir) + tf_model.decoder.save_pretrained(decoder_dir) + + if hasattr(tf_model, "enc_to_dec_proj"): + enc_to_dec_proj_weight = torch.transpose( + torch.from_numpy(tf_model.enc_to_dec_proj.kernel.numpy()), 1, 0 + ) + enc_to_dec_proj_bias = torch.from_numpy(tf_model.enc_to_dec_proj.bias.numpy()) + + del _tf_model + del tf_model + gc.collect() + + model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained( + encoder_dir, decoder_dir, encoder_from_tf=True, decoder_from_tf=True + ) + # This is only for copying some specific attributes of this particular model. + model.config = config + + if hasattr(model, "enc_to_dec_proj"): + model.enc_to_dec_proj.weight.data = enc_to_dec_proj_weight.contiguous() + model.enc_to_dec_proj.bias.data = enc_to_dec_proj_bias.contiguous() + + return model + + # At the moment fast initialization is not supported for composite models + if kwargs.get("_fast_init", False): + logger.warning( + "Fast initialization is currently not supported for VisionEncoderDecoderModel. " + "Falling back to slow initialization..." + ) + kwargs["_fast_init"] = False + + return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + + @classmethod + def from_encoder_decoder_pretrained( + cls, + encoder_pretrained_model_name_or_path: str = None, + decoder_pretrained_model_name_or_path: str = None, + *model_args, + **kwargs, + ) -> PreTrainedModel: + r""" + Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model + checkpoints. + + + The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train + the model, you need to first set it back in training mode with `model.train()`. + + Params: + encoder_pretrained_model_name_or_path (`str`, *optional*): + Information necessary to initiate the image encoder. Can be either: + + - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. An + example is `google/vit-base-patch16-224-in21k`. + - A path to a *directory* containing model weights saved using + [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. + - A path or url to a *tensorflow index checkpoint file* (e.g, `./tf_model/model.ckpt.index`). In + this case, `from_tf` should be set to `True` and a configuration object should be provided as + `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a + PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards. + + decoder_pretrained_model_name_or_path (`str`, *optional*, defaults to `None`): + Information necessary to initiate the text decoder. Can be either: + + - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. + - A path to a *directory* containing model weights saved using + [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. + - A path or url to a *tensorflow index checkpoint file* (e.g, `./tf_model/model.ckpt.index`). In + this case, `from_tf` should be set to `True` and a configuration object should be provided as + `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a + PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards. + + model_args (remaining positional arguments, *optional*): + All remaning positional arguments will be passed to the underlying model's `__init__` method. + + kwargs (remaining dictionary of keyword arguments, *optional*): + Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., + `output_attentions=True`). + + - To update the encoder configuration, use the prefix *encoder_* for each configuration parameter. + - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter. + - To update the parent model configuration, do not use a prefix for each configuration parameter. + + Behaves differently depending on whether a `config` is provided or automatically loaded. + + Example: + + ```python + >>> from transformers import VisionEncoderDecoderModel + + >>> # initialize a vit-bert from a pretrained ViT and a pretrained BERT model. Note that the cross-attention layers will be randomly initialized + >>> model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained( + ... "google/vit-base-patch16-224-in21k", "google-bert/bert-base-uncased" + ... ) + >>> # saving model after fine-tuning + >>> model.save_pretrained("./vit-bert") + >>> # load fine-tuned model + >>> model = VisionEncoderDecoderModel.from_pretrained("./vit-bert") + ```""" + + kwargs_encoder = { + argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_") + } + + kwargs_decoder = { + argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_") + } + + # remove encoder, decoder kwargs from kwargs + for key in kwargs_encoder.keys(): + del kwargs["encoder_" + key] + for key in kwargs_decoder.keys(): + del kwargs["decoder_" + key] + + # Load and initialize the encoder and decoder + # The distinction between encoder and decoder at the model level is made + # by the value of the flag `is_decoder` that we need to set correctly. + encoder = kwargs_encoder.pop("model", None) + if encoder is None: + if encoder_pretrained_model_name_or_path is None: + raise ValueError( + "If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has " + "to be defined." + ) + + if "config" not in kwargs_encoder: + encoder_config, kwargs_encoder = AutoConfig.from_pretrained( + encoder_pretrained_model_name_or_path, **kwargs_encoder, return_unused_kwargs=True + ) + + if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True: + logger.info( + f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model " + "from a decoder model. Cross-attention and casual mask are disabled." + ) + encoder_config.is_decoder = False + encoder_config.add_cross_attention = False + + kwargs_encoder["config"] = encoder_config + + encoder = AutoModel.from_pretrained(encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder) + + decoder = kwargs_decoder.pop("model", None) + if decoder is None: + if decoder_pretrained_model_name_or_path is None: + raise ValueError( + "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has " + "to be defined." + ) + + if "config" not in kwargs_decoder: + decoder_config, kwargs_decoder = AutoConfig.from_pretrained( + decoder_pretrained_model_name_or_path, **kwargs_decoder, return_unused_kwargs=True + ) + + if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False: + logger.info( + f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention" + f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if" + f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers." + ) + decoder_config.is_decoder = True + decoder_config.add_cross_attention = True + + kwargs_decoder["config"] = decoder_config + + if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False: + logger.warning( + f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. " + f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, " + "make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` " + "passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a " + "`decoder_config` to `.from_encoder_decoder_pretrained(...)`" + ) + + decoder = AutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder) + + # instantiate config with corresponding kwargs + config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs) + + # make sure input & output embeddings is not tied + config.tie_word_embeddings = False + return cls(encoder=encoder, decoder=decoder, config=config) + + @add_start_docstrings_to_model_forward(VISION_ENCODER_DECODER_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: Optional[torch.FloatTensor] = None, + decoder_input_ids: Optional[torch.LongTensor] = None, + decoder_attention_mask: Optional[torch.BoolTensor] = None, + encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + decoder_inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + **kwargs, + ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]: + r""" + Returns: + + Examples: + + ```python + >>> from transformers import AutoProcessor, VisionEncoderDecoderModel + >>> import requests + >>> from PIL import Image + >>> import torch + + >>> processor = AutoProcessor.from_pretrained("microsoft/trocr-base-handwritten") + >>> model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten") + + >>> # load image from the IAM dataset + >>> url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB") + + >>> # training + >>> model.config.decoder_start_token_id = processor.tokenizer.eos_token_id + >>> model.config.pad_token_id = processor.tokenizer.pad_token_id + >>> model.config.vocab_size = model.config.decoder.vocab_size + + >>> pixel_values = processor(image, return_tensors="pt").pixel_values + >>> text = "hello world" + >>> labels = processor.tokenizer(text, return_tensors="pt").input_ids + >>> outputs = model(pixel_values=pixel_values, labels=labels) + >>> loss = outputs.loss + + >>> # inference (generation) + >>> generated_ids = model.generate(pixel_values) + >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")} + + kwargs_decoder = { + argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_") + } + + if encoder_outputs is None: + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + encoder_outputs = self.encoder( + pixel_values=pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs_encoder, + ) + elif isinstance(encoder_outputs, tuple): + encoder_outputs = BaseModelOutput(*encoder_outputs) + + encoder_hidden_states = encoder_outputs[0] + + # optionally project encoder_hidden_states + if ( + self.encoder.config.hidden_size != self.decoder.config.hidden_size + and self.decoder.config.cross_attention_hidden_size is None + ): + encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states) + + # else: + encoder_attention_mask = None + + if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None): + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + # Decode + decoder_outputs = self.decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + inputs_embeds=decoder_inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + use_cache=use_cache, + past_key_values=past_key_values, + return_dict=return_dict, + **kwargs_decoder, + ) + + # Compute loss independent from decoder (as some shift the logits inside them) + loss = None + if labels is not None: + logits = decoder_outputs.logits if return_dict else decoder_outputs[0] + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.reshape(-1, self.decoder.config.vocab_size), labels.reshape(-1)) + + if not return_dict: + if loss is not None: + return (loss,) + decoder_outputs + encoder_outputs + else: + return decoder_outputs + encoder_outputs + + return Seq2SeqLMOutput( + loss=loss, + logits=decoder_outputs.logits, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): + return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id) + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs + ): + decoder_inputs = self.decoder.prepare_inputs_for_generation(input_ids, past_key_values=past_key_values) + decoder_attention_mask = decoder_inputs["attention_mask"] if "attention_mask" in decoder_inputs else None + input_dict = { + "attention_mask": attention_mask, + "decoder_attention_mask": decoder_attention_mask, + "decoder_input_ids": decoder_inputs["input_ids"], + "encoder_outputs": encoder_outputs, + "past_key_values": decoder_inputs["past_key_values"], + "use_cache": use_cache, + } + return input_dict + + def resize_token_embeddings(self, *args, **kwargs): + raise NotImplementedError( + "Resizing the embedding layers via the VisionEncoderDecoderModel directly is not supported.Please use the" + " respective methods of the wrapped decoder object (model.decoder.resize_token_embeddings(...))" + ) + + def _reorder_cache(self, past_key_values, beam_idx): + # apply decoder cache reordering here + return self.decoder._reorder_cache(past_key_values, beam_idx) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..27c117274b645cfb6de7accf6f14c25301433239 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/__init__.py @@ -0,0 +1,89 @@ +# Copyright 2021 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import ( + OptionalDependencyNotAvailable, + _LazyModule, + is_flax_available, + is_tf_available, + is_torch_available, +) + + +_import_structure = { + "configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"], + "processing_vision_text_dual_encoder": ["VisionTextDualEncoderProcessor"], +} + + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_vision_text_dual_encoder"] = ["VisionTextDualEncoderModel"] + + +try: + if not is_flax_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_flax_vision_text_dual_encoder"] = ["FlaxVisionTextDualEncoderModel"] + +try: + if not is_tf_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_tf_vision_text_dual_encoder"] = ["TFVisionTextDualEncoderModel"] + + +if TYPE_CHECKING: + from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig + from .processing_vision_text_dual_encoder import VisionTextDualEncoderProcessor + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_vision_text_dual_encoder import VisionTextDualEncoderModel + + try: + if not is_flax_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_flax_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel + + try: + if not is_tf_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_tf_vision_text_dual_encoder import TFVisionTextDualEncoderModel + + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 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0000000000000000000000000000000000000000..b6ffd504fa34fd4962cd923837bd08372f9abe40 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/__pycache__/processing_vision_text_dual_encoder.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/configuration_vision_text_dual_encoder.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/configuration_vision_text_dual_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..aab76f71db6b7928a8073e39b1da39f9f1815c4e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/configuration_vision_text_dual_encoder.py @@ -0,0 +1,119 @@ +# coding=utf-8 +# Copyright The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" VisionTextDualEncoder model configuration""" + + +from ...configuration_utils import PretrainedConfig +from ...utils import logging +from ..auto.configuration_auto import AutoConfig +from ..chinese_clip.configuration_chinese_clip import ChineseCLIPVisionConfig +from ..clip.configuration_clip import CLIPVisionConfig +from ..siglip.configuration_siglip import SiglipVisionConfig + + +logger = logging.get_logger(__name__) + +VISION_MODEL_CONFIGS = { + "clip_vision_model": CLIPVisionConfig, + "chinese_clip_vision_model": ChineseCLIPVisionConfig, + "siglip_vision_model": SiglipVisionConfig, +} + + +class VisionTextDualEncoderConfig(PretrainedConfig): + r""" + [`VisionTextDualEncoderConfig`] is the configuration class to store the configuration of a + [`VisionTextDualEncoderModel`]. It is used to instantiate [`VisionTextDualEncoderModel`] model according to the + specified arguments, defining the text model and vision model configs. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + projection_dim (`int`, *optional*, defaults to 512): + Dimentionality of text and vision projection layers. + logit_scale_init_value (`float`, *optional*, defaults to 2.6592): + The inital value of the *logit_scale* paramter. Default is used as per the original CLIP implementation. + kwargs (*optional*): + Dictionary of keyword arguments. + + Examples: + + ```python + >>> from transformers import ViTConfig, BertConfig, VisionTextDualEncoderConfig, VisionTextDualEncoderModel + + >>> # Initializing a BERT and ViT configuration + >>> config_vision = ViTConfig() + >>> config_text = BertConfig() + + >>> config = VisionTextDualEncoderConfig.from_vision_text_configs(config_vision, config_text, projection_dim=512) + + >>> # Initializing a BERT and ViT model (with random weights) + >>> model = VisionTextDualEncoderModel(config=config) + + >>> # Accessing the model configuration + >>> config_vision = model.config.vision_config + >>> config_text = model.config.text_config + + >>> # Saving the model, including its configuration + >>> model.save_pretrained("vit-bert") + + >>> # loading model and config from pretrained folder + >>> vision_text_config = VisionTextDualEncoderConfig.from_pretrained("vit-bert") + >>> model = VisionTextDualEncoderModel.from_pretrained("vit-bert", config=vision_text_config) + ```""" + + model_type = "vision-text-dual-encoder" + is_composition = True + + def __init__(self, projection_dim=512, logit_scale_init_value=2.6592, **kwargs): + super().__init__(**kwargs) + + if "vision_config" not in kwargs: + raise ValueError("`vision_config` can not be `None`.") + + if "text_config" not in kwargs: + raise ValueError("`text_config` can not be `None`.") + + vision_config = kwargs.pop("vision_config") + text_config = kwargs.pop("text_config") + + vision_model_type = vision_config.pop("model_type") + text_model_type = text_config.pop("model_type") + + vision_config_class = VISION_MODEL_CONFIGS.get(vision_model_type) + if vision_config_class is not None: + self.vision_config = vision_config_class(**vision_config) + else: + self.vision_config = AutoConfig.for_model(vision_model_type, **vision_config) + if hasattr(self.vision_config, "vision_config"): + self.vision_config = self.vision_config.vision_config + + self.text_config = AutoConfig.for_model(text_model_type, **text_config) + + self.projection_dim = projection_dim + self.logit_scale_init_value = logit_scale_init_value + + @classmethod + def from_vision_text_configs(cls, vision_config: PretrainedConfig, text_config: PretrainedConfig, **kwargs): + r""" + Instantiate a [`VisionTextDualEncoderConfig`] (or a derived class) from text model configuration and vision + model configuration. + + Returns: + [`VisionTextDualEncoderConfig`]: An instance of a configuration object + """ + + return cls(vision_config=vision_config.to_dict(), text_config=text_config.to_dict(), **kwargs) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..ba8bf7091b3f94a5ab4cb205d4280db691a3be20 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py @@ -0,0 +1,599 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Flax VisionTextDualEncoder model.""" + + +from typing import Optional, Tuple + +import flax.linen as nn +import jax +import jax.numpy as jnp +from flax.core.frozen_dict import FrozenDict, freeze, unfreeze +from flax.traverse_util import flatten_dict, unflatten_dict + +from ...modeling_flax_utils import FlaxPreTrainedModel, append_replace_return_docstrings, overwrite_call_docstring +from ...utils import add_start_docstrings, logging +from ..auto.configuration_auto import AutoConfig +from ..auto.modeling_flax_auto import FLAX_MODEL_MAPPING, FlaxAutoModel +from ..clip.modeling_flax_clip import FlaxCLIPOutput, FlaxCLIPVisionModel +from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "VisionTextDualEncoderConfig" + +VISION_TEXT_DUAL_ENCODER_START_DOCSTRING = r""" + This class can be used to initialize a vision-text dual encoder model with any pretrained vision autoencoding model + as the vision encoder and any pretrained text model as the text encoder. The vision and text encoders are loaded + via the [`~FlaxAutoModel.from_pretrained`] method. The projection layers are automatically added to the model and + should be fine-tuned on a downstream task, like contrastive image-text modeling. + + In [LiT: Zero-Shot Transfer with Locked-image Text Tuning](https://arxiv.org/abs/2111.07991) it is shown how + leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvment + on new zero-shot vision tasks such as image classification or retrieval. + + After such a Vision-Text-Dual-Encoder model has been trained/fine-tuned, it can be saved/loaded just like any other + models (see the examples for more information). + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a + [flax.linen.Module](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html) subclass. Use it + as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and + behavior. + + Finally, this model supports inherent JAX features such as: + + - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) + - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) + - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) + - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) + + Parameters: + config ([`VisionTextDualEncoderConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. + dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): + The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and + `jax.numpy.bfloat16` (on TPUs). + + This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If + specified all the computation will be performed with the given `dtype`. + + **Note that this only specifies the dtype of the computation and does not influence the dtype of model + parameters.** + + If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and + [`~FlaxPreTrainedModel.to_bf16`]. +""" + + +VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING = r""" + Args: + input_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using + an image processor (e.g. if you use ViT as the encoder, you should use [`AutoImageProcessor`]). See + [`ViTImageProcessor.__call__`] for details. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class FlaxVisionTextDualEncoderModule(nn.Module): + config: VisionTextDualEncoderConfig + dtype: jnp.dtype = jnp.float32 + + def setup(self): + vision_config = self.config.vision_config + text_config = self.config.text_config + + self.vision_embed_dim = vision_config.hidden_size + self.text_embed_dim = text_config.hidden_size + self.projection_dim = self.config.projection_dim + + vision_module = FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__, FlaxCLIPVisionModel).module_class + text_module = FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class + + self.vision_model = vision_module(vision_config, dtype=self.dtype) + self.text_model = text_module(text_config, dtype=self.dtype) + + self.visual_projection = nn.Dense( + self.projection_dim, + dtype=self.dtype, + kernel_init=jax.nn.initializers.normal(0.02), + use_bias=False, + ) + self.text_projection = nn.Dense( + self.projection_dim, + dtype=self.dtype, + kernel_init=jax.nn.initializers.normal(0.02), + use_bias=False, + ) + + self.logit_scale = self.param( + "logit_scale", lambda _, shape: jnp.ones(shape) * self.config.logit_scale_init_value, [] + ) + + def __call__( + self, + input_ids=None, + pixel_values=None, + attention_mask=None, + position_ids=None, + token_type_ids=None, + deterministic: bool = True, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + return_dict = return_dict if return_dict is not None else self.config.return_dict + + vision_outputs = self.vision_model( + pixel_values=pixel_values, + deterministic=deterministic, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + text_outputs = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + deterministic=deterministic, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + image_embeds = vision_outputs[1] + image_embeds = self.visual_projection(image_embeds) + + text_embeds = text_outputs[1] + text_embeds = self.text_projection(text_embeds) + + # normalized features + image_embeds = image_embeds / jnp.linalg.norm(image_embeds, axis=-1, keepdims=True) + text_embeds = text_embeds / jnp.linalg.norm(text_embeds, axis=-1, keepdims=True) + + # cosine similarity as logits + logit_scale = jnp.exp(self.logit_scale) + logits_per_text = jnp.matmul(text_embeds, image_embeds.T) * logit_scale + logits_per_image = logits_per_text.T + + if not return_dict: + return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs) + + return FlaxCLIPOutput( + logits_per_image=logits_per_image, + logits_per_text=logits_per_text, + text_embeds=text_embeds, + image_embeds=image_embeds, + text_model_output=text_outputs, + vision_model_output=vision_outputs, + ) + + +@add_start_docstrings(VISION_TEXT_DUAL_ENCODER_START_DOCSTRING) +class FlaxVisionTextDualEncoderModel(FlaxPreTrainedModel): + config_class = VisionTextDualEncoderConfig + module_class = FlaxVisionTextDualEncoderModule + + def __init__( + self, + config: VisionTextDualEncoderConfig, + input_shape: Optional[Tuple] = None, + seed: int = 0, + dtype: jnp.dtype = jnp.float32, + _do_init: bool = True, + **kwargs, + ): + if not _do_init: + raise ValueError( + "`FlaxVisionTextDualEncoderModel` cannot be created without initializing, `_do_init` must be `True`." + ) + + if input_shape is None: + input_shape = ((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3)) + + module = self.module_class(config=config, dtype=dtype, **kwargs) + super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype) + + def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: + # init input tensor + input_ids = jnp.zeros(input_shape[0], dtype="i4") + position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape[0]) + token_type_ids = jnp.ones_like(input_ids) + attention_mask = jnp.ones_like(input_ids) + + pixel_values = jax.random.normal(rng, input_shape[1]) + + params_rng, dropout_rng = jax.random.split(rng) + rngs = {"params": params_rng, "dropout": dropout_rng} + + random_params = self.module.init(rngs, input_ids, pixel_values, attention_mask, position_ids, token_type_ids)[ + "params" + ] + + if params is not None: + random_params = flatten_dict(unfreeze(random_params)) + params = flatten_dict(unfreeze(params)) + for missing_key in self._missing_keys: + params[missing_key] = random_params[missing_key] + self._missing_keys = set() + return freeze(unflatten_dict(params)) + else: + return random_params + + def __call__( + self, + input_ids, + pixel_values, + attention_mask=None, + position_ids=None, + token_type_ids=None, + params: dict = None, + dropout_rng: jax.random.PRNGKey = None, + train: bool = False, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) + + if position_ids is None: + position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) + + if token_type_ids is None: + token_type_ids = jnp.zeros_like(input_ids) + + if attention_mask is None: + attention_mask = jnp.ones_like(input_ids) + + # Handle any PRNG if needed + rngs = {} + if dropout_rng is not None: + rngs["dropout"] = dropout_rng + + return self.module.apply( + {"params": params or self.params}, + jnp.array(input_ids, dtype="i4"), + jnp.array(pixel_values, dtype=jnp.float32), + jnp.array(attention_mask, dtype="i4"), + jnp.array(position_ids, dtype="i4"), + jnp.array(token_type_ids, dtype="i4"), + not train, + output_attentions, + output_hidden_states, + return_dict, + rngs=rngs, + ) + + def get_text_features( + self, + input_ids, + attention_mask=None, + position_ids=None, + token_type_ids=None, + params: dict = None, + dropout_rng: jax.random.PRNGKey = None, + train=False, + ): + r""" + Args: + input_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you + provide it. + + Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + + Returns: + text_features (`jnp.ndarray` of shape `(batch_size, output_dim`): The text embeddings obtained by applying + the projection layer to the pooled output of text model. + """ + if position_ids is None: + position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) + + if token_type_ids is None: + token_type_ids = jnp.zeros_like(input_ids) + + if attention_mask is None: + attention_mask = jnp.ones_like(input_ids) + + # Handle any PRNG if needed + rngs = {} + if dropout_rng is not None: + rngs["dropout"] = dropout_rng + + def _get_features(module, input_ids, attention_mask, position_ids, token_type_ids, deterministic): + text_outputs = module.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + token_type_ids=token_type_ids, + deterministic=deterministic, + ) + pooled_output = text_outputs[1] + text_features = module.text_projection(pooled_output) + return text_features + + return self.module.apply( + {"params": params or self.params}, + jnp.array(input_ids, dtype="i4"), + jnp.array(attention_mask, dtype="i4"), + jnp.array(position_ids, dtype="i4"), + jnp.array(token_type_ids, dtype="i4"), + not train, + method=_get_features, + rngs=rngs, + ) + + def get_image_features( + self, pixel_values, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train=False + ): + r""" + Args: + pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained + using [`ImageFeatureExtractionMixin`]. See [`ImageFeatureExtractionMixin.__call__`] for details. + + Returns: + image_features (`jnp.ndarray` of shape `(batch_size, output_dim`): The image embeddings obtained by + applying the projection layer to the pooled output of vision model. + """ + + # Handle any PRNG if needed + rngs = {} + if dropout_rng is not None: + rngs["dropout"] = dropout_rng + + def _get_features(module, pixel_values, deterministic): + vision_outputs = module.vision_model(pixel_values=pixel_values, deterministic=deterministic) + pooled_output = vision_outputs[1] # pooled_output + image_features = module.visual_projection(pooled_output) + return image_features + + return self.module.apply( + {"params": params or self.params}, + jnp.array(pixel_values, dtype=jnp.float32), + not train, + method=_get_features, + rngs=rngs, + ) + + @classmethod + def from_vision_text_pretrained( + cls, + vision_model_name_or_path: str = None, + text_model_name_or_path: str = None, + *model_args, + **kwargs, + ) -> FlaxPreTrainedModel: + """ + Params: + vision_model_name_or_path (`str`, *optional*, defaults to `None`): + Information necessary to initiate the vision model. Can be either: + + - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. + - A path to a *directory* containing model weights saved using + [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. + - A path or url to a *PyTorch checkpoint folder* (e.g, `./pt_model`). In this case, `from_pt` + should be set to `True` and a configuration object should be provided as `config` argument. This + loading path is slower than converting the PyTorch checkpoint in a Flax model using the provided + conversion scripts and loading the Flax model afterwards. + + text_model_name_or_path (`str`, *optional*): + Information necessary to initiate the text model. Can be either: + + - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. + - A path to a *directory* containing model weights saved using + [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. + - A path or url to a *PyTorch checkpoint folder* (e.g, `./pt_model`). In this case, `from_pt` + should be set to `True` and a configuration object should be provided as `config` argument. This + loading path is slower than converting the PyTorch checkpoint in a Flax model using the provided + conversion scripts and loading the Flax model afterwards. + + model_args (remaining positional arguments, *optional*): + All remaning positional arguments will be passed to the underlying model's `__init__` method. + + kwargs (remaining dictionary of keyword arguments, *optional*): + Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., + `output_attentions=True`). + + - To update the text configuration, use the prefix *text_* for each configuration parameter. + - To update the vision configuration, use the prefix *vision_* for each configuration parameter. + - To update the parent model configuration, do not use a prefix for each configuration parameter. + + Behaves differently depending on whether a `config` is provided or automatically loaded. + + Example: + + ```python + >>> from transformers import FlaxVisionTextDualEncoderModel + + >>> # initialize a model from pretrained ViT and BERT models. Note that the projection layers will be randomly initialized. + >>> model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained( + ... "google/vit-base-patch16-224", "google-bert/bert-base-uncased" + ... ) + >>> # saving model after fine-tuning + >>> model.save_pretrained("./vit-bert") + >>> # load fine-tuned model + >>> model = FlaxVisionTextDualEncoderModel.from_pretrained("./vit-bert") + ```""" + + kwargs_vision = { + argument[len("vision_") :]: value for argument, value in kwargs.items() if argument.startswith("vision_") + } + + kwargs_text = { + argument[len("text_") :]: value for argument, value in kwargs.items() if argument.startswith("text_") + } + + # remove text, vision kwargs from kwargs + for key in kwargs_vision.keys(): + del kwargs["vision_" + key] + for key in kwargs_text.keys(): + del kwargs["text_" + key] + + # Load and initialize the text and vision model + vision_model = kwargs_vision.pop("model", None) + if vision_model is None: + if vision_model_name_or_path is None: + raise ValueError( + "If `vision_model` is not defined as an argument, a `vision_model_name_or_path` has to be defined" + ) + + if "config" not in kwargs_vision: + vision_config = AutoConfig.from_pretrained(vision_model_name_or_path) + + if vision_config.model_type == "clip": + kwargs_vision["config"] = vision_config.vision_config + vision_model = FlaxCLIPVisionModel.from_pretrained( + vision_model_name_or_path, *model_args, **kwargs_vision + ) + else: + kwargs_vision["config"] = vision_config + vision_model = FlaxAutoModel.from_pretrained(vision_model_name_or_path, *model_args, **kwargs_vision) + + text_model = kwargs_text.pop("model", None) + if text_model is None: + if text_model_name_or_path is None: + raise ValueError( + "If `text_model` is not defined as an argument, a `text_model_name_or_path` has to be defined" + ) + + if "config" not in kwargs_text: + text_config = AutoConfig.from_pretrained(text_model_name_or_path) + kwargs_text["config"] = text_config + + text_model = FlaxAutoModel.from_pretrained(text_model_name_or_path, *model_args, **kwargs_text) + + # instantiate config with corresponding kwargs + dtype = kwargs.pop("dtype", jnp.float32) + config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_model.config, text_model.config, **kwargs) + + # init model + model = cls(config, *model_args, dtype=dtype, **kwargs) + + model.params["vision_model"] = vision_model.params + model.params["text_model"] = text_model.params + + # the projection layers are always newly initialized when loading the model + # using pre-trained vision and text model. + logger.warning( + "The projection layer and logit scale weights `[('visual_projection', 'kernel'), ('text_projection'," + " 'kernel'), ('logit_scale',)]` are newly initialized. You should probably TRAIN this model on a" + " down-stream task to be able to use it for predictions and inference." + ) + + return model + + +VISION_TEXT_DUAL_ENCODER_MODEL_DOCSTRING = r""" + Returns: + + Examples: + + ```python + >>> from PIL import Image + >>> import requests + >>> import jax + >>> from transformers import ( + ... FlaxVisionTextDualEncoderModel, + ... VisionTextDualEncoderProcessor, + ... AutoImageProcessor, + ... AutoTokenizer, + ... ) + + >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") + >>> image_processor = AutoImageProcesor.from_pretrained("google/vit-base-patch16-224") + >>> processor = VisionTextDualEncoderProcessor(image_processor, tokenizer) + >>> model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained( + ... "google/vit-base-patch16-224", "google-bert/bert-base-uncased" + ... ) + + >>> # contrastive training + >>> urls = [ + ... "http://images.cocodataset.org/val2017/000000039769.jpg", + ... "https://farm3.staticflickr.com/2674/5850229113_4fe05d5265_z.jpg", + ... ] + >>> images = [Image.open(requests.get(url, stream=True).raw) for url in urls] + >>> inputs = processor( + ... text=["a photo of a cat", "a photo of a dog"], images=images, return_tensors="np", padding=True + ... ) + >>> outputs = model( + ... input_ids=inputs.input_ids, + ... attention_mask=inputs.attention_mask, + ... pixel_values=inputs.pixel_values, + ... ) + >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score + + >>> # save and load from pretrained + >>> model.save_pretrained("vit-bert") + >>> model = FlaxVisionTextDualEncoderModel.from_pretrained("vit-bert") + + >>> # inference + >>> outputs = model(**inputs) + >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score + >>> probs = jax.nn.softmax(logits_per_image, axis=1) # we can take the softmax to get the label probabilities + ``` +""" + +overwrite_call_docstring( + FlaxVisionTextDualEncoderModel, + VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING + VISION_TEXT_DUAL_ENCODER_MODEL_DOCSTRING, +) +append_replace_return_docstrings( + FlaxVisionTextDualEncoderModel, output_type=FlaxCLIPOutput, config_class=_CONFIG_FOR_DOC +) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..6f7e30d3f6fa6f23c3c963746aa52d23073daa3a --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py @@ -0,0 +1,622 @@ +# coding=utf-8 +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""TensorFlow VisionTextDualEncoder model.""" + + +from __future__ import annotations + +import re +from typing import Optional, Tuple, Union + +import tensorflow as tf + +from ...configuration_utils import PretrainedConfig +from ...modeling_tf_utils import TFPreTrainedModel, keras, unpack_inputs +from ...tf_utils import shape_list +from ...utils import ( + DUMMY_INPUTS, + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) +from ..auto.configuration_auto import AutoConfig +from ..auto.modeling_tf_auto import TFAutoModel +from ..clip.modeling_tf_clip import CLIPVisionConfig, TFCLIPOutput, TFCLIPVisionModel +from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "VisionTextDualEncoderConfig" + +VISION_TEXT_DUAL_ENCODER_START_DOCSTRING = r""" + This class can be used to initialize a vision-text dual encoder model with any pretrained vision autoencoding model + as the vision encoder and any pretrained text model as the text encoder. The vision and text encoders are loaded + via the [`~TFAutoModel.from_pretrained`] method. The projection layers are automatically added to the model and + should be fine-tuned on a downstream task, like contrastive image-text modeling. + + In [LiT: Zero-Shot Transfer with Locked-image Text Tuning](https://arxiv.org/abs/2111.07991) it is shown how + leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvment + on new zero-shot vision tasks such as image classification or retrieval. + + After such a Vision-Text-Dual-Encoder model has been trained/fine-tuned, it can be saved/loaded just like any other + models (see the examples for more information). + + This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a Keras [Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a + regular Keras Model and refer to the TF documentation for all matter related to general usage and behavior. + + Parameters: + config ([`VisionEncoderDecoderConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +VISION_TEXT_DUAL_ENCODER_TEXT_INPUTS_DOCSTRING = r""" + Args: + input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + +VISION_TEXT_DUAL_ENCODER_VISION_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using + [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + +VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING = r""" + Args: + input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using + an image processor (e.g. if you use ViT as the encoder, you should use [`AutoImageProcessor`]). See + [`ViTImageProcessor.__call__`] for details. + return_loss (`bool`, *optional*): + Whether or not to return the contrastive loss. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +# Copied from transformers.models.clip.modeling_tf_clip.contrastive_loss +def contrastive_loss(logits: tf.Tensor) -> tf.Tensor: + return tf.math.reduce_mean( + keras.metrics.sparse_categorical_crossentropy( + y_true=tf.range(shape_list(logits)[0]), y_pred=logits, from_logits=True + ) + ) + + +# Copied from transformers.models.clip.modeling_tf_clip.clip_loss +def clip_loss(similarity: tf.Tensor) -> tf.Tensor: + caption_loss = contrastive_loss(similarity) + image_loss = contrastive_loss(tf.transpose(similarity)) + return (caption_loss + image_loss) / 2.0 + + +@add_start_docstrings(VISION_TEXT_DUAL_ENCODER_START_DOCSTRING) +class TFVisionTextDualEncoderModel(TFPreTrainedModel): + config_class = VisionTextDualEncoderConfig + base_model_prefix = "vision_text_dual_encoder" + load_weight_prefix = "tf_vision_text_dual_encoder_model" + + def __init__( + self, + config: Optional[VisionTextDualEncoderConfig] = None, + vision_model: Optional[TFPreTrainedModel] = None, + text_model: Optional[TFPreTrainedModel] = None, + ): + if config is None and (vision_model is None or text_model is None): + raise ValueError("Either a configuration or an vision and a text model has to be provided") + + if config is None: + config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_model.config, text_model.config) + else: + if not isinstance(config, self.config_class): + raise ValueError(f"config: {config} has to be of type {self.config_class}") + + # initialize with config + super().__init__(config) + + if vision_model is None: + if isinstance(config.vision_config, CLIPVisionConfig): + vision_model = TFCLIPVisionModel.from_config(config.vision_config, name="vision_model") + else: + vision_model = TFAutoModel.from_config(config.vision_config, name="vision_model") + + if text_model is None: + text_model = TFAutoModel.from_config(config.text_config, name="text_model") + + self.vision_model = vision_model + self.text_model = text_model + + # make sure that the individual model's config refers to the shared config + # so that the updates to the config will be synced + self.vision_model.config = self.config.vision_config + self.text_model.config = self.config.text_config + + self.vision_embed_dim = config.vision_config.hidden_size + self.text_embed_dim = config.text_config.hidden_size + self.projection_dim = config.projection_dim + + self.visual_projection = keras.layers.Dense(self.projection_dim, use_bias=False, name="visual_projection") + self.text_projection = keras.layers.Dense(self.projection_dim, use_bias=False, name="text_projection") + self.logit_scale = None + self.config = config + + def build(self, input_shape=None): + if self.built: + return + self.built = True + # Build in the build() method to make sure the names are right + initializer = keras.initializers.Constant(self.config.logit_scale_init_value) + self.logit_scale = self.add_weight(shape=(1,), initializer=initializer, name="logit_scale") + + if getattr(self, "visual_projection", None) is not None: + with tf.name_scope(self.visual_projection.name): + self.visual_projection.build([None, None, self.vision_embed_dim]) + if getattr(self, "text_projection", None) is not None: + with tf.name_scope(self.text_projection.name): + self.text_projection.build([None, None, self.text_embed_dim]) + with tf.name_scope(self.vision_model.name): + self.vision_model.build(None) + with tf.name_scope(self.text_model.name): + self.text_model.build(None) + + def tf_to_pt_weight_rename(self, tf_weight): + # Matt: The TF and PT weights don't align because our TF base classes have an extra layer compared to PT models + # (the main model stem is in the MainLayer class). If we remove that layer, then weight names sync up as normal. + # However, the name of that extra layer is the name of the MainLayer in the base model. + if "vision_model" in tf_weight: + if tf_weight.count("vision_model") == 1: + return (re.sub(r"vision_model\..*?\.", "vision_model.", tf_weight),) + elif tf_weight.count("vision_model") == 2: + return (re.sub(r"vision_model\..*?\.vision_model", "vision_model.vision_model", tf_weight),) + else: + raise ValueError( + f"Unexpected weight name {tf_weight}. Please file an issue on the" + " Transformers repo to let us know about this error!" + ) + elif "text_model" in tf_weight: + return (re.sub(r"text_model\..*?\.", "text_model.", tf_weight),) + else: + return (tf_weight,) + + @add_start_docstrings_to_model_forward(VISION_TEXT_DUAL_ENCODER_TEXT_INPUTS_DOCSTRING) + def get_text_features( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + token_type_ids=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + r""" + Returns: + text_features (`tf.Tensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying + the projection layer to the pooled output of [`TFCLIPTextModel`]. + + Examples: + + ```python + >>> from transformers import TFVisionTextDualEncoderModel, AutoTokenizer + + >>> model = TFVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian", from_pt=True) + >>> tokenizer = AutoTokenizer.from_pretrained("clip-italian/clip-italian") + + >>> inputs = tokenizer(["una foto di un gatto", "una foto di un cane"], padding=True, return_tensors="np") + >>> text_features = model.get_text_features(**inputs) + ```""" + text_outputs = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + token_type_ids=token_type_ids, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + pooled_output = text_outputs[1] + text_features = self.text_projection(pooled_output) + + return text_features + + @add_start_docstrings_to_model_forward(VISION_TEXT_DUAL_ENCODER_VISION_INPUTS_DOCSTRING) + def get_image_features( + self, + pixel_values=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + r""" + Returns: + image_features (`tf.Tensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying + the projection layer to the pooled output of [`TFCLIPVisionModel`]. + + Examples: + + ```python + >>> from PIL import Image + >>> import requests + >>> from transformers import TFVisionTextDualEncoderModel, AutoImageProcessor + + >>> model = TFVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian", from_pt=True) + >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> inputs = image_processor(images=image, return_tensors="np") + + >>> image_features = model.get_image_features(**inputs) + ```""" + vision_outputs = self.vision_model( + pixel_values=pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + pooled_output = vision_outputs[1] # pooled_output + image_features = self.visual_projection(pooled_output) + + return image_features + + @unpack_inputs + @add_start_docstrings_to_model_forward(VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=TFCLIPOutput, config_class=_CONFIG_FOR_DOC) + def call( + self, + input_ids: tf.Tensor | None = None, + pixel_values: tf.Tensor | None = None, + attention_mask: tf.Tensor | None = None, + position_ids: tf.Tensor | None = None, + return_loss: Optional[bool] = None, + token_type_ids: tf.Tensor | None = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + training: bool = False, + ) -> Union[Tuple[tf.Tensor], TFCLIPOutput]: + r""" + Returns: + + Examples: + + ```python + >>> from PIL import Image + >>> import requests + >>> from transformers import ( + ... TFVisionTextDualEncoderModel, + ... VisionTextDualEncoderProcessor, + ... AutoImageProcessor, + ... AutoTokenizer, + ... ) + + >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") + >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224") + >>> processor = VisionTextDualEncoderProcessor(image_processor, tokenizer) + >>> model = TFVisionTextDualEncoderModel.from_vision_text_pretrained( + ... "google/vit-base-patch16-224", "google-bert/bert-base-uncased" + ... ) + + >>> # contrastive training + >>> urls = [ + ... "http://images.cocodataset.org/val2017/000000039769.jpg", + ... "https://farm3.staticflickr.com/2674/5850229113_4fe05d5265_z.jpg", + ... ] + >>> images = [Image.open(requests.get(url, stream=True).raw) for url in urls] + >>> inputs = processor( + ... text=["a photo of a cat", "a photo of a dog"], images=images, return_tensors="np", padding=True + ... ) + >>> outputs = model( + ... input_ids=inputs.input_ids, + ... attention_mask=inputs.attention_mask, + ... pixel_values=inputs.pixel_values, + ... return_loss=True, + ... ) + >>> loss, logits_per_image = outputs.loss, outputs.logits_per_image # this is the image-text similarity score + + >>> # save and load from pretrained + >>> model.save_pretrained("vit-bert") + >>> model = TFVisionTextDualEncoderModel.from_pretrained("vit-bert") + + >>> # inference + >>> outputs = model(**inputs) + >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score + >>> probs = tf.nn.softmax(logits_per_image, axis=1) # we can take the softmax to get the label probabilities + ```""" + return_dict = return_dict if return_dict is not None else self.config.return_dict + + vision_outputs = self.vision_model( + pixel_values=pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + training=training, + ) + + text_outputs = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + training=training, + ) + + image_embeds = vision_outputs[1] # pooler_output + image_embeds = self.visual_projection(image_embeds) + + text_embeds = text_outputs[1] # pooler_output + text_embeds = self.text_projection(text_embeds) + + # normalized features + image_embeds = image_embeds / tf.norm(image_embeds, axis=-1, keepdims=True) + text_embeds = text_embeds / tf.norm(text_embeds, axis=-1, keepdims=True) + + # cosine similarity as logits + logit_scale = tf.math.exp(self.logit_scale) + logits_per_text = tf.matmul(text_embeds, image_embeds, transpose_b=True) * logit_scale + logits_per_image = tf.transpose(logits_per_text) + + loss = None + if return_loss: + loss = clip_loss(logits_per_text) + if loss.shape.rank == 0: + loss = tf.expand_dims(loss, 0) + + if not return_dict: + output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs) + return ((loss,) + output) if loss is not None else output + + return TFCLIPOutput( + loss=loss, + logits_per_image=logits_per_image, + logits_per_text=logits_per_text, + text_embeds=text_embeds, + image_embeds=image_embeds, + text_model_output=text_outputs, + vision_model_output=vision_outputs, + ) + + @classmethod + def from_vision_text_pretrained( + cls, + vision_model_name_or_path: str = None, + text_model_name_or_path: str = None, + *model_args, + **kwargs, + ) -> TFPreTrainedModel: + """ + Params: + vision_model_name_or_path (`str`, *optional*, defaults to `None`): + Information necessary to initiate the vision model. Can be either: + + - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. + - A path to a *directory* containing model weights saved using + [`~TFPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. + - A path or url to a *PyTorch checkpoint folder* (e.g, `./pt_model`). In this case, `from_pt` + should be set to `True` and a configuration object should be provided as `config` argument. + + text_model_name_or_path (`str`, *optional*): + Information necessary to initiate the text model. Can be either: + + - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. + - A path to a *directory* containing model weights saved using + [`~TFPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. + - A path or url to a *PyTorch checkpoint folder* (e.g, `./pt_model`). In this case, `from_pt` + should be set to `True` and a configuration object should be provided as `config` argument. + + model_args (remaining positional arguments, *optional*): + All remaning positional arguments will be passed to the underlying model's `__init__` method. + + kwargs (remaining dictionary of keyword arguments, *optional*): + Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., + `output_attentions=True`). + + - To update the text configuration, use the prefix *text_* for each configuration parameter. + - To update the vision configuration, use the prefix *vision_* for each configuration parameter. + - To update the parent model configuration, do not use a prefix for each configuration parameter. + + Behaves differently depending on whether a `config` is provided or automatically loaded. + + Example: + + ```python + >>> from transformers import TFVisionTextDualEncoderModel + + >>> # initialize a model from pretrained ViT and BERT models. Note that the projection layers will be randomly initialized. + >>> model = TFVisionTextDualEncoderModel.from_vision_text_pretrained( + ... "google/vit-base-patch16-224", "google-bert/bert-base-uncased" + ... ) + >>> # saving model after fine-tuning + >>> model.save_pretrained("./vit-bert") + >>> # load fine-tuned model + >>> model = TFVisionTextDualEncoderModel.from_pretrained("./vit-bert") + ```""" + kwargs_vision = { + argument[len("vision_") :]: value for argument, value in kwargs.items() if argument.startswith("vision_") + } + + kwargs_text = { + argument[len("text_") :]: value for argument, value in kwargs.items() if argument.startswith("text_") + } + + # remove vision, text kwargs from kwargs + for key in kwargs_vision.keys(): + del kwargs["vision_" + key] + for key in kwargs_text.keys(): + del kwargs["text_" + key] + + # Load and initialize the vision and text model + vision_model = kwargs_vision.pop("model", None) + if vision_model is None: + if vision_model_name_or_path is None: + raise ValueError( + "If `vision_model` is not defined as an argument, a `vision_model_name_or_path` has to be defined" + ) + kwargs_vision["name"] = "vision_model" + kwargs_vision["load_weight_prefix"] = cls.load_weight_prefix + + vision_config_dict, unused_args = PretrainedConfig.get_config_dict(vision_model_name_or_path, **kwargs) + if vision_config_dict.get("model_type", None) == "clip_vision_model": + vision_config = CLIPVisionConfig.from_dict(vision_config_dict) + else: + vision_config = AutoConfig.from_pretrained(vision_model_name_or_path) + + if vision_config.model_type == "clip_vision_model": + kwargs_vision["config"] = vision_config + vision_class = TFCLIPVisionModel + elif vision_config.model_type == "clip": + kwargs_vision["config"] = vision_config.vision_config + vision_class = TFCLIPVisionModel + else: + kwargs_vision["config"] = vision_config + vision_class = TFAutoModel + vision_model = vision_class.from_pretrained(vision_model_name_or_path, *model_args, **kwargs_vision) + + text_model = kwargs_text.pop("model", None) + if text_model is None: + if text_model_name_or_path is None: + raise ValueError( + "If `text_model` is not defined as an argument, a `text_model_name_or_path` has to be defined" + ) + kwargs_text["name"] = "text_model" + kwargs_text["load_weight_prefix"] = cls.load_weight_prefix + + if "config" not in kwargs_text: + text_config = AutoConfig.from_pretrained(text_model_name_or_path) + kwargs_text["config"] = text_config + + text_model = TFAutoModel.from_pretrained(text_model_name_or_path, *model_args, **kwargs_text) + + # instantiate config with corresponding kwargs + config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_model.config, text_model.config, **kwargs) + + # init model + model = cls(config=config, vision_model=vision_model, text_model=text_model) + + # the projection layers are always newly initialized when loading the model + # using pre-trained vision and text model. + logger.warning( + "The projection layer and logit scale weights `['visual_projection.weight', 'text_projection.weight'," + " 'logit_scale']` are newly initialized. You should probably TRAIN this model on a down-stream task to be" + " able to use it for predictions and inference." + ) + + if vision_model.name != "vision_model": + raise ValueError("vision model must be created with the name `vision_model`.") + if text_model.name != "text_model": + raise ValueError("text model must be created with the name `text_model`.") + + model.build_in_name_scope() # Ensure model is fully built + + return model + + @property + def dummy_inputs(self): + """ + Dummy inputs to build the network. + + Returns: + `Dict[str, tf.Tensor]`: The dummy inputs. + """ + input_ids = tf.constant(DUMMY_INPUTS, dtype=tf.int32) + batch_size, seq_len = input_ids.shape + + VISION_DUMMY_INPUTS = tf.random.uniform( + shape=( + batch_size, + self.config.vision_config.num_channels, + self.config.vision_config.image_size, + self.config.vision_config.image_size, + ), + dtype=tf.float32, + ) + pixel_values = tf.constant(VISION_DUMMY_INPUTS) + dummy = {"pixel_values": pixel_values, "input_ids": input_ids} + return dummy diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..cd4d5bd7a1f1971656a1c188ec0689d619f77116 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py @@ -0,0 +1,533 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch VisionTextDualEncoder model.""" + + +from typing import Optional, Tuple, Union + +import torch +from torch import nn + +from ...modeling_utils import PreTrainedModel +from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings +from ..auto.configuration_auto import AutoConfig +from ..auto.modeling_auto import AutoModel +from ..clip.modeling_clip import CLIPOutput, CLIPVisionConfig, CLIPVisionModel +from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "VisionTextDualEncoderConfig" + +VISION_TEXT_DUAL_ENCODER_START_DOCSTRING = r""" + This class can be used to initialize a vision-text dual encoder model with any pretrained vision autoencoding model + as the vision encoder and any pretrained text model as the text encoder. The vision and text encoders are loaded + via the [`~AutoModel.from_pretrained`] method. The projection layers are automatically added to the model and + should be fine-tuned on a downstream task, like contrastive image-text modeling. + + In [LiT: Zero-Shot Transfer with Locked-image Text Tuning](https://arxiv.org/abs/2111.07991) it is shown how + leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvment + on new zero-shot vision tasks such as image classification or retrieval. + + After such a Vision-Text-Dual-Encoder model has been trained/fine-tuned, it can be saved/loaded just like any other + models (see the examples for more information). + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`VisionEncoderDecoderConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +VISION_TEXT_DUAL_ENCODER_TEXT_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + +VISION_TEXT_DUAL_ENCODER_VISION_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using + [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + +VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using + an image processor (e.g. if you use ViT as the encoder, you should use [`AutoImageProcessor`]). See + [`ViTImageProcessor.__call__`] for details. + return_loss (`bool`, *optional*): + Whether or not to return the contrastive loss. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +# Copied from transformers.models.clip.modeling_clip.contrastive_loss +def contrastive_loss(logits: torch.Tensor) -> torch.Tensor: + return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device)) + + +# Copied from transformers.models.clip.modeling_clip.clip_loss +def clip_loss(similarity: torch.Tensor) -> torch.Tensor: + caption_loss = contrastive_loss(similarity) + image_loss = contrastive_loss(similarity.t()) + return (caption_loss + image_loss) / 2.0 + + +@add_start_docstrings(VISION_TEXT_DUAL_ENCODER_START_DOCSTRING) +class VisionTextDualEncoderModel(PreTrainedModel): + config_class = VisionTextDualEncoderConfig + base_model_prefix = "vision_text_dual_encoder" + + def __init__( + self, + config: Optional[VisionTextDualEncoderConfig] = None, + vision_model: Optional[PreTrainedModel] = None, + text_model: Optional[PreTrainedModel] = None, + ): + if config is None and (vision_model is None or text_model is None): + raise ValueError("Either a configuration or an vision and a text model has to be provided") + + if config is None: + config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_model.config, text_model.config) + else: + if not isinstance(config, self.config_class): + raise ValueError(f"config: {config} has to be of type {self.config_class}") + + # initialize with config + super().__init__(config) + + if vision_model is None: + if isinstance(config.vision_config, CLIPVisionConfig): + vision_model = CLIPVisionModel(config.vision_config) + else: + vision_model = AutoModel.from_config(config.vision_config) + + if text_model is None: + text_model = AutoModel.from_config(config.text_config) + + self.vision_model = vision_model + self.text_model = text_model + + # make sure that the individual model's config refers to the shared config + # so that the updates to the config will be synced + self.vision_model.config = self.config.vision_config + self.text_model.config = self.config.text_config + + self.vision_embed_dim = config.vision_config.hidden_size + self.text_embed_dim = config.text_config.hidden_size + self.projection_dim = config.projection_dim + + self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False) + self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False) + self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value)) + + @add_start_docstrings_to_model_forward(VISION_TEXT_DUAL_ENCODER_TEXT_INPUTS_DOCSTRING) + def get_text_features( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + token_type_ids=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + r""" + Returns: + text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by + applying the projection layer to the pooled output of [`CLIPTextModel`]. + + Examples: + + ```python + >>> from transformers import VisionTextDualEncoderModel, AutoTokenizer + + >>> model = VisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian") + >>> tokenizer = AutoTokenizer.from_pretrained("clip-italian/clip-italian") + + >>> inputs = tokenizer(["una foto di un gatto", "una foto di un cane"], padding=True, return_tensors="pt") + >>> text_features = model.get_text_features(**inputs) + ```""" + text_outputs = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + token_type_ids=token_type_ids, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + pooled_output = text_outputs[1] + text_features = self.text_projection(pooled_output) + + return text_features + + @add_start_docstrings_to_model_forward(VISION_TEXT_DUAL_ENCODER_VISION_INPUTS_DOCSTRING) + def get_image_features( + self, + pixel_values=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + r""" + Returns: + image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by + applying the projection layer to the pooled output of [`CLIPVisionModel`]. + + Examples: + + ```python + >>> from PIL import Image + >>> import requests + >>> from transformers import VisionTextDualEncoderModel, AutoImageProcessor + + >>> model = VisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian") + >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> inputs = image_processor(images=image, return_tensors="pt") + + >>> image_features = model.get_image_features(**inputs) + ```""" + vision_outputs = self.vision_model( + pixel_values=pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + pooled_output = vision_outputs[1] # pooled_output + image_features = self.visual_projection(pooled_output) + + return image_features + + @add_start_docstrings_to_model_forward(VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CLIPOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + pixel_values: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + return_loss: Optional[bool] = None, + token_type_ids: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor], CLIPOutput]: + r""" + Returns: + + Examples: + + ```python + >>> from PIL import Image + >>> import requests + >>> from transformers import ( + ... VisionTextDualEncoderModel, + ... VisionTextDualEncoderProcessor, + ... AutoImageProcessor, + ... AutoTokenizer, + ... ) + + >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") + >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224") + >>> processor = VisionTextDualEncoderProcessor(image_processor, tokenizer) + >>> model = VisionTextDualEncoderModel.from_vision_text_pretrained( + ... "google/vit-base-patch16-224", "google-bert/bert-base-uncased" + ... ) + + >>> # contrastive training + >>> urls = [ + ... "http://images.cocodataset.org/val2017/000000039769.jpg", + ... "https://farm3.staticflickr.com/2674/5850229113_4fe05d5265_z.jpg", + ... ] + >>> images = [Image.open(requests.get(url, stream=True).raw) for url in urls] + >>> inputs = processor( + ... text=["a photo of a cat", "a photo of a dog"], images=images, return_tensors="pt", padding=True + ... ) + >>> outputs = model( + ... input_ids=inputs.input_ids, + ... attention_mask=inputs.attention_mask, + ... pixel_values=inputs.pixel_values, + ... return_loss=True, + ... ) + >>> loss, logits_per_image = outputs.loss, outputs.logits_per_image # this is the image-text similarity score + + >>> # save and load from pretrained + >>> model.save_pretrained("vit-bert") + >>> model = VisionTextDualEncoderModel.from_pretrained("vit-bert") + + >>> # inference + >>> outputs = model(**inputs) + >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score + >>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities + ```""" + return_dict = return_dict if return_dict is not None else self.config.return_dict + + vision_outputs = self.vision_model( + pixel_values=pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + text_outputs = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + image_embeds = vision_outputs[1] # pooler_output + image_embeds = self.visual_projection(image_embeds) + + text_embeds = text_outputs[1] # pooler_output + text_embeds = self.text_projection(text_embeds) + + # normalized features + image_embeds = image_embeds / image_embeds.norm(dim=-1, keepdim=True) + text_embeds = text_embeds / text_embeds.norm(dim=-1, keepdim=True) + + # cosine similarity as logits + logit_scale = self.logit_scale.exp() + logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale + logits_per_image = logits_per_text.T + + loss = None + if return_loss: + loss = clip_loss(logits_per_text) + + if not return_dict: + output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs) + return ((loss,) + output) if loss is not None else output + + return CLIPOutput( + loss=loss, + logits_per_image=logits_per_image, + logits_per_text=logits_per_text, + text_embeds=text_embeds, + image_embeds=image_embeds, + text_model_output=text_outputs, + vision_model_output=vision_outputs, + ) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + # At the moment fast initialization is not supported + # for composite models + kwargs["_fast_init"] = False + return super().from_pretrained(*args, **kwargs) + + @classmethod + def from_vision_text_pretrained( + cls, + vision_model_name_or_path: str = None, + text_model_name_or_path: str = None, + *model_args, + **kwargs, + ) -> PreTrainedModel: + """ + Params: + vision_model_name_or_path (`str`, *optional*, defaults to `None`): + Information necessary to initiate the vision model. Can be either: + + - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. + - A path to a *directory* containing model weights saved using + [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. + - A path or url to a *PyTorch checkpoint folder* (e.g, `./pt_model`). In this case, `from_pt` + should be set to `True` and a configuration object should be provided as `config` argument. This + loading path is slower than converting the PyTorch checkpoint in a Flax model using the provided + conversion scripts and loading the Flax model afterwards. + + text_model_name_or_path (`str`, *optional*): + Information necessary to initiate the text model. Can be either: + + - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. + - A path to a *directory* containing model weights saved using + [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. + - A path or url to a *PyTorch checkpoint folder* (e.g, `./pt_model`). In this case, `from_pt` + should be set to `True` and a configuration object should be provided as `config` argument. This + loading path is slower than converting the PyTorch checkpoint in a Flax model using the provided + conversion scripts and loading the Flax model afterwards. + + model_args (remaining positional arguments, *optional*): + All remaning positional arguments will be passed to the underlying model's `__init__` method. + + kwargs (remaining dictionary of keyword arguments, *optional*): + Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., + `output_attentions=True`). + + - To update the text configuration, use the prefix *text_* for each configuration parameter. + - To update the vision configuration, use the prefix *vision_* for each configuration parameter. + - To update the parent model configuration, do not use a prefix for each configuration parameter. + + Behaves differently depending on whether a `config` is provided or automatically loaded. + + Example: + + ```python + >>> from transformers import VisionTextDualEncoderModel + + >>> # initialize a model from pretrained ViT and BERT models. Note that the projection layers will be randomly initialized. + >>> model = VisionTextDualEncoderModel.from_vision_text_pretrained( + ... "google/vit-base-patch16-224", "google-bert/bert-base-uncased" + ... ) + >>> # saving model after fine-tuning + >>> model.save_pretrained("./vit-bert") + >>> # load fine-tuned model + >>> model = VisionTextDualEncoderModel.from_pretrained("./vit-bert") + ```""" + kwargs_vision = { + argument[len("vision_") :]: value for argument, value in kwargs.items() if argument.startswith("vision_") + } + + kwargs_text = { + argument[len("text_") :]: value for argument, value in kwargs.items() if argument.startswith("text_") + } + + # remove vision, text kwargs from kwargs + for key in kwargs_vision.keys(): + del kwargs["vision_" + key] + for key in kwargs_text.keys(): + del kwargs["text_" + key] + + # Load and initialize the vision and text model + vision_model = kwargs_vision.pop("model", None) + if vision_model is None: + if vision_model_name_or_path is None: + raise ValueError( + "If `vision_model` is not defined as an argument, a `vision_model_name_or_path` has to be defined" + ) + + if "config" not in kwargs_vision: + vision_config = AutoConfig.from_pretrained(vision_model_name_or_path) + + if vision_config.model_type == "clip": + kwargs_vision["config"] = vision_config.vision_config + vision_model = CLIPVisionModel.from_pretrained(vision_model_name_or_path, *model_args, **kwargs_vision) + # TODO: Should we use the pre-trained projection as well ? + else: + kwargs_vision["config"] = vision_config + vision_model = AutoModel.from_pretrained(vision_model_name_or_path, *model_args, **kwargs_vision) + + text_model = kwargs_text.pop("model", None) + if text_model is None: + if text_model_name_or_path is None: + raise ValueError( + "If `text_model` is not defined as an argument, a `text_model_name_or_path` has to be defined" + ) + + if "config" not in kwargs_text: + text_config = AutoConfig.from_pretrained(text_model_name_or_path) + kwargs_text["config"] = text_config + + text_model = AutoModel.from_pretrained(text_model_name_or_path, *model_args, **kwargs_text) + + # instantiate config with corresponding kwargs + config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_model.config, text_model.config, **kwargs) + + # init model + model = cls(config=config, vision_model=vision_model, text_model=text_model) + + # the projection layers are always newly initialized when loading the model + # using pre-trained vision and text model. + logger.warning( + "The projection layer and logit scale weights `['visual_projection.weight', 'text_projection.weight'," + " 'logit_scale']` are newly initialized. You should probably TRAIN this model on a down-stream task to be" + " able to use it for predictions and inference." + ) + + return model diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/processing_vision_text_dual_encoder.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/processing_vision_text_dual_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..0d723ed10bf067b5ab3f0dbf35bbc2eb14e178f7 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vision_text_dual_encoder/processing_vision_text_dual_encoder.py @@ -0,0 +1,150 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Processor class for VisionTextDualEncoder +""" + +import warnings + +from ...processing_utils import ProcessorMixin +from ...tokenization_utils_base import BatchEncoding + + +class VisionTextDualEncoderProcessor(ProcessorMixin): + r""" + Constructs a VisionTextDualEncoder processor which wraps an image processor and a tokenizer into a single + processor. + + [`VisionTextDualEncoderProcessor`] offers all the functionalities of [`AutoImageProcessor`] and [`AutoTokenizer`]. + See the [`~VisionTextDualEncoderProcessor.__call__`] and [`~VisionTextDualEncoderProcessor.decode`] for more + information. + + Args: + image_processor ([`AutoImageProcessor`], *optional*): + The image processor is a required input. + tokenizer ([`PreTrainedTokenizer`], *optional*): + The tokenizer is a required input. + """ + + attributes = ["image_processor", "tokenizer"] + image_processor_class = "AutoImageProcessor" + tokenizer_class = "AutoTokenizer" + + def __init__(self, image_processor=None, tokenizer=None, **kwargs): + feature_extractor = None + if "feature_extractor" in kwargs: + warnings.warn( + "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`" + " instead.", + FutureWarning, + ) + feature_extractor = kwargs.pop("feature_extractor") + + image_processor = image_processor if image_processor is not None else feature_extractor + if image_processor is None: + raise ValueError("You have to specify an image_processor.") + if tokenizer is None: + raise ValueError("You have to specify a tokenizer.") + + super().__init__(image_processor, tokenizer) + self.current_processor = self.image_processor + + def __call__(self, text=None, images=None, return_tensors=None, **kwargs): + """ + Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` + and `kwargs` arguments to VisionTextDualEncoderTokenizer's [`~PreTrainedTokenizer.__call__`] if `text` is not + `None` to encode the text. To prepare the image(s), this method forwards the `images` and `kwargs` arguments to + AutoImageProcessor's [`~AutoImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring + of the above two methods for more information. + + Args: + text (`str`, `List[str]`, `List[List[str]]`): + The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings + (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set + `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). + images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): + The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch + tensor. Both channels-first and channels-last formats are supported. + + return_tensors (`str` or [`~utils.TensorType`], *optional*): + If set, will return tensors of a particular framework. Acceptable values are: + + - `'tf'`: Return TensorFlow `tf.constant` objects. + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return NumPy `np.ndarray` objects. + - `'jax'`: Return JAX `jnp.ndarray` objects. + + Returns: + [`BatchEncoding`]: A [`BatchEncoding`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not + `None`). + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + """ + + if text is None and images is None: + raise ValueError("You have to specify either text or images. Both cannot be none.") + + if text is not None: + encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs) + + if images is not None: + image_features = self.image_processor(images, return_tensors=return_tensors, **kwargs) + + if text is not None and images is not None: + encoding["pixel_values"] = image_features.pixel_values + return encoding + elif text is not None: + return encoding + else: + return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors) + + def batch_decode(self, *args, **kwargs): + """ + This method forwards all its arguments to VisionTextDualEncoderTokenizer's + [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. + """ + return self.tokenizer.batch_decode(*args, **kwargs) + + def decode(self, *args, **kwargs): + """ + This method forwards all its arguments to VisionTextDualEncoderTokenizer's [`~PreTrainedTokenizer.decode`]. + Please refer to the docstring of this method for more information. + """ + return self.tokenizer.decode(*args, **kwargs) + + @property + def model_input_names(self): + tokenizer_input_names = self.tokenizer.model_input_names + image_processor_input_names = self.image_processor.model_input_names + return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) + + @property + def feature_extractor_class(self): + warnings.warn( + "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.", + FutureWarning, + ) + return self.image_processor_class + + @property + def feature_extractor(self): + warnings.warn( + "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.", + FutureWarning, + ) + return self.image_processor diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/visual_bert/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/visual_bert/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a752f1fa0c147676b75cd35e5a6a37bef6a62333 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/visual_bert/__init__.py @@ -0,0 +1,65 @@ +# Copyright 2021 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available + + +_import_structure = {"configuration_visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"]} + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_visual_bert"] = [ + "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "VisualBertForMultipleChoice", + "VisualBertForPreTraining", + "VisualBertForQuestionAnswering", + "VisualBertForRegionToPhraseAlignment", + "VisualBertForVisualReasoning", + "VisualBertLayer", + "VisualBertModel", + "VisualBertPreTrainedModel", + ] + + +if TYPE_CHECKING: + from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_visual_bert import ( + VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST, + VisualBertForMultipleChoice, + VisualBertForPreTraining, + VisualBertForQuestionAnswering, + VisualBertForRegionToPhraseAlignment, + VisualBertForVisualReasoning, + VisualBertLayer, + VisualBertModel, + VisualBertPreTrainedModel, + ) + + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, 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a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/visual_bert/configuration_visual_bert.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/visual_bert/configuration_visual_bert.py new file mode 100644 index 0000000000000000000000000000000000000000..2edf5466e347b8ec51ae3f84e90668c053e8750d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/visual_bert/configuration_visual_bert.py @@ -0,0 +1,135 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" VisualBERT model configuration""" + +from ...configuration_utils import PretrainedConfig +from ...utils import logging + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class VisualBertConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`VisualBertModel`]. It is used to instantiate an + VisualBERT model according to the specified arguments, defining the model architecture. Instantiating a + configuration with the defaults will yield a similar configuration to that of the VisualBERT + [uclanlp/visualbert-vqa-coco-pre](https://huggingface.co/uclanlp/visualbert-vqa-coco-pre) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 30522): + Vocabulary size of the VisualBERT model. Defines the number of different tokens that can be represented by + the `inputs_ids` passed when calling [`VisualBertModel`]. Vocabulary size of the model. Defines the + different tokens that can be represented by the `inputs_ids` passed to the forward method of + [`VisualBertModel`]. + hidden_size (`int`, *optional*, defaults to 768): + Dimensionality of the encoder layers and the pooler layer. + visual_embedding_dim (`int`, *optional*, defaults to 512): + Dimensionality of the visual embeddings to be passed to the model. + num_hidden_layers (`int`, *optional*, defaults to 12): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 12): + Number of attention heads for each attention layer in the Transformer encoder. + intermediate_size (`int`, *optional*, defaults to 3072): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"` and `"gelu_new"` are supported. + hidden_dropout_prob (`float`, *optional*, defaults to 0.1): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): + The dropout ratio for the attention probabilities. + max_position_embeddings (`int`, *optional*, defaults to 512): + The maximum sequence length that this model might ever be used with. Typically set this to something large + just in case (e.g., 512 or 1024 or 2048). + type_vocab_size (`int`, *optional*, defaults to 2): + The vocabulary size of the `token_type_ids` passed when calling [`VisualBertModel`]. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + layer_norm_eps (`float`, *optional*, defaults to 1e-12): + The epsilon used by the layer normalization layers. + bypass_transformer (`bool`, *optional*, defaults to `False`): + Whether or not the model should bypass the transformer for the visual embeddings. If set to `True`, the + model directly concatenates the visual embeddings from [`VisualBertEmbeddings`] with text output from + transformers, and then pass it to a self-attention layer. + special_visual_initialize (`bool`, *optional*, defaults to `True`): + Whether or not the visual token type and position type embedding weights should be initialized the same as + the textual token type and positive type embeddings. When set to `True`, the weights of the textual token + type and position type embeddings are copied to the respective visual embedding layers. + + + Example: + + ```python + >>> from transformers import VisualBertConfig, VisualBertModel + + >>> # Initializing a VisualBERT visualbert-vqa-coco-pre style configuration + >>> configuration = VisualBertConfig.from_pretrained("uclanlp/visualbert-vqa-coco-pre") + + >>> # Initializing a model (with random weights) from the visualbert-vqa-coco-pre style configuration + >>> model = VisualBertModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "visual_bert" + + def __init__( + self, + vocab_size=30522, + hidden_size=768, + visual_embedding_dim=512, + num_hidden_layers=12, + num_attention_heads=12, + intermediate_size=3072, + hidden_act="gelu", + hidden_dropout_prob=0.1, + attention_probs_dropout_prob=0.1, + max_position_embeddings=512, + type_vocab_size=2, + initializer_range=0.02, + layer_norm_eps=1e-12, + bypass_transformer=False, + special_visual_initialize=True, + pad_token_id=1, + bos_token_id=0, + eos_token_id=2, + **kwargs, + ): + super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) + + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.visual_embedding_dim = visual_embedding_dim + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.initializer_range = initializer_range + self.type_vocab_size = type_vocab_size + self.layer_norm_eps = layer_norm_eps + self.bypass_transformer = bypass_transformer + self.special_visual_initialize = special_visual_initialize diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/visual_bert/convert_visual_bert_original_pytorch_checkpoint_to_pytorch.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/visual_bert/convert_visual_bert_original_pytorch_checkpoint_to_pytorch.py new file mode 100644 index 0000000000000000000000000000000000000000..d1e95630bd000ff01ba941f200560b52a31db9cf --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/visual_bert/convert_visual_bert_original_pytorch_checkpoint_to_pytorch.py @@ -0,0 +1,150 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert VisualBert checkpoint.""" + + +import argparse +from collections import OrderedDict +from pathlib import Path + +import torch + +from transformers import ( + VisualBertConfig, + VisualBertForMultipleChoice, + VisualBertForPreTraining, + VisualBertForQuestionAnswering, + VisualBertForVisualReasoning, +) +from transformers.utils import logging + + +logging.set_verbosity_info() +logger = logging.get_logger(__name__) + +rename_keys_prefix = [ + ("bert.bert", "visual_bert"), + ("bert.cls", "cls"), + ("bert.classifier", "cls"), + ("token_type_embeddings_visual", "visual_token_type_embeddings"), + ("position_embeddings_visual", "visual_position_embeddings"), + ("projection", "visual_projection"), +] + +ACCEPTABLE_CHECKPOINTS = [ + "nlvr2_coco_pre_trained.th", + "nlvr2_fine_tuned.th", + "nlvr2_pre_trained.th", + "vcr_coco_pre_train.th", + "vcr_fine_tune.th", + "vcr_pre_train.th", + "vqa_coco_pre_trained.th", + "vqa_fine_tuned.th", + "vqa_pre_trained.th", +] + + +def load_state_dict(checkpoint_path): + sd = torch.load(checkpoint_path, map_location="cpu") + return sd + + +def get_new_dict(d, config, rename_keys_prefix=rename_keys_prefix): + new_d = OrderedDict() + new_d["visual_bert.embeddings.position_ids"] = torch.arange(config.max_position_embeddings).expand((1, -1)) + # detector_d = OrderedDict() + for key in d: + if "detector" in key: + # detector_d[key.replace('detector.','')] = d[key] + continue + new_key = key + for name_pair in rename_keys_prefix: + new_key = new_key.replace(name_pair[0], name_pair[1]) + new_d[new_key] = d[key] + if key == "bert.cls.predictions.decoder.weight": + # Old bert code didn't have `decoder.bias`, but was added separately + new_d["cls.predictions.decoder.bias"] = new_d["cls.predictions.bias"] + return new_d + + +@torch.no_grad() +def convert_visual_bert_checkpoint(checkpoint_path, pytorch_dump_folder_path): + """ + Copy/paste/tweak model's weights to our VisualBERT structure. + """ + + assert ( + checkpoint_path.split("/")[-1] in ACCEPTABLE_CHECKPOINTS + ), f"The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}." + + # Get Config + if "pre" in checkpoint_path: + model_type = "pretraining" + if "vcr" in checkpoint_path: + config_params = {"visual_embedding_dim": 512} + elif "vqa_advanced" in checkpoint_path: + config_params = {"visual_embedding_dim": 2048} + elif "vqa" in checkpoint_path: + config_params = {"visual_embedding_dim": 2048} + elif "nlvr" in checkpoint_path: + config_params = {"visual_embedding_dim": 1024} + else: + raise NotImplementedError(f"No implementation found for `{checkpoint_path}`.") + else: + if "vcr" in checkpoint_path: + config_params = {"visual_embedding_dim": 512} + model_type = "multichoice" + elif "vqa_advanced" in checkpoint_path: + config_params = {"visual_embedding_dim": 2048} + model_type = "vqa_advanced" + elif "vqa" in checkpoint_path: + config_params = {"visual_embedding_dim": 2048, "num_labels": 3129} + model_type = "vqa" + elif "nlvr" in checkpoint_path: + config_params = { + "visual_embedding_dim": 1024, + "num_labels": 2, + } + model_type = "nlvr" + + config = VisualBertConfig(**config_params) + + # Load State Dict + state_dict = load_state_dict(checkpoint_path) + + new_state_dict = get_new_dict(state_dict, config) + + if model_type == "pretraining": + model = VisualBertForPreTraining(config) + elif model_type == "vqa": + model = VisualBertForQuestionAnswering(config) + elif model_type == "nlvr": + model = VisualBertForVisualReasoning(config) + elif model_type == "multichoice": + model = VisualBertForMultipleChoice(config) + + model.load_state_dict(new_state_dict) + # Save Checkpoints + Path(pytorch_dump_folder_path).mkdir(exist_ok=True) + model.save_pretrained(pytorch_dump_folder_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + # Required parameters + parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.") + parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.") + args = parser.parse_args() + convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/visual_bert/modeling_visual_bert.