id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
10,756 | import gc
import os
import tempfile
import warnings
from typing import Optional
import tensorflow as tf
from ...configuration_utils import PretrainedConfig
from ...modeling_tf_outputs import TFBaseModelOutput, TFSeq2SeqLMOutput
from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFPreTrainedModel, get_initia... | null |
10,757 | import gc
import os
import tempfile
from typing import Optional
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 ... | Shift input ids one token to the right. |
10,758 | 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 ...deepspeed import is_deepspeed_zero3_enabled
from ...m... | 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 comp... |
10,759 | import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
Wav2Vec2FeatureExtractor,
Wav2Vec2PhonemeCTCTokenizer,
Wav2Vec2Processor,
logging,
)
logger = logging.ge... | Copy/paste/tweak model's weights to transformers design. |
10,760 | import argparse
import torch
from transformers import (
Wav2Vec2FeatureExtractor,
WavLMConfig,
WavLMForAudioFrameClassification,
WavLMForSequenceClassification,
WavLMForXVector,
logging,
)
def convert_classification(base_model_name, hf_config, downstream_dict):
model = WavLMForSequenceClassi... | Copy/paste/tweak model's weights to transformers design. |
10,761 | import argparse
import torch
from transformers import WavLMConfig, WavLMModel, logging
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig
def recursively_load_weights(fairseq_model, hf_model):
unused_weights = []
fairseq_dict = fairseq_model.state_dict(... | null |
10,762 | import math
import warnings
from typing import Optional, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...deepspeed import is_deepspeed_zero3_enabled
from ...mod... | 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 comp... |
10,763 | 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, gelu
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
BaseM... | 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`. Args: x: torch.Tensor x: Returns: torch.Tensor |
10,764 | import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from tr... | Copy/paste/tweak roberta's weights to our BERT structure. |
10,765 | import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `bytes_to_unicode` function. Wr... | Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like... |
10,766 | import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `get_pairs` function. Write a P... | Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). |
10,767 | from typing import Callable, Optional, Tuple
import numpy as np
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen import partitioning as nn_partitioning
from flax.linen.attentio... | 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`. Args: input_ids: jnp.ndarray padding_idx: int Returns: jnp.ndarray |
10,768 | import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple
import torch
import torch.nn as nn
from torch import Tensor
from classy_visio... | null |
10,769 | import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import torch
import torch.nn as nn
from torch import Tensor
import timm
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetY32gf, Re... | null |
10,770 | import copy
import math
import random
from typing import Optional, Tuple
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
from ...modeling_utils import Pr... | Make causal mask used for bi-directional self-attention. |
10,771 | import copy
import math
import random
from typing import Optional, Tuple
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
from ...modeling_utils import Pr... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
10,772 | import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `get_pairs` function. Write a Python function `def get_pairs(word)` to solve the followi... | Return set of symbol pairs in a word. word is represented as tuple of symbols (symbols being variable-length strings) |
10,773 | import argparse
import json
from pathlib import Path
import torch
from PIL import Image
import requests
from huggingface_hub import hf_hub_download
from transformers import BeitConfig, BeitFeatureExtractor, BeitForImageClassification, BeitForMaskedImageModeling
from transformers.image_utils import PILImageResampling
fr... | Copy/paste/tweak model's weights to our BEiT structure. |
10,774 | 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
ACCEPTA... | Copy/paste/tweak model's weights to our VisualBERT structure. |
10,775 | import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from sew_asapp import tasks
from transformers import (
SEWDConfig,
SEWDForCTC,
SEWDModel,
Wav2Vec2CTCTokenizer,
Wav2Vec2FeatureExtractor,
Wav2Vec2Processor,
logging,
)
logger = logging.get_... | Copy/paste/tweak model's weights to transformers design. |
10,776 | import math
import warnings
from collections.abc import Sequence
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, LayerNorm
from transformers.deepspeed import is_deepspeed_zero3_enabled
from ...activations... | 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 comp... |
10,777 | import math
import warnings
from collections.abc import Sequence
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, LayerNorm
from transformers.deepspeed import is_deepspeed_zero3_enabled
from ...activations... | Build relative position according to the query and key We assume the absolute position of query \\(P_q\\) is range from (0, query_size) and the absolute position of key \\(P_k\\) is range from (0, key_size), The relative positions from query to key is \\(R_{q \\rightarrow k} = P_q - P_k\\) Args: query_size (int): the l... |
10,778 | import math
import warnings
from collections.abc import Sequence
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, LayerNorm
from transformers.deepspeed import is_deepspeed_zero3_enabled
from ...activations... | null |
10,779 | import math
import warnings
from collections.abc import Sequence
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, LayerNorm
from transformers.deepspeed import is_deepspeed_zero3_enabled
from ...activations... | null |
10,780 | import math
import warnings
from collections.abc import Sequence
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, LayerNorm
from transformers.deepspeed import is_deepspeed_zero3_enabled
from ...activations... | null |