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/visual_bert/modeling_visual_bert.py new file mode 100644 index 0000000000000000000000000000000000000000..07c8b7a4b5173ce046578ccb0b28e2461a6efd4a --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/visual_bert/modeling_visual_bert.py @@ -0,0 +1,1590 @@ +# coding=utf-8 +# Copyright 2021 The UCLA NLP Authors and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch VisualBERT model.""" + + +import math +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss, KLDivLoss, LogSoftmax + +from ...activations import ACT2FN +from ...modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPooling, + MultipleChoiceModelOutput, + SequenceClassifierOutput, +) +from ...modeling_utils import PreTrainedModel +from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer +from ...utils import ( + ModelOutput, + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) +from .configuration_visual_bert import VisualBertConfig + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "VisualBertConfig" +_CHECKPOINT_FOR_DOC = "uclanlp/visualbert-vqa-coco-pre" + + +from ..deprecated._archive_maps import VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +class VisualBertEmbeddings(nn.Module): + """Construct the embeddings from word, position and token_type embeddings and visual embeddings.""" + + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) + self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False + ) + + # For Visual Features + # Token type and position embedding for image features + self.visual_token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) + self.visual_position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) + + if config.special_visual_initialize: + self.visual_token_type_embeddings.weight.data = nn.Parameter( + self.token_type_embeddings.weight.data.clone(), requires_grad=True + ) + self.visual_position_embeddings.weight.data = nn.Parameter( + self.position_embeddings.weight.data.clone(), requires_grad=True + ) + + self.visual_projection = nn.Linear(config.visual_embedding_dim, config.hidden_size) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + visual_embeds=None, + visual_token_type_ids=None, + image_text_alignment=None, + ): + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + if position_ids is None: + position_ids = self.position_ids[:, :seq_length] + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + if token_type_ids is None: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) + + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + + # Absolute Position Embeddings + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + + if visual_embeds is not None: + if visual_token_type_ids is None: + visual_token_type_ids = torch.ones( + visual_embeds.size()[:-1], dtype=torch.long, device=self.position_ids.device + ) + + visual_embeds = self.visual_projection(visual_embeds) + visual_token_type_embeddings = self.visual_token_type_embeddings(visual_token_type_ids) + + if image_text_alignment is not None: + # image_text_alignment = Batch x image_length x alignment_number. + # Each element denotes the position of the word corresponding to the image feature. -1 is the padding value. + + dtype = token_type_embeddings.dtype + image_text_alignment_mask = (image_text_alignment != -1).long() + # Get rid of the -1. + image_text_alignment = image_text_alignment_mask * image_text_alignment + + # Batch x image_length x alignment length x dim + visual_position_embeddings = self.position_embeddings(image_text_alignment) + visual_position_embeddings *= image_text_alignment_mask.to(dtype=dtype).unsqueeze(-1) + visual_position_embeddings = visual_position_embeddings.sum(2) + + # We want to averge along the alignment_number dimension. + image_text_alignment_mask = image_text_alignment_mask.to(dtype=dtype).sum(2) + + if (image_text_alignment_mask == 0).sum() != 0: + image_text_alignment_mask[image_text_alignment_mask == 0] = 1 # Avoid divide by zero error + logger.warning( + "Found 0 values in `image_text_alignment_mask`. Setting them to 1 to avoid divide-by-zero" + " error." + ) + visual_position_embeddings = visual_position_embeddings / image_text_alignment_mask.unsqueeze(-1) + + visual_position_ids = torch.zeros( + *visual_embeds.size()[:-1], dtype=torch.long, device=visual_embeds.device + ) + + # When fine-tuning the detector , the image_text_alignment is sometimes padded too long. + if visual_position_embeddings.size(1) != visual_embeds.size(1): + if visual_position_embeddings.size(1) < visual_embeds.size(1): + raise ValueError( + f"Visual position embeddings length: {visual_position_embeddings.size(1)} " + f"should be the same as `visual_embeds` length: {visual_embeds.size(1)}" + ) + visual_position_embeddings = visual_position_embeddings[:, : visual_embeds.size(1), :] + + visual_position_embeddings = visual_position_embeddings + self.visual_position_embeddings( + visual_position_ids + ) + else: + visual_position_ids = torch.zeros( + *visual_embeds.size()[:-1], dtype=torch.long, device=visual_embeds.device + ) + visual_position_embeddings = self.visual_position_embeddings(visual_position_ids) + + visual_embeddings = visual_embeds + visual_position_embeddings + visual_token_type_embeddings + + embeddings = torch.cat((embeddings, visual_embeddings), dim=1) + + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class VisualBertSelfAttention(nn.Module): + def __init__(self, config): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + output_attentions=False, + ): + mixed_query_layer = self.query(hidden_states) + + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in VisualBertSelfAttentionModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->VisualBert +class VisualBertSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class VisualBertAttention(nn.Module): + def __init__(self, config): + super().__init__() + self.self = VisualBertSelfAttention(config) + self.output = VisualBertSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + output_attentions=False, + ): + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + output_attentions, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->VisualBert +class VisualBertIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->VisualBert +class VisualBertOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class VisualBertLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = VisualBertAttention(config) + self.intermediate = VisualBertIntermediate(config) + self.output = VisualBertOutput(config) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + output_attentions=False, + ): + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + ) + attention_output = self_attention_outputs[0] + + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + outputs = (layer_output,) + outputs + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +class VisualBertEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList([VisualBertLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer_module.__call__, + hidden_states, + attention_mask, + layer_head_mask, + output_attentions, + ) + else: + layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions) + + hidden_states = layer_outputs[0] + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + all_hidden_states, + all_self_attentions, + ] + if v is not None + ) + return BaseModelOutput( + last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->VisualBert +class VisualBertPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->VisualBert +class VisualBertPredictionHeadTransform(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if isinstance(config.hidden_act, str): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->VisualBert +class VisualBertLMPredictionHead(nn.Module): + def __init__(self, config): + super().__init__() + self.transform = VisualBertPredictionHeadTransform(config) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` + self.decoder.bias = self.bias + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + hidden_states = self.decoder(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertPreTrainingHeads with Bert->VisualBert +class VisualBertPreTrainingHeads(nn.Module): + def __init__(self, config): + super().__init__() + self.predictions = VisualBertLMPredictionHead(config) + self.seq_relationship = nn.Linear(config.hidden_size, 2) + + def forward(self, sequence_output, pooled_output): + prediction_scores = self.predictions(sequence_output) + seq_relationship_score = self.seq_relationship(pooled_output) + return prediction_scores, seq_relationship_score + + +class VisualBertPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = VisualBertConfig + base_model_prefix = "visual_bert" + supports_gradient_checkpointing = True + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, (nn.Linear, nn.Embedding)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + + +@dataclass +class VisualBertForPreTrainingOutput(ModelOutput): + """ + Output type of [`VisualBertForPreTraining`]. + + Args: + loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`): + Total loss as the sum of the masked language modeling loss and the sentence-image prediction + (classification) loss. + prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`): + Prediction scores of the sentence-image prediction (classification) head (scores of True/False continuation + before SoftMax). + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + """ + + loss: Optional[torch.FloatTensor] = None + prediction_logits: torch.FloatTensor = None + seq_relationship_logits: torch.FloatTensor = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + + +VISUAL_BERT_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`VisualBertConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +VISUAL_BERT_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + + visual_embeds (`torch.FloatTensor` of shape `(batch_size, visual_seq_length, visual_embedding_dim)`, *optional*): + The embedded representation of the visual inputs, generally derived using using an object detector. + + visual_attention_mask (`torch.FloatTensor` of shape `(batch_size, visual_seq_length)`, *optional*): + Mask to avoid performing attention on visual embeddings. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + visual_token_type_ids (`torch.LongTensor` of shape `(batch_size, visual_seq_length)`, *optional*): + Segment token indices to indicate different portions of the visual embeds. + + [What are token type IDs?](../glossary#token-type-ids) The authors of VisualBERT set the + *visual_token_type_ids* to *1* for all tokens. + + image_text_alignment (`torch.LongTensor` of shape `(batch_size, visual_seq_length, alignment_number)`, *optional*): + Image-Text alignment uses to decide the position IDs of the visual embeddings. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare VisualBert Model transformer outputting raw hidden-states without any specific head on top.", + VISUAL_BERT_START_DOCSTRING, +) +class VisualBertModel(VisualBertPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) following the architecture described in [Attention is + all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, + Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. + """ + + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = VisualBertEmbeddings(config) + self.encoder = VisualBertEncoder(config) + + self.pooler = VisualBertPooler(config) if add_pooling_layer else None + + self.bypass_transformer = config.bypass_transformer + + if self.bypass_transformer: + self.additional_layer = VisualBertLayer(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + @add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.LongTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.LongTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + visual_embeds: Optional[torch.FloatTensor] = None, + visual_attention_mask: Optional[torch.LongTensor] = None, + visual_token_type_ids: Optional[torch.LongTensor] = None, + image_text_alignment: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPooling]: + r""" + + Returns: + + Example: + + ```python + # Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image. + from transformers import AutoTokenizer, VisualBertModel + import torch + + tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") + model = VisualBertModel.from_pretrained("uclanlp/visualbert-vqa-coco-pre") + + inputs = tokenizer("The capital of France is Paris.", return_tensors="pt") + visual_embeds = get_visual_embeddings(image).unsqueeze(0) + visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long) + visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float) + + inputs.update( + { + "visual_embeds": visual_embeds, + "visual_token_type_ids": visual_token_type_ids, + "visual_attention_mask": visual_attention_mask, + } + ) + + outputs = model(**inputs) + + last_hidden_states = outputs.last_hidden_state + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + if visual_embeds is not None: + visual_input_shape = visual_embeds.size()[:-1] + + if attention_mask is None: + attention_mask = torch.ones(input_shape, device=device) + + if visual_embeds is not None and visual_attention_mask is None: + visual_attention_mask = torch.ones(visual_input_shape, device=device) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + if visual_embeds is not None: + combined_attention_mask = torch.cat((attention_mask, visual_attention_mask), dim=-1) + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + combined_attention_mask, (batch_size, input_shape + visual_input_shape) + ) + + else: + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, (batch_size, input_shape) + ) + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + visual_embeds=visual_embeds, + visual_token_type_ids=visual_token_type_ids, + image_text_alignment=image_text_alignment, + ) + + if self.bypass_transformer and visual_embeds is not None: + text_length = input_ids.size(1) + text_embedding_output = embedding_output[:, :text_length, :] + visual_embedding_output = embedding_output[:, text_length:, :] + + text_extended_attention_mask = extended_attention_mask[:, :, text_length, :text_length] + + encoded_outputs = self.encoder( + text_embedding_output, + attention_mask=text_extended_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoded_outputs[0] + concatenated_input = torch.cat((sequence_output, visual_embedding_output), dim=1) + sequence_output = self.additional_layer(concatenated_input, extended_attention_mask) + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + else: + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPooling( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +@add_start_docstrings( + """ + VisualBert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a + `sentence-image prediction (classification)` head. + """, + VISUAL_BERT_START_DOCSTRING, +) +class VisualBertForPreTraining(VisualBertPreTrainedModel): + _tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"] + + def __init__(self, config): + super().__init__(config) + + self.visual_bert = VisualBertModel(config) + self.cls = VisualBertPreTrainingHeads(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.cls.predictions.decoder + + def set_output_embeddings(self, new_embeddings): + self.cls.predictions.decoder = new_embeddings + + @add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @replace_return_docstrings(output_type=VisualBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.LongTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.LongTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + visual_embeds: Optional[torch.FloatTensor] = None, + visual_attention_mask: Optional[torch.LongTensor] = None, + visual_token_type_ids: Optional[torch.LongTensor] = None, + image_text_alignment: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + labels: Optional[torch.LongTensor] = None, + sentence_image_labels: Optional[torch.LongTensor] = None, + ) -> Union[Tuple[torch.Tensor], VisualBertForPreTrainingOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, total_sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + sentence_image_labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sentence-image prediction (classification) loss. Input should be a sequence pair + (see `input_ids` docstring) Indices should be in `[0, 1]`: + + - 0 indicates sequence B is a matching pair of sequence A for the given image, + - 1 indicates sequence B is a random sequence w.r.t A for the given image. + + Returns: + + Example: + + ```python + # Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image in the batch. + from transformers import AutoTokenizer, VisualBertForPreTraining + + tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") + model = VisualBertForPreTraining.from_pretrained("uclanlp/visualbert-vqa-coco-pre") + + inputs = tokenizer("The capital of France is [MASK].", return_tensors="pt") + visual_embeds = get_visual_embeddings(image).unsqueeze(0) + visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long) + visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float) + + inputs.update( + { + "visual_embeds": visual_embeds, + "visual_token_type_ids": visual_token_type_ids, + "visual_attention_mask": visual_attention_mask, + } + ) + max_length = inputs["input_ids"].shape[-1] + visual_embeds.shape[-2] + labels = tokenizer( + "The capital of France is Paris.", return_tensors="pt", padding="max_length", max_length=max_length + )["input_ids"] + sentence_image_labels = torch.tensor(1).unsqueeze(0) # Batch_size + + + outputs = model(**inputs, labels=labels, sentence_image_labels=sentence_image_labels) + loss = outputs.loss + prediction_logits = outputs.prediction_logits + seq_relationship_logits = outputs.seq_relationship_logits + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.visual_bert( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + visual_embeds=visual_embeds, + visual_attention_mask=visual_attention_mask, + visual_token_type_ids=visual_token_type_ids, + image_text_alignment=image_text_alignment, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output, pooled_output = outputs[:2] + prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) + + total_loss = None + if labels is not None and sentence_image_labels is not None: + total_size = attention_mask.size(-1) + visual_attention_mask.size(-1) + if labels.size(-1) != total_size: + raise ValueError( + "The labels provided should have same sequence length as total attention mask. " + f"Found labels with sequence length {labels.size(-1)}, expected {total_size}." + ) + + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + sentence_image_loss = loss_fct(seq_relationship_score.view(-1, 2), sentence_image_labels.view(-1)) + total_loss = masked_lm_loss + sentence_image_loss + + if labels is not None and sentence_image_labels is None: + total_size = attention_mask.size(-1) + visual_attention_mask.size(-1) + if labels.size(-1) != total_size: + raise ValueError( + "The labels provided should have same sequence length as total attention mask. " + f"Found labels with sequence length {labels.size(-1)}, expected {total_size}." + ) + + loss_fct = CrossEntropyLoss() + total_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (prediction_scores, seq_relationship_score) + outputs[2:] + return ((total_loss,) + output) if total_loss is not None else output + + return VisualBertForPreTrainingOutput( + loss=total_loss, + prediction_logits=prediction_scores, + seq_relationship_logits=seq_relationship_score, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + """ + VisualBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and + a softmax) e.g. for VCR tasks. + """, + VISUAL_BERT_START_DOCSTRING, +) +class VisualBertForMultipleChoice(VisualBertPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.visual_bert = VisualBertModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.cls = nn.Linear(config.hidden_size, 1) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward( + VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") + ) + @replace_return_docstrings(output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.LongTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.LongTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + visual_embeds: Optional[torch.FloatTensor] = None, + visual_attention_mask: Optional[torch.LongTensor] = None, + visual_token_type_ids: Optional[torch.LongTensor] = None, + image_text_alignment: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + labels: Optional[torch.LongTensor] = None, + ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., + num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See + `input_ids` above) + + Returns: + + Example: + + ```python + # Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image in the batch. + from transformers import AutoTokenizer, VisualBertForMultipleChoice + import torch + + tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") + model = VisualBertForMultipleChoice.from_pretrained("uclanlp/visualbert-vcr") + + prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced." + choice0 = "It is eaten with a fork and a knife." + choice1 = "It is eaten while held in the hand." + + visual_embeds = get_visual_embeddings(image) + # (batch_size, num_choices, visual_seq_length, visual_embedding_dim) + visual_embeds = visual_embeds.expand(1, 2, *visual_embeds.shape) + visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long) + visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float) + + labels = torch.tensor(0).unsqueeze(0) # choice0 is correct (according to Wikipedia ;)), batch size 1 + + encoding = tokenizer([[prompt, prompt], [choice0, choice1]], return_tensors="pt", padding=True) + # batch size is 1 + inputs_dict = {k: v.unsqueeze(0) for k, v in encoding.items()} + inputs_dict.update( + { + "visual_embeds": visual_embeds, + "visual_attention_mask": visual_attention_mask, + "visual_token_type_ids": visual_token_type_ids, + "labels": labels, + } + ) + outputs = model(**inputs_dict) + + loss = outputs.loss + logits = outputs.logits + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] + + input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None + attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None + token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None + position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None + inputs_embeds = ( + inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) + if inputs_embeds is not None + else None + ) + + visual_embeds = ( + visual_embeds.view(-1, visual_embeds.size(-2), visual_embeds.size(-1)) + if visual_embeds is not None + else None + ) + visual_attention_mask = ( + visual_attention_mask.view(-1, visual_attention_mask.size(-1)) + if visual_attention_mask is not None + else None + ) + visual_token_type_ids = ( + visual_token_type_ids.view(-1, visual_token_type_ids.size(-1)) + if visual_token_type_ids is not None + else None + ) + + outputs = self.visual_bert( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + visual_embeds=visual_embeds, + visual_attention_mask=visual_attention_mask, + visual_token_type_ids=visual_token_type_ids, + image_text_alignment=image_text_alignment, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + _, pooled_output = outputs[0], outputs[1] + + pooled_output = self.dropout(pooled_output) + logits = self.cls(pooled_output) + reshaped_logits = logits.view(-1, num_choices) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(reshaped_logits, labels) + + if not return_dict: + output = (reshaped_logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return MultipleChoiceModelOutput( + loss=loss, + logits=reshaped_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + """ + VisualBert Model with a classification/regression head on top (a dropout and a linear layer on top of the pooled + output) for VQA. + """, + VISUAL_BERT_START_DOCSTRING, +) +class VisualBertForQuestionAnswering(VisualBertPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + + self.visual_bert = VisualBertModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.cls = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.LongTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.LongTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + visual_embeds: Optional[torch.FloatTensor] = None, + visual_attention_mask: Optional[torch.LongTensor] = None, + visual_token_type_ids: Optional[torch.LongTensor] = None, + image_text_alignment: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + labels: Optional[torch.LongTensor] = None, + ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, total_sequence_length)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. A KLDivLoss is computed between the labels and the returned logits. + + Returns: + + Example: + + ```python + # Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image in the batch. + from transformers import AutoTokenizer, VisualBertForQuestionAnswering + import torch + + tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") + model = VisualBertForQuestionAnswering.from_pretrained("uclanlp/visualbert-vqa") + + text = "Who is eating the apple?" + inputs = tokenizer(text, return_tensors="pt") + visual_embeds = get_visual_embeddings(image).unsqueeze(0) + visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long) + visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float) + + inputs.update( + { + "visual_embeds": visual_embeds, + "visual_token_type_ids": visual_token_type_ids, + "visual_attention_mask": visual_attention_mask, + } + ) + + labels = torch.tensor([[0.0, 1.0]]).unsqueeze(0) # Batch size 1, Num labels 2 + + outputs = model(**inputs, labels=labels) + loss = outputs.loss + scores = outputs.logits + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # Get the index of the last text token + index_to_gather = attention_mask.sum(1) - 2 # as in original code + + outputs = self.visual_bert( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + visual_embeds=visual_embeds, + visual_attention_mask=visual_attention_mask, + visual_token_type_ids=visual_token_type_ids, + image_text_alignment=image_text_alignment, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + # TO-CHECK: From the original code + index_to_gather = ( + index_to_gather.unsqueeze(-1).unsqueeze(-1).expand(index_to_gather.size(0), 1, sequence_output.size(-1)) + ) + pooled_output = torch.gather(sequence_output, 1, index_to_gather) + + pooled_output = self.dropout(pooled_output) + logits = self.cls(pooled_output) + reshaped_logits = logits.view(-1, self.num_labels) + + loss = None + if labels is not None: + loss_fct = nn.KLDivLoss(reduction="batchmean") + log_softmax = nn.LogSoftmax(dim=-1) + reshaped_logits = log_softmax(reshaped_logits) + loss = loss_fct(reshaped_logits, labels.contiguous()) + if not return_dict: + output = (reshaped_logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=reshaped_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + """ + VisualBert Model with a sequence classification head on top (a dropout and a linear layer on top of the pooled + output) for Visual Reasoning e.g. for NLVR task. + """, + VISUAL_BERT_START_DOCSTRING, +) +class VisualBertForVisualReasoning(VisualBertPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + + self.visual_bert = VisualBertModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.cls = nn.Linear(config.hidden_size, config.num_labels) # 2 + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.LongTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.LongTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + visual_embeds: Optional[torch.FloatTensor] = None, + visual_attention_mask: Optional[torch.LongTensor] = None, + visual_token_type_ids: Optional[torch.LongTensor] = None, + image_text_alignment: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + labels: Optional[torch.LongTensor] = None, + ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. A classification loss is computed (Cross-Entropy) against these labels. + + Returns: + + Example: + + ```python + # Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image in the batch. + from transformers import AutoTokenizer, VisualBertForVisualReasoning + import torch + + tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") + model = VisualBertForVisualReasoning.from_pretrained("uclanlp/visualbert-nlvr2") + + text = "Who is eating the apple?" + inputs = tokenizer(text, return_tensors="pt") + visual_embeds = get_visual_embeddings(image).unsqueeze(0) + visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long) + visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float) + + inputs.update( + { + "visual_embeds": visual_embeds, + "visual_token_type_ids": visual_token_type_ids, + "visual_attention_mask": visual_attention_mask, + } + ) + + labels = torch.tensor(1).unsqueeze(0) # Batch size 1, Num choices 2 + + outputs = model(**inputs, labels=labels) + loss = outputs.loss + scores = outputs.logits + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.visual_bert( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + visual_embeds=visual_embeds, + visual_attention_mask=visual_attention_mask, + visual_token_type_ids=visual_token_type_ids, + image_text_alignment=image_text_alignment, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + # sequence_output = outputs[0] + pooled_output = outputs[1] + pooled_output = self.dropout(pooled_output) + logits = self.cls(pooled_output) + reshaped_logits = logits.contiguous() + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(reshaped_logits, labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=reshaped_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class VisualBertRegionToPhraseAttention(nn.Module): + def __init__(self, config): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0: + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + self.num_attention_heads = 1 # config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward(self, query, key, attention_mask): + attention_mask = attention_mask.to(query.dtype) + attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) + attention_mask = (1.0 - attention_mask) * torch.finfo(query.dtype).min + + mixed_query_layer = self.query(query) + mixed_key_layer = self.key(key) + + query_layer = self.transpose_for_scores(mixed_query_layer) + key_layer = self.transpose_for_scores(mixed_key_layer) + + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + + attention_scores = attention_scores + attention_mask + + attention_scores = attention_scores.squeeze(1) + return attention_scores + + +@add_start_docstrings( + """ + VisualBert Model with a Masked Language Modeling head and an attention layer on top for Region-to-Phrase Alignment + e.g. for Flickr30 Entities task. + """, + VISUAL_BERT_START_DOCSTRING, +) +class VisualBertForRegionToPhraseAlignment(VisualBertPreTrainedModel): + _tied_weights_keys = ["cls.predictions.decoder.bias"] + + def __init__(self, config): + super().__init__(config) + + self.visual_bert = VisualBertModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.cls = VisualBertPreTrainingHeads(config) + self.attention = VisualBertRegionToPhraseAttention(config) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.LongTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.LongTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + visual_embeds: Optional[torch.FloatTensor] = None, + visual_attention_mask: Optional[torch.LongTensor] = None, + visual_token_type_ids: Optional[torch.LongTensor] = None, + image_text_alignment: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + region_to_phrase_position: Optional[torch.LongTensor] = None, + labels: Optional[torch.LongTensor] = None, + ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: + r""" + region_to_phrase_position (`torch.LongTensor` of shape `(batch_size, total_sequence_length)`, *optional*): + The positions depicting the position of the image embedding corresponding to the textual tokens. + + labels (`torch.LongTensor` of shape `(batch_size, total_sequence_length, visual_sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. KLDivLoss is computed against these labels and the + outputs from the attention layer. + + Returns: + + Example: + + ```python + # Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image in the batch. + from transformers import AutoTokenizer, VisualBertForRegionToPhraseAlignment + import torch + + tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") + model = VisualBertForRegionToPhraseAlignment.from_pretrained("uclanlp/visualbert-vqa-coco-pre") + + text = "Who is eating the apple?" + inputs = tokenizer(text, return_tensors="pt") + visual_embeds = get_visual_embeddings(image).unsqueeze(0) + visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long) + visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float) + region_to_phrase_position = torch.ones((1, inputs["input_ids"].shape[-1] + visual_embeds.shape[-2])) + + inputs.update( + { + "region_to_phrase_position": region_to_phrase_position, + "visual_embeds": visual_embeds, + "visual_token_type_ids": visual_token_type_ids, + "visual_attention_mask": visual_attention_mask, + } + ) + + labels = torch.ones( + (1, inputs["input_ids"].shape[-1] + visual_embeds.shape[-2], visual_embeds.shape[-2]) + ) # Batch size 1 + + outputs = model(**inputs, labels=labels) + loss = outputs.loss + scores = outputs.logits + ```""" + if region_to_phrase_position is None: + raise ValueError("`region_to_phrase_position` should not be None when using Flickr Model.") + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.visual_bert( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + visual_embeds=visual_embeds, + visual_attention_mask=visual_attention_mask, + visual_token_type_ids=visual_token_type_ids, + image_text_alignment=image_text_alignment, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + region_to_phrase_position_mask = (region_to_phrase_position != -1).long() + + # Make the -1 become 0 + region_to_phrase_position = region_to_phrase_position * region_to_phrase_position_mask + + # Selected_positions = batch x selected position x dim + expanded_region_to_phrase_positions = region_to_phrase_position.unsqueeze(2).expand( + region_to_phrase_position.size(0), region_to_phrase_position.size(1), sequence_output.size(2) + ) + selected_positions = sequence_output.gather(1, expanded_region_to_phrase_positions) + + # Visual Features = batch x visual_feature_length x dim + # This will need separate image and visual masks. + visual_features = sequence_output[:, attention_mask.size(1) :] + + if visual_features.size(1) != visual_attention_mask.size(1): + raise ValueError( + f"Visual features length :{visual_features.size(1)} should be the same" + f" as visual attention mask length: {visual_attention_mask.size(1)}." + ) + + logits = self.attention(selected_positions, visual_features, visual_attention_mask) + + loss = None + + if labels is not None: + # scores = batch x selected position x visual_feature + # scores = selected_positions.bmm(visual_features.transpose(1,2)) + # label = batch x selected_postion x needed position + loss_fct = KLDivLoss(reduction="batchmean") + log_softmax = LogSoftmax(dim=-1) + scores = log_softmax(logits) + labels = labels.contiguous() + loss = loss_fct(scores, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d426ec93bf5859bc3ba040421c54ae4eefbbb32e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/__init__.py @@ -0,0 +1,121 @@ +# Copyright 2021 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import ( + OptionalDependencyNotAvailable, + _LazyModule, + is_flax_available, + is_tf_available, + is_torch_available, + is_vision_available, +) + + +_import_structure = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]} + +try: + if not is_vision_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["feature_extraction_vit"] = ["ViTFeatureExtractor"] + _import_structure["image_processing_vit"] = ["ViTImageProcessor"] + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_vit"] = [ + "VIT_PRETRAINED_MODEL_ARCHIVE_LIST", + "ViTForImageClassification", + "ViTForMaskedImageModeling", + "ViTModel", + "ViTPreTrainedModel", + ] + +try: + if not is_tf_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_tf_vit"] = [ + "TFViTForImageClassification", + "TFViTModel", + "TFViTPreTrainedModel", + ] + +try: + if not is_flax_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_flax_vit"] = [ + "FlaxViTForImageClassification", + "FlaxViTModel", + "FlaxViTPreTrainedModel", + ] + +if TYPE_CHECKING: + from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig + + try: + if not is_vision_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .feature_extraction_vit import ViTFeatureExtractor + from .image_processing_vit import ViTImageProcessor + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_vit import ( + VIT_PRETRAINED_MODEL_ARCHIVE_LIST, + ViTForImageClassification, + ViTForMaskedImageModeling, + ViTModel, + ViTPreTrainedModel, + ) + + try: + if not is_tf_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel + + try: + if not is_flax_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel + + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a7e7e897fb6ec2074278a90606c61616cf738117 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/__pycache__/__init__.cpython-312.pyc differ diff --git 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a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/configuration_vit.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/configuration_vit.py new file mode 100644 index 0000000000000000000000000000000000000000..4b505b5d9cbb6d22e2797de2d0767da31bc11857 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/configuration_vit.py @@ -0,0 +1,141 @@ +# coding=utf-8 +# Copyright 2021 Google AI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" ViT model configuration""" + +from collections import OrderedDict +from typing import Mapping + +from packaging import version + +from ...configuration_utils import PretrainedConfig +from ...onnx import OnnxConfig +from ...utils import logging + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class ViTConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`ViTModel`]. It is used to instantiate an ViT + model according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the ViT + [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + hidden_size (`int`, *optional*, defaults to 768): + Dimensionality of the encoder layers and the pooler layer. + num_hidden_layers (`int`, *optional*, defaults to 12): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 12): + Number of attention heads for each attention layer in the Transformer encoder. + intermediate_size (`int`, *optional*, defaults to 3072): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"` and `"gelu_new"` are supported. + hidden_dropout_prob (`float`, *optional*, defaults to 0.0): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + layer_norm_eps (`float`, *optional*, defaults to 1e-12): + The epsilon used by the layer normalization layers. + image_size (`int`, *optional*, defaults to 224): + The size (resolution) of each image. + patch_size (`int`, *optional*, defaults to 16): + The size (resolution) of each patch. + num_channels (`int`, *optional*, defaults to 3): + The number of input channels. + qkv_bias (`bool`, *optional*, defaults to `True`): + Whether to add a bias to the queries, keys and values. + encoder_stride (`int`, *optional*, defaults to 16): + Factor to increase the spatial resolution by in the decoder head for masked image modeling. + + Example: + + ```python + >>> from transformers import ViTConfig, ViTModel + + >>> # Initializing a ViT vit-base-patch16-224 style configuration + >>> configuration = ViTConfig() + + >>> # Initializing a model (with random weights) from the vit-base-patch16-224 style configuration + >>> model = ViTModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "vit" + + def __init__( + self, + hidden_size=768, + num_hidden_layers=12, + num_attention_heads=12, + intermediate_size=3072, + hidden_act="gelu", + hidden_dropout_prob=0.0, + attention_probs_dropout_prob=0.0, + initializer_range=0.02, + layer_norm_eps=1e-12, + image_size=224, + patch_size=16, + num_channels=3, + qkv_bias=True, + encoder_stride=16, + **kwargs, + ): + super().__init__(**kwargs) + + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.initializer_range = initializer_range + self.layer_norm_eps = layer_norm_eps + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.qkv_bias = qkv_bias + self.encoder_stride = encoder_stride + + +class ViTOnnxConfig(OnnxConfig): + torch_onnx_minimum_version = version.parse("1.11") + + @property + def inputs(self) -> Mapping[str, Mapping[int, str]]: + return OrderedDict( + [ + ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), + ] + ) + + @property + def atol_for_validation(self) -> float: + return 1e-4 diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/convert_dino_to_pytorch.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/convert_dino_to_pytorch.py new file mode 100644 index 0000000000000000000000000000000000000000..7eec823ad5d1d80a5a438693dbaee49189d7731f --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/convert_dino_to_pytorch.py @@ -0,0 +1,219 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert ViT checkpoints trained with the DINO method.""" + + +import argparse +import json +from pathlib import Path + +import requests +import torch +from huggingface_hub import hf_hub_download +from PIL import Image + +from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel +from transformers.utils import logging + + +logging.set_verbosity_info() +logger = logging.get_logger(__name__) + + +# here we list all keys to be renamed (original name on the left, our name on the right) +def create_rename_keys(config, base_model=False): + rename_keys = [] + for i in range(config.num_hidden_layers): + # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms + rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight")) + rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")) + rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")) + rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")) + rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")) + rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")) + rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")) + rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")) + rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")) + rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")) + + # projection layer + position embeddings + rename_keys.extend( + [ + ("cls_token", "vit.embeddings.cls_token"), + ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"), + ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"), + ("pos_embed", "vit.embeddings.position_embeddings"), + ] + ) + + if base_model: + # layernorm + pooler + rename_keys.extend( + [ + ("norm.weight", "layernorm.weight"), + ("norm.bias", "layernorm.bias"), + ] + ) + + # if just the base model, we should remove "vit" from all keys that start with "vit" + rename_keys = [(pair[0], pair[1][4:]) if pair[1].startswith("vit") else pair for pair in rename_keys] + else: + # layernorm + classification head + rename_keys.extend( + [ + ("norm.weight", "vit.layernorm.weight"), + ("norm.bias", "vit.layernorm.bias"), + ("head.weight", "classifier.weight"), + ("head.bias", "classifier.bias"), + ] + ) + + return rename_keys + + +# we split up the matrix of each encoder layer into queries, keys and values +def read_in_q_k_v(state_dict, config, base_model=False): + for i in range(config.num_hidden_layers): + if base_model: + prefix = "" + else: + prefix = "vit." + # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) + in_proj_weight = state_dict.pop(f"blocks.{i}.attn.qkv.weight") + in_proj_bias = state_dict.pop(f"blocks.{i}.attn.qkv.bias") + # next, add query, keys and values (in that order) to the state dict + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[ + : config.hidden_size, : + ] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.bias"] = in_proj_bias[: config.hidden_size] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[ + config.hidden_size : config.hidden_size * 2, : + ] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.bias"] = in_proj_bias[ + config.hidden_size : config.hidden_size * 2 + ] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[ + -config.hidden_size :, : + ] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.bias"] = in_proj_bias[-config.hidden_size :] + + +def remove_classification_head_(state_dict): + ignore_keys = ["head.weight", "head.bias"] + for k in ignore_keys: + state_dict.pop(k, None) + + +def rename_key(dct, old, new): + val = dct.pop(old) + dct[new] = val + + +# We will verify our results on an image of cute cats +def prepare_img(): + url = "http://images.cocodataset.org/val2017/000000039769.jpg" + im = Image.open(requests.get(url, stream=True).raw) + return im + + +@torch.no_grad() +def convert_vit_checkpoint(model_name, pytorch_dump_folder_path, base_model=True): + """ + Copy/paste/tweak model's weights to our ViT structure. + """ + + # define default ViT configuration + config = ViTConfig() + # patch_size + if model_name[-1] == "8": + config.patch_size = 8 + # set labels if required + if not base_model: + config.num_labels = 1000 + repo_id = "huggingface/label-files" + filename = "imagenet-1k-id2label.json" + id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) + id2label = {int(k): v for k, v in id2label.items()} + config.id2label = id2label + config.label2id = {v: k for k, v in id2label.items()} + # size of the architecture + if model_name in ["dino_vits8", "dino_vits16"]: + config.hidden_size = 384 + config.intermediate_size = 1536 + config.num_hidden_layers = 12 + config.num_attention_heads = 6 + + # load original model from torch hub + original_model = torch.hub.load("facebookresearch/dino:main", model_name) + original_model.eval() + + # load state_dict of original model, remove and rename some keys + state_dict = original_model.state_dict() + if base_model: + remove_classification_head_(state_dict) + rename_keys = create_rename_keys(config, base_model=base_model) + for src, dest in rename_keys: + rename_key(state_dict, src, dest) + read_in_q_k_v(state_dict, config, base_model) + + # load HuggingFace model + if base_model: + model = ViTModel(config, add_pooling_layer=False).eval() + else: + model = ViTForImageClassification(config).eval() + model.load_state_dict(state_dict) + + # Check outputs on an image, prepared by ViTImageProcessor + image_processor = ViTImageProcessor() + encoding = image_processor(images=prepare_img(), return_tensors="pt") + pixel_values = encoding["pixel_values"] + outputs = model(pixel_values) + + if base_model: + final_hidden_state_cls_token = original_model(pixel_values) + assert torch.allclose(final_hidden_state_cls_token, outputs.last_hidden_state[:, 0, :], atol=1e-1) + else: + logits = original_model(pixel_values) + assert logits.shape == outputs.logits.shape + assert torch.allclose(logits, outputs.logits, atol=1e-3) + + Path(pytorch_dump_folder_path).mkdir(exist_ok=True) + print(f"Saving model {model_name} to {pytorch_dump_folder_path}") + model.save_pretrained(pytorch_dump_folder_path) + print(f"Saving image processor to {pytorch_dump_folder_path}") + image_processor.save_pretrained(pytorch_dump_folder_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + # Required parameters + parser.add_argument( + "--model_name", + default="dino_vitb16", + type=str, + help="Name of the model trained with DINO you'd like to convert.", + ) + parser.add_argument( + "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." + ) + parser.add_argument( + "--base_model", + action="store_true", + help="Whether to only convert the base model (no projection head weights).", + ) + + parser.set_defaults(base_model=True) + args = parser.parse_args() + convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/convert_vit_timm_to_pytorch.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/convert_vit_timm_to_pytorch.py new file mode 100644 index 0000000000000000000000000000000000000000..0ccd9b9f6685fe375955fdee7298c17cf308de86 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/convert_vit_timm_to_pytorch.py @@ -0,0 +1,255 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert ViT and non-distilled DeiT checkpoints from the timm library.""" + + +import argparse +from pathlib import Path + +import requests +import timm +import torch +from PIL import Image +from timm.data import ImageNetInfo, infer_imagenet_subset + +from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel +from transformers.utils import logging + + +logging.set_verbosity_info() +logger = logging.get_logger(__name__) + + +# here we list all keys to be renamed (original name on the left, our name on the right) +def create_rename_keys(config, base_model=False): + rename_keys = [] + for i in range(config.num_hidden_layers): + # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms + rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight")) + rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")) + rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")) + rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")) + rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")) + rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")) + rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")) + rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")) + rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")) + rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")) + + # projection layer + position embeddings + rename_keys.extend( + [ + ("cls_token", "vit.embeddings.cls_token"), + ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"), + ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"), + ("pos_embed", "vit.embeddings.position_embeddings"), + ] + ) + + if base_model: + # layernorm + rename_keys.extend( + [ + ("norm.weight", "layernorm.weight"), + ("norm.bias", "layernorm.bias"), + ] + ) + + # if just the base model, we should remove "vit" from all keys that start with "vit" + rename_keys = [(pair[0], pair[1][4:]) if pair[1].startswith("vit") else pair for pair in rename_keys] + else: + # layernorm + classification head + rename_keys.extend( + [ + ("norm.weight", "vit.layernorm.weight"), + ("norm.bias", "vit.layernorm.bias"), + ("head.weight", "classifier.weight"), + ("head.bias", "classifier.bias"), + ] + ) + + return rename_keys + + +# we split up the matrix of each encoder layer into queries, keys and values +def read_in_q_k_v(state_dict, config, base_model=False): + for i in range(config.num_hidden_layers): + if base_model: + prefix = "" + else: + prefix = "vit." + # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) + in_proj_weight = state_dict.pop(f"blocks.{i}.attn.qkv.weight") + in_proj_bias = state_dict.pop(f"blocks.{i}.attn.qkv.bias") + # next, add query, keys and values (in that order) to the state dict + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[ + : config.hidden_size, : + ] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.bias"] = in_proj_bias[: config.hidden_size] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[ + config.hidden_size : config.hidden_size * 2, : + ] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.bias"] = in_proj_bias[ + config.hidden_size : config.hidden_size * 2 + ] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[ + -config.hidden_size :, : + ] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.bias"] = in_proj_bias[-config.hidden_size :] + + +def remove_classification_head_(state_dict): + ignore_keys = ["head.weight", "head.bias"] + for k in ignore_keys: + state_dict.pop(k, None) + + +def rename_key(dct, old, new): + val = dct.pop(old) + dct[new] = val + + +# We will verify our results on an image of cute cats +def prepare_img(): + url = "http://images.cocodataset.org/val2017/000000039769.jpg" + im = Image.open(requests.get(url, stream=True).raw) + return im + + +@torch.no_grad() +def convert_vit_checkpoint(vit_name, pytorch_dump_folder_path): + """ + Copy/paste/tweak model's weights to our ViT structure. + """ + + # define default ViT configuration + config = ViTConfig() + base_model = False + + # load original model from timm + timm_model = timm.create_model(vit_name, pretrained=True) + timm_model.eval() + + # detect unsupported ViT models in transformers + # fc_norm is present + if not isinstance(getattr(timm_model, "fc_norm", None), torch.nn.Identity): + raise ValueError(f"{vit_name} is not supported in transformers because of the presence of fc_norm.") + + # use of global average pooling in combination (or without) class token + if getattr(timm_model, "global_pool", None) == "avg": + raise ValueError(f"{vit_name} is not supported in transformers because of use of global average pooling.") + + # CLIP style vit with norm_pre layer present + if "clip" in vit_name and not isinstance(getattr(timm_model, "norm_pre", None), torch.nn.Identity): + raise ValueError( + f"{vit_name} is not supported in transformers because it's a CLIP style ViT with norm_pre layer." + ) + + # SigLIP style vit with attn_pool layer present + if "siglip" in vit_name and getattr(timm_model, "global_pool", None) == "map": + raise ValueError( + f"{vit_name} is not supported in transformers because it's a SigLIP style ViT with attn_pool." + ) + + # use of layer scale in ViT model blocks + if not isinstance(getattr(timm_model.blocks[0], "ls1", None), torch.nn.Identity) or not isinstance( + getattr(timm_model.blocks[0], "ls2", None), torch.nn.Identity + ): + raise ValueError(f"{vit_name} is not supported in transformers because it uses a layer scale in its blocks.") + + # Hybrid ResNet-ViTs + if not isinstance(timm_model.patch_embed, timm.layers.PatchEmbed): + raise ValueError(f"{vit_name} is not supported in transformers because it is a hybrid ResNet-ViT.") + + # get patch size and image size from the patch embedding submodule + config.patch_size = timm_model.patch_embed.patch_size[0] + config.image_size = timm_model.patch_embed.img_size[0] + + # retrieve architecture-specific parameters from the timm model + config.hidden_size = timm_model.embed_dim + config.intermediate_size = timm_model.blocks[0].mlp.fc1.out_features + config.num_hidden_layers = len(timm_model.blocks) + config.num_attention_heads = timm_model.blocks[0].attn.num_heads + + # check whether the model has a classification head or not + if timm_model.num_classes != 0: + config.num_labels = timm_model.num_classes + # infer ImageNet subset from timm model + imagenet_subset = infer_imagenet_subset(timm_model) + dataset_info = ImageNetInfo(imagenet_subset) + config.id2label = {i: dataset_info.index_to_label_name(i) for i in range(dataset_info.num_classes())} + config.label2id = {v: k for k, v in config.id2label.items()} + else: + print(f"{vit_name} is going to be converted as a feature extractor only.") + base_model = True + + # load state_dict of original model + state_dict = timm_model.state_dict() + + # remove and rename some keys in the state dict + if base_model: + remove_classification_head_(state_dict) + rename_keys = create_rename_keys(config, base_model) + for src, dest in rename_keys: + rename_key(state_dict, src, dest) + read_in_q_k_v(state_dict, config, base_model) + + # load HuggingFace model + if base_model: + model = ViTModel(config, add_pooling_layer=False).eval() + else: + model = ViTForImageClassification(config).eval() + model.load_state_dict(state_dict) + + # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor + if "deit" in vit_name: + image_processor = DeiTImageProcessor(size=config.image_size) + else: + image_processor = ViTImageProcessor(size=config.image_size) + encoding = image_processor(images=prepare_img(), return_tensors="pt") + pixel_values = encoding["pixel_values"] + outputs = model(pixel_values) + + if base_model: + timm_pooled_output = timm_model.forward_features(pixel_values) + assert timm_pooled_output.shape == outputs.last_hidden_state.shape + assert torch.allclose(timm_pooled_output, outputs.last_hidden_state, atol=1e-1) + else: + timm_logits = timm_model(pixel_values) + assert timm_logits.shape == outputs.logits.shape + assert torch.allclose(timm_logits, outputs.logits, atol=1e-3) + + Path(pytorch_dump_folder_path).mkdir(exist_ok=True) + print(f"Saving model {vit_name} to {pytorch_dump_folder_path}") + model.save_pretrained(pytorch_dump_folder_path) + print(f"Saving image processor to {pytorch_dump_folder_path}") + image_processor.save_pretrained(pytorch_dump_folder_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + # Required parameters + parser.add_argument( + "--vit_name", + default="vit_base_patch16_224", + type=str, + help="Name of the ViT timm model you'd like to convert.", + ) + parser.add_argument( + "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." + ) + + args = parser.parse_args() + convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/feature_extraction_vit.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/feature_extraction_vit.py new file mode 100644 index 0000000000000000000000000000000000000000..54d47c0f3ad59b217b56d8522ca9a356dbc3c9db --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/feature_extraction_vit.py @@ -0,0 +1,33 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Feature extractor class for ViT.""" + +import warnings + +from ...utils import logging +from .image_processing_vit import ViTImageProcessor + + +logger = logging.get_logger(__name__) + + +class ViTFeatureExtractor(ViTImageProcessor): + def __init__(self, *args, **kwargs) -> None: + warnings.warn( + "The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please" + " use ViTImageProcessor instead.", + FutureWarning, + ) + super().__init__(*args, **kwargs) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/image_processing_vit.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/image_processing_vit.py new file mode 100644 index 0000000000000000000000000000000000000000..4c7d8de714f72d996d69273eb83c20b4c685a115 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/image_processing_vit.py @@ -0,0 +1,289 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for ViT.""" + +from typing import Dict, List, Optional, Union + +import numpy as np + +from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict +from ...image_transforms import resize, to_channel_dimension_format +from ...image_utils import ( + IMAGENET_STANDARD_MEAN, + IMAGENET_STANDARD_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + infer_channel_dimension_format, + is_scaled_image, + make_list_of_images, + to_numpy_array, + valid_images, + validate_kwargs, + validate_preprocess_arguments, +) +from ...utils import TensorType, logging + + +logger = logging.get_logger(__name__) + + +class ViTImageProcessor(BaseImageProcessor): + r""" + Constructs a ViT image processor. + + Args: + do_resize (`bool`, *optional*, defaults to `True`): + Whether to resize the image's (height, width) dimensions to the specified `(size["height"], + size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method. + size (`dict`, *optional*, defaults to `{"height": 224, "width": 224}`): + Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess` + method. + resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): + Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the + `preprocess` method. + do_rescale (`bool`, *optional*, defaults to `True`): + Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` + parameter in the `preprocess` method. + rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): + Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the + `preprocess` method. + do_normalize (`bool`, *optional*, defaults to `True`): + Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` + method. + image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): + Mean to use if normalizing the image. This is a float or list of floats the length of the number of + channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. + image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): + Standard deviation to use if normalizing the image. This is a float or list of floats the length of the + number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. + """ + + model_input_names = ["pixel_values"] + + def __init__( + self, + do_resize: bool = True, + size: Optional[Dict[str, int]] = None, + resample: PILImageResampling = PILImageResampling.BILINEAR, + do_rescale: bool = True, + rescale_factor: Union[int, float] = 1 / 255, + do_normalize: bool = True, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + **kwargs, + ) -> None: + super().__init__(**kwargs) + size = size if size is not None else {"height": 224, "width": 224} + size = get_size_dict(size) + self.do_resize = do_resize + self.do_rescale = do_rescale + self.do_normalize = do_normalize + self.size = size + self.resample = resample + self.rescale_factor = rescale_factor + self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN + self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD + self._valid_processor_keys = [ + "images", + "do_resize", + "size", + "resample", + "do_rescale", + "rescale_factor", + "do_normalize", + "image_mean", + "image_std", + "return_tensors", + "data_format", + "input_data_format", + ] + + def resize( + self, + image: np.ndarray, + size: Dict[str, int], + resample: PILImageResampling = PILImageResampling.BILINEAR, + data_format: Optional[Union[str, ChannelDimension]] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ) -> np.ndarray: + """ + Resize an image to `(size["height"], size["width"])`. + + Args: + image (`np.ndarray`): + Image to resize. + size (`Dict[str, int]`): + Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image. + resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`): + `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`. + data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format for the output image. If unset, the channel dimension format of the input + image is used. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format for the input image. If unset, the channel dimension format is inferred + from the input image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. + + Returns: + `np.ndarray`: The resized image. + """ + size = get_size_dict(size) + if "height" not in size or "width" not in size: + raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}") + output_size = (size["height"], size["width"]) + return resize( + image, + size=output_size, + resample=resample, + data_format=data_format, + input_data_format=input_data_format, + **kwargs, + ) + + def preprocess( + self, + images: ImageInput, + do_resize: Optional[bool] = None, + size: Dict[str, int] = None, + resample: PILImageResampling = None, + do_rescale: Optional[bool] = None, + rescale_factor: Optional[float] = None, + do_normalize: Optional[bool] = None, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + return_tensors: Optional[Union[str, TensorType]] = None, + data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ): + """ + Preprocess an image or batch of images. + + Args: + images (`ImageInput`): + Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If + passing in images with pixel values between 0 and 1, set `do_rescale=False`. + do_resize (`bool`, *optional*, defaults to `self.do_resize`): + Whether to resize the image. + size (`Dict[str, int]`, *optional*, defaults to `self.size`): + Dictionary in the format `{"height": h, "width": w}` specifying the size of the output image after + resizing. + resample (`PILImageResampling` filter, *optional*, defaults to `self.resample`): + `PILImageResampling` filter to use if resizing the image e.g. `PILImageResampling.BILINEAR`. Only has + an effect if `do_resize` is set to `True`. + do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): + Whether to rescale the image values between [0 - 1]. + rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): + Rescale factor to rescale the image by if `do_rescale` is set to `True`. + do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): + Whether to normalize the image. + image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): + Image mean to use if `do_normalize` is set to `True`. + image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): + Image standard deviation to use if `do_normalize` is set to `True`. + return_tensors (`str` or `TensorType`, *optional*): + The type of tensors to return. Can be one of: + - Unset: Return a list of `np.ndarray`. + - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. + - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. + - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. + - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. + data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): + The channel dimension format for the output image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - Unset: Use the channel dimension format of the input image. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format for the input image. If unset, the channel dimension format is inferred + from the input image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. + """ + do_resize = do_resize if do_resize is not None else self.do_resize + do_rescale = do_rescale if do_rescale is not None else self.do_rescale + do_normalize = do_normalize if do_normalize is not None else self.do_normalize + resample = resample if resample is not None else self.resample + rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor + image_mean = image_mean if image_mean is not None else self.image_mean + image_std = image_std if image_std is not None else self.image_std + + size = size if size is not None else self.size + size_dict = get_size_dict(size) + + images = make_list_of_images(images) + + validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys) + + if not valid_images(images): + raise ValueError( + "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " + "torch.Tensor, tf.Tensor or jax.ndarray." + ) + validate_preprocess_arguments( + do_rescale=do_rescale, + rescale_factor=rescale_factor, + do_normalize=do_normalize, + image_mean=image_mean, + image_std=image_std, + do_resize=do_resize, + size=size, + resample=resample, + ) + + # All transformations expect numpy arrays. + images = [to_numpy_array(image) for image in images] + + if is_scaled_image(images[0]) and do_rescale: + logger.warning_once( + "It looks like you are trying to rescale already rescaled images. If the input" + " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." + ) + + if input_data_format is None: + # We assume that all images have the same channel dimension format. + input_data_format = infer_channel_dimension_format(images[0]) + + if do_resize: + images = [ + self.resize(image=image, size=size_dict, resample=resample, input_data_format=input_data_format) + for image in images + ] + + if do_rescale: + images = [ + self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) + for image in images + ] + + if do_normalize: + images = [ + self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format) + for image in images + ] + + images = [ + to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images + ] + + data = {"pixel_values": images} + return BatchFeature(data=data, tensor_type=return_tensors) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/modeling_flax_vit.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/modeling_flax_vit.py new file mode 100644 index 0000000000000000000000000000000000000000..586c8b62f6dad084cb3034c355e279908a6ba725 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/modeling_flax_vit.py @@ -0,0 +1,673 @@ +# coding=utf-8 +# Copyright 2021 The Google Flax Team Authors and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Optional, Tuple + +import flax.linen as nn +import jax +import jax.numpy as jnp +from flax.core.frozen_dict import FrozenDict, freeze, unfreeze +from flax.linen.attention import dot_product_attention_weights +from flax.traverse_util import flatten_dict, unflatten_dict + +from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxBaseModelOutputWithPooling, FlaxSequenceClassifierOutput +from ...modeling_flax_utils import ( + ACT2FN, + FlaxPreTrainedModel, + append_replace_return_docstrings, + overwrite_call_docstring, +) +from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward +from .configuration_vit import ViTConfig + + +VIT_START_DOCSTRING = r""" + + This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading, saving and converting weights from PyTorch models) + + This model is also a + [flax.linen.Module](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html) subclass. Use it as + a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and + behavior. + + Finally, this model supports inherent JAX features such as: + + - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) + - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) + - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) + - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) + + Parameters: + config ([`ViTConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. + dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): + The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and + `jax.numpy.bfloat16` (on TPUs). + + This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If + specified all the computation will be performed with the given `dtype`. + + **Note that this only specifies the dtype of the computation and does not influence the dtype of model + parameters.** + + If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and + [`~FlaxPreTrainedModel.to_bf16`]. +""" + +VIT_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`] + for details. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class FlaxViTPatchEmbeddings(nn.Module): + config: ViTConfig + dtype: jnp.dtype = jnp.float32 # the dtype of the computation + + def setup(self): + image_size = self.config.image_size + patch_size = self.config.patch_size + num_patches = (image_size // patch_size) * (image_size // patch_size) + self.num_patches = num_patches + self.num_channels = self.config.num_channels + self.projection = nn.Conv( + self.config.hidden_size, + kernel_size=(patch_size, patch_size), + strides=(patch_size, patch_size), + padding="VALID", + dtype=self.dtype, + kernel_init=jax.nn.initializers.variance_scaling( + self.config.initializer_range**2, "fan_in", "truncated_normal" + ), + ) + + def __call__(self, pixel_values): + num_channels = pixel_values.shape[-1] + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + ) + embeddings = self.projection(pixel_values) + batch_size, _, _, channels = embeddings.shape + return jnp.reshape(embeddings, (batch_size, -1, channels)) + + +class FlaxViTEmbeddings(nn.Module): + """Construct the CLS token, position and patch embeddings.""" + + config: ViTConfig + dtype: jnp.dtype = jnp.float32 # the dtype of the computation + + def setup(self): + self.cls_token = self.param( + "cls_token", + jax.nn.initializers.variance_scaling(self.config.initializer_range**2, "fan_in", "truncated_normal"), + (1, 1, self.config.hidden_size), + ) + self.patch_embeddings = FlaxViTPatchEmbeddings(self.config, dtype=self.dtype) + num_patches = self.patch_embeddings.num_patches + self.position_embeddings = self.param( + "position_embeddings", + jax.nn.initializers.variance_scaling(self.config.initializer_range**2, "fan_in", "truncated_normal"), + (1, num_patches + 1, self.config.hidden_size), + ) + self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) + + def __call__(self, pixel_values, deterministic=True): + batch_size = pixel_values.shape[0] + + embeddings = self.patch_embeddings(pixel_values) + + cls_tokens = jnp.broadcast_to(self.cls_token, (batch_size, 1, self.config.hidden_size)) + embeddings = jnp.concatenate((cls_tokens, embeddings), axis=1) + embeddings = embeddings + self.position_embeddings + embeddings = self.dropout(embeddings, deterministic=deterministic) + return embeddings + + +class FlaxViTSelfAttention(nn.Module): + config: ViTConfig + dtype: jnp.dtype = jnp.float32 # the dtype of the computation + + def setup(self): + if self.config.hidden_size % self.config.num_attention_heads != 0: + raise ValueError( + "`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads`:" + " {self.config.num_attention_heads}" + ) + + self.query = nn.Dense( + self.config.hidden_size, + dtype=self.dtype, + kernel_init=jax.nn.initializers.variance_scaling( + self.config.initializer_range**2, mode="fan_in", distribution="truncated_normal" + ), + use_bias=self.config.qkv_bias, + ) + self.key = nn.Dense( + self.config.hidden_size, + dtype=self.dtype, + kernel_init=jax.nn.initializers.variance_scaling( + self.config.initializer_range**2, mode="fan_in", distribution="truncated_normal" + ), + use_bias=self.config.qkv_bias, + ) + self.value = nn.Dense( + self.config.hidden_size, + dtype=self.dtype, + kernel_init=jax.nn.initializers.variance_scaling( + self.config.initializer_range**2, mode="fan_in", distribution="truncated_normal" + ), + use_bias=self.config.qkv_bias, + ) + + def __call__(self, hidden_states, deterministic: bool = True, output_attentions: bool = False): + head_dim = self.config.hidden_size // self.config.num_attention_heads + + query_states = self.query(hidden_states).reshape( + hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim) + ) + value_states = self.value(hidden_states).reshape( + hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim) + ) + key_states = self.key(hidden_states).reshape( + hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim) + ) + + dropout_rng = None + if not deterministic and self.config.attention_probs_dropout_prob > 0.0: + dropout_rng = self.make_rng("dropout") + + attn_weights = dot_product_attention_weights( + query_states, + key_states, + dropout_rng=dropout_rng, + dropout_rate=self.config.attention_probs_dropout_prob, + broadcast_dropout=True, + deterministic=deterministic, + dtype=self.dtype, + precision=None, + ) + + attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) + attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,)) + + outputs = (attn_output, attn_weights) if output_attentions else (attn_output,) + return outputs + + +class FlaxViTSelfOutput(nn.Module): + config: ViTConfig + dtype: jnp.dtype = jnp.float32 # the dtype of the computation + + def setup(self): + self.dense = nn.Dense( + self.config.hidden_size, + kernel_init=jax.nn.initializers.variance_scaling( + self.config.initializer_range**2, "fan_in", "truncated_normal" + ), + dtype=self.dtype, + ) + self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) + + def __call__(self, hidden_states, input_tensor, deterministic: bool = True): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states, deterministic=deterministic) + return hidden_states + + +class FlaxViTAttention(nn.Module): + config: ViTConfig + dtype: jnp.dtype = jnp.float32 + + def setup(self): + self.attention = FlaxViTSelfAttention(self.config, dtype=self.dtype) + self.output = FlaxViTSelfOutput(self.config, dtype=self.dtype) + + def __call__(self, hidden_states, deterministic=True, output_attentions: bool = False): + attn_outputs = self.attention(hidden_states, deterministic=deterministic, output_attentions=output_attentions) + attn_output = attn_outputs[0] + hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_outputs[1],) + + return outputs + + +class FlaxViTIntermediate(nn.Module): + config: ViTConfig + dtype: jnp.dtype = jnp.float32 # the dtype of the computation + + def setup(self): + self.dense = nn.Dense( + self.config.intermediate_size, + kernel_init=jax.nn.initializers.variance_scaling( + self.config.initializer_range**2, "fan_in", "truncated_normal" + ), + dtype=self.dtype, + ) + self.activation = ACT2FN[self.config.hidden_act] + + def __call__(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.activation(hidden_states) + return hidden_states + + +class FlaxViTOutput(nn.Module): + config: ViTConfig + dtype: jnp.dtype = jnp.float32 # the dtype of the computation + + def setup(self): + self.dense = nn.Dense( + self.config.hidden_size, + kernel_init=jax.nn.initializers.variance_scaling( + self.config.initializer_range**2, "fan_in", "truncated_normal" + ), + dtype=self.dtype, + ) + self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) + + def __call__(self, hidden_states, attention_output, deterministic: bool = True): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states, deterministic=deterministic) + hidden_states = hidden_states + attention_output + return hidden_states + + +class FlaxViTLayer(nn.Module): + config: ViTConfig + dtype: jnp.dtype = jnp.float32 # the dtype of the computation + + def setup(self): + self.attention = FlaxViTAttention(self.config, dtype=self.dtype) + self.intermediate = FlaxViTIntermediate(self.config, dtype=self.dtype) + self.output = FlaxViTOutput(self.config, dtype=self.dtype) + self.layernorm_before = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) + self.layernorm_after = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) + + def __call__(self, hidden_states, deterministic: bool = True, output_attentions: bool = False): + attention_outputs = self.attention( + self.layernorm_before(hidden_states), # in ViT, layernorm is applied before self-attention + deterministic=deterministic, + output_attentions=output_attentions, + ) + + attention_output = attention_outputs[0] + + # first residual connection + attention_output = attention_output + hidden_states + + # in ViT, layernorm is also applied after self-attention + layer_output = self.layernorm_after(attention_output) + + hidden_states = self.intermediate(layer_output) + hidden_states = self.output(hidden_states, attention_output, deterministic=deterministic) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attention_outputs[1],) + return outputs + + +class FlaxViTLayerCollection(nn.Module): + config: ViTConfig + dtype: jnp.dtype = jnp.float32 # the dtype of the computation + + def setup(self): + self.layers = [ + FlaxViTLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers) + ] + + def __call__( + self, + hidden_states, + deterministic: bool = True, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ): + all_attentions = () if output_attentions else None + all_hidden_states = () if output_hidden_states else None + + for i, layer in enumerate(self.layers): + if output_hidden_states: + all_hidden_states += (hidden_states,) + + layer_outputs = layer(hidden_states, deterministic=deterministic, output_attentions=output_attentions) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_attentions += (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states += (hidden_states,) + + outputs = (hidden_states,) + if not return_dict: + return tuple(v for v in outputs if v is not None) + + return FlaxBaseModelOutput( + last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions + ) + + +class FlaxViTEncoder(nn.Module): + config: ViTConfig + dtype: jnp.dtype = jnp.float32 # the dtype of the computation + + def setup(self): + self.layer = FlaxViTLayerCollection(self.config, dtype=self.dtype) + + def __call__( + self, + hidden_states, + deterministic: bool = True, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ): + return self.layer( + hidden_states, + deterministic=deterministic, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + +class FlaxViTPooler(nn.Module): + config: ViTConfig + dtype: jnp.dtype = jnp.float32 # the dtype of the computation + + def setup(self): + self.dense = nn.Dense( + self.config.hidden_size, + kernel_init=jax.nn.initializers.variance_scaling( + self.config.initializer_range**2, "fan_in", "truncated_normal" + ), + dtype=self.dtype, + ) + + def __call__(self, hidden_states): + cls_hidden_state = hidden_states[:, 0] + cls_hidden_state = self.dense(cls_hidden_state) + return nn.tanh(cls_hidden_state) + + +class FlaxViTPreTrainedModel(FlaxPreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = ViTConfig + base_model_prefix = "vit" + main_input_name = "pixel_values" + module_class: nn.Module = None + + def __init__( + self, + config: ViTConfig, + input_shape=None, + seed: int = 0, + dtype: jnp.dtype = jnp.float32, + _do_init: bool = True, + **kwargs, + ): + module = self.module_class(config=config, dtype=dtype, **kwargs) + if input_shape is None: + input_shape = (1, config.image_size, config.image_size, config.num_channels) + super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) + + def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: + # init input tensors + pixel_values = jnp.zeros(input_shape, dtype=self.dtype) + + params_rng, dropout_rng = jax.random.split(rng) + rngs = {"params": params_rng, "dropout": dropout_rng} + + random_params = self.module.init(rngs, pixel_values, return_dict=False)["params"] + + if params is not None: + random_params = flatten_dict(unfreeze(random_params)) + params = flatten_dict(unfreeze(params)) + for missing_key in self._missing_keys: + params[missing_key] = random_params[missing_key] + self._missing_keys = set() + return freeze(unflatten_dict(params)) + else: + return random_params + + @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + def __call__( + self, + pixel_values, + params: dict = None, + dropout_rng: jax.random.PRNGKey = None, + train: bool = False, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) + # Handle any PRNG if needed + rngs = {} + if dropout_rng is not None: + rngs["dropout"] = dropout_rng + + return self.module.apply( + {"params": params or self.params}, + jnp.array(pixel_values, dtype=jnp.float32), + not train, + output_attentions, + output_hidden_states, + return_dict, + rngs=rngs, + ) + + +class FlaxViTModule(nn.Module): + config: ViTConfig + dtype: jnp.dtype = jnp.float32 # the dtype of the computation + add_pooling_layer: bool = True + + def setup(self): + self.embeddings = FlaxViTEmbeddings(self.config, dtype=self.dtype) + self.encoder = FlaxViTEncoder(self.config, dtype=self.dtype) + self.layernorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) + self.pooler = FlaxViTPooler(self.config, dtype=self.dtype) if self.add_pooling_layer else None + + def __call__( + self, + pixel_values, + deterministic: bool = True, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ): + hidden_states = self.embeddings(pixel_values, deterministic=deterministic) + + outputs = self.encoder( + hidden_states, + deterministic=deterministic, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = outputs[0] + hidden_states = self.layernorm(hidden_states) + pooled = self.pooler(hidden_states) if self.add_pooling_layer else None + + if not return_dict: + # if pooled is None, don't return it + if pooled is None: + return (hidden_states,) + outputs[1:] + return (hidden_states, pooled) + outputs[1:] + + return FlaxBaseModelOutputWithPooling( + last_hidden_state=hidden_states, + pooler_output=pooled, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + "The bare ViT Model transformer outputting raw hidden-states without any specific head on top.", + VIT_START_DOCSTRING, +) +class FlaxViTModel(FlaxViTPreTrainedModel): + module_class = FlaxViTModule + + +FLAX_VISION_MODEL_DOCSTRING = """ + Returns: + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, FlaxViTModel + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k") + >>> model = FlaxViTModel.from_pretrained("google/vit-base-patch16-224-in21k") + + >>> inputs = image_processor(images=image, return_tensors="np") + >>> outputs = model(**inputs) + >>> last_hidden_states = outputs.last_hidden_state + ``` +""" + +overwrite_call_docstring(FlaxViTModel, FLAX_VISION_MODEL_DOCSTRING) +append_replace_return_docstrings(FlaxViTModel, output_type=FlaxBaseModelOutputWithPooling, config_class=ViTConfig) + + +class FlaxViTForImageClassificationModule(nn.Module): + config: ViTConfig + dtype: jnp.dtype = jnp.float32 + + def setup(self): + self.vit = FlaxViTModule(config=self.config, dtype=self.dtype, add_pooling_layer=False) + self.classifier = nn.Dense( + self.config.num_labels, + dtype=self.dtype, + kernel_init=jax.nn.initializers.variance_scaling( + self.config.initializer_range**2, "fan_in", "truncated_normal" + ), + ) + + def __call__( + self, + pixel_values=None, + deterministic: bool = True, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.vit( + pixel_values, + deterministic=deterministic, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + logits = self.classifier(hidden_states[:, 0, :]) + + if not return_dict: + output = (logits,) + outputs[2:] + return output + + return FlaxSequenceClassifierOutput( + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + """ + ViT Model transformer with an image classification head on top (a linear layer on top of the final hidden state of + the [CLS] token) e.g. for ImageNet. + """, + VIT_START_DOCSTRING, +) +class FlaxViTForImageClassification(FlaxViTPreTrainedModel): + module_class = FlaxViTForImageClassificationModule + + +FLAX_VISION_CLASSIF_DOCSTRING = """ + Returns: + + Example: + + ```python + >>> from transformers import AutoImageProcessor, FlaxViTForImageClassification + >>> from PIL import Image + >>> import jax + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224") + >>> model = FlaxViTForImageClassification.from_pretrained("google/vit-base-patch16-224") + + >>> inputs = image_processor(images=image, return_tensors="np") + >>> outputs = model(**inputs) + >>> logits = outputs.logits + + >>> # model predicts one of the 1000 ImageNet classes + >>> predicted_class_idx = jax.numpy.argmax(logits, axis=-1) + >>> print("Predicted class:", model.config.id2label[predicted_class_idx.item()]) + ``` +""" + +overwrite_call_docstring(FlaxViTForImageClassification, FLAX_VISION_CLASSIF_DOCSTRING) +append_replace_return_docstrings( + FlaxViTForImageClassification, output_type=FlaxSequenceClassifierOutput, config_class=ViTConfig +) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/modeling_tf_vit.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/modeling_tf_vit.py new file mode 100644 index 0000000000000000000000000000000000000000..ac5cf691e9f8a7ce537866c30c6d6004fd6e029f --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/modeling_tf_vit.py @@ -0,0 +1,905 @@ +# coding=utf-8 +# Copyright 2021 Google AI, Ross Wightman, The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" TF 2.0 ViT model.""" + + +from __future__ import annotations + +import collections.abc +import math +from typing import Optional, Tuple, Union + +import numpy as np +import tensorflow as tf + +from ...activations_tf import get_tf_activation +from ...modeling_tf_outputs import TFBaseModelOutput, TFBaseModelOutputWithPooling, TFSequenceClassifierOutput +from ...modeling_tf_utils import ( + TFModelInputType, + TFPreTrainedModel, + TFSequenceClassificationLoss, + get_initializer, + keras, + keras_serializable, + unpack_inputs, +) +from ...tf_utils import shape_list, stable_softmax +from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging +from .configuration_vit import ViTConfig + + +logger = logging.get_logger(__name__) + +# General docstring +_CONFIG_FOR_DOC = "ViTConfig" + +# Base docstring +_CHECKPOINT_FOR_DOC = "google/vit-base-patch16-224-in21k" +_EXPECTED_OUTPUT_SHAPE = [1, 197, 768] + +# Image classification docstring +_IMAGE_CLASS_CHECKPOINT = "google/vit-base-patch16-224" +_IMAGE_CLASS_EXPECTED_OUTPUT = "Egyptian cat" + + +class TFViTEmbeddings(keras.layers.Layer): + """ + Construct the CLS token, position and patch embeddings. + + """ + + def __init__(self, config: ViTConfig, **kwargs): + super().__init__(**kwargs) + + self.patch_embeddings = TFViTPatchEmbeddings(config, name="patch_embeddings") + self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) + self.config = config + + def build(self, input_shape=None): + num_patches = self.patch_embeddings.num_patches + self.cls_token = self.add_weight( + shape=(1, 1, self.config.hidden_size), + initializer=get_initializer(self.config.initializer_range), + trainable=True, + name="cls_token", + ) + self.position_embeddings = self.add_weight( + shape=(1, num_patches + 1, self.config.hidden_size), + initializer=get_initializer(self.config.initializer_range), + trainable=True, + name="position_embeddings", + ) + + if self.built: + return + self.built = True + if getattr(self, "patch_embeddings", None) is not None: + with tf.name_scope(self.patch_embeddings.name): + self.patch_embeddings.build(None) + + def interpolate_pos_encoding(self, embeddings, height, width) -> tf.Tensor: + """ + This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher + resolution images. + + Source: + https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174 + """ + + batch_size, seq_len, dim = shape_list(embeddings) + num_patches = seq_len - 1 + + _, num_positions, _ = shape_list(self.position_embeddings) + num_positions -= 1 + + if num_patches == num_positions and height == width: + return self.position_embeddings + class_pos_embed = self.position_embeddings[:, :1] + patch_pos_embed = self.position_embeddings[:, 1:] + h0 = height // self.config.patch_size + w0 = width // self.config.patch_size + patch_pos_embed = tf.image.resize( + images=tf.reshape( + patch_pos_embed, shape=(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim) + ), + size=(h0, w0), + method="bicubic", + ) + + shape = shape_list(patch_pos_embed) + assert h0 == shape[-3] and w0 == shape[-2] + patch_pos_embed = tf.reshape(tensor=patch_pos_embed, shape=(1, -1, dim)) + return tf.concat(values=(class_pos_embed, patch_pos_embed), axis=1) + + def call( + self, pixel_values: tf.Tensor, interpolate_pos_encoding: bool = False, training: bool = False + ) -> tf.Tensor: + batch_size, num_channels, height, width = shape_list(pixel_values) + embeddings = self.patch_embeddings( + pixel_values, interpolate_pos_encoding=interpolate_pos_encoding, training=training + ) + + # add the [CLS] token to the embedded patch tokens + cls_tokens = tf.repeat(self.cls_token, repeats=batch_size, axis=0) + embeddings = tf.concat((cls_tokens, embeddings), axis=1) + + # add positional encoding to each token + if interpolate_pos_encoding: + embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) + else: + embeddings = embeddings + self.position_embeddings + + embeddings = self.dropout(embeddings, training=training) + + return embeddings + + +# Based on timm implementation, which can be found here: +# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py +class TFViTPatchEmbeddings(keras.layers.Layer): + """ + This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial + `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a + Transformer. + """ + + def __init__(self, config: ViTConfig, **kwargs): + super().__init__(**kwargs) + image_size, patch_size = config.image_size, config.patch_size + num_channels, hidden_size = config.num_channels, config.hidden_size + + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) + self.image_size = image_size + self.patch_size = patch_size + self.num_patches = num_patches + self.num_channels = num_channels + self.config = config + + self.projection = keras.layers.Conv2D( + filters=hidden_size, + kernel_size=patch_size, + strides=patch_size, + padding="valid", + data_format="channels_last", + use_bias=True, + kernel_initializer=get_initializer(self.config.initializer_range), + bias_initializer="zeros", + name="projection", + ) + + def call( + self, pixel_values: tf.Tensor, interpolate_pos_encoding: bool = False, training: bool = False + ) -> tf.Tensor: + batch_size, num_channels, height, width = shape_list(pixel_values) + if tf.executing_eagerly() and num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + ) + if not interpolate_pos_encoding: + if tf.executing_eagerly(): + if height != self.image_size[0] or width != self.image_size[1]: + raise ValueError( + f"Input image size ({height}*{width}) doesn't match model" + f" ({self.image_size[0]}*{self.image_size[1]})." + ) + + # When running on CPU, `keras.layers.Conv2D` doesn't support `NCHW` format. + # So change the input format from `NCHW` to `NHWC`. + # shape = (batch_size, in_height, in_width, in_channels=num_channels) + pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1)) + + projection = self.projection(pixel_values) + + # Change the 2D spatial dimensions to a single temporal dimension. + # shape = (batch_size, num_patches, out_channels=embed_dim) + num_patches = (width // self.patch_size[1]) * (height // self.patch_size[0]) + embeddings = tf.reshape(tensor=projection, shape=(batch_size, num_patches, -1)) + + return embeddings + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "projection", None) is not None: + with tf.name_scope(self.projection.name): + self.projection.build([None, None, None, self.num_channels]) + + +class TFViTSelfAttention(keras.layers.Layer): + def __init__(self, config: ViTConfig, **kwargs): + super().__init__(**kwargs) + + if config.hidden_size % config.num_attention_heads != 0: + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number " + f"of attention heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + self.sqrt_att_head_size = math.sqrt(self.attention_head_size) + + self.query = keras.layers.Dense( + units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query" + ) + self.key = keras.layers.Dense( + units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key" + ) + self.value = keras.layers.Dense( + units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value" + ) + self.dropout = keras.layers.Dropout(rate=config.attention_probs_dropout_prob) + self.config = config + + def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor: + # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size] + tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size)) + + # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size] + return tf.transpose(tensor, perm=[0, 2, 1, 3]) + + def call( + self, + hidden_states: tf.Tensor, + head_mask: tf.Tensor, + output_attentions: bool, + training: bool = False, + ) -> Tuple[tf.Tensor]: + batch_size = shape_list(hidden_states)[0] + mixed_query_layer = self.query(inputs=hidden_states) + mixed_key_layer = self.key(inputs=hidden_states) + mixed_value_layer = self.value(inputs=hidden_states) + query_layer = self.transpose_for_scores(mixed_query_layer, batch_size) + key_layer = self.transpose_for_scores(mixed_key_layer, batch_size) + value_layer = self.transpose_for_scores(mixed_value_layer, batch_size) + + # Take the dot product between "query" and "key" to get the raw attention scores. + # (batch size, num_heads, seq_len_q, seq_len_k) + attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) + dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype) + attention_scores = tf.divide(attention_scores, dk) + + # Normalize the attention scores to probabilities. + attention_probs = stable_softmax(logits=attention_scores, axis=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(inputs=attention_probs, training=training) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = tf.multiply(attention_probs, head_mask) + + attention_output = tf.matmul(attention_probs, value_layer) + attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3]) + + # (batch_size, seq_len_q, all_head_size) + attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size)) + outputs = (attention_output, attention_probs) if output_attentions else (attention_output,) + + return outputs + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "query", None) is not None: + with tf.name_scope(self.query.name): + self.query.build([None, None, self.config.hidden_size]) + if getattr(self, "key", None) is not None: + with tf.name_scope(self.key.name): + self.key.build([None, None, self.config.hidden_size]) + if getattr(self, "value", None) is not None: + with tf.name_scope(self.value.name): + self.value.build([None, None, self.config.hidden_size]) + + +class TFViTSelfOutput(keras.layers.Layer): + """ + The residual connection is defined in TFViTLayer instead of here (as is the case with other models), due to the + layernorm applied before each block. + """ + + def __init__(self, config: ViTConfig, **kwargs): + super().__init__(**kwargs) + + self.dense = keras.layers.Dense( + units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" + ) + self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) + self.config = config + + def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: + hidden_states = self.dense(inputs=hidden_states) + hidden_states = self.dropout(inputs=hidden_states, training=training) + + return hidden_states + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "dense", None) is not None: + with tf.name_scope(self.dense.name): + self.dense.build([None, None, self.config.hidden_size]) + + +class TFViTAttention(keras.layers.Layer): + def __init__(self, config: ViTConfig, **kwargs): + super().__init__(**kwargs) + + self.self_attention = TFViTSelfAttention(config, name="attention") + self.dense_output = TFViTSelfOutput(config, name="output") + + def prune_heads(self, heads): + raise NotImplementedError + + def call( + self, + input_tensor: tf.Tensor, + head_mask: tf.Tensor, + output_attentions: bool, + training: bool = False, + ) -> Tuple[tf.Tensor]: + self_outputs = self.self_attention( + hidden_states=input_tensor, head_mask=head_mask, output_attentions=output_attentions, training=training + ) + attention_output = self.dense_output( + hidden_states=self_outputs[0], input_tensor=input_tensor, training=training + ) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + + return outputs + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "self_attention", None) is not None: + with tf.name_scope(self.self_attention.name): + self.self_attention.build(None) + if getattr(self, "dense_output", None) is not None: + with tf.name_scope(self.dense_output.name): + self.dense_output.build(None) + + +class TFViTIntermediate(keras.layers.Layer): + def __init__(self, config: ViTConfig, **kwargs): + super().__init__(**kwargs) + + self.dense = keras.layers.Dense( + units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" + ) + + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = get_tf_activation(config.hidden_act) + else: + self.intermediate_act_fn = config.hidden_act + self.config = config + + def call(self, hidden_states: tf.Tensor) -> tf.Tensor: + hidden_states = self.dense(inputs=hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + + return hidden_states + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "dense", None) is not None: + with tf.name_scope(self.dense.name): + self.dense.build([None, None, self.config.hidden_size]) + + +class TFViTOutput(keras.layers.Layer): + def __init__(self, config: ViTConfig, **kwargs): + super().__init__(**kwargs) + + self.dense = keras.layers.Dense( + units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" + ) + self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) + self.config = config + + def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: + hidden_states = self.dense(inputs=hidden_states) + hidden_states = self.dropout(inputs=hidden_states, training=training) + hidden_states = hidden_states + input_tensor + + return hidden_states + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "dense", None) is not None: + with tf.name_scope(self.dense.name): + self.dense.build([None, None, self.config.intermediate_size]) + + +class TFViTLayer(keras.layers.Layer): + """This corresponds to the Block class in the timm implementation.""" + + def __init__(self, config: ViTConfig, **kwargs): + super().__init__(**kwargs) + + self.attention = TFViTAttention(config, name="attention") + self.intermediate = TFViTIntermediate(config, name="intermediate") + self.vit_output = TFViTOutput(config, name="output") + + self.layernorm_before = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_before") + self.layernorm_after = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_after") + self.config = config + + def call( + self, + hidden_states: tf.Tensor, + head_mask: tf.Tensor, + output_attentions: bool, + training: bool = False, + ) -> Tuple[tf.Tensor]: + attention_outputs = self.attention( + # in ViT, layernorm is applied before self-attention + input_tensor=self.layernorm_before(inputs=hidden_states), + head_mask=head_mask, + output_attentions=output_attentions, + training=training, + ) + attention_output = attention_outputs[0] + + # first residual connection + hidden_states = attention_output + hidden_states + + # in ViT, layernorm is also applied after self-attention + layer_output = self.layernorm_after(inputs=hidden_states) + + intermediate_output = self.intermediate(hidden_states=layer_output) + + # second residual connection is done here + layer_output = self.vit_output( + hidden_states=intermediate_output, input_tensor=hidden_states, training=training + ) + outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them + + return outputs + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "attention", None) is not None: + with tf.name_scope(self.attention.name): + self.attention.build(None) + if getattr(self, "intermediate", None) is not None: + with tf.name_scope(self.intermediate.name): + self.intermediate.build(None) + if getattr(self, "vit_output", None) is not None: + with tf.name_scope(self.vit_output.name): + self.vit_output.build(None) + if getattr(self, "layernorm_before", None) is not None: + with tf.name_scope(self.layernorm_before.name): + self.layernorm_before.build([None, None, self.config.hidden_size]) + if getattr(self, "layernorm_after", None) is not None: + with tf.name_scope(self.layernorm_after.name): + self.layernorm_after.build([None, None, self.config.hidden_size]) + + +class TFViTEncoder(keras.layers.Layer): + def __init__(self, config: ViTConfig, **kwargs): + super().__init__(**kwargs) + + self.layer = [TFViTLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)] + + def call( + self, + hidden_states: tf.Tensor, + head_mask: tf.Tensor, + output_attentions: bool, + output_hidden_states: bool, + return_dict: bool, + training: bool = False, + ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]: + all_hidden_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_outputs = layer_module( + hidden_states=hidden_states, + head_mask=head_mask[i], + output_attentions=output_attentions, + training=training, + ) + hidden_states = layer_outputs[0] + + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + # Add last layer + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None) + + return TFBaseModelOutput( + last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions + ) + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "layer", None) is not None: + for layer in self.layer: + with tf.name_scope(layer.name): + layer.build(None) + + +@keras_serializable +class TFViTMainLayer(keras.layers.Layer): + config_class = ViTConfig + + def __init__(self, config: ViTConfig, add_pooling_layer: bool = True, **kwargs): + super().__init__(**kwargs) + + self.config = config + + self.embeddings = TFViTEmbeddings(config, name="embeddings") + self.encoder = TFViTEncoder(config, name="encoder") + self.layernorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm") + self.pooler = TFViTPooler(config, name="pooler") if add_pooling_layer else None + + def get_input_embeddings(self) -> keras.layers.Layer: + return self.embeddings.patch_embeddings + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + raise NotImplementedError + + @unpack_inputs + def call( + self, + pixel_values: TFModelInputType | None = None, + head_mask: np.ndarray | tf.Tensor | None = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + interpolate_pos_encoding: Optional[bool] = None, + return_dict: Optional[bool] = None, + training: bool = False, + ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]: + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + embedding_output = self.embeddings( + pixel_values=pixel_values, + interpolate_pos_encoding=interpolate_pos_encoding, + training=training, + ) + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + if head_mask is not None: + raise NotImplementedError + else: + head_mask = [None] * self.config.num_hidden_layers + + encoder_outputs = self.encoder( + hidden_states=embedding_output, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + training=training, + ) + + sequence_output = encoder_outputs[0] + sequence_output = self.layernorm(inputs=sequence_output) + pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return TFBaseModelOutputWithPooling( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "embeddings", None) is not None: + with tf.name_scope(self.embeddings.name): + self.embeddings.build(None) + if getattr(self, "encoder", None) is not None: + with tf.name_scope(self.encoder.name): + self.encoder.build(None) + if getattr(self, "layernorm", None) is not None: + with tf.name_scope(self.layernorm.name): + self.layernorm.build([None, None, self.config.hidden_size]) + if getattr(self, "pooler", None) is not None: + with tf.name_scope(self.pooler.name): + self.pooler.build(None) + + +class TFViTPreTrainedModel(TFPreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = ViTConfig + base_model_prefix = "vit" + main_input_name = "pixel_values" + + +VIT_START_DOCSTRING = r""" + + This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it + as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and + behavior. + + + + TensorFlow models and layers in `transformers` accept two formats as input: + + - having all inputs as keyword arguments (like PyTorch models), or + - having all inputs as a list, tuple or dict in the first positional argument. + + The reason the second format is supported is that Keras methods prefer this format when passing inputs to models + and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just + pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second + format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with + the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first + positional argument: + + - a single Tensor with `pixel_values` only and nothing else: `model(pixel_values)` + - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: + `model([pixel_values, attention_mask])` or `model([pixel_values, attention_mask, token_type_ids])` + - a dictionary with one or several input Tensors associated to the input names given in the docstring: + `model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})` + + Note that when creating models and layers with + [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry + about any of this, as you can just pass inputs like you would to any other Python function! + + + + Args: + config ([`ViTConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. +""" + +VIT_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`] + for details. + + head_mask (`np.ndarray` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the + config will be used instead. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. This argument can be used only in eager mode, in graph mode the value in the config will be + used instead. + interpolate_pos_encoding (`bool`, *optional*): + Whether to interpolate the pre-trained position encodings. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in + eager mode, in graph mode the value will always be set to True. + training (`bool`, *optional*, defaults to `False``): + Whether or not to use the model in training mode (some modules like dropout modules have different + behaviors between training and evaluation). +""" + + +@add_start_docstrings( + "The bare ViT Model transformer outputting raw hidden-states without any specific head on top.", + VIT_START_DOCSTRING, +) +class TFViTModel(TFViTPreTrainedModel): + def __init__(self, config: ViTConfig, *inputs, add_pooling_layer=True, **kwargs): + super().__init__(config, *inputs, **kwargs) + + self.vit = TFViTMainLayer(config, add_pooling_layer=add_pooling_layer, name="vit") + + @unpack_inputs + @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=TFBaseModelOutputWithPooling, + config_class=_CONFIG_FOR_DOC, + modality="vision", + expected_output=_EXPECTED_OUTPUT_SHAPE, + ) + def call( + self, + pixel_values: TFModelInputType | None = None, + head_mask: np.ndarray | tf.Tensor | None = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + interpolate_pos_encoding: Optional[bool] = None, + return_dict: Optional[bool] = None, + training: bool = False, + ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]: + outputs = self.vit( + pixel_values=pixel_values, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + interpolate_pos_encoding=interpolate_pos_encoding, + return_dict=return_dict, + training=training, + ) + + return outputs + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "vit", None) is not None: + with tf.name_scope(self.vit.name): + self.vit.build(None) + + +class TFViTPooler(keras.layers.Layer): + def __init__(self, config: ViTConfig, **kwargs): + super().__init__(**kwargs) + + self.dense = keras.layers.Dense( + units=config.hidden_size, + kernel_initializer=get_initializer(config.initializer_range), + activation="tanh", + name="dense", + ) + self.config = config + + def call(self, hidden_states: tf.Tensor) -> tf.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(inputs=first_token_tensor) + + return pooled_output + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "dense", None) is not None: + with tf.name_scope(self.dense.name): + self.dense.build([None, None, self.config.hidden_size]) + + +@add_start_docstrings( + """ + ViT Model transformer with an image classification head on top (a linear layer on top of the final hidden state of + the [CLS] token) e.g. for ImageNet. + + + + Note that it's possible to fine-tune ViT on higher resolution images than the ones it has been trained on, by + setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained + position embeddings to the higher resolution. + + + """, + VIT_START_DOCSTRING, +) +class TFViTForImageClassification(TFViTPreTrainedModel, TFSequenceClassificationLoss): + def __init__(self, config: ViTConfig, *inputs, **kwargs): + super().__init__(config, *inputs, **kwargs) + + self.num_labels = config.num_labels + self.vit = TFViTMainLayer(config, add_pooling_layer=False, name="vit") + + # Classifier head + self.classifier = keras.layers.Dense( + units=config.num_labels, + kernel_initializer=get_initializer(config.initializer_range), + name="classifier", + ) + self.config = config + + @unpack_inputs + @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_IMAGE_CLASS_CHECKPOINT, + output_type=TFSequenceClassifierOutput, + config_class=_CONFIG_FOR_DOC, + expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, + ) + def call( + self, + pixel_values: TFModelInputType | None = None, + head_mask: np.ndarray | tf.Tensor | None = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + interpolate_pos_encoding: Optional[bool] = None, + return_dict: Optional[bool] = None, + labels: np.ndarray | tf.Tensor | None = None, + training: Optional[bool] = False, + ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]: + r""" + labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + + outputs = self.vit( + pixel_values=pixel_values, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + interpolate_pos_encoding=interpolate_pos_encoding, + return_dict=return_dict, + training=training, + ) + sequence_output = outputs[0] + logits = self.classifier(inputs=sequence_output[:, 0, :]) + loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return TFSequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "vit", None) is not None: + with tf.name_scope(self.vit.name): + self.vit.build(None) + if getattr(self, "classifier", None) is not None: + with tf.name_scope(self.classifier.name): + self.classifier.build([None, None, self.config.hidden_size]) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/modeling_vit.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/modeling_vit.py new file mode 100644 index 0000000000000000000000000000000000000000..4ccdd1deaf4ca1ed4fe30e936c66f8109a4fafd6 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit/modeling_vit.py @@ -0,0 +1,838 @@ +# coding=utf-8 +# Copyright 2021 Google AI, Ross Wightman, The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch ViT model.""" + + +import collections.abc +import math +from typing import Dict, List, Optional, Set, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ...activations import ACT2FN +from ...modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPooling, + ImageClassifierOutput, + MaskedImageModelingOutput, +) +from ...modeling_utils import PreTrainedModel +from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer +from ...utils import ( + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) +from .configuration_vit import ViTConfig + + +logger = logging.get_logger(__name__) + +# General docstring +_CONFIG_FOR_DOC = "ViTConfig" + +# Base docstring +_CHECKPOINT_FOR_DOC = "google/vit-base-patch16-224-in21k" +_EXPECTED_OUTPUT_SHAPE = [1, 197, 768] + +# Image classification docstring +_IMAGE_CLASS_CHECKPOINT = "google/vit-base-patch16-224" +_IMAGE_CLASS_EXPECTED_OUTPUT = "Egyptian cat" + + +from ..deprecated._archive_maps import VIT_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +class ViTEmbeddings(nn.Module): + """ + Construct the CLS token, position and patch embeddings. Optionally, also the mask token. + """ + + def __init__(self, config: ViTConfig, use_mask_token: bool = False) -> None: + super().__init__() + + self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_size)) + self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None + self.patch_embeddings = ViTPatchEmbeddings(config) + num_patches = self.patch_embeddings.num_patches + self.position_embeddings = nn.Parameter(torch.randn(1, num_patches + 1, config.hidden_size)) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.config = config + + def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: + """ + This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher + resolution images. + + Source: + https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174 + """ + + num_patches = embeddings.shape[1] - 1 + num_positions = self.position_embeddings.shape[1] - 1 + if num_patches == num_positions and height == width: + return self.position_embeddings + class_pos_embed = self.position_embeddings[:, 0] + patch_pos_embed = self.position_embeddings[:, 1:] + dim = embeddings.shape[-1] + h0 = height // self.config.patch_size + w0 = width // self.config.patch_size + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + h0, w0 = h0 + 0.1, w0 + 0.1 + patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim) + patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed, + scale_factor=(h0 / math.sqrt(num_positions), w0 / math.sqrt(num_positions)), + mode="bicubic", + align_corners=False, + ) + assert int(h0) == patch_pos_embed.shape[-2] and int(w0) == patch_pos_embed.shape[-1] + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) + + def forward( + self, + pixel_values: torch.Tensor, + bool_masked_pos: Optional[torch.BoolTensor] = None, + interpolate_pos_encoding: bool = False, + ) -> torch.Tensor: + batch_size, num_channels, height, width = pixel_values.shape + embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding) + + if bool_masked_pos is not None: + seq_length = embeddings.shape[1] + mask_tokens = self.mask_token.expand(batch_size, seq_length, -1) + # replace the masked visual tokens by mask_tokens + mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens) + embeddings = embeddings * (1.0 - mask) + mask_tokens * mask + + # add the [CLS] token to the embedded patch tokens + cls_tokens = self.cls_token.expand(batch_size, -1, -1) + embeddings = torch.cat((cls_tokens, embeddings), dim=1) + + # add positional encoding to each token + if interpolate_pos_encoding: + embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) + else: + embeddings = embeddings + self.position_embeddings + + embeddings = self.dropout(embeddings) + + return embeddings + + +class ViTPatchEmbeddings(nn.Module): + """ + This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial + `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a + Transformer. + """ + + def __init__(self, config): + super().__init__() + image_size, patch_size = config.image_size, config.patch_size + num_channels, hidden_size = config.num_channels, config.hidden_size + + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_patches = num_patches + + self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) + + def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor: + batch_size, num_channels, height, width = pixel_values.shape + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + f" Expected {self.num_channels} but got {num_channels}." + ) + if not interpolate_pos_encoding: + if height != self.image_size[0] or width != self.image_size[1]: + raise ValueError( + f"Input image size ({height}*{width}) doesn't match model" + f" ({self.image_size[0]}*{self.image_size[1]})." + ) + embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2) + return embeddings + + +class ViTSelfAttention(nn.Module): + def __init__(self, config: ViTConfig) -> None: + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " + f"heads {config.num_attention_heads}." + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + mixed_query_layer = self.query(hidden_states) + + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + query_layer = self.transpose_for_scores(mixed_query_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +class ViTSelfOutput(nn.Module): + """ + The residual connection is defined in ViTLayer instead of here (as is the case with other models), due to the + layernorm applied before each block. + """ + + def __init__(self, config: ViTConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + + return hidden_states + + +class ViTAttention(nn.Module): + def __init__(self, config: ViTConfig) -> None: + super().__init__() + self.attention = ViTSelfAttention(config) + self.output = ViTSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads: Set[int]) -> None: + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.attention.query = prune_linear_layer(self.attention.query, index) + self.attention.key = prune_linear_layer(self.attention.key, index) + self.attention.value = prune_linear_layer(self.attention.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) + self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + self_outputs = self.attention(hidden_states, head_mask, output_attentions) + + attention_output = self.output(self_outputs[0], hidden_states) + + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +class ViTIntermediate(nn.Module): + def __init__(self, config: ViTConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + + return hidden_states + + +class ViTOutput(nn.Module): + def __init__(self, config: ViTConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + + hidden_states = hidden_states + input_tensor + + return hidden_states + + +class ViTLayer(nn.Module): + """This corresponds to the Block class in the timm implementation.""" + + def __init__(self, config: ViTConfig) -> None: + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = ViTAttention(config) + self.intermediate = ViTIntermediate(config) + self.output = ViTOutput(config) + self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + self_attention_outputs = self.attention( + self.layernorm_before(hidden_states), # in ViT, layernorm is applied before self-attention + head_mask, + output_attentions=output_attentions, + ) + attention_output = self_attention_outputs[0] + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + # first residual connection + hidden_states = attention_output + hidden_states + + # in ViT, layernorm is also applied after self-attention + layer_output = self.layernorm_after(hidden_states) + layer_output = self.intermediate(layer_output) + + # second residual connection is done here + layer_output = self.output(layer_output, hidden_states) + + outputs = (layer_output,) + outputs + + return outputs + + +class ViTEncoder(nn.Module): + def __init__(self, config: ViTConfig) -> None: + super().__init__() + self.config = config + self.layer = nn.ModuleList([ViTLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ) -> Union[tuple, BaseModelOutput]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer_module.__call__, + hidden_states, + layer_head_mask, + output_attentions, + ) + else: + layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +class ViTPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = ViTConfig + base_model_prefix = "vit" + main_input_name = "pixel_values" + supports_gradient_checkpointing = True + _no_split_modules = ["ViTEmbeddings", "ViTLayer"] + + def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None: + """Initialize the weights""" + if isinstance(module, (nn.Linear, nn.Conv2d)): + # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid + # `trunc_normal_cpu` not implemented in `half` issues + module.weight.data = nn.init.trunc_normal_( + module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range + ).to(module.weight.dtype) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + elif isinstance(module, ViTEmbeddings): + module.position_embeddings.data = nn.init.trunc_normal_( + module.position_embeddings.data.to(torch.float32), + mean=0.0, + std=self.config.initializer_range, + ).to(module.position_embeddings.dtype) + + module.cls_token.data = nn.init.trunc_normal_( + module.cls_token.data.to(torch.float32), + mean=0.0, + std=self.config.initializer_range, + ).to(module.cls_token.dtype) + + +VIT_START_DOCSTRING = r""" + This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it + as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and + behavior. + + Parameters: + config ([`ViTConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +VIT_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`] + for details. + + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + interpolate_pos_encoding (`bool`, *optional*): + Whether to interpolate the pre-trained position encodings. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare ViT Model transformer outputting raw hidden-states without any specific head on top.", + VIT_START_DOCSTRING, +) +class ViTModel(ViTPreTrainedModel): + def __init__(self, config: ViTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False): + super().__init__(config) + self.config = config + + self.embeddings = ViTEmbeddings(config, use_mask_token=use_mask_token) + self.encoder = ViTEncoder(config) + + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.pooler = ViTPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> ViTPatchEmbeddings: + return self.embeddings.patch_embeddings + + def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=BaseModelOutputWithPooling, + config_class=_CONFIG_FOR_DOC, + modality="vision", + expected_output=_EXPECTED_OUTPUT_SHAPE, + ) + def forward( + self, + pixel_values: Optional[torch.Tensor] = None, + bool_masked_pos: Optional[torch.BoolTensor] = None, + head_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + interpolate_pos_encoding: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPooling]: + r""" + bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*): + Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + # TODO: maybe have a cleaner way to cast the input (from `ImageProcessor` side?) + expected_dtype = self.embeddings.patch_embeddings.projection.weight.dtype + if pixel_values.dtype != expected_dtype: + pixel_values = pixel_values.to(expected_dtype) + + embedding_output = self.embeddings( + pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding + ) + + encoder_outputs = self.encoder( + embedding_output, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + sequence_output = self.layernorm(sequence_output) + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,) + return head_outputs + encoder_outputs[1:] + + return BaseModelOutputWithPooling( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +class ViTPooler(nn.Module): + def __init__(self, config: ViTConfig): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +@add_start_docstrings( + """ViT Model with a decoder on top for masked image modeling, as proposed in [SimMIM](https://arxiv.org/abs/2111.09886). + + + + Note that we provide a script to pre-train this model on custom data in our [examples + directory](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining). + + + """, + VIT_START_DOCSTRING, +) +class ViTForMaskedImageModeling(ViTPreTrainedModel): + def __init__(self, config: ViTConfig) -> None: + super().__init__(config) + + self.vit = ViTModel(config, add_pooling_layer=False, use_mask_token=True) + + self.decoder = nn.Sequential( + nn.Conv2d( + in_channels=config.hidden_size, + out_channels=config.encoder_stride**2 * config.num_channels, + kernel_size=1, + ), + nn.PixelShuffle(config.encoder_stride), + ) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=MaskedImageModelingOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: Optional[torch.Tensor] = None, + bool_masked_pos: Optional[torch.BoolTensor] = None, + head_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + interpolate_pos_encoding: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[tuple, MaskedImageModelingOutput]: + r""" + bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`): + Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). + + Returns: + + Examples: + ```python + >>> from transformers import AutoImageProcessor, ViTForMaskedImageModeling + >>> import torch + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k") + >>> model = ViTForMaskedImageModeling.from_pretrained("google/vit-base-patch16-224-in21k") + + >>> num_patches = (model.config.image_size // model.config.patch_size) ** 2 + >>> pixel_values = image_processor(images=image, return_tensors="pt").pixel_values + >>> # create random boolean mask of shape (batch_size, num_patches) + >>> bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool() + + >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos) + >>> loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction + >>> list(reconstructed_pixel_values.shape) + [1, 3, 224, 224] + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if bool_masked_pos is not None and (self.config.patch_size != self.config.encoder_stride): + raise ValueError( + "When `bool_masked_pos` is provided, `patch_size` must be equal to `encoder_stride` to ensure that " + "the reconstructed image has the same dimensions as the input. " + f"Got `patch_size` = {self.config.patch_size} and `encoder_stride` = {self.config.encoder_stride}." + ) + + outputs = self.vit( + pixel_values, + bool_masked_pos=bool_masked_pos, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + interpolate_pos_encoding=interpolate_pos_encoding, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + # Reshape to (batch_size, num_channels, height, width) + sequence_output = sequence_output[:, 1:] + batch_size, sequence_length, num_channels = sequence_output.shape + height = width = math.floor(sequence_length**0.5) + sequence_output = sequence_output.permute(0, 2, 1).reshape(batch_size, num_channels, height, width) + + # Reconstruct pixel values + reconstructed_pixel_values = self.decoder(sequence_output) + + masked_im_loss = None + if bool_masked_pos is not None: + size = self.config.image_size // self.config.patch_size + bool_masked_pos = bool_masked_pos.reshape(-1, size, size) + mask = ( + bool_masked_pos.repeat_interleave(self.config.patch_size, 1) + .repeat_interleave(self.config.patch_size, 2) + .unsqueeze(1) + .contiguous() + ) + reconstruction_loss = nn.functional.l1_loss(pixel_values, reconstructed_pixel_values, reduction="none") + masked_im_loss = (reconstruction_loss * mask).sum() / (mask.sum() + 1e-5) / self.config.num_channels + + if not return_dict: + output = (reconstructed_pixel_values,) + outputs[1:] + return ((masked_im_loss,) + output) if masked_im_loss is not None else output + + return MaskedImageModelingOutput( + loss=masked_im_loss, + reconstruction=reconstructed_pixel_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + """ + ViT Model transformer with an image classification head on top (a linear layer on top of the final hidden state of + the [CLS] token) e.g. for ImageNet. + + + + Note that it's possible to fine-tune ViT on higher resolution images than the ones it has been trained on, by + setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained + position embeddings to the higher resolution. + + + """, + VIT_START_DOCSTRING, +) +class ViTForImageClassification(ViTPreTrainedModel): + def __init__(self, config: ViTConfig) -> None: + super().__init__(config) + + self.num_labels = config.num_labels + self.vit = ViTModel(config, add_pooling_layer=False) + + # Classifier head + self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity() + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_IMAGE_CLASS_CHECKPOINT, + output_type=ImageClassifierOutput, + config_class=_CONFIG_FOR_DOC, + expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, + ) + def forward( + self, + pixel_values: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + interpolate_pos_encoding: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[tuple, ImageClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.vit( + pixel_values, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + interpolate_pos_encoding=interpolate_pos_encoding, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + logits = self.classifier(sequence_output[:, 0, :]) + + loss = None + if labels is not None: + # move labels to correct device to enable model parallelism + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return ImageClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_hybrid/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_hybrid/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..47342d3a26043897fd48035c684e886632015169 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_hybrid/__init__.py @@ -0,0 +1,71 @@ +# Copyright 2022 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available + + +_import_structure = {"configuration_vit_hybrid": ["VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTHybridConfig"]} + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_vit_hybrid"] = [ + "VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST", + "ViTHybridForImageClassification", + "ViTHybridModel", + "ViTHybridPreTrainedModel", + ] + +try: + if not is_vision_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["image_processing_vit_hybrid"] = ["ViTHybridImageProcessor"] + + +if TYPE_CHECKING: + from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_vit_hybrid import ( + VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST, + ViTHybridForImageClassification, + ViTHybridModel, + ViTHybridPreTrainedModel, + ) + + try: + if not is_vision_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .image_processing_vit_hybrid 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b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_hybrid/configuration_vit_hybrid.py new file mode 100644 index 0000000000000000000000000000000000000000..8a8a808ec60d05e289404fcd2436b8c1aa19dfc8 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_hybrid/configuration_vit_hybrid.py @@ -0,0 +1,173 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" ViT Hybrid model configuration""" + + +from ...configuration_utils import PretrainedConfig +from ...utils import logging +from ..auto.configuration_auto import CONFIG_MAPPING +from ..bit import BitConfig + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class ViTHybridConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`ViTHybridModel`]. It is used to instantiate a ViT + Hybrid model according to the specified arguments, defining the model architecture. Instantiating a configuration + with the defaults will yield a similar configuration to that of the ViT Hybrid + [google/vit-hybrid-base-bit-384](https://huggingface.co/google/vit-hybrid-base-bit-384) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + backbone_config (`Union[Dict[str, Any], PretrainedConfig]`, *optional*): + The configuration of the backbone in a dictionary or the config object of the backbone. + backbone (`str`, *optional*): + Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this + will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone` + is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights. + use_pretrained_backbone (`bool`, *optional*, defaults to `False`): + Whether to use pretrained weights for the backbone. + use_timm_backbone (`bool`, *optional*, defaults to `False`): + Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers + library. + backbone_kwargs (`dict`, *optional*): + Keyword arguments to be passed to AutoBackbone when loading from a checkpoint + e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set. + hidden_size (`int`, *optional*, defaults to 768): + Dimensionality of the encoder layers and the pooler layer. + num_hidden_layers (`int`, *optional*, defaults to 12): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 12): + Number of attention heads for each attention layer in the Transformer encoder. + intermediate_size (`int`, *optional*, defaults to 3072): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"` and `"gelu_new"` are supported. + hidden_dropout_prob (`float`, *optional*, defaults to 0.0): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + layer_norm_eps (`float`, *optional*, defaults to 1e-12): + The epsilon used by the layer normalization layers. + image_size (`int`, *optional*, defaults to 224): + The size (resolution) of each image. + patch_size (`int`, *optional*, defaults to 1): + The size (resolution) of each patch. + num_channels (`int`, *optional*, defaults to 3): + The number of input channels. + backbone_featmap_shape (`List[int]`, *optional*, defaults to `[1, 1024, 24, 24]`): + Used only for the `hybrid` embedding type. The shape of the feature maps of the backbone. + qkv_bias (`bool`, *optional*, defaults to `True`): + Whether to add a bias to the queries, keys and values. + + Example: + + ```python + >>> from transformers import ViTHybridConfig, ViTHybridModel + + >>> # Initializing a ViT Hybrid vit-hybrid-base-bit-384 style configuration + >>> configuration = ViTHybridConfig() + + >>> # Initializing a model (with random weights) from the vit-hybrid-base-bit-384 style configuration + >>> model = ViTHybridModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "vit-hybrid" + + def __init__( + self, + backbone_config=None, + backbone=None, + use_pretrained_backbone=False, + use_timm_backbone=False, + backbone_kwargs=None, + hidden_size=768, + num_hidden_layers=12, + num_attention_heads=12, + intermediate_size=3072, + hidden_act="gelu", + hidden_dropout_prob=0.0, + attention_probs_dropout_prob=0.0, + initializer_range=0.02, + layer_norm_eps=1e-12, + image_size=224, + patch_size=1, + num_channels=3, + backbone_featmap_shape=[1, 1024, 24, 24], + qkv_bias=True, + **kwargs, + ): + super().__init__(**kwargs) + if use_pretrained_backbone: + raise ValueError("Pretrained backbones are not supported yet.") + + if backbone_config is not None and backbone is not None: + raise ValueError("You can't specify both `backbone` and `backbone_config`.") + + if backbone_config is None and backbone is None: + logger.info("`backbone_config` is `None`. Initializing the config with a `BiT` backbone.") + backbone_config = { + "global_padding": "same", + "layer_type": "bottleneck", + "depths": [3, 4, 9], + "out_features": ["stage3"], + "embedding_dynamic_padding": True, + } + + if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None: + raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.") + + if isinstance(backbone_config, dict): + if "model_type" in backbone_config: + backbone_config_class = CONFIG_MAPPING[backbone_config["model_type"]] + else: + logger.info( + "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class." + ) + backbone_config_class = BitConfig + backbone_config = backbone_config_class(**backbone_config) + + self.backbone_featmap_shape = backbone_featmap_shape + self.backbone_config = backbone_config + self.backbone = backbone + self.use_pretrained_backbone = use_pretrained_backbone + self.use_timm_backbone = use_timm_backbone + self.backbone_kwargs = backbone_kwargs + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.initializer_range = initializer_range + self.layer_norm_eps = layer_norm_eps + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.qkv_bias = qkv_bias diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_hybrid/convert_vit_hybrid_timm_to_pytorch.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_hybrid/convert_vit_hybrid_timm_to_pytorch.py new file mode 100644 index 0000000000000000000000000000000000000000..e88ee246ba1c1d088e4c309b881649eea7fc4f2e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_hybrid/convert_vit_hybrid_timm_to_pytorch.py @@ -0,0 +1,283 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert ViT hybrid checkpoints from the timm library.""" + + +import argparse +import json +from pathlib import Path + +import requests +import timm +import torch +from huggingface_hub import hf_hub_download +from PIL import Image +from timm.data import resolve_data_config +from timm.data.transforms_factory import create_transform + +from transformers import ( + BitConfig, + ViTHybridConfig, + ViTHybridForImageClassification, + ViTHybridImageProcessor, + ViTHybridModel, +) +from transformers.image_utils import PILImageResampling +from transformers.utils import logging + + +logging.set_verbosity_info() +logger = logging.get_logger(__name__) + + +# here we list all keys to be renamed (original name on the left, our name on the right) +def create_rename_keys(config, base_model=False): + rename_keys = [] + + # fmt: off + # stem: + rename_keys.append(("cls_token", "vit.embeddings.cls_token")) + rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings")) + + rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight")) + rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias")) + + # backbone + rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight")) + rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight")) + rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias")) + + for stage_idx in range(len(config.backbone_config.depths)): + for layer_idx in range(config.backbone_config.depths[stage_idx]): + rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight")) + rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight")) + rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias")) + rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight")) + rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight")) + rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias")) + rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight")) + rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight")) + rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias")) + + rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight")) + rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight")) + rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias")) + + # transformer encoder + for i in range(config.num_hidden_layers): + # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms + rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight")) + rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")) + rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")) + rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")) + rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")) + rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")) + rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")) + rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")) + rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")) + rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")) + + if base_model: + # layernorm + pooler + rename_keys.extend( + [ + ("norm.weight", "layernorm.weight"), + ("norm.bias", "layernorm.bias"), + ("pre_logits.fc.weight", "pooler.dense.weight"), + ("pre_logits.fc.bias", "pooler.dense.bias"), + ] + ) + + # if just the base model, we should remove "vit" from all keys that start with "vit" + rename_keys = [(pair[0], pair[1][4:]) if pair[1].startswith("vit") else pair for pair in rename_keys] + else: + # layernorm + classification head + rename_keys.extend( + [ + ("norm.weight", "vit.layernorm.weight"), + ("norm.bias", "vit.layernorm.bias"), + ("head.weight", "classifier.weight"), + ("head.bias", "classifier.bias"), + ] + ) + # fmt: on + + return rename_keys + + +# we split up the matrix of each encoder layer into queries, keys and values +def read_in_q_k_v(state_dict, config, base_model=False): + for i in range(config.num_hidden_layers): + if base_model: + prefix = "" + else: + prefix = "vit." + # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) + in_proj_weight = state_dict.pop(f"blocks.{i}.attn.qkv.weight") + in_proj_bias = state_dict.pop(f"blocks.{i}.attn.qkv.bias") + # next, add query, keys and values (in that order) to the state dict + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[ + : config.hidden_size, : + ] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.bias"] = in_proj_bias[: config.hidden_size] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[ + config.hidden_size : config.hidden_size * 2, : + ] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.bias"] = in_proj_bias[ + config.hidden_size : config.hidden_size * 2 + ] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[ + -config.hidden_size :, : + ] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.bias"] = in_proj_bias[-config.hidden_size :] + + +def remove_classification_head_(state_dict): + ignore_keys = ["head.weight", "head.bias"] + for k in ignore_keys: + state_dict.pop(k, None) + + +def rename_key(dct, old, new): + val = dct.pop(old) + dct[new] = val + + +# We will verify our results on an image of cute cats +def prepare_img(): + url = "http://images.cocodataset.org/val2017/000000039769.jpg" + im = Image.open(requests.get(url, stream=True).raw) + return im + + +@torch.no_grad() +def convert_vit_checkpoint(vit_name, pytorch_dump_folder_path, push_to_hub=False): + """ + Copy/paste/tweak model's weights to our ViT structure. + """ + + # define default ViT hybrid configuration + backbone_config = BitConfig( + global_padding="same", + layer_type="bottleneck", + depths=(3, 4, 9), + out_features=["stage3"], + embedding_dynamic_padding=True, + ) + config = ViTHybridConfig(backbone_config=backbone_config, image_size=384, num_labels=1000) + base_model = False + + # load original model from timm + timm_model = timm.create_model(vit_name, pretrained=True) + timm_model.eval() + + # load state_dict of original model, remove and rename some keys + state_dict = timm_model.state_dict() + if base_model: + remove_classification_head_(state_dict) + rename_keys = create_rename_keys(config, base_model) + for src, dest in rename_keys: + rename_key(state_dict, src, dest) + read_in_q_k_v(state_dict, config, base_model) + + repo_id = "huggingface/label-files" + filename = "imagenet-1k-id2label.json" + id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) + id2label = {int(k): v for k, v in id2label.items()} + config.id2label = id2label + config.label2id = {v: k for k, v in id2label.items()} + + # load HuggingFace model + if vit_name[-5:] == "in21k": + model = ViTHybridModel(config).eval() + else: + model = ViTHybridForImageClassification(config).eval() + model.load_state_dict(state_dict) + + # create image processor + transform = create_transform(**resolve_data_config({}, model=timm_model)) + timm_transforms = transform.transforms + + pillow_resamplings = { + "bilinear": PILImageResampling.BILINEAR, + "bicubic": PILImageResampling.BICUBIC, + "nearest": PILImageResampling.NEAREST, + } + + processor = ViTHybridImageProcessor( + do_resize=True, + size={"shortest_edge": timm_transforms[0].size}, + resample=pillow_resamplings[timm_transforms[0].interpolation.value], + do_center_crop=True, + crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}, + do_normalize=True, + image_mean=timm_transforms[-1].mean.tolist(), + image_std=timm_transforms[-1].std.tolist(), + ) + + image = prepare_img() + timm_pixel_values = transform(image).unsqueeze(0) + pixel_values = processor(image, return_tensors="pt").pixel_values + + # verify pixel values + assert torch.allclose(timm_pixel_values, pixel_values) + + # verify logits + with torch.no_grad(): + outputs = model(pixel_values) + logits = outputs.logits + + print("Predicted class:", logits.argmax(-1).item()) + if base_model: + timm_pooled_output = timm_model.forward_features(pixel_values) + assert timm_pooled_output.shape == outputs.pooler_output.shape + assert torch.allclose(timm_pooled_output, outputs.pooler_output, atol=1e-3) + else: + timm_logits = timm_model(pixel_values) + assert timm_logits.shape == outputs.logits.shape + assert torch.allclose(timm_logits, outputs.logits, atol=1e-3) + print("Looks ok!") + + if pytorch_dump_folder_path is not None: + Path(pytorch_dump_folder_path).mkdir(exist_ok=True) + print(f"Saving model {vit_name} to {pytorch_dump_folder_path}") + model.save_pretrained(pytorch_dump_folder_path) + print(f"Saving processor to {pytorch_dump_folder_path}") + processor.save_pretrained(pytorch_dump_folder_path) + + if push_to_hub: + print(f"Pushing model and processor to the hub {vit_name}") + model.push_to_hub(f"ybelkada/{vit_name}") + processor.push_to_hub(f"ybelkada/{vit_name}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + # Required parameters + parser.add_argument( + "--vit_name", + default="vit_base_r50_s16_384", + type=str, + help="Name of the hybrid ViT timm model you'd like to convert.", + ) + parser.add_argument( + "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." + ) + parser.add_argument( + "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub." + ) + + args = parser.parse_args() + convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_hybrid/image_processing_vit_hybrid.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_hybrid/image_processing_vit_hybrid.py new file mode 100644 index 0000000000000000000000000000000000000000..4bb3f70b49bb5b4d12a8a5a40f4adc522ca0fdef --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_hybrid/image_processing_vit_hybrid.py @@ -0,0 +1,344 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for ViT hybrid.""" + +from typing import Dict, List, Optional, Union + +import numpy as np + +from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict +from ...image_transforms import ( + convert_to_rgb, + get_resize_output_image_size, + resize, + to_channel_dimension_format, +) +from ...image_utils import ( + OPENAI_CLIP_MEAN, + OPENAI_CLIP_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + infer_channel_dimension_format, + is_scaled_image, + make_list_of_images, + to_numpy_array, + valid_images, + validate_kwargs, + validate_preprocess_arguments, +) +from ...utils import TensorType, is_vision_available, logging + + +logger = logging.get_logger(__name__) + + +if is_vision_available(): + import PIL + + +class ViTHybridImageProcessor(BaseImageProcessor): + r""" + Constructs a ViT Hybrid image processor. + + Args: + do_resize (`bool`, *optional*, defaults to `True`): + Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by + `do_resize` in the `preprocess` method. + size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`): + Size of the image after resizing. The shortest edge of the image is resized to size["shortest_edge"], with + the longest edge resized to keep the input aspect ratio. Can be overridden by `size` in the `preprocess` + method. + resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): + Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method. + do_center_crop (`bool`, *optional*, defaults to `True`): + Whether to center crop the image to the specified `crop_size`. Can be overridden by `do_center_crop` in the + `preprocess` method. + crop_size (`Dict[str, int]` *optional*, defaults to 224): + Size of the output image after applying `center_crop`. Can be overridden by `crop_size` in the `preprocess` + method. + do_rescale (`bool`, *optional*, defaults to `True`): + Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in + the `preprocess` method. + rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): + Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess` + method. + do_normalize: + Whether to normalize the image. Can be overridden by `do_normalize` in the `preprocess` method. + image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): + Mean to use if normalizing the image. This is a float or list of floats the length of the number of + channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. + image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): + Standard deviation to use if normalizing the image. This is a float or list of floats the length of the + number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. + Can be overridden by the `image_std` parameter in the `preprocess` method. + do_convert_rgb (`bool`, *optional*, defaults to `True`): + Whether to convert the image to RGB. + """ + + model_input_names = ["pixel_values"] + + def __init__( + self, + do_resize: bool = True, + size: Dict[str, int] = None, + resample: PILImageResampling = PILImageResampling.BICUBIC, + do_center_crop: bool = True, + crop_size: Dict[str, int] = None, + do_rescale: bool = True, + rescale_factor: Union[int, float] = 1 / 255, + do_normalize: bool = True, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + do_convert_rgb: bool = True, + **kwargs, + ) -> None: + super().__init__(**kwargs) + size = size if size is not None else {"shortest_edge": 224} + size = get_size_dict(size, default_to_square=False) + crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224} + crop_size = get_size_dict(crop_size, default_to_square=True, param_name="crop_size") + + self.do_resize = do_resize + self.size = size + self.resample = resample + self.do_center_crop = do_center_crop + self.crop_size = crop_size + self.do_rescale = do_rescale + self.rescale_factor = rescale_factor + self.do_normalize = do_normalize + self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN + self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD + self.do_convert_rgb = do_convert_rgb + self._valid_processor_keys = [ + "images", + "do_resize", + "size", + "resample", + "do_center_crop", + "crop_size", + "do_rescale", + "rescale_factor", + "do_normalize", + "image_mean", + "image_std", + "do_convert_rgb", + "return_tensors", + "data_format", + "input_data_format", + ] + + # Copied from transformers.models.clip.image_processing_clip.CLIPImageProcessor.resize + def resize( + self, + image: np.ndarray, + size: Dict[str, int], + resample: PILImageResampling = PILImageResampling.BICUBIC, + data_format: Optional[Union[str, ChannelDimension]] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ) -> np.ndarray: + """ + Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge + resized to keep the input aspect ratio. + + Args: + image (`np.ndarray`): + Image to resize. + size (`Dict[str, int]`): + Size of the output image. + resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): + Resampling filter to use when resiizing the image. + data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format of the image. If not provided, it will be the same as the input image. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format of the input image. If not provided, it will be inferred. + """ + default_to_square = True + if "shortest_edge" in size: + size = size["shortest_edge"] + default_to_square = False + elif "height" in size and "width" in size: + size = (size["height"], size["width"]) + else: + raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.") + + output_size = get_resize_output_image_size( + image, + size=size, + default_to_square=default_to_square, + input_data_format=input_data_format, + ) + return resize( + image, + size=output_size, + resample=resample, + data_format=data_format, + input_data_format=input_data_format, + **kwargs, + ) + + def preprocess( + self, + images: ImageInput, + do_resize: bool = None, + size: Dict[str, int] = None, + resample: PILImageResampling = None, + do_center_crop: bool = None, + crop_size: int = None, + do_rescale: bool = None, + rescale_factor: float = None, + do_normalize: bool = None, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + do_convert_rgb: bool = None, + return_tensors: Optional[Union[str, TensorType]] = None, + data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ) -> PIL.Image.Image: + """ + Preprocess an image or batch of images. + + Args: + images (`ImageInput`): + Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If + passing in images with pixel values between 0 and 1, set `do_rescale=False`. + do_resize (`bool`, *optional*, defaults to `self.do_resize`): + Whether to resize the image. + size (`Dict[str, int]`, *optional*, defaults to `self.size`): + Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with + the longest edge resized to keep the input aspect ratio. + resample (`int`, *optional*, defaults to `self.resample`): + Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only + has an effect if `do_resize` is set to `True`. + do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`): + Whether to center crop the image. + crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`): + Size of the center crop. Only has an effect if `do_center_crop` is set to `True`. + do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): + Whether to rescale the image. + rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): + Rescale factor to rescale the image by if `do_rescale` is set to `True`. + do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): + Whether to normalize the image. + image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): + Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`. + image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): + Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to + `True`. + do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`): + Whether to convert the image to RGB. + return_tensors (`str` or `TensorType`, *optional*): + The type of tensors to return. Can be one of: + - Unset: Return a list of `np.ndarray`. + - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. + - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. + - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. + - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. + data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): + The channel dimension format for the output image. Can be one of: + - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - Unset: defaults to the channel dimension format of the input image. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format for the input image. If unset, the channel dimension format is inferred + from the input image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. + """ + do_resize = do_resize if do_resize is not None else self.do_resize + size = size if size is not None else self.size + size = get_size_dict(size, param_name="size", default_to_square=False) + resample = resample if resample is not None else self.resample + do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop + crop_size = crop_size if crop_size is not None else self.crop_size + crop_size = get_size_dict(crop_size, param_name="crop_size", default_to_square=True) + do_rescale = do_rescale if do_rescale is not None else self.do_rescale + rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor + do_normalize = do_normalize if do_normalize is not None else self.do_normalize + image_mean = image_mean if image_mean is not None else self.image_mean + image_std = image_std if image_std is not None else self.image_std + do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb + + images = make_list_of_images(images) + + validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys) + + if not valid_images(images): + raise ValueError( + "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " + "torch.Tensor, tf.Tensor or jax.ndarray." + ) + validate_preprocess_arguments( + do_rescale=do_rescale, + rescale_factor=rescale_factor, + do_normalize=do_normalize, + image_mean=image_mean, + image_std=image_std, + do_center_crop=do_center_crop, + crop_size=crop_size, + do_resize=do_resize, + size=size, + resample=resample, + ) + + # PIL RGBA images are converted to RGB + if do_convert_rgb: + images = [convert_to_rgb(image) for image in images] + + # All transformations expect numpy arrays. + images = [to_numpy_array(image) for image in images] + + if is_scaled_image(images[0]) and do_rescale: + logger.warning_once( + "It looks like you are trying to rescale already rescaled images. If the input" + " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." + ) + + if input_data_format is None: + # We assume that all images have the same channel dimension format. + input_data_format = infer_channel_dimension_format(images[0]) + + if do_resize: + images = [ + self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format) + for image in images + ] + + if do_center_crop: + images = [ + self.center_crop(image=image, size=crop_size, input_data_format=input_data_format) for image in images + ] + + if do_rescale: + images = [ + self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) + for image in images + ] + + if do_normalize: + images = [ + self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format) + for image in images + ] + + images = [ + to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images + ] + + data = {"pixel_values": images} + return BatchFeature(data=data, tensor_type=return_tensors) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_hybrid/modeling_vit_hybrid.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_hybrid/modeling_vit_hybrid.py new file mode 100644 index 0000000000000000000000000000000000000000..6fe9f8d2b6c9bdafa347dc6a10ac4d6adbdbfdcb --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_hybrid/modeling_vit_hybrid.py @@ -0,0 +1,724 @@ +# coding=utf-8 +# Copyright 2022 Google AI, Ross Wightman, The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch ViT Hybrid model.""" + + +import collections.abc +import math +from typing import Dict, List, Optional, Set, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ...activations import ACT2FN +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput +from ...modeling_utils import PreTrainedModel +from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer +from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging +from ...utils.backbone_utils import load_backbone +from .configuration_vit_hybrid import ViTHybridConfig + + +logger = logging.get_logger(__name__) + +# General docstring +_CONFIG_FOR_DOC = "ViTHybridConfig" + +# Base docstring +_CHECKPOINT_FOR_DOC = "google/vit-hybrid-base-bit-384" +_EXPECTED_OUTPUT_SHAPE = [1, 197, 768] + +# Image classification docstring +_IMAGE_CLASS_CHECKPOINT = "google/vit-hybrid-base-bit-384" +_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat" + + +from ..deprecated._archive_maps import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +class ViTHybridEmbeddings(nn.Module): + """ + Construct the CLS token, position and patch embeddings. Optionally, also the mask token. + """ + + # Copied from transformers.models.vit.modeling_vit.ViTEmbeddings.__init__ with ViT->ViTHybrid + def __init__(self, config: ViTHybridConfig, use_mask_token: bool = False) -> None: + super().__init__() + + self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_size)) + self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None + self.patch_embeddings = ViTHybridPatchEmbeddings(config) + num_patches = self.patch_embeddings.num_patches + self.position_embeddings = nn.Parameter(torch.randn(1, num_patches + 1, config.hidden_size)) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.config = config + + def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: + """ + This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher + resolution images. + + Source: + https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174 + """ + + num_patches = embeddings.shape[1] - 1 + num_positions = self.position_embeddings.shape[1] - 1 + if num_patches == num_positions and height == width: + return self.position_embeddings + class_pos_embed = self.position_embeddings[:, 0] + patch_pos_embed = self.position_embeddings[:, 1:] + dim = embeddings.shape[-1] + height = height // self.config.patch_size + width = width // self.config.patch_size + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + height, width = height + 0.1, width + 0.1 + patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim) + patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed, + scale_factor=(height / math.sqrt(num_positions), width / math.sqrt(num_positions)), + mode="bicubic", + align_corners=False, + ) + if int(height) != patch_pos_embed.shape[-2] or int(width) != patch_pos_embed.shape[-1]: + raise ValueError(f"Invalid height or width: {height}, {width}") + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) + + def forward( + self, + pixel_values: torch.Tensor, + bool_masked_pos: Optional[torch.BoolTensor] = None, + interpolate_pos_encoding: bool = False, + ) -> torch.Tensor: + batch_size, num_channels, height, width = pixel_values.shape + embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding) + + if bool_masked_pos is not None: + seq_length = embeddings.shape[1] + mask_tokens = self.mask_token.expand(batch_size, seq_length, -1) + # replace the masked visual tokens by mask_tokens + mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens) + embeddings = embeddings * (1.0 - mask) + mask_tokens * mask + + # add the [CLS] token to the embedded patch tokens + cls_tokens = self.cls_token.expand(batch_size, -1, -1) + embeddings = torch.cat((cls_tokens, embeddings), dim=1) + + # add positional encoding to each token + if interpolate_pos_encoding: + embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) + else: + embeddings = embeddings + self.position_embeddings + + embeddings = self.dropout(embeddings) + + return embeddings + + +class ViTHybridPatchEmbeddings(nn.Module): + """ + This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial + `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a + Transformer. + """ + + def __init__(self, config, feature_size=None): + super().__init__() + image_size, patch_size = config.image_size, config.patch_size + num_channels, hidden_size = config.num_channels, config.hidden_size + + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + + self.backbone = load_backbone(config) + if self.backbone.config.model_type != "bit": + raise ValueError(f"Backbone model type {self.backbone.model_type} is not supported.") + feature_dim = self.backbone.channels[-1] + + if feature_size is None: + feature_map = config.backbone_featmap_shape + + feature_size = feature_map[-2:] + feature_dim = feature_map[1] + else: + feature_size = ( + feature_size if isinstance(feature_size, collections.abc.Iterable) else (feature_size, feature_size) + ) + feature_dim = self.backbone.channels[-1] + + self.grid_size = (feature_size[0] // patch_size[0], feature_size[1] // patch_size[1]) + self.num_patches = self.grid_size[0] * self.grid_size[1] + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + + self.projection = nn.Conv2d(feature_dim, hidden_size, kernel_size=patch_size, stride=patch_size) + + def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor: + _, num_channels, height, width = pixel_values.shape + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + ) + if not interpolate_pos_encoding: + if height != self.image_size[0] or width != self.image_size[1]: + raise ValueError( + f"Input image size ({height}*{width}) doesn't match model" + f" ({self.image_size[0]}*{self.image_size[1]})." + ) + + features = self.backbone(pixel_values).feature_maps[-1] + embeddings = self.projection(features).flatten(2).transpose(1, 2) + + return embeddings + + +# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->ViTHybrid +class ViTHybridSelfAttention(nn.Module): + def __init__(self, config: ViTHybridConfig) -> None: + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " + f"heads {config.num_attention_heads}." + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + mixed_query_layer = self.query(hidden_states) + + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + query_layer = self.transpose_for_scores(mixed_query_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->ViTHybrid +class ViTHybridSelfOutput(nn.Module): + """ + The residual connection is defined in ViTHybridLayer instead of here (as is the case with other models), due to the + layernorm applied before each block. + """ + + def __init__(self, config: ViTHybridConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + + return hidden_states + + +# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->ViTHybrid +class ViTHybridAttention(nn.Module): + def __init__(self, config: ViTHybridConfig) -> None: + super().__init__() + self.attention = ViTHybridSelfAttention(config) + self.output = ViTHybridSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads: Set[int]) -> None: + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.attention.query = prune_linear_layer(self.attention.query, index) + self.attention.key = prune_linear_layer(self.attention.key, index) + self.attention.value = prune_linear_layer(self.attention.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) + self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + self_outputs = self.attention(hidden_states, head_mask, output_attentions) + + attention_output = self.output(self_outputs[0], hidden_states) + + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +# Copied from transformers.models.vit.modeling_vit.ViTIntermediate with ViT->ViTHybrid +class ViTHybridIntermediate(nn.Module): + def __init__(self, config: ViTHybridConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + + return hidden_states + + +# Copied from transformers.models.vit.modeling_vit.ViTOutput with ViT->ViTHybrid +class ViTHybridOutput(nn.Module): + def __init__(self, config: ViTHybridConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + + hidden_states = hidden_states + input_tensor + + return hidden_states + + +class ViTHybridLayer(nn.Module): + """This corresponds to the Block class in the timm implementation.""" + + def __init__(self, config: ViTHybridConfig) -> None: + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = ViTHybridAttention(config) + self.intermediate = ViTHybridIntermediate(config) + self.output = ViTHybridOutput(config) + self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + self_attention_outputs = self.attention( + self.layernorm_before(hidden_states), # in ViTHybrid, layernorm is applied before self-attention + head_mask, + output_attentions=output_attentions, + ) + attention_output = self_attention_outputs[0] + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + # first residual connection + # We assign to correct device for `accelerate`, check: https://github.com/huggingface/transformers/pull/20705/ + hidden_states = attention_output + hidden_states.to(attention_output.device) + + # in ViTHybrid, layernorm is also applied after self-attention + layer_output = self.layernorm_after(hidden_states) + layer_output = self.intermediate(layer_output) + + # second residual connection is done here + layer_output = self.output(layer_output, hidden_states) + + outputs = (layer_output,) + outputs + + return outputs + + +# Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->ViTHybrid +class ViTHybridEncoder(nn.Module): + def __init__(self, config: ViTHybridConfig) -> None: + super().__init__() + self.config = config + self.layer = nn.ModuleList([ViTHybridLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ) -> Union[tuple, BaseModelOutput]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer_module.__call__, + hidden_states, + layer_head_mask, + output_attentions, + ) + else: + layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +# Copied from transformers.models.vit.modeling_vit.ViTPreTrainedModel with ViT->ViTHybrid +class ViTHybridPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = ViTHybridConfig + base_model_prefix = "vit" + main_input_name = "pixel_values" + supports_gradient_checkpointing = True + _no_split_modules = ["ViTHybridEmbeddings", "ViTHybridLayer"] + + def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None: + """Initialize the weights""" + if isinstance(module, (nn.Linear, nn.Conv2d)): + # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid + # `trunc_normal_cpu` not implemented in `half` issues + module.weight.data = nn.init.trunc_normal_( + module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range + ).to(module.weight.dtype) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + elif isinstance(module, ViTHybridEmbeddings): + module.position_embeddings.data = nn.init.trunc_normal_( + module.position_embeddings.data.to(torch.float32), + mean=0.0, + std=self.config.initializer_range, + ).to(module.position_embeddings.dtype) + + module.cls_token.data = nn.init.trunc_normal_( + module.cls_token.data.to(torch.float32), + mean=0.0, + std=self.config.initializer_range, + ).to(module.cls_token.dtype) + + +VIT_START_DOCSTRING = r""" + This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it + as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and + behavior. + + Parameters: + config ([`ViTHybridConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +VIT_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See + [`ViTHybridImageProcessor.__call__`] for details. + + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare ViT Hybrid Model transformer outputting raw hidden-states without any specific head on top.", + VIT_START_DOCSTRING, +) +# Copied from transformers.models.vit.modeling_vit.ViTModel with ViT->ViTHybrid +class ViTHybridModel(ViTHybridPreTrainedModel): + def __init__(self, config: ViTHybridConfig, add_pooling_layer: bool = True, use_mask_token: bool = False): + super().__init__(config) + self.config = config + + self.embeddings = ViTHybridEmbeddings(config, use_mask_token=use_mask_token) + self.encoder = ViTHybridEncoder(config) + + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.pooler = ViTHybridPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> ViTHybridPatchEmbeddings: + return self.embeddings.patch_embeddings + + def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=BaseModelOutputWithPooling, + config_class=_CONFIG_FOR_DOC, + modality="vision", + expected_output=_EXPECTED_OUTPUT_SHAPE, + ) + def forward( + self, + pixel_values: Optional[torch.Tensor] = None, + bool_masked_pos: Optional[torch.BoolTensor] = None, + head_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + interpolate_pos_encoding: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPooling]: + r""" + bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*): + Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + # TODO: maybe have a cleaner way to cast the input (from `ImageProcessor` side?) + expected_dtype = self.embeddings.patch_embeddings.projection.weight.dtype + if pixel_values.dtype != expected_dtype: + pixel_values = pixel_values.to(expected_dtype) + + embedding_output = self.embeddings( + pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding + ) + + encoder_outputs = self.encoder( + embedding_output, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + sequence_output = self.layernorm(sequence_output) + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,) + return head_outputs + encoder_outputs[1:] + + return BaseModelOutputWithPooling( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +# Copied from transformers.models.vit.modeling_vit.ViTPooler with ViT->ViTHybrid +class ViTHybridPooler(nn.Module): + def __init__(self, config: ViTHybridConfig): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +@add_start_docstrings( + """ + ViT Hybrid Model transformer with an image classification head on top (a linear layer on top of the final hidden + state of the [CLS] token) e.g. for ImageNet. + """, + VIT_START_DOCSTRING, +) +# Copied from transformers.models.vit.modeling_vit.ViTForImageClassification with ViT->ViTHybrid +class ViTHybridForImageClassification(ViTHybridPreTrainedModel): + def __init__(self, config: ViTHybridConfig) -> None: + super().__init__(config) + + self.num_labels = config.num_labels + self.vit = ViTHybridModel(config, add_pooling_layer=False) + + # Classifier head + self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity() + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_IMAGE_CLASS_CHECKPOINT, + output_type=ImageClassifierOutput, + config_class=_CONFIG_FOR_DOC, + expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, + ) + def forward( + self, + pixel_values: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + interpolate_pos_encoding: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[tuple, ImageClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.vit( + pixel_values, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + interpolate_pos_encoding=interpolate_pos_encoding, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + logits = self.classifier(sequence_output[:, 0, :]) + + loss = None + if labels is not None: + # move labels to correct device to enable model parallelism + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return ImageClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_mae/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_mae/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bfd200e9dcb9130c8612b97295d0721403271555 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_mae/__init__.py @@ -0,0 +1,82 @@ +# Copyright 2020 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import ( + OptionalDependencyNotAvailable, + _LazyModule, + is_flax_available, + is_tf_available, + is_torch_available, +) + + +_import_structure = {"configuration_vit_mae": ["VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMAEConfig"]} + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_vit_mae"] = [ + "VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST", + "ViTMAEForPreTraining", + "ViTMAELayer", + "ViTMAEModel", + "ViTMAEPreTrainedModel", + ] + +try: + if not is_tf_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_tf_vit_mae"] = [ + "TFViTMAEForPreTraining", + "TFViTMAEModel", + "TFViTMAEPreTrainedModel", + ] + +if TYPE_CHECKING: + from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_vit_mae import ( + VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST, + ViTMAEForPreTraining, + ViTMAELayer, + ViTMAEModel, + ViTMAEPreTrainedModel, + ) + + try: + if not is_tf_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel + + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_mae/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_mae/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2be8a8fa4cee2738e7071017ccb1c732ae489813 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_mae/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_mae/__pycache__/configuration_vit_mae.cpython-312.pyc 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b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_mae/configuration_vit_mae.py new file mode 100644 index 0000000000000000000000000000000000000000..c5866ef40b497c509734b668f4f044e8b93b1883 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_mae/configuration_vit_mae.py @@ -0,0 +1,140 @@ +# coding=utf-8 +# Copyright 2022 Facebook AI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" ViT MAE model configuration""" + +from ...configuration_utils import PretrainedConfig +from ...utils import logging + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class ViTMAEConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`ViTMAEModel`]. It is used to instantiate an ViT + MAE model according to the specified arguments, defining the model architecture. Instantiating a configuration with + the defaults will yield a similar configuration to that of the ViT + [facebook/vit-mae-base](https://huggingface.co/facebook/vit-mae-base) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + hidden_size (`int`, *optional*, defaults to 768): + Dimensionality of the encoder layers and the pooler layer. + num_hidden_layers (`int`, *optional*, defaults to 12): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 12): + Number of attention heads for each attention layer in the Transformer encoder. + intermediate_size (`int`, *optional*, defaults to 3072): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"` and `"gelu_new"` are supported. + hidden_dropout_prob (`float`, *optional*, defaults to 0.0): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + layer_norm_eps (`float`, *optional*, defaults to 1e-12): + The epsilon used by the layer normalization layers. + image_size (`int`, *optional*, defaults to 224): + The size (resolution) of each image. + patch_size (`int`, *optional*, defaults to 16): + The size (resolution) of each patch. + num_channels (`int`, *optional*, defaults to 3): + The number of input channels. + qkv_bias (`bool`, *optional*, defaults to `True`): + Whether to add a bias to the queries, keys and values. + decoder_num_attention_heads (`int`, *optional*, defaults to 16): + Number of attention heads for each attention layer in the decoder. + decoder_hidden_size (`int`, *optional*, defaults to 512): + Dimensionality of the decoder. + decoder_num_hidden_layers (`int`, *optional*, defaults to 8): + Number of hidden layers in the decoder. + decoder_intermediate_size (`int`, *optional*, defaults to 2048): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the decoder. + mask_ratio (`float`, *optional*, defaults to 0.75): + The ratio of the number of masked tokens in the input sequence. + norm_pix_loss (`bool`, *optional*, defaults to `False`): + Whether or not to train with normalized pixels (see Table 3 in the paper). Using normalized pixels improved + representation quality in the experiments of the authors. + + Example: + + ```python + >>> from transformers import ViTMAEConfig, ViTMAEModel + + >>> # Initializing a ViT MAE vit-mae-base style configuration + >>> configuration = ViTMAEConfig() + + >>> # Initializing a model (with random weights) from the vit-mae-base style configuration + >>> model = ViTMAEModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "vit_mae" + + def __init__( + self, + hidden_size=768, + num_hidden_layers=12, + num_attention_heads=12, + intermediate_size=3072, + hidden_act="gelu", + hidden_dropout_prob=0.0, + attention_probs_dropout_prob=0.0, + initializer_range=0.02, + layer_norm_eps=1e-12, + image_size=224, + patch_size=16, + num_channels=3, + qkv_bias=True, + decoder_num_attention_heads=16, + decoder_hidden_size=512, + decoder_num_hidden_layers=8, + decoder_intermediate_size=2048, + mask_ratio=0.75, + norm_pix_loss=False, + **kwargs, + ): + super().__init__(**kwargs) + + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.initializer_range = initializer_range + self.layer_norm_eps = layer_norm_eps + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.qkv_bias = qkv_bias + self.decoder_num_attention_heads = decoder_num_attention_heads + self.decoder_hidden_size = decoder_hidden_size + self.decoder_num_hidden_layers = decoder_num_hidden_layers + self.decoder_intermediate_size = decoder_intermediate_size + self.mask_ratio = mask_ratio + self.norm_pix_loss = norm_pix_loss diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_mae/convert_vit_mae_to_pytorch.