10,781 | import math
import warnings
from collections.abc import Sequence
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, LayerNorm
from transformers.deepspeed import is_deepspeed_zero3_enabled
from ...activations... | null |
10,782 | import torch
import trajectory.utils as utils
from transformers import TrajectoryTransformerModel
The provided code snippet includes necessary dependencies for implementing the `convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch` function. Write a Python function `def convert_trajectory_transformer_... | Converting Sequential blocks to ModuleList |
10,783 | import math
import os
from dataclasses import dataclass
from typing import Optional, Tuple
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_docstring... | Load tf checkpoints in a pytorch model. |
10,784 | import io
import pathlib
import warnings
from collections import defaultdict
from typing import Dict, List, Optional, Set, Tuple, Union
import numpy as np
from PIL import Image
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import ImageFeatureExtractionMixin, is_torch_t... | Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format (x_0, y_0, x_1, y_1). |
10,785 | import io
import pathlib
import warnings
from collections import defaultdict
from typing import Dict, List, Optional, Set, Tuple, Union
import numpy as np
from PIL import Image
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import ImageFeatureExtractionMixin, is_torch_t... | Converts a NumPy array of bounding boxes of shape (number of bounding boxes, 4) of corners format (x_0, y_0, x_1, y_1) to center format (center_x, center_y, width, height). |
10,786 | import io
import pathlib
import warnings
from collections import defaultdict
from typing import Dict, List, Optional, Set, Tuple, Union
import numpy as np
from PIL import Image
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import ImageFeatureExtractionMixin, is_torch_t... | Compute the bounding boxes around the provided panoptic segmentation masks. The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions. Returns a [N, 4] tensor, with the boxes in corner (xyxy) format. |
10,787 | import io
import pathlib
import warnings
from collections import defaultdict
from typing import Dict, List, Optional, Set, Tuple, Union
import numpy as np
from PIL import Image
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import ImageFeatureExtractionMixin, is_torch_t... | null |
10,788 | import io
import pathlib
import warnings
from collections import defaultdict
from typing import Dict, List, Optional, Set, Tuple, Union
import numpy as np
from PIL import Image
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import ImageFeatureExtractionMixin, is_torch_t... | null |
10,789 | import io
import pathlib
import warnings
from collections import defaultdict
from typing import Dict, List, Optional, Set, Tuple, Union
import numpy as np
from PIL import Image
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import ImageFeatureExtractionMixin, is_torch_t... | Converts given segmentation map of shape (height, width) to the run-length encoding (RLE) format. Args: segmentation (`torch.Tensor` or `numpy.array`): A segmentation map of shape `(height, width)` where each value denotes a segment or class id. Returns: `List[List]`: A list of lists, where each list is the run-length ... |
10,790 | import io
import pathlib
import warnings
from collections import defaultdict
from typing import Dict, List, Optional, Set, Tuple, Union
import numpy as np
from PIL import Image
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import ImageFeatureExtractionMixin, is_torch_t... | Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and `labels`. Args: masks (`torch.Tensor`): A tensor of shape `(num_queries, height, width)`. scores (`torch.Tensor`): A tensor of shape `(num_queries)`. labels (`torch.Tensor`): A tensor of shape `(num_queries... |
10,791 | import io
import pathlib
import warnings
from collections import defaultdict
from typing import Dict, List, Optional, Set, Tuple, Union
import numpy as np
from PIL import Image
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import ImageFeatureExtractionMixin, is_torch_t... | null |
10,792 | import math
import random
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput
from ...modeling_utils import Pre... | null |
10,793 | import math
import random
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput
from ...modeling_utils import Pre... | Expands attention_mask from `[batch_size, seq_len]` to `[batch_size, 1, target_seq_len, source_seq_len]`. |
10,794 | import math
import random
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput
from ...modeling_utils import Pre... | null |
10,795 | import math
import random
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput
from ...modeling_utils import Pre... | null |
10,796 | import math
import random
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput
from ...modeling_utils import Pre... | 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). |
10,797 | import math
import random
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput
from ...modeling_utils import Pre... | 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 th... |
10,798 | import math
import random
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput
from ...modeling_utils import Pre... | 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) |
10,799 | import math
import random
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput
from ...modeling_utils import Pre... | Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format (x_0, y_0, x_1, y_1). |
10,800 | import math
import random
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput
from ...modeling_utils import Pre... | null |
10,801 | import argparse
import json
from collections import OrderedDict
from pathlib import Path
import torch
from PIL import Image
import requests
from huggingface_hub import hf_hub_download
from transformers import DetrConfig, DetrFeatureExtractor, DetrForObjectDetection, DetrForSegmentation
from transformers.utils import lo... | Copy/paste/tweak model's weights to our DETR structure. |
10,802 | import json
import os
import re
import unicodedata
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `whitespace_tokenize` fun... | Runs basic whitespace cleaning and splitting on a piece of text. |
10,803 | import json
import os
import re