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_mae/convert_vit_mae_to_pytorch.py new file mode 100644 index 0000000000000000000000000000000000000000..47e77593f6fd3ad7c2b7ff2c329b84f432060c7d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_mae/convert_vit_mae_to_pytorch.py @@ -0,0 +1,178 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert ViT MAE checkpoints from the original repository: https://github.com/facebookresearch/mae""" + +import argparse + +import requests +import torch +from PIL import Image + +from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor + + +def rename_key(name): + if "cls_token" in name: + name = name.replace("cls_token", "vit.embeddings.cls_token") + if "mask_token" in name: + name = name.replace("mask_token", "decoder.mask_token") + if "decoder_pos_embed" in name: + name = name.replace("decoder_pos_embed", "decoder.decoder_pos_embed") + if "pos_embed" in name and "decoder" not in name: + name = name.replace("pos_embed", "vit.embeddings.position_embeddings") + if "patch_embed.proj" in name: + name = name.replace("patch_embed.proj", "vit.embeddings.patch_embeddings.projection") + if "patch_embed.norm" in name: + name = name.replace("patch_embed.norm", "vit.embeddings.norm") + if "decoder_blocks" in name: + name = name.replace("decoder_blocks", "decoder.decoder_layers") + if "blocks" in name: + name = name.replace("blocks", "vit.encoder.layer") + if "attn.proj" in name: + name = name.replace("attn.proj", "attention.output.dense") + if "attn" in name: + name = name.replace("attn", "attention.self") + if "norm1" in name: + name = name.replace("norm1", "layernorm_before") + if "norm2" in name: + name = name.replace("norm2", "layernorm_after") + if "mlp.fc1" in name: + name = name.replace("mlp.fc1", "intermediate.dense") + if "mlp.fc2" in name: + name = name.replace("mlp.fc2", "output.dense") + if "decoder_embed" in name: + name = name.replace("decoder_embed", "decoder.decoder_embed") + if "decoder_norm" in name: + name = name.replace("decoder_norm", "decoder.decoder_norm") + if "decoder_pred" in name: + name = name.replace("decoder_pred", "decoder.decoder_pred") + if "norm.weight" in name and "decoder" not in name: + name = name.replace("norm.weight", "vit.layernorm.weight") + if "norm.bias" in name and "decoder" not in name: + name = name.replace("norm.bias", "vit.layernorm.bias") + + return name + + +def convert_state_dict(orig_state_dict, config): + for key in orig_state_dict.copy().keys(): + val = orig_state_dict.pop(key) + + if "qkv" in key: + key_split = key.split(".") + layer_num = int(key_split[1]) + if "decoder_blocks" in key: + dim = config.decoder_hidden_size + prefix = "decoder.decoder_layers." + if "weight" in key: + orig_state_dict[f"{prefix}{layer_num}.attention.attention.query.weight"] = val[:dim, :] + orig_state_dict[f"{prefix}{layer_num}.attention.attention.key.weight"] = val[dim : dim * 2, :] + orig_state_dict[f"{prefix}{layer_num}.attention.attention.value.weight"] = val[-dim:, :] + elif "bias" in key: + orig_state_dict[f"{prefix}{layer_num}.attention.attention.query.bias"] = val[:dim] + orig_state_dict[f"{prefix}{layer_num}.attention.attention.key.bias"] = val[dim : dim * 2] + orig_state_dict[f"{prefix}{layer_num}.attention.attention.value.bias"] = val[-dim:] + else: + dim = config.hidden_size + prefix = "vit.encoder.layer." + if "weight" in key: + orig_state_dict[f"{prefix}{layer_num}.attention.attention.query.weight"] = val[:dim, :] + orig_state_dict[f"{prefix}{layer_num}.attention.attention.key.weight"] = val[dim : dim * 2, :] + orig_state_dict[f"{prefix}{layer_num}.attention.attention.value.weight"] = val[-dim:, :] + elif "bias" in key: + orig_state_dict[f"{prefix}{layer_num}.attention.attention.query.bias"] = val[:dim] + orig_state_dict[f"{prefix}{layer_num}.attention.attention.key.bias"] = val[dim : dim * 2] + orig_state_dict[f"{prefix}{layer_num}.attention.attention.value.bias"] = val[-dim:] + + else: + orig_state_dict[rename_key(key)] = val + + return orig_state_dict + + +def convert_vit_mae_checkpoint(checkpoint_url, pytorch_dump_folder_path): + config = ViTMAEConfig() + if "large" in checkpoint_url: + config.hidden_size = 1024 + config.intermediate_size = 4096 + config.num_hidden_layers = 24 + config.num_attention_heads = 16 + elif "huge" in checkpoint_url: + config.patch_size = 14 + config.hidden_size = 1280 + config.intermediate_size = 5120 + config.num_hidden_layers = 32 + config.num_attention_heads = 16 + + model = ViTMAEForPreTraining(config) + + state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")["model"] + + image_processor = ViTMAEImageProcessor(size=config.image_size) + + new_state_dict = convert_state_dict(state_dict, config) + + model.load_state_dict(new_state_dict) + model.eval() + + url = "https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg" + + image = Image.open(requests.get(url, stream=True).raw) + image_processor = ViTMAEImageProcessor(size=config.image_size) + inputs = image_processor(images=image, return_tensors="pt") + + # forward pass + torch.manual_seed(2) + outputs = model(**inputs) + logits = outputs.logits + + if "large" in checkpoint_url: + expected_slice = torch.tensor( + [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]] + ) + elif "huge" in checkpoint_url: + expected_slice = torch.tensor( + [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]] + ) + else: + expected_slice = torch.tensor( + [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]] + ) + + # verify logits + assert torch.allclose(logits[0, :3, :3], expected_slice, atol=1e-4) + + print(f"Saving model to {pytorch_dump_folder_path}") + model.save_pretrained(pytorch_dump_folder_path) + + print(f"Saving image processor to {pytorch_dump_folder_path}") + image_processor.save_pretrained(pytorch_dump_folder_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + # Required parameters + parser.add_argument( + "--checkpoint_url", + default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth", + type=str, + help="URL of the checkpoint you'd like to convert.", + ) + parser.add_argument( + "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." + ) + + args = parser.parse_args() + convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_mae/modeling_tf_vit_mae.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_mae/modeling_tf_vit_mae.py new file mode 100644 index 0000000000000000000000000000000000000000..fff8234e06bc0a080b6bf840da8c8412c82253b5 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_mae/modeling_tf_vit_mae.py @@ -0,0 +1,1271 @@ +# coding=utf-8 +# Copyright 2022 Facebook AI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" TF 2.0 ViT MAE (masked autoencoder) model.""" + + +from __future__ import annotations + +import collections.abc +import math +from copy import deepcopy +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import numpy as np +import tensorflow as tf + +from ...activations_tf import get_tf_activation +from ...file_utils import ( + ModelOutput, + add_start_docstrings, + add_start_docstrings_to_model_forward, + replace_return_docstrings, +) +from ...modeling_tf_outputs import TFBaseModelOutput +from ...modeling_tf_utils import ( + TFModelInputType, + TFPreTrainedModel, + get_initializer, + keras, + keras_serializable, + unpack_inputs, +) +from ...tf_utils import shape_list, stable_softmax +from ...utils import logging +from .configuration_vit_mae import ViTMAEConfig + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "ViTMAEConfig" +_CHECKPOINT_FOR_DOC = "facebook/vit-mae-base" + + +@dataclass +class TFViTMAEModelOutput(ModelOutput): + """ + Class for TFViTMAEModel's outputs, with potential hidden states and attentions. + + Args: + last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + mask (`tf.Tensor` of shape `(batch_size, sequence_length)`): + Tensor indicating which patches are masked (1) and which are not (0). + ids_restore (`tf.Tensor` of shape `(batch_size, sequence_length)`): + Tensor containing the original index of the (shuffled) masked patches. + hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape + `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus + the initial embedding outputs. + attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in + the self-attention heads. + """ + + last_hidden_state: tf.Tensor = None + mask: tf.Tensor = None + ids_restore: tf.Tensor = None + hidden_states: Tuple[tf.Tensor] | None = None + attentions: Tuple[tf.Tensor] | None = None + + +@dataclass +class TFViTMAEDecoderOutput(ModelOutput): + """ + Class for TFViTMAEDecoder's outputs, with potential hidden states and attentions. + + Args: + logits (`tf.Tensor` of shape `(batch_size, sequence_length, patch_size ** 2 * num_channels)`): + Pixel reconstruction logits. + hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape + `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus + the initial embedding outputs. + attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in + the self-attention heads. + """ + + logits: tf.Tensor = None + hidden_states: Tuple[tf.Tensor] | None = None + attentions: Tuple[tf.Tensor] | None = None + + +@dataclass +class TFViTMAEForPreTrainingOutput(ModelOutput): + """ + Class for TFViTMAEForPreTraining's outputs, with potential hidden states and attentions. + + Args: + loss (`tf.Tensor` of shape `(1,)`): + Pixel reconstruction loss. + logits (`tf.Tensor` of shape `(batch_size, sequence_length, patch_size ** 2 * num_channels)`): + Pixel reconstruction logits. + mask (`tf.Tensor` of shape `(batch_size, sequence_length)`): + Tensor indicating which patches are masked (1) and which are not (0). + ids_restore (`tf.Tensor` of shape `(batch_size, sequence_length)`): + Tensor containing the original index of the (shuffled) masked patches. + hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape + `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus + the initial embedding outputs. + attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in + the self-attention heads. + """ + + loss: tf.Tensor | None = None + logits: tf.Tensor = None + mask: tf.Tensor = None + ids_restore: tf.Tensor = None + hidden_states: Tuple[tf.Tensor] | None = None + attentions: Tuple[tf.Tensor] | None = None + + +def get_2d_sincos_pos_embed(embed_dim, grid_size, add_cls_token=False): + """ + Create 2D sin/cos positional embeddings. + + Args: + embed_dim (`int`): + Embedding dimension. + grid_size (`int`): + The grid height and width. + add_cls_token (`bool`, *optional*, defaults to `False`): + Whether or not to add a classification (CLS) token. + + Returns: + (`tf.Tensor` of shape (grid_size*grid_size, embed_dim) or (1+grid_size*grid_size, embed_dim): the position + embeddings (with or without classification token) + """ + grid_h = tf.range(grid_size, dtype=tf.float32) + grid_w = tf.range(grid_size, dtype=tf.float32) + grid = tf.meshgrid(grid_w, grid_h) # here w goes first + grid = tf.stack(grid, axis=0) + + grid = tf.reshape(grid, [2, 1, grid_size, grid_size]) + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + if add_cls_token: + pos_embed = tf.concat([tf.zeros((1, embed_dim)), pos_embed], axis=0) + return pos_embed + + +def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): + if embed_dim % 2 != 0: + raise ValueError("embed_dim must be even") + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) + + emb = tf.concat([emb_h, emb_w], axis=1) # (H*W, D) + return emb + + +def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): + """ + embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D) + """ + if embed_dim % 2 != 0: + raise ValueError("embed_dim must be even") + + omega = tf.range(embed_dim // 2, dtype="float32") + omega /= embed_dim / 2.0 + omega = 1.0 / 10000**omega # (D/2,) + + pos = tf.reshape(pos, [-1]) # (M,) + out = tf.einsum("m,d->md", pos, omega) # (M, D/2), outer product + + # half of the positions get sinusoidal pattern and the rest gets + # cosine pattern and then they are concatenated + emb_sin = tf.sin(out) # (M, D/2) + emb_cos = tf.cos(out) # (M, D/2) + + emb = tf.concat([emb_sin, emb_cos], axis=1) # (M, D) + return emb + + +class TFViTMAEEmbeddings(keras.layers.Layer): + """ + Construct the CLS token, position and patch embeddings. + + """ + + def __init__(self, config: ViTMAEConfig, **kwargs): + super().__init__(**kwargs) + + self.patch_embeddings = TFViTMAEPatchEmbeddings(config, name="patch_embeddings") + self.num_patches = self.patch_embeddings.num_patches + + self.config = config + + def build(self, input_shape=None): + self.cls_token = self.add_weight( + shape=(1, 1, self.config.hidden_size), + initializer=tf.random_normal_initializer(stddev=self.config.initializer_range), + trainable=True, + name="cls_token", + ) + self.position_embeddings = self.add_weight( + shape=(1, self.num_patches + 1, self.config.hidden_size), + initializer="zeros", + trainable=False, # fixed sin-cos embedding + name="position_embeddings", + ) + pos_embed = get_2d_sincos_pos_embed( + self.position_embeddings.shape[-1], + int(self.patch_embeddings.num_patches**0.5), + add_cls_token=True, + )[None, ...] + self.position_embeddings.assign(pos_embed) + + if self.built: + return + self.built = True + if getattr(self, "patch_embeddings", None) is not None: + with tf.name_scope(self.patch_embeddings.name): + self.patch_embeddings.build(None) + + def random_masking(self, sequence: tf.Tensor, noise: tf.Tensor | None = None): + """ + Perform per-sample random masking by per-sample shuffling. Per-sample shuffling is done by argsort random + noise. + + Args: + sequence (`tf.Tensor` of shape `(batch_size, sequence_length, dim)`) + noise (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) which is + mainly used for testing purposes to control randomness and maintain the reproducibility + """ + batch_size, seq_length, dim = shape_list(sequence) + len_keep = int(seq_length * (1 - self.config.mask_ratio)) + + if noise is None: + noise = tf.random.uniform(shape=(batch_size, seq_length), minval=0.0, maxval=1.0) # noise in [0, 1) + + # sort noise for each sample + ids_shuffle = tf.argsort(noise, axis=1) # ascend: small is keep, large is remove + ids_restore = tf.argsort(ids_shuffle, axis=1) + + # keep the first subset + ids_keep = ids_shuffle[:, :len_keep] + sequence_unmasked = tf.gather( + sequence, + axis=1, + batch_dims=1, + indices=ids_keep, + ) + + # generate the binary mask: 0 is keep, 1 is remove + # this hack is needed because TF's EagerTensors don't support + # assignment + mask_keep = tf.zeros((batch_size, len_keep)) + mask_remove = tf.ones((batch_size, seq_length - len_keep)) + mask = tf.concat([mask_keep, mask_remove], axis=-1) + + # unshuffle to get the binary mask + mask = tf.gather(mask, axis=1, batch_dims=1, indices=ids_restore) + + return sequence_unmasked, mask, ids_restore + + def call(self, pixel_values: tf.Tensor, noise: tf.Tensor = None) -> tf.Tensor: + embeddings = self.patch_embeddings(pixel_values) + + # add position embeddings w/o cls token + embeddings = embeddings + self.position_embeddings[:, 1:, :] + + # masking: length -> length * config.mask_ratio + embeddings, mask, ids_restore = self.random_masking(embeddings, noise) + + # append cls token + cls_token = self.cls_token + self.position_embeddings[:, :1, :] + cls_tokens = tf.tile(cls_token, (shape_list(embeddings)[0], 1, 1)) + embeddings = tf.concat([cls_tokens, embeddings], axis=1) + + return embeddings, mask, ids_restore + + +class TFViTMAEPatchEmbeddings(keras.layers.Layer): + """ + This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial + `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a + Transformer. + """ + + def __init__(self, config: ViTMAEConfig, **kwargs): + super().__init__(**kwargs) + image_size, patch_size = config.image_size, config.patch_size + num_channels, hidden_size = config.num_channels, config.hidden_size + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) + self.image_size = image_size + self.patch_size = patch_size + self.num_patches = num_patches + self.num_channels = num_channels + self.config = config + + self.projection = keras.layers.Conv2D( + filters=hidden_size, + kernel_size=patch_size, + strides=patch_size, + padding="valid", + data_format="channels_last", + kernel_initializer="glorot_uniform", # following torch.nn.Linear + bias_initializer="zeros", + name="projection", + ) + + def call(self, pixel_values: tf.Tensor, training: bool = False) -> tf.Tensor: + batch_size, num_channels, height, width = shape_list(pixel_values) + if tf.executing_eagerly(): + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the" + " configuration." + ) + if height != self.image_size[0] or width != self.image_size[1]: + raise ValueError( + f"Input image size ({height}*{width}) doesn't match model" + f" ({self.image_size[0]}*{self.image_size[1]})." + ) + + # When running on CPU, `keras.layers.Conv2D` doesn't support `NCHW` format. + # So change the input format from `NCHW` to `NHWC`. + # shape = (batch_size, in_height, in_width, in_channels=num_channels) + pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1)) + + projection = self.projection(pixel_values) + + # Change the 2D spatial dimensions to a single temporal dimension. + # shape = (batch_size, num_patches, out_channels=embed_dim) + num_patches = (width // self.patch_size[1]) * (height // self.patch_size[0]) + x = tf.reshape(tensor=projection, shape=(batch_size, num_patches, -1)) + + return x + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "projection", None) is not None: + with tf.name_scope(self.projection.name): + self.projection.build([None, None, None, self.num_channels]) + + +# Copied from transformers.models.vit.modeling_tf_vit.TFViTSelfAttention with ViT->ViTMAE +class TFViTMAESelfAttention(keras.layers.Layer): + def __init__(self, config: ViTMAEConfig, **kwargs): + super().__init__(**kwargs) + + if config.hidden_size % config.num_attention_heads != 0: + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number " + f"of attention heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + self.sqrt_att_head_size = math.sqrt(self.attention_head_size) + + self.query = keras.layers.Dense( + units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query" + ) + self.key = keras.layers.Dense( + units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key" + ) + self.value = keras.layers.Dense( + units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value" + ) + self.dropout = keras.layers.Dropout(rate=config.attention_probs_dropout_prob) + self.config = config + + def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor: + # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size] + tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size)) + + # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size] + return tf.transpose(tensor, perm=[0, 2, 1, 3]) + + def call( + self, + hidden_states: tf.Tensor, + head_mask: tf.Tensor, + output_attentions: bool, + training: bool = False, + ) -> Tuple[tf.Tensor]: + batch_size = shape_list(hidden_states)[0] + mixed_query_layer = self.query(inputs=hidden_states) + mixed_key_layer = self.key(inputs=hidden_states) + mixed_value_layer = self.value(inputs=hidden_states) + query_layer = self.transpose_for_scores(mixed_query_layer, batch_size) + key_layer = self.transpose_for_scores(mixed_key_layer, batch_size) + value_layer = self.transpose_for_scores(mixed_value_layer, batch_size) + + # Take the dot product between "query" and "key" to get the raw attention scores. + # (batch size, num_heads, seq_len_q, seq_len_k) + attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) + dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype) + attention_scores = tf.divide(attention_scores, dk) + + # Normalize the attention scores to probabilities. + attention_probs = stable_softmax(logits=attention_scores, axis=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(inputs=attention_probs, training=training) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = tf.multiply(attention_probs, head_mask) + + attention_output = tf.matmul(attention_probs, value_layer) + attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3]) + + # (batch_size, seq_len_q, all_head_size) + attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size)) + outputs = (attention_output, attention_probs) if output_attentions else (attention_output,) + + return outputs + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "query", None) is not None: + with tf.name_scope(self.query.name): + self.query.build([None, None, self.config.hidden_size]) + if getattr(self, "key", None) is not None: + with tf.name_scope(self.key.name): + self.key.build([None, None, self.config.hidden_size]) + if getattr(self, "value", None) is not None: + with tf.name_scope(self.value.name): + self.value.build([None, None, self.config.hidden_size]) + + +# Copied from transformers.models.vit.modeling_tf_vit.TFViTSelfOutput with ViT->ViTMAE +class TFViTMAESelfOutput(keras.layers.Layer): + """ + The residual connection is defined in TFViTMAELayer instead of here (as is the case with other models), due to the + layernorm applied before each block. + """ + + def __init__(self, config: ViTMAEConfig, **kwargs): + super().__init__(**kwargs) + + self.dense = keras.layers.Dense( + units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" + ) + self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) + self.config = config + + def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: + hidden_states = self.dense(inputs=hidden_states) + hidden_states = self.dropout(inputs=hidden_states, training=training) + + return hidden_states + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "dense", None) is not None: + with tf.name_scope(self.dense.name): + self.dense.build([None, None, self.config.hidden_size]) + + +# Copied from transformers.models.vit.modeling_tf_vit.TFViTAttention with ViT->ViTMAE +class TFViTMAEAttention(keras.layers.Layer): + def __init__(self, config: ViTMAEConfig, **kwargs): + super().__init__(**kwargs) + + self.self_attention = TFViTMAESelfAttention(config, name="attention") + self.dense_output = TFViTMAESelfOutput(config, name="output") + + def prune_heads(self, heads): + raise NotImplementedError + + def call( + self, + input_tensor: tf.Tensor, + head_mask: tf.Tensor, + output_attentions: bool, + training: bool = False, + ) -> Tuple[tf.Tensor]: + self_outputs = self.self_attention( + hidden_states=input_tensor, head_mask=head_mask, output_attentions=output_attentions, training=training + ) + attention_output = self.dense_output( + hidden_states=self_outputs[0], input_tensor=input_tensor, training=training + ) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + + return outputs + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "self_attention", None) is not None: + with tf.name_scope(self.self_attention.name): + self.self_attention.build(None) + if getattr(self, "dense_output", None) is not None: + with tf.name_scope(self.dense_output.name): + self.dense_output.build(None) + + +# Copied from transformers.models.vit.modeling_tf_vit.TFViTIntermediate with ViT->ViTMAE +class TFViTMAEIntermediate(keras.layers.Layer): + def __init__(self, config: ViTMAEConfig, **kwargs): + super().__init__(**kwargs) + + self.dense = keras.layers.Dense( + units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" + ) + + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = get_tf_activation(config.hidden_act) + else: + self.intermediate_act_fn = config.hidden_act + self.config = config + + def call(self, hidden_states: tf.Tensor) -> tf.Tensor: + hidden_states = self.dense(inputs=hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + + return hidden_states + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "dense", None) is not None: + with tf.name_scope(self.dense.name): + self.dense.build([None, None, self.config.hidden_size]) + + +# Copied from transformers.models.vit.modeling_tf_vit.TFViTOutput with ViT->ViTMAE +class TFViTMAEOutput(keras.layers.Layer): + def __init__(self, config: ViTMAEConfig, **kwargs): + super().__init__(**kwargs) + + self.dense = keras.layers.Dense( + units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" + ) + self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) + self.config = config + + def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: + hidden_states = self.dense(inputs=hidden_states) + hidden_states = self.dropout(inputs=hidden_states, training=training) + hidden_states = hidden_states + input_tensor + + return hidden_states + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "dense", None) is not None: + with tf.name_scope(self.dense.name): + self.dense.build([None, None, self.config.intermediate_size]) + + +# Copied from transformers.models.vit.modeling_tf_vit.TFViTLayer with ViT->ViTMAE +class TFViTMAELayer(keras.layers.Layer): + """This corresponds to the Block class in the timm implementation.""" + + def __init__(self, config: ViTMAEConfig, **kwargs): + super().__init__(**kwargs) + + self.attention = TFViTMAEAttention(config, name="attention") + self.intermediate = TFViTMAEIntermediate(config, name="intermediate") + self.vit_output = TFViTMAEOutput(config, name="output") + + self.layernorm_before = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_before") + self.layernorm_after = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_after") + self.config = config + + def call( + self, + hidden_states: tf.Tensor, + head_mask: tf.Tensor, + output_attentions: bool, + training: bool = False, + ) -> Tuple[tf.Tensor]: + attention_outputs = self.attention( + # in ViTMAE, layernorm is applied before self-attention + input_tensor=self.layernorm_before(inputs=hidden_states), + head_mask=head_mask, + output_attentions=output_attentions, + training=training, + ) + attention_output = attention_outputs[0] + + # first residual connection + hidden_states = attention_output + hidden_states + + # in ViTMAE, layernorm is also applied after self-attention + layer_output = self.layernorm_after(inputs=hidden_states) + + intermediate_output = self.intermediate(hidden_states=layer_output) + + # second residual connection is done here + layer_output = self.vit_output( + hidden_states=intermediate_output, input_tensor=hidden_states, training=training + ) + outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them + + return outputs + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "attention", None) is not None: + with tf.name_scope(self.attention.name): + self.attention.build(None) + if getattr(self, "intermediate", None) is not None: + with tf.name_scope(self.intermediate.name): + self.intermediate.build(None) + if getattr(self, "vit_output", None) is not None: + with tf.name_scope(self.vit_output.name): + self.vit_output.build(None) + if getattr(self, "layernorm_before", None) is not None: + with tf.name_scope(self.layernorm_before.name): + self.layernorm_before.build([None, None, self.config.hidden_size]) + if getattr(self, "layernorm_after", None) is not None: + with tf.name_scope(self.layernorm_after.name): + self.layernorm_after.build([None, None, self.config.hidden_size]) + + +# Copied from transformers.models.vit.modeling_tf_vit.TFViTEncoder with ViT->ViTMAE +class TFViTMAEEncoder(keras.layers.Layer): + def __init__(self, config: ViTMAEConfig, **kwargs): + super().__init__(**kwargs) + + self.layer = [TFViTMAELayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)] + + def call( + self, + hidden_states: tf.Tensor, + head_mask: tf.Tensor, + output_attentions: bool, + output_hidden_states: bool, + return_dict: bool, + training: bool = False, + ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]: + all_hidden_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_outputs = layer_module( + hidden_states=hidden_states, + head_mask=head_mask[i], + output_attentions=output_attentions, + training=training, + ) + hidden_states = layer_outputs[0] + + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + # Add last layer + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None) + + return TFBaseModelOutput( + last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions + ) + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "layer", None) is not None: + for layer in self.layer: + with tf.name_scope(layer.name): + layer.build(None) + + +@keras_serializable +class TFViTMAEMainLayer(keras.layers.Layer): + config_class = ViTMAEConfig + + def __init__(self, config: ViTMAEConfig, **kwargs): + super().__init__(**kwargs) + + self.config = config + + self.embeddings = TFViTMAEEmbeddings(config, name="embeddings") + self.encoder = TFViTMAEEncoder(config, name="encoder") + self.layernorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm") + + def get_input_embeddings(self) -> keras.layers.Layer: + return self.embeddings.patch_embeddings + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + raise NotImplementedError + + @unpack_inputs + def call( + self, + pixel_values: TFModelInputType | None = None, + noise: tf.Tensor = None, + head_mask: np.ndarray | tf.Tensor | None = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + training: bool = False, + ) -> Union[TFViTMAEModelOutput, Tuple[tf.Tensor]]: + embedding_output, mask, ids_restore = self.embeddings( + pixel_values=pixel_values, training=training, noise=noise + ) + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + if head_mask is not None: + raise NotImplementedError + else: + head_mask = [None] * self.config.num_hidden_layers + + encoder_outputs = self.encoder( + embedding_output, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + training=training, + ) + + sequence_output = encoder_outputs[0] + sequence_output = self.layernorm(inputs=sequence_output) + + if not return_dict: + return (sequence_output, mask, ids_restore) + encoder_outputs[1:] + + return TFViTMAEModelOutput( + last_hidden_state=sequence_output, + mask=mask, + ids_restore=ids_restore, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "embeddings", None) is not None: + with tf.name_scope(self.embeddings.name): + self.embeddings.build(None) + if getattr(self, "encoder", None) is not None: + with tf.name_scope(self.encoder.name): + self.encoder.build(None) + if getattr(self, "layernorm", None) is not None: + with tf.name_scope(self.layernorm.name): + self.layernorm.build([None, None, self.config.hidden_size]) + + +class TFViTMAEPreTrainedModel(TFPreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = ViTMAEConfig + base_model_prefix = "vit" + main_input_name = "pixel_values" + + +VIT_MAE_START_DOCSTRING = r""" + This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it + as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and + behavior. + + + + TensorFlow models and layers in `transformers` accept two formats as input: + + - having all inputs as keyword arguments (like PyTorch models), or + - having all inputs as a list, tuple or dict in the first positional argument. + + The reason the second format is supported is that Keras methods prefer this format when passing inputs to models + and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just + pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second + format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with + the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first + positional argument: + + - a single Tensor with `pixel_values` only and nothing else: `model(pixel_values)` + - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: + `model([pixel_values, attention_mask])` or `model([pixel_values, attention_mask, token_type_ids])` + - a dictionary with one or several input Tensors associated to the input names given in the docstring: + `model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})` + + Note that when creating models and layers with + [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry + about any of this, as you can just pass inputs like you would to any other Python function! + + + + Args: + config ([`ViTMAEConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. +""" + +VIT_MAE_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`] + for details. + + head_mask (`np.ndarray` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the + config will be used instead. + + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. This argument can be used only in eager mode, in graph mode the value in the config will be + used instead. + + return_dict (`bool`, *optional*): + Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used + in eager mode, in graph mode the value will always be set to True. + + training (`bool`, *optional*, defaults to `False``): + Whether or not to use the model in training mode (some modules like dropout modules have different + behaviors between training and evaluation). +""" + + +@add_start_docstrings( + "The bare ViTMAE Model transformer outputting raw hidden-states without any specific head on top.", + VIT_MAE_START_DOCSTRING, +) +class TFViTMAEModel(TFViTMAEPreTrainedModel): + def __init__(self, config: ViTMAEConfig, *inputs, **kwargs): + super().__init__(config, *inputs, **kwargs) + + self.vit = TFViTMAEMainLayer(config, name="vit") + + def get_input_embeddings(self): + return self.vit.get_input_embeddings() + + @unpack_inputs + @add_start_docstrings_to_model_forward(VIT_MAE_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=TFViTMAEModelOutput, config_class=_CONFIG_FOR_DOC) + def call( + self, + pixel_values: TFModelInputType | None = None, + noise: tf.Tensor = None, + head_mask: np.ndarray | tf.Tensor | None = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + training: bool = False, + ) -> Union[TFViTMAEModelOutput, Tuple[tf.Tensor]]: + r""" + Returns: + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, TFViTMAEModel + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("facebook/vit-mae-base") + >>> model = TFViTMAEModel.from_pretrained("facebook/vit-mae-base") + + >>> inputs = image_processor(images=image, return_tensors="tf") + >>> outputs = model(**inputs) + >>> last_hidden_states = outputs.last_hidden_state + ```""" + outputs = self.vit( + pixel_values=pixel_values, + noise=noise, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + training=training, + ) + + return outputs + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "vit", None) is not None: + with tf.name_scope(self.vit.name): + self.vit.build(None) + + +class TFViTMAEDecoder(keras.layers.Layer): + def __init__(self, config, num_patches, **kwargs): + super().__init__(**kwargs) + self.decoder_embed = keras.layers.Dense(config.decoder_hidden_size, name="decoder_embed") + + decoder_config = deepcopy(config) + decoder_config.hidden_size = config.decoder_hidden_size + decoder_config.num_hidden_layers = config.decoder_num_hidden_layers + decoder_config.num_attention_heads = config.decoder_num_attention_heads + decoder_config.intermediate_size = config.decoder_intermediate_size + self.decoder_layers = [ + TFViTMAELayer(decoder_config, name=f"decoder_layers.{j}") for j in range(config.decoder_num_hidden_layers) + ] + + self.decoder_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="decoder_norm") + self.decoder_pred = keras.layers.Dense( + config.patch_size**2 * config.num_channels, + kernel_initializer=get_initializer(config.initializer_range), + name="decoder_pred", + ) # encoder to decoder + self.config = config + self.num_patches = num_patches + + def build(self, input_shape=None): + self.mask_token = self.add_weight( + shape=(1, 1, self.config.decoder_hidden_size), + initializer=tf.random_normal_initializer(stddev=self.config.initializer_range), + trainable=True, + name="mask_token", + ) + self.decoder_pos_embed = self.add_weight( + shape=(1, self.num_patches + 1, self.config.decoder_hidden_size), + initializer="zeros", + trainable=False, + name="decoder_pos_embed", + ) + decoder_pos_embed = get_2d_sincos_pos_embed( + self.decoder_pos_embed.shape[-1], + int(self.num_patches**0.5), + add_cls_token=True, + )[None, ...] + self.decoder_pos_embed.assign(decoder_pos_embed) + + if self.built: + return + self.built = True + if getattr(self, "decoder_embed", None) is not None: + with tf.name_scope(self.decoder_embed.name): + self.decoder_embed.build([None, None, self.config.hidden_size]) + if getattr(self, "decoder_norm", None) is not None: + with tf.name_scope(self.decoder_norm.name): + self.decoder_norm.build([None, None, self.config.decoder_hidden_size]) + if getattr(self, "decoder_pred", None) is not None: + with tf.name_scope(self.decoder_pred.name): + self.decoder_pred.build([None, None, self.config.decoder_hidden_size]) + if getattr(self, "decoder_layers", None) is not None: + for layer in self.decoder_layers: + with tf.name_scope(layer.name): + layer.build(None) + + def call( + self, + hidden_states, + ids_restore, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + ): + # embed tokens + x = self.decoder_embed(hidden_states) + + # append mask tokens to sequence + mask_tokens = tf.tile( + self.mask_token, + (shape_list(x)[0], shape_list(ids_restore)[1] + 1 - shape_list(x)[1], 1), + ) + x_ = tf.concat([x[:, 1:, :], mask_tokens], axis=1) # no cls token + x_ = tf.gather(x_, axis=1, batch_dims=1, indices=ids_restore) # unshuffle + x = tf.concat([x[:, :1, :], x_], axis=1) # append cls token + + # add pos embed + hidden_states = x + self.decoder_pos_embed + + # apply Transformer layers (blocks) + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + for i, layer_module in enumerate(self.decoder_layers): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_outputs = layer_module( + hidden_states, + head_mask=None, + output_attentions=output_attentions, + ) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + hidden_states = self.decoder_norm(hidden_states) + + # predictor projection + logits = self.decoder_pred(hidden_states) + + # remove cls token + logits = logits[:, 1:, :] + + if not return_dict: + return tuple(v for v in [logits, all_hidden_states, all_self_attentions] if v is not None) + return TFViTMAEDecoderOutput(logits=logits, hidden_states=all_hidden_states, attentions=all_self_attentions) + + +@add_start_docstrings( + "The ViTMAE Model transformer with the decoder on top for self-supervised pre-training.", + VIT_MAE_START_DOCSTRING, +) +class TFViTMAEForPreTraining(TFViTMAEPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.config = config + + self.vit = TFViTMAEMainLayer(config, name="vit") + self.decoder = TFViTMAEDecoder( + config, + num_patches=self.vit.embeddings.num_patches, + name="decoder", + ) + + def get_input_embeddings(self): + return self.vit.get_input_embeddings() + + def _prune_heads(self, heads_to_prune): + raise NotImplementedError + + def patchify(self, pixel_values): + """ + Args: + pixel_values (`tf.Tensor` of shape `(batch_size, height, width, num_channels)` or `(batch_size, num_channels, height, width)`): + Pixel values. + + Returns: + `tf.Tensor` of shape `(batch_size, num_patches, patch_size**2 * num_channels)`: + Patchified pixel values. + """ + patch_size, num_channels = self.config.patch_size, self.config.num_channels + # make sure channels are last + if shape_list(pixel_values)[1] == num_channels: + pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1)) + + # sanity checks + tf.debugging.assert_equal( + shape_list(pixel_values)[1], + shape_list(pixel_values)[2], + message="Make sure the pixel values have a squared size", + ) + tf.debugging.assert_equal( + shape_list(pixel_values)[1] % patch_size, + 0, + message="Make sure the pixel values have a size that is divisible by the patch size", + ) + tf.debugging.assert_equal( + shape_list(pixel_values)[3], + num_channels, + message=( + "Make sure the number of channels of the pixel values is equal to the one set in the configuration" + ), + ) + + # patchify + batch_size = shape_list(pixel_values)[0] + num_patches_one_direction = shape_list(pixel_values)[2] // patch_size + patchified_pixel_values = tf.reshape( + pixel_values, + (batch_size, num_patches_one_direction, patch_size, num_patches_one_direction, patch_size, num_channels), + ) + patchified_pixel_values = tf.einsum("nhpwqc->nhwpqc", patchified_pixel_values) + patchified_pixel_values = tf.reshape( + patchified_pixel_values, + (batch_size, num_patches_one_direction * num_patches_one_direction, patch_size**2 * num_channels), + ) + return patchified_pixel_values + + def unpatchify(self, patchified_pixel_values): + """ + Args: + patchified_pixel_values (`tf.Tensor` of shape `(batch_size, num_patches, patch_size**2 * num_channels)`: + Patchified pixel values. + + Returns: + `tf.Tensor` of shape `(batch_size, height, width, num_channels)`: + Pixel values. + """ + patch_size, num_channels = self.config.patch_size, self.config.num_channels + num_patches_one_direction = int(shape_list(patchified_pixel_values)[1] ** 0.5) + # sanity check + tf.debugging.assert_equal( + num_patches_one_direction * num_patches_one_direction, + shape_list(patchified_pixel_values)[1], + message="Make sure that the number of patches can be squared", + ) + + # unpatchify + batch_size = shape_list(patchified_pixel_values)[0] + patchified_pixel_values = tf.reshape( + patchified_pixel_values, + (batch_size, num_patches_one_direction, num_patches_one_direction, patch_size, patch_size, num_channels), + ) + patchified_pixel_values = tf.einsum("nhwpqc->nhpwqc", patchified_pixel_values) + pixel_values = tf.reshape( + patchified_pixel_values, + (batch_size, num_patches_one_direction * patch_size, num_patches_one_direction * patch_size, num_channels), + ) + return pixel_values + + def forward_loss(self, pixel_values, pred, mask): + """ + Args: + pixel_values (`tf.Tensor` of shape `(batch_size, height, width, num_channels)`): + Pixel values. + pred (`tf.Tensor` of shape `(batch_size, num_patches, patch_size**2 * num_channels)`: + Predicted pixel values. + mask (`tf.Tensor` of shape `(batch_size, sequence_length)`): + Tensor indicating which patches are masked (1) and which are not (0). + + Returns: + `tf.Tensor`: Pixel reconstruction loss. + """ + target = self.patchify(pixel_values) + if self.config.norm_pix_loss: + mean = tf.reduce_mean(target, axis=-1, keepdims=True) + var = tf.math.reduce_variance(target, axis=-1, keepdims=True) + target = (target - mean) / (var + 1.0e-6) ** 0.5 + + loss = (pred - target) ** 2 + loss = tf.reduce_mean(loss, axis=-1) # [batch_size, num_patches], mean loss per patch + + loss = tf.reduce_sum(loss * mask) / tf.reduce_sum(mask) # mean loss on removed patches + loss = tf.reshape(loss, (1,)) + return loss + + @unpack_inputs + @add_start_docstrings_to_model_forward(VIT_MAE_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=TFViTMAEForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) + def call( + self, + pixel_values: TFModelInputType | None = None, + noise: tf.Tensor = None, + head_mask: np.ndarray | tf.Tensor | None = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + training: bool = False, + ) -> Union[TFViTMAEForPreTrainingOutput, Tuple[tf.Tensor]]: + r""" + Returns: + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, TFViTMAEForPreTraining + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("facebook/vit-mae-base") + >>> model = TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base") + + >>> inputs = image_processor(images=image, return_tensors="pt") + >>> outputs = model(**inputs) + >>> loss = outputs.loss + >>> mask = outputs.mask + >>> ids_restore = outputs.ids_restore + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.vit( + pixel_values=pixel_values, + noise=noise, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + training=training, + ) + + latent = outputs.last_hidden_state + ids_restore = outputs.ids_restore + mask = outputs.mask + + decoder_outputs = self.decoder(latent, ids_restore) # [batch_size, num_patches, patch_size**2*3] + logits = decoder_outputs.logits + + loss = self.forward_loss(pixel_values, logits, mask) + + if not return_dict: + output = (logits, mask, ids_restore) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return TFViTMAEForPreTrainingOutput( + loss=loss, + logits=logits, + mask=mask, + ids_restore=ids_restore, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "vit", None) is not None: + with tf.name_scope(self.vit.name): + self.vit.build(None) + if getattr(self, "decoder", None) is not None: + with tf.name_scope(self.decoder.name): + self.decoder.build(None) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_mae/modeling_vit_mae.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_mae/modeling_vit_mae.py new file mode 100644 index 0000000000000000000000000000000000000000..bfbe59ea903a1a2199afb048b3645606399f053b --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_mae/modeling_vit_mae.py @@ -0,0 +1,1008 @@ +# coding=utf-8 +# Copyright 2022 Facebook AI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch ViT MAE (masked autoencoder) model.""" + + +import collections.abc +import math +from copy import deepcopy +from dataclasses import dataclass +from typing import Optional, Set, Tuple, Union + +import numpy as np +import torch +import torch.utils.checkpoint +from torch import nn + +from ...activations import ACT2FN +from ...modeling_outputs import BaseModelOutput +from ...modeling_utils import PreTrainedModel +from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer +from ...utils import ( + ModelOutput, + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) +from .configuration_vit_mae import ViTMAEConfig + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "ViTMAEConfig" +_CHECKPOINT_FOR_DOC = "facebook/vit-mae-base" + + +from ..deprecated._archive_maps import VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +@dataclass +class ViTMAEModelOutput(ModelOutput): + """ + Class for ViTMAEModel's outputs, with potential hidden states and attentions. + + Args: + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): + Tensor indicating which patches are masked (1) and which are not (0). + ids_restore (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Tensor containing the original index of the (shuffled) masked patches. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer + plus the initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in + the self-attention heads. + """ + + last_hidden_state: torch.FloatTensor = None + mask: torch.LongTensor = None + ids_restore: torch.LongTensor = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + + +@dataclass +class ViTMAEDecoderOutput(ModelOutput): + """ + Class for ViTMAEDecoder's outputs, with potential hidden states and attentions. + + Args: + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, patch_size ** 2 * num_channels)`): + Pixel reconstruction logits. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer + plus the initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in + the self-attention heads. + """ + + logits: torch.FloatTensor = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + + +@dataclass +class ViTMAEForPreTrainingOutput(ModelOutput): + """ + Class for ViTMAEForPreTraining's outputs, with potential hidden states and attentions. + + Args: + loss (`torch.FloatTensor` of shape `(1,)`): + Pixel reconstruction loss. + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, patch_size ** 2 * num_channels)`): + Pixel reconstruction logits. + mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): + Tensor indicating which patches are masked (1) and which are not (0). + ids_restore (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Tensor containing the original index of the (shuffled) masked patches. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer + plus the initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in + the self-attention heads. + """ + + loss: Optional[torch.FloatTensor] = None + logits: torch.FloatTensor = None + mask: torch.LongTensor = None + ids_restore: torch.LongTensor = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + + +def get_2d_sincos_pos_embed(embed_dim, grid_size, add_cls_token=False): + """ + Create 2D sin/cos positional embeddings. + + Args: + embed_dim (`int`): + Embedding dimension. + grid_size (`int`): + The grid height and width. + add_cls_token (`bool`, *optional*, defaults to `False`): + Whether or not to add a classification (CLS) token. + + Returns: + (`torch.FloatTensor` of shape (grid_size*grid_size, embed_dim) or (1+grid_size*grid_size, embed_dim): the + position embeddings (with or without classification token) + """ + grid_h = np.arange(grid_size, dtype=np.float32) + grid_w = np.arange(grid_size, dtype=np.float32) + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) + + grid = grid.reshape([2, 1, grid_size, grid_size]) + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + if add_cls_token: + pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0) + return pos_embed + + +def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): + if embed_dim % 2 != 0: + raise ValueError("embed_dim must be even") + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) + + emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) + return emb + + +def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): + """ + embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D) + """ + if embed_dim % 2 != 0: + raise ValueError("embed_dim must be even") + + omega = np.arange(embed_dim // 2, dtype=float) + omega /= embed_dim / 2.0 + omega = 1.0 / 10000**omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product + + emb_sin = np.sin(out) # (M, D/2) + emb_cos = np.cos(out) # (M, D/2) + + emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) + return emb + + +class ViTMAEEmbeddings(nn.Module): + """ + Construct the CLS token, position and patch embeddings. + + """ + + def __init__(self, config): + super().__init__() + + self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) + self.patch_embeddings = ViTMAEPatchEmbeddings(config) + self.num_patches = self.patch_embeddings.num_patches + # fixed sin-cos embedding + self.position_embeddings = nn.Parameter( + torch.zeros(1, self.num_patches + 1, config.hidden_size), requires_grad=False + ) + self.config = config + self.initialize_weights() + + def initialize_weights(self): + # initialize (and freeze) position embeddings by sin-cos embedding + pos_embed = get_2d_sincos_pos_embed( + self.position_embeddings.shape[-1], int(self.patch_embeddings.num_patches**0.5), add_cls_token=True + ) + self.position_embeddings.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) + + # initialize patch_embeddings like nn.Linear (instead of nn.Conv2d) + w = self.patch_embeddings.projection.weight.data + torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1])) + + # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.) + torch.nn.init.normal_(self.cls_token, std=self.config.initializer_range) + + def random_masking(self, sequence, noise=None): + """ + Perform per-sample random masking by per-sample shuffling. Per-sample shuffling is done by argsort random + noise. + + Args: + sequence (`torch.LongTensor` of shape `(batch_size, sequence_length, dim)`) + noise (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*) which is + mainly used for testing purposes to control randomness and maintain the reproducibility + """ + batch_size, seq_length, dim = sequence.shape + len_keep = int(seq_length * (1 - self.config.mask_ratio)) + + if noise is None: + noise = torch.rand(batch_size, seq_length, device=sequence.device) # noise in [0, 1] + + # sort noise for each sample + ids_shuffle = torch.argsort(noise, dim=1) # ascend: small is keep, large is remove + ids_restore = torch.argsort(ids_shuffle, dim=1) + + # keep the first subset + ids_keep = ids_shuffle[:, :len_keep] + sequence_unmasked = torch.gather(sequence, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, dim)) + + # generate the binary mask: 0 is keep, 1 is remove + mask = torch.ones([batch_size, seq_length], device=sequence.device) + mask[:, :len_keep] = 0 + # unshuffle to get the binary mask + mask = torch.gather(mask, dim=1, index=ids_restore) + + return sequence_unmasked, mask, ids_restore + + def forward(self, pixel_values, noise=None): + batch_size, num_channels, height, width = pixel_values.shape + embeddings = self.patch_embeddings(pixel_values) + + # add position embeddings w/o cls token + embeddings = embeddings + self.position_embeddings[:, 1:, :] + + # masking: length -> length * config.mask_ratio + embeddings, mask, ids_restore = self.random_masking(embeddings, noise) + + # append cls token + cls_token = self.cls_token + self.position_embeddings[:, :1, :] + cls_tokens = cls_token.expand(embeddings.shape[0], -1, -1) + embeddings = torch.cat((cls_tokens, embeddings), dim=1) + + return embeddings, mask, ids_restore + + +class ViTMAEPatchEmbeddings(nn.Module): + """ + This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial + `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a + Transformer. + """ + + def __init__(self, config): + super().__init__() + image_size, patch_size = config.image_size, config.patch_size + num_channels, hidden_size = config.num_channels, config.hidden_size + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_patches = num_patches + + self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) + + def forward(self, pixel_values): + batch_size, num_channels, height, width = pixel_values.shape + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + ) + if height != self.image_size[0] or width != self.image_size[1]: + raise ValueError( + f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})." + ) + x = self.projection(pixel_values).flatten(2).transpose(1, 2) + return x + + +# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention ViT->ViTMAE +class ViTMAESelfAttention(nn.Module): + def __init__(self, config: ViTMAEConfig) -> None: + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " + f"heads {config.num_attention_heads}." + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + mixed_query_layer = self.query(hidden_states) + + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + query_layer = self.transpose_for_scores(mixed_query_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->ViTMAE +class ViTMAESelfOutput(nn.Module): + """ + The residual connection is defined in ViTMAELayer instead of here (as is the case with other models), due to the + layernorm applied before each block. + """ + + def __init__(self, config: ViTMAEConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + + return hidden_states + + +# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->ViTMAE +class ViTMAEAttention(nn.Module): + def __init__(self, config: ViTMAEConfig) -> None: + super().__init__() + self.attention = ViTMAESelfAttention(config) + self.output = ViTMAESelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads: Set[int]) -> None: + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.attention.query = prune_linear_layer(self.attention.query, index) + self.attention.key = prune_linear_layer(self.attention.key, index) + self.attention.value = prune_linear_layer(self.attention.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) + self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + self_outputs = self.attention(hidden_states, head_mask, output_attentions) + + attention_output = self.output(self_outputs[0], hidden_states) + + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +# Copied from transformers.models.vit.modeling_vit.ViTIntermediate ViT->ViTMAE +class ViTMAEIntermediate(nn.Module): + def __init__(self, config: ViTMAEConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + + return hidden_states + + +# Copied from transformers.models.vit.modeling_vit.ViTOutput ViT->ViTMAE +class ViTMAEOutput(nn.Module): + def __init__(self, config: ViTMAEConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + + hidden_states = hidden_states + input_tensor + + return hidden_states + + +# Copied from transformers.models.vit.modeling_vit.ViTLayer with ViT->ViTMAE +class ViTMAELayer(nn.Module): + """This corresponds to the Block class in the timm implementation.""" + + def __init__(self, config: ViTMAEConfig) -> None: + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = ViTMAEAttention(config) + self.intermediate = ViTMAEIntermediate(config) + self.output = ViTMAEOutput(config) + self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + self_attention_outputs = self.attention( + self.layernorm_before(hidden_states), # in ViTMAE, layernorm is applied before self-attention + head_mask, + output_attentions=output_attentions, + ) + attention_output = self_attention_outputs[0] + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + # first residual connection + hidden_states = attention_output + hidden_states + + # in ViTMAE, layernorm is also applied after self-attention + layer_output = self.layernorm_after(hidden_states) + layer_output = self.intermediate(layer_output) + + # second residual connection is done here + layer_output = self.output(layer_output, hidden_states) + + outputs = (layer_output,) + outputs + + return outputs + + +# Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->ViTMAE +class ViTMAEEncoder(nn.Module): + def __init__(self, config: ViTMAEConfig) -> None: + super().__init__() + self.config = config + self.layer = nn.ModuleList([ViTMAELayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ) -> Union[tuple, BaseModelOutput]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer_module.__call__, + hidden_states, + layer_head_mask, + output_attentions, + ) + else: + layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +class ViTMAEPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = ViTMAEConfig + base_model_prefix = "vit" + main_input_name = "pixel_values" + supports_gradient_checkpointing = True + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, (nn.Linear, nn.Conv2d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + +VIT_MAE_START_DOCSTRING = r""" + This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it + as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and + behavior. + + Parameters: + config ([`ViTMAEConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +VIT_MAE_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`] + for details. + + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare ViTMAE Model transformer outputting raw hidden-states without any specific head on top.", + VIT_MAE_START_DOCSTRING, +) +class ViTMAEModel(ViTMAEPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.config = config + + self.embeddings = ViTMAEEmbeddings(config) + self.encoder = ViTMAEEncoder(config) + + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.patch_embeddings + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + @add_start_docstrings_to_model_forward(VIT_MAE_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=ViTMAEModelOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: Optional[torch.FloatTensor] = None, + noise: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, ViTMAEModelOutput]: + r""" + Returns: + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, ViTMAEModel + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("facebook/vit-mae-base") + >>> model = ViTMAEModel.from_pretrained("facebook/vit-mae-base") + + >>> inputs = image_processor(images=image, return_tensors="pt") + >>> outputs = model(**inputs) + >>> last_hidden_states = outputs.last_hidden_state + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output, mask, ids_restore = self.embeddings(pixel_values, noise=noise) + + encoder_outputs = self.encoder( + embedding_output, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + sequence_output = self.layernorm(sequence_output) + + if not return_dict: + return (sequence_output, mask, ids_restore) + encoder_outputs[1:] + + return ViTMAEModelOutput( + last_hidden_state=sequence_output, + mask=mask, + ids_restore=ids_restore, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +class ViTMAEDecoder(nn.Module): + def __init__(self, config, num_patches): + super().__init__() + self.decoder_embed = nn.Linear(config.hidden_size, config.decoder_hidden_size, bias=True) + self.mask_token = nn.Parameter(torch.zeros(1, 1, config.decoder_hidden_size)) + self.decoder_pos_embed = nn.Parameter( + torch.zeros(1, num_patches + 1, config.decoder_hidden_size), requires_grad=False + ) # fixed sin-cos embedding + + decoder_config = deepcopy(config) + decoder_config.hidden_size = config.decoder_hidden_size + decoder_config.num_hidden_layers = config.decoder_num_hidden_layers + decoder_config.num_attention_heads = config.decoder_num_attention_heads + decoder_config.intermediate_size = config.decoder_intermediate_size + self.decoder_layers = nn.ModuleList( + [ViTMAELayer(decoder_config) for _ in range(config.decoder_num_hidden_layers)] + ) + + self.decoder_norm = nn.LayerNorm(config.decoder_hidden_size, eps=config.layer_norm_eps) + self.decoder_pred = nn.Linear( + config.decoder_hidden_size, config.patch_size**2 * config.num_channels, bias=True + ) # encoder to decoder + self.gradient_checkpointing = False + self.config = config + self.initialize_weights(num_patches) + + def initialize_weights(self, num_patches): + # initialize (and freeze) position embeddings by sin-cos embedding + decoder_pos_embed = get_2d_sincos_pos_embed( + self.decoder_pos_embed.shape[-1], int(num_patches**0.5), add_cls_token=True + ) + self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0)) + + # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.) + torch.nn.init.normal_(self.mask_token, std=self.config.initializer_range) + + def forward( + self, + hidden_states, + ids_restore, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + ): + # embed tokens + x = self.decoder_embed(hidden_states) + + # append mask tokens to sequence + mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] + 1 - x.shape[1], 1) + x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1) # no cls token + x_ = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2])) # unshuffle + x = torch.cat([x[:, :1, :], x_], dim=1) # append cls token + + # add pos embed + hidden_states = x + self.decoder_pos_embed + + # apply Transformer layers (blocks) + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + for i, layer_module in enumerate(self.decoder_layers): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer_module.__call__, + hidden_states, + None, + output_attentions, + ) + else: + layer_outputs = layer_module(hidden_states, head_mask=None, output_attentions=output_attentions) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + hidden_states = self.decoder_norm(hidden_states) + + # predictor projection + logits = self.decoder_pred(hidden_states) + + # remove cls token + logits = logits[:, 1:, :] + + if not return_dict: + return tuple(v for v in [logits, all_hidden_states, all_self_attentions] if v is not None) + return ViTMAEDecoderOutput( + logits=logits, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +@add_start_docstrings( + """The ViTMAE Model transformer with the decoder on top for self-supervised pre-training. + + + + Note that we provide a script to pre-train this model on custom data in our [examples + directory](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining). + + + + """, + VIT_MAE_START_DOCSTRING, +) +class ViTMAEForPreTraining(ViTMAEPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.config = config + + self.vit = ViTMAEModel(config) + self.decoder = ViTMAEDecoder(config, num_patches=self.vit.embeddings.num_patches) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.vit.embeddings.patch_embeddings + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + def patchify(self, pixel_values): + """ + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. + + Returns: + `torch.FloatTensor` of shape `(batch_size, num_patches, patch_size**2 * num_channels)`: + Patchified pixel values. + """ + patch_size, num_channels = self.config.patch_size, self.config.num_channels + # sanity checks + if (pixel_values.shape[2] != pixel_values.shape[3]) or (pixel_values.shape[2] % patch_size != 0): + raise ValueError("Make sure the pixel values have a squared size that is divisible by the patch size") + if pixel_values.shape[1] != num_channels: + raise ValueError( + "Make sure the number of channels of the pixel values is equal to the one set in the configuration" + ) + + # patchify + batch_size = pixel_values.shape[0] + num_patches_one_direction = pixel_values.shape[2] // patch_size + patchified_pixel_values = pixel_values.reshape( + batch_size, num_channels, num_patches_one_direction, patch_size, num_patches_one_direction, patch_size + ) + patchified_pixel_values = torch.einsum("nchpwq->nhwpqc", patchified_pixel_values) + patchified_pixel_values = patchified_pixel_values.reshape( + batch_size, num_patches_one_direction * num_patches_one_direction, patch_size**2 * num_channels + ) + return patchified_pixel_values + + def unpatchify(self, patchified_pixel_values): + """ + Args: + patchified_pixel_values (`torch.FloatTensor` of shape `(batch_size, num_patches, patch_size**2 * num_channels)`: + Patchified pixel values. + + Returns: + `torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`: + Pixel values. + """ + patch_size, num_channels = self.config.patch_size, self.config.num_channels + num_patches_one_direction = int(patchified_pixel_values.shape[1] ** 0.5) + # sanity check + if num_patches_one_direction**2 != patchified_pixel_values.shape[1]: + raise ValueError("Make sure that the number of patches can be squared") + + # unpatchify + batch_size = patchified_pixel_values.shape[0] + patchified_pixel_values = patchified_pixel_values.reshape( + batch_size, + num_patches_one_direction, + num_patches_one_direction, + patch_size, + patch_size, + num_channels, + ) + patchified_pixel_values = torch.einsum("nhwpqc->nchpwq", patchified_pixel_values) + pixel_values = patchified_pixel_values.reshape( + batch_size, + num_channels, + num_patches_one_direction * patch_size, + num_patches_one_direction * patch_size, + ) + return pixel_values + + def forward_loss(self, pixel_values, pred, mask): + """ + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. + pred (`torch.FloatTensor` of shape `(batch_size, num_patches, patch_size**2 * num_channels)`: + Predicted pixel values. + mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): + Tensor indicating which patches are masked (1) and which are not (0). + + Returns: + `torch.FloatTensor`: Pixel reconstruction loss. + """ + target = self.patchify(pixel_values) + if self.config.norm_pix_loss: + mean = target.mean(dim=-1, keepdim=True) + var = target.var(dim=-1, keepdim=True) + target = (target - mean) / (var + 1.0e-6) ** 0.5 + + loss = (pred - target) ** 2 + loss = loss.mean(dim=-1) # [N, L], mean loss per patch + + loss = (loss * mask).sum() / mask.sum() # mean loss on removed patches + return loss + + @add_start_docstrings_to_model_forward(VIT_MAE_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=ViTMAEForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: Optional[torch.FloatTensor] = None, + noise: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, ViTMAEForPreTrainingOutput]: + r""" + Returns: + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, ViTMAEForPreTraining + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("facebook/vit-mae-base") + >>> model = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base") + + >>> inputs = image_processor(images=image, return_tensors="pt") + >>> outputs = model(**inputs) + >>> loss = outputs.loss + >>> mask = outputs.mask + >>> ids_restore = outputs.ids_restore + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.vit( + pixel_values, + noise=noise, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + latent = outputs.last_hidden_state + ids_restore = outputs.ids_restore + mask = outputs.mask + + decoder_outputs = self.decoder(latent, ids_restore) + logits = decoder_outputs.logits # shape (batch_size, num_patches, patch_size*patch_size*num_channels) + + loss = self.forward_loss(pixel_values, logits, mask) + + if not return_dict: + output = (logits, mask, ids_restore) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return ViTMAEForPreTrainingOutput( + loss=loss, + logits=logits, + mask=mask, + ids_restore=ids_restore, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c36cb750cfa4e6273de0a8a2646236ee14b516d1 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/__init__.py @@ -0,0 +1,53 @@ +# Copyright 2020 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available + + +_import_structure = {"configuration_vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"]} + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_vit_msn"] = [ + "VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST", + "ViTMSNModel", + "ViTMSNForImageClassification", + "ViTMSNPreTrainedModel", + ] + +if TYPE_CHECKING: + from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_vit_msn import ( + VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST, + ViTMSNForImageClassification, + ViTMSNModel, + ViTMSNPreTrainedModel, + ) + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..dfea6ab0e9f32a3b5991ba76e34a25df71c3828c Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/__pycache__/configuration_vit_msn.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/__pycache__/configuration_vit_msn.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c3fd81f0107ba57e4325396fddece8f8b06e1dea Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/__pycache__/configuration_vit_msn.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/__pycache__/convert_msn_to_pytorch.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/__pycache__/convert_msn_to_pytorch.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..44c86779e00ad49a457e6654f4fb84d29990a390 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/__pycache__/convert_msn_to_pytorch.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/configuration_vit_msn.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/configuration_vit_msn.py new file mode 100644 index 0000000000000000000000000000000000000000..296434346625f56e265787ad7e567188d45d02dc --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/configuration_vit_msn.py @@ -0,0 +1,116 @@ +# coding=utf-8 +# Copyright 2022 Facebook AI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" ViT MSN model configuration""" + + +from ...configuration_utils import PretrainedConfig +from ...utils import logging + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class ViTMSNConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`ViTMSNModel`]. It is used to instantiate an ViT + MSN model according to the specified arguments, defining the model architecture. Instantiating a configuration with + the defaults will yield a similar configuration to that of the ViT + [facebook/vit_msn_base](https://huggingface.co/facebook/vit_msn_base) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + hidden_size (`int`, *optional*, defaults to 768): + Dimensionality of the encoder layers and the pooler layer. + num_hidden_layers (`int`, *optional*, defaults to 12): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 12): + Number of attention heads for each attention layer in the Transformer encoder. + intermediate_size (`int`, *optional*, defaults to 3072): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"` and `"gelu_new"` are supported. + hidden_dropout_prob (`float`, *optional*, defaults to 0.0): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + layer_norm_eps (`float`, *optional*, defaults to 1e-06): + The epsilon used by the layer normalization layers. + image_size (`int`, *optional*, defaults to 224): + The size (resolution) of each image. + patch_size (`int`, *optional*, defaults to 16): + The size (resolution) of each patch. + num_channels (`int`, *optional*, defaults to 3): + The number of input channels. + qkv_bias (`bool`, *optional*, defaults to `True`): + Whether to add a bias to the queries, keys and values. + + Example: + + ```python + >>> from transformers import ViTMSNModel, ViTMSNConfig + + >>> # Initializing a ViT MSN vit-msn-base style configuration + >>> configuration = ViTConfig() + + >>> # Initializing a model from the vit-msn-base style configuration + >>> model = ViTMSNModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "vit_msn" + + def __init__( + self, + hidden_size=768, + num_hidden_layers=12, + num_attention_heads=12, + intermediate_size=3072, + hidden_act="gelu", + hidden_dropout_prob=0.0, + attention_probs_dropout_prob=0.0, + initializer_range=0.02, + layer_norm_eps=1e-06, + image_size=224, + patch_size=16, + num_channels=3, + qkv_bias=True, + **kwargs, + ): + super().__init__(**kwargs) + + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.initializer_range = initializer_range + self.layer_norm_eps = layer_norm_eps + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.qkv_bias = qkv_bias diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/convert_msn_to_pytorch.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/convert_msn_to_pytorch.py new file mode 100644 index 0000000000000000000000000000000000000000..899c74f183205e9fdc18984a1f15e877bc64fe31 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/convert_msn_to_pytorch.py @@ -0,0 +1,245 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert ViT MSN checkpoints from the original repository: https://github.com/facebookresearch/msn""" + +import argparse +import json + +import requests +import torch +from huggingface_hub import hf_hub_download +from PIL import Image + +from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel +from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD + + +torch.set_grad_enabled(False) + + +# here we list all keys to be renamed (original name on the left, our name on the right) +def create_rename_keys(config, base_model=False): + rename_keys = [] + for i in range(config.num_hidden_layers): + # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms + rename_keys.append((f"module.blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight")) + rename_keys.append((f"module.blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")) + rename_keys.append( + (f"module.blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight") + ) + rename_keys.append((f"module.blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")) + rename_keys.append((f"module.blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")) + rename_keys.append((f"module.blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")) + rename_keys.append((f"module.blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")) + rename_keys.append((f"module.blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")) + rename_keys.append((f"module.blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")) + rename_keys.append((f"module.blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")) + + # projection layer + position embeddings + rename_keys.extend( + [ + ("module.cls_token", "vit.embeddings.cls_token"), + ("module.patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"), + ("module.patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"), + ("module.pos_embed", "vit.embeddings.position_embeddings"), + ] + ) + + if base_model: + # layernorm + pooler + rename_keys.extend( + [ + ("module.norm.weight", "layernorm.weight"), + ("module.norm.bias", "layernorm.bias"), + ] + ) + + # if just the base model, we should remove "vit" from all keys that start with "vit" + rename_keys = [(pair[0], pair[1][4:]) if pair[1].startswith("vit") else pair for pair in rename_keys] + else: + # layernorm + classification head + rename_keys.extend( + [ + ("norm.weight", "vit.layernorm.weight"), + ("norm.bias", "vit.layernorm.bias"), + ("head.weight", "classifier.weight"), + ("head.bias", "classifier.bias"), + ] + ) + + return rename_keys + + +# we split up the matrix of each encoder layer into queries, keys and values +def read_in_q_k_v(state_dict, config, base_model=False): + for i in range(config.num_hidden_layers): + if base_model: + prefix = "" + else: + prefix = "vit." + # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) + in_proj_weight = state_dict.pop(f"module.blocks.{i}.attn.qkv.weight") + in_proj_bias = state_dict.pop(f"module.blocks.{i}.attn.qkv.bias") + # next, add query, keys and values (in that order) to the state dict + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[ + : config.hidden_size, : + ] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.bias"] = in_proj_bias[: config.hidden_size] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[ + config.hidden_size : config.hidden_size * 2, : + ] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.bias"] = in_proj_bias[ + config.hidden_size : config.hidden_size * 2 + ] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[ + -config.hidden_size :, : + ] + state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.bias"] = in_proj_bias[-config.hidden_size :] + + +def remove_classification_head_(state_dict): + ignore_keys = ["head.weight", "head.bias"] + for k in ignore_keys: + state_dict.pop(k, None) + + +def remove_projection_head(state_dict): + # projection head is used in the self-supervised pre-training in MSN, + # for downstream task it's not needed. + ignore_keys = [ + "module.fc.fc1.weight", + "module.fc.fc1.bias", + "module.fc.bn1.weight", + "module.fc.bn1.bias", + "module.fc.bn1.running_mean", + "module.fc.bn1.running_var", + "module.fc.bn1.num_batches_tracked", + "module.fc.fc2.weight", + "module.fc.fc2.bias", + "module.fc.bn2.weight", + "module.fc.bn2.bias", + "module.fc.bn2.running_mean", + "module.fc.bn2.running_var", + "module.fc.bn2.num_batches_tracked", + "module.fc.fc3.weight", + "module.fc.fc3.bias", + ] + for k in ignore_keys: + state_dict.pop(k, None) + + +def rename_key(dct, old, new): + val = dct.pop(old) + dct[new] = val + + +def convert_vit_msn_checkpoint(checkpoint_url, pytorch_dump_folder_path): + config = ViTMSNConfig() + config.num_labels = 1000 + + repo_id = "datasets/huggingface/label-files" + filename = "imagenet-1k-id2label.json" + id2label = json.load(open(hf_hub_download(repo_id, filename), "r")) + id2label = {int(k): v for k, v in id2label.items()} + config.id2label = id2label + config.label2id = {v: k for k, v in id2label.items()} + + if "s16" in checkpoint_url: + config.hidden_size = 384 + config.intermediate_size = 1536 + config.num_attention_heads = 6 + elif "l16" in checkpoint_url: + config.hidden_size = 1024 + config.intermediate_size = 4096 + config.num_hidden_layers = 24 + config.num_attention_heads = 16 + config.hidden_dropout_prob = 0.1 + elif "b4" in checkpoint_url: + config.patch_size = 4 + elif "l7" in checkpoint_url: + config.patch_size = 7 + config.hidden_size = 1024 + config.intermediate_size = 4096 + config.num_hidden_layers = 24 + config.num_attention_heads = 16 + config.hidden_dropout_prob = 0.1 + + model = ViTMSNModel(config) + + state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")["target_encoder"] + + image_processor = ViTImageProcessor(size=config.image_size) + + remove_projection_head(state_dict) + rename_keys = create_rename_keys(config, base_model=True) + + for src, dest in rename_keys: + rename_key(state_dict, src, dest) + read_in_q_k_v(state_dict, config, base_model=True) + + model.load_state_dict(state_dict) + model.eval() + + url = "http://images.cocodataset.org/val2017/000000039769.jpg" + + image = Image.open(requests.get(url, stream=True).raw) + image_processor = ViTImageProcessor( + size=config.image_size, image_mean=IMAGENET_DEFAULT_MEAN, image_std=IMAGENET_DEFAULT_STD + ) + inputs = image_processor(images=image, return_tensors="pt") + + # forward pass + torch.manual_seed(2) + outputs = model(**inputs) + last_hidden_state = outputs.last_hidden_state + + # The following Colab Notebook was used to generate these outputs: + # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb + if "s16" in checkpoint_url: + expected_slice = torch.tensor([[-1.0915, -1.4876, -1.1809]]) + elif "b16" in checkpoint_url: + expected_slice = torch.tensor([[14.2889, -18.9045, 11.7281]]) + elif "l16" in checkpoint_url: + expected_slice = torch.tensor([[41.5028, -22.8681, 45.6475]]) + elif "b4" in checkpoint_url: + expected_slice = torch.tensor([[-4.3868, 5.2932, -0.4137]]) + else: + expected_slice = torch.tensor([[-0.1792, -0.6465, 2.4263]]) + + # verify logits + assert torch.allclose(last_hidden_state[:, 0, :3], expected_slice, atol=1e-4) + + print(f"Saving model to {pytorch_dump_folder_path}") + model.save_pretrained(pytorch_dump_folder_path) + + print(f"Saving image processor to {pytorch_dump_folder_path}") + image_processor.save_pretrained(pytorch_dump_folder_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + # Required parameters + parser.add_argument( + "--checkpoint_url", + default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar", + type=str, + help="URL of the checkpoint you'd like to convert.", + ) + parser.add_argument( + "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." + ) + + args = parser.parse_args() + convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/modeling_vit_msn.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/modeling_vit_msn.py new file mode 100644 index 0000000000000000000000000000000000000000..9c2269a3ae546fde6b04d05350b9c7b33481aa54 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vit_msn/modeling_vit_msn.py @@ -0,0 +1,688 @@ +# coding=utf-8 +# Copyright 2022 Facebook AI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch ViT MSN (masked siamese network) model.""" + + +import collections.abc +import math +from typing import Dict, List, Optional, Set, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ...activations import ACT2FN +from ...modeling_outputs import BaseModelOutput, ImageClassifierOutput +from ...modeling_utils import PreTrainedModel +from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer +from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings +from .configuration_vit_msn import ViTMSNConfig + + +logger = logging.get_logger(__name__) + + +_CONFIG_FOR_DOC = "ViTMSNConfig" +_CHECKPOINT_FOR_DOC = "facebook/vit-msn-small" + +from ..deprecated._archive_maps import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +class ViTMSNEmbeddings(nn.Module): + """ + Construct the CLS token, position and patch embeddings. Optionally, also the mask token. + """ + + def __init__(self, config: ViTMSNConfig, use_mask_token: bool = False) -> None: + super().__init__() + + self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) + self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None + self.patch_embeddings = ViTMSNPatchEmbeddings(config) + num_patches = self.patch_embeddings.num_patches + self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size)) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.config = config + + def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: + """ + This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher + resolution images. + + Source: + https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174 + """ + + num_patches = embeddings.shape[1] - 1 + num_positions = self.position_embeddings.shape[1] - 1 + if num_patches == num_positions and height == width: + return self.position_embeddings + class_pos_embed = self.position_embeddings[:, 0] + patch_pos_embed = self.position_embeddings[:, 1:] + dim = embeddings.shape[-1] + patch_window_height = height // self.config.patch_size + patch_window_width = width // self.config.patch_size + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + patch_window_height, patch_window_width = patch_window_height + 0.1, patch_window_width + 0.1 + patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim) + patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed, + scale_factor=( + patch_window_height / math.sqrt(num_positions), + patch_window_width / math.sqrt(num_positions), + ), + mode="bicubic", + align_corners=False, + ) + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) + + def forward( + self, + pixel_values: torch.Tensor, + bool_masked_pos: Optional[torch.BoolTensor] = None, + interpolate_pos_encoding: bool = False, + ) -> torch.Tensor: + batch_size, num_channels, height, width = pixel_values.shape + embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding) + + if bool_masked_pos is not None: + seq_length = embeddings.shape[1] + mask_tokens = self.mask_token.expand(batch_size, seq_length, -1) + # replace the masked visual tokens by mask_tokens + mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens) + embeddings = embeddings * (1.0 - mask) + mask_tokens * mask + + # add the [CLS] token to the embedded patch tokens + cls_tokens = self.cls_token.expand(batch_size, -1, -1) + embeddings = torch.cat((cls_tokens, embeddings), dim=1) + + # add positional encoding to each token + if interpolate_pos_encoding: + embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) + else: + embeddings = embeddings + self.position_embeddings + + embeddings = self.dropout(embeddings) + + return embeddings + + +# Copied from transformers.models.vit.modeling_vit.ViTPatchEmbeddings with ViT->ViTMSN +class ViTMSNPatchEmbeddings(nn.Module): + """ + This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial + `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a + Transformer. + """ + + def __init__(self, config): + super().__init__() + image_size, patch_size = config.image_size, config.patch_size + num_channels, hidden_size = config.num_channels, config.hidden_size + + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_patches = num_patches + + self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) + + def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor: + batch_size, num_channels, height, width = pixel_values.shape + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + f" Expected {self.num_channels} but got {num_channels}." + ) + if not interpolate_pos_encoding: + if height != self.image_size[0] or width != self.image_size[1]: + raise ValueError( + f"Input image size ({height}*{width}) doesn't match model" + f" ({self.image_size[0]}*{self.image_size[1]})." + ) + embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2) + return embeddings + + +# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->ViTMSN +class ViTMSNSelfAttention(nn.Module): + def __init__(self, config: ViTMSNConfig) -> None: + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " + f"heads {config.num_attention_heads}." + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + mixed_query_layer = self.query(hidden_states) + + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + query_layer = self.transpose_for_scores(mixed_query_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->ViTMSN +class ViTMSNSelfOutput(nn.Module): + """ + The residual connection is defined in ViTMSNLayer instead of here (as is the case with other models), due to the + layernorm applied before each block. + """ + + def __init__(self, config: ViTMSNConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + + return hidden_states + + +# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->ViTMSN +class ViTMSNAttention(nn.Module): + def __init__(self, config: ViTMSNConfig) -> None: + super().__init__() + self.attention = ViTMSNSelfAttention(config) + self.output = ViTMSNSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads: Set[int]) -> None: + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.attention.query = prune_linear_layer(self.attention.query, index) + self.attention.key = prune_linear_layer(self.attention.key, index) + self.attention.value = prune_linear_layer(self.attention.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) + self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + self_outputs = self.attention(hidden_states, head_mask, output_attentions) + + attention_output = self.output(self_outputs[0], hidden_states) + + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +# Copied from transformers.models.vit.modeling_vit.ViTIntermediate with ViT->ViTMSN +class ViTMSNIntermediate(nn.Module): + def __init__(self, config: ViTMSNConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + + return hidden_states + + +# Copied from transformers.models.vit.modeling_vit.ViTOutput with ViT->ViTMSN +class ViTMSNOutput(nn.Module): + def __init__(self, config: ViTMSNConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + + hidden_states = hidden_states + input_tensor + + return hidden_states + + +# Copied from transformers.models.vit.modeling_vit.ViTLayer with ViT->ViTMSN +class ViTMSNLayer(nn.Module): + """This corresponds to the Block class in the timm implementation.""" + + def __init__(self, config: ViTMSNConfig) -> None: + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = ViTMSNAttention(config) + self.intermediate = ViTMSNIntermediate(config) + self.output = ViTMSNOutput(config) + self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + self_attention_outputs = self.attention( + self.layernorm_before(hidden_states), # in ViTMSN, layernorm is applied before self-attention + head_mask, + output_attentions=output_attentions, + ) + attention_output = self_attention_outputs[0] + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + # first residual connection + hidden_states = attention_output + hidden_states + + # in ViTMSN, layernorm is also applied after self-attention + layer_output = self.layernorm_after(hidden_states) + layer_output = self.intermediate(layer_output) + + # second residual connection is done here + layer_output = self.output(layer_output, hidden_states) + + outputs = (layer_output,) + outputs + + return outputs + + +# Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->ViTMSN +class ViTMSNEncoder(nn.Module): + def __init__(self, config: ViTMSNConfig) -> None: + super().__init__() + self.config = config + self.layer = nn.ModuleList([ViTMSNLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ) -> Union[tuple, BaseModelOutput]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer_module.__call__, + hidden_states, + layer_head_mask, + output_attentions, + ) + else: + layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +class ViTMSNPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = ViTMSNConfig + base_model_prefix = "vit" + main_input_name = "pixel_values" + supports_gradient_checkpointing = True + + # todo: Resort to https://github.com/facebookresearch/msn/blob/main/src/deit.py#L200-#L211 + # when creating pre-training scripts. + def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None: + """Initialize the weights""" + if isinstance(module, (nn.Linear, nn.Conv2d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + +VIT_MSN_START_DOCSTRING = r""" + This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it + as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and + behavior. + + Parameters: + config ([`ViTMSNConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +VIT_MSN_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`] + for details. + + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + interpolate_pos_encoding (`bool`, *optional*): + Whether to interpolate the pre-trained position encodings. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare ViTMSN Model outputting raw hidden-states without any specific head on top.", + VIT_MSN_START_DOCSTRING, +) +class ViTMSNModel(ViTMSNPreTrainedModel): + def __init__(self, config: ViTMSNConfig, use_mask_token: bool = False): + super().__init__(config) + self.config = config + + self.embeddings = ViTMSNEmbeddings(config, use_mask_token=use_mask_token) + self.encoder = ViTMSNEncoder(config) + + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> ViTMSNPatchEmbeddings: + return self.embeddings.patch_embeddings + + def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + @add_start_docstrings_to_model_forward(VIT_MSN_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: Optional[torch.Tensor] = None, + bool_masked_pos: Optional[torch.BoolTensor] = None, + head_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + interpolate_pos_encoding: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[tuple, BaseModelOutput]: + r""" + bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*): + Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). + + Returns: + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, ViTMSNModel + >>> import torch + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("facebook/vit-msn-small") + >>> model = ViTMSNModel.from_pretrained("facebook/vit-msn-small") + >>> inputs = image_processor(images=image, return_tensors="pt") + >>> with torch.no_grad(): + ... outputs = model(**inputs) + >>> last_hidden_states = outputs.last_hidden_state + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding + ) + + encoder_outputs = self.encoder( + embedding_output, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + sequence_output = self.layernorm(sequence_output) + + if not return_dict: + head_outputs = (sequence_output,) + return head_outputs + encoder_outputs[1:] + + return BaseModelOutput( + last_hidden_state=sequence_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +# Caution: We don't have the weights for the classification head yet. This class +# is here for the users that are interested to fine-tune the base model (ViTMSNModel). +@add_start_docstrings( + """ + ViTMSN Model with an image classification head on top e.g. for ImageNet. + """, + VIT_MSN_START_DOCSTRING, +) +class ViTMSNForImageClassification(ViTMSNPreTrainedModel): + def __init__(self, config: ViTMSNConfig) -> None: + super().__init__(config) + + self.num_labels = config.num_labels + self.vit = ViTMSNModel(config) + + # Classifier head + self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity() + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(VIT_MSN_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=ImageClassifierOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + interpolate_pos_encoding: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[tuple, ImageClassifierOutput]: + r""" + Returns: + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, ViTMSNForImageClassification + >>> import torch + >>> from PIL import Image + >>> import requests + + >>> torch.manual_seed(2) # doctest: +IGNORE_RESULT + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("facebook/vit-msn-small") + >>> model = ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small") + + >>> inputs = image_processor(images=image, return_tensors="pt") + >>> with torch.no_grad(): + ... logits = model(**inputs).logits + >>> # model predicts one of the 1000 ImageNet classes + >>> predicted_label = logits.argmax(-1).item() + >>> print(model.config.id2label[predicted_label]) + tusker + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.vit( + pixel_values, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + interpolate_pos_encoding=interpolate_pos_encoding, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + logits = self.classifier(sequence_output[:, 0, :]) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return ImageClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitdet/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitdet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8ccc1365820d6923f17d3e72cc80868590801f5e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitdet/__init__.py @@ -0,0 +1,57 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import ( + OptionalDependencyNotAvailable, + _LazyModule, + is_torch_available, +) + + +_import_structure = {"configuration_vitdet": ["VITDET_PRETRAINED_CONFIG_ARCHIVE_MAP", "VitDetConfig"]} + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_vitdet"] = [ + "VITDET_PRETRAINED_MODEL_ARCHIVE_LIST", + "VitDetModel", + "VitDetPreTrainedModel", + "VitDetBackbone", + ] + +if TYPE_CHECKING: + from .configuration_vitdet import VITDET_PRETRAINED_CONFIG_ARCHIVE_MAP, VitDetConfig + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_vitdet import ( + VITDET_PRETRAINED_MODEL_ARCHIVE_LIST, + VitDetBackbone, + VitDetModel, + VitDetPreTrainedModel, + ) + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitdet/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitdet/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..adc85ca8ad1b0e421c5223ac094d7692297ff9cc Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitdet/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitdet/__pycache__/configuration_vitdet.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitdet/__pycache__/configuration_vitdet.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..99d9e339c58ce163afe48027b2905a1791dca49b Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitdet/__pycache__/configuration_vitdet.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitdet/modeling_vitdet.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitdet/modeling_vitdet.py new file mode 100644 index 0000000000000000000000000000000000000000..985f00b7e54f56e43615d0276387b78bb3385612 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitdet/modeling_vitdet.py @@ -0,0 +1,878 @@ +# coding=utf-8 +# Copyright 2023 Meta AI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch ViTDet backbone.""" + + +import collections.abc +import math +from typing import Dict, List, Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn + +from ...activations import ACT2FN +from ...modeling_outputs import BackboneOutput, BaseModelOutput +from ...modeling_utils import PreTrainedModel +from ...utils import ( + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) +from ...utils.backbone_utils import BackboneMixin +from .configuration_vitdet import VitDetConfig + + +logger = logging.get_logger(__name__) + +# General docstring +_CONFIG_FOR_DOC = "VitDetConfig" + + +from ..deprecated._archive_maps import VITDET_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +class VitDetEmbeddings(nn.Module): + """ + This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial + `hidden_states` (patch embeddings) to be consumed by a Transformer. + """ + + def __init__(self, config): + super().__init__() + image_size, patch_size = config.pretrain_image_size, config.patch_size + num_channels, hidden_size = config.num_channels, config.hidden_size + + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_patches = num_patches + + if config.use_absolute_position_embeddings: + # Initialize absolute positional embedding with pretrain image size. + num_positions = num_patches + 1 + self.position_embeddings = nn.Parameter(torch.zeros(1, num_positions, config.hidden_size)) + else: + self.position_embeddings = None + + self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) + + def get_absolute_positions(self, abs_pos_embeddings, has_cls_token, height, width): + """ + Calculate absolute positional embeddings. If needed, resize embeddings and remove cls_token dimension for the + original embeddings. + + Args: + abs_pos_embeddings (`torch.Tensor`): + Absolute positional embeddings with (1, num_position, num_channels). + has_cls_token (`bool`): + If true, has 1 embedding in abs_pos_embeddings for cls token. + height (`int`): + Height of input image tokens. + width (`int`): + Width of input image tokens. + + Returns: + Absolute positional embeddings after processing with shape (1, height, width, num_channels) + """ + if has_cls_token: + abs_pos_embeddings = abs_pos_embeddings[:, 1:] + num_position = abs_pos_embeddings.shape[1] + size = int(math.sqrt(num_position)) # This is a constant and can be recorded as such in the ONNX export. + if size * size != num_position: + raise ValueError("Absolute position embeddings must be a square number.") + + if torch.jit.is_tracing() or (size != height or size != width): + # nn.functional.interpolate is a noop in case size == height and size == width - we need to always capture this path with jit.trace. + new_abs_pos_embeddings = nn.functional.interpolate( + abs_pos_embeddings.reshape(1, size, size, -1).permute(0, 3, 1, 2), + size=(height, width), + mode="bicubic", + align_corners=False, + ) + + return new_abs_pos_embeddings.permute(0, 2, 3, 1) + else: + return abs_pos_embeddings.reshape(1, height, width, -1) + + def forward(self, pixel_values: torch.Tensor) -> torch.Tensor: + num_channels = pixel_values.shape[1] + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + f" Expected {self.num_channels} but got {num_channels}." + ) + embeddings = self.projection(pixel_values) + + if self.position_embeddings is not None: + # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels) + embeddings = embeddings.permute(0, 2, 3, 1) + # add position embeddings + embeddings = embeddings + self.get_absolute_positions( + self.position_embeddings, True, embeddings.shape[1], embeddings.shape[2] + ) + # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width) + embeddings = embeddings.permute(0, 3, 1, 2) + + return embeddings + + +@torch.jit.script_if_tracing # nn.functional.interpolate's `size` needs to be dynamic. +def get_rel_pos(q_size, k_size, rel_pos): + """ + Get relative positional embeddings according to the relative positions of query and key sizes. + + Args: + q_size (`int`): + Size of query q. + k_size (`int`): + Size of key k. + rel_pos (`torch.Tensor`): + Relative position embeddings (num_embeddings, num_channels). + + Returns: + Extracted positional embeddings according to relative positions. + """ + max_rel_dist = int(2 * max(q_size, k_size) - 1) + # Interpolate rel pos if needed. + if rel_pos.shape[0] != max_rel_dist: + # Interpolate rel position embeddings. + rel_pos_resized = nn.functional.interpolate( + rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), + size=max_rel_dist, + mode="linear", + ) + rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) + else: + rel_pos_resized = rel_pos + + # Scale the coords with short length if shapes for q and k are different. + q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0) + k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0) + relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) + + return rel_pos_resized[relative_coords.long()] + + +def add_decomposed_relative_positions(attn, queries, rel_pos_h, rel_pos_w, q_size, k_size): + """ + Calculate decomposed Relative Positional Embeddings as introduced in + [MViT2](https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py). + + Args: + attn (`torch.Tensor`): + Attention map. + queries (`torch.Tensor`): + Query q in the attention layer with shape (batch_size, queries_height * queries_width, num_channels). + rel_pos_h (`torch.Tensor`): + Relative position embeddings (Lh, num_channels) for height axis. + rel_pos_w (`torch.Tensor`): + Relative position embeddings (Lw, num_channels) for width axis. + q_size (`Tuple[int]`): + Spatial sequence size of query q with (queries_height, queries_width). + k_size (`Tuple[int]`]): + Spatial sequence size of key k with (keys_height, keys_width). + + Returns: + attn (Tensor): attention map with added relative positional embeddings. + """ + queries_height, queries_width = q_size + keys_height, keys_width = k_size + relative_height = get_rel_pos(queries_height, keys_height, rel_pos_h) + relative_width = get_rel_pos(queries_width, keys_width, rel_pos_w) + + batch_size, _, dim = queries.shape + r_q = queries.reshape(batch_size, queries_height, queries_width, dim) + relative_height = torch.einsum("bhwc,hkc->bhwk", r_q, relative_height) + relative_weight = torch.einsum("bhwc,wkc->bhwk", r_q, relative_width) + + attn = ( + attn.view(batch_size, queries_height, queries_width, keys_height, keys_width) + + relative_height[:, :, :, :, None] + + relative_weight[:, :, :, None, :] + ).view(batch_size, queries_height * queries_width, keys_height * keys_width) + + return attn + + +class VitDetAttention(nn.Module): + """Multi-head Attention block with relative position embeddings.""" + + def __init__(self, config, input_size=None): + """ + Args: + config (`VitDetConfig`): + Model configuration. + input_size (`Tuple[int]`, *optional*): + Input resolution, only required in case relative position embeddings are added. + """ + super().__init__() + + dim = config.hidden_size + num_heads = config.num_attention_heads + + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim**-0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=config.qkv_bias) + self.proj = nn.Linear(dim, dim) + + self.use_relative_position_embeddings = config.use_relative_position_embeddings + if self.use_relative_position_embeddings: + # initialize relative positional embeddings + self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim)) + self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim)) + + def forward(self, hidden_state, output_attentions=False): + batch_size, height, width, _ = hidden_state.shape + # qkv with shape (3, batch_size, num_heads, height * width, num_channels) + qkv = self.qkv(hidden_state).reshape(batch_size, height * width, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + # queries, keys and values have shape (batch_size * num_heads, height * width, num_channels) + queries, keys, values = qkv.reshape(3, batch_size * self.num_heads, height * width, -1).unbind(0) + + attention_scores = (queries * self.scale) @ keys.transpose(-2, -1) + + if self.use_relative_position_embeddings: + attention_scores = add_decomposed_relative_positions( + attention_scores, queries, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width) + ) + + attention_probs = attention_scores.softmax(dim=-1) + + hidden_state = attention_probs @ values + hidden_state = hidden_state.view(batch_size, self.num_heads, height, width, -1) + hidden_state = hidden_state.permute(0, 2, 3, 1, 4) + hidden_state = hidden_state.reshape(batch_size, height, width, -1) + hidden_state = self.proj(hidden_state) + + if output_attentions: + attention_probs = attention_probs.reshape( + batch_size, self.num_heads, attention_probs.shape[-2], attention_probs.shape[-1] + ) + outputs = (hidden_state, attention_probs) + else: + outputs = (hidden_state,) + + return outputs + + +# Copied from transformers.models.beit.modeling_beit.drop_path +def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor: + """ + Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks, + however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the + layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the + argument. + """ + if drop_prob == 0.0 or not training: + return input + keep_prob = 1 - drop_prob + shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device) + random_tensor.floor_() # binarize + output = input.div(keep_prob) * random_tensor + return output + + +# Copied from transformers.models.beit.modeling_beit.BeitDropPath +class VitDetDropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob: Optional[float] = None) -> None: + super().__init__() + self.drop_prob = drop_prob + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + return drop_path(hidden_states, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return "p={}".format(self.drop_prob) + + +class VitDetLayerNorm(nn.Module): + """ + A LayerNorm variant, popularized by Transformers, that performs point-wise mean and variance normalization over the + channel dimension for inputs that have shape (batch_size, channels, height, width). + https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 + """ + + def __init__(self, normalized_shape, eps=1e-6): + super().__init__() + self.weight = nn.Parameter(torch.ones(normalized_shape)) + self.bias = nn.Parameter(torch.zeros(normalized_shape)) + self.eps = eps + self.normalized_shape = (normalized_shape,) + + def forward(self, x): + u = x.mean(1, keepdim=True) + s = (x - u).pow(2).mean(1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.eps) + x = self.weight[:, None, None] * x + self.bias[:, None, None] + return x + + +class VitDetResBottleneckBlock(nn.Module): + """ + The standard bottleneck residual block without the last activation layer. It contains 3 conv layers with kernels + 1x1, 3x3, 1x1. + """ + + def __init__(self, config, in_channels, out_channels, bottleneck_channels): + """ + Args: + config (`VitDetConfig`): + Model configuration. + in_channels (`int`): + Number of input channels. + out_channels (`int`): + Number of output channels. + bottleneck_channels (`int`): + Number of output channels for the 3x3 "bottleneck" conv layers. + """ + super().__init__() + self.conv1 = nn.Conv2d(in_channels, bottleneck_channels, 1, bias=False) + self.norm1 = VitDetLayerNorm(bottleneck_channels) + self.act1 = ACT2FN[config.hidden_act] + + self.conv2 = nn.Conv2d(bottleneck_channels, bottleneck_channels, 3, padding=1, bias=False) + self.norm2 = VitDetLayerNorm(bottleneck_channels) + self.act2 = ACT2FN[config.hidden_act] + + self.conv3 = nn.Conv2d(bottleneck_channels, out_channels, 1, bias=False) + self.norm3 = VitDetLayerNorm(out_channels) + + def forward(self, x): + out = x + for layer in self.children(): + out = layer(out) + + out = x + out + return out + + +class VitDetMlp(nn.Module): + def __init__(self, config, in_features: int, hidden_features: int) -> None: + super().__init__() + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = ACT2FN[config.hidden_act] + self.fc2 = nn.Linear(hidden_features, in_features) + self.drop = nn.Dropout(config.dropout_prob) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + + return x + + +def window_partition(hidden_state, window_size): + """ + Partition into non-overlapping windows with padding if needed. + + Args: + hidden_state (`torch.Tensor`): + Input tokens with [batch_size, height, width, num_channels]. + window_size (`int`): + Window size. + + Returns: + `tuple(torch.FloatTensor)` comprising various elements: + - windows: windows after partition with [batch_size * num_windows, window_size, window_size, num_channels]. + - (padded_height, padded_width): padded height and width before partition + """ + batch_size, height, width, num_channels = hidden_state.shape + + pad_height = (window_size - height % window_size) % window_size + pad_width = (window_size - width % window_size) % window_size + + # Noop in case pad_width == 0 and pad_height == 0. + hidden_state = nn.functional.pad(hidden_state, (0, 0, 0, pad_width, 0, pad_height)) + + padded_height, padded_width = height + pad_height, width + pad_width + + hidden_state = hidden_state.view( + batch_size, padded_height // window_size, window_size, padded_width // window_size, window_size, num_channels + ) + windows = hidden_state.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, num_channels) + return windows, (padded_height, padded_width) + + +def window_unpartition(windows, window_size, pad_height_width, height_width): + """ + Window unpartition into original sequences and removing padding. + + Args: + windows (`torch.Tensor`): + Input tokens with [batch_size * num_windows, window_size, window_size, num_channels]. + window_size (`int`): + Window size. + pad_height_width (`Tuple[int]`): + Padded height and width (padded_height, padded_width). + height_width (`Tuple[int]`): + Original height and width before padding. + + Returns: + hidden_state: unpartitioned sequences with [batch_size, height, width, num_channels]. + """ + padded_height, padded_width = pad_height_width + height, width = height_width + batch_size = windows.shape[0] // (padded_height * padded_width // window_size // window_size) + hidden_state = windows.view( + batch_size, padded_height // window_size, padded_width // window_size, window_size, window_size, -1 + ) + hidden_state = hidden_state.permute(0, 1, 3, 2, 4, 5).contiguous() + hidden_state = hidden_state.view(batch_size, padded_height, padded_width, -1) + + # We always have height <= padded_height and width <= padded_width + hidden_state = hidden_state[:, :height, :width, :].contiguous() + return hidden_state + + +class VitDetLayer(nn.Module): + """This corresponds to the Block class in the original implementation.""" + + def __init__( + self, config: VitDetConfig, drop_path_rate: float = 0, window_size: int = 0, use_residual_block: bool = False + ) -> None: + super().__init__() + + dim = config.hidden_size + input_size = (config.image_size // config.patch_size, config.image_size // config.patch_size) + + self.norm1 = nn.LayerNorm(dim, eps=config.layer_norm_eps) + self.attention = VitDetAttention( + config, input_size=input_size if window_size == 0 else (window_size, window_size) + ) + + self.drop_path = VitDetDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity() + self.norm2 = nn.LayerNorm(dim, eps=config.layer_norm_eps) + self.mlp = VitDetMlp(config=config, in_features=dim, hidden_features=int(dim * config.mlp_ratio)) + + self.window_size = window_size + + self.use_residual_block = use_residual_block + if self.use_residual_block: + # Use a residual block with bottleneck channel as dim // 2 + self.residual = VitDetResBottleneckBlock( + config=config, + in_channels=dim, + out_channels=dim, + bottleneck_channels=dim // 2, + ) + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + hidden_states = hidden_states.permute(0, 2, 3, 1) + + shortcut = hidden_states + + hidden_states = self.norm1(hidden_states) + + # Window partition + if self.window_size > 0: + height, width = hidden_states.shape[1], hidden_states.shape[2] + hidden_states, pad_height_width = window_partition(hidden_states, self.window_size) + + self_attention_outputs = self.attention( + hidden_states, + output_attentions=output_attentions, + ) + hidden_states = self_attention_outputs[0] + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + # Reverse window partition + if self.window_size > 0: + hidden_states = window_unpartition(hidden_states, self.window_size, pad_height_width, (height, width)) + + # first residual connection + hidden_states = shortcut + self.drop_path(hidden_states) + + hidden_states = hidden_states + self.drop_path(self.mlp(self.norm2(hidden_states))) + + hidden_states = hidden_states.permute(0, 3, 1, 2) + + if self.use_residual_block: + hidden_states = self.residual(hidden_states) + + outputs = (hidden_states,) + outputs + + return outputs + + +class VitDetEncoder(nn.Module): + def __init__(self, config: VitDetConfig) -> None: + super().__init__() + self.config = config + depth = config.num_hidden_layers + + # stochastic depth decay rule + drop_path_rate = [x.item() for x in torch.linspace(0, config.drop_path_rate, depth)] + + layers = [] + for i in range(depth): + layers.append( + VitDetLayer( + config, + drop_path_rate=drop_path_rate[i], + window_size=config.window_size if i in config.window_block_indices else 0, + use_residual_block=i in config.residual_block_indices, + ) + ) + + self.layer = nn.ModuleList(layers) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ) -> Union[tuple, BaseModelOutput]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer_module.__call__, + hidden_states, + layer_head_mask, + output_attentions, + ) + else: + layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +def caffe2_msra_fill(module: nn.Module) -> None: + """ + Initialize `module.weight` using the "MSRAFill" implemented in Caffe2. Also initializes `module.bias` to 0. + + Source: https://detectron2.readthedocs.io/en/latest/_modules/fvcore/nn/weight_init.html. + + Args: + module (torch.nn.Module): module to initialize. + """ + nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu") + if module.bias is not None: + nn.init.constant_(module.bias, 0) + + +class VitDetPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = VitDetConfig + base_model_prefix = "vitdet" + main_input_name = "pixel_values" + supports_gradient_checkpointing = True + _no_split_modules = [] + + def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None: + """Initialize the weights""" + if isinstance(module, (nn.Linear, nn.Conv2d)): + # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid + # `trunc_normal_cpu` not implemented in `half` issues + module.weight.data = nn.init.trunc_normal_( + module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range + ).to(module.weight.dtype) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + elif isinstance(module, VitDetEmbeddings): + module.position_embeddings.data = nn.init.trunc_normal_( + module.position_embeddings.data.to(torch.float32), + mean=0.0, + std=self.config.initializer_range, + ).to(module.position_embeddings.dtype) + + elif isinstance(module, VitDetAttention) and self.config.use_relative_position_embeddings: + module.rel_pos_h.data = nn.init.trunc_normal_( + module.rel_pos_h.data.to(torch.float32), + mean=0.0, + std=self.config.initializer_range, + ) + module.rel_pos_w.data = nn.init.trunc_normal_( + module.rel_pos_w.data.to(torch.float32), + mean=0.0, + std=self.config.initializer_range, + ) + + elif isinstance(module, VitDetResBottleneckBlock): + for layer in [module.conv1, module.conv2, module.conv3]: + caffe2_msra_fill(layer) + for layer in [module.norm1, module.norm2]: + layer.weight.data.fill_(1.0) + layer.bias.data.zero_() + # zero init last norm layer. + module.norm3.weight.data.zero_() + module.norm3.bias.data.zero_() + + +VITDET_START_DOCSTRING = r""" + This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it + as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and + behavior. + + Parameters: + config ([`VitDetConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +VITDET_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`] + for details. + + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare VitDet Transformer model outputting raw hidden-states without any specific head on top.", + VITDET_START_DOCSTRING, +) +class VitDetModel(VitDetPreTrainedModel): + def __init__(self, config: VitDetConfig): + super().__init__(config) + self.config = config + + self.embeddings = VitDetEmbeddings(config) + self.encoder = VitDetEncoder(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> VitDetEmbeddings: + return self.embeddings.projection + + def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + @add_start_docstrings_to_model_forward(VITDET_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutput]: + """ + Returns: + + Examples: + + ```python + >>> from transformers import VitDetConfig, VitDetModel + >>> import torch + + >>> config = VitDetConfig() + >>> model = VitDetModel(config) + + >>> pixel_values = torch.randn(1, 3, 224, 224) + + >>> with torch.no_grad(): + ... outputs = model(pixel_values) + + >>> last_hidden_states = outputs.last_hidden_state + >>> list(last_hidden_states.shape) + [1, 768, 14, 14] + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings(pixel_values) + + encoder_outputs = self.encoder( + embedding_output, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + + if not return_dict: + return (sequence_output,) + encoder_outputs[1:] + + return BaseModelOutput( + last_hidden_state=sequence_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +@add_start_docstrings( + """ + ViTDet backbone, to be used with frameworks like Mask R-CNN. + """, + VITDET_START_DOCSTRING, +) +class VitDetBackbone(VitDetPreTrainedModel, BackboneMixin): + def __init__(self, config): + super().__init__(config) + super()._init_backbone(config) + + self.embeddings = VitDetEmbeddings(config) + self.encoder = VitDetEncoder(config) + self.num_features = [config.hidden_size for _ in range(config.num_hidden_layers + 1)] + + # initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> VitDetEmbeddings: + return self.embeddings.projection + + @add_start_docstrings_to_model_forward(VITDET_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: torch.Tensor, + output_hidden_states: Optional[bool] = None, + output_attentions: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> BackboneOutput: + """ + Returns: + + Examples: + + ```python + >>> from transformers import VitDetConfig, VitDetBackbone + >>> import torch + + >>> config = VitDetConfig() + >>> model = VitDetBackbone(config) + + >>> pixel_values = torch.randn(1, 3, 224, 224) + + >>> with torch.no_grad(): + ... outputs = model(pixel_values) + + >>> feature_maps = outputs.feature_maps + >>> list(feature_maps[-1].shape) + [1, 768, 14, 14] + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + + embedding_output = self.embeddings(pixel_values) + + outputs = self.encoder( + embedding_output, + output_hidden_states=True, + output_attentions=output_attentions, + return_dict=return_dict, + ) + + hidden_states = outputs.hidden_states if return_dict else outputs[1] + + feature_maps = () + for stage, hidden_state in zip(self.stage_names, hidden_states): + if stage in self.out_features: + feature_maps += (hidden_state,) + + if not return_dict: + if output_hidden_states: + output = (feature_maps,) + outputs[1:] + else: + output = (feature_maps,) + outputs[2:] + return output + + return BackboneOutput( + feature_maps=feature_maps, + hidden_states=outputs.hidden_states if output_hidden_states else None, + attentions=outputs.attentions, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitmatte/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitmatte/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..abbfae97c220302447fecb3ae71c36e09a704b6d --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitmatte/__init__.py @@ -0,0 +1,72 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import ( + OptionalDependencyNotAvailable, + _LazyModule, + is_torch_available, + is_vision_available, +) + + +_import_structure = {"configuration_vitmatte": ["VITMATTE_PRETRAINED_CONFIG_ARCHIVE_MAP", "VitMatteConfig"]} + +try: + if not is_vision_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["image_processing_vitmatte"] = ["VitMatteImageProcessor"] + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_vitmatte"] = [ + "VITMATTE_PRETRAINED_MODEL_ARCHIVE_LIST", + "VitMattePreTrainedModel", + "VitMatteForImageMatting", + ] + +if TYPE_CHECKING: + from .configuration_vitmatte import VITMATTE_PRETRAINED_CONFIG_ARCHIVE_MAP, VitMatteConfig + + try: + if not is_vision_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .image_processing_vitmatte import VitMatteImageProcessor + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_vitmatte import ( + VITMATTE_PRETRAINED_MODEL_ARCHIVE_LIST, + VitMatteForImageMatting, + VitMattePreTrainedModel, + ) + +else: + import sys + + sys.modules[__name__] = 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b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitmatte/configuration_vitmatte.py new file mode 100644 index 0000000000000000000000000000000000000000..275640d1d079a174b8a50c1b1bcab7af9b3b5485 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitmatte/configuration_vitmatte.py @@ -0,0 +1,136 @@ +# coding=utf-8 +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" VitMatte model configuration""" + +import copy +from typing import List + +from ...configuration_utils import PretrainedConfig +from ...utils import logging +from ..auto.configuration_auto import CONFIG_MAPPING + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import VITMATTE_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class VitMatteConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of [`VitMatteForImageMatting`]. It is used to + instantiate a ViTMatte model according to the specified arguments, defining the model architecture. Instantiating a + configuration with the defaults will yield a similar configuration to that of the ViTMatte + [hustvl/vitmatte-small-composition-1k](https://huggingface.co/hustvl/vitmatte-small-composition-1k) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + backbone_config (`PretrainedConfig` or `dict`, *optional*, defaults to `VitDetConfig()`): + The configuration of the backbone model. + backbone (`str`, *optional*): + Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this + will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone` + is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights. + use_pretrained_backbone (`bool`, *optional*, defaults to `False`): + Whether to use pretrained weights for the backbone. + use_timm_backbone (`bool`, *optional*, defaults to `False`): + Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers + library. + backbone_kwargs (`dict`, *optional*): + Keyword arguments to be passed to AutoBackbone when loading from a checkpoint + e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set. + hidden_size (`int`, *optional*, defaults to 384): + The number of input channels of the decoder. + batch_norm_eps (`float`, *optional*, defaults to 1e-05): + The epsilon used by the batch norm layers. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + convstream_hidden_sizes (`List[int]`, *optional*, defaults to `[48, 96, 192]`): + The output channels of the ConvStream module. + fusion_hidden_sizes (`List[int]`, *optional*, defaults to `[256, 128, 64, 32]`): + The output channels of the Fusion blocks. + + Example: + + ```python + >>> from transformers import VitMatteConfig, VitMatteForImageMatting + + >>> # Initializing a ViTMatte hustvl/vitmatte-small-composition-1k style configuration + >>> configuration = VitMatteConfig() + + >>> # Initializing a model (with random weights) from the hustvl/vitmatte-small-composition-1k style configuration + >>> model = VitMatteForImageMatting(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "vitmatte" + + def __init__( + self, + backbone_config: PretrainedConfig = None, + backbone=None, + use_pretrained_backbone=False, + use_timm_backbone=False, + backbone_kwargs=None, + hidden_size: int = 384, + batch_norm_eps: float = 1e-5, + initializer_range: float = 0.02, + convstream_hidden_sizes: List[int] = [48, 96, 192], + fusion_hidden_sizes: List[int] = [256, 128, 64, 32], + **kwargs, + ): + super().__init__(**kwargs) + + if use_pretrained_backbone: + raise ValueError("Pretrained backbones are not supported yet.") + + if backbone_config is not None and backbone is not None: + raise ValueError("You can't specify both `backbone` and `backbone_config`.") + + if backbone_config is None and backbone is None: + logger.info("`backbone_config` is `None`. Initializing the config with the default `VitDet` backbone.") + backbone_config = CONFIG_MAPPING["vitdet"](out_features=["stage4"]) + elif isinstance(backbone_config, dict): + backbone_model_type = backbone_config.get("model_type") + config_class = CONFIG_MAPPING[backbone_model_type] + backbone_config = config_class.from_dict(backbone_config) + + if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None: + raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.") + + self.backbone_config = backbone_config + self.backbone = backbone + self.use_pretrained_backbone = use_pretrained_backbone + self.use_timm_backbone = use_timm_backbone + self.backbone_kwargs = backbone_kwargs + self.batch_norm_eps = batch_norm_eps + self.hidden_size = hidden_size + self.initializer_range = initializer_range + self.convstream_hidden_sizes = convstream_hidden_sizes + self.fusion_hidden_sizes = fusion_hidden_sizes + + def to_dict(self): + """ + Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns: + `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, + """ + output = copy.deepcopy(self.__dict__) + output["backbone_config"] = self.backbone_config.to_dict() + output["model_type"] = self.__class__.model_type + return output diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitmatte/convert_vitmatte_to_hf.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitmatte/convert_vitmatte_to_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..bcc05563337198021c91f56356533bf87c1e6e9f --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitmatte/convert_vitmatte_to_hf.py @@ -0,0 +1,170 @@ +# coding=utf-8 +# Copyright 2023 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert VitMatte checkpoints from the original repository. + +URL: https://github.com/hustvl/ViTMatte +""" + +import argparse + +import requests +import torch +from huggingface_hub import hf_hub_download +from PIL import Image + +from transformers import VitDetConfig, VitMatteConfig, VitMatteForImageMatting, VitMatteImageProcessor + + +def get_config(model_name): + hidden_size = 384 if "small" in model_name else 768 + num_attention_heads = 6 if "small" in model_name else 12 + + backbone_config = VitDetConfig( + num_channels=4, + image_size=512, + pretrain_image_size=224, + patch_size=16, + hidden_size=hidden_size, + num_attention_heads=num_attention_heads, + use_absolute_position_embeddings=True, + use_relative_position_embeddings=True, + window_size=14, + # 2, 5, 8, 11 for global attention + window_block_indices=[0, 1, 3, 4, 6, 7, 9, 10], + residual_block_indices=[2, 5, 8, 11], + out_features=["stage12"], + ) + + return VitMatteConfig(backbone_config=backbone_config, hidden_size=hidden_size) + + +# here we list all keys to be renamed (original name on the left, our name on the right) +def create_rename_keys(config): + rename_keys = [] + + # fmt: off + # stem + rename_keys.append(("backbone.pos_embed", "backbone.embeddings.position_embeddings")) + rename_keys.append(("backbone.patch_embed.proj.weight", "backbone.embeddings.projection.weight")) + rename_keys.append(("backbone.patch_embed.proj.bias", "backbone.embeddings.projection.bias")) + # fmt: on + + return rename_keys + + +def rename_key(dct, old, new): + val = dct.pop(old) + dct[new] = val + + +def convert_vitmatte_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub): + config = get_config(model_name) + + # load original state dict + model_name_to_filename = { + "vitmatte-small-composition-1k": "ViTMatte_S_Com.pth", + "vitmatte-base-composition-1k": "ViTMatte_B_Com.pth", + "vitmatte-small-distinctions-646": "ViTMatte_S_DIS.pth", + "vitmatte-base-distinctions-646": "ViTMatte_B_DIS.pth", + } + + filename = model_name_to_filename[model_name] + filepath = hf_hub_download(repo_id="nielsr/vitmatte-checkpoints", filename=filename, repo_type="model") + state_dict = torch.load(filepath, map_location="cpu") + + # rename keys + for key in state_dict.copy().keys(): + val = state_dict.pop(key) + if "backbone.blocks" in key: + key = key.replace("backbone.blocks", "backbone.encoder.layer") + if "attn" in key: + key = key.replace("attn", "attention") + if "fusion_blks" in key: + key = key.replace("fusion_blks", "fusion_blocks") + if "bn" in key: + key = key.replace("bn", "batch_norm") + state_dict[key] = val + + # rename keys + rename_keys = create_rename_keys(config) + for src, dest in rename_keys: + rename_key(state_dict, src, dest) + + # create model + processor = VitMatteImageProcessor() + model = VitMatteForImageMatting(config) + model.eval() + + # load state dict + model.load_state_dict(state_dict) + + # verify on dummy image + trimap + url = "https://github.com/hustvl/ViTMatte/blob/main/demo/bulb_rgb.png?raw=true" + image = Image.open(requests.get(url, stream=True).raw).convert("RGB") + url = "https://github.com/hustvl/ViTMatte/blob/main/demo/bulb_trimap.png?raw=true" + trimap = Image.open(requests.get(url, stream=True).raw) + + pixel_values = processor(images=image, trimaps=trimap.convert("L"), return_tensors="pt").pixel_values + + with torch.no_grad(): + alphas = model(pixel_values).alphas + + if model_name == "vitmatte-small-composition-1k": + expected_slice = torch.tensor([[0.9977, 0.9987, 0.9990], [0.9980, 0.9998, 0.9998], [0.9983, 0.9998, 0.9998]]) + elif model_name == "vitmatte-base-composition-1k": + expected_slice = torch.tensor([[0.9972, 0.9971, 0.9981], [0.9948, 0.9987, 0.9994], [0.9963, 0.9992, 0.9995]]) + elif model_name == "vitmatte-small-distinctions-646": + expected_slice = torch.tensor([[0.9880, 0.9970, 0.9972], [0.9960, 0.9996, 0.9997], [0.9963, 0.9996, 0.9997]]) + elif model_name == "vitmatte-base-distinctions-646": + expected_slice = torch.tensor([[0.9963, 0.9998, 0.9999], [0.9995, 1.0000, 1.0000], [0.9992, 0.9999, 1.0000]]) + + assert torch.allclose(alphas[0, 0, :3, :3], expected_slice, atol=1e-4) + print("Looks ok!") + + if pytorch_dump_folder_path is not None: + print(f"Saving model and processor of {model_name} to {pytorch_dump_folder_path}") + model.save_pretrained(pytorch_dump_folder_path) + processor.save_pretrained(pytorch_dump_folder_path) + + if push_to_hub: + print(f"Pushing model and processor for {model_name} to hub") + model.push_to_hub(f"hustvl/{model_name}") + processor.push_to_hub(f"hustvl/{model_name}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + # Required parameters + parser.add_argument( + "--model_name", + default="vitmatte-small-composition-1k", + type=str, + choices=[ + "vitmatte-small-composition-1k", + "vitmatte-base-composition-1k", + "vitmatte-small-distinctions-646", + "vitmatte-base-distinctions-646", + ], + help="Name of the VitMatte model you'd like to convert.", + ) + parser.add_argument( + "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." + ) + parser.add_argument( + "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." + ) + + args = parser.parse_args() + convert_vitmatte_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitmatte/image_processing_vitmatte.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitmatte/image_processing_vitmatte.py new file mode 100644 index 0000000000000000000000000000000000000000..d7310bc0dd26ba5c26fbc44588d7349605350a77 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitmatte/image_processing_vitmatte.py @@ -0,0 +1,286 @@ +# coding=utf-8 +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for ViTMatte.""" + +from typing import List, Optional, Union + +import numpy as np + +from ...image_processing_utils import BaseImageProcessor, BatchFeature +from ...image_transforms import pad, to_channel_dimension_format +from ...image_utils import ( + IMAGENET_STANDARD_MEAN, + IMAGENET_STANDARD_STD, + ChannelDimension, + ImageInput, + get_image_size, + infer_channel_dimension_format, + is_scaled_image, + make_list_of_images, + to_numpy_array, + valid_images, + validate_kwargs, + validate_preprocess_arguments, +) +from ...utils import TensorType, logging + + +logger = logging.get_logger(__name__) + + +class VitMatteImageProcessor(BaseImageProcessor): + r""" + Constructs a ViTMatte image processor. + + Args: + do_rescale (`bool`, *optional*, defaults to `True`): + Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` + parameter in the `preprocess` method. + rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): + Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the + `preprocess` method. + do_normalize (`bool`, *optional*, defaults to `True`): + Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` + method. + image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): + Mean to use if normalizing the image. This is a float or list of floats the length of the number of + channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. + image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): + Standard deviation to use if normalizing the image. This is a float or list of floats the length of the + number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. + do_pad (`bool`, *optional*, defaults to `True`): + Whether to pad the image to make the width and height divisible by `size_divisibility`. Can be overridden + by the `do_pad` parameter in the `preprocess` method. + size_divisibility (`int`, *optional*, defaults to 32): + The width and height of the image will be padded to be divisible by this number. + """ + + model_input_names = ["pixel_values"] + + def __init__( + self, + do_rescale: bool = True, + rescale_factor: Union[int, float] = 1 / 255, + do_normalize: bool = True, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + do_pad: bool = True, + size_divisibility: int = 32, + **kwargs, + ) -> None: + super().__init__(**kwargs) + self.do_rescale = do_rescale + self.do_normalize = do_normalize + self.do_pad = do_pad + self.rescale_factor = rescale_factor + self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN + self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD + self.size_divisibility = size_divisibility + self._valid_processor_keys = [ + "images", + "trimaps", + "do_rescale", + "rescale_factor", + "do_normalize", + "image_mean", + "image_std", + "do_pad", + "size_divisibility", + "return_tensors", + "data_format", + "input_data_format", + ] + + def pad_image( + self, + image: np.ndarray, + size_divisibility: int = 32, + data_format: Optional[Union[str, ChannelDimension]] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + ) -> np.ndarray: + """ + Args: + image (`np.ndarray`): + Image to pad. + size_divisibility (`int`, *optional*, defaults to 32): + The width and height of the image will be padded to be divisible by this number. + data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): + The channel dimension format for the output image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - Unset: Use the channel dimension format of the input image. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format for the input image. If unset, the channel dimension format is inferred + from the input image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. + """ + if input_data_format is None: + input_data_format = infer_channel_dimension_format(image) + + height, width = get_image_size(image, input_data_format) + + if height % size_divisibility != 0 or width % size_divisibility != 0: + pad_height = size_divisibility - height % size_divisibility + pad_width = size_divisibility - width % size_divisibility + padding = ((0, pad_height), (0, pad_width)) + image = pad(image, padding=padding, data_format=data_format, input_data_format=input_data_format) + + if data_format is not None: + image = to_channel_dimension_format(image, data_format, input_data_format) + + return image + + def preprocess( + self, + images: ImageInput, + trimaps: ImageInput, + do_rescale: Optional[bool] = None, + rescale_factor: Optional[float] = None, + do_normalize: Optional[bool] = None, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + do_pad: Optional[bool] = None, + size_divisibility: Optional[int] = None, + return_tensors: Optional[Union[str, TensorType]] = None, + data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ): + """ + Preprocess an image or batch of images. + + Args: + images (`ImageInput`): + Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If + passing in images with pixel values between 0 and 1, set `do_rescale=False`. + trimaps (`ImageInput`): + Trimap to preprocess. + do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): + Whether to rescale the image values between [0 - 1]. + rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): + Rescale factor to rescale the image by if `do_rescale` is set to `True`. + do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): + Whether to normalize the image. + image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): + Image mean to use if `do_normalize` is set to `True`. + image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): + Image standard deviation to use if `do_normalize` is set to `True`. + do_pad (`bool`, *optional*, defaults to `self.do_pad`): + Whether to pad the image. + size_divisibility (`int`, *optional*, defaults to `self.size_divisibility`): + The size divisibility to pad the image to if `do_pad` is set to `True`. + return_tensors (`str` or `TensorType`, *optional*): + The type of tensors to return. Can be one of: + - Unset: Return a list of `np.ndarray`. + - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. + - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. + - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. + - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. + data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): + The channel dimension format for the output image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - Unset: Use the channel dimension format of the input image. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format for the input image. If unset, the channel dimension format is inferred + from the input image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. + """ + do_rescale = do_rescale if do_rescale is not None else self.do_rescale + do_normalize = do_normalize if do_normalize is not None else self.do_normalize + do_pad = do_pad if do_pad is not None else self.do_pad + rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor + image_mean = image_mean if image_mean is not None else self.image_mean + image_std = image_std if image_std is not None else self.image_std + size_divisibility = size_divisibility if size_divisibility is not None else self.size_divisibility + + images = make_list_of_images(images) + trimaps = make_list_of_images(trimaps, expected_ndims=2) + + validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys) + + if not valid_images(trimaps): + raise ValueError( + "Invalid trimap type. Must be of type PIL.Image.Image, numpy.ndarray, " + "torch.Tensor, tf.Tensor or jax.ndarray." + ) + + if not valid_images(images): + raise ValueError( + "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " + "torch.Tensor, tf.Tensor or jax.ndarray." + ) + validate_preprocess_arguments( + do_rescale=do_rescale, + rescale_factor=rescale_factor, + do_normalize=do_normalize, + image_mean=image_mean, + image_std=image_std, + do_pad=do_pad, + size_divisibility=size_divisibility, + ) + + # All transformations expect numpy arrays. + images = [to_numpy_array(image) for image in images] + trimaps = [to_numpy_array(trimap) for trimap in trimaps] + + if is_scaled_image(images[0]) and do_rescale: + logger.warning_once( + "It looks like you are trying to rescale already rescaled images. If the input" + " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." + ) + + if input_data_format is None: + # We assume that all images have the same channel dimension format. + input_data_format = infer_channel_dimension_format(images[0]) + + if do_rescale: + images = [ + self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) + for image in images + ] + trimaps = [ + self.rescale(image=trimap, scale=rescale_factor, input_data_format=input_data_format) + for trimap in trimaps + ] + + if do_normalize: + images = [ + self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format) + for image in images + ] + + # concatenate images and trimaps + images = [ + np.concatenate([image, np.expand_dims(trimap, axis=-1)], axis=-1) for image, trimap in zip(images, trimaps) + ] + + if do_pad: + images = [ + self.pad_image(image, size_divisibility=size_divisibility, input_data_format=input_data_format) + for image in images + ] + + images = [ + to_channel_dimension_format(image=image, channel_dim=data_format, input_channel_dim=input_data_format) + for image in images + ] + + data = {"pixel_values": images} + return BatchFeature(data=data, tensor_type=return_tensors) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitmatte/modeling_vitmatte.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitmatte/modeling_vitmatte.py new file mode 100644 index 0000000000000000000000000000000000000000..f371c608607a5f30858b1def5c0dea9658e5ec6a --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vitmatte/modeling_vitmatte.py @@ -0,0 +1,333 @@ +# coding=utf-8 +# Copyright 2023 HUST-VL and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch ViTMatte model.""" + +from dataclasses import dataclass +from typing import Optional, Tuple + +import torch +from torch import nn + +from ...modeling_utils import PreTrainedModel +from ...utils import ( + ModelOutput, + add_start_docstrings, + add_start_docstrings_to_model_forward, + replace_return_docstrings, +) +from ...utils.backbone_utils import load_backbone +from ..deprecated._archive_maps import VITMATTE_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 +from .configuration_vitmatte import VitMatteConfig + + +# General docstring +_CONFIG_FOR_DOC = "VitMatteConfig" + + +@dataclass +class ImageMattingOutput(ModelOutput): + """ + Class for outputs of image matting models. + + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Loss. + alphas (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Estimated alpha values. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states + (also called feature maps) of the model at the output of each stage. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + """ + + loss: Optional[torch.FloatTensor] = None + alphas: torch.FloatTensor = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + + +class VitMattePreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = VitMatteConfig + main_input_name = "pixel_values" + supports_gradient_checkpointing = True + + def _init_weights(self, module): + if isinstance(module, nn.Conv2d): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + + +class VitMatteBasicConv3x3(nn.Module): + """ + Basic convolution layers including: Conv3x3, BatchNorm2d, ReLU layers. + """ + + def __init__(self, config, in_channels, out_channels, stride=2, padding=1): + super().__init__() + self.conv = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=3, + stride=stride, + padding=padding, + bias=False, + ) + self.batch_norm = nn.BatchNorm2d(out_channels, eps=config.batch_norm_eps) + self.relu = nn.ReLU() + + def forward(self, hidden_state): + hidden_state = self.conv(hidden_state) + hidden_state = self.batch_norm(hidden_state) + hidden_state = self.relu(hidden_state) + + return hidden_state + + +class VitMatteConvStream(nn.Module): + """ + Simple ConvStream containing a series of basic conv3x3 layers to extract detail features. + """ + + def __init__(self, config): + super().__init__() + + in_channels = config.backbone_config.num_channels + out_channels = config.convstream_hidden_sizes + + self.convs = nn.ModuleList() + self.conv_chans = [in_channels] + out_channels + + for i in range(len(self.conv_chans) - 1): + in_chan_ = self.conv_chans[i] + out_chan_ = self.conv_chans[i + 1] + self.convs.append(VitMatteBasicConv3x3(config, in_chan_, out_chan_)) + + def forward(self, pixel_values): + out_dict = {"detailed_feature_map_0": pixel_values} + embeddings = pixel_values + for i in range(len(self.convs)): + embeddings = self.convs[i](embeddings) + name_ = "detailed_feature_map_" + str(i + 1) + out_dict[name_] = embeddings + + return out_dict + + +class VitMatteFusionBlock(nn.Module): + """ + Simple fusion block to fuse features from ConvStream and Plain Vision Transformer. + """ + + def __init__(self, config, in_channels, out_channels): + super().__init__() + self.conv = VitMatteBasicConv3x3(config, in_channels, out_channels, stride=1, padding=1) + + def forward(self, features, detailed_feature_map): + upscaled_features = nn.functional.interpolate(features, scale_factor=2, mode="bilinear", align_corners=False) + out = torch.cat([detailed_feature_map, upscaled_features], dim=1) + out = self.conv(out) + + return out + + +class VitMatteHead(nn.Module): + """ + Simple Matting Head, containing only conv3x3 and conv1x1 layers. + """ + + def __init__(self, config): + super().__init__() + + in_channels = config.fusion_hidden_sizes[-1] + mid_channels = 16 + + self.matting_convs = nn.Sequential( + nn.Conv2d(in_channels, mid_channels, kernel_size=3, stride=1, padding=1), + nn.BatchNorm2d(mid_channels), + nn.ReLU(True), + nn.Conv2d(mid_channels, 1, kernel_size=1, stride=1, padding=0), + ) + + def forward(self, hidden_state): + hidden_state = self.matting_convs(hidden_state) + + return hidden_state + + +class VitMatteDetailCaptureModule(nn.Module): + """ + Simple and lightweight Detail Capture Module for ViT Matting. + """ + + def __init__(self, config): + super().__init__() + if len(config.fusion_hidden_sizes) != len(config.convstream_hidden_sizes) + 1: + raise ValueError( + "The length of fusion_hidden_sizes should be equal to the length of convstream_hidden_sizes + 1." + ) + + self.config = config + self.convstream = VitMatteConvStream(config) + self.conv_chans = self.convstream.conv_chans + + self.fusion_blocks = nn.ModuleList() + self.fusion_channels = [config.hidden_size] + config.fusion_hidden_sizes + + for i in range(len(self.fusion_channels) - 1): + self.fusion_blocks.append( + VitMatteFusionBlock( + config=config, + in_channels=self.fusion_channels[i] + self.conv_chans[-(i + 1)], + out_channels=self.fusion_channels[i + 1], + ) + ) + + self.matting_head = VitMatteHead(config) + + def forward(self, features, pixel_values): + detail_features = self.convstream(pixel_values) + for i in range(len(self.fusion_blocks)): + detailed_feature_map_name = "detailed_feature_map_" + str(len(self.fusion_blocks) - i - 1) + features = self.fusion_blocks[i](features, detail_features[detailed_feature_map_name]) + + alphas = torch.sigmoid(self.matting_head(features)) + + return alphas + + +VITMATTE_START_DOCSTRING = r""" + Parameters: + This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use + it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and + behavior. + config ([`UperNetConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +VITMATTE_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using + [`AutoImageProcessor`]. See [`VitMatteImageProcessor.__call__`] for details. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See + `attentions` under returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under + returned tensors for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + """ViTMatte framework leveraging any vision backbone e.g. for ADE20k, CityScapes.""", + VITMATTE_START_DOCSTRING, +) +class VitMatteForImageMatting(VitMattePreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.config = config + + self.backbone = load_backbone(config) + self.decoder = VitMatteDetailCaptureModule(config) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(VITMATTE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @replace_return_docstrings(output_type=ImageMattingOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + labels: Optional[torch.Tensor] = None, + return_dict: Optional[bool] = None, + ): + """ + labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): + Ground truth image matting for computing the loss. + + Returns: + + Examples: + + ```python + >>> from transformers import VitMatteImageProcessor, VitMatteForImageMatting + >>> import torch + >>> from PIL import Image + >>> from huggingface_hub import hf_hub_download + + >>> processor = VitMatteImageProcessor.from_pretrained("hustvl/vitmatte-small-composition-1k") + >>> model = VitMatteForImageMatting.from_pretrained("hustvl/vitmatte-small-composition-1k") + + >>> filepath = hf_hub_download( + ... repo_id="hf-internal-testing/image-matting-fixtures", filename="image.png", repo_type="dataset" + ... ) + >>> image = Image.open(filepath).convert("RGB") + >>> filepath = hf_hub_download( + ... repo_id="hf-internal-testing/image-matting-fixtures", filename="trimap.png", repo_type="dataset" + ... ) + >>> trimap = Image.open(filepath).convert("L") + + >>> # prepare image + trimap for the model + >>> inputs = processor(images=image, trimaps=trimap, return_tensors="pt") + + >>> with torch.no_grad(): + ... alphas = model(**inputs).alphas + >>> print(alphas.shape) + torch.Size([1, 1, 640, 960]) + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + + outputs = self.backbone.forward_with_filtered_kwargs( + pixel_values, output_hidden_states=output_hidden_states, output_attentions=output_attentions + ) + + features = outputs.feature_maps[-1] + alphas = self.decoder(features, pixel_values) + + loss = None + if labels is not None: + raise NotImplementedError("Training is not yet supported") + + if not return_dict: + output = (alphas,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return ImageMattingOutput( + loss=loss, + alphas=alphas, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..79c18048e7c776dbee992b249cd53098f544daaa --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/__init__.py @@ -0,0 +1,67 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import ( + OptionalDependencyNotAvailable, + _LazyModule, + is_sentencepiece_available, + is_speech_available, + is_torch_available, +) + + +_import_structure = { + "configuration_vits": [ + "VITS_PRETRAINED_CONFIG_ARCHIVE_MAP", + "VitsConfig", + ], + "tokenization_vits": ["VitsTokenizer"], +} + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_vits"] = [ + "VITS_PRETRAINED_MODEL_ARCHIVE_LIST", + "VitsModel", + "VitsPreTrainedModel", + ] + +if TYPE_CHECKING: + from .configuration_vits import ( + VITS_PRETRAINED_CONFIG_ARCHIVE_MAP, + VitsConfig, + ) + from .tokenization_vits import VitsTokenizer + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_vits import ( + VITS_PRETRAINED_MODEL_ARCHIVE_LIST, + VitsModel, + VitsPreTrainedModel, + ) + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b4678dcf9da812dfaa2ac1e7ea8172221f83ee81 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/__pycache__/convert_original_checkpoint.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/__pycache__/convert_original_checkpoint.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7917db5ae5db09a6527594449e44d30a646ea5ee Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/__pycache__/convert_original_checkpoint.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/__pycache__/modeling_vits.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/__pycache__/modeling_vits.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b76014f591a581375b748a1665df26892288d654 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/__pycache__/modeling_vits.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/configuration_vits.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/configuration_vits.py new file mode 100644 index 0000000000000000000000000000000000000000..5538e53d4be1b84b70ff18e75df851b17c9edcb7 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/configuration_vits.py @@ -0,0 +1,254 @@ +# coding=utf-8 +# Copyright 2023 The Kakao Enterprise Authors and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" VITS model configuration""" + + +from ...configuration_utils import PretrainedConfig +from ...utils import logging + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import VITS_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class VitsConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`VitsModel`]. It is used to instantiate a VITS + model according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the VITS + [facebook/mms-tts-eng](https://huggingface.co/facebook/mms-tts-eng) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + vocab_size (`int`, *optional*, defaults to 38): + Vocabulary size of the VITS model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed to the forward method of [`VitsModel`]. + hidden_size (`int`, *optional*, defaults to 192): + Dimensionality of the text encoder layers. + num_hidden_layers (`int`, *optional*, defaults to 6): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 2): + Number of attention heads for each attention layer in the Transformer encoder. + window_size (`int`, *optional*, defaults to 4): + Window size for the relative positional embeddings in the attention layers of the Transformer encoder. + use_bias (`bool`, *optional*, defaults to `True`): + Whether to use bias in the key, query, value projection layers in the Transformer encoder. + ffn_dim (`int`, *optional*, defaults to 768): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + layerdrop (`float`, *optional*, defaults to 0.1): + The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) + for more details. + ffn_kernel_size (`int`, *optional*, defaults to 3): + Kernel size of the 1D convolution layers used by the feed-forward network in the Transformer encoder. + flow_size (`int`, *optional*, defaults to 192): + Dimensionality of the flow layers. + spectrogram_bins (`int`, *optional*, defaults to 513): + Number of frequency bins in the target spectrogram. + hidden_act (`str` or `function`, *optional*, defaults to `"relu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"` and `"gelu_new"` are supported. + hidden_dropout (`float`, *optional*, defaults to 0.1): + The dropout probability for all fully connected layers in the embeddings and encoder. + attention_dropout (`float`, *optional*, defaults to 0.1): + The dropout ratio for the attention probabilities. + activation_dropout (`float`, *optional*, defaults to 0.1): + The dropout ratio for activations inside the fully connected layer. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + layer_norm_eps (`float`, *optional*, defaults to 1e-05): + The epsilon used by the layer normalization layers. + use_stochastic_duration_prediction (`bool`, *optional*, defaults to `True`): + Whether to use the stochastic duration prediction module or the regular duration predictor. + num_speakers (`int`, *optional*, defaults to 1): + Number of speakers if this is a multi-speaker model. + speaker_embedding_size (`int`, *optional*, defaults to 0): + Number of channels used by the speaker embeddings. Is zero for single-speaker models. + upsample_initial_channel (`int`, *optional*, defaults to 512): + The number of input channels into the HiFi-GAN upsampling network. + upsample_rates (`Tuple[int]` or `List[int]`, *optional*, defaults to `[8, 8, 2, 2]`): + A tuple of integers defining the stride of each 1D convolutional layer in the HiFi-GAN upsampling network. + The length of `upsample_rates` defines the number of convolutional layers and has to match the length of + `upsample_kernel_sizes`. + upsample_kernel_sizes (`Tuple[int]` or `List[int]`, *optional*, defaults to `[16, 16, 4, 4]`): + A tuple of integers defining the kernel size of each 1D convolutional layer in the HiFi-GAN upsampling + network. The length of `upsample_kernel_sizes` defines the number of convolutional layers and has to match + the length of `upsample_rates`. + resblock_kernel_sizes (`Tuple[int]` or `List[int]`, *optional*, defaults to `[3, 7, 11]`): + A tuple of integers defining the kernel sizes of the 1D convolutional layers in the HiFi-GAN + multi-receptive field fusion (MRF) module. + resblock_dilation_sizes (`Tuple[Tuple[int]]` or `List[List[int]]`, *optional*, defaults to `[[1, 3, 5], [1, 3, 5], [1, 3, 5]]`): + A nested tuple of integers defining the dilation rates of the dilated 1D convolutional layers in the + HiFi-GAN multi-receptive field fusion (MRF) module. + leaky_relu_slope (`float`, *optional*, defaults to 0.1): + The angle of the negative slope used by the leaky ReLU activation. + depth_separable_channels (`int`, *optional*, defaults to 2): + Number of channels to use in each depth-separable block. + depth_separable_num_layers (`int`, *optional*, defaults to 3): + Number of convolutional layers to use in each depth-separable block. + duration_predictor_flow_bins (`int`, *optional*, defaults to 10): + Number of channels to map using the unonstrained rational spline in the duration predictor model. + duration_predictor_tail_bound (`float`, *optional*, defaults to 5.0): + Value of the tail bin boundary when computing the unconstrained rational spline in the duration predictor + model. + duration_predictor_kernel_size (`int`, *optional*, defaults to 3): + Kernel size of the 1D convolution layers used in the duration predictor model. + duration_predictor_dropout (`float`, *optional*, defaults to 0.5): + The dropout ratio for the duration predictor model. + duration_predictor_num_flows (`int`, *optional*, defaults to 4): + Number of flow stages used by the duration predictor model. + duration_predictor_filter_channels (`int`, *optional*, defaults to 256): + Number of channels for the convolution layers used in the duration predictor model. + prior_encoder_num_flows (`int`, *optional*, defaults to 4): + Number of flow stages used by the prior encoder flow model. + prior_encoder_num_wavenet_layers (`int`, *optional*, defaults to 4): + Number of WaveNet layers used by the prior encoder flow model. + posterior_encoder_num_wavenet_layers (`int`, *optional*, defaults to 16): + Number of WaveNet layers used by the posterior encoder model. + wavenet_kernel_size (`int`, *optional*, defaults to 5): + Kernel size of the 1D convolution layers used in the WaveNet model. + wavenet_dilation_rate (`int`, *optional*, defaults to 1): + Dilation rates of the dilated 1D convolutional layers used in the WaveNet model. + wavenet_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the WaveNet layers. + speaking_rate (`float`, *optional*, defaults to 1.0): + Speaking rate. Larger values give faster synthesised speech. + noise_scale (`float`, *optional*, defaults to 0.667): + How random the speech prediction is. Larger values create more variation in the predicted speech. + noise_scale_duration (`float`, *optional*, defaults to 0.8): + How random the duration prediction is. Larger values create more variation in the predicted durations. + sampling_rate (`int`, *optional*, defaults to 16000): + The sampling rate at which the output audio waveform is digitalized expressed in hertz (Hz). + + Example: + + ```python + >>> from transformers import VitsModel, VitsConfig + + >>> # Initializing a "facebook/mms-tts-eng" style configuration + >>> configuration = VitsConfig() + + >>> # Initializing a model (with random weights) from the "facebook/mms-tts-eng" style configuration + >>> model = VitsModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "vits" + + def __init__( + self, + vocab_size=38, + hidden_size=192, + num_hidden_layers=6, + num_attention_heads=2, + window_size=4, + use_bias=True, + ffn_dim=768, + layerdrop=0.1, + ffn_kernel_size=3, + flow_size=192, + spectrogram_bins=513, + hidden_act="relu", + hidden_dropout=0.1, + attention_dropout=0.1, + activation_dropout=0.1, + initializer_range=0.02, + layer_norm_eps=1e-5, + use_stochastic_duration_prediction=True, + num_speakers=1, + speaker_embedding_size=0, + upsample_initial_channel=512, + upsample_rates=[8, 8, 2, 2], + upsample_kernel_sizes=[16, 16, 4, 4], + resblock_kernel_sizes=[3, 7, 11], + resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]], + leaky_relu_slope=0.1, + depth_separable_channels=2, + depth_separable_num_layers=3, + duration_predictor_flow_bins=10, + duration_predictor_tail_bound=5.0, + duration_predictor_kernel_size=3, + duration_predictor_dropout=0.5, + duration_predictor_num_flows=4, + duration_predictor_filter_channels=256, + prior_encoder_num_flows=4, + prior_encoder_num_wavenet_layers=4, + posterior_encoder_num_wavenet_layers=16, + wavenet_kernel_size=5, + wavenet_dilation_rate=1, + wavenet_dropout=0.0, + speaking_rate=1.0, + noise_scale=0.667, + noise_scale_duration=0.8, + sampling_rate=16_000, + **kwargs, + ): + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.window_size = window_size + self.use_bias = use_bias + self.ffn_dim = ffn_dim + self.layerdrop = layerdrop + self.ffn_kernel_size = ffn_kernel_size + self.flow_size = flow_size + self.spectrogram_bins = spectrogram_bins + self.hidden_act = hidden_act + self.hidden_dropout = hidden_dropout + self.attention_dropout = attention_dropout + self.activation_dropout = activation_dropout + self.initializer_range = initializer_range + self.layer_norm_eps = layer_norm_eps + self.use_stochastic_duration_prediction = use_stochastic_duration_prediction + self.num_speakers = num_speakers + self.speaker_embedding_size = speaker_embedding_size + self.upsample_initial_channel = upsample_initial_channel + self.upsample_rates = upsample_rates + self.upsample_kernel_sizes = upsample_kernel_sizes + self.resblock_kernel_sizes = resblock_kernel_sizes + self.resblock_dilation_sizes = resblock_dilation_sizes + self.leaky_relu_slope = leaky_relu_slope + self.depth_separable_channels = depth_separable_channels + self.depth_separable_num_layers = depth_separable_num_layers + self.duration_predictor_flow_bins = duration_predictor_flow_bins + self.duration_predictor_tail_bound = duration_predictor_tail_bound + self.duration_predictor_kernel_size = duration_predictor_kernel_size + self.duration_predictor_dropout = duration_predictor_dropout + self.duration_predictor_num_flows = duration_predictor_num_flows + self.duration_predictor_filter_channels = duration_predictor_filter_channels + self.prior_encoder_num_flows = prior_encoder_num_flows + self.prior_encoder_num_wavenet_layers = prior_encoder_num_wavenet_layers + self.posterior_encoder_num_wavenet_layers = posterior_encoder_num_wavenet_layers + self.wavenet_kernel_size = wavenet_kernel_size + self.wavenet_dilation_rate = wavenet_dilation_rate + self.wavenet_dropout = wavenet_dropout + self.speaking_rate = speaking_rate + self.noise_scale = noise_scale + self.noise_scale_duration = noise_scale_duration + self.sampling_rate = sampling_rate + + if len(upsample_kernel_sizes) != len(upsample_rates): + raise ValueError( + f"The length of `upsample_kernel_sizes` ({len(upsample_kernel_sizes)}) must match the length of " + f"`upsample_rates` ({len(upsample_rates)})" + ) + + super().__init__(**kwargs) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/convert_original_checkpoint.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/convert_original_checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..267f72ccd08fc26f7bdd1a56747a1dbc8d697cb0 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/convert_original_checkpoint.py @@ -0,0 +1,390 @@ +# coding=utf-8 +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert VITS checkpoint.""" + +import argparse +import json +import tempfile + +import torch +from huggingface_hub import hf_hub_download + +from transformers import VitsConfig, VitsModel, VitsTokenizer, logging + + +logging.set_verbosity_info() +logger = logging.get_logger("transformers.models.vits") + +MAPPING_TEXT_ENCODER = { + "enc_p.emb": "text_encoder.embed_tokens", + "enc_p.encoder.attn_layers.*.conv_k": "text_encoder.encoder.layers.*.attention.k_proj", + "enc_p.encoder.attn_layers.*.conv_v": "text_encoder.encoder.layers.*.attention.v_proj", + "enc_p.encoder.attn_layers.*.conv_q": "text_encoder.encoder.layers.*.attention.q_proj", + "enc_p.encoder.attn_layers.*.conv_o": "text_encoder.encoder.layers.*.attention.out_proj", + "enc_p.encoder.attn_layers.*.emb_rel_k": "text_encoder.encoder.layers.*.attention.emb_rel_k", + "enc_p.encoder.attn_layers.*.emb_rel_v": "text_encoder.encoder.layers.*.attention.emb_rel_v", + "enc_p.encoder.norm_layers_1.*.gamma": "text_encoder.encoder.layers.*.layer_norm.weight", + "enc_p.encoder.norm_layers_1.*.beta": "text_encoder.encoder.layers.*.layer_norm.bias", + "enc_p.encoder.ffn_layers.*.conv_1": "text_encoder.encoder.layers.*.feed_forward.conv_1", + "enc_p.encoder.ffn_layers.*.conv_2": "text_encoder.encoder.layers.*.feed_forward.conv_2", + "enc_p.encoder.norm_layers_2.*.gamma": "text_encoder.encoder.layers.*.final_layer_norm.weight", + "enc_p.encoder.norm_layers_2.*.beta": "text_encoder.encoder.layers.*.final_layer_norm.bias", + "enc_p.proj": "text_encoder.project", +} +MAPPING_STOCHASTIC_DURATION_PREDICTOR = { + "dp.pre": "duration_predictor.conv_pre", + "dp.proj": "duration_predictor.conv_proj", + "dp.convs.convs_sep.*": "duration_predictor.conv_dds.convs_dilated.*", + "dp.convs.convs_1x1.*": "duration_predictor.conv_dds.convs_pointwise.*", + "dp.convs.norms_1.*.gamma": "duration_predictor.conv_dds.norms_1.*.weight", + "dp.convs.norms_1.*.beta": "duration_predictor.conv_dds.norms_1.*.bias", + "dp.convs.norms_2.*.gamma": "duration_predictor.conv_dds.norms_2.*.weight", + "dp.convs.norms_2.*.beta": "duration_predictor.conv_dds.norms_2.*.bias", + "dp.flows.0.logs": "duration_predictor.flows.0.log_scale", + "dp.flows.0.m": "duration_predictor.flows.0.translate", + "dp.flows.*.pre": "duration_predictor.flows.*.conv_pre", + "dp.flows.*.proj": "duration_predictor.flows.*.conv_proj", + "dp.flows.*.convs.convs_1x1.0": "duration_predictor.flows.*.conv_dds.convs_pointwise.0", + "dp.flows.*.convs.convs_1x1.1": "duration_predictor.flows.*.conv_dds.convs_pointwise.1", + "dp.flows.*.convs.convs_1x1.2": "duration_predictor.flows.*.conv_dds.convs_pointwise.2", + "dp.flows.*.convs.convs_sep.0": "duration_predictor.flows.*.conv_dds.convs_dilated.0", + "dp.flows.*.convs.convs_sep.1": "duration_predictor.flows.*.conv_dds.convs_dilated.1", + "dp.flows.*.convs.convs_sep.2": "duration_predictor.flows.*.conv_dds.convs_dilated.2", + "dp.flows.*.convs.norms_1.0.gamma": "duration_predictor.flows.*.conv_dds.norms_1.0.weight", + "dp.flows.*.convs.norms_1.0.beta": "duration_predictor.flows.*.conv_dds.norms_1.0.bias", + "dp.flows.*.convs.norms_1.1.gamma": "duration_predictor.flows.*.conv_dds.norms_1.1.weight", + "dp.flows.*.convs.norms_1.1.beta": "duration_predictor.flows.*.conv_dds.norms_1.1.bias", + "dp.flows.*.convs.norms_1.2.gamma": "duration_predictor.flows.*.conv_dds.norms_1.2.weight", + "dp.flows.*.convs.norms_1.2.beta": "duration_predictor.flows.*.conv_dds.norms_1.2.bias", + "dp.flows.*.convs.norms_2.0.gamma": "duration_predictor.flows.*.conv_dds.norms_2.0.weight", + "dp.flows.*.convs.norms_2.0.beta": "duration_predictor.flows.*.conv_dds.norms_2.0.bias", + "dp.flows.*.convs.norms_2.1.gamma": "duration_predictor.flows.*.conv_dds.norms_2.1.weight", + "dp.flows.*.convs.norms_2.1.beta": "duration_predictor.flows.*.conv_dds.norms_2.1.bias", + "dp.flows.*.convs.norms_2.2.gamma": "duration_predictor.flows.*.conv_dds.norms_2.2.weight", + "dp.flows.*.convs.norms_2.2.beta": "duration_predictor.flows.*.conv_dds.norms_2.2.bias", + "dp.post_pre": "duration_predictor.post_conv_pre", + "dp.post_proj": "duration_predictor.post_conv_proj", + "dp.post_convs.convs_sep.*": "duration_predictor.post_conv_dds.convs_dilated.*", + "dp.post_convs.convs_1x1.*": "duration_predictor.post_conv_dds.convs_pointwise.*", + "dp.post_convs.norms_1.*.gamma": "duration_predictor.post_conv_dds.norms_1.*.weight", + "dp.post_convs.norms_1.*.beta": "duration_predictor.post_conv_dds.norms_1.*.bias", + "dp.post_convs.norms_2.*.gamma": "duration_predictor.post_conv_dds.norms_2.*.weight", + "dp.post_convs.norms_2.*.beta": "duration_predictor.post_conv_dds.norms_2.*.bias", + "dp.post_flows.0.logs": "duration_predictor.post_flows.0.log_scale", + "dp.post_flows.0.m": "duration_predictor.post_flows.0.translate", + "dp.post_flows.*.pre": "duration_predictor.post_flows.*.conv_pre", + "dp.post_flows.*.proj": "duration_predictor.post_flows.*.conv_proj", + "dp.post_flows.*.convs.convs_1x1.0": "duration_predictor.post_flows.*.conv_dds.convs_pointwise.0", + "dp.post_flows.*.convs.convs_1x1.1": "duration_predictor.post_flows.*.conv_dds.convs_pointwise.1", + "dp.post_flows.*.convs.convs_1x1.2": "duration_predictor.post_flows.*.conv_dds.convs_pointwise.2", + "dp.post_flows.*.convs.convs_sep.0": "duration_predictor.post_flows.*.conv_dds.convs_dilated.0", + "dp.post_flows.*.convs.convs_sep.1": "duration_predictor.post_flows.*.conv_dds.convs_dilated.1", + "dp.post_flows.*.convs.convs_sep.2": "duration_predictor.post_flows.*.conv_dds.convs_dilated.2", + "dp.post_flows.*.convs.norms_1.0.gamma": "duration_predictor.post_flows.*.conv_dds.norms_1.0.weight", + "dp.post_flows.*.convs.norms_1.0.beta": "duration_predictor.post_flows.*.conv_dds.norms_1.0.bias", + "dp.post_flows.*.convs.norms_1.1.gamma": "duration_predictor.post_flows.*.conv_dds.norms_1.1.weight", + "dp.post_flows.*.convs.norms_1.1.beta": "duration_predictor.post_flows.*.conv_dds.norms_1.1.bias", + "dp.post_flows.*.convs.norms_1.2.gamma": "duration_predictor.post_flows.*.conv_dds.norms_1.2.weight", + "dp.post_flows.*.convs.norms_1.2.beta": "duration_predictor.post_flows.*.conv_dds.norms_1.2.bias", + "dp.post_flows.*.convs.norms_2.0.gamma": "duration_predictor.post_flows.*.conv_dds.norms_2.0.weight", + "dp.post_flows.*.convs.norms_2.0.beta": "duration_predictor.post_flows.*.conv_dds.norms_2.0.bias", + "dp.post_flows.*.convs.norms_2.1.gamma": "duration_predictor.post_flows.*.conv_dds.norms_2.1.weight", + "dp.post_flows.*.convs.norms_2.1.beta": "duration_predictor.post_flows.*.conv_dds.norms_2.1.bias", + "dp.post_flows.*.convs.norms_2.2.gamma": "duration_predictor.post_flows.*.conv_dds.norms_2.2.weight", + "dp.post_flows.*.convs.norms_2.2.beta": "duration_predictor.post_flows.*.conv_dds.norms_2.2.bias", + "dp.cond": "duration_predictor.cond", # num_speakers > 1 +} +MAPPING_FLOW = { + "flow.flows.*.pre": "flow.flows.*.conv_pre", + "flow.flows.*.enc.in_layers.0": "flow.flows.*.wavenet.in_layers.0", + "flow.flows.*.enc.in_layers.1": "flow.flows.*.wavenet.in_layers.1", + "flow.flows.*.enc.in_layers.2": "flow.flows.*.wavenet.in_layers.2", + "flow.flows.*.enc.in_layers.3": "flow.flows.*.wavenet.in_layers.3", + "flow.flows.*.enc.res_skip_layers.0": "flow.flows.*.wavenet.res_skip_layers.0", + "flow.flows.*.enc.res_skip_layers.1": "flow.flows.*.wavenet.res_skip_layers.1", + "flow.flows.*.enc.res_skip_layers.2": "flow.flows.*.wavenet.res_skip_layers.2", + "flow.flows.*.enc.res_skip_layers.3": "flow.flows.*.wavenet.res_skip_layers.3", + "flow.flows.*.enc.cond_layer": "flow.flows.*.wavenet.cond_layer", # num_speakers > 1 + "flow.flows.*.post": "flow.flows.*.conv_post", +} +MAPPING_GENERATOR = { + "dec.conv_pre": "decoder.conv_pre", + "dec.ups.0": "decoder.upsampler.0", + "dec.ups.1": "decoder.upsampler.1", + "dec.ups.2": "decoder.upsampler.2", + "dec.ups.3": "decoder.upsampler.3", + "dec.resblocks.*.convs1.0": "decoder.resblocks.*.convs1.0", + "dec.resblocks.*.convs1.1": "decoder.resblocks.*.convs1.1", + "dec.resblocks.*.convs1.2": "decoder.resblocks.*.convs1.2", + "dec.resblocks.*.convs2.0": "decoder.resblocks.*.convs2.0", + "dec.resblocks.*.convs2.1": "decoder.resblocks.*.convs2.1", + "dec.resblocks.*.convs2.2": "decoder.resblocks.*.convs2.2", + "dec.conv_post": "decoder.conv_post", + "dec.cond": "decoder.cond", # num_speakers > 1 +} +MAPPING_POSTERIOR_ENCODER = { + "enc_q.pre": "posterior_encoder.conv_pre", + "enc_q.enc.in_layers.*": "posterior_encoder.wavenet.in_layers.*", + "enc_q.enc.res_skip_layers.*": "posterior_encoder.wavenet.res_skip_layers.*", + "enc_q.enc.cond_layer": "posterior_encoder.wavenet.cond_layer", # num_speakers > 1 + "enc_q.proj": "posterior_encoder.conv_proj", +} +MAPPING = { + **MAPPING_TEXT_ENCODER, + **MAPPING_STOCHASTIC_DURATION_PREDICTOR, + **MAPPING_FLOW, + **MAPPING_GENERATOR, + **MAPPING_POSTERIOR_ENCODER, + "emb_g": "embed_speaker", # num_speakers > 1 +} +TOP_LEVEL_KEYS = [] +IGNORE_KEYS = [] + + +def set_recursively(hf_pointer, key, value, full_name, weight_type): + for attribute in key.split("."): + hf_pointer = getattr(hf_pointer, attribute) + + if weight_type is not None: + hf_shape = getattr(hf_pointer, weight_type).shape + else: + hf_shape = hf_pointer.shape + + # strip off the kernel dimension at the end (original weights are Conv1d) + if key.endswith(".k_proj") or key.endswith(".v_proj") or key.endswith(".q_proj") or key.endswith(".out_proj"): + value = value.squeeze(-1) + + if hf_shape != value.shape: + raise ValueError( + f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be" + f" {value.shape} for {full_name}" + ) + + if weight_type == "weight": + hf_pointer.weight.data = value + elif weight_type == "weight_g": + hf_pointer.weight_g.data = value + elif weight_type == "weight_v": + hf_pointer.weight_v.data = value + elif weight_type == "bias": + hf_pointer.bias.data = value + elif weight_type == "running_mean": + hf_pointer.running_mean.data = value + elif weight_type == "running_var": + hf_pointer.running_var.data = value + elif weight_type == "num_batches_tracked": + hf_pointer.num_batches_tracked.data = value + else: + hf_pointer.data = value + + logger.info(f"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.") + + +def should_ignore(name, ignore_keys): + for key in ignore_keys: + if key.endswith(".*"): + if name.startswith(key[:-1]): + return True + elif ".*." in key: + prefix, suffix = key.split(".*.") + if prefix in name and suffix in name: + return True + elif key in name: + return True + return False + + +def recursively_load_weights(fairseq_dict, hf_model): + unused_weights = [] + + for name, value in fairseq_dict.items(): + if should_ignore(name, IGNORE_KEYS): + logger.info(f"{name} was ignored") + continue + + is_used = False + for key, mapped_key in MAPPING.items(): + if key.endswith(".*"): + key = key[:-1] + elif "*" in key: + prefix, suffix = key.split(".*.") + if prefix in name and suffix in name: + key = suffix + + if key in name: + is_used = True + if mapped_key.endswith(".*"): + layer_index = name.split(key)[-1].split(".")[0] + mapped_key = mapped_key.replace("*", layer_index) + elif "*" in mapped_key: + layer_index = name.split(key)[0].split(".")[-2] + + # remap the layer index since we removed the Flip layers + if "flow.flows" in mapped_key: + layer_index = str(int(layer_index) // 2) + if "duration_predictor.flows" in mapped_key or "duration_predictor.post_flows" in mapped_key: + layer_index = str(int(layer_index) // 2 + 1) + + mapped_key = mapped_key.replace("*", layer_index) + if "weight_g" in name: + weight_type = "weight_g" + elif "weight_v" in name: + weight_type = "weight_v" + elif "bias" in name: + weight_type = "bias" + elif "weight" in name: + weight_type = "weight" + elif "running_mean" in name: + weight_type = "running_mean" + elif "running_var" in name: + weight_type = "running_var" + elif "num_batches_tracked" in name: + weight_type = "num_batches_tracked" + else: + weight_type = None + set_recursively(hf_model, mapped_key, value, name, weight_type) + continue + if not is_used: + unused_weights.append(name) + + logger.warning(f"Unused weights: {unused_weights}") + + +@torch.no_grad() +def convert_checkpoint( + pytorch_dump_folder_path, + checkpoint_path=None, + config_path=None, + vocab_path=None, + language=None, + num_speakers=None, + sampling_rate=None, + repo_id=None, +): + """ + Copy/paste/tweak model's weights to transformers design. + """ + if config_path is not None: + config = VitsConfig.from_pretrained(config_path) + else: + config = VitsConfig() + + if num_speakers: + config.num_speakers = num_speakers + config.speaker_embedding_size = 256 + + if sampling_rate: + config.sampling_rate = sampling_rate + + if checkpoint_path is None: + logger.info(f"***Converting model: facebook/mms-tts {language}***") + + vocab_path = hf_hub_download( + repo_id="facebook/mms-tts", + filename="vocab.txt", + subfolder=f"models/{language}", + ) + config_file = hf_hub_download( + repo_id="facebook/mms-tts", + filename="config.json", + subfolder=f"models/{language}", + ) + checkpoint_path = hf_hub_download( + repo_id="facebook/mms-tts", + filename="G_100000.pth", + subfolder=f"models/{language}", + ) + + with open(config_file, "r") as f: + data = f.read() + hps = json.loads(data) + + is_uroman = hps["data"]["training_files"].split(".")[-1] == "uroman" + if is_uroman: + logger.warning("For this checkpoint, you should use `uroman` to convert input text before tokenizing it!") + else: + logger.info(f"***Converting model: {checkpoint_path}***") + is_uroman = False + + # original VITS checkpoint + if vocab_path is None: + _pad = "_" + _punctuation = ';:,.!?¡¿—…"«»“” ' + _letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" + _letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ" + symbols = _pad + _punctuation + _letters + _letters_ipa + symbol_to_id = {s: i for i, s in enumerate(symbols)} + phonemize = True + else: + # Save vocab as temporary json file + symbols = [line.replace("\n", "") for line in open(vocab_path, encoding="utf-8").readlines()] + symbol_to_id = {s: i for i, s in enumerate(symbols)} + # MMS-TTS does not use a token, so we set to the token used to space characters + _pad = symbols[0] + phonemize = False + + with tempfile.NamedTemporaryFile() as tf: + with open(tf.name, "w", encoding="utf-8") as f: + f.write(json.dumps(symbol_to_id, indent=2, sort_keys=True, ensure_ascii=False) + "\n") + + tokenizer = VitsTokenizer(tf.name, language=language, phonemize=phonemize, is_uroman=is_uroman, pad_token=_pad) + + config.vocab_size = len(symbols) + model = VitsModel(config) + + model.decoder.apply_weight_norm() + + orig_checkpoint = torch.load(checkpoint_path, map_location=torch.device("cpu")) + recursively_load_weights(orig_checkpoint["model"], model) + + model.decoder.remove_weight_norm() + + model.save_pretrained(pytorch_dump_folder_path) + tokenizer.save_pretrained(pytorch_dump_folder_path) + + if repo_id: + print("Pushing to the hub...") + tokenizer.push_to_hub(repo_id) + model.push_to_hub(repo_id) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--checkpoint_path", default=None, type=str, help="Local path to original checkpoint") + parser.add_argument("--vocab_path", default=None, type=str, help="Path to vocab.txt") + parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert") + parser.add_argument("--language", default=None, type=str, help="Tokenizer language (three-letter code)") + parser.add_argument("--num_speakers", default=None, type=int, help="Number of speakers") + parser.add_argument( + "--sampling_rate", default=None, type=int, help="Sampling rate on which the model was trained." + ) + parser.add_argument( + "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model." + ) + parser.add_argument( + "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub." + ) + + args = parser.parse_args() + convert_checkpoint( + args.pytorch_dump_folder_path, + args.checkpoint_path, + args.config_path, + args.vocab_path, + args.language, + args.num_speakers, + args.sampling_rate, + args.push_to_hub, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/tokenization_vits.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/tokenization_vits.py new file mode 100644 index 0000000000000000000000000000000000000000..c8b115c176bcefa5c297049e23be899553bf4066 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vits/tokenization_vits.py @@ -0,0 +1,237 @@ +# coding=utf-8 +# Copyright 2023 The Kakao Enterprise Authors, the MMS-TTS Authors and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization class for VITS.""" + + +import json +import os +import re +from typing import Any, Dict, List, Optional, Tuple, Union + +from ...tokenization_utils import PreTrainedTokenizer +from ...utils import is_phonemizer_available, logging + + +if is_phonemizer_available(): + import phonemizer + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "vocab.json"} + + +def has_non_roman_characters(input_string): + # Find any character outside the ASCII range + non_roman_pattern = re.compile(r"[^\x00-\x7F]") + + # Search the input string for non-Roman characters + match = non_roman_pattern.search(input_string) + has_non_roman = match is not None + return has_non_roman + + +class VitsTokenizer(PreTrainedTokenizer): + """ + Construct a VITS tokenizer. Also supports MMS-TTS. + + This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to + this superclass for more information regarding those methods. + + Args: + vocab_file (`str`): + Path to the vocabulary file. + language (`str`, *optional*): + Language identifier. + add_blank (`bool`, *optional*, defaults to `True`): + Whether to insert token id 0 in between the other tokens. + normalize (`bool`, *optional*, defaults to `True`): + Whether to normalize the input text by removing all casing and punctuation. + phonemize (`bool`, *optional*, defaults to `True`): + Whether to convert the input text into phonemes. + is_uroman (`bool`, *optional*, defaults to `False`): + Whether the `uroman` Romanizer needs to be applied to the input text prior to tokenizing. + """ + + vocab_files_names = VOCAB_FILES_NAMES + model_input_names = ["input_ids", "attention_mask"] + + def __init__( + self, + vocab_file, + pad_token="", + unk_token="", + language=None, + add_blank=True, + normalize=True, + phonemize=True, + is_uroman=False, + **kwargs, + ) -> None: + with open(vocab_file, encoding="utf-8") as vocab_handle: + self.encoder = json.load(vocab_handle) + + self.decoder = {v: k for k, v in self.encoder.items()} + self.language = language + self.add_blank = add_blank + self.normalize = normalize + self.phonemize = phonemize + + self.is_uroman = is_uroman + + super().__init__( + pad_token=pad_token, + unk_token=unk_token, + language=language, + add_blank=add_blank, + normalize=normalize, + phonemize=phonemize, + is_uroman=is_uroman, + **kwargs, + ) + + @property + def vocab_size(self): + return len(self.encoder) + + def get_vocab(self): + vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} + vocab.update(self.added_tokens_encoder) + return vocab + + def normalize_text(self, input_string): + """Lowercase the input string, respecting any special token ids that may be part or entirely upper-cased.""" + all_vocabulary = list(self.encoder.keys()) + list(self.added_tokens_encoder.keys()) + filtered_text = "" + + i = 0 + while i < len(input_string): + found_match = False + for word in all_vocabulary: + if input_string[i : i + len(word)] == word: + filtered_text += word + i += len(word) + found_match = True + break + + if not found_match: + filtered_text += input_string[i].lower() + i += 1 + + return filtered_text + + def _preprocess_char(self, text): + """Special treatment of characters in certain languages""" + if self.language == "ron": + text = text.replace("ț", "ţ") + return text + + def prepare_for_tokenization( + self, text: str, is_split_into_words: bool = False, normalize: Optional[bool] = None, **kwargs + ) -> Tuple[str, Dict[str, Any]]: + """ + Performs any necessary transformations before tokenization. + + This method should pop the arguments from kwargs and return the remaining `kwargs` as well. We test the + `kwargs` at the end of the encoding process to be sure all the arguments have been used. + + Args: + text (`str`): + The text to prepare. + is_split_into_words (`bool`, *optional*, defaults to `False`): + Whether or not the input is already pre-tokenized (e.g., split into words). If set to `True`, the + tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace) + which it will tokenize. + normalize (`bool`, *optional*, defaults to `None`): + Whether or not to apply punctuation and casing normalization to the text inputs. Typically, VITS is + trained on lower-cased and un-punctuated text. Hence, normalization is used to ensure that the input + text consists only of lower-case characters. + kwargs (`Dict[str, Any]`, *optional*): + Keyword arguments to use for the tokenization. + + Returns: + `Tuple[str, Dict[str, Any]]`: The prepared text and the unused kwargs. + """ + normalize = normalize if normalize is not None else self.normalize + + if normalize: + # normalise for casing + text = self.normalize_text(text) + + filtered_text = self._preprocess_char(text) + + if has_non_roman_characters(filtered_text) and self.is_uroman: + logger.warning( + "Text to the tokenizer contains non-Roman characters. Ensure the `uroman` Romanizer is " + "applied to the text prior to passing it to the tokenizer. See " + "`https://github.com/isi-nlp/uroman` for details." + ) + + if self.phonemize: + if not is_phonemizer_available(): + raise ImportError("Please install the `phonemizer` Python package to use this tokenizer.") + + filtered_text = phonemizer.phonemize( + filtered_text, + language="en-us", + backend="espeak", + strip=True, + preserve_punctuation=True, + with_stress=True, + ) + filtered_text = re.sub(r"\s+", " ", filtered_text) + elif normalize: + # strip any chars outside of the vocab (punctuation) + filtered_text = "".join(list(filter(lambda char: char in self.encoder, filtered_text))).strip() + + return filtered_text, kwargs + + def _tokenize(self, text: str) -> List[str]: + """Tokenize a string by inserting the `` token at the boundary between adjacent characters.""" + tokens = list(text) + + if self.add_blank: + interspersed = [self._convert_id_to_token(0)] * (len(tokens) * 2 + 1) + interspersed[1::2] = tokens + tokens = interspersed + + return tokens + + def convert_tokens_to_string(self, tokens: List[str]) -> str: + if self.add_blank and len(tokens) > 1: + tokens = tokens[1::2] + return "".join(tokens) + + def _convert_token_to_id(self, token): + """Converts a token (str) in an id using the vocab.""" + return self.encoder.get(token, self.encoder.get(self.unk_token)) + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + return self.decoder.get(index) + + def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Union[Tuple[str], None]: + if not os.path.isdir(save_directory): + logger.error(f"Vocabulary path ({save_directory}) should be a directory") + return + + vocab_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] + ) + + with open(vocab_file, "w", encoding="utf-8") as f: + f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") + + return (vocab_file,) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vivit/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vivit/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ec446b79707255023729510b0cbf3b3ac5801862 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vivit/__init__.py @@ -0,0 +1,78 @@ +# flake8: noqa +# There's no way to ignore "F401 '...' imported but unused" warnings in this +# module, but to preserve other warnings. So, don't check this module at all. + +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +# rely on isort to merge the imports +from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available + + +_import_structure = { + "configuration_vivit": ["VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VivitConfig"], +} +try: + if not is_vision_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["image_processing_vivit"] = ["VivitImageProcessor"] + + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_vivit"] = [ + "VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST", + "VivitModel", + "VivitPreTrainedModel", + "VivitForVideoClassification", + ] + + +if TYPE_CHECKING: + from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig + + try: + if not is_vision_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .image_processing_vivit import VivitImageProcessor + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_vivit import ( + VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST, + VivitForVideoClassification, + VivitModel, + VivitPreTrainedModel, + ) + + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vivit/__pycache__/image_processing_vivit.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vivit/__pycache__/image_processing_vivit.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3c798d585cde0a4b3920727ca93a7b0320cb6d02 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vivit/__pycache__/image_processing_vivit.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vivit/configuration_vivit.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vivit/configuration_vivit.py new file mode 100644 index 0000000000000000000000000000000000000000..28ac13496f82f83a68e08982775d19787cb44485 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vivit/configuration_vivit.py @@ -0,0 +1,119 @@ +# coding=utf-8 +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" ViViT model configuration""" + +from ...configuration_utils import PretrainedConfig +from ...utils import logging + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class VivitConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`VivitModel`]. It is used to instantiate a ViViT + model according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the ViViT + [google/vivit-b-16x2-kinetics400](https://huggingface.co/google/vivit-b-16x2-kinetics400) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + image_size (`int`, *optional*, defaults to 224): + The size (resolution) of each image. + num_frames (`int`, *optional*, defaults to 32): + The number of frames in each video. + tubelet_size (`List[int]`, *optional*, defaults to `[2, 16, 16]`): + The size (resolution) of each tubelet. + num_channels (`int`, *optional*, defaults to 3): + The number of input channels. + hidden_size (`int`, *optional*, defaults to 768): + Dimensionality of the encoder layers and the pooler layer. + num_hidden_layers (`int`, *optional*, defaults to 12): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 12): + Number of attention heads for each attention layer in the Transformer encoder. + intermediate_size (`int`, *optional*, defaults to 3072): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + hidden_act (`str` or `function`, *optional*, defaults to `"gelu_fast"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"`, `"gelu_fast"` and `"gelu_new"` are supported. + hidden_dropout_prob (`float`, *optional*, defaults to 0.0): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + layer_norm_eps (`float`, *optional*, defaults to 1e-06): + The epsilon used by the layer normalization layers. + qkv_bias (`bool`, *optional*, defaults to `True`): + Whether to add a bias to the queries, keys and values. + + Example: + + ```python + >>> from transformers import VivitConfig, VivitModel + + >>> # Initializing a ViViT google/vivit-b-16x2-kinetics400 style configuration + >>> configuration = VivitConfig() + + >>> # Initializing a model (with random weights) from the google/vivit-b-16x2-kinetics400 style configuration + >>> model = VivitModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "vivit" + + def __init__( + self, + image_size=224, + num_frames=32, + tubelet_size=[2, 16, 16], + num_channels=3, + hidden_size=768, + num_hidden_layers=12, + num_attention_heads=12, + intermediate_size=3072, + hidden_act="gelu_fast", + hidden_dropout_prob=0.0, + attention_probs_dropout_prob=0.0, + initializer_range=0.02, + layer_norm_eps=1e-06, + qkv_bias=True, + **kwargs, + ): + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.initializer_range = initializer_range + self.layer_norm_eps = layer_norm_eps + + self.image_size = image_size + self.num_frames = num_frames + self.tubelet_size = tubelet_size + self.num_channels = num_channels + self.qkv_bias = qkv_bias + + super().__init__(**kwargs) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vivit/convert_vivit_flax_to_pytorch.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vivit/convert_vivit_flax_to_pytorch.py new file mode 100644 index 0000000000000000000000000000000000000000..571dfe896ef5f630ed0cf7abcadcde6dc63a457c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vivit/convert_vivit_flax_to_pytorch.py @@ -0,0 +1,230 @@ +# coding=utf-8 +# Copyright 2023 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert Flax ViViT checkpoints from the original repository to PyTorch. URL: +https://github.com/google-research/scenic/tree/main/scenic/projects/vivit +""" +import argparse +import json +import os.path +from collections import OrderedDict + +import numpy as np +import requests +import torch +from flax.training.checkpoints import restore_checkpoint +from huggingface_hub import hf_hub_download + +from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor +from transformers.image_utils import PILImageResampling + + +def download_checkpoint(path): + url = "https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint" + + with open(path, "wb") as f: + with requests.get(url, stream=True) as req: + for chunk in req.iter_content(chunk_size=2048): + f.write(chunk) + + +def get_vivit_config() -> VivitConfig: + config = VivitConfig() + + config.num_labels = 400 + repo_id = "huggingface/label-files" + filename = "kinetics400-id2label.json" + + id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) + id2label = {int(k): v for k, v in id2label.items()} + config.id2label = id2label + config.label2id = {v: k for k, v in id2label.items()} + return config + + +# We will verify our results on a video of eating spaghetti +# Frame indices used: [ 47, 51, 55, 59, 63, 67, 71, 75, 80, 84, 88, 92, 96, 100, 104, 108, 113, 117, +# 121, 125, 129, 133, 137, 141, 146, 150, 154, 158, 162, 166, 170, 174] +def prepare_video(): + file = hf_hub_download( + repo_id="hf-internal-testing/spaghetti-video", filename="eating_spaghetti_32_frames.npy", repo_type="dataset" + ) + video = np.load(file) + return list(video) + + +def transform_attention(current: np.ndarray): + if np.ndim(current) == 2: + return transform_attention_bias(current) + + elif np.ndim(current) == 3: + return transform_attention_kernel(current) + + else: + raise Exception(f"Invalid number of dimesions: {np.ndim(current)}") + + +def transform_attention_bias(current: np.ndarray): + return current.flatten() + + +def transform_attention_kernel(current: np.ndarray): + return np.reshape(current, (current.shape[0], current.shape[1] * current.shape[2])).T + + +def transform_attention_output_weight(current: np.ndarray): + return np.reshape(current, (current.shape[0] * current.shape[1], current.shape[2])).T + + +def transform_state_encoder_block(state_dict, i): + state = state_dict["optimizer"]["target"]["Transformer"][f"encoderblock_{i}"] + + prefix = f"encoder.layer.{i}." + new_state = { + prefix + "intermediate.dense.bias": state["MlpBlock_0"]["Dense_0"]["bias"], + prefix + "intermediate.dense.weight": np.transpose(state["MlpBlock_0"]["Dense_0"]["kernel"]), + prefix + "output.dense.bias": state["MlpBlock_0"]["Dense_1"]["bias"], + prefix + "output.dense.weight": np.transpose(state["MlpBlock_0"]["Dense_1"]["kernel"]), + prefix + "layernorm_before.bias": state["LayerNorm_0"]["bias"], + prefix + "layernorm_before.weight": state["LayerNorm_0"]["scale"], + prefix + "layernorm_after.bias": state["LayerNorm_1"]["bias"], + prefix + "layernorm_after.weight": state["LayerNorm_1"]["scale"], + prefix + "attention.attention.query.bias": transform_attention( + state["MultiHeadDotProductAttention_0"]["query"]["bias"] + ), + prefix + "attention.attention.query.weight": transform_attention( + state["MultiHeadDotProductAttention_0"]["query"]["kernel"] + ), + prefix + "attention.attention.key.bias": transform_attention( + state["MultiHeadDotProductAttention_0"]["key"]["bias"] + ), + prefix + "attention.attention.key.weight": transform_attention( + state["MultiHeadDotProductAttention_0"]["key"]["kernel"] + ), + prefix + "attention.attention.value.bias": transform_attention( + state["MultiHeadDotProductAttention_0"]["value"]["bias"] + ), + prefix + "attention.attention.value.weight": transform_attention( + state["MultiHeadDotProductAttention_0"]["value"]["kernel"] + ), + prefix + "attention.output.dense.bias": state["MultiHeadDotProductAttention_0"]["out"]["bias"], + prefix + "attention.output.dense.weight": transform_attention_output_weight( + state["MultiHeadDotProductAttention_0"]["out"]["kernel"] + ), + } + + return new_state + + +def get_n_layers(state_dict): + return sum([1 if "encoderblock_" in k else 0 for k in state_dict["optimizer"]["target"]["Transformer"].keys()]) + + +def transform_state(state_dict, classification_head=False): + transformer_layers = get_n_layers(state_dict) + + new_state = OrderedDict() + + new_state["layernorm.bias"] = state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["bias"] + new_state["layernorm.weight"] = state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["scale"] + + new_state["embeddings.patch_embeddings.projection.weight"] = np.transpose( + state_dict["optimizer"]["target"]["embedding"]["kernel"], (4, 3, 0, 1, 2) + ) + new_state["embeddings.patch_embeddings.projection.bias"] = state_dict["optimizer"]["target"]["embedding"]["bias"] + + new_state["embeddings.cls_token"] = state_dict["optimizer"]["target"]["cls"] + new_state["embeddings.position_embeddings"] = state_dict["optimizer"]["target"]["Transformer"]["posembed_input"][ + "pos_embedding" + ] + + for i in range(transformer_layers): + new_state.update(transform_state_encoder_block(state_dict, i)) + + if classification_head: + new_state = {"vivit." + k: v for k, v in new_state.items()} + new_state["classifier.weight"] = np.transpose(state_dict["optimizer"]["target"]["output_projection"]["kernel"]) + new_state["classifier.bias"] = np.transpose(state_dict["optimizer"]["target"]["output_projection"]["bias"]) + + return {k: torch.tensor(v) for k, v in new_state.items()} + + +# checks that image processor settings are the same as in the original implementation +# original: https://github.com/google-research/scenic/blob/main/scenic/projects/vivit/data/video_tfrecord_dataset.py +# dataset specific config: +# https://github.com/google-research/scenic/blob/main/scenic/projects/vivit/configs/kinetics400/vivit_base_k400.py +def get_processor() -> VivitImageProcessor: + extractor = VivitImageProcessor() + + assert extractor.do_resize is True + assert extractor.size == {"shortest_edge": 256} + assert extractor.do_center_crop is True + assert extractor.crop_size == {"width": 224, "height": 224} + assert extractor.resample == PILImageResampling.BILINEAR + + # here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py + # one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1 + # which effectively means no normalization (and ViViT does not overwrite those when calling this func) + assert extractor.do_normalize is False + assert extractor.do_rescale is True + assert extractor.rescale_factor == 1 / 255 + + # zero-centering = True in original implementation + assert extractor.do_zero_centering is True + + return extractor + + +def convert(output_path: str): + flax_model_path = "checkpoint" + + if not os.path.exists(flax_model_path): + download_checkpoint(flax_model_path) + + state_dict = restore_checkpoint(flax_model_path, None) + new_state = transform_state(state_dict, classification_head=True) + + config = get_vivit_config() + + assert config.image_size == 224 + assert config.num_frames == 32 + + model = VivitForVideoClassification(config) + model.load_state_dict(new_state) + model.eval() + + extractor = get_processor() + + video = prepare_video() + inputs = extractor(video, return_tensors="pt") + + outputs = model(**inputs) + + expected_shape = torch.Size([1, 400]) + expected_slice = torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]) + + assert outputs.logits.shape == expected_shape + assert torch.allclose(outputs.logits[0, :5], expected_slice, atol=1e-4), outputs.logits[0, :5] + + model.save_pretrained(output_path) + extractor.save_pretrained(output_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--output_model_name", "-o", type=str, help="Output path for the converted HuggingFace model") + + args = parser.parse_args() + convert(args.output_model_name) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vivit/image_processing_vivit.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vivit/image_processing_vivit.py new file mode 100644 index 0000000000000000000000000000000000000000..9b62aedc234e88513d0d7b475939fdc9a766ec8c --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vivit/image_processing_vivit.py @@ -0,0 +1,423 @@ +# coding=utf-8 +# Copyright 2023 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for Vivit.""" +from typing import Dict, List, Optional, Union + +import numpy as np + +from transformers.utils import is_vision_available +from transformers.utils.generic import TensorType + +from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict +from ...image_transforms import ( + get_resize_output_image_size, + rescale, + resize, + to_channel_dimension_format, +) +from ...image_utils import ( + IMAGENET_STANDARD_MEAN, + IMAGENET_STANDARD_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + infer_channel_dimension_format, + is_scaled_image, + is_valid_image, + to_numpy_array, + valid_images, + validate_kwargs, + validate_preprocess_arguments, +) +from ...utils import logging + + +if is_vision_available(): + import PIL + +logger = logging.get_logger(__name__) + + +def make_batched(videos) -> List[List[ImageInput]]: + if isinstance(videos, (list, tuple)) and isinstance(videos[0], (list, tuple)) and is_valid_image(videos[0][0]): + return videos + + elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]): + return [videos] + + elif is_valid_image(videos): + return [[videos]] + + raise ValueError(f"Could not make batched video from {videos}") + + +class VivitImageProcessor(BaseImageProcessor): + r""" + Constructs a Vivit image processor. + + Args: + do_resize (`bool`, *optional*, defaults to `True`): + Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the + `do_resize` parameter in the `preprocess` method. + size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 256}`): + Size of the output image after resizing. The shortest edge of the image will be resized to + `size["shortest_edge"]` while maintaining the aspect ratio of the original image. Can be overriden by + `size` in the `preprocess` method. + resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): + Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the + `preprocess` method. + do_center_crop (`bool`, *optional*, defaults to `True`): + Whether to center crop the image to the specified `crop_size`. Can be overridden by the `do_center_crop` + parameter in the `preprocess` method. + crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`): + Size of the image after applying the center crop. Can be overridden by the `crop_size` parameter in the + `preprocess` method. + do_rescale (`bool`, *optional*, defaults to `True`): + Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` + parameter in the `preprocess` method. + rescale_factor (`int` or `float`, *optional*, defaults to `1/127.5`): + Defines the scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter + in the `preprocess` method. + offset (`bool`, *optional*, defaults to `True`): + Whether to scale the image in both negative and positive directions. Can be overriden by the `offset` in + the `preprocess` method. + do_normalize (`bool`, *optional*, defaults to `True`): + Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` + method. + image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): + Mean to use if normalizing the image. This is a float or list of floats the length of the number of + channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. + image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): + Standard deviation to use if normalizing the image. This is a float or list of floats the length of the + number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. + """ + + model_input_names = ["pixel_values"] + + def __init__( + self, + do_resize: bool = True, + size: Dict[str, int] = None, + resample: PILImageResampling = PILImageResampling.BILINEAR, + do_center_crop: bool = True, + crop_size: Dict[str, int] = None, + do_rescale: bool = True, + rescale_factor: Union[int, float] = 1 / 127.5, + offset: bool = True, + do_normalize: bool = True, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + **kwargs, + ) -> None: + super().__init__(**kwargs) + size = size if size is not None else {"shortest_edge": 256} + size = get_size_dict(size, default_to_square=False) + crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224} + crop_size = get_size_dict(crop_size, param_name="crop_size") + + self.do_resize = do_resize + self.size = size + self.do_center_crop = do_center_crop + self.crop_size = crop_size + self.resample = resample + self.do_rescale = do_rescale + self.rescale_factor = rescale_factor + self.offset = offset + self.do_normalize = do_normalize + self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN + self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD + self._valid_processor_keys = [ + "videos", + "do_resize", + "size", + "resample", + "do_center_crop", + "crop_size", + "do_rescale", + "rescale_factor", + "offset", + "do_normalize", + "image_mean", + "image_std", + "return_tensors", + "data_format", + "input_data_format", + ] + + def resize( + self, + image: np.ndarray, + size: Dict[str, int], + resample: PILImageResampling = PILImageResampling.BILINEAR, + data_format: Optional[Union[str, ChannelDimension]] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ) -> np.ndarray: + """ + Resize an image. + + Args: + image (`np.ndarray`): + Image to resize. + size (`Dict[str, int]`): + Size of the output image. If `size` is of the form `{"height": h, "width": w}`, the output image will + have the size `(h, w)`. If `size` is of the form `{"shortest_edge": s}`, the output image will have its + shortest edge of length `s` while keeping the aspect ratio of the original image. + resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`): + Resampling filter to use when resiizing the image. + data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format of the image. If not provided, it will be the same as the input image. + input_data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format of the input image. If not provided, it will be inferred. + """ + size = get_size_dict(size, default_to_square=False) + if "shortest_edge" in size: + output_size = get_resize_output_image_size( + image, size["shortest_edge"], default_to_square=False, input_data_format=input_data_format + ) + elif "height" in size and "width" in size: + output_size = (size["height"], size["width"]) + else: + raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}") + return resize( + image, + size=output_size, + resample=resample, + data_format=data_format, + input_data_format=input_data_format, + **kwargs, + ) + + # Copied from transformers.models.efficientnet.image_processing_efficientnet.EfficientNetImageProcessor.rescale + def rescale( + self, + image: np.ndarray, + scale: Union[int, float], + offset: bool = True, + data_format: Optional[Union[str, ChannelDimension]] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ): + """ + Rescale an image by a scale factor. + + If `offset` is `True`, the image has its values rescaled by `scale` and then offset by 1. If `scale` is + 1/127.5, the image is rescaled between [-1, 1]. + image = image * scale - 1 + + If `offset` is `False`, and `scale` is 1/255, the image is rescaled between [0, 1]. + image = image * scale + + Args: + image (`np.ndarray`): + Image to rescale. + scale (`int` or `float`): + Scale to apply to the image. + offset (`bool`, *optional*): + Whether to scale the image in both negative and positive directions. + data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format of the image. If not provided, it will be the same as the input image. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format of the input image. If not provided, it will be inferred. + """ + rescaled_image = rescale( + image, scale=scale, data_format=data_format, input_data_format=input_data_format, **kwargs + ) + + if offset: + rescaled_image = rescaled_image - 1 + + return rescaled_image + + def _preprocess_image( + self, + image: ImageInput, + do_resize: bool = None, + size: Dict[str, int] = None, + resample: PILImageResampling = None, + do_center_crop: bool = None, + crop_size: Dict[str, int] = None, + do_rescale: bool = None, + rescale_factor: float = None, + offset: bool = None, + do_normalize: bool = None, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + ) -> np.ndarray: + """Preprocesses a single image.""" + + validate_preprocess_arguments( + do_rescale=do_rescale, + rescale_factor=rescale_factor, + do_normalize=do_normalize, + image_mean=image_mean, + image_std=image_std, + do_center_crop=do_center_crop, + crop_size=crop_size, + do_resize=do_resize, + size=size, + resample=resample, + ) + + if offset and not do_rescale: + raise ValueError("For offset, do_rescale must also be set to True.") + + # All transformations expect numpy arrays. + image = to_numpy_array(image) + + if is_scaled_image(image) and do_rescale: + logger.warning_once( + "It looks like you are trying to rescale already rescaled images. If the input" + " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." + ) + + if input_data_format is None: + input_data_format = infer_channel_dimension_format(image) + + if do_resize: + image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format) + + if do_center_crop: + image = self.center_crop(image, size=crop_size, input_data_format=input_data_format) + + if do_rescale: + image = self.rescale(image=image, scale=rescale_factor, offset=offset, input_data_format=input_data_format) + + if do_normalize: + image = self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format) + + image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) + return image + + def preprocess( + self, + videos: ImageInput, + do_resize: bool = None, + size: Dict[str, int] = None, + resample: PILImageResampling = None, + do_center_crop: bool = None, + crop_size: Dict[str, int] = None, + do_rescale: bool = None, + rescale_factor: float = None, + offset: bool = None, + do_normalize: bool = None, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + return_tensors: Optional[Union[str, TensorType]] = None, + data_format: ChannelDimension = ChannelDimension.FIRST, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ) -> PIL.Image.Image: + """ + Preprocess an image or batch of images. + + Args: + videos (`ImageInput`): + Video frames to preprocess. Expects a single or batch of video frames with pixel values ranging from 0 + to 255. If passing in frames with pixel values between 0 and 1, set `do_rescale=False`. + do_resize (`bool`, *optional*, defaults to `self.do_resize`): + Whether to resize the image. + size (`Dict[str, int]`, *optional*, defaults to `self.size`): + Size of the image after applying resize. + resample (`PILImageResampling`, *optional*, defaults to `self.resample`): + Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only + has an effect if `do_resize` is set to `True`. + do_center_crop (`bool`, *optional*, defaults to `self.do_centre_crop`): + Whether to centre crop the image. + crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`): + Size of the image after applying the centre crop. + do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): + Whether to rescale the image values between `[-1 - 1]` if `offset` is `True`, `[0, 1]` otherwise. + rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): + Rescale factor to rescale the image by if `do_rescale` is set to `True`. + offset (`bool`, *optional*, defaults to `self.offset`): + Whether to scale the image in both negative and positive directions. + do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): + Whether to normalize the image. + image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): + Image mean. + image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): + Image standard deviation. + return_tensors (`str` or `TensorType`, *optional*): + The type of tensors to return. Can be one of: + - Unset: Return a list of `np.ndarray`. + - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. + - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. + - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. + - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. + data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): + The channel dimension format for the output image. Can be one of: + - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - Unset: Use the inferred channel dimension format of the input image. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format for the input image. If unset, the channel dimension format is inferred + from the input image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. + """ + do_resize = do_resize if do_resize is not None else self.do_resize + resample = resample if resample is not None else self.resample + do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop + do_rescale = do_rescale if do_rescale is not None else self.do_rescale + rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor + offset = offset if offset is not None else self.offset + do_normalize = do_normalize if do_normalize is not None else self.do_normalize + image_mean = image_mean if image_mean is not None else self.image_mean + image_std = image_std if image_std is not None else self.image_std + + size = size if size is not None else self.size + size = get_size_dict(size, default_to_square=False) + crop_size = crop_size if crop_size is not None else self.crop_size + crop_size = get_size_dict(crop_size, param_name="crop_size") + + validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys) + + if not valid_images(videos): + raise ValueError( + "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " + "torch.Tensor, tf.Tensor or jax.ndarray." + ) + + videos = make_batched(videos) + + videos = [ + [ + self._preprocess_image( + image=img, + do_resize=do_resize, + size=size, + resample=resample, + do_center_crop=do_center_crop, + crop_size=crop_size, + do_rescale=do_rescale, + rescale_factor=rescale_factor, + offset=offset, + do_normalize=do_normalize, + image_mean=image_mean, + image_std=image_std, + data_format=data_format, + input_data_format=input_data_format, + ) + for img in video + ] + for video in videos + ] + + data = {"pixel_values": videos} + return BatchFeature(data=data, tensor_type=return_tensors) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vivit/modeling_vivit.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vivit/modeling_vivit.py new file mode 100644 index 0000000000000000000000000000000000000000..08efb85e1f0254c9ab87f75fb514d6c5079d3141 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/vivit/modeling_vivit.py @@ -0,0 +1,743 @@ +# coding=utf-8 +# Copyright 2023 Google AI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch ViViT model.""" + + +import math +from typing import Optional, Set, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss, MSELoss + +from ...activations import ACT2FN +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput +from ...modeling_utils import PreTrainedModel +from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer +from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings +from .configuration_vivit import VivitConfig + + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "google/vivit-b-16x2-kinetics400" +_CONFIG_FOR_DOC = "VivitConfig" + + +from ..deprecated._archive_maps import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +class VivitTubeletEmbeddings(nn.Module): + """ + Construct Vivit Tubelet embeddings. + + This module turns a batch of videos of shape (batch_size, num_frames, num_channels, height, width) into a tensor of + shape (batch_size, seq_len, hidden_size) to be consumed by a Transformer encoder. + + The seq_len (the number of patches) equals (number of frames // tubelet_size[0]) * (height // tubelet_size[1]) * + (width // tubelet_size[2]). + """ + + def __init__(self, config): + super().__init__() + self.num_frames = config.num_frames + self.image_size = config.image_size + self.patch_size = config.tubelet_size + self.num_patches = ( + (self.image_size // self.patch_size[2]) + * (self.image_size // self.patch_size[1]) + * (self.num_frames // self.patch_size[0]) + ) + self.embed_dim = config.hidden_size + + self.projection = nn.Conv3d( + config.num_channels, config.hidden_size, kernel_size=config.tubelet_size, stride=config.tubelet_size + ) + + def forward(self, pixel_values): + batch_size, num_frames, num_channels, height, width = pixel_values.shape + if height != self.image_size or width != self.image_size: + raise ValueError( + f"Input image size ({height}*{width}) doesn't match model ({self.image_size}*{self.image_size})." + ) + + # permute to (batch_size, num_channels, num_frames, height, width) + pixel_values = pixel_values.permute(0, 2, 1, 3, 4) + + x = self.projection(pixel_values) + # out_batch_size, out_num_channels, out_num_frames, out_height, out_width = x.shape + x = self.projection(pixel_values).flatten(2).transpose(1, 2) + return x + + +class VivitEmbeddings(nn.Module): + """ + Vivit Embeddings. + + Creates embeddings from a video using VivitTubeletEmbeddings, adds CLS token and positional embeddings. + """ + + def __init__(self, config): + super().__init__() + + self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) + self.patch_embeddings = VivitTubeletEmbeddings(config) + + self.position_embeddings = nn.Parameter( + torch.zeros(1, self.patch_embeddings.num_patches + 1, config.hidden_size) + ) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.config = config + + def forward(self, pixel_values): + batch_size = pixel_values.shape[0] + embeddings = self.patch_embeddings(pixel_values) + + cls_tokens = self.cls_token.tile([batch_size, 1, 1]) + + embeddings = torch.cat((cls_tokens, embeddings), dim=1) + + # add positional encoding to each token + embeddings = embeddings + self.position_embeddings + + embeddings = self.dropout(embeddings) + + return embeddings + + +# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->Vivit +class VivitSelfAttention(nn.Module): + def __init__(self, config: VivitConfig) -> None: + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " + f"heads {config.num_attention_heads}." + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + mixed_query_layer = self.query(hidden_states) + + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + query_layer = self.transpose_for_scores(mixed_query_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->Vivit +class VivitSelfOutput(nn.Module): + """ + The residual connection is defined in VivitLayer instead of here (as is the case with other models), due to the + layernorm applied before each block. + """ + + def __init__(self, config: VivitConfig) -> None: + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + + return hidden_states + + +# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->Vivit +class VivitAttention(nn.Module): + def __init__(self, config: VivitConfig) -> None: + super().__init__() + self.attention = VivitSelfAttention(config) + self.output = VivitSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads: Set[int]) -> None: + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.attention.query = prune_linear_layer(self.attention.query, index) + self.attention.key = prune_linear_layer(self.attention.key, index) + self.attention.value = prune_linear_layer(self.attention.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) + self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + self_outputs = self.attention(hidden_states, head_mask, output_attentions) + + attention_output = self.output(self_outputs[0], hidden_states) + + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +class VivitIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + hidden_states = self.dropout(hidden_states) + + return hidden_states + + +class VivitOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + + hidden_states = self.dropout(hidden_states) + + hidden_states = hidden_states + input_tensor + + return hidden_states + + +class VivitLayer(nn.Module): + """This corresponds to the EncoderBlock class in the scenic/vivit implementation.""" + + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = VivitAttention(config) + self.intermediate = VivitIntermediate(config) + self.output = VivitOutput(config) + self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states, head_mask=None, output_attentions=False): + self_attention_outputs = self.attention( + # in Vivit, layernorm is applied before self-attention + self.layernorm_before(hidden_states), + head_mask, + output_attentions=output_attentions, + ) + attention_output = self_attention_outputs[0] + # add self attentions if we output attention weights + outputs = self_attention_outputs[1:] + + # first residual connection + hidden_states = attention_output + hidden_states + + # in Vivit, layernorm is also applied after self-attention + layer_output = self.layernorm_after(hidden_states) + layer_output = self.intermediate(layer_output) + + # second residual connection is done here + layer_output = self.output(layer_output, hidden_states) + + outputs = (layer_output,) + outputs + + return outputs + + +class VivitEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList([VivitLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + head_mask=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer_module.__call__, + hidden_states, + layer_head_mask, + output_attentions, + ) + else: + layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +class VivitPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class VivitPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = VivitConfig + base_model_prefix = "vivit" + main_input_name = "pixel_values" + supports_gradient_checkpointing = True + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, (nn.Linear, nn.Conv3d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + elif isinstance(module, nn.Parameter): + module.data.normal_(mean=0.0, std=self.config.initializer_range) + + +VIVIT_START_DOCSTRING = r""" + This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it + as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and + behavior. + + Parameters: + config ([`VivitConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +VIVIT_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`VivitImageProcessor`]. See + [`VivitImageProcessor.preprocess`] for details. + + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare ViViT Transformer model outputting raw hidden-states without any specific head on top.", + VIVIT_START_DOCSTRING, +) +class VivitModel(VivitPreTrainedModel): + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = VivitEmbeddings(config) + self.encoder = VivitEncoder(config) + + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.pooler = VivitPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.patch_embeddings + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. + + Args: + heads_to_prune: + dict of {layer_num: list of heads to prune in this layer} + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + @add_start_docstrings_to_model_forward(VIVIT_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.FloatTensor], BaseModelOutputWithPooling]: + r""" + Returns: + + Examples: + + ```python + >>> import av + >>> import numpy as np + + >>> from transformers import VivitImageProcessor, VivitModel + >>> from huggingface_hub import hf_hub_download + + >>> np.random.seed(0) + + + >>> def read_video_pyav(container, indices): + ... ''' + ... Decode the video with PyAV decoder. + ... Args: + ... container (`av.container.input.InputContainer`): PyAV container. + ... indices (`List[int]`): List of frame indices to decode. + ... Returns: + ... result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3). + ... ''' + ... frames = [] + ... container.seek(0) + ... start_index = indices[0] + ... end_index = indices[-1] + ... for i, frame in enumerate(container.decode(video=0)): + ... if i > end_index: + ... break + ... if i >= start_index and i in indices: + ... frames.append(frame) + ... return np.stack([x.to_ndarray(format="rgb24") for x in frames]) + + + >>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len): + ... ''' + ... Sample a given number of frame indices from the video. + ... Args: + ... clip_len (`int`): Total number of frames to sample. + ... frame_sample_rate (`int`): Sample every n-th frame. + ... seg_len (`int`): Maximum allowed index of sample's last frame. + ... Returns: + ... indices (`List[int]`): List of sampled frame indices + ... ''' + ... converted_len = int(clip_len * frame_sample_rate) + ... end_idx = np.random.randint(converted_len, seg_len) + ... start_idx = end_idx - converted_len + ... indices = np.linspace(start_idx, end_idx, num=clip_len) + ... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64) + ... return indices + + + >>> # video clip consists of 300 frames (10 seconds at 30 FPS) + >>> file_path = hf_hub_download( + ... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset" + ... ) + >>> container = av.open(file_path) + + >>> # sample 32 frames + >>> indices = sample_frame_indices(clip_len=32, frame_sample_rate=1, seg_len=container.streams.video[0].frames) + >>> video = read_video_pyav(container=container, indices=indices) + + >>> image_processor = VivitImageProcessor.from_pretrained("google/vivit-b-16x2-kinetics400") + >>> model = VivitModel.from_pretrained("google/vivit-b-16x2-kinetics400") + + >>> # prepare video for the model + >>> inputs = image_processor(list(video), return_tensors="pt") + + >>> # forward pass + >>> outputs = model(**inputs) + >>> last_hidden_states = outputs.last_hidden_state + >>> list(last_hidden_states.shape) + [1, 3137, 768] + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings(pixel_values) + + encoder_outputs = self.encoder( + embedding_output, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + sequence_output = self.layernorm(sequence_output) + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPooling( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +@add_start_docstrings( + """ViViT Transformer model with a video classification head on top (a linear layer on top of the final hidden state of the +[CLS] token) e.g. for Kinetics-400.""", + VIVIT_START_DOCSTRING, +) +class VivitForVideoClassification(VivitPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.num_labels = config.num_labels + self.vivit = VivitModel(config, add_pooling_layer=False) + + # Classifier head + self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity() + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(VIVIT_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=ImageClassifierOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.FloatTensor], ImageClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + + Returns: + + Examples: + + ```python + >>> import av + >>> import numpy as np + >>> import torch + + >>> from transformers import VivitImageProcessor, VivitForVideoClassification + >>> from huggingface_hub import hf_hub_download + + >>> np.random.seed(0) + + + >>> def read_video_pyav(container, indices): + ... ''' + ... Decode the video with PyAV decoder. + ... Args: + ... container (`av.container.input.InputContainer`): PyAV container. + ... indices (`List[int]`): List of frame indices to decode. + ... Returns: + ... result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3). + ... ''' + ... frames = [] + ... container.seek(0) + ... start_index = indices[0] + ... end_index = indices[-1] + ... for i, frame in enumerate(container.decode(video=0)): + ... if i > end_index: + ... break + ... if i >= start_index and i in indices: + ... frames.append(frame) + ... return np.stack([x.to_ndarray(format="rgb24") for x in frames]) + + + >>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len): + ... ''' + ... Sample a given number of frame indices from the video. + ... Args: + ... clip_len (`int`): Total number of frames to sample. + ... frame_sample_rate (`int`): Sample every n-th frame. + ... seg_len (`int`): Maximum allowed index of sample's last frame. + ... Returns: + ... indices (`List[int]`): List of sampled frame indices + ... ''' + ... converted_len = int(clip_len * frame_sample_rate) + ... end_idx = np.random.randint(converted_len, seg_len) + ... start_idx = end_idx - converted_len + ... indices = np.linspace(start_idx, end_idx, num=clip_len) + ... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64) + ... return indices + + + >>> # video clip consists of 300 frames (10 seconds at 30 FPS) + >>> file_path = hf_hub_download( + ... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset" + ... ) + >>> container = av.open(file_path) + + >>> # sample 32 frames + >>> indices = sample_frame_indices(clip_len=32, frame_sample_rate=4, seg_len=container.streams.video[0].frames) + >>> video = read_video_pyav(container=container, indices=indices) + + >>> image_processor = VivitImageProcessor.from_pretrained("google/vivit-b-16x2-kinetics400") + >>> model = VivitForVideoClassification.from_pretrained("google/vivit-b-16x2-kinetics400") + + >>> inputs = image_processor(list(video), return_tensors="pt") + + >>> with torch.no_grad(): + ... outputs = model(**inputs) + ... logits = outputs.logits + + >>> # model predicts one of the 400 Kinetics-400 classes + >>> predicted_label = logits.argmax(-1).item() + >>> print(model.config.id2label[predicted_label]) + LABEL_116 + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.vivit( + pixel_values, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + logits = self.classifier(sequence_output[:, 0, :]) + + loss = None + if labels is not None: + if self.num_labels == 1: + # We are doing regression + loss_fct = MSELoss() + loss = loss_fct(logits.view(-1), labels.view(-1)) + else: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return ImageClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b3abdb99ec722d6f5e13b136d89b664a79527840 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/__init__.py @@ -0,0 +1,134 @@ +# Copyright 2021 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import ( + OptionalDependencyNotAvailable, + _LazyModule, + is_flax_available, + is_tf_available, + is_torch_available, +) + + +_import_structure = { + "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"], + "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"], + "processing_wav2vec2": ["Wav2Vec2Processor"], + "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"], +} + + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_wav2vec2"] = [ + "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST", + "Wav2Vec2ForAudioFrameClassification", + "Wav2Vec2ForCTC", + "Wav2Vec2ForMaskedLM", + "Wav2Vec2ForPreTraining", + "Wav2Vec2ForSequenceClassification", + "Wav2Vec2ForXVector", + "Wav2Vec2Model", + "Wav2Vec2PreTrainedModel", + ] + +try: + if not is_tf_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_tf_wav2vec2"] = [ + "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST", + "TFWav2Vec2ForCTC", + "TFWav2Vec2Model", + "TFWav2Vec2PreTrainedModel", + "TFWav2Vec2ForSequenceClassification", + ] + +try: + if not is_flax_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_flax_wav2vec2"] = [ + "FlaxWav2Vec2ForCTC", + "FlaxWav2Vec2ForPreTraining", + "FlaxWav2Vec2Model", + "FlaxWav2Vec2PreTrainedModel", + ] + + +if TYPE_CHECKING: + from .configuration_wav2vec2 import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, Wav2Vec2Config + from .feature_extraction_wav2vec2 import Wav2Vec2FeatureExtractor + from .processing_wav2vec2 import Wav2Vec2Processor + from .tokenization_wav2vec2 import Wav2Vec2CTCTokenizer, Wav2Vec2Tokenizer + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_wav2vec2 import ( + WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, + Wav2Vec2ForAudioFrameClassification, + Wav2Vec2ForCTC, + Wav2Vec2ForMaskedLM, + Wav2Vec2ForPreTraining, + Wav2Vec2ForSequenceClassification, + Wav2Vec2ForXVector, + Wav2Vec2Model, + Wav2Vec2PreTrainedModel, + ) + + try: + if not is_tf_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_tf_wav2vec2 import ( + TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, + TFWav2Vec2ForCTC, + TFWav2Vec2ForSequenceClassification, + TFWav2Vec2Model, + TFWav2Vec2PreTrainedModel, + ) + + try: + if not is_flax_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_tf_wav2vec2 import ( + FlaxWav2Vec2ForCTC, + FlaxWav2Vec2ForPreTraining, + FlaxWav2Vec2Model, + FlaxWav2Vec2PreTrainedModel, + ) + + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/__pycache__/configuration_wav2vec2.cpython-312.pyc 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a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/configuration_wav2vec2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/configuration_wav2vec2.py new file mode 100644 index 0000000000000000000000000000000000000000..252674bb3da3fd87cd4f92936792c214c16484e1 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/configuration_wav2vec2.py @@ -0,0 +1,347 @@ +# coding=utf-8 +# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Wav2Vec2 model configuration""" + +import functools +import operator + +from ...configuration_utils import PretrainedConfig +from ...utils import logging + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class Wav2Vec2Config(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`Wav2Vec2Model`]. It is used to instantiate an + Wav2Vec2 model according to the specified arguments, defining the model architecture. Instantiating a configuration + with the defaults will yield a similar configuration to that of the Wav2Vec2 + [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 32): + Vocabulary size of the Wav2Vec2 model. Defines the number of different tokens that can be represented by + the `inputs_ids` passed when calling [`Wav2Vec2Model`] or [`TFWav2Vec2Model`]. Vocabulary size of the + model. Defines the different tokens that can be represented by the *inputs_ids* passed to the forward + method of [`Wav2Vec2Model`]. + hidden_size (`int`, *optional*, defaults to 768): + Dimensionality of the encoder layers and the pooler layer. + num_hidden_layers (`int`, *optional*, defaults to 12): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 12): + Number of attention heads for each attention layer in the Transformer encoder. + intermediate_size (`int`, *optional*, defaults to 3072): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"` and `"gelu_new"` are supported. + hidden_dropout (`float`, *optional*, defaults to 0.1): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + activation_dropout (`float`, *optional*, defaults to 0.1): + The dropout ratio for activations inside the fully connected layer. + attention_dropout (`float`, *optional*, defaults to 0.1): + The dropout ratio for the attention probabilities. + final_dropout (`float`, *optional*, defaults to 0.1): + The dropout probability for the final projection layer of [`Wav2Vec2ForCTC`]. + layerdrop (`float`, *optional*, defaults to 0.1): + The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more + details. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + layer_norm_eps (`float`, *optional*, defaults to 1e-12): + The epsilon used by the layer normalization layers. + feat_extract_norm (`str`, *optional*, defaults to `"group"`): + The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group + normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D + convolutional layers. + feat_proj_dropout (`float`, *optional*, defaults to 0.0): + The dropout probability for output of the feature encoder. + feat_extract_activation (`str, `optional`, defaults to `"gelu"`): + The non-linear activation function (function or string) in the 1D convolutional layers of the feature + extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. + feat_quantizer_dropout (`float`, *optional*, defaults to 0.0): + The dropout probability for quantized feature encoder states. + conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`): + A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the + feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers. + conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`): + A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length + of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*. + conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`): + A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The + length of *conv_kernel* defines the number of convolutional layers and has to match the length of + *conv_dim*. + conv_bias (`bool`, *optional*, defaults to `False`): + Whether the 1D convolutional layers have a bias. + num_conv_pos_embeddings (`int`, *optional*, defaults to 128): + Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional + embeddings layer. + num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16): + Number of groups of 1D convolutional positional embeddings layer. + do_stable_layer_norm (`bool`, *optional*, defaults to `False`): + Whether to apply *stable* layer norm architecture of the Transformer encoder. `do_stable_layer_norm is + True` corresponds to applying layer norm before the attention layer, whereas `do_stable_layer_norm is + False` corresponds to applying layer norm after the attention layer. + apply_spec_augment (`bool`, *optional*, defaults to `True`): + Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see + [SpecAugment: A Simple Data Augmentation Method for Automatic Speech + Recognition](https://arxiv.org/abs/1904.08779). + mask_time_prob (`float`, *optional*, defaults to 0.05): + Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking + procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If + reasoning from the propability of each feature vector to be chosen as the start of the vector span to be + masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the + actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`. + mask_time_length (`int`, *optional*, defaults to 10): + Length of vector span along the time axis. + mask_time_min_masks (`int`, *optional*, defaults to 2),: + The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step, + irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length < + mask_time_min_masks'' + mask_feature_prob (`float`, *optional*, defaults to 0.0): + Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The + masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over + the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector + span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap + may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is + True`. + mask_feature_length (`int`, *optional*, defaults to 10): + Length of vector span along the feature axis. + mask_feature_min_masks (`int`, *optional*, defaults to 0),: + The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time + step, irrespectively of `mask_feature_prob`. Only relevant if + ''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks'' + num_codevectors_per_group (`int`, *optional*, defaults to 320): + Number of entries in each quantization codebook (group). + num_codevector_groups (`int`, *optional*, defaults to 2): + Number of codevector groups for product codevector quantization. + contrastive_logits_temperature (`float`, *optional*, defaults to 0.1): + The temperature *kappa* in the contrastive loss. + feat_quantizer_dropout (`float`, *optional*, defaults to 0.0): + The dropout probability for the output of the feature encoder that's used by the quantizer. + num_negatives (`int`, *optional*, defaults to 100): + Number of negative samples for the contrastive loss. + codevector_dim (`int`, *optional*, defaults to 256): + Dimensionality of the quantized feature vectors. + proj_codevector_dim (`int`, *optional*, defaults to 256): + Dimensionality of the final projection of both the quantized and the transformer features. + diversity_loss_weight (`int`, *optional*, defaults to 0.1): + The weight of the codebook diversity loss component. + ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`): + Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an + instance of [`Wav2Vec2ForCTC`]. + ctc_zero_infinity (`bool`, *optional*, defaults to `False`): + Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly + occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance + of [`Wav2Vec2ForCTC`]. + use_weighted_layer_sum (`bool`, *optional*, defaults to `False`): + Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an + instance of [`Wav2Vec2ForSequenceClassification`]. + classifier_proj_size (`int`, *optional*, defaults to 256): + Dimensionality of the projection before token mean-pooling for classification. + tdnn_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 1500)`): + A tuple of integers defining the number of output channels of each 1D convolutional layer in the *TDNN* + module of the *XVector* model. The length of *tdnn_dim* defines the number of *TDNN* layers. + tdnn_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 3, 3, 1, 1)`): + A tuple of integers defining the kernel size of each 1D convolutional layer in the *TDNN* module of the + *XVector* model. The length of *tdnn_kernel* has to match the length of *tdnn_dim*. + tdnn_dilation (`Tuple[int]` or `List[int]`, *optional*, defaults to `(1, 2, 3, 1, 1)`): + A tuple of integers defining the dilation factor of each 1D convolutional layer in *TDNN* module of the + *XVector* model. The length of *tdnn_dilation* has to match the length of *tdnn_dim*. + xvector_output_dim (`int`, *optional*, defaults to 512): + Dimensionality of the *XVector* embedding vectors. + add_adapter (`bool`, *optional*, defaults to `False`): + Whether a convolutional network should be stacked on top of the Wav2Vec2 Encoder. Can be very useful for + warm-starting Wav2Vec2 for SpeechEncoderDecoder models. + adapter_kernel_size (`int`, *optional*, defaults to 3): + Kernel size of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`. + adapter_stride (`int`, *optional*, defaults to 2): + Stride of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`. + num_adapter_layers (`int`, *optional*, defaults to 3): + Number of convolutional layers that should be used in the adapter network. Only relevant if `add_adapter is + True`. + adapter_attn_dim (`int`, *optional*): + Dimension of the attention adapter weights to be used in each attention block. An example of a model using + attention adapters is [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all). + output_hidden_size (`int`, *optional*): + Dimensionality of the encoder output layer. If not defined, this defaults to *hidden-size*. Only relevant + if `add_adapter is True`. + + Example: + + ```python + >>> from transformers import Wav2Vec2Config, Wav2Vec2Model + + >>> # Initializing a Wav2Vec2 facebook/wav2vec2-base-960h style configuration + >>> configuration = Wav2Vec2Config() + + >>> # Initializing a model (with random weights) from the facebook/wav2vec2-base-960h style configuration + >>> model = Wav2Vec2Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "wav2vec2" + + def __init__( + self, + vocab_size=32, + hidden_size=768, + num_hidden_layers=12, + num_attention_heads=12, + intermediate_size=3072, + hidden_act="gelu", + hidden_dropout=0.1, + activation_dropout=0.1, + attention_dropout=0.1, + feat_proj_dropout=0.0, + feat_quantizer_dropout=0.0, + final_dropout=0.1, + layerdrop=0.1, + initializer_range=0.02, + layer_norm_eps=1e-5, + feat_extract_norm="group", + feat_extract_activation="gelu", + conv_dim=(512, 512, 512, 512, 512, 512, 512), + conv_stride=(5, 2, 2, 2, 2, 2, 2), + conv_kernel=(10, 3, 3, 3, 3, 2, 2), + conv_bias=False, + num_conv_pos_embeddings=128, + num_conv_pos_embedding_groups=16, + do_stable_layer_norm=False, + apply_spec_augment=True, + mask_time_prob=0.05, + mask_time_length=10, + mask_time_min_masks=2, + mask_feature_prob=0.0, + mask_feature_length=10, + mask_feature_min_masks=0, + num_codevectors_per_group=320, + num_codevector_groups=2, + contrastive_logits_temperature=0.1, + num_negatives=100, + codevector_dim=256, + proj_codevector_dim=256, + diversity_loss_weight=0.1, + ctc_loss_reduction="sum", + ctc_zero_infinity=False, + use_weighted_layer_sum=False, + classifier_proj_size=256, + tdnn_dim=(512, 512, 512, 512, 1500), + tdnn_kernel=(5, 3, 3, 1, 1), + tdnn_dilation=(1, 2, 3, 1, 1), + xvector_output_dim=512, + pad_token_id=0, + bos_token_id=1, + eos_token_id=2, + add_adapter=False, + adapter_kernel_size=3, + adapter_stride=2, + num_adapter_layers=3, + output_hidden_size=None, + adapter_attn_dim=None, + **kwargs, + ): + super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id) + self.hidden_size = hidden_size + self.feat_extract_norm = feat_extract_norm + self.feat_extract_activation = feat_extract_activation + self.conv_dim = list(conv_dim) + self.conv_stride = list(conv_stride) + self.conv_kernel = list(conv_kernel) + self.conv_bias = conv_bias + self.num_conv_pos_embeddings = num_conv_pos_embeddings + self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups + self.num_feat_extract_layers = len(self.conv_dim) + self.num_hidden_layers = num_hidden_layers + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.num_attention_heads = num_attention_heads + self.hidden_dropout = hidden_dropout + self.attention_dropout = attention_dropout + self.activation_dropout = activation_dropout + self.feat_proj_dropout = feat_proj_dropout + self.final_dropout = final_dropout + self.layerdrop = layerdrop + self.layer_norm_eps = layer_norm_eps + self.initializer_range = initializer_range + self.vocab_size = vocab_size + self.do_stable_layer_norm = do_stable_layer_norm + self.use_weighted_layer_sum = use_weighted_layer_sum + + if ( + (len(self.conv_stride) != self.num_feat_extract_layers) + or (len(self.conv_kernel) != self.num_feat_extract_layers) + or (len(self.conv_dim) != self.num_feat_extract_layers) + ): + raise ValueError( + "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` ==" + " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) =" + f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`," + f" `len(config.conv_kernel) = {len(self.conv_kernel)}`." + ) + + # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 + self.apply_spec_augment = apply_spec_augment + self.mask_time_prob = mask_time_prob + self.mask_time_length = mask_time_length + self.mask_time_min_masks = mask_time_min_masks + self.mask_feature_prob = mask_feature_prob + self.mask_feature_length = mask_feature_length + self.mask_feature_min_masks = mask_feature_min_masks + + # parameters for pretraining with codevector quantized representations + self.num_codevectors_per_group = num_codevectors_per_group + self.num_codevector_groups = num_codevector_groups + self.contrastive_logits_temperature = contrastive_logits_temperature + self.feat_quantizer_dropout = feat_quantizer_dropout + self.num_negatives = num_negatives + self.codevector_dim = codevector_dim + self.proj_codevector_dim = proj_codevector_dim + self.diversity_loss_weight = diversity_loss_weight + + # ctc loss + self.ctc_loss_reduction = ctc_loss_reduction + self.ctc_zero_infinity = ctc_zero_infinity + + # adapter + self.add_adapter = add_adapter + self.adapter_kernel_size = adapter_kernel_size + self.adapter_stride = adapter_stride + self.num_adapter_layers = num_adapter_layers + self.output_hidden_size = output_hidden_size or hidden_size + self.adapter_attn_dim = adapter_attn_dim + + # SequenceClassification-specific parameter. Feel free to ignore for other classes. + self.classifier_proj_size = classifier_proj_size + + # XVector-specific parameters. Feel free to ignore for other classes. + self.tdnn_dim = list(tdnn_dim) + self.tdnn_kernel = list(tdnn_kernel) + self.tdnn_dilation = list(tdnn_dilation) + self.xvector_output_dim = xvector_output_dim + + @property + def inputs_to_logits_ratio(self): + return functools.reduce(operator.mul, self.conv_stride, 1) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py new file mode 100644 index 0000000000000000000000000000000000000000..28554691c6e2bb3ca59c381cb3648fbebbe5e9e6 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py @@ -0,0 +1,371 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert Wav2Vec2 checkpoint.""" + + +import argparse +import json +import os + +import fairseq +import torch +from fairseq.data import Dictionary + +from transformers import ( + Wav2Vec2Config, + Wav2Vec2CTCTokenizer, + Wav2Vec2FeatureExtractor, + Wav2Vec2ForCTC, + Wav2Vec2ForPreTraining, + Wav2Vec2Processor, + logging, +) +from transformers.models.wav2vec2.modeling_wav2vec2 import Wav2Vec2ForSequenceClassification + + +logging.set_verbosity_info() +logger = logging.get_logger(__name__) + +MAPPING = { + "post_extract_proj": "feature_projection.projection", + "encoder.pos_conv.0": "encoder.pos_conv_embed.conv", + "self_attn.k_proj": "encoder.layers.*.attention.k_proj", + "self_attn.v_proj": "encoder.layers.*.attention.v_proj", + "self_attn.q_proj": "encoder.layers.*.attention.q_proj", + "self_attn.out_proj": "encoder.layers.*.attention.out_proj", + "self_attn_layer_norm": "encoder.layers.*.layer_norm", + "fc1": "encoder.layers.*.feed_forward.intermediate_dense", + "fc2": "encoder.layers.*.feed_forward.output_dense", + "final_layer_norm": "encoder.layers.*.final_layer_norm", + "encoder.layer_norm": "encoder.layer_norm", + "adapter_layer": "encoder.layers.*.adapter_layer", + "w2v_model.layer_norm": "feature_projection.layer_norm", + "quantizer.weight_proj": "quantizer.weight_proj", + "quantizer.vars": "quantizer.codevectors", + "project_q": "project_q", + "final_proj": "project_hid", + "w2v_encoder.proj": "lm_head", + "mask_emb": "masked_spec_embed", + "pooling_layer.linear": "projector", + "pooling_layer.projection": "classifier", +} +TOP_LEVEL_KEYS = [ + "lm_head", + "quantizer.weight_proj", + "quantizer.codevectors", + "project_q", + "project_hid", + "projector", + "classifier", +] + + +def read_txt_into_dict(filename): + result = {} + with open(filename, "r") as file: + for line_number, line in enumerate(file): + line = line.strip() + if line: + words = line.split() + key = line_number + value = words[0] + result[key] = value + return result + + +def set_recursively(key, value, full_name, weight_type, hf_pointer): + for attribute in key.split("."): + hf_pointer = getattr(hf_pointer, attribute) + + hf_param_name = None + for param_key in PARAM_MAPPING.keys(): + if full_name.endswith(param_key): + hf_param_name = PARAM_MAPPING[full_name.split(".")[-1]] + weight_type = "param" + + if weight_type is not None and weight_type != "param": + hf_shape = getattr(hf_pointer, weight_type).shape + elif weight_type is not None and weight_type == "param": + shape_pointer = hf_pointer + for attribute in hf_param_name.split("."): + shape_pointer = getattr(shape_pointer, attribute) + hf_shape = shape_pointer.shape + + # let's reduce dimension + value = value[0] + else: + hf_shape = hf_pointer.shape + + if hf_shape != value.shape: + raise ValueError( + f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be" + f" {value.shape} for {full_name}" + ) + + if weight_type == "weight": + hf_pointer.weight.data = value + elif weight_type == "weight_g": + hf_pointer.weight_g.data = value + elif weight_type == "weight_v": + hf_pointer.weight_v.data = value + elif weight_type == "bias": + hf_pointer.bias.data = value + elif weight_type == "param": + for attribute in hf_param_name.split("."): + hf_pointer = getattr(hf_pointer, attribute) + hf_pointer.data = value + else: + hf_pointer.data = value + + logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.") + + +def rename_dict(key, value, full_name, weight_type, hf_dict): + hf_param_name = None + for param_key in PARAM_MAPPING.keys(): + if full_name.endswith(param_key): + hf_param_name = PARAM_MAPPING[full_name.split(".")[-1]] + weight_type = "param" + + if weight_type is not None and weight_type != "param": + full_key = ".".join([key, weight_type]) + elif weight_type is not None and weight_type == "param": + full_key = ".".join([key, hf_param_name]) + else: + full_key = key + + hf_dict[full_key] = value if "lm_head" in full_key else value[0] + + +PARAM_MAPPING = { + "W_a": "linear_1.weight", + "W_b": "linear_2.weight", + "b_a": "linear_1.bias", + "b_b": "linear_2.bias", + "ln_W": "norm.weight", + "ln_b": "norm.bias", +} + + +def load_wav2vec2_layer(name, value, hf_model=None, hf_dict=None): + is_used = False + for key, mapped_key in MAPPING.items(): + mapped_key = "wav2vec2." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key + if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]: + is_used = True + if "*" in mapped_key: + layer_index = name.split(key)[0].split(".")[-2] + mapped_key = mapped_key.replace("*", layer_index) + if "weight_g" in name: + weight_type = "weight_g" + elif "weight_v" in name: + weight_type = "weight_v" + elif "bias" in name: + weight_type = "bias" + elif "weight" in name: + # TODO: don't match quantizer.weight_proj + weight_type = "weight" + else: + weight_type = None + if hf_dict is not None: + rename_dict(mapped_key, value, name, weight_type, hf_dict) + else: + set_recursively(mapped_key, value, name, weight_type, hf_model) + return is_used + return is_used + + +def recursively_load_weights(fairseq_model, hf_model, is_headless): + unused_weights = [] + fairseq_dict = fairseq_model.state_dict() + + feature_extractor = hf_model.wav2vec2.feature_extractor + + for name, value in fairseq_dict.items(): + is_used = False + if "conv_layers" in name: + load_conv_layer( + name, + value, + feature_extractor, + unused_weights, + hf_model.config.feat_extract_norm == "group", + ) + is_used = True + else: + is_used = load_wav2vec2_layer(name, value, hf_model) + if not is_used: + unused_weights.append(name) + + logger.warning(f"Unused weights: {unused_weights}") + + +def load_conv_layer(full_name, value, feature_extractor, unused_weights, use_group_norm): + name = full_name.split("conv_layers.")[-1] + items = name.split(".") + layer_id = int(items[0]) + type_id = int(items[1]) + + if type_id == 0: + if "bias" in name: + if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape: + raise ValueError( + f"{full_name} has size {value.shape}, but" + f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found." + ) + feature_extractor.conv_layers[layer_id].conv.bias.data = value + logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.") + elif "weight" in name: + if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape: + raise ValueError( + f"{full_name} has size {value.shape}, but" + f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found." + ) + feature_extractor.conv_layers[layer_id].conv.weight.data = value + logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.") + elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): + if "bias" in name: + if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape: + raise ValueError( + f"{full_name} has size {value.shape}, but" + f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found." + ) + feature_extractor.conv_layers[layer_id].layer_norm.bias.data = value + logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.") + elif "weight" in name: + if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape: + raise ValueError( + f"{full_name} has size {value.shape}, but" + f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found." + ) + feature_extractor.conv_layers[layer_id].layer_norm.weight.data = value + logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.") + else: + unused_weights.append(full_name) + + +@torch.no_grad() +def convert_wav2vec2_checkpoint( + checkpoint_path, pytorch_dump_folder_path, config_path=None, dict_path=None, is_finetuned=True, is_seq_class=False +): + """ + Copy/paste/tweak model's weights to transformers design. + """ + if config_path is not None: + config = Wav2Vec2Config.from_pretrained(config_path) + else: + config = Wav2Vec2Config() + + if is_seq_class: + id2label = read_txt_into_dict(dict_path) + config.id2label = id2label + hf_wav2vec = Wav2Vec2ForSequenceClassification(config) + feature_extractor = Wav2Vec2FeatureExtractor( + feature_size=1, + sampling_rate=16000, + padding_value=0, + do_normalize=True, + return_attention_mask=True, + ) + feature_extractor.save_pretrained(pytorch_dump_folder_path) + + elif is_finetuned: + if dict_path: + target_dict = Dictionary.load(dict_path) + + # important change bos & pad token id since CTC symbol is and + # not as in fairseq + config.bos_token_id = target_dict.pad_index + config.pad_token_id = target_dict.bos_index + config.eos_token_id = target_dict.eos_index + config.vocab_size = len(target_dict.symbols) + vocab_path = os.path.join(pytorch_dump_folder_path, "vocab.json") + if not os.path.isdir(pytorch_dump_folder_path): + logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(pytorch_dump_folder_path)) + return + os.makedirs(pytorch_dump_folder_path, exist_ok=True) + vocab_dict = target_dict.indices + + # fairseq has the and switched + vocab_dict[""] = 0 + vocab_dict[""] = 1 + with open(vocab_path, "w", encoding="utf-8") as vocab_handle: + json.dump(vocab_dict, vocab_handle) + tokenizer = Wav2Vec2CTCTokenizer( + vocab_path, + unk_token=target_dict.unk_word, + pad_token=target_dict.pad_word, + bos_token=target_dict.bos_word, + eos_token=target_dict.eos_word, + word_delimiter_token="|", + do_lower_case=False, + ) + return_attention_mask = True if config.feat_extract_norm == "layer" else False + feature_extractor = Wav2Vec2FeatureExtractor( + feature_size=1, + sampling_rate=16000, + padding_value=0, + do_normalize=True, + return_attention_mask=return_attention_mask, + ) + processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) + processor.save_pretrained(pytorch_dump_folder_path) + + hf_wav2vec = Wav2Vec2ForCTC(config) + else: + hf_wav2vec = Wav2Vec2ForPreTraining(config) + + if is_finetuned or is_seq_class: + model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task( + [checkpoint_path], arg_overrides={"data": "/".join(dict_path.split("/")[:-1])} + ) + else: + task_arg = argparse.Namespace(task="audio_pretraining") + task = fairseq.tasks.setup_task(task_arg) + + model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path], task=task) + + model = model[0].eval() + + recursively_load_weights(model, hf_wav2vec, not is_finetuned) + + hf_wav2vec.save_pretrained(pytorch_dump_folder_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") + parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint") + parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model") + parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert") + parser.add_argument( + "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not" + ) + parser.add_argument( + "--is_seq_class", + action="store_true", + help="Whether the model to convert is a fine-tuned sequence classification model or not", + ) + args = parser.parse_args() + + is_finetuned = not args.not_finetuned and not args.is_seq_class + convert_wav2vec2_checkpoint( + args.checkpoint_path, + args.pytorch_dump_folder_path, + args.config_path, + args.dict_path, + is_finetuned, + args.is_seq_class, + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/convert_wav2vec2_original_s3prl_checkpoint_to_pytorch.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/convert_wav2vec2_original_s3prl_checkpoint_to_pytorch.py new file mode 100644 index 0000000000000000000000000000000000000000..bcc9fd95a4d2448656c0d1d1b521a79cbd7bc8f7 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/convert_wav2vec2_original_s3prl_checkpoint_to_pytorch.py @@ -0,0 +1,110 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert Hubert checkpoint.""" + + +import argparse + +import torch + +from transformers import ( + Wav2Vec2Config, + Wav2Vec2FeatureExtractor, + Wav2Vec2ForAudioFrameClassification, + Wav2Vec2ForSequenceClassification, + Wav2Vec2ForXVector, + logging, +) + + +logging.set_verbosity_info() +logger = logging.get_logger(__name__) + + +def convert_classification(base_model_name, hf_config, downstream_dict): + model = Wav2Vec2ForSequenceClassification.from_pretrained(base_model_name, config=hf_config) + model.projector.weight.data = downstream_dict["projector.weight"] + model.projector.bias.data = downstream_dict["projector.bias"] + model.classifier.weight.data = downstream_dict["model.post_net.linear.weight"] + model.classifier.bias.data = downstream_dict["model.post_net.linear.bias"] + return model + + +def convert_diarization(base_model_name, hf_config, downstream_dict): + model = Wav2Vec2ForAudioFrameClassification.from_pretrained(base_model_name, config=hf_config) + model.classifier.weight.data = downstream_dict["model.linear.weight"] + model.classifier.bias.data = downstream_dict["model.linear.bias"] + return model + + +def convert_xvector(base_model_name, hf_config, downstream_dict): + model = Wav2Vec2ForXVector.from_pretrained(base_model_name, config=hf_config) + model.projector.weight.data = downstream_dict["connector.weight"] + model.projector.bias.data = downstream_dict["connector.bias"] + for i, kernel_size in enumerate(hf_config.tdnn_kernel): + model.tdnn[i].kernel.weight.data = downstream_dict[ + f"model.framelevel_feature_extractor.module.{i}.kernel.weight" + ] + model.tdnn[i].kernel.bias.data = downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"] + + model.feature_extractor.weight.data = downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"] + model.feature_extractor.bias.data = downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"] + model.classifier.weight.data = downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"] + model.classifier.bias.data = downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"] + model.objective.weight.data = downstream_dict["objective.W"] + return model + + +@torch.no_grad() +def convert_s3prl_checkpoint(base_model_name, config_path, checkpoint_path, model_dump_path): + """ + Copy/paste/tweak model's weights to transformers design. + """ + checkpoint = torch.load(checkpoint_path, map_location="cpu") + + downstream_dict = checkpoint["Downstream"] + + hf_config = Wav2Vec2Config.from_pretrained(config_path) + hf_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( + base_model_name, return_attention_mask=True, do_normalize=False + ) + + arch = hf_config.architectures[0] + if arch.endswith("ForSequenceClassification"): + hf_model = convert_classification(base_model_name, hf_config, downstream_dict) + elif arch.endswith("ForAudioFrameClassification"): + hf_model = convert_diarization(base_model_name, hf_config, downstream_dict) + elif arch.endswith("ForXVector"): + hf_model = convert_xvector(base_model_name, hf_config, downstream_dict) + else: + raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}") + + if hf_config.use_weighted_layer_sum: + hf_model.layer_weights.data = checkpoint["Featurizer"]["weights"] + + hf_feature_extractor.save_pretrained(model_dump_path) + hf_model.save_pretrained(model_dump_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model." + ) + parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.") + parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.") + parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.") + args = parser.parse_args() + convert_s3prl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/feature_extraction_wav2vec2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/feature_extraction_wav2vec2.py new file mode 100644 index 0000000000000000000000000000000000000000..2c2066739ddd49fbd0e5451143f22b131826cd89 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/feature_extraction_wav2vec2.py @@ -0,0 +1,240 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Feature extractor class for Wav2Vec2 +""" + +from typing import List, Optional, Union + +import numpy as np + +from ...feature_extraction_sequence_utils import SequenceFeatureExtractor +from ...feature_extraction_utils import BatchFeature +from ...utils import PaddingStrategy, TensorType, logging + + +logger = logging.get_logger(__name__) + + +class Wav2Vec2FeatureExtractor(SequenceFeatureExtractor): + r""" + Constructs a Wav2Vec2 feature extractor. + + This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains + most of the main methods. Users should refer to this superclass for more information regarding those methods. + + Args: + feature_size (`int`, defaults to 1): + The feature dimension of the extracted features. + sampling_rate (`int`, defaults to 16000): + The sampling rate at which the audio files should be digitalized expressed in hertz (Hz). + padding_value (`float`, defaults to 0.0): + The value that is used to fill the padding values. + do_normalize (`bool`, *optional*, defaults to `True`): + Whether or not to zero-mean unit-variance normalize the input. Normalizing can help to significantly + improve the performance for some models, *e.g.*, + [wav2vec2-lv60](https://huggingface.co/models?search=lv60). + return_attention_mask (`bool`, *optional*, defaults to `False`): + Whether or not [`~Wav2Vec2FeatureExtractor.