import unicodedata
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `get_pairs` function. Wri... | Return set of symbol pairs in a word. word is represented as tuple of symbols (symbols being variable-length strings) |
10,804 | import json
import os
import re
import unicodedata
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `text_standardize` functi... | fixes some issues the spacy tokenizer had on books corpus also does some whitespace standardization |
10,805 | import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
def convert_openai_checkpoint_to_pytorch(openai_checkpoint_folder_path, openai_config_file, pytorch_dump_folder_path):
# Construct m... | null |
10,806 | import json
import math
import os
from dataclasses import dataclass
from typing import Any, Dict, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import gelu_new, silu
from ...modeling_outputs import BaseModelOutput, CausalLM... | Load tf pre-trained weights in a pytorch model (from NumPy arrays here) |
10,807 | import math
from typing import Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN, gelu
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPooling,
MaskedLMOutput,
MultipleChoiceM... | 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`. :param torch.Tensor x: :return torch.Tensor: |
10,808 | import collections
import os
import unicodedata
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `load_voca... | Loads a vocabulary file into a dictionary. |
10,809 | import collections
import os
import unicodedata
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `whitespac... | Runs basic whitespace cleaning and splitting on a piece of text. |
10,810 | import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from PIL import Image
from torchvision.transforms import functional as F
from huggingface_hub import hf_hub_download
from transformers import DetrFeatureExtractor, TableTransformerConfig, TableTransformerForObjectDetection
from tr... | Copy/paste/tweak model's weights to our DETR structure. |
10,811 | import math
import random
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput
from ...modeling_utils import Pre... | null |
10,812 | import math
import random
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput
from ...modeling_utils import Pre... | Expands attention_mask from `[batch_size, seq_len]` to `[batch_size, 1, target_seq_len, source_seq_len]`. |
10,813 | import math
import random
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput
from ...modeling_utils import Pre... | null |
10,814 | import math
import random
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput
from ...modeling_utils import Pre... | 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). |
10,815 | import math
import random
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput
from ...modeling_utils import Pre... | 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 th... |
10,816 | import math
import random
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput
from ...modeling_utils import Pre... | 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) |
10,817 | import math
import random
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput
from ...modeling_utils import Pre... | null |
10,818 | import math
import random
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput
from ...modeling_utils import Pre... | Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format (x_0, y_0, x_1, y_1). |
10,819 | from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
The provided code snippet includes necessary dependen... | Custom torch.Tensor.unfold implementation to enable the export to ONNX. |
10,820 | from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
The provided code snippet includes necessary dependen... | Custom implementation for GPTNeoAttentionMixin._get_block_length_and_num_blocks to enable the export to ONNX as original implementation uses Python variables and control flow. |
10,821 | import argparse
import json
from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path):
# Initialise PyTorch model
config_json = json.load(open(config_file, ... | null |
10,822 | import os
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 (
BaseModelOutputWithPast,
BaseModelOutputWithPastAndCrossAttent... | Load tf checkpoints in a pytorch model |
10,823 | import math
import random
from functools import partial
from typing import Callable, Optional, Tuple
import numpy as np
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen.attenti... | Shift input ids one token to the right. |
10,824 | import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
def rename_state_dict_key(k):
if k == "embeddings.weight":
return "shared.weight"
for parlai_name, hf_name in PATTERNS:
k = k.replace(parlai_name, hf_... | Copy/paste/tweak model's weights to our BERT structure. |
10,825 | import os
import random
import warnings
from typing import List, Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPastAndCrossAttentions,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
... | null |
10,826 | import os
import random
import warnings
from typing import List, Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPastAndCrossAttentions,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
... | Make causal mask used for bi-directional self-attention. |
10,827 | import os
import random
import warnings
from typing import List, Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPastAndCrossAttentions,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
10,828 | import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `bytes_to_unicod... | Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like... |
10,829 | import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `get_pairs` func... | Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). |
10,830 | import copy
import math
import os
import random
import warnings
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,
BaseModelOutp... | Shift input ids one token to the right. |
10,831 | import copy
import math
import os
import random
import warnings
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,
BaseModelOutp... | Make causal mask used for bi-directional self-attention. |
10,832 | import copy
import math
import os
import random
import warnings
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,
BaseModelOutp... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