__call__`] should return `attention_mask`. + + + + Wav2Vec2 models that have set `config.feat_extract_norm == "group"`, such as + [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), have **not** been trained using + `attention_mask`. For such models, `input_values` should simply be padded with 0 and no `attention_mask` + should be passed. + + For Wav2Vec2 models that have set `config.feat_extract_norm == "layer"`, such as + [wav2vec2-lv60](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self), `attention_mask` should be + passed for batched inference. + + """ + + model_input_names = ["input_values", "attention_mask"] + + def __init__( + self, + feature_size=1, + sampling_rate=16000, + padding_value=0.0, + return_attention_mask=False, + do_normalize=True, + **kwargs, + ): + super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs) + self.return_attention_mask = return_attention_mask + self.do_normalize = do_normalize + + @staticmethod + def zero_mean_unit_var_norm( + input_values: List[np.ndarray], attention_mask: List[np.ndarray], padding_value: float = 0.0 + ) -> List[np.ndarray]: + """ + Every array in the list is normalized to have zero mean and unit variance + """ + if attention_mask is not None: + attention_mask = np.array(attention_mask, np.int32) + normed_input_values = [] + + for vector, length in zip(input_values, attention_mask.sum(-1)): + normed_slice = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7) + if length < normed_slice.shape[0]: + normed_slice[length:] = padding_value + + normed_input_values.append(normed_slice) + else: + normed_input_values = [(x - x.mean()) / np.sqrt(x.var() + 1e-7) for x in input_values] + + return normed_input_values + + def __call__( + self, + raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]], + padding: Union[bool, str, PaddingStrategy] = False, + max_length: Optional[int] = None, + truncation: bool = False, + pad_to_multiple_of: Optional[int] = None, + return_attention_mask: Optional[bool] = None, + return_tensors: Optional[Union[str, TensorType]] = None, + sampling_rate: Optional[int] = None, + **kwargs, + ) -> BatchFeature: + """ + Main method to featurize and prepare for the model one or several sequence(s). + + Args: + raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`): + The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float + values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not + stereo, i.e. single float per timestep. + padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): + Select a strategy to pad the returned sequences (according to the model's padding side and padding + index) among: + + - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single + sequence if provided). + - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum + acceptable input length for the model if that argument is not provided. + - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different + lengths). + max_length (`int`, *optional*): + Maximum length of the returned list and optionally padding length (see above). + truncation (`bool`): + Activates truncation to cut input sequences longer than *max_length* to *max_length*. + pad_to_multiple_of (`int`, *optional*): + If set will pad the sequence to a multiple of the provided value. + + This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability + `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128. + return_attention_mask (`bool`, *optional*): + Whether to return the attention mask. If left to the default, will return the attention mask according + to the specific feature_extractor's default. + + [What are attention masks?](../glossary#attention-mask) + + + + Wav2Vec2 models that have set `config.feat_extract_norm == "group"`, such as + [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), have **not** been trained using + `attention_mask`. For such models, `input_values` should simply be padded with 0 and no + `attention_mask` should be passed. + + For Wav2Vec2 models that have set `config.feat_extract_norm == "layer"`, such as + [wav2vec2-lv60](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self), `attention_mask` should + be passed for batched inference. + + + + return_tensors (`str` or [`~utils.TensorType`], *optional*): + If set, will return tensors instead of list of python integers. Acceptable values are: + + - `'tf'`: Return TensorFlow `tf.constant` objects. + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return Numpy `np.ndarray` objects. + sampling_rate (`int`, *optional*): + The sampling rate at which the `raw_speech` input was sampled. It is strongly recommended to pass + `sampling_rate` at the forward call to prevent silent errors. + padding_value (`float`, defaults to 0.0): + """ + + if sampling_rate is not None: + if sampling_rate != self.sampling_rate: + raise ValueError( + f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of" + f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with" + f" {self.sampling_rate} and not {sampling_rate}." + ) + else: + logger.warning( + "It is strongly recommended to pass the ``sampling_rate`` argument to this function. " + "Failing to do so can result in silent errors that might be hard to debug." + ) + + is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1 + if is_batched_numpy and len(raw_speech.shape) > 2: + raise ValueError(f"Only mono-channel audio is supported for input to {self}") + is_batched = is_batched_numpy or ( + isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list))) + ) + + # always return batch + if not is_batched: + raw_speech = [raw_speech] + + # convert into correct format for padding + encoded_inputs = BatchFeature({"input_values": raw_speech}) + + padded_inputs = self.pad( + encoded_inputs, + padding=padding, + max_length=max_length, + truncation=truncation, + pad_to_multiple_of=pad_to_multiple_of, + return_attention_mask=return_attention_mask, + ) + + # convert input values to correct format + input_values = padded_inputs["input_values"] + if not isinstance(input_values[0], np.ndarray): + padded_inputs["input_values"] = [np.asarray(array, dtype=np.float32) for array in input_values] + elif ( + not isinstance(input_values, np.ndarray) + and isinstance(input_values[0], np.ndarray) + and input_values[0].dtype is np.dtype(np.float64) + ): + padded_inputs["input_values"] = [array.astype(np.float32) for array in input_values] + elif isinstance(input_values, np.ndarray) and input_values.dtype is np.dtype(np.float64): + padded_inputs["input_values"] = input_values.astype(np.float32) + + # convert attention_mask to correct format + attention_mask = padded_inputs.get("attention_mask") + if attention_mask is not None: + padded_inputs["attention_mask"] = [np.asarray(array, dtype=np.int32) for array in attention_mask] + + # zero-mean and unit-variance normalization + if self.do_normalize: + attention_mask = ( + attention_mask + if self._get_padding_strategies(padding, max_length=max_length) is not PaddingStrategy.DO_NOT_PAD + else None + ) + padded_inputs["input_values"] = self.zero_mean_unit_var_norm( + padded_inputs["input_values"], attention_mask=attention_mask, padding_value=self.padding_value + ) + + if return_tensors is not None: + padded_inputs = padded_inputs.convert_to_tensors(return_tensors) + + return padded_inputs diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/modeling_flax_wav2vec2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/modeling_flax_wav2vec2.py new file mode 100644 index 0000000000000000000000000000000000000000..86cfb5e089ea006116541a5af3eacd17f0554a89 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/modeling_flax_wav2vec2.py @@ -0,0 +1,1425 @@ +# coding=utf-8 +# Copyright 2021 The Fairseq Authors and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Flax Wav2Vec2 model.""" + +from functools import partial +from typing import Optional, Tuple, Union + +import flax +import flax.linen as nn +import jax +import jax.numpy as jnp +import numpy as np +from flax.core.frozen_dict import FrozenDict, freeze, unfreeze +from flax.linen.attention import dot_product_attention_weights +from flax.traverse_util import flatten_dict, unflatten_dict +from jax import lax + +from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutput +from ...modeling_flax_utils import ( + ACT2FN, + FlaxPreTrainedModel, + append_replace_return_docstrings, + overwrite_call_docstring, +) +from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging +from .configuration_wav2vec2 import Wav2Vec2Config + + +logger = logging.get_logger(__name__) + + +@flax.struct.dataclass +class FlaxWav2Vec2BaseModelOutput(ModelOutput): + """ + Output type of [`FlaxWav2Vec2BaseModelOutput`], with potential hidden states and attentions. + + Args: + last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + extract_features (`jnp.ndarray` of shape `(batch_size, sequence_length, last_conv_dim)`): + Sequence of extracted feature vectors of the last convolutional layer of the model with `last_conv_dim` + being the dimension of the last convolutional layer. + hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape + `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + """ + + last_hidden_state: jnp.ndarray = None + extract_features: jnp.ndarray = None + hidden_states: Optional[Tuple[jnp.ndarray]] = None + attentions: Optional[Tuple[jnp.ndarray]] = None + + +@flax.struct.dataclass +class FlaxWav2Vec2ForPreTrainingOutput(ModelOutput): + """ + Output type of [`FlaxWav2Vec2ForPreTrainingOutput`], with potential hidden states and attentions. + + Args: + loss (*optional*, returned when model is in train mode, `jnp.ndarray` of shape `(1,)`): + Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the [official + paper](https://arxiv.org/pdf/2006.11477.pdf) . (classification) loss. + projected_states (`jnp.ndarray` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`): + Hidden-states of the model projected to *config.proj_codevector_dim* that can be used to predict the masked + projected quantized states. + projected_quantized_states (`jnp.ndarray` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`): + Quantized extracted feature vectors projected to *config.proj_codevector_dim* representing the positive + target vectors for contrastive loss. + hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape + `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + """ + + projected_states: jnp.ndarray = None + projected_quantized_states: jnp.ndarray = None + codevector_perplexity: jnp.ndarray = None + hidden_states: Optional[Tuple[jnp.ndarray]] = None + attentions: Optional[Tuple[jnp.ndarray]] = None + + +def _compute_mask_indices( + shape: Tuple[int, int], + mask_prob: float, + mask_length: int, + attention_mask: Optional[np.ndarray] = None, + min_masks: int = 0, +) -> np.ndarray: + """ + Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for + ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on + CPU as part of the preprocessing during training. + + Args: + shape: the shape for which to compute masks. + should be of size 2 where first element is batch size and 2nd is timesteps + mask_prob: + probability for each token to be chosen as start of the span to be masked. this will be multiplied by + number of timesteps divided by length of mask span to mask approximately this percentage of all elements. + however due to overlaps, the actual number will be smaller (unless no_overlap is True) + mask_length: size of the mask + min_masks: minimum number of masked spans + + """ + batch_size, sequence_length = shape + + if mask_length < 1: + raise ValueError("`mask_length` has to be bigger than 0.") + + if mask_length > sequence_length: + raise ValueError( + f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and" + f" `sequence_length`: {sequence_length}`" + ) + + # compute number of masked spans in batch + num_masked_spans = int(mask_prob * sequence_length / mask_length + np.random.rand(1).item()) + num_masked_spans = max(num_masked_spans, min_masks) + + # make sure num masked indices <= sequence_length + if num_masked_spans * mask_length > sequence_length: + num_masked_spans = sequence_length // mask_length + + # SpecAugment mask to fill + spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool) + + # get random indices to mask + spec_aug_mask_idxs = np.array( + [ + np.random.choice(np.arange(sequence_length - (mask_length - 1)), num_masked_spans, replace=False) + for _ in range(batch_size) + ] + ) + + # expand masked indices to masked spans + spec_aug_mask_idxs = np.broadcast_to(spec_aug_mask_idxs[:, :, None], (batch_size, num_masked_spans, mask_length)) + spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, num_masked_spans * mask_length) + + offsets = np.arange(mask_length)[None, None, :] + offsets = np.broadcast_to(offsets, (batch_size, num_masked_spans, mask_length)).reshape( + batch_size, num_masked_spans * mask_length + ) + spec_aug_mask_idxs = spec_aug_mask_idxs + offsets + + # scatter indices to mask + np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1) + + if attention_mask is not None: + # make sure padded input ids cannot be masked + spec_aug_mask = np.where(attention_mask, spec_aug_mask, False) + + return spec_aug_mask + + +def _sample_negative_indices(features_shape: Tuple, num_negatives: int, attention_mask: Optional[np.ndarray] = None): + """ + Sample `num_negatives` vectors from feature vectors. + """ + batch_size, sequence_length, hidden_size = features_shape + if sequence_length <= 1: + raise ValueError( + "`features should have `sequence_length` > 1, but are of shape " + f"(batch_size, sequence_length, hidden_size) = ({batch_size, sequence_length, hidden_size})." + ) + + # get `num_negatives` random vector indices from the same utterance + sampled_negative_indices = [] + for batch_idx in range(batch_size): + high = attention_mask[batch_idx].sum() - 1 if attention_mask is not None else sequence_length - 1 + sampled_indices_slice = np.random.randint(0, high, size=(num_negatives * sequence_length,)) + sampled_negative_indices.append(sampled_indices_slice) + + sampled_negative_indices = np.asarray(sampled_negative_indices, dtype=np.int32) + + # generate indices of the positive vectors themselves, repeat them `num_negatives` times + feature_indices = np.broadcast_to(np.arange(sequence_length)[:, None], (sequence_length, num_negatives)).flatten() + + # avoid sampling the same positive vector, but keep the distribution uniform + sampled_negative_indices[sampled_negative_indices >= feature_indices] += 1 + + # correct for batch size + for batch_idx in range(1, batch_size): + sampled_negative_indices[batch_idx] += batch_idx * sequence_length + + return sampled_negative_indices + + +WAV_2_VEC_2_START_DOCSTRING = r""" + Wav2Vec2 was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech + Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael + Auli. + + This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a Flax Linen + [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a + regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. + + Finally, this model supports inherent JAX features such as: + + - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) + - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) + - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) + - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) + + Parameters: + config ([`Wav2Vec2Config`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. + dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): + The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and + `jax.numpy.bfloat16` (on TPUs). + + This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If + specified all the computation will be performed with the given `dtype`. + + **Note that this only specifies the dtype of the computation and does not influence the dtype of model + parameters.** + + If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and + [`~FlaxPreTrainedModel.to_bf16`]. +""" + + +WAV_2_VEC_2_INPUTS_DOCSTRING = r""" + Args: + input_values (`jnp.ndarray` of shape `(batch_size, sequence_length)`): + Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file + into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install + soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and + conversion into a tensor of type `jnp.ndarray`. See [`Wav2Vec2Processor.__call__`] for details. + attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0, + 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) .. warning:: `attention_mask` should only be passed + if the corresponding processor has `config.return_attention_mask == True`. For all models whose processor + has `config.return_attention_mask == False`, such as + [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), `attention_mask` should **not** be + passed to avoid degraded performance when doing batched inference. For such models `input_values` should + simply be padded with 0 and passed without `attention_mask`. Be aware that these models also yield slightly + different results depending on whether `input_values` is padded or not. + mask_time_indices (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): + Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict + masked extracted features in *config.proj_codevector_dim* space. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class FlaxWav2Vec2LayerNormConvLayer(nn.Module): + config: Wav2Vec2Config + layer_id: int = 0 + dtype: jnp.dtype = jnp.float32 + + def setup(self): + self.in_conv_dim = self.config.conv_dim[self.layer_id] if self.layer_id > 0 else 1 + self.out_conv_dim = self.config.conv_dim[self.layer_id] + + self.conv = nn.Conv( + features=self.config.conv_dim[self.layer_id], + kernel_size=(self.config.conv_kernel[self.layer_id],), + strides=(self.config.conv_stride[self.layer_id],), + use_bias=self.config.conv_bias, + kernel_init=jax.nn.initializers.he_normal(), + padding="VALID", + dtype=self.dtype, + ) + self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) + self.activation = ACT2FN[self.config.feat_extract_activation] + + def __call__(self, hidden_states): + hidden_states = self.conv(hidden_states) + hidden_states = self.layer_norm(hidden_states) + hidden_states = self.activation(hidden_states) + return hidden_states + + +class FlaxConvWithWeightNorm(nn.Module): + config: Wav2Vec2Config + dtype: jnp.dtype = jnp.float32 + + def setup(self): + self.conv = nn.Conv( + features=self.config.hidden_size, + kernel_size=(self.config.num_conv_pos_embeddings,), + kernel_init=jax.nn.initializers.he_normal(), + padding="VALID", + feature_group_count=self.config.num_conv_pos_embedding_groups, + dtype=self.dtype, + ) + weight_shape = ( + self.conv.features, + self.conv.features // self.conv.feature_group_count, + self.conv.kernel_size[0], + ) + self.weight_v = self.param("weight_v", jax.nn.initializers.he_normal(), weight_shape) + self.weight_g = self.param("weight_g", lambda _: jnp.linalg.norm(self.weight_v, axis=(0, 1))[None, None, :]) + self.bias = self.param("bias", jax.nn.initializers.zeros, (self.conv.features,)) + self.prev_padding = self.conv.kernel_size[0] // 2 + + def _get_normed_weights(self): + weight_v_norm = jnp.linalg.norm(self.weight_v, axis=(0, 1))[None, None, :] + normed_weight_v = jnp.divide(self.weight_v, weight_v_norm) + normed_kernel = jnp.multiply(normed_weight_v, self.weight_g) + return normed_kernel + + def __call__(self, hidden_states): + kernel = self._get_normed_weights() + hidden_states = jnp.pad(hidden_states, ((0, 0), (self.prev_padding, self.prev_padding), (0, 0))) + hidden_states = self.conv.apply({"params": {"kernel": kernel.T, "bias": self.bias}}, hidden_states) + return hidden_states + + +class FlaxWav2Vec2PositionalConvEmbedding(nn.Module): + config: Wav2Vec2Config + dtype: jnp.dtype = jnp.float32 + + def setup(self): + self.conv = FlaxConvWithWeightNorm(self.config, dtype=self.dtype) + self.activation = ACT2FN[self.config.feat_extract_activation] + self.num_pad_remove = 1 if self.config.num_conv_pos_embeddings % 2 == 0 else 0 + + def __call__(self, hidden_states): + hidden_states = hidden_states.transpose((0, 1, 2)) + + hidden_states = self.conv(hidden_states) + + if self.num_pad_remove > 0: + hidden_states = hidden_states[:, : -self.num_pad_remove, :] + hidden_states = self.activation(hidden_states) + + hidden_states = hidden_states.transpose((0, 1, 2)) + return hidden_states + + +class FlaxConvLayersCollection(nn.Module): + config: Wav2Vec2Config + dtype: jnp.dtype = jnp.float32 + + def setup(self): + if self.config.feat_extract_norm == "layer": + self.layers = [ + FlaxWav2Vec2LayerNormConvLayer(self.config, layer_id=i, name=str(i), dtype=self.dtype) + for i in range(self.config.num_feat_extract_layers) + ] + elif self.config.feat_extract_norm == "group": + raise NotImplementedError("At the moment only ``config.feat_extact_norm == 'layer'`` is supported") + else: + raise ValueError( + f"`config.feat_extract_norm` is {self.config.feat_extract_norm}, but has to be one of ['group'," + " 'layer']" + ) + + def __call__(self, hidden_states): + for i, conv_layer in enumerate(self.layers): + hidden_states = conv_layer(hidden_states) + return hidden_states + + +class FlaxWav2Vec2FeatureEncoder(nn.Module): + """Construct the features from raw audio waveform""" + + config: Wav2Vec2Config + dtype: jnp.dtype = jnp.float32 + + def setup(self): + self.conv_layers = FlaxConvLayersCollection(self.config, dtype=self.dtype) + + def __call__(self, input_values, freeze_feature_encoder=False): + hidden_states = input_values[:, :, None] + hidden_states = self.conv_layers(hidden_states) + if freeze_feature_encoder: + hidden_states = jax.lax.stop_gradient(hidden_states) + return hidden_states + + +class FlaxWav2Vec2FeatureProjection(nn.Module): + config: Wav2Vec2Config + dtype: jnp.dtype = jnp.float32 + + def setup(self): + self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) + self.projection = nn.Dense( + self.config.hidden_size, + kernel_init=jax.nn.initializers.normal(self.config.initializer_range), + dtype=self.dtype, + ) + self.dropout = nn.Dropout(rate=self.config.feat_proj_dropout) + + def __call__(self, hidden_states, deterministic=True): + norm_hidden_states = self.layer_norm(hidden_states) + hidden_states = self.projection(norm_hidden_states) + hidden_states = self.dropout(hidden_states, deterministic=deterministic) + return hidden_states, norm_hidden_states + + +class FlaxWav2Vec2Attention(nn.Module): + config: Wav2Vec2Config + embed_dim: int + num_heads: int + dropout: float = 0.0 + bias: bool = True + dtype: jnp.dtype = jnp.float32 # the dtype of the computation + + def setup(self) -> None: + self.head_dim = self.embed_dim // self.num_heads + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {self.num_heads})." + ) + + dense = partial( + nn.Dense, + self.embed_dim, + use_bias=self.bias, + dtype=self.dtype, + kernel_init=jax.nn.initializers.normal(self.config.initializer_range), + ) + + self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense() + self.out_proj = dense() + + self.dropout_layer = nn.Dropout(rate=self.dropout) + + def _split_heads(self, hidden_states): + return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim)) + + def _merge_heads(self, hidden_states): + return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,)) + + def __call__( + self, + hidden_states: jnp.ndarray, + key_value_states: Optional[jnp.ndarray] = None, + attention_mask: Optional[jnp.ndarray] = None, + deterministic: bool = True, + ) -> Tuple[jnp.ndarray]: + """Input shape: Batch x Time x Channel""" + + # get query proj + query_states = self.q_proj(hidden_states) + + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = self._split_heads(query_states) + key_states = self._split_heads(key_states) + value_states = self._split_heads(value_states) + + if attention_mask is not None: + attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) + + # Convert the boolean attention mask to an attention bias. + if attention_mask is not None: + # attention mask in the form of attention bias + attention_bias = lax.select( + attention_mask > 0, + jnp.full(attention_mask.shape, 0.0).astype(self.dtype), + jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype), + ) + else: + attention_bias = None + + dropout_rng = None + if not deterministic and self.dropout > 0.0: + dropout_rng = self.make_rng("dropout") + + attn_weights = dot_product_attention_weights( + query_states, + key_states, + bias=attention_bias, + dropout_rng=dropout_rng, + dropout_rate=self.dropout, + broadcast_dropout=True, + deterministic=deterministic, + dtype=self.dtype, + precision=None, + ) + + attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) + attn_output = self._merge_heads(attn_output) + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +class FlaxWav2Vec2FeedForward(nn.Module): + config: Wav2Vec2Config + dtype: jnp.dtype = jnp.float32 + + def setup(self): + self.intermediate_dropout = nn.Dropout(rate=self.config.activation_dropout) + + self.intermediate_dense = nn.Dense( + self.config.intermediate_size, + kernel_init=jax.nn.initializers.normal(self.config.initializer_range), + dtype=self.dtype, + ) + if isinstance(self.config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[self.config.hidden_act] + else: + self.intermediate_act_fn = self.config.hidden_act + + self.output_dense = nn.Dense( + self.config.hidden_size, + kernel_init=jax.nn.initializers.normal(self.config.initializer_range), + dtype=self.dtype, + ) + self.output_dropout = nn.Dropout(rate=self.config.hidden_dropout) + + def __call__(self, hidden_states, deterministic=True): + hidden_states = self.intermediate_dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + hidden_states = self.intermediate_dropout(hidden_states, deterministic=deterministic) + + hidden_states = self.output_dense(hidden_states) + hidden_states = self.output_dropout(hidden_states, deterministic=deterministic) + return hidden_states + + +class FlaxWav2Vec2EncoderLayerStableLayerNorm(nn.Module): + config: Wav2Vec2Config + dtype: jnp.dtype = jnp.float32 + + def setup(self): + self.attention = FlaxWav2Vec2Attention( + config=self.config, + embed_dim=self.config.hidden_size, + num_heads=self.config.num_attention_heads, + dropout=self.config.attention_dropout, + dtype=self.dtype, + ) + self.dropout = nn.Dropout(rate=self.config.hidden_dropout) + self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) + self.feed_forward = FlaxWav2Vec2FeedForward(self.config, dtype=self.dtype) + self.final_layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) + + def __call__(self, hidden_states, attention_mask=None, deterministic=True, output_attentions=False): + attn_residual = hidden_states + hidden_states = self.layer_norm(hidden_states) + hidden_states, attn_weights = self.attention( + hidden_states, attention_mask=attention_mask, deterministic=deterministic + ) + hidden_states = self.dropout(hidden_states, deterministic=deterministic) + hidden_states = attn_residual + hidden_states + hidden_states = hidden_states + self.feed_forward( + self.final_layer_norm(hidden_states), deterministic=deterministic + ) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +class FlaxWav2Vec2EncoderLayerStableLayerNormCollection(nn.Module): + config: Wav2Vec2Config + dtype: jnp.dtype = jnp.float32 + + def setup(self): + self.layers = [ + FlaxWav2Vec2EncoderLayerStableLayerNorm(self.config, name=str(i), dtype=self.dtype) + for i in range(self.config.num_hidden_layers) + ] + + def __call__( + self, + hidden_states, + attention_mask=None, + deterministic: bool = True, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ): + all_attentions = () if output_attentions else None + all_hidden_states = () if output_hidden_states else None + + for i, layer in enumerate(self.layers): + if output_hidden_states: + all_hidden_states += (hidden_states,) + + layer_outputs = layer( + hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions + ) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_attentions += (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states += (hidden_states,) + + outputs = (hidden_states, all_hidden_states, all_attentions) + + if not return_dict: + return tuple(v for v in outputs if v is not None) + + return FlaxBaseModelOutput( + last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions + ) + + +class FlaxWav2Vec2StableLayerNormEncoder(nn.Module): + config: Wav2Vec2Config + dtype: jnp.dtype = jnp.float32 + + def setup(self): + self.pos_conv_embed = FlaxWav2Vec2PositionalConvEmbedding(self.config, dtype=self.dtype) + self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) + self.dropout = nn.Dropout(rate=self.config.hidden_dropout) + self.layers = FlaxWav2Vec2EncoderLayerStableLayerNormCollection(self.config, dtype=self.dtype) + + def __call__( + self, + hidden_states, + attention_mask=None, + deterministic=True, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + ): + if attention_mask is not None: + # make sure padded tokens are not attended to + hidden_states = jnp.where( + jnp.broadcast_to(attention_mask[:, :, None], hidden_states.shape), hidden_states, 0 + ) + + position_embeddings = self.pos_conv_embed(hidden_states) + + hidden_states = hidden_states + position_embeddings + hidden_states = self.dropout(hidden_states, deterministic=deterministic) + + outputs = self.layers( + hidden_states, + attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + last_hidden_state = self.layer_norm(outputs[0]) + + # update the last element in `hidden_states` after applying `layernorm` above + hidden_states = None + if output_hidden_states: + hidden_states = outputs[1] + hidden_states = hidden_states[:-1] + (last_hidden_state,) + + if not return_dict: + outputs = (last_hidden_state, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:]) + return tuple(v for v in outputs if v is not None) + + return FlaxBaseModelOutput( + last_hidden_state=last_hidden_state, hidden_states=hidden_states, attentions=outputs.attentions + ) + + +class FlaxWav2Vec2GumbelVectorQuantizer(nn.Module): + """ + Vector quantization using gumbel softmax. See [CATEGORICAL REPARAMETERIZATION WITH + GUMBEL-SOFTMAX](https://arxiv.org/pdf/1611.01144.pdf) for more information. + """ + + config: Wav2Vec2Config + dtype: jnp.dtype = jnp.float32 + + def setup(self): + self.num_groups = self.config.num_codevector_groups + self.num_vars = self.config.num_codevectors_per_group + + if self.config.codevector_dim % self.num_groups != 0: + raise ValueError( + f"`config.codevector_dim {self.config.codevector_dim} must be divisible by" + f" `config.num_codevector_groups` {self.num_groups} for concatenation" + ) + + # storage for codebook variables (codewords) + self.codevectors = self.param( + "codevectors", + jax.nn.initializers.uniform(), + (1, self.num_groups * self.num_vars, self.config.codevector_dim // self.num_groups), + ) + self.weight_proj = nn.Dense( + self.num_groups * self.num_vars, + kernel_init=jax.nn.initializers.normal(1.0), + dtype=self.dtype, + ) + + @staticmethod + def _compute_perplexity(probs, mask=None): + if mask is not None: + mask_extended = jnp.broadcast_to(mask.flatten()[:, None, None], probs.shape) + probs = jnp.where(mask_extended, probs, jnp.zeros_like(probs)) + marginal_probs = probs.sum(axis=0) / mask.sum() + else: + marginal_probs = probs.mean(axis=0) + + perplexity = jnp.exp(-jnp.sum(marginal_probs * jnp.log(marginal_probs + 1e-7), axis=-1)).sum() + return perplexity + + def __call__(self, hidden_states, mask_time_indices=None, deterministic=True, temperature=1): + batch_size, sequence_length, hidden_size = hidden_states.shape + + # project to codevector dim + hidden_states = self.weight_proj(hidden_states) + hidden_states = hidden_states.reshape(batch_size * sequence_length * self.num_groups, -1) + + if not deterministic: + # sample code vector probs via gumbel in differentiateable way + gumbel_rng = self.make_rng("gumbel") + gumbels = jax.random.gumbel(gumbel_rng, hidden_states.shape) + codevector_probs = nn.softmax((hidden_states + gumbels) / temperature) + + # compute perplexity + codevector_soft_dist = nn.softmax( + hidden_states.reshape(batch_size * sequence_length, self.num_groups, -1), axis=-1 + ) + perplexity = self._compute_perplexity(codevector_soft_dist, mask_time_indices) + else: + # take argmax in non-differentiable way + # comptute hard codevector distribution (one hot) + codevector_idx = hidden_states.argmax(axis=-1) + codevector_probs = jax.nn.one_hot(codevector_idx, hidden_states.shape[-1]) * 1.0 + codevector_probs = codevector_probs.reshape(batch_size * sequence_length, self.num_groups, -1) + perplexity = self._compute_perplexity(codevector_probs, mask_time_indices) + + codevector_probs = codevector_probs.reshape(batch_size * sequence_length, -1) + # use probs to retrieve codevectors + codevectors_per_group = jnp.expand_dims(codevector_probs, axis=-1) * self.codevectors + codevectors = codevectors_per_group.reshape(batch_size * sequence_length, self.num_groups, self.num_vars, -1) + codevectors = codevectors.sum(-2).reshape(batch_size, sequence_length, -1) + + return codevectors, perplexity + + +class FlaxWav2Vec2Adapter(nn.Module): + config: Wav2Vec2Config + dtype: jnp.dtype = jnp.float32 + + def setup(self): + # hidden_states require down-projection if feature dims don't match + if self.config.output_hidden_size != self.config.hidden_size: + self.proj = nn.Dense( + self.config.output_hidden_size, + kernel_init=jax.nn.initializers.normal(self.config.initializer_range), + dtype=self.dtype, + ) + self.proj_layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) + else: + self.proj = self.proj_layer_norm = None + + self.layers = FlaxWav2Vec2AdapterLayersCollection(self.config, dtype=self.dtype) + + def __call__(self, hidden_states, deterministic=True): + # down-project hidden_states if required + if self.proj is not None and self.proj_layer_norm is not None: + hidden_states = self.proj(hidden_states) + hidden_states = self.proj_layer_norm(hidden_states) + + hidden_states = self.layers(hidden_states) + + return hidden_states + + +class FlaxWav2Vec2AdapterLayer(nn.Module): + config: Wav2Vec2Config + dtype: jnp.dtype = jnp.float32 + + def setup(self): + self.conv = nn.Conv( + features=2 * self.config.output_hidden_size, + kernel_size=(self.config.adapter_kernel_size,), + strides=(self.config.adapter_stride,), + padding=((1, 1),), + kernel_init=jax.nn.initializers.normal(self.config.initializer_range), + dtype=self.dtype, + ) + + def __call__(self, hidden_states): + hidden_states = self.conv(hidden_states) + hidden_states = nn.glu(hidden_states, axis=2) + + return hidden_states + + +class FlaxWav2Vec2AdapterLayersCollection(nn.Module): + config: Wav2Vec2Config + dtype: jnp.dtype = jnp.float32 + + def setup(self): + self.layers = [ + FlaxWav2Vec2AdapterLayer(self.config, name=str(i), dtype=self.dtype) + for i in range(self.config.num_adapter_layers) + ] + + def __call__(self, hidden_states): + for conv_layer in self.layers: + hidden_states = conv_layer(hidden_states) + + return hidden_states + + +class FlaxWav2Vec2PreTrainedModel(FlaxPreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = Wav2Vec2Config + base_model_prefix: str = "wav2vec2" + main_input_name = "input_values" + module_class: nn.Module = None + + def __init__( + self, + config: Wav2Vec2Config, + input_shape: Tuple = (1, 1024), + seed: int = 0, + dtype: jnp.dtype = jnp.float32, + _do_init: bool = True, + **kwargs, + ): + module = self.module_class(config=config, dtype=dtype, **kwargs) + super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) + + def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: + # init input tensors + input_values = jnp.zeros(input_shape, dtype="i4") + attention_mask = jnp.ones_like(input_values) + params_rng, dropout_rng = jax.random.split(rng, 2) + rngs = {"params": params_rng, "dropout": dropout_rng} + + random_params = self.module.init(rngs, input_values, attention_mask, return_dict=False)["params"] + + if params is not None: + random_params = flatten_dict(unfreeze(random_params)) + params = flatten_dict(unfreeze(params)) + for missing_key in self._missing_keys: + params[missing_key] = random_params[missing_key] + self._missing_keys = set() + return freeze(unflatten_dict(params)) + else: + return random_params + + @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING) + def __call__( + self, + input_values, + attention_mask=None, + mask_time_indices=None, + params: dict = None, + dropout_rng: jax.random.PRNGKey = None, + train: bool = False, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + freeze_feature_encoder: bool = False, + return_dict: Optional[bool] = None, + ): + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + batch_size, sequence_length = input_values.shape + + if attention_mask is None: + attention_mask = jnp.ones((batch_size, sequence_length)) + + # Handle any PRNG if needed + rngs = {} + if dropout_rng is not None: + rngs["dropout"] = dropout_rng + + inputs = {"params": params or self.params} + + return self.module.apply( + inputs, + jnp.array(input_values, dtype="f4"), + jnp.array(attention_mask, dtype="i4"), + mask_time_indices, + not train, + output_attentions, + output_hidden_states, + freeze_feature_encoder, + return_dict, + rngs=rngs, + ) + + def _get_feat_extract_output_lengths( + self, input_lengths: Union[jnp.ndarray, int], add_adapter: Optional[bool] = None + ): + return self.module._get_feat_extract_output_lengths(input_lengths, add_adapter=add_adapter) + + +class FlaxWav2Vec2Module(nn.Module): + config: Wav2Vec2Config + dtype: jnp.dtype = jnp.float32 + + def setup(self): + self.feature_extractor = FlaxWav2Vec2FeatureEncoder(self.config, dtype=self.dtype) + self.feature_projection = FlaxWav2Vec2FeatureProjection(self.config, dtype=self.dtype) + self.masked_spec_embed = self.param( + "masked_spec_embed", jax.nn.initializers.uniform(), (self.config.hidden_size,) + ) + + if self.config.do_stable_layer_norm: + self.encoder = FlaxWav2Vec2StableLayerNormEncoder(self.config, dtype=self.dtype) + else: + raise NotImplementedError("``config.do_stable_layer_norm is False`` is currently not supported.") + + self.adapter = FlaxWav2Vec2Adapter(self.config, dtype=self.dtype) if self.config.add_adapter else None + + def __call__( + self, + input_values, + attention_mask=None, + mask_time_indices=None, + deterministic=True, + output_attentions=None, + output_hidden_states=None, + freeze_feature_encoder=False, + return_dict=None, + ): + extract_features = self.feature_extractor(input_values, freeze_feature_encoder=freeze_feature_encoder) + + # make sure that no loss is computed on padded inputs + if attention_mask is not None: + # compute reduced attention_mask corresponding to feature vectors + attention_mask = self._get_feature_vector_attention_mask( + extract_features.shape[1], attention_mask, add_adapter=False + ) + + hidden_states, extract_features = self.feature_projection(extract_features, deterministic=deterministic) + if mask_time_indices is not None: # apply SpecAugment along time axis with given indices + hidden_states = jnp.where( + jnp.broadcast_to(mask_time_indices[:, :, None], hidden_states.shape), + jnp.broadcast_to(self.masked_spec_embed[None, None, :], hidden_states.shape), + hidden_states, + ) + + encoder_outputs = self.encoder( + hidden_states, + attention_mask=attention_mask, + deterministic=deterministic, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = encoder_outputs[0] + + if self.adapter is not None: + hidden_states = self.adapter(hidden_states) + + if not return_dict: + return (hidden_states, extract_features) + encoder_outputs[1:] + + return FlaxWav2Vec2BaseModelOutput( + last_hidden_state=hidden_states, + extract_features=extract_features, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + def _get_feat_extract_output_lengths( + self, input_lengths: Union[jnp.ndarray, int], add_adapter: Optional[bool] = None + ): + """ + Computes the output length of the convolutional layers + """ + + add_adapter = self.config.add_adapter if add_adapter is None else add_adapter + + def _conv_out_length(input_length, kernel_size, stride): + # 1D convolutional layer output length formula taken + # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html + return (input_length - kernel_size) // stride + 1 + + for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): + input_lengths = _conv_out_length(input_lengths, kernel_size, stride) + + if add_adapter: + for _ in range(self.config.num_adapter_layers): + input_lengths = _conv_out_length(input_lengths, 1, self.config.adapter_stride) + + return input_lengths + + def _get_feature_vector_attention_mask( + self, feature_vector_length: int, attention_mask: jnp.ndarray, add_adapter=None + ): + # Effectively attention_mask.sum(-1), but not inplace to be able to run + # on inference mode. + non_padded_lengths = attention_mask.cumsum(axis=-1)[:, -1] + + output_lengths = self._get_feat_extract_output_lengths(non_padded_lengths, add_adapter=add_adapter) + + batch_size = attention_mask.shape[0] + + attention_mask = jnp.zeros((batch_size, feature_vector_length), dtype=attention_mask.dtype) + # these two operations makes sure that all values + # before the output lengths indices are attended to + attention_mask = attention_mask.at[jnp.arange(attention_mask.shape[0]), output_lengths - 1].set(1) + attention_mask = jnp.flip(jnp.flip(attention_mask, -1).cumsum(-1), -1).astype("bool") + return attention_mask + + +@add_start_docstrings( + "The bare Wav2Vec2 Model transformer outputting raw hidden-states without any specific head on top.", + WAV_2_VEC_2_START_DOCSTRING, +) +class FlaxWav2Vec2Model(FlaxWav2Vec2PreTrainedModel): + module_class = FlaxWav2Vec2Module + + +FLAX_WAV2VEC2_MODEL_DOCSTRING = """ + Returns: + + Example: + + ```python + >>> from transformers import AutoProcessor, FlaxWav2Vec2Model + >>> from datasets import load_dataset + >>> import soundfile as sf + + >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-large-lv60") + >>> model = FlaxWav2Vec2Model.from_pretrained("facebook/wav2vec2-large-lv60") + + + >>> def map_to_array(batch): + ... speech, _ = sf.read(batch["file"]) + ... batch["speech"] = speech + ... return batch + + + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + >>> ds = ds.map(map_to_array) + + >>> input_values = processor( + ... ds["speech"][0], sampling_rate=16_000, return_tensors="np" + ... ).input_values # Batch size 1 + >>> hidden_states = model(input_values).last_hidden_state + ``` +""" + +overwrite_call_docstring( + FlaxWav2Vec2Model, + WAV_2_VEC_2_INPUTS_DOCSTRING + FLAX_WAV2VEC2_MODEL_DOCSTRING, +) +append_replace_return_docstrings( + FlaxWav2Vec2Model, output_type=FlaxWav2Vec2BaseModelOutput, config_class=Wav2Vec2Config +) + + +class FlaxWav2Vec2ForCTCModule(nn.Module): + config: Wav2Vec2Config + dtype: jnp.dtype = jnp.float32 + + def setup(self): + self.wav2vec2 = FlaxWav2Vec2Module(self.config, dtype=self.dtype) + self.dropout = nn.Dropout(rate=self.config.final_dropout) + self.lm_head = nn.Dense( + self.config.vocab_size, + kernel_init=jax.nn.initializers.normal(self.config.initializer_range), + dtype=self.dtype, + ) + + def __call__( + self, + input_values, + attention_mask=None, + mask_time_indices=None, + deterministic=True, + output_attentions=None, + output_hidden_states=None, + freeze_feature_encoder=False, + return_dict=None, + ): + outputs = self.wav2vec2( + input_values, + attention_mask=attention_mask, + mask_time_indices=mask_time_indices, + deterministic=deterministic, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + freeze_feature_encoder=freeze_feature_encoder, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + hidden_states = self.dropout(hidden_states, deterministic=deterministic) + + logits = self.lm_head(hidden_states) + + if not return_dict: + return (logits,) + outputs[2:] + + return FlaxCausalLMOutput(logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions) + + def _get_feat_extract_output_lengths( + self, + input_lengths: Union[jnp.ndarray, int], + add_adapter: Optional[bool] = None, + ): + """ + Computes the output length of the convolutional layers + """ + + add_adapter = self.config.add_adapter if add_adapter is None else add_adapter + + def _conv_out_length(input_length, kernel_size, stride): + # 1D convolutional layer output length formula taken + # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html + return (input_length - kernel_size) // stride + 1 + + for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): + input_lengths = _conv_out_length(input_lengths, kernel_size, stride) + + if add_adapter: + for _ in range(self.config.num_adapter_layers): + input_lengths = _conv_out_length(input_lengths, 1, self.config.adapter_stride) + + return input_lengths + + +@add_start_docstrings( + "Wav2Vec2 Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).", + WAV_2_VEC_2_START_DOCSTRING, +) +class FlaxWav2Vec2ForCTC(FlaxWav2Vec2PreTrainedModel): + module_class = FlaxWav2Vec2ForCTCModule + + +FLAX_WAV2VEC2_FOR_CTC_DOCSTRING = """ + Returns: + + Example: + + ```python + >>> import jax.numpy as jnp + >>> from transformers import AutoProcessor, FlaxWav2Vec2ForCTC + >>> from datasets import load_dataset + >>> import soundfile as sf + + >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-large-960h-lv60") + >>> model = FlaxWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60") + + + >>> def map_to_array(batch): + ... speech, _ = sf.read(batch["file"]) + ... batch["speech"] = speech + ... return batch + + + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + >>> ds = ds.map(map_to_array) + + >>> input_values = processor( + ... ds["speech"][0], sampling_rate=16_000, return_tensors="np" + ... ).input_values # Batch size 1 + >>> logits = model(input_values).logits + >>> predicted_ids = jnp.argmax(logits, axis=-1) + + >>> transcription = processor.decode(predicted_ids[0]) + >>> # should give: "A MAN SAID TO THE UNIVERSE SIR I EXIST" + ``` +""" + +overwrite_call_docstring( + FlaxWav2Vec2ForCTC, + WAV_2_VEC_2_INPUTS_DOCSTRING + FLAX_WAV2VEC2_FOR_CTC_DOCSTRING, +) +append_replace_return_docstrings(FlaxWav2Vec2ForCTC, output_type=FlaxCausalLMOutput, config_class=Wav2Vec2Config) + + +class FlaxWav2Vec2ForPreTrainingModule(nn.Module): + config: Wav2Vec2Config + dtype: jnp.dtype = jnp.float32 + + def setup(self): + self.wav2vec2 = FlaxWav2Vec2Module(self.config, dtype=self.dtype) + self.dropout_features = nn.Dropout(self.config.feat_quantizer_dropout) + + self.quantizer = FlaxWav2Vec2GumbelVectorQuantizer(self.config, dtype=self.dtype) + self.project_q = nn.Dense( + self.config.proj_codevector_dim, + kernel_init=jax.nn.initializers.normal(self.config.initializer_range), + dtype=self.dtype, + ) + self.project_hid = nn.Dense( + self.config.proj_codevector_dim, + kernel_init=jax.nn.initializers.normal(self.config.initializer_range), + dtype=self.dtype, + ) + + def __call__( + self, + input_values, + attention_mask=None, + mask_time_indices=None, + gumbel_temperature: int = 1, + deterministic: bool = True, + output_attentions=None, + output_hidden_states=None, + freeze_feature_encoder=False, + return_dict=None, + ): + r""" + Returns: + + Example: + + ```python + + ```""" + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.wav2vec2( + input_values, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + mask_time_indices=mask_time_indices, + deterministic=deterministic, + freeze_feature_encoder=freeze_feature_encoder, + return_dict=return_dict, + ) + + # project all transformed features (including masked) to final vq dim + transformer_features = self.project_hid(outputs[0]) + + # quantize all (unmasked) extracted features and project to final vq dim + extract_features = self.dropout_features(outputs[1], deterministic=deterministic) + quantized_features, codevector_perplexity = self.quantizer( + extract_features, mask_time_indices, deterministic=deterministic, temperature=gumbel_temperature + ) + quantized_features = self.project_q(quantized_features) + + if not return_dict: + return (transformer_features, quantized_features, codevector_perplexity) + outputs[2:] + + return FlaxWav2Vec2ForPreTrainingOutput( + projected_states=transformer_features, + projected_quantized_states=quantized_features, + codevector_perplexity=codevector_perplexity, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def _get_feat_extract_output_lengths( + self, input_lengths: Union[jnp.ndarray, int], add_adapter: Optional[bool] = None + ): + """ + Computes the output length of the convolutional layers + """ + + add_adapter = self.config.add_adapter if add_adapter is None else add_adapter + + def _conv_out_length(input_length, kernel_size, stride): + # 1D convolutional layer output length formula taken + # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html + return (input_length - kernel_size) // stride + 1 + + for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): + input_lengths = _conv_out_length(input_lengths, kernel_size, stride) + + if add_adapter: + for _ in range(self.config.num_adapter_layers): + input_lengths = _conv_out_length(input_lengths, 1, self.config.adapter_stride) + + return input_lengths + + +@add_start_docstrings("""Wav2Vec2 Model with a quantizer and `VQ` head on top.""", WAV_2_VEC_2_START_DOCSTRING) +class FlaxWav2Vec2ForPreTraining(FlaxWav2Vec2PreTrainedModel): + module_class = FlaxWav2Vec2ForPreTrainingModule + + @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING) + # overwrite since has `gumbel_temperature` input + def __call__( + self, + input_values, + attention_mask=None, + mask_time_indices=None, + gumbel_temperature: int = 1, + params: dict = None, + dropout_rng: jax.random.PRNGKey = None, + gumbel_rng: jax.random.PRNGKey = None, + train: bool = False, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + freeze_feature_encoder: bool = False, + return_dict: Optional[bool] = None, + ): + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + batch_size, sequence_length = input_values.shape + + if attention_mask is None: + attention_mask = jnp.ones((batch_size, sequence_length)) + + # Handle any PRNG if needed + rngs = {} + if dropout_rng is not None: + rngs["dropout"] = dropout_rng + + if gumbel_rng is not None: + rngs["gumbel"] = gumbel_rng + + inputs = {"params": params or self.params} + + return self.module.apply( + inputs, + jnp.array(input_values, dtype="f4"), + jnp.array(attention_mask, dtype="i4"), + mask_time_indices, + gumbel_temperature, + not train, + output_attentions, + output_hidden_states, + freeze_feature_encoder, + return_dict, + rngs=rngs, + ) + + +FLAX_WAV2VEC2_FOR_PRETRAINING_DOCSTRING = """ + Returns: + + Example: + + ```python + >>> import optax + >>> import numpy as np + >>> import jax.numpy as jnp + >>> from transformers import AutoFeatureExtractor, FlaxWav2Vec2ForPreTraining + >>> from transformers.models.wav2vec2.modeling_flax_wav2vec2 import _compute_mask_indices + >>> from datasets import load_dataset + >>> import soundfile as sf + + >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-large-lv60") + >>> model = FlaxWav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-large-lv60") + + + >>> def map_to_array(batch): + ... speech, _ = sf.read(batch["file"]) + ... batch["speech"] = speech + ... return batch + + + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + >>> ds = ds.map(map_to_array) + + >>> input_values = feature_extractor(ds["speech"][0], return_tensors="np").input_values # Batch size 1 + + >>> # compute masked indices + >>> batch_size, raw_sequence_length = input_values.shape + >>> sequence_length = model._get_feat_extract_output_lengths(raw_sequence_length) + >>> mask_time_indices = _compute_mask_indices((batch_size, sequence_length), mask_prob=0.2, mask_length=2) + + >>> outputs = model(input_values, mask_time_indices=mask_time_indices) + + >>> # compute cosine similarity between predicted (=projected_states) and target (=projected_quantized_states) + >>> cosine_sim = optax.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states) + + >>> # show that cosine similarity is much higher than random + >>> assert np.asarray(cosine_sim)[mask_time_indices].mean() > 0.5 + ``` +""" + +overwrite_call_docstring( + FlaxWav2Vec2ForPreTraining, + WAV_2_VEC_2_INPUTS_DOCSTRING + FLAX_WAV2VEC2_FOR_PRETRAINING_DOCSTRING, +) +append_replace_return_docstrings( + FlaxWav2Vec2ForPreTraining, output_type=FlaxWav2Vec2ForPreTrainingOutput, config_class=Wav2Vec2Config +) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/modeling_tf_wav2vec2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/modeling_tf_wav2vec2.py new file mode 100644 index 0000000000000000000000000000000000000000..a8e39b0754af75564b1bf823622ec0d2eb2b76c7 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/modeling_tf_wav2vec2.py @@ -0,0 +1,1860 @@ +# coding=utf-8 +# Copyright 2021 The Fairseq Authors and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" TensorFlow Wav2Vec2 model.""" + + +from __future__ import annotations + +import warnings +from dataclasses import dataclass +from typing import Any, Optional, Tuple, Union + +import numpy as np +import tensorflow as tf + +from ...activations_tf import get_tf_activation +from ...modeling_tf_outputs import TFBaseModelOutput, TFCausalLMOutput, TFSequenceClassifierOutput +from ...modeling_tf_utils import ( + TFPreTrainedModel, + get_initializer, + keras, + keras_serializable, + unpack_inputs, +) +from ...tf_utils import shape_list, stable_softmax +from ...utils import ( + ModelOutput, + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) +from .configuration_wav2vec2 import Wav2Vec2Config + + +logger = logging.get_logger(__name__) + + +_HIDDEN_STATES_START_POSITION = 2 + +_CHECKPOINT_FOR_DOC = "facebook/wav2vec2-base-960h" +_CONFIG_FOR_DOC = "Wav2Vec2Config" + + +from ..deprecated._archive_maps import TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +LARGE_NEGATIVE = -1e8 + + +@dataclass +class TFWav2Vec2BaseModelOutput(ModelOutput): + """ + Output type of [`TFWav2Vec2BaseModelOutput`], with potential hidden states and attentions. + + Args: + last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + extract_features (`tf.Tensor` of shape `(batch_size, sequence_length, conv_dim[-1])`): + Sequence of extracted feature vectors of the last convolutional layer of the model. + hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape + `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the initial embedding outputs. + attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + """ + + last_hidden_state: tf.Tensor = None + extract_features: tf.Tensor = None + hidden_states: Tuple[tf.Tensor] | None = None + attentions: Tuple[tf.Tensor] | None = None + + +def _sample_without_replacement(distribution, num_samples): + """ + Categorical sampling without replacement is currently not implemented. The gumbel-max trick will do for now - see + https://github.com/tensorflow/tensorflow/issues/9260 for more info + """ + z = -tf.math.log(tf.random.uniform(shape_list(distribution), 0, 1)) + _, indices = tf.nn.top_k(distribution + z, num_samples) + return indices + + +def _scatter_values_on_batch_indices(values, batch_indices, output_shape): + """ + Scatter function as in PyTorch with indices in format (batch_dim, indixes) + """ + indices_shape = shape_list(batch_indices) + # broadcast batch dim to indices_shape + broad_casted_batch_dims = tf.reshape( + tf.broadcast_to(tf.expand_dims(tf.range(indices_shape[0]), axis=-1), indices_shape), [1, -1] + ) + # transform batch_indices to pair_indices + pair_indices = tf.transpose(tf.concat([broad_casted_batch_dims, tf.reshape(batch_indices, [1, -1])], 0)) + # scatter values to pair indices + return tf.scatter_nd(pair_indices, tf.reshape(values, [-1]), output_shape) + + +def _compute_mask_indices( + shape: Tuple[int, int], + mask_prob: float, + mask_length: int, + min_masks: int = 0, +) -> tf.Tensor: + """ + Computes random mask spans for a given shape + + Args: + shape: the shape for which to compute masks. + should be of size 2 where first element is batch size and 2nd is timesteps + attention_mask: optional padding mask of the same size as shape, which will prevent masking padded elements + mask_prob: + probability for each token to be chosen as start of the span to be masked. this will be multiplied by + number of timesteps divided by length of mask span to mask approximately this percentage of all elements. + however due to overlaps, the actual number will be smaller (unless no_overlap is True) + mask_length: size of the mask + min_masks: minimum number of masked spans + + Adapted from [fairseq's + data_utils.py](https://github.com/pytorch/fairseq/blob/e0788f7007a8473a76db573985031f3c94201e79/fairseq/data/data_utils.py#L376). + """ + batch_size, sequence_length = shape + + if mask_length < 1: + raise ValueError("`mask_length` has to be bigger than 0.") + + tf.debugging.assert_less( + mask_length, + sequence_length, + message=( + f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and" + f" `sequence_length`: {sequence_length}`" + ), + ) + + # compute number of masked spans in batch + num_masked_spans = mask_prob * tf.cast(sequence_length, tf.float32) / mask_length + tf.random.uniform((1,)) + num_masked_spans = tf.maximum(num_masked_spans, min_masks) + num_masked_spans = tf.cast(num_masked_spans, tf.int32) + + # make sure num masked indices <= sequence_length + num_masked_spans = tf.math.minimum(sequence_length // mask_length, num_masked_spans) + num_masked_spans = tf.squeeze(num_masked_spans) + + # SpecAugment mask to fill + spec_aug_mask = tf.zeros((batch_size, sequence_length), dtype=tf.int32) + + # uniform distribution to sample from, make sure that offset samples are < sequence_length + uniform_dist = tf.ones((batch_size, sequence_length - (mask_length - 1))) + + # get random indices to mask + spec_aug_mask_idxs = _sample_without_replacement(uniform_dist, num_masked_spans) + + # expand masked indices to masked spans + spec_aug_mask_idxs = tf.expand_dims(spec_aug_mask_idxs, -1) + spec_aug_mask_idxs = tf.tile(spec_aug_mask_idxs, (1, 1, mask_length)) + spec_aug_mask_idxs = tf.reshape(spec_aug_mask_idxs, (batch_size, num_masked_spans * mask_length)) + + offsets = tf.range(mask_length)[tf.newaxis, tf.newaxis, :] + offsets = tf.tile(offsets, (batch_size, num_masked_spans, 1)) + offsets = tf.reshape(offsets, (batch_size, num_masked_spans * mask_length)) + + spec_aug_mask_idxs = spec_aug_mask_idxs + offsets + + # scatter indices to mask + spec_aug_mask = _scatter_values_on_batch_indices( + tf.ones_like(spec_aug_mask_idxs), spec_aug_mask_idxs, tf.shape(spec_aug_mask) + ) + + return spec_aug_mask + + +# Copied from transformers.models.bart.modeling_tf_bart._expand_mask +def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None): + """ + Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. + """ + src_len = shape_list(mask)[1] + tgt_len = tgt_len if tgt_len is not None else src_len + one_cst = tf.constant(1.0) + mask = tf.cast(mask, dtype=one_cst.dtype) + expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1)) + + return (one_cst - expanded_mask) * LARGE_NEGATIVE + + +class TFWav2Vec2GroupNorm(keras.layers.Layer): + """ + From tensorflow-addons https://www.tensorflow.org/addons/api_docs/python/tfa/layers/GroupNormalization + """ + + def __init__( + self, + groups: int = 32, + axis: int = -1, + epsilon: float = 1e-3, + center: bool = True, + scale: bool = True, + beta_initializer: keras.initializers.Initializer = "zeros", + gamma_initializer: keras.initializers.Initializer = "ones", + beta_regularizer: keras.regularizers.Regularizer = None, + gamma_regularizer: keras.regularizers.Regularizer = None, + beta_constraint: keras.constraints.Constraint = None, + gamma_constraint: keras.constraints.Constraint = None, + **kwargs, + ): + super().__init__(**kwargs) + self.supports_masking = True + self.groups = groups + self.axis = axis + self.epsilon = epsilon + self.center = center + self.scale = scale + self.beta_initializer = keras.initializers.get(beta_initializer) + self.gamma_initializer = keras.initializers.get(gamma_initializer) + self.beta_regularizer = keras.regularizers.get(beta_regularizer) + self.gamma_regularizer = keras.regularizers.get(gamma_regularizer) + self.beta_constraint = keras.constraints.get(beta_constraint) + self.gamma_constraint = keras.constraints.get(gamma_constraint) + self._check_axis() + + def build(self, input_shape): + self._check_if_input_shape_is_none(input_shape) + self._set_number_of_groups_for_instance_norm(input_shape) + self._check_size_of_dimensions(input_shape) + self._create_input_spec(input_shape) + + self._add_gamma_weight(input_shape) + self._add_beta_weight(input_shape) + self.built = True + super().build(input_shape) + + def call(self, inputs): + input_shape = keras.backend.int_shape(inputs) + tensor_input_shape = tf.shape(inputs) + + reshaped_inputs, group_shape = self._reshape_into_groups(inputs, input_shape, tensor_input_shape) + + normalized_inputs = self._apply_normalization(reshaped_inputs, input_shape) + + is_instance_norm = (input_shape[self.axis] // self.groups) == 1 + if not is_instance_norm: + outputs = tf.reshape(normalized_inputs, tensor_input_shape) + else: + outputs = normalized_inputs + + return outputs + + def get_config(self): + config = { + "groups": self.groups, + "axis": self.axis, + "epsilon": self.epsilon, + "center": self.center, + "scale": self.scale, + "beta_initializer": keras.initializers.serialize(self.beta_initializer), + "gamma_initializer": keras.initializers.serialize(self.gamma_initializer), + "beta_regularizer": keras.regularizers.serialize(self.beta_regularizer), + "gamma_regularizer": keras.regularizers.serialize(self.gamma_regularizer), + "beta_constraint": keras.constraints.serialize(self.beta_constraint), + "gamma_constraint": keras.constraints.serialize(self.gamma_constraint), + } + base_config = super().get_config() + return {**base_config, **config} + + def compute_output_shape(self, input_shape): + return input_shape + + def _reshape_into_groups(self, inputs, input_shape, tensor_input_shape): + group_shape = [tensor_input_shape[i] for i in range(len(input_shape))] + is_instance_norm = (input_shape[self.axis] // self.groups) == 1 + if not is_instance_norm: + group_shape[self.axis] = input_shape[self.axis] // self.groups + group_shape.insert(self.axis, self.groups) + group_shape = tf.stack(group_shape) + reshaped_inputs = tf.reshape(inputs, group_shape) + return reshaped_inputs, group_shape + else: + return inputs, group_shape + + def _apply_normalization(self, reshaped_inputs, input_shape): + group_shape = keras.backend.int_shape(reshaped_inputs) + group_reduction_axes = list(range(1, len(group_shape))) + is_instance_norm = (input_shape[self.axis] // self.groups) == 1 + if not is_instance_norm: + axis = -2 if self.axis == -1 else self.axis - 1 + else: + axis = -1 if self.axis == -1 else self.axis - 1 + group_reduction_axes.pop(axis) + + mean, variance = tf.nn.moments(reshaped_inputs, group_reduction_axes, keepdims=True) + + gamma, beta = self._get_reshaped_weights(input_shape) + normalized_inputs = tf.nn.batch_normalization( + reshaped_inputs, + mean=mean, + variance=variance, + scale=gamma, + offset=beta, + variance_epsilon=self.epsilon, + ) + return normalized_inputs + + def _get_reshaped_weights(self, input_shape): + broadcast_shape = self._create_broadcast_shape(input_shape) + gamma = None + beta = None + if self.scale: + gamma = tf.reshape(self.gamma, broadcast_shape) + + if self.center: + beta = tf.reshape(self.beta, broadcast_shape) + return gamma, beta + + def _check_if_input_shape_is_none(self, input_shape): + dim = input_shape[self.axis] + if dim is None: + raise ValueError( + "Axis " + + str(self.axis) + + " of input tensor should have a defined dimension but the layer received an input with shape " + + str(input_shape) + + "." + ) + + def _set_number_of_groups_for_instance_norm(self, input_shape): + dim = input_shape[self.axis] + + if self.groups == -1: + self.groups = dim + + def _check_size_of_dimensions(self, input_shape): + dim = input_shape[self.axis] + if dim < self.groups: + raise ValueError( + "Number of groups (" + + str(self.groups) + + ") cannot be more than the number of channels (" + + str(dim) + + ")." + ) + + if dim % self.groups != 0: + raise ValueError( + "Number of groups (" + + str(self.groups) + + ") must be a multiple of the number of channels (" + + str(dim) + + ")." + ) + + def _check_axis(self): + if self.axis == 0: + raise ValueError( + "You are trying to normalize your batch axis. Do you want to use tf.layer.batch_normalization instead" + ) + + def _create_input_spec(self, input_shape): + dim = input_shape[self.axis] + self.input_spec = keras.layers.InputSpec(ndim=len(input_shape), axes={self.axis: dim}) + + def _add_gamma_weight(self, input_shape): + dim = input_shape[self.axis] + shape = (dim,) + + if self.scale: + self.gamma = self.add_weight( + shape=shape, + name="gamma", + initializer=self.gamma_initializer, + regularizer=self.gamma_regularizer, + constraint=self.gamma_constraint, + ) + else: + self.gamma = None + + def _add_beta_weight(self, input_shape): + dim = input_shape[self.axis] + shape = (dim,) + + if self.center: + self.beta = self.add_weight( + shape=shape, + name="beta", + initializer=self.beta_initializer, + regularizer=self.beta_regularizer, + constraint=self.beta_constraint, + ) + else: + self.beta = None + + def _create_broadcast_shape(self, input_shape): + broadcast_shape = [1] * len(input_shape) + is_instance_norm = (input_shape[self.axis] // self.groups) == 1 + if not is_instance_norm: + broadcast_shape[self.axis] = input_shape[self.axis] // self.groups + broadcast_shape.insert(self.axis, self.groups) + else: + broadcast_shape[self.axis] = self.groups + return broadcast_shape + + +class TFWav2Vec2WeightNormConv1D(keras.layers.Conv1D): + """Adapted from https://www.tensorflow.org/probability/api_docs/python/tfp/layers/weight_norm/WeightNorm""" + + def __init__(self, filters, kernel_size, groups, explicit_padding, **kwargs): + super().__init__( + filters=filters, + kernel_size=kernel_size, + groups=groups, + padding="valid", + use_bias=True, + bias_initializer="he_normal", + **kwargs, + ) + self.explicit_padding = explicit_padding + self.filter_axis = 2 + self.kernel_norm_axes = tf.constant([0, 1]) + + def _init_norm(self): + """Set the norm of the weight vector.""" + kernel_norm = tf.sqrt(tf.reduce_sum(tf.square(self.weight_v), axis=self.kernel_norm_axes)) + self.weight_g.assign(kernel_norm[:, tf.newaxis, tf.newaxis]) + + def _normalize_kernel(self): + """Generate normalized weights.""" + kernel = tf.nn.l2_normalize(self.weight_v, axis=self.kernel_norm_axes) * tf.transpose(self.weight_g) + self.kernel = tf.transpose(kernel) + + def build(self, input_shape): + if not self.built: + super().build(input_shape) + + self.kernel = tf.Variable(tf.transpose(self.kernel), name="weight_v", trainable=True) + self.weight_v = self.kernel + + self.weight_g = self.add_weight( + name="weight_g", + shape=(int(self.weight_v.shape[self.filter_axis]), 1, 1), + initializer="ones", + dtype=self.weight_v.dtype, + trainable=True, + ) + self._init_norm() + self.bias = self.add_weight(name="bias", shape=(self.filters,), initializer="zeros", trainable=True) + + def call(self, inputs): + # TODO Matt: Assigning to attributes in call() is deeply sinful in TensorFlow, as it should be idempotent. + # This whole layer should be replaced by a layer that doesn't inherit from Conv1D, but instead calls + # a functional 1d convolution with normalized weights that it generates (but does not store!) + self._normalize_kernel() + + padded_inputs = tf.pad(inputs, ((0, 0), (self.explicit_padding, self.explicit_padding), (0, 0))) + output = super().call(padded_inputs) + + return output + + +class TFWav2Vec2NoLayerNormConvLayer(keras.layers.Layer): + def __init__(self, config: Wav2Vec2Config, layer_id: int = 0, **kwargs: Any) -> None: + super().__init__(**kwargs) + self.in_conv_dim = config.conv_dim[layer_id] if layer_id > 0 else 1 + self.out_conv_dim = config.conv_dim[layer_id] + + self.conv = keras.layers.Conv1D( + filters=self.out_conv_dim, + kernel_size=config.conv_kernel[layer_id], + strides=config.conv_stride[layer_id], + use_bias=config.conv_bias, + name="conv", + ) + self.activation = get_tf_activation(config.feat_extract_activation) + + def call(self, hidden_states: tf.Tensor) -> tf.Tensor: + hidden_states = self.conv(hidden_states) + hidden_states = self.activation(hidden_states) + return hidden_states + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "conv", None) is not None: + with tf.name_scope(self.conv.name): + self.conv.build([None, None, self.in_conv_dim]) + + +class TFWav2Vec2LayerNormConvLayer(keras.layers.Layer): + def __init__(self, config: Wav2Vec2Config, layer_id: int = 0, **kwargs: Any) -> None: + super().__init__(**kwargs) + self.in_conv_dim = config.conv_dim[layer_id] if layer_id > 0 else 1 + self.out_conv_dim = config.conv_dim[layer_id] + + self.conv = keras.layers.Conv1D( + filters=self.out_conv_dim, + kernel_size=config.conv_kernel[layer_id], + strides=config.conv_stride[layer_id], + use_bias=config.conv_bias, + name="conv", + ) + self.layer_norm = keras.layers.LayerNormalization(name="layer_norm", epsilon=config.layer_norm_eps) + self.activation = get_tf_activation(config.feat_extract_activation) + + def call(self, hidden_states: tf.Tensor) -> tf.Tensor: + hidden_states = self.conv(hidden_states) + hidden_states = self.layer_norm(hidden_states) + hidden_states = self.activation(hidden_states) + return hidden_states + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "conv", None) is not None: + with tf.name_scope(self.conv.name): + self.conv.build([None, None, self.in_conv_dim]) + if getattr(self, "layer_norm", None) is not None: + with tf.name_scope(self.layer_norm.name): + self.layer_norm.build([None, None, self.out_conv_dim]) + + +class TFWav2Vec2GroupNormConvLayer(keras.layers.Layer): + def __init__(self, config: Wav2Vec2Config, layer_id: int = 0, **kwargs: Any) -> None: + super().__init__(**kwargs) + self.in_conv_dim = config.conv_dim[layer_id] if layer_id > 0 else 1 + self.out_conv_dim = config.conv_dim[layer_id] + + self.conv = keras.layers.Conv1D( + filters=self.out_conv_dim, + kernel_size=config.conv_kernel[layer_id], + strides=config.conv_stride[layer_id], + use_bias=config.conv_bias, + name="conv", + ) + self.activation = get_tf_activation(config.feat_extract_activation) + self.layer_norm = TFWav2Vec2GroupNorm( + groups=self.out_conv_dim, epsilon=config.layer_norm_eps, name="layer_norm" + ) + + def call(self, hidden_states: tf.Tensor) -> tf.Tensor: + hidden_states = self.conv(hidden_states) + hidden_states = self.layer_norm(hidden_states) + hidden_states = self.activation(hidden_states) + return hidden_states + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "conv", None) is not None: + with tf.name_scope(self.conv.name): + self.conv.build([None, None, self.in_conv_dim]) + if getattr(self, "layer_norm", None) is not None: + with tf.name_scope(self.layer_norm.name): + self.layer_norm.build([None, None, self.out_conv_dim]) + + +class TFWav2Vec2PositionalConvEmbedding(keras.layers.Layer): + def __init__(self, config: Wav2Vec2Config, **kwargs: Any) -> None: + super().__init__(**kwargs) + self.conv = TFWav2Vec2WeightNormConv1D( + filters=config.hidden_size, + kernel_size=config.num_conv_pos_embeddings, + groups=config.num_conv_pos_embedding_groups, + explicit_padding=config.num_conv_pos_embeddings // 2, + name="conv", + ) + self.padding = TFWav2Vec2SamePadLayer(config.num_conv_pos_embeddings) + self.activation = get_tf_activation(config.feat_extract_activation) + self.config = config + + def call(self, hidden_states: tf.Tensor) -> tf.Tensor: + hidden_states = self.conv(hidden_states) + hidden_states = self.padding(hidden_states) + hidden_states = self.activation(hidden_states) + return hidden_states + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "conv", None) is not None: + with tf.name_scope(self.conv.name): + self.conv.build([None, None, self.config.hidden_size]) + + +class TFWav2Vec2SamePadLayer(keras.layers.Layer): + def __init__(self, num_conv_pos_embeddings, **kwargs): + super().__init__(**kwargs) + self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0 + + def call(self, hidden_states): + if self.num_pad_remove > 0: + hidden_states = hidden_states[:, : -self.num_pad_remove, :] + return hidden_states + + +class TFWav2Vec2FeatureEncoder(keras.layers.Layer): + def __init__(self, config: Wav2Vec2Config, **kwargs: Any) -> None: + super().__init__(**kwargs) + + if config.feat_extract_norm == "group": + conv_layers = [TFWav2Vec2GroupNormConvLayer(config, layer_id=0, name=f"conv_layers.{0}")] + [ + TFWav2Vec2NoLayerNormConvLayer(config, layer_id=i + 1, name=f"conv_layers.{i+1}") + for i in range(config.num_feat_extract_layers - 1) + ] + elif config.feat_extract_norm == "layer": + conv_layers = [ + TFWav2Vec2LayerNormConvLayer(config, layer_id=i, name=f"conv_layers.{i}") + for i in range(config.num_feat_extract_layers) + ] + else: + raise ValueError( + f"`config.feat_extract_norm` is {config.feat_extract_norm}, but has to be one of ['group', 'layer']" + ) + self.conv_layers = conv_layers + + def call(self, input_values): + hidden_states = tf.expand_dims(input_values, -1) + for conv_layer in self.conv_layers: + hidden_states = conv_layer(hidden_states) + return hidden_states + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "conv_layers", None) is not None: + for conv_layer in self.conv_layers: + with tf.name_scope(conv_layer.name): + conv_layer.build(None) + + +class TFWav2Vec2FeatureExtractor(TFWav2Vec2FeatureEncoder): + def __init__(self, config, **kwargs): + super().__init__(config, **kwargs) + warnings.warn( + f"The class `{self.__class__.__name__}` has been depreciated " + "and will be removed in Transformers v5. " + f"Use `{self.__class__.__bases__[0].__name__}` instead.", + FutureWarning, + ) + + +class TFWav2Vec2FeatureProjection(keras.layers.Layer): + def __init__(self, config: Wav2Vec2Config, **kwargs): + super().__init__(**kwargs) + + self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm") + self.projection = keras.layers.Dense( + units=config.hidden_size, + kernel_initializer=get_initializer(config.initializer_range), + bias_initializer="zeros", + name="projection", + ) + self.dropout = keras.layers.Dropout(rate=config.feat_proj_dropout) + self.config = config + + def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor: + norm_hidden_states = self.layer_norm(hidden_states) + hidden_states = self.projection(norm_hidden_states) + hidden_states = self.dropout(hidden_states, training=training) + return hidden_states, norm_hidden_states + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "layer_norm", None) is not None: + with tf.name_scope(self.layer_norm.name): + self.layer_norm.build([None, None, self.config.conv_dim[-1]]) + if getattr(self, "projection", None) is not None: + with tf.name_scope(self.projection.name): + self.projection.build([None, None, self.config.conv_dim[-1]]) + + +# Copied from transformers.models.bart.modeling_tf_bart.TFBartAttention with TFBart->TFWav2Vec2 +class TFWav2Vec2Attention(keras.layers.Layer): + """Multi-headed attention from "Attention Is All You Need""" + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + is_decoder: bool = False, + bias: bool = True, + **kwargs, + ): + super().__init__(**kwargs) + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.dropout = keras.layers.Dropout(dropout) + self.head_dim = embed_dim // num_heads + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + + self.k_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj") + self.q_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj") + self.v_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj") + self.out_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj") + + def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int): + return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3)) + + def call( + self, + hidden_states: tf.Tensor, + key_value_states: tf.Tensor | None = None, + past_key_value: Tuple[Tuple[tf.Tensor]] | None = None, + attention_mask: tf.Tensor | None = None, + layer_head_mask: tf.Tensor | None = None, + training: Optional[bool] = False, + ) -> Tuple[tf.Tensor, tf.Tensor | None]: + """Input shape: Batch x Time x Channel""" + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + bsz, tgt_len, embed_dim = shape_list(hidden_states) + + # get query proj + query_states = self.q_proj(hidden_states) * self.scaling + # get key, value proj + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_states = past_key_value[0] + value_states = past_key_value[1] + elif is_cross_attention: + # cross_attentions + key_states = self._shape(self.k_proj(key_value_states), -1, bsz) + value_states = self._shape(self.v_proj(key_value_states), -1, bsz) + elif past_key_value is not None: + # reuse k, v, self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + key_states = tf.concat([past_key_value[0], key_states], axis=2) + value_states = tf.concat([past_key_value[1], value_states], axis=2) + else: + # self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + + if self.is_decoder: + # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_states, value_states) + + proj_shape = (bsz * self.num_heads, -1, self.head_dim) + query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape) + key_states = tf.reshape(key_states, proj_shape) + value_states = tf.reshape(value_states, proj_shape) + + src_len = shape_list(key_states)[1] + attn_weights = tf.matmul(query_states, key_states, transpose_b=True) + + tf.debugging.assert_equal( + shape_list(attn_weights), + [bsz * self.num_heads, tgt_len, src_len], + message=( + f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" + f" {shape_list(attn_weights)}" + ), + ) + + if attention_mask is not None: + tf.debugging.assert_equal( + shape_list(attention_mask), + [bsz, 1, tgt_len, src_len], + message=( + f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is" + f" {shape_list(attention_mask)}" + ), + ) + + attention_mask = tf.cast(attention_mask, dtype=attn_weights.dtype) + attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask + attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len)) + + attn_weights = stable_softmax(attn_weights, axis=-1) + + if layer_head_mask is not None: + tf.debugging.assert_equal( + shape_list(layer_head_mask), + [self.num_heads], + message=( + f"Head mask for a single layer should be of size {(self.num_heads)}, but is" + f" {shape_list(layer_head_mask)}" + ), + ) + + attn_weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape( + attn_weights, (bsz, self.num_heads, tgt_len, src_len) + ) + attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len)) + + attn_probs = self.dropout(attn_weights, training=training) + attn_output = tf.matmul(attn_probs, value_states) + + tf.debugging.assert_equal( + shape_list(attn_output), + [bsz * self.num_heads, tgt_len, self.head_dim], + message=( + f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" + f" {shape_list(attn_output)}" + ), + ) + + attn_output = tf.transpose( + tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3) + ) + attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim)) + + attn_output = self.out_proj(attn_output) + attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + + return attn_output, attn_weights, past_key_value + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "k_proj", None) is not None: + with tf.name_scope(self.k_proj.name): + self.k_proj.build([None, None, self.embed_dim]) + if getattr(self, "q_proj", None) is not None: + with tf.name_scope(self.q_proj.name): + self.q_proj.build([None, None, self.embed_dim]) + if getattr(self, "v_proj", None) is not None: + with tf.name_scope(self.v_proj.name): + self.v_proj.build([None, None, self.embed_dim]) + if getattr(self, "out_proj", None) is not None: + with tf.name_scope(self.out_proj.name): + self.out_proj.build([None, None, self.embed_dim]) + + +class TFWav2Vec2FeedForward(keras.layers.Layer): + def __init__(self, config: Wav2Vec2Config, **kwargs): + super().__init__(**kwargs) + + self.intermediate_dropout = keras.layers.Dropout(config.activation_dropout) + + self.intermediate_dense = keras.layers.Dense( + units=config.intermediate_size, + kernel_initializer=get_initializer(config.initializer_range), + bias_initializer="zeros", + name="intermediate_dense", + ) + self.intermediate_act_fn = get_tf_activation(config.hidden_act) + + self.output_dense = keras.layers.Dense( + units=config.hidden_size, + kernel_initializer=get_initializer(config.initializer_range), + bias_initializer="zeros", + name="output_dense", + ) + self.output_dropout = keras.layers.Dropout(config.hidden_dropout) + self.config = config + + def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor: + hidden_states = self.intermediate_dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + hidden_states = self.intermediate_dropout(hidden_states, training=training) + + hidden_states = self.output_dense(hidden_states) + hidden_states = self.output_dropout(hidden_states, training=training) + return hidden_states + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "intermediate_dense", None) is not None: + with tf.name_scope(self.intermediate_dense.name): + self.intermediate_dense.build([None, None, self.config.hidden_size]) + if getattr(self, "output_dense", None) is not None: + with tf.name_scope(self.output_dense.name): + self.output_dense.build([None, None, self.config.intermediate_size]) + + +class TFWav2Vec2EncoderLayer(keras.layers.Layer): + def __init__(self, config: Wav2Vec2Config, **kwargs): + super().__init__(**kwargs) + self.attention = TFWav2Vec2Attention( + embed_dim=config.hidden_size, + num_heads=config.num_attention_heads, + dropout=config.attention_dropout, + is_decoder=False, + name="attention", + ) + self.dropout = keras.layers.Dropout(config.hidden_dropout) + self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm") + self.feed_forward = TFWav2Vec2FeedForward(config, name="feed_forward") + self.final_layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="final_layer_norm") + self.config = config + + def call( + self, + hidden_states: tf.Tensor, + attention_mask: tf.Tensor | None = None, + output_attentions: Optional[bool] = False, + training: bool = False, + ) -> Tuple[tf.Tensor]: + attn_residual = hidden_states + hidden_states, attn_weights, _ = self.attention( + hidden_states, attention_mask=attention_mask, training=training + ) + hidden_states = self.dropout(hidden_states, training=training) + hidden_states = attn_residual + hidden_states + + hidden_states = self.layer_norm(hidden_states) + hidden_states = hidden_states + self.feed_forward(hidden_states) + hidden_states = self.final_layer_norm(hidden_states) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "attention", None) is not None: + with tf.name_scope(self.attention.name): + self.attention.build(None) + if getattr(self, "layer_norm", None) is not None: + with tf.name_scope(self.layer_norm.name): + self.layer_norm.build([None, None, self.config.hidden_size]) + if getattr(self, "feed_forward", None) is not None: + with tf.name_scope(self.feed_forward.name): + self.feed_forward.build(None) + if getattr(self, "final_layer_norm", None) is not None: + with tf.name_scope(self.final_layer_norm.name): + self.final_layer_norm.build([None, None, self.config.hidden_size]) + + +class TFWav2Vec2EncoderLayerStableLayerNorm(keras.layers.Layer): + def __init__(self, config: Wav2Vec2Config, **kwargs): + super().__init__(**kwargs) + self.attention = TFWav2Vec2Attention( + embed_dim=config.hidden_size, + num_heads=config.num_attention_heads, + dropout=config.attention_dropout, + is_decoder=False, + name="attention", + ) + self.dropout = keras.layers.Dropout(config.hidden_dropout) + self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm") + self.feed_forward = TFWav2Vec2FeedForward(config, name="feed_forward") + self.final_layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="final_layer_norm") + self.config = config + + def call( + self, + hidden_states: tf.Tensor, + attention_mask: tf.Tensor | None = None, + output_attentions: Optional[bool] = False, + training: bool = False, + ) -> Tuple[tf.Tensor]: + attn_residual = hidden_states + hidden_states = self.layer_norm(hidden_states) + hidden_states, attn_weights, _ = self.attention( + hidden_states, attention_mask=attention_mask, training=training + ) + hidden_states = self.dropout(hidden_states, training=training) + hidden_states = attn_residual + hidden_states + hidden_states = hidden_states + self.feed_forward(self.final_layer_norm(hidden_states)) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "attention", None) is not None: + with tf.name_scope(self.attention.name): + self.attention.build(None) + if getattr(self, "layer_norm", None) is not None: + with tf.name_scope(self.layer_norm.name): + self.layer_norm.build([None, None, self.config.hidden_size]) + if getattr(self, "feed_forward", None) is not None: + with tf.name_scope(self.feed_forward.name): + self.feed_forward.build(None) + if getattr(self, "final_layer_norm", None) is not None: + with tf.name_scope(self.final_layer_norm.name): + self.final_layer_norm.build([None, None, self.config.hidden_size]) + + +class TFWav2Vec2Encoder(keras.layers.Layer): + def __init__(self, config: Wav2Vec2Config, **kwargs): + super().__init__(**kwargs) + self.config = config + self.pos_conv_embed = TFWav2Vec2PositionalConvEmbedding(config, name="pos_conv_embed") + self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm") + self.dropout = keras.layers.Dropout(config.hidden_dropout) + self.layer = [TFWav2Vec2EncoderLayer(config, name=f"layers.{i}") for i in range(config.num_hidden_layers)] + + def call( + self, + hidden_states: tf.Tensor, + attention_mask: tf.Tensor | None = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + training: Optional[bool] = False, + ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + if attention_mask is not None: + hidden_states = hidden_states * tf.expand_dims(attention_mask, -1) + attention_mask = _expand_mask(attention_mask) + else: + attention_mask = None + + position_embeddings = self.pos_conv_embed(hidden_states) + hidden_states = hidden_states + position_embeddings + hidden_states = self.layer_norm(hidden_states) + hidden_states = self.dropout(hidden_states, training=training) + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + dropout_probability = np.random.uniform(0, 1) + if training and (dropout_probability < self.config.layerdrop): # skip the layer + continue + + layer_outputs = layer_module( + hidden_states=hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + training=training, + ) + hidden_states = layer_outputs[0] + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + # Add last layer + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return TFBaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "pos_conv_embed", None) is not None: + with tf.name_scope(self.pos_conv_embed.name): + self.pos_conv_embed.build(None) + if getattr(self, "layer_norm", None) is not None: + with tf.name_scope(self.layer_norm.name): + self.layer_norm.build([None, None, self.config.hidden_size]) + if getattr(self, "layer", None) is not None: + for layer in self.layer: + with tf.name_scope(layer.name): + layer.build(None) + + +class TFWav2Vec2EncoderStableLayerNorm(keras.layers.Layer): + def __init__(self, config: Wav2Vec2Config, **kwargs): + super().__init__(**kwargs) + self.config = config + self.pos_conv_embed = TFWav2Vec2PositionalConvEmbedding(config, name="pos_conv_embed") + self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm") + self.dropout = keras.layers.Dropout(config.hidden_dropout) + self.layer = [ + TFWav2Vec2EncoderLayerStableLayerNorm(config, name=f"layers.{i}") for i in range(config.num_hidden_layers) + ] + + def call( + self, + hidden_states: tf.Tensor, + attention_mask: tf.Tensor | None = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + training: Optional[bool] = False, + ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + if attention_mask is not None: + hidden_states = hidden_states * tf.expand_dims(attention_mask, -1) + attention_mask = _expand_mask(attention_mask) + else: + attention_mask = None + + position_embeddings = self.pos_conv_embed(hidden_states) + hidden_states = hidden_states + position_embeddings + hidden_states = self.dropout(hidden_states, training=training) + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + dropout_probability = np.random.uniform(0, 1) + if training and (dropout_probability < self.config.layerdrop): # skip the layer + continue + + layer_outputs = layer_module( + hidden_states=hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + training=training, + ) + hidden_states = layer_outputs[0] + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + hidden_states = self.layer_norm(hidden_states) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return TFBaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "pos_conv_embed", None) is not None: + with tf.name_scope(self.pos_conv_embed.name): + self.pos_conv_embed.build(None) + if getattr(self, "layer_norm", None) is not None: + with tf.name_scope(self.layer_norm.name): + self.layer_norm.build([None, None, self.config.hidden_size]) + if getattr(self, "layer", None) is not None: + for layer in self.layer: + with tf.name_scope(layer.name): + layer.build(None) + + +@keras_serializable +class TFWav2Vec2MainLayer(keras.layers.Layer): + config_class = Wav2Vec2Config + + def __init__(self, config: Wav2Vec2Config, **kwargs): + super().__init__(**kwargs) + self.config = config + self.feature_extractor = TFWav2Vec2FeatureEncoder(config, name="feature_extractor") + self.feature_projection = TFWav2Vec2FeatureProjection(config, name="feature_projection") + + if config.do_stable_layer_norm: + self.encoder = TFWav2Vec2EncoderStableLayerNorm(config, name="encoder") + else: + self.encoder = TFWav2Vec2Encoder(config, name="encoder") + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if self.config.mask_time_prob > 0.0 or self.config.mask_feature_prob > 0.0: + self.masked_spec_embed = self.add_weight( + shape=(self.config.hidden_size,), initializer="uniform", trainable=True, name="masked_spec_embed" + ) + if getattr(self, "feature_extractor", None) is not None: + with tf.name_scope(self.feature_extractor.name): + self.feature_extractor.build(None) + if getattr(self, "feature_projection", None) is not None: + with tf.name_scope(self.feature_projection.name): + self.feature_projection.build(None) + if getattr(self, "encoder", None) is not None: + with tf.name_scope(self.encoder.name): + self.encoder.build(None) + + def _get_feat_extract_output_lengths(self, input_lengths: tf.Tensor): + """ + Computes the output length of the convolutional layers + """ + + def _conv_out_length(input_length, kernel_size, stride): + # 1D convolutional layer output length formula taken + # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html + return (input_length - kernel_size) // stride + 1 + + for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): + input_lengths = _conv_out_length(input_lengths, kernel_size, stride) + + return input_lengths + + def _mask_hidden_states(self, hidden_states: tf.Tensor, mask_time_indices: tf.Tensor | None = None): + """ + Masks extracted features along time axis and/or along feature axis according to + [SpecAugment](https://arxiv.org/abs/1904.08779). + """ + batch_size, sequence_length, hidden_size = shape_list(hidden_states) + + # `config.apply_spec_augment` can set masking to False + if not getattr(self.config, "apply_spec_augment", True): + return hidden_states + + if mask_time_indices is not None: + # apply SpecAugment along time axis with given mask_time_indices + hidden_states = tf.where( + tf.cast(mask_time_indices[:, :, tf.newaxis], tf.bool), + self.masked_spec_embed[tf.newaxis, tf.newaxis, :], + hidden_states, + ) + + elif self.config.mask_time_prob > 0: + # generate indices & apply SpecAugment along time axis + mask_time_indices = _compute_mask_indices( + (batch_size, sequence_length), + mask_prob=self.config.mask_time_prob, + mask_length=self.config.mask_time_length, + min_masks=2, + ) + hidden_states = tf.where( + tf.cast(mask_time_indices[:, :, tf.newaxis], tf.bool), + self.masked_spec_embed[tf.newaxis, tf.newaxis, :], + hidden_states, + ) + + # apply SpecAugment along feature axis + if self.config.mask_feature_prob > 0: + mask_feature_indices = _compute_mask_indices( + (batch_size, hidden_size), + mask_prob=self.config.mask_feature_prob, + mask_length=self.config.mask_feature_length, + ) + hidden_states = tf.where(mask_feature_indices[:, tf.newaxis, :], hidden_states, 0) + + return hidden_states + + @unpack_inputs + def call( + self, + input_values: tf.Tensor, + attention_mask: tf.Tensor | None = None, + token_type_ids: tf.Tensor | None = None, + position_ids: tf.Tensor | None = None, + head_mask: tf.Tensor | None = None, + inputs_embeds: tf.Tensor | None = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + training: bool = False, + **kwargs: Any, + ): + extract_features = self.feature_extractor(tf.cast(input_values, tf.float32), training=training) + # extract_features = tf.transpose(extract_features, perm=(0, 2, 1)) + + if attention_mask is not None: + # compute real output lengths according to convolution formula + output_lengths = self._get_feat_extract_output_lengths(tf.reduce_sum(attention_mask, -1)) + + attention_mask = tf.sequence_mask( + output_lengths, maxlen=shape_list(extract_features)[1], dtype=extract_features.dtype + ) + + hidden_states, extract_features = self.feature_projection(extract_features, training=training) + + mask_time_indices = kwargs.get("mask_time_indices", None) + if training: + hidden_states = self._mask_hidden_states(hidden_states, mask_time_indices=mask_time_indices) + + encoder_outputs = self.encoder( + hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + training=training, + ) + hidden_states = encoder_outputs[0] + + if not return_dict: + return (hidden_states, extract_features) + encoder_outputs[1:] + + return TFWav2Vec2BaseModelOutput( + last_hidden_state=hidden_states, + extract_features=extract_features, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +class TFWav2Vec2PreTrainedModel(TFPreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = Wav2Vec2Config + base_model_prefix = "wav2vec2" + main_input_name = "input_values" + + @property + def input_signature(self): + return { + "input_values": tf.TensorSpec((None, None), tf.float32, name="input_values"), + "attention_mask": tf.TensorSpec((None, None), tf.float32, name="attention_mask"), + } + + @property + def dummy_inputs(self): + return { + "input_values": tf.random.uniform(shape=(1, 500), dtype=tf.float32), + "attention_mask": tf.ones(shape=(1, 500), dtype=tf.float32), + } + + def __init__(self, config, *inputs, **kwargs): + super().