10,833 | import math
import warnings
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
from torch.nn import functional as F
from ...file_utils import add_code_sample_docstrings, add_start_docstrin... | Make causal mask used for self-attention. |
10,834 | import math
import warnings
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
from torch.nn import functional as F
from ...file_utils import add_code_sample_docstrings, add_start_docstrin... | Expands attention_mask from `[batch_size, src_length]` to `[batch_size, 1, tgt_length, src_length]`. |
10,835 | import math
import warnings
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
from torch.nn import functional as F
from ...file_utils import add_code_sample_docstrings, add_start_docstrin... | Link to paper: https://arxiv.org/abs/2108.12409 Alibi tensor is not causal as the original paper mentions, it relies on a translation invariance of softmax for quick implementation: with l being a tensor, and a fixed value `softmax(l+a) = softmax(l)`. Based on https://github.com/ofirpress/attention_with_linear_biases/b... |
10,836 | import math
import warnings
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
from torch.nn import functional as F
from ...file_utils import add_code_sample_docstrings, add_start_docstrin... | Dropout add function Args: x (`torch.tensor`, *required*): input tensor residual (`torch.tensor`, *required*): esidual tensor prob (`float`, *required*): dropout probability training (`bool`, *required*): training mode |
10,837 | import math
import warnings
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
from torch.nn import functional as F
from ...file_utils import add_code_sample_docstrings, add_start_docstrin... | Custom bias GELU function. Adapted from Megatron-DeepSpeed code. Here we use a simple implementation (inference) to make the model jitable. Args: x (`torch.tensor`, *required*): input hidden states |
10,838 | import math
import warnings
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
from torch.nn import functional as F
from ...file_utils import add_code_sample_docstrings, add_start_docstrin... | gradient of tanh approximation of gelu gradient of actual gelu is: 0.5 * (1. + torch.erf(x * 0.70710678)) + 0.3989423 * x * torch.exp(-0.5 * x * x) Args: g (`torch.tensor`, *required*): gradient output tensor x (`torch.tensor`, *required*): input tensor |
10,839 | import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
WEIGHTS_TO_AVERAGE_ENDSWITH = [
"word_embeddings_layernorm.weight",
"word_embeddings_layernorm.bias",... | null |
10,840 | import copy
import math
import random
import warnings
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_outputs import (
BaseModelOutput,... | Shift input ids one token to the right. |
10,841 | import copy
import math
import random
import warnings
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_outputs import (
BaseModelOutput,... | Make causal mask used for bi-directional self-attention. |
10,842 | import copy
import math
import random
import warnings
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_outputs import (
BaseModelOutput,... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
10,843 | import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils import logging
SAM... | Copy/paste/tweak model's weights to our BERT structure. |
10,846 | import random
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,
TFBaseModelOutputWithPastAndCrossAttentions,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
from ...model... | null |
10,847 | import random
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,
TFBaseModelOutputWithPastAndCrossAttentions,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
from ...model... | Make causal mask used for bi-directional self-attention. |
10,848 | import random
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,
TFBaseModelOutputWithPastAndCrossAttentions,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
from ...model... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
10,849 | import math
import random
from functools import partial
from typing import Callable, Optional, Tuple
import numpy as np
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen.attenti... | Shift input ids one token to the right. |
10,850 | import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
Wav2Vec2FeatureExtractor,
logging,
)
def convert_classification(base_model_name, hf_config, downstream_dict):
mo... | Copy/paste/tweak model's weights to transformers design. |
10,851 | 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 ...deepspeed import is_deepspeed_zero3_enabled
from ...m... | 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 comp... |
10,852 | import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
def recursively_load_weights(fairseq_model, hf_model):
unused_weights = []
fairseq_dict = fairseq_model.state_dict()
feature_extractor = hf_model.unispeech_sat.fea... | Copy/paste/tweak model's weights to transformers design. |
10,853 | import collections
import os
import sys
import unicodedata
from typing import Dict, List, Optional, Tuple, Union
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...tokenization_utils_base import (
BatchEncoding,
EncodedInput,
PreTokenizedInput,
Te... | Loads a vocabulary file into a dictionary. |
10,854 | import collections
import os
import sys
import unicodedata
from typing import Dict, List, Optional, Tuple, Union
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...tokenization_utils_base import (
BatchEncoding,
EncodedInput,
PreTokenizedInput,
Te... | Runs basic whitespace cleaning and splitting on a piece of text. |
10,855 | import collections
import os
import sys
import unicodedata
from typing import Dict, List, Optional, Tuple, Union
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...tokenization_utils_base import (
BatchEncoding,
EncodedInput,
PreTokenizedInput,
Te... | null |
10,856 | import math
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,
QuestionAns... | Adapted from Mesh Tensorflow: https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 Translate relative position to a bucket number for relative attention. The relative position is defined as memory_position - query_position, i.e. the dist... |
10,857 | import math
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,
QuestionAns... | null |
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