__init__(config, *inputs, **kwargs) + logger.warning( + f"\n{self.__class__.__name__} has backpropagation operations that are NOT supported on CPU. If you wish " + "to train/fine-tune this model, you need a GPU or a TPU" + ) + + def _get_feat_extract_output_lengths(self, input_lengths, add_adapter=None): + """ + Computes the output length of the convolutional layers + """ + add_adapter = self.config.add_adapter if add_adapter is None else add_adapter + + def _conv_out_length(input_length, kernel_size, stride): + return tf.math.floordiv(input_length - kernel_size, stride) + 1 + + for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): + input_lengths = _conv_out_length(input_lengths, kernel_size, stride) + + if add_adapter: + for _ in range(self.config.num_adapter_layers): + input_lengths = _conv_out_length(input_lengths, 1, self.config.adapter_stride) + return input_lengths + + def _get_feature_vector_attention_mask( + self, feature_vector_length: int, attention_mask: tf.Tensor, add_adapter=None + ): + non_padded_lengths = tf.math.cumsum(attention_mask, axis=-1)[:, -1] + output_lengths = self._get_feat_extract_output_lengths(non_padded_lengths, add_adapter=add_adapter) + output_lengths = tf.cast(output_lengths, tf.int32) + batch_size = tf.shape(attention_mask)[0] + # check device here + attention_mask = tf.zeros( + (batch_size, feature_vector_length), dtype=attention_mask.dtype, name="attention_mask" + ) # these two operations makes sure that all values before the output lengths idxs are attended to + ## check device + attention_mask = tf.tensor_scatter_nd_update( + attention_mask, + indices=tf.stack([tf.range(batch_size), output_lengths - 1], axis=1), + updates=tf.ones([batch_size], dtype=attention_mask.dtype), + ) + attention_mask = tf.reverse(attention_mask, axis=[-1]) + attention_mask = tf.cumsum(attention_mask, axis=-1) + attention_mask = tf.reverse(attention_mask, axis=[-1]) + attention_mask = tf.cast(attention_mask, tf.bool) + return attention_mask + + +WAV_2_VEC_2_START_DOCSTRING = r""" + + This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it + as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and + behavior. + + + + TensorFlow models and layers in `transformers` accept two formats as input: + + - having all inputs as keyword arguments (like PyTorch models), or + - having all inputs as a list, tuple or dict in the first positional argument. + + The reason the second format is supported is that Keras methods prefer this format when passing inputs to models + and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just + pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second + format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with + the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first + positional argument: + + - a single Tensor with `input_values` only and nothing else: `model(input_values)` + - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: + `model([input_values, attention_mask])` or `model([input_values, attention_mask, token_type_ids])` + - a dictionary with one or several input Tensors associated to the input names given in the docstring: + `model({"input_values": input_values, "token_type_ids": token_type_ids})` + + Note that when creating models and layers with + [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry + about any of this, as you can just pass inputs like you would to any other Python function! + + + + Args: + config ([`Wav2Vec2Config`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +WAV_2_VEC_2_INPUTS_DOCSTRING = r""" + Args: + input_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` `Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and + [`PreTrainedTokenizer.encode`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`np.ndarray` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_values` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_values` indices into associated vectors + than the model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the + config will be used instead. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. This argument can be used only in eager mode, in graph mode the value in the config will be + used instead. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in + eager mode, in graph mode the value will always be set to True. + training (`bool`, *optional*, defaults to `False``): + Whether or not to use the model in training mode (some modules like dropout modules have different + behaviors between training and evaluation). +""" + + +@add_start_docstrings( + "The bare TFWav2Vec2 Model transformer outputing raw hidden-states without any specific head on top.", + WAV_2_VEC_2_START_DOCSTRING, +) +class TFWav2Vec2Model(TFWav2Vec2PreTrainedModel): + def __init__(self, config: Wav2Vec2Config, *inputs, **kwargs): + super().__init__(config, *inputs, **kwargs) + self.config = config + self.wav2vec2 = TFWav2Vec2MainLayer(config, name="wav2vec2") + + @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=TFBaseModelOutput, config_class=_CONFIG_FOR_DOC) + @unpack_inputs + def call( + self, + input_values: tf.Tensor, + attention_mask: tf.Tensor | None = None, + token_type_ids: tf.Tensor | None = None, + position_ids: tf.Tensor | None = None, + head_mask: tf.Tensor | None = None, + inputs_embeds: tf.Tensor | None = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + training: bool = False, + ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]: + """ + + Returns: + + Example: + + ```python + >>> from transformers import AutoProcessor, TFWav2Vec2Model + >>> from datasets import load_dataset + >>> import soundfile as sf + + >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h") + >>> model = TFWav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h") + + + >>> def map_to_array(batch): + ... speech, _ = sf.read(batch["file"]) + ... batch["speech"] = speech + ... return batch + + + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + >>> ds = ds.map(map_to_array) + + >>> input_values = processor(ds["speech"][0], return_tensors="tf").input_values # Batch size 1 + >>> hidden_states = model(input_values).last_hidden_state + ```""" + + output_hidden_states = output_hidden_states if output_hidden_states else self.config.output_hidden_states + output_attentions = output_attentions if output_attentions else self.config.output_attentions + return_dict = return_dict if return_dict else self.config.return_dict + + outputs = self.wav2vec2( + input_values=input_values, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + training=training, + ) + + return outputs + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "wav2vec2", None) is not None: + with tf.name_scope(self.wav2vec2.name): + self.wav2vec2.build(None) + + +@add_start_docstrings( + """TFWav2Vec2 Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).""", + WAV_2_VEC_2_START_DOCSTRING, +) +class TFWav2Vec2ForCTC(TFWav2Vec2PreTrainedModel): + def __init__(self, config: Wav2Vec2Config, *inputs, **kwargs): + super().__init__(config, *inputs, **kwargs) + + self.wav2vec2 = TFWav2Vec2MainLayer(config, name="wav2vec2") + self.dropout = keras.layers.Dropout(config.final_dropout) + self.lm_head = keras.layers.Dense(config.vocab_size, name="lm_head") + self.output_hidden_size = ( + config.output_hidden_size if hasattr(config, "add_adapter") and config.add_adapter else config.hidden_size + ) + + def freeze_feature_extractor(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameters will + not be updated during training. + """ + warnings.warn( + "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. " + "Please use the equivalent `freeze_feature_encoder` method instead.", + FutureWarning, + ) + self.freeze_feature_encoder() + + def freeze_feature_encoder(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameter will + not be updated during training. + """ + self.wav2vec2.feature_extractor.trainable = False + + @unpack_inputs + @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=TFCausalLMOutput, config_class=_CONFIG_FOR_DOC) + def call( + self, + input_values: tf.Tensor, + attention_mask: tf.Tensor | None = None, + token_type_ids: tf.Tensor | None = None, + position_ids: tf.Tensor | None = None, + head_mask: tf.Tensor | None = None, + inputs_embeds: tf.Tensor | None = None, + output_attentions: Optional[bool] = None, + labels: tf.Tensor | None = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + training: Optional[bool] = False, + ) -> Union[TFCausalLMOutput, Tuple[tf.Tensor]]: + r""" + labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_values` docstring) Tokens with indices set to `-100` are ignored (masked), + the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + + Returns: + + Example: + + ```python + >>> import tensorflow as tf + >>> from transformers import AutoProcessor, TFWav2Vec2ForCTC + >>> from datasets import load_dataset + >>> import soundfile as sf + + >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h") + >>> model = TFWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") + + + >>> def map_to_array(batch): + ... speech, _ = sf.read(batch["file"]) + ... batch["speech"] = speech + ... return batch + + + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + >>> ds = ds.map(map_to_array) + + >>> input_values = processor(ds["speech"][0], return_tensors="tf").input_values # Batch size 1 + >>> logits = model(input_values).logits + >>> predicted_ids = tf.argmax(logits, axis=-1) + + >>> transcription = processor.decode(predicted_ids[0]) + + >>> # compute loss + >>> target_transcription = "A MAN SAID TO THE UNIVERSE SIR I EXIST" + + >>> # Pass transcription as `text` to encode labels + >>> labels = processor(text=transcription, return_tensors="tf").input_ids + + >>> loss = model(input_values, labels=labels).loss + ```""" + + outputs = self.wav2vec2( + input_values=input_values, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + training=training, + ) + hidden_states = outputs[0] + hidden_states = self.dropout(hidden_states, training=training) + + logits = self.lm_head(hidden_states) + + if labels is not None: + if tf.reduce_max(labels) >= self.config.vocab_size: + raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}") + + attention_mask = ( + attention_mask if attention_mask is not None else tf.ones_like(input_values, dtype=tf.float32) + ) + input_lengths = self.wav2vec2._get_feat_extract_output_lengths(tf.reduce_sum(attention_mask, axis=-1)) + + # assuming that padded tokens are filled with -100 + # when not being attended to + labels_mask = tf.cast(labels >= 0, tf.int32) + target_lengths = tf.reduce_sum(labels_mask, axis=-1) + + loss = tf.nn.ctc_loss( + logits=logits, + labels=labels, + logit_length=input_lengths, + label_length=target_lengths, + blank_index=self.config.pad_token_id, + logits_time_major=False, + ) + + if self.config.ctc_loss_reduction == "sum": + loss = tf.reduce_sum(loss) + if self.config.ctc_loss_reduction == "mean": + loss = tf.reduce_mean(loss) + + loss = tf.reshape(loss, (1,)) + else: + loss = None + + if not return_dict: + output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:] + return ((loss,) + output) if loss is not None else output + + return TFCausalLMOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "wav2vec2", None) is not None: + with tf.name_scope(self.wav2vec2.name): + self.wav2vec2.build(None) + if getattr(self, "lm_head", None) is not None: + with tf.name_scope(self.lm_head.name): + self.lm_head.build([None, None, self.output_hidden_size]) + + +class TFWav2Vec2ForSequenceClassification(TFWav2Vec2PreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.wav2vec2 = TFWav2Vec2MainLayer(config, name="wav2vec2") + self.num_layers = config.num_hidden_layers + 1 + with tf.name_scope(self._name_scope()): + if config.use_weighted_layer_sum: + self.layer_weights = self.add_weight( + shape=(self.num_layers,), initializer="ones", trainable=True, name="layer_weights" + ) + self.config = config + self.projector = keras.layers.Dense(units=config.classifier_proj_size, name="projector") + self.classifier = keras.layers.Dense(units=config.num_labels, activation=None, name="classifier") + + def freeze_feature_extractor(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameters will + not be updated during training. + """ + warnings.warn( + "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. " + "Please use the equivalent `freeze_feature_encoder` method instead.", + FutureWarning, + ) + self.freeze_feature_encoder() + + def freeze_feature_encoder(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameter will + not be updated during training. + """ + self.wav2vec2.feature_extractor.trainable = False + + def freeze_base_model(self): + """ + Calling this function will disable the gradient computation for the base model so that its parameters will not + be updated during training. Only the classification head will be updated. + """ + for layer in self.wav2vec2.layers: + layer.trainable = False + + @unpack_inputs + def call( + self, + input_values: tf.Tensor, + attention_mask: tf.Tensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + labels: tf.Tensor | None = None, + training: bool = False, + ) -> TFSequenceClassifierOutput | Tuple[tf.Tensor]: + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states + + outputs = self.wav2vec2( + input_values, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + training=training, + ) + if self.config.use_weighted_layer_sum: + hidden_states = outputs[_HIDDEN_STATES_START_POSITION] + hidden_states = tf.stack(hidden_states, axis=1) + norm_weights = tf.nn.softmax(self.layer_weights, axis=-1) + hidden_states = tf.reduce_sum(hidden_states * tf.reshape(norm_weights, [-1, 1, 1]), axis=1) + else: + hidden_states = outputs[0] + + hidden_states = self.projector(hidden_states) + if attention_mask is None: + pooled_output = tf.reduce_mean(hidden_states, axis=1) + else: + padding_mask = self._get_feature_vector_attention_mask(shape_list(hidden_states)[1], attention_mask) + padding_mask_float = tf.cast(padding_mask, hidden_states.dtype) + hidden_states = tf.multiply(hidden_states, tf.expand_dims(padding_mask_float, axis=-1)) + pooled_output = tf.divide( + tf.reduce_sum(hidden_states, axis=1), tf.expand_dims(tf.reduce_sum(padding_mask_float, axis=1), axis=1) + ) + logits = self.classifier(pooled_output) + loss = None + if labels is not None: + loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True) + loss = loss_fn(tf.reshape(labels, [-1]), tf.reshape(logits, [-1, self.config.num_labels])) + if not return_dict: + output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:] + return ((loss,) + output) if loss is not None else output + + return TFSequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def build(self, input_shape=None): + if self.built: + return + self.built = True + if getattr(self, "wav2vec2", None) is not None: + with tf.name_scope(self.wav2vec2.name): + self.wav2vec2.build(None) + if getattr(self, "projector", None) is not None: + with tf.name_scope(self.projector.name): + self.projector.build([None, None, self.config.hidden_size]) + if getattr(self, "classifier", None) is not None: + with tf.name_scope(self.classifier.name): + self.classifier.build([None, None, self.config.classifier_proj_size]) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/processing_wav2vec2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/processing_wav2vec2.py new file mode 100644 index 0000000000000000000000000000000000000000..dc6e9d14ee66cdad94cc1b5c09176f0dd9d0c3a1 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/processing_wav2vec2.py @@ -0,0 +1,165 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Speech processor class for Wav2Vec2 +""" +import warnings +from contextlib import contextmanager + +from ...processing_utils import ProcessorMixin +from .feature_extraction_wav2vec2 import Wav2Vec2FeatureExtractor +from .tokenization_wav2vec2 import Wav2Vec2CTCTokenizer + + +class Wav2Vec2Processor(ProcessorMixin): + r""" + Constructs a Wav2Vec2 processor which wraps a Wav2Vec2 feature extractor and a Wav2Vec2 CTC tokenizer into a single + processor. + + [`Wav2Vec2Processor`] offers all the functionalities of [`Wav2Vec2FeatureExtractor`] and [`PreTrainedTokenizer`]. + See the docstring of [`~Wav2Vec2Processor.__call__`] and [`~Wav2Vec2Processor.decode`] for more information. + + Args: + feature_extractor (`Wav2Vec2FeatureExtractor`): + An instance of [`Wav2Vec2FeatureExtractor`]. The feature extractor is a required input. + tokenizer ([`PreTrainedTokenizer`]): + An instance of [`PreTrainedTokenizer`]. The tokenizer is a required input. + """ + + feature_extractor_class = "Wav2Vec2FeatureExtractor" + tokenizer_class = "AutoTokenizer" + + def __init__(self, feature_extractor, tokenizer): + super().__init__(feature_extractor, tokenizer) + self.current_processor = self.feature_extractor + self._in_target_context_manager = False + + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path, **kwargs): + try: + return super().from_pretrained(pretrained_model_name_or_path, **kwargs) + except OSError: + warnings.warn( + f"Loading a tokenizer inside {cls.__name__} from a config that does not" + " include a `tokenizer_class` attribute is deprecated and will be " + "removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`" + " attribute to either your `config.json` or `tokenizer_config.json` " + "file to suppress this warning: ", + FutureWarning, + ) + + feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(pretrained_model_name_or_path, **kwargs) + tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs) + + return cls(feature_extractor=feature_extractor, tokenizer=tokenizer) + + def __call__(self, *args, **kwargs): + """ + When used in normal mode, this method forwards all its arguments to Wav2Vec2FeatureExtractor's + [`~Wav2Vec2FeatureExtractor.__call__`] and returns its output. If used in the context + [`~Wav2Vec2Processor.as_target_processor`] this method forwards all its arguments to PreTrainedTokenizer's + [`~PreTrainedTokenizer.__call__`]. Please refer to the docstring of the above two methods for more information. + """ + # For backward compatibility + if self._in_target_context_manager: + return self.current_processor(*args, **kwargs) + + if "raw_speech" in kwargs: + warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.") + audio = kwargs.pop("raw_speech") + else: + audio = kwargs.pop("audio", None) + sampling_rate = kwargs.pop("sampling_rate", None) + text = kwargs.pop("text", None) + if len(args) > 0: + audio = args[0] + args = args[1:] + + if audio is None and text is None: + raise ValueError("You need to specify either an `audio` or `text` input to process.") + + if audio is not None: + inputs = self.feature_extractor(audio, *args, sampling_rate=sampling_rate, **kwargs) + if text is not None: + encodings = self.tokenizer(text, **kwargs) + + if text is None: + return inputs + elif audio is None: + return encodings + else: + inputs["labels"] = encodings["input_ids"] + return inputs + + def pad(self, *args, **kwargs): + """ + When used in normal mode, this method forwards all its arguments to Wav2Vec2FeatureExtractor's + [`~Wav2Vec2FeatureExtractor.pad`] and returns its output. If used in the context + [`~Wav2Vec2Processor.as_target_processor`] this method forwards all its arguments to PreTrainedTokenizer's + [`~PreTrainedTokenizer.pad`]. Please refer to the docstring of the above two methods for more information. + """ + # For backward compatibility + if self._in_target_context_manager: + return self.current_processor.pad(*args, **kwargs) + + input_features = kwargs.pop("input_features", None) + labels = kwargs.pop("labels", None) + if len(args) > 0: + input_features = args[0] + args = args[1:] + + if input_features is not None: + input_features = self.feature_extractor.pad(input_features, *args, **kwargs) + if labels is not None: + labels = self.tokenizer.pad(labels, **kwargs) + + if labels is None: + return input_features + elif input_features is None: + return labels + else: + input_features["labels"] = labels["input_ids"] + return input_features + + def batch_decode(self, *args, **kwargs): + """ + This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please + refer to the docstring of this method for more information. + """ + return self.tokenizer.batch_decode(*args, **kwargs) + + def decode(self, *args, **kwargs): + """ + This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer + to the docstring of this method for more information. + """ + return self.tokenizer.decode(*args, **kwargs) + + @contextmanager + def as_target_processor(self): + """ + Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning + Wav2Vec2. + """ + warnings.warn( + "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your " + "labels by using the argument `text` of the regular `__call__` method (either in the same call as " + "your audio inputs, or in a separate call." + ) + self._in_target_context_manager = True + self.current_processor = self.tokenizer + yield + self.current_processor = self.feature_extractor + self._in_target_context_manager = False diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/tokenization_wav2vec2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/tokenization_wav2vec2.py new file mode 100644 index 0000000000000000000000000000000000000000..34848a841e9f712377c483db0b095a3816ca7ac3 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2/tokenization_wav2vec2.py @@ -0,0 +1,915 @@ +# coding=utf-8 +# Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization class for Wav2Vec2.""" + +import json +import os +import warnings +from dataclasses import dataclass +from itertools import groupby +from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union + +import numpy as np + +from ...tokenization_utils import PreTrainedTokenizer +from ...tokenization_utils_base import AddedToken, BatchEncoding +from ...utils import ( + ModelOutput, + PaddingStrategy, + TensorType, + add_end_docstrings, + is_flax_available, + is_tf_available, + is_torch_available, + logging, + to_py_obj, +) + + +logger = logging.get_logger(__name__) + + +if TYPE_CHECKING: + if is_torch_available(): + import torch + if is_tf_available(): + import tensorflow as tf + if is_flax_available(): + import jax.numpy as jnp # noqa: F401 + + +VOCAB_FILES_NAMES = { + "vocab_file": "vocab.json", + "tokenizer_config_file": "tokenizer_config.json", +} + + +# Wav2Vec2 has no max input length + +WAV2VEC2_KWARGS_DOCSTRING = r""" + padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): + Activates and controls padding. Accepts the following values: + + - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single + sequence if provided). + - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum + acceptable input length for the model if that argument is not provided. + - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different + lengths). + max_length (`int`, *optional*): + Controls the maximum length to use by one of the truncation/padding parameters. + + If left unset or set to `None`, this will use the predefined model maximum length if a maximum length + is required by one of the truncation/padding parameters. If the model has no specific maximum input + length (like XLNet) truncation/padding to a maximum length will be deactivated. + pad_to_multiple_of (`int`, *optional*): + If set will pad the sequence to a multiple of the provided value. This is especially useful to enable + the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta). + return_tensors (`str` or [`~utils.TensorType`], *optional*): + If set, will return tensors instead of list of python integers. Acceptable values are: + + - `'tf'`: Return TensorFlow `tf.constant` objects. + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return Numpy `np.ndarray` objects. + verbose (`bool`, *optional*, defaults to `True`): + Whether or not to print more information and warnings. +""" + +ListOfDict = List[Dict[str, Union[int, str]]] + + +@dataclass +class Wav2Vec2CTCTokenizerOutput(ModelOutput): + """ + Output type of [` Wav2Vec2CTCTokenizer`], with transcription. + + Args: + text (list of `str` or `str`): + Decoded logits in text from. Usually the speech transcription. + char_offsets (list of `List[Dict[str, Union[int, str]]]` or `List[Dict[str, Union[int, str]]]`): + Offsets of the decoded characters. In combination with sampling rate and model downsampling rate char + offsets can be used to compute time stamps for each charater. Total logit score of the beam associated with + produced text. + word_offsets (list of `List[Dict[str, Union[int, str]]]` or `List[Dict[str, Union[int, str]]]`): + Offsets of the decoded words. In combination with sampling rate and model downsampling rate word offsets + can be used to compute time stamps for each word. + """ + + text: Union[List[str], str] + char_offsets: Union[List[ListOfDict], ListOfDict] = None + word_offsets: Union[List[ListOfDict], ListOfDict] = None + + +class Wav2Vec2CTCTokenizer(PreTrainedTokenizer): + """ + Constructs a Wav2Vec2CTC tokenizer. + + This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to + the superclass for more information regarding such methods. + + Args: + vocab_file (`str`): + File containing the vocabulary. + bos_token (`str`, *optional*, defaults to `""`): + The beginning of sentence token. + eos_token (`str`, *optional*, defaults to `""`): + The end of sentence token. + unk_token (`str`, *optional*, defaults to `""`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + pad_token (`str`, *optional*, defaults to `""`): + The token used for padding, for example when batching sequences of different lengths. + word_delimiter_token (`str`, *optional*, defaults to `"|"`): + The token used for defining the end of a word. + do_lower_case (`bool`, *optional*, defaults to `False`): + Whether or not to accept lowercase input and lowercase the output when decoding. + target_lang (`str`, *optional*): + A target language the tokenizer should set by default. `target_lang` has to be defined for multi-lingual, + nested vocabulary such as [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all). + + **kwargs + Additional keyword arguments passed along to [`PreTrainedTokenizer`] + """ + + vocab_files_names = VOCAB_FILES_NAMES + model_input_names = ["input_ids", "attention_mask"] + + def __init__( + self, + vocab_file, + bos_token="", + eos_token="", + unk_token="", + pad_token="", + word_delimiter_token="|", + replace_word_delimiter_char=" ", + do_lower_case=False, + target_lang=None, + **kwargs, + ): + self._word_delimiter_token = word_delimiter_token + + self.do_lower_case = do_lower_case + self.replace_word_delimiter_char = replace_word_delimiter_char + self.target_lang = target_lang + + with open(vocab_file, encoding="utf-8") as vocab_handle: + self.vocab = json.load(vocab_handle) + + # if target lang is defined vocab must be a nested dict + # with each target lang being one vocabulary + if target_lang is not None: + self.encoder = self.vocab[target_lang] + else: + self.encoder = self.vocab + + self.decoder = {v: k for k, v in self.encoder.items()} + + super().__init__( + unk_token=unk_token, + bos_token=bos_token, + eos_token=eos_token, + pad_token=pad_token, + do_lower_case=do_lower_case, + word_delimiter_token=word_delimiter_token, + replace_word_delimiter_char=replace_word_delimiter_char, + target_lang=target_lang, + **kwargs, + ) + + # make sure that tokens made of several + # characters are not split at tokenization + for token in self.encoder.keys(): + if len(token) > 1: + self.add_tokens(AddedToken(token, rstrip=True, lstrip=True, normalized=False)) + + def set_target_lang(self, target_lang: str): + """ + Set the target language of a nested multi-lingual dictionary + """ + if self.vocab == self.encoder: + raise ValueError(f"{self.vocab} is not a multi-lingual, nested tokenizer. Cannot set target language.") + + if target_lang not in self.vocab: + raise ValueError(f"{target_lang} does not exist. Choose one of {', '.join(self.vocab.keys())}.") + + self.target_lang = target_lang + self.init_kwargs["target_lang"] = target_lang + self.encoder = self.vocab[target_lang] + self.decoder = {v: k for k, v in self.encoder.items()} + + # make sure that tokens made of several + # characters are not split at tokenization + for token in self.encoder.keys(): + if len(token) > 1: + self.add_tokens(AddedToken(token, rstrip=True, lstrip=True, normalized=False)) + + @property + def word_delimiter_token(self) -> str: + """ + `str`: Word delimiter token. Log an error if used while not having been set. + """ + if self._word_delimiter_token is None and self.verbose: + logger.error("Using word_delimiter_token, but it is not set yet.") + return None + return str(self._word_delimiter_token) + + @property + def word_delimiter_token_id(self) -> Optional[int]: + """ + `Optional[int]`: Id of the word_delimiter_token in the vocabulary. Returns `None` if the token has not been + set. + """ + if self._word_delimiter_token is None: + return None + return self.convert_tokens_to_ids(self.word_delimiter_token) + + @word_delimiter_token.setter + def word_delimiter_token(self, value): + self._word_delimiter_token = value + + @word_delimiter_token_id.setter + def word_delimiter_token_id(self, value): + self._word_delimiter_token = self.convert_tokens_to_ids(value) + + @property + def vocab_size(self) -> int: + return len(self.decoder) + + def get_vocab(self) -> Dict: + vocab = dict(self.encoder) + vocab.update(self.added_tokens_encoder) + return vocab + + def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int: + # Overwritten to never strip! + to_add = [] + for token in new_tokens: + if isinstance(token, str): + to_add.append(AddedToken(token, rstrip=False, lstrip=False, normalized=False)) + else: + to_add.append(token) + + return super()._add_tokens(to_add, special_tokens) + + def _tokenize(self, text, **kwargs): + """ + Converts a string into a sequence of tokens (string), using the tokenizer. + """ + if self.do_lower_case: + text = text.upper() + + return list(text.replace(" ", self.word_delimiter_token)) + + def _convert_token_to_id(self, token: str) -> int: + """Converts a token (str) in an index (integer) using the vocab.""" + return self.encoder.get(token, self.encoder.get(self.unk_token)) + + def _convert_id_to_token(self, index: int) -> str: + """Converts an index (integer) in a token (str) using the vocab.""" + result = self.decoder.get(index, self.unk_token) + return result + + def convert_tokens_to_string( + self, + tokens: List[str], + group_tokens: bool = True, + spaces_between_special_tokens: bool = False, + output_char_offsets: bool = False, + output_word_offsets: bool = False, + ) -> Dict[str, Union[str, float]]: + """ + Converts a connectionist-temporal-classification (CTC) output tokens into a single string. + """ + if len(tokens) == 0: + return {"text": "", "char_offsets": [], "word_offsets": []} + # group same tokens into non-repeating tokens in CTC style decoding + if group_tokens: + chars, char_repetitions = zip(*((token, len(list(group_iter))) for token, group_iter in groupby(tokens))) + else: + chars = tokens + char_repetitions = len(tokens) * [1] + + # filter self.pad_token which is used as CTC-blank token + processed_chars = list(filter(lambda char: char != self.pad_token, chars)) + + # replace delimiter token + processed_chars = [ + self.replace_word_delimiter_char if char == self.word_delimiter_token else char for char in processed_chars + ] + + # retrieve offsets + char_offsets = word_offsets = None + if output_char_offsets or output_word_offsets: + char_offsets = self._compute_offsets(char_repetitions, chars, self.pad_token) + + if len(char_offsets) != len(processed_chars): + raise ValueError( + f"`char_offsets`: {char_offsets} and `processed_tokens`: {processed_chars}" + " have to be of the same length, but are: " + f"`len(offsets)`: {len(char_offsets)} and `len(processed_tokens)`:" + f" {len(processed_chars)}" + ) + + # set tokens to correct processed token + for i, char in enumerate(processed_chars): + char_offsets[i]["char"] = char + + # retrieve word offsets from character offsets + word_offsets = None + if output_word_offsets: + word_offsets = self._get_word_offsets(char_offsets, self.replace_word_delimiter_char) + + # don't output chars if not set to True + if not output_char_offsets: + char_offsets = None + + # join to string + join_char = " " if spaces_between_special_tokens else "" + string = join_char.join(processed_chars).strip() + + if self.do_lower_case: + string = string.lower() + + return {"text": string, "char_offsets": char_offsets, "word_offsets": word_offsets} + + @staticmethod + def _compute_offsets( + char_repetitions: List[int], chars: List[str], ctc_token: int + ) -> List[Dict[str, Union[str, int]]]: + end_indices = np.asarray(char_repetitions).cumsum() + start_indices = np.concatenate(([0], end_indices[:-1])) + + offsets = [ + {"char": t, "start_offset": s, "end_offset": e} for t, s, e in zip(chars, start_indices, end_indices) + ] + + # filter out CTC token + offsets = list(filter(lambda offsets: offsets["char"] != ctc_token, offsets)) + return offsets + + @staticmethod + def _get_word_offsets( + offsets: Dict[str, Union[str, float]], word_delimiter_char: str = " " + ) -> Dict[str, Union[str, float]]: + word_offsets = [] + + last_state = "SPACE" + word = "" + start_offset = 0 + end_offset = 0 + for i, offset in enumerate(offsets): + char = offset["char"] + state = "SPACE" if char == word_delimiter_char else "WORD" + + if state == last_state: + # If we are in the same state as before, we simply repeat what we've done before + end_offset = offset["end_offset"] + word += char + else: + # Switching state + if state == "SPACE": + # Finishing a word + word_offsets.append({"word": word, "start_offset": start_offset, "end_offset": end_offset}) + else: + # Starting a new word + start_offset = offset["start_offset"] + end_offset = offset["end_offset"] + word = char + + last_state = state + if last_state == "WORD": + word_offsets.append({"word": word, "start_offset": start_offset, "end_offset": end_offset}) + + return word_offsets + + def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs): + if is_split_into_words: + text = " " + text + return (text, kwargs) + + def _decode( + self, + token_ids: List[int], + skip_special_tokens: bool = False, + clean_up_tokenization_spaces: bool = None, + group_tokens: bool = True, + spaces_between_special_tokens: bool = False, + output_word_offsets: Optional[bool] = False, + output_char_offsets: Optional[bool] = False, + ) -> str: + """ + special _decode function is needed for Wav2Vec2Tokenizer because added tokens should be treated exactly the + same as tokens of the base vocabulary and therefore the function `convert_tokens_to_string` has to be called on + the whole token list and not individually on added tokens + """ + filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens) + + result = [] + for token in filtered_tokens: + if skip_special_tokens and ( + token in self.all_special_ids or (token != self.pad_token and token in self.all_special_tokens) + ): + continue + result.append(token) + + string_output = self.convert_tokens_to_string( + result, + group_tokens=group_tokens, + spaces_between_special_tokens=spaces_between_special_tokens, + output_word_offsets=output_word_offsets, + output_char_offsets=output_char_offsets, + ) + + text = string_output["text"] + + clean_up_tokenization_spaces = ( + clean_up_tokenization_spaces + if clean_up_tokenization_spaces is not None + else self.clean_up_tokenization_spaces + ) + if clean_up_tokenization_spaces: + text = self.clean_up_tokenization(text) + + if output_word_offsets or output_char_offsets: + return Wav2Vec2CTCTokenizerOutput( + text=text, + char_offsets=string_output["char_offsets"], + word_offsets=string_output["word_offsets"], + ) + else: + return text + + # overwritten from `tokenization_utils_base.py` because tokenizer can output + # `ModelOutput` which should not be a list for batched output and + # because we need docs for `output_char_offsets` here + def batch_decode( + self, + sequences: Union[List[int], List[List[int]], "np.ndarray", "torch.Tensor", "tf.Tensor"], + skip_special_tokens: bool = False, + clean_up_tokenization_spaces: bool = None, + output_char_offsets: bool = False, + output_word_offsets: bool = False, + **kwargs, + ) -> List[str]: + """ + Convert a list of lists of token ids into a list of strings by calling decode. + + Args: + sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`): + List of tokenized input ids. Can be obtained using the `__call__` method. + skip_special_tokens (`bool`, *optional*, defaults to `False`): + Whether or not to remove special tokens in the decoding. + clean_up_tokenization_spaces (`bool`, *optional*): + Whether or not to clean up the tokenization spaces. + output_char_offsets (`bool`, *optional*, defaults to `False`): + Whether or not to output character offsets. Character offsets can be used in combination with the + sampling rate and model downsampling rate to compute the time-stamps of transcribed characters. + + + + Please take a look at the Example of [`~Wav2Vec2CTCTokenizer.decode`] to better understand how to make + use of `output_char_offsets`. [`~Wav2Vec2CTCTokenizer.batch_decode`] works the same way with batched + output. + + + + output_word_offsets (`bool`, *optional*, defaults to `False`): + Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate + and model downsampling rate to compute the time-stamps of transcribed words. + + + + Please take a look at the Example of [`~Wav2Vec2CTCTokenizer.decode`] to better understand how to make + use of `output_word_offsets`. [`~Wav2Vec2CTCTokenizer.batch_decode`] works the same way with batched + output. + + + + kwargs (additional keyword arguments, *optional*): + Will be passed to the underlying model specific decode method. + + Returns: + `List[str]` or [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`]: The list of decoded + sentences. Will be a [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`] when + `output_char_offsets == True` or `output_word_offsets == True`. + """ + batch_decoded = [ + self.decode( + seq, + skip_special_tokens=skip_special_tokens, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + output_char_offsets=output_char_offsets, + output_word_offsets=output_word_offsets, + **kwargs, + ) + for seq in sequences + ] + if output_char_offsets or output_word_offsets: + # transform list of dicts to dict of lists + return Wav2Vec2CTCTokenizerOutput({k: [d[k] for d in batch_decoded] for k in batch_decoded[0]}) + + return batch_decoded + + # overwritten from `tokenization_utils_base.py` because we need docs for `output_char_offsets` + # and `output_word_offsets` here + def decode( + self, + token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"], + skip_special_tokens: bool = False, + clean_up_tokenization_spaces: bool = None, + output_char_offsets: bool = False, + output_word_offsets: bool = False, + **kwargs, + ) -> str: + """ + Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special + tokens and clean up tokenization spaces. + + Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`. + + Args: + token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`): + List of tokenized input ids. Can be obtained using the `__call__` method. + skip_special_tokens (`bool`, *optional*, defaults to `False`): + Whether or not to remove special tokens in the decoding. + clean_up_tokenization_spaces (`bool`, *optional*): + Whether or not to clean up the tokenization spaces. + output_char_offsets (`bool`, *optional*, defaults to `False`): + Whether or not to output character offsets. Character offsets can be used in combination with the + sampling rate and model downsampling rate to compute the time-stamps of transcribed characters. + + + + Please take a look at the example below to better understand how to make use of `output_char_offsets`. + + + + output_word_offsets (`bool`, *optional*, defaults to `False`): + Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate + and model downsampling rate to compute the time-stamps of transcribed words. + + + + Please take a look at the example below to better understand how to make use of `output_word_offsets`. + + + + kwargs (additional keyword arguments, *optional*): + Will be passed to the underlying model specific decode method. + + Returns: + `str` or [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`]: The list of decoded + sentences. Will be a [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`] when + `output_char_offsets == True` or `output_word_offsets == True`. + + Example: + + ```python + >>> # Let's see how to retrieve time steps for a model + >>> from transformers import AutoTokenizer, AutoFeatureExtractor, AutoModelForCTC + >>> from datasets import load_dataset + >>> import datasets + >>> import torch + + >>> # import model, feature extractor, tokenizer + >>> model = AutoModelForCTC.from_pretrained("facebook/wav2vec2-base-960h") + >>> tokenizer = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h") + >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h") + + >>> # load first sample of English common_voice + >>> dataset = load_dataset("mozilla-foundation/common_voice_11_0", "en", split="train", streaming=True) + >>> dataset = dataset.cast_column("audio", datasets.Audio(sampling_rate=16_000)) + >>> dataset_iter = iter(dataset) + >>> sample = next(dataset_iter) + + >>> # forward sample through model to get greedily predicted transcription ids + >>> input_values = feature_extractor(sample["audio"]["array"], return_tensors="pt").input_values + >>> logits = model(input_values).logits[0] + >>> pred_ids = torch.argmax(logits, axis=-1) + + >>> # retrieve word stamps (analogous commands for `output_char_offsets`) + >>> outputs = tokenizer.decode(pred_ids, output_word_offsets=True) + >>> # compute `time_offset` in seconds as product of downsampling ratio and sampling_rate + >>> time_offset = model.config.inputs_to_logits_ratio / feature_extractor.sampling_rate + + >>> word_offsets = [ + ... { + ... "word": d["word"], + ... "start_time": round(d["start_offset"] * time_offset, 2), + ... "end_time": round(d["end_offset"] * time_offset, 2), + ... } + ... for d in outputs.word_offsets + ... ] + >>> # compare word offsets with audio `en_train_0/common_voice_en_19121553.mp3` online on the dataset viewer: + >>> # https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/en + >>> word_offsets[:3] + [{'word': 'THE', 'start_time': 0.7, 'end_time': 0.78}, {'word': 'TRICK', 'start_time': 0.88, 'end_time': 1.08}, {'word': 'APPEARS', 'start_time': 1.2, 'end_time': 1.64}] + ```""" + # Convert inputs to python lists + token_ids = to_py_obj(token_ids) + + return self._decode( + token_ids=token_ids, + skip_special_tokens=skip_special_tokens, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + output_char_offsets=output_char_offsets, + output_word_offsets=output_word_offsets, + **kwargs, + ) + + def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: + if not os.path.isdir(save_directory): + logger.error(f"Vocabulary path ({save_directory}) should be a directory") + return + vocab_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] + ) + + with open(vocab_file, "w", encoding="utf-8") as f: + f.write(json.dumps(self.vocab, indent=2, sort_keys=True, ensure_ascii=False) + "\n") + + return (vocab_file,) + + +class Wav2Vec2Tokenizer(PreTrainedTokenizer): + """ + Constructs a Wav2Vec2 tokenizer. + + This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to + the superclass for more information regarding such methods. + + Args: + vocab_file (`str`): + File containing the vocabulary. + bos_token (`str`, *optional*, defaults to `""`): + The beginning of sentence token. + eos_token (`str`, *optional*, defaults to `""`): + The end of sentence token. + unk_token (`str`, *optional*, defaults to `""`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + pad_token (`str`, *optional*, defaults to `""`): + The token used for padding, for example when batching sequences of different lengths. + word_delimiter_token (`str`, *optional*, defaults to `"|"`): + The token used for defining the end of a word. + do_lower_case (`bool`, *optional*, defaults to `False`): + Whether or not to lowercase the output when decoding. + do_normalize (`bool`, *optional*, defaults to `False`): + Whether or not to zero-mean unit-variance normalize the input. Normalizing can help to significantly + improve the performance for some models, *e.g.*, + [wav2vec2-lv60](https://huggingface.co/models?search=lv60). + return_attention_mask (`bool`, *optional*, defaults to `False`): + Whether or not [`~Wav2Vec2Tokenizer.__call__`] should return `attention_mask`. + + + + Wav2Vec2 models that have set `config.feat_extract_norm == "group"`, such as + [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), have **not** been trained using + `attention_mask`. For such models, `input_values` should simply be padded with 0 and no `attention_mask` + should be passed. + + For Wav2Vec2 models that have set `config.feat_extract_norm == "layer"`, such as + [wav2vec2-lv60](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self), `attention_mask` should be + passed for batched inference. + + + + **kwargs + Additional keyword arguments passed along to [`PreTrainedTokenizer`] + """ + + vocab_files_names = VOCAB_FILES_NAMES + pretrained_vocab_files_map = { + "vocab_file": { + "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/vocab.json" + }, + "tokenizer_config_file": { + "facebook/wav2vec2-base-960h": ( + "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/tokenizer.json" + ), + }, + } + model_input_names = ["input_values", "attention_mask"] + + def __init__( + self, + vocab_file, + bos_token="", + eos_token="", + unk_token="", + pad_token="", + word_delimiter_token="|", + do_lower_case=False, + do_normalize=False, + return_attention_mask=False, + **kwargs, + ): + warnings.warn( + "The class `Wav2Vec2Tokenizer` is deprecated and will be removed in version 5 of Transformers. Please use" + " `Wav2Vec2Processor` or `Wav2Vec2CTCTokenizer` instead.", + FutureWarning, + ) + + self._word_delimiter_token = word_delimiter_token + + self.do_lower_case = do_lower_case + self.return_attention_mask = return_attention_mask + self.do_normalize = do_normalize + + with open(vocab_file, encoding="utf-8") as vocab_handle: + self.encoder = json.load(vocab_handle) + + self.decoder = {v: k for k, v in self.encoder.items()} + + super().__init__( + unk_token=unk_token, + bos_token=bos_token, + eos_token=eos_token, + pad_token=pad_token, + do_lower_case=do_lower_case, + do_normalize=do_normalize, + return_attention_mask=return_attention_mask, + word_delimiter_token=word_delimiter_token, + **kwargs, + ) + + @property + def word_delimiter_token(self) -> str: + """ + `str`: Padding token. Log an error if used while not having been set. + """ + if self._word_delimiter_token is None and self.verbose: + logger.error("Using word_delimiter_token, but it is not set yet.") + return None + return str(self._word_delimiter_token) + + @property + def word_delimiter_token_id(self) -> Optional[int]: + """ + `Optional[int]`: Id of the word_delimiter_token in the vocabulary. Returns `None` if the token has not been + set. + """ + if self._word_delimiter_token is None: + return None + return self.convert_tokens_to_ids(self.word_delimiter_token) + + @word_delimiter_token.setter + def word_delimiter_token(self, value): + self._word_delimiter_token = value + + @word_delimiter_token_id.setter + def word_delimiter_token_id(self, value): + self._word_delimiter_token = self.convert_tokens_to_ids(value) + + @add_end_docstrings(WAV2VEC2_KWARGS_DOCSTRING) + def __call__( + self, + raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]], + padding: Union[bool, str, PaddingStrategy] = False, + max_length: Optional[int] = None, + pad_to_multiple_of: Optional[int] = None, + return_tensors: Optional[Union[str, TensorType]] = None, + verbose: bool = True, + **kwargs, + ) -> BatchEncoding: + """ + Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of + sequences. + + Args: + raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`): + The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float + values, a list of numpy array or a list of list of float values. Must be mono channel audio, not + stereo, i.e. single float per timestep. + """ + + is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1 + if is_batched_numpy and len(raw_speech.shape) > 2: + raise ValueError(f"Only mono-channel audio is supported for input to {self}") + is_batched = is_batched_numpy or ( + isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list))) + ) + + # make sure input is in list format + if is_batched and not isinstance(raw_speech[0], np.ndarray): + raw_speech = [np.asarray(speech) for speech in raw_speech] + elif not is_batched and not isinstance(raw_speech, np.ndarray): + raw_speech = np.asarray(raw_speech) + + # always return batch + if not is_batched: + raw_speech = [raw_speech] + + # zero-mean and unit-variance normalization + if self.do_normalize: + raw_speech = [(x - np.mean(x)) / np.sqrt(np.var(x) + 1e-5) for x in raw_speech] + + # convert into correct format for padding + encoded_inputs = BatchEncoding({"input_values": raw_speech}) + + padded_inputs = self.pad( + encoded_inputs, + padding=padding, + max_length=max_length, + pad_to_multiple_of=pad_to_multiple_of, + return_attention_mask=self.return_attention_mask, + return_tensors=return_tensors, + verbose=verbose, + ) + + return padded_inputs + + @property + def vocab_size(self) -> int: + return len(self.decoder) + + def get_vocab(self) -> Dict: + return dict(self.encoder, **self.added_tokens_encoder) + + def _convert_token_to_id(self, token: str) -> int: + """Converts a token (str) in an index (integer) using the vocab.""" + return self.encoder.get(token, self.encoder.get(self.unk_token)) + + def _convert_id_to_token(self, index: int) -> str: + """Converts an index (integer) in a token (str) using the vocab.""" + result = self.decoder.get(index, self.unk_token) + return result + + def convert_tokens_to_string(self, tokens: List[str]) -> str: + """ + Converts a connectionist-temporal-classification (CTC) output tokens into a single string. + """ + # group same tokens into non-repeating tokens in CTC style decoding + grouped_tokens = [token_group[0] for token_group in groupby(tokens)] + + # filter self.pad_token which is used as CTC-blank token + filtered_tokens = list(filter(lambda token: token != self.pad_token, grouped_tokens)) + + # replace delimiter token + string = "".join([" " if token == self.word_delimiter_token else token for token in filtered_tokens]).strip() + + if self.do_lower_case: + string = string.lower() + + return string + + def _decode( + self, + token_ids: List[int], + skip_special_tokens: bool = False, + clean_up_tokenization_spaces: bool = None, + **kwargs, + ) -> str: + """ + special _decode function is needed for Wav2Vec2Tokenizer because added tokens should be treated exactly the + same as tokens of the base vocabulary and therefore the function `convert_tokens_to_string` has to be called on + the whole token list and not individually on added tokens + """ + filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens) + + result = [] + for token in filtered_tokens: + if skip_special_tokens and ( + token in self.all_special_ids or (token != self.pad_token and token in self.all_special_tokens) + ): + continue + result.append(token) + + text = self.convert_tokens_to_string(result) + + clean_up_tokenization_spaces = ( + clean_up_tokenization_spaces + if clean_up_tokenization_spaces is not None + else self.clean_up_tokenization_spaces + ) + if clean_up_tokenization_spaces: + clean_text = self.clean_up_tokenization(text) + return clean_text + else: + return text + + def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: + if not os.path.isdir(save_directory): + logger.error(f"Vocabulary path ({save_directory}) should be a directory") + return + vocab_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] + ) + + with open(vocab_file, "w", encoding="utf-8") as f: + f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") + + return (vocab_file,) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2_bert/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2_bert/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..594f108bcaad969b69904b3b21d101be6a7484c3 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2_bert/__init__.py @@ -0,0 +1,70 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available + + +_import_structure = { + "configuration_wav2vec2_bert": [ + "WAV2VEC2_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", + "Wav2Vec2BertConfig", + ], + "processing_wav2vec2_bert": ["Wav2Vec2BertProcessor"], +} + + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_wav2vec2_bert"] = [ + "WAV2VEC2_BERT_PRETRAINED_MODEL_ARCHIVE_LIST", + "Wav2Vec2BertForAudioFrameClassification", + "Wav2Vec2BertForCTC", + "Wav2Vec2BertForSequenceClassification", + "Wav2Vec2BertForXVector", + "Wav2Vec2BertModel", + "Wav2Vec2BertPreTrainedModel", + ] + +if TYPE_CHECKING: + from .configuration_wav2vec2_bert import ( + WAV2VEC2_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, + Wav2Vec2BertConfig, + ) + from .processing_wav2vec2_bert import Wav2Vec2BertProcessor + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_wav2vec2_bert import ( + WAV2VEC2_BERT_PRETRAINED_MODEL_ARCHIVE_LIST, + Wav2Vec2BertForAudioFrameClassification, + Wav2Vec2BertForCTC, + Wav2Vec2BertForSequenceClassification, + Wav2Vec2BertForXVector, + Wav2Vec2BertModel, + Wav2Vec2BertPreTrainedModel, + ) + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2_bert/__pycache__/__init__.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2_bert/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b0dbc7100b9af80826656434e038a9bffab382b6 Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2_bert/__pycache__/__init__.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2_bert/__pycache__/configuration_wav2vec2_bert.cpython-312.pyc 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a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2_bert/__pycache__/processing_wav2vec2_bert.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2_bert/__pycache__/processing_wav2vec2_bert.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e106020bb617484ef8235c83342a59953560b74b Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2_bert/__pycache__/processing_wav2vec2_bert.cpython-312.pyc differ diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2_bert/configuration_wav2vec2_bert.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2_bert/configuration_wav2vec2_bert.py new file mode 100644 index 0000000000000000000000000000000000000000..4183c1e4c06e7bfd029851f00f5ef2227600709b --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2_bert/configuration_wav2vec2_bert.py @@ -0,0 +1,314 @@ +# coding=utf-8 +# Copyright 2024 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Wav2Vec2Bert model configuration""" + + +from ...configuration_utils import PretrainedConfig +from ...utils import logging + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import WAV2VEC2_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class Wav2Vec2BertConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`Wav2Vec2BertModel`]. It is used to + instantiate an Wav2Vec2Bert model according to the specified arguments, defining the model architecture. + Instantiating a configuration with the defaults will yield a similar configuration to that of the Wav2Vec2Bert + [facebook/wav2vec2-bert-rel-pos-large](https://huggingface.co/facebook/wav2vec2-bert-rel-pos-large) + architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*): + Vocabulary size of the Wav2Vec2Bert model. Defines the number of different tokens that can be + represented by the `inputs_ids` passed when calling [`Wav2Vec2BertModel`]. Vocabulary size of the + model. Defines the different tokens that can be represented by the *inputs_ids* passed to the forward + method of [`Wav2Vec2BertModel`]. + hidden_size (`int`, *optional*, defaults to 1024): + Dimensionality of the encoder layers and the pooler layer. + num_hidden_layers (`int`, *optional*, defaults to 24): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 16): + Number of attention heads for each attention layer in the Transformer encoder. + intermediate_size (`int`, *optional*, defaults to 4096): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + feature_projection_input_dim (`int`, *optional*, defaults to 160): + Input dimension of this model, i.e the dimension after processing input audios with [`SeamlessM4TFeatureExtractor`] or [`Wav2Vec2BertProcessor`]. + hidden_act (`str` or `function`, *optional*, defaults to `"swish"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"`, `"swish"` and `"gelu_new"` are supported. + hidden_dropout (`float`, *optional*, defaults to 0.0): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + activation_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for activations inside the fully connected layer. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + feat_proj_dropout (`float`, *optional*, defaults to 0.0): + The dropout probability for the feature projection. + final_dropout (`float`, *optional*, defaults to 0.1): + The dropout probability for the final projection layer of [`Wav2Vec2BertForCTC`]. + layerdrop (`float`, *optional*, defaults to 0.1): + The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more + details. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + layer_norm_eps (`float`, *optional*, defaults to 1e-05): + The epsilon used by the layer normalization layers. + apply_spec_augment (`bool`, *optional*, defaults to `True`): + Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see + [SpecAugment: A Simple Data Augmentation Method for Automatic Speech + Recognition](https://arxiv.org/abs/1904.08779). + mask_time_prob (`float`, *optional*, defaults to 0.05): + Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking + procecure generates `mask_time_prob*len(time_axis)/mask_time_length ``independent masks over the axis. If + reasoning from the propability of each feature vector to be chosen as the start of the vector span to be + masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the + actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`. + mask_time_length (`int`, *optional*, defaults to 10): + Length of vector span along the time axis. + mask_time_min_masks (`int`, *optional*, defaults to 2): + The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step, + irrespectively of `mask_feature_prob`. Only relevant if `mask_time_prob*len(time_axis)/mask_time_length < + mask_time_min_masks`. + mask_feature_prob (`float`, *optional*, defaults to 0.0): + Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The + masking procecure generates `mask_feature_prob*len(feature_axis)/mask_time_length` independent masks over + the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector + span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap + may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is + True`. + mask_feature_length (`int`, *optional*, defaults to 10): + Length of vector span along the feature axis. + mask_feature_min_masks (`int`, *optional*, defaults to 0): + The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time + step, irrespectively of `mask_feature_prob`. Only relevant if + `mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks`. + ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`): + Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an + instance of [`Wav2Vec2BertForCTC`]. + ctc_zero_infinity (`bool`, *optional*, defaults to `False`): + Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly + occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance + of [`Wav2Vec2BertForCTC`]. + use_weighted_layer_sum (`bool`, *optional*, defaults to `False`): + Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an + instance of [`Wav2Vec2BertForSequenceClassification`]. + classifier_proj_size (`int`, *optional*, defaults to 768): + Dimensionality of the projection before token mean-pooling for classification. + tdnn_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 1500)`): + A tuple of integers defining the number of output channels of each 1D convolutional layer in the *TDNN* + module of the *XVector* model. The length of *tdnn_dim* defines the number of *TDNN* layers. + tdnn_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 3, 3, 1, 1)`): + A tuple of integers defining the kernel size of each 1D convolutional layer in the *TDNN* module of the + *XVector* model. The length of *tdnn_kernel* has to match the length of *tdnn_dim*. + tdnn_dilation (`Tuple[int]` or `List[int]`, *optional*, defaults to `(1, 2, 3, 1, 1)`): + A tuple of integers defining the dilation factor of each 1D convolutional layer in *TDNN* module of the + *XVector* model. The length of *tdnn_dilation* has to match the length of *tdnn_dim*. + xvector_output_dim (`int`, *optional*, defaults to 512): + Dimensionality of the *XVector* embedding vectors. + pad_token_id (`int`, *optional*, defaults to 0): The id of the _beginning-of-stream_ token. + bos_token_id (`int`, *optional*, defaults to 1): The id of the _padding_ token. + eos_token_id (`int`, *optional*, defaults to 2): The id of the _end-of-stream_ token. + add_adapter (`bool`, *optional*, defaults to `False`): + Whether a convolutional attention network should be stacked on top of the Wav2Vec2Bert Encoder. Can be very + useful for warm-starting Wav2Vec2Bert for SpeechEncoderDecoder models. + adapter_kernel_size (`int`, *optional*, defaults to 3): + Kernel size of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`. + adapter_stride (`int`, *optional*, defaults to 2): + Stride of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`. + num_adapter_layers (`int`, *optional*, defaults to 1): + Number of convolutional layers that should be used in the adapter network. Only relevant if `add_adapter is + True`. + adapter_act (`str` or `function`, *optional*, defaults to `"relu"`): + The non-linear activation function (function or string) in the adapter layers. If string, `"gelu"`, + `"relu"`, `"selu"`, `"swish"` and `"gelu_new"` are supported. + use_intermediate_ffn_before_adapter (`bool`, *optional*, defaults to `False`): + Whether an intermediate feed-forward block should be stacked on top of the Wav2Vec2Bert Encoder and before the adapter network. + Only relevant if `add_adapter is True`. + output_hidden_size (`int`, *optional*): + Dimensionality of the encoder output layer. If not defined, this defaults to *hidden-size*. Only relevant + if `add_adapter is True`. + position_embeddings_type (`str`, *optional*, defaults to `"relative_key"`): + Can be specified to : + - `rotary`, for rotary position embeddings. + - `relative`, for relative position embeddings. + - `relative_key`, for relative position embeddings as defined by Shaw in [Self-Attention + with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). + If left to `None`, no relative position embeddings is applied. + rotary_embedding_base (`int`, *optional*, defaults to 10000): + If `"rotary"` position embeddings are used, defines the size of the embedding base. + max_source_positions (`int`, *optional*, defaults to 5000): + if `"relative"` position embeddings are used, defines the maximum source input positions. + left_max_position_embeddings (`int`, *optional*, defaults to 64): + If `"relative_key"` (aka Shaw) position embeddings are used, defines the left clipping value for relative positions. + right_max_position_embeddings (`int`, *optional*, defaults to 8): + If `"relative_key"` (aka Shaw) position embeddings are used, defines the right clipping value for relative positions. + conv_depthwise_kernel_size (`int`, *optional*, defaults to 31): + Kernel size of convolutional depthwise 1D layer in Conformer blocks. + conformer_conv_dropout (`float`, *optional*, defaults to 0.1): + The dropout probability for all convolutional layers in Conformer blocks. + Example: + + ```python + >>> from transformers import Wav2Vec2BertConfig, Wav2Vec2BertModel + + >>> # Initializing a Wav2Vec2Bert facebook/wav2vec2-bert-rel-pos-large style configuration + >>> configuration = Wav2Vec2BertConfig() + + >>> # Initializing a model (with random weights) from the facebook/wav2vec2-bert-rel-pos-large style configuration + >>> model = Wav2Vec2BertModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "wav2vec2-bert" + + def __init__( + self, + vocab_size=None, + hidden_size=1024, + num_hidden_layers=24, + num_attention_heads=16, + intermediate_size=4096, + feature_projection_input_dim=160, + hidden_act="swish", + hidden_dropout=0.0, + activation_dropout=0.0, + attention_dropout=0.0, + feat_proj_dropout=0.0, + final_dropout=0.1, + layerdrop=0.1, + initializer_range=0.02, + layer_norm_eps=1e-5, + apply_spec_augment=True, + mask_time_prob=0.05, + mask_time_length=10, + mask_time_min_masks=2, + mask_feature_prob=0.0, + mask_feature_length=10, + mask_feature_min_masks=0, + ctc_loss_reduction="sum", + ctc_zero_infinity=False, + use_weighted_layer_sum=False, + classifier_proj_size=768, + tdnn_dim=(512, 512, 512, 512, 1500), + tdnn_kernel=(5, 3, 3, 1, 1), + tdnn_dilation=(1, 2, 3, 1, 1), + xvector_output_dim=512, + pad_token_id=0, + bos_token_id=1, + eos_token_id=2, + add_adapter=False, + adapter_kernel_size=3, + adapter_stride=2, + num_adapter_layers=1, + adapter_act="relu", + use_intermediate_ffn_before_adapter=False, + output_hidden_size=None, + position_embeddings_type="relative_key", + rotary_embedding_base=10000, + max_source_positions=5000, + left_max_position_embeddings=64, + right_max_position_embeddings=8, + conv_depthwise_kernel_size=31, + conformer_conv_dropout=0.1, + **kwargs, + ): + super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id) + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.num_attention_heads = num_attention_heads + self.feature_projection_input_dim = feature_projection_input_dim + self.hidden_dropout = hidden_dropout + self.attention_dropout = attention_dropout + self.activation_dropout = activation_dropout + self.feat_proj_dropout = feat_proj_dropout + self.final_dropout = final_dropout + self.layerdrop = layerdrop + self.layer_norm_eps = layer_norm_eps + self.initializer_range = initializer_range + self.vocab_size = vocab_size + self.use_weighted_layer_sum = use_weighted_layer_sum + self.max_source_positions = max_source_positions + + if position_embeddings_type is not None and position_embeddings_type not in [ + "rotary", + "relative", + "relative_key", + ]: + raise ValueError( + """ + `position_embeddings_type` is not valid. It must be one of the following values: + `["rotary", "relative", "relative_key"]` or left as `None`. + """ + ) + self.position_embeddings_type = position_embeddings_type + self.rotary_embedding_base = rotary_embedding_base + self.left_max_position_embeddings = left_max_position_embeddings + self.right_max_position_embeddings = right_max_position_embeddings + + # Conformer-block related + self.conv_depthwise_kernel_size = conv_depthwise_kernel_size + self.conformer_conv_dropout = conformer_conv_dropout + + # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 + self.apply_spec_augment = apply_spec_augment + self.mask_time_prob = mask_time_prob + self.mask_time_length = mask_time_length + self.mask_time_min_masks = mask_time_min_masks + self.mask_feature_prob = mask_feature_prob + self.mask_feature_length = mask_feature_length + self.mask_feature_min_masks = mask_feature_min_masks + + # ctc loss + self.ctc_loss_reduction = ctc_loss_reduction + self.ctc_zero_infinity = ctc_zero_infinity + + # adapter + self.add_adapter = add_adapter + self.adapter_kernel_size = adapter_kernel_size + self.adapter_stride = adapter_stride + self.num_adapter_layers = num_adapter_layers + self.adapter_act = adapter_act + self.output_hidden_size = output_hidden_size if output_hidden_size is not None else hidden_size + if use_intermediate_ffn_before_adapter and not add_adapter: + raise ValueError("`use_intermediate_ffn_before_adapter` is `True` but `add_adapter` is `False`.") + self.use_intermediate_ffn_before_adapter = use_intermediate_ffn_before_adapter + + # SequenceClassification-specific parameter. Feel free to ignore for other classes. + self.classifier_proj_size = classifier_proj_size + + # XVector-specific parameters. Feel free to ignore for other classes. + self.tdnn_dim = list(tdnn_dim) + self.tdnn_kernel = list(tdnn_kernel) + self.tdnn_dilation = list(tdnn_dilation) + self.xvector_output_dim = xvector_output_dim + + @property + def inputs_to_logits_ratio(self): + ratio = self.feature_projection_input_dim * 2 + if self.add_adapter: + ratio = ratio * (self.adapter_stride**self.num_adapter_layers) + return ratio diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2_bert/convert_wav2vec2_seamless_checkpoint.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2_bert/convert_wav2vec2_seamless_checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..8b77cd71f7f7e0513ae1a74eb3947dca1353659e --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2_bert/convert_wav2vec2_seamless_checkpoint.py @@ -0,0 +1,218 @@ +# coding=utf-8 +# Copyright 2024 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert Wav2Vec2Bert BERT checkpoint.""" + + +import argparse + +import torch +import torchaudio +from fairseq2.data import Collater +from fairseq2.data.audio import WaveformToFbankConverter +from fairseq2.nn.padding import get_seqs_and_padding_mask +from seamless_communication.models.conformer_shaw import load_conformer_shaw_model + +from transformers import ( + SeamlessM4TFeatureExtractor, + Wav2Vec2BertConfig, + Wav2Vec2BertModel, + logging, +) + + +logging.set_verbosity_info() +logger = logging.get_logger(__name__) + + +wav2vec_convert_list = [ + ("encoder_frontend.model_dim_proj", "feature_projection.projection"), + ("encoder_frontend.post_extract_layer_norm", "feature_projection.layer_norm"), + ("encoder_frontend.pos_encoder.conv", "encoder.pos_conv_embed.conv"), + ("encoder.inner.layers", "encoder.layers"), + ("encoder.inner_layer_norm", "encoder.layer_norm"), + ("encoder.adaptor_layers", "adapter.layers"), + ("inner_proj", "intermediate_dense"), + ("self_attn.output_proj", "self_attn.linear_out"), + ("output_proj", "output_dense"), + ("self_attn.k_proj", "self_attn.linear_k"), + ("self_attn.v_proj", "self_attn.linear_v"), + ("self_attn.q_proj", "self_attn.linear_q"), + ("self_attn.sdpa.u_bias", "self_attn.pos_bias_u"), + ("self_attn.sdpa.v_bias", "self_attn.pos_bias_v"), + ("self_attn.sdpa.rel_k_embed", "self_attn.distance_embedding"), + ("self_attn.sdpa.r_proj", "self_attn.linear_pos"), + ("conv.pointwise_conv1", "conv_module.pointwise_conv1"), + ("conv.pointwise_conv2", "conv_module.pointwise_conv2"), + ("conv.depthwise_conv", "conv_module.depthwise_conv"), + ("conv.layer_norm", "conv_module.depthwise_layer_norm"), + ("conv_layer_norm", "conv_module.layer_norm"), + ("encoder.proj1", "intermediate_ffn.intermediate_dense"), + ("encoder.proj2", "intermediate_ffn.output_dense"), + ("encoder.layer_norm", "inner_layer_norm"), + ("masker.temporal_mask_embed", "masked_spec_embed"), +] + +keys_to_remove = { + "quantizer.entry_proj", + "final_proj", + "final_target_proj", + "quantizer.entries", + "quantizer.num_updates", +} + + +def param_count(model): + return sum(p[1].numel() for p in model.named_parameters() if "final_proj" not in p[0]) + + +def _convert_model( + original_model, + hf_model, + convert_list, +): + state_dict = original_model.state_dict() + + for k, v in list(state_dict.items()): + new_key = k + for old_layer_name, new_layer_name in convert_list: + if old_layer_name in new_key: + new_key = new_key.replace(old_layer_name, new_layer_name) + + # must do it by hand + if ".layer_norm" in new_key and new_key.split(".layer_norm")[0][-1].isnumeric(): + new_key = new_key.replace("layer_norm", "final_layer_norm") + + add_key = True + for key in keys_to_remove: + if key in new_key: + state_dict.pop(k) + add_key = False + break + + if add_key: + state_dict[new_key] = state_dict.pop(k) + + extra_keys = set(state_dict.keys()) - set(hf_model.state_dict().keys()) + extra_keys = set({k for k in extra_keys if "num_updates" not in k}) # filter unecessary param + missing_keys = set(hf_model.state_dict().keys()) - set(state_dict.keys()) + if len(extra_keys) != 0: + raise ValueError(f"extra keys found: {extra_keys}") + if len(missing_keys) != 0: + raise ValueError(f"missing keys: {missing_keys}") + hf_model.load_state_dict(state_dict, strict=True) + n_params = param_count(hf_model) + + logger.info(f"model loaded: {round(n_params/1e6,1)}M params") + + hf_model.eval() + del state_dict + + return hf_model + + +@torch.no_grad() +def convert_wav2vec2_bert_checkpoint( + checkpoint_path, + pytorch_dump_folder_path, + config_path=None, + repo_id=None, +): + """ + Copy/paste/tweak model's weights to transformers design. + """ + if config_path is not None: + config = Wav2Vec2BertConfig.from_pretrained(config_path, hidden_act="swish") + else: + config = Wav2Vec2BertConfig(apply_spec_augment=False) + + hf_wav2vec = Wav2Vec2BertModel(config) + + model = load_conformer_shaw_model(checkpoint_path, dtype=torch.float32) + model.eval() + + hf_wav2vec = _convert_model(model, hf_wav2vec, wav2vec_convert_list) + + hf_wav2vec.save_pretrained(pytorch_dump_folder_path) + + if repo_id: + hf_wav2vec.push_to_hub(repo_id, create_pr=True) + + # save feature extractor + fe = SeamlessM4TFeatureExtractor(padding_value=1) + fe._set_processor_class("Wav2Vec2BertProcessor") + fe.save_pretrained(pytorch_dump_folder_path) + + if repo_id: + fe.push_to_hub(repo_id, create_pr=True) + + if args.audio_path: + waveform, sample_rate = torchaudio.load(args.audio_path) + waveform = torchaudio.functional.resample(waveform, sample_rate, fe.sampling_rate) + + fbank_converter = WaveformToFbankConverter( + num_mel_bins=80, + waveform_scale=2**15, + channel_last=True, + standardize=True, + dtype=torch.float32, + ) + collater = Collater(pad_value=1) + + decoded_audio = {"waveform": waveform.T, "sample_rate": fe.sampling_rate, "format": -1} + src = collater(fbank_converter(decoded_audio))["fbank"] + seqs, padding_mask = get_seqs_and_padding_mask(src) + + with torch.inference_mode(): + seqs, padding_mask = model.encoder_frontend(seqs, padding_mask) + original_output, padding_mask = model.encoder(seqs, padding_mask) + + hf_wav2vec.eval() + + inputs = fe(waveform, return_tensors="pt", padding=True) + with torch.no_grad(): + outputs = hf_wav2vec(**inputs) + + torch.testing.assert_close(original_output, outputs.last_hidden_state, atol=5e-3, rtol=5e-3) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--pytorch_dump_folder_path", + default=None, + type=str, + help="Path to the output PyTorch model.", + ) + parser.add_argument( + "--checkpoint_path", default="conformer_shaw", type=str, help="Path to seamless communication checkpoint" + ) + parser.add_argument( + "--config_path", + default=None, + type=str, + help="Path to hf config.json of model to convert", + ) + parser.add_argument("--repo_id", default=None, type=str, help="Push to this repo id if precised.") + parser.add_argument( + "--audio_path", + default=None, + type=str, + help="If specified, check that the original model and the converted model produce the same outputs.", + ) + + args = parser.parse_args() + convert_wav2vec2_bert_checkpoint( + args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.repo_id + ) diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py new file mode 100644 index 0000000000000000000000000000000000000000..6519faa931d688a30b5792904be0f1bb28de3a38 --- /dev/null +++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py @@ -0,0 +1,1671 @@ +# coding=utf-8 +# Copyright 2024 The Seamless Authors and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch Wav2Vec2-BERT model.""" + +import math +import warnings +from typing import Optional, Tuple, Union + +import numpy as np +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss + +from ...activations import ACT2FN +from ...integrations.deepspeed import is_deepspeed_zero3_enabled +from ...modeling_attn_mask_utils import _prepare_4d_attention_mask +from ...modeling_outputs import ( + BaseModelOutput, + CausalLMOutput, + SequenceClassifierOutput, + TokenClassifierOutput, + Wav2Vec2BaseModelOutput, + XVectorOutput, +) +from ...modeling_utils import PreTrainedModel +from ...utils import ( + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_peft_available, + logging, +) +from .configuration_wav2vec2_bert import Wav2Vec2BertConfig + + +logger = logging.get_logger(__name__) + + +_HIDDEN_STATES_START_POSITION = 2 + +# General docstring +_CONFIG_FOR_DOC = "Wav2Vec2BertConfig" + +# Base docstring +_BASE_CHECKPOINT_FOR_DOC = "facebook/w2v-bert-2.0" +_PRETRAINED_CHECKPOINT_FOR_DOC = "hf-audio/wav2vec2-bert-CV16-en" +_EXPECTED_OUTPUT_SHAPE = [1, 146, 1024] + +# CTC docstring +_CTC_EXPECTED_OUTPUT = "'mr quilter is the apostle of the middle classes and we are glad to welcome his gospel'" +_CTC_EXPECTED_LOSS = 17.04 + + +from ..deprecated._archive_maps import WAV2VEC2_BERT_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +# Copied from transformers.models.seamless_m4t_v2.modeling_seamless_m4t_v2._compute_new_attention_mask +def _compute_new_attention_mask(hidden_states: torch.Tensor, seq_lens: torch.Tensor): + """ + Computes an attention mask of the form `(batch, seq_len)` with an attention for each element in the batch that + stops at the corresponding element in `seq_lens`. + Args: + hidden_states (`torch.FloatTensor` of shape `(batch, seq_len, *)`): + The sequences to mask, where `*` is any number of sequence-specific dimensions including none. + seq_lens (`torch.Tensor` of shape `(batch)`: + Each element represents the length of the sequence at the same index in `hidden_states` + Returns: + `torch.FloatTensor`: The float attention mask of shape `(batch, seq_len)` + """ + batch_size, mask_seq_len = hidden_states.shape[:2] + + indices = torch.arange(mask_seq_len, device=seq_lens.device).expand(batch_size, -1) + + bool_mask = indices >= seq_lens.unsqueeze(1).expand(-1, mask_seq_len) + + mask = hidden_states.new_ones((batch_size, mask_seq_len)) + + mask = mask.masked_fill(bool_mask, 0) + + return mask + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2._compute_mask_indices +def _compute_mask_indices( + shape: Tuple[int, int], + mask_prob: float, + mask_length: int, + attention_mask: Optional[torch.LongTensor] = None, + min_masks: int = 0, +) -> np.ndarray: + """ + Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for + ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on + CPU as part of the preprocessing during training. + + Args: + shape: The shape for which to compute masks. This should be of a tuple of size 2 where + the first element is the batch size and the second element is the length of the axis to span. + mask_prob: The percentage of the whole axis (between 0 and 1) which will be masked. The number of + independently generated mask spans of length `mask_length` is computed by + `mask_prob*shape[1]/mask_length`. Note that due to overlaps, `mask_prob` is an upper bound and the + actual percentage will be smaller. + mask_length: size of the mask + min_masks: minimum number of masked spans + attention_mask: A (right-padded) attention mask which independently shortens the feature axis of + each batch dimension. + """ + batch_size, sequence_length = shape + + if mask_length < 1: + raise ValueError("`mask_length` has to be bigger than 0.") + + if mask_length > sequence_length: + raise ValueError( + f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length}" + f" and `sequence_length`: {sequence_length}`" + ) + + # epsilon is used for probabilistic rounding + epsilon = np.random.rand(1).item() + + def compute_num_masked_span(input_length): + """Given input length, compute how many spans should be masked""" + num_masked_span = int(mask_prob * input_length / mask_length + epsilon) + num_masked_span = max(num_masked_span, min_masks) + + # make sure num masked span <= sequence_length + if num_masked_span * mask_length > sequence_length: + num_masked_span = sequence_length // mask_length + + # make sure num_masked span is also <= input_length - (mask_length - 1) + if input_length - (mask_length - 1) < num_masked_span: + num_masked_span = max(input_length - (mask_length - 1), 0) + + return num_masked_span + + # compute number of masked spans in batch + input_lengths = ( + attention_mask.sum(-1).detach().tolist() + if attention_mask is not None + else [sequence_length for _ in range(batch_size)] + ) + + # SpecAugment mask to fill + spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool) + spec_aug_mask_idxs = [] + + max_num_masked_span = compute_num_masked_span(sequence_length) + + if max_num_masked_span == 0: + return spec_aug_mask + + for input_length in input_lengths: + # compute num of masked spans for this input + num_masked_span = compute_num_masked_span(input_length) + + # get random indices to mask + spec_aug_mask_idx = np.random.choice( + np.arange(input_length - (mask_length - 1)), num_masked_span, replace=False + ) + + # pick first sampled index that will serve as a dummy index to pad vector + # to ensure same dimension for all batches due to probabilistic rounding + # Picking first sample just pads those vectors twice. + if len(spec_aug_mask_idx) == 0: + # this case can only happen if `input_length` is strictly smaller then + # `sequence_length` in which case the last token has to be a padding + # token which we can use as a dummy mask id + dummy_mask_idx = sequence_length - 1 + else: + dummy_mask_idx = spec_aug_mask_idx[0] + + spec_aug_mask_idx = np.concatenate( + [spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx] + ) + spec_aug_mask_idxs.append(spec_aug_mask_idx) + + spec_aug_mask_idxs = np.array(spec_aug_mask_idxs) + + # expand masked indices to masked spans + spec_aug_mask_idxs = np.broadcast_to( + spec_aug_mask_idxs[:, :, None], (batch_size, max_num_masked_span, mask_length) + ) + spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, max_num_masked_span * mask_length) + + # add offset to the starting indexes so that indexes now create a span + offsets = np.arange(mask_length)[None, None, :] + offsets = np.broadcast_to(offsets, (batch_size, max_num_masked_span, mask_length)).reshape( + batch_size, max_num_masked_span * mask_length + ) + spec_aug_mask_idxs = spec_aug_mask_idxs + offsets + + # ensure that we cannot have indices larger than sequence_length + if spec_aug_mask_idxs.max() > sequence_length - 1: + spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1 + + # scatter indices to mask + np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1) + + return spec_aug_mask + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2._sample_negative_indices +def _sample_negative_indices( + features_shape: Tuple, num_negatives: int, mask_time_indices: Optional[np.ndarray] = None +): + """ + Sample `num_negatives` vectors from feature vectors. + """ + batch_size, sequence_length = features_shape + + # generate indices of the positive vectors themselves, repeat them `num_negatives` times + sequence_length_range = np.arange(sequence_length) + + # get `num_negatives` random vector indices from the same utterance + sampled_negative_indices = np.zeros(shape=(batch_size, sequence_length, num_negatives), dtype=np.int32) + + mask_time_indices = ( + mask_time_indices.astype(bool) if mask_time_indices is not None else np.ones(features_shape, dtype=bool) + ) + + for batch_idx in range(batch_size): + high = mask_time_indices[batch_idx].sum() - 1 + mapped_masked_indices = sequence_length_range[mask_time_indices[batch_idx]] + + feature_indices = np.broadcast_to(np.arange(high + 1)[:, None], (high + 1, num_negatives)) + sampled_indices = np.random.randint(0, high, size=(high + 1, num_negatives)) + # avoid sampling the same positive vector, but keep the distribution uniform + sampled_indices[sampled_indices >= feature_indices] += 1 + + # remap to actual indices + sampled_negative_indices[batch_idx][mask_time_indices[batch_idx]] = mapped_masked_indices[sampled_indices] + + # correct for batch size + sampled_negative_indices[batch_idx] += batch_idx * sequence_length + + return sampled_negative_indices + + +# Copied from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerRotaryPositionalEmbedding with Wav2Vec2Conformer->Wav2Vec2Bert +class Wav2Vec2BertRotaryPositionalEmbedding(nn.Module): + """Rotary positional embedding + Reference : https://blog.eleuther.ai/rotary-embeddings/ Paper: https://arxiv.org/pdf/2104.09864.pdf + """ + + def __init__(self, config): + super().__init__() + dim = config.hidden_size // config.num_attention_heads + base = config.rotary_embedding_base + + inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).float() / dim)) + # Ignore copy + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.cached_sequence_length = None + self.cached_rotary_positional_embedding = None + + def forward(self, hidden_states): + sequence_length = hidden_states.shape[1] + + if sequence_length == self.cached_sequence_length and self.cached_rotary_positional_embedding is not None: + return self.cached_rotary_positional_embedding + + self.cached_sequence_length = sequence_length + # Embeddings are computed in the dtype of the inv_freq constant + time_stamps = torch.arange(sequence_length).type_as(self.inv_freq) + freqs = torch.einsum("i,j->ij", time_stamps, self.inv_freq) + embeddings = torch.cat((freqs, freqs), dim=-1) + + cos_embeddings = embeddings.cos()[:, None, None, :] + sin_embeddings = embeddings.sin()[:, None, None, :] + # Computed embeddings are cast to the dtype of the hidden state inputs + self.cached_rotary_positional_embedding = torch.stack([cos_embeddings, sin_embeddings]).type_as(hidden_states) + return self.cached_rotary_positional_embedding + + +# Copied from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerRelPositionalEmbedding with Wav2Vec2Conformer->Wav2Vec2Bert +class Wav2Vec2BertRelPositionalEmbedding(nn.Module): + """Relative positional encoding module.""" + + def __init__(self, config): + super().__init__() + self.max_len = config.max_source_positions + self.d_model = config.hidden_size + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, self.max_len)) + + def extend_pe(self, x): + # Reset the positional encodings + if self.pe is not None: + # self.pe contains both positive and negative parts + # the length of self.pe is 2 * input_len - 1 + if self.pe.size(1) >= x.size(1) * 2 - 1: + if self.pe.dtype != x.dtype or self.pe.device != x.device: + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + # Suppose `i` is the position of query vector and `j` is the + # position of key vector. We use positive relative positions when keys + # are to the left (i>j) and negative relative positions otherwise (i