Input:
# 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.
""" Testing suite for the PyTorch BLIP-2 model. """

import inspect
import tempfile
import unittest

import numpy as np
import requests

from transformers import CONFIG_MAPPING, Blip2Config, Blip2QFormerConfig, Blip2VisionConfig
from transformers.testing_utils import (
    require_torch,
    require_torch_multi_accelerator,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
    ModelTesterMixin,
    _config_zero_init,
    floats_tensor,
    ids_tensor,
    random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin

if is_torch_available():
    import torch
    from torch import nn

    from transformers import Blip2ForConditionalGeneration, Blip2Model, Blip2VisionModel
    from transformers.models.blip_2.modeling_blip_2 import BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
    from PIL import Image

    from transformers import Blip2Processor

class Blip2VisionModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        image_size=30,
        patch_size=2,
        num_channels=3,
        is_training=True,
        hidden_size=32,
        projection_dim=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        dropout=0.1,
        attention_dropout=0.1,
        initializer_range=1e-10,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.is_training = is_training
        self.hidden_size = hidden_size
        self.projection_dim = projection_dim
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.initializer_range = initializer_range
        self.scope = scope

        # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        num_patches = (image_size // patch_size) ** 2
        self.seq_length = num_patches + 1

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
        config = self.get_config()

        return config, pixel_values

    def get_config(self):
        return Blip2VisionConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            hidden_size=self.hidden_size,
            projection_dim=self.projection_dim,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            intermediate_size=self.intermediate_size,
            dropout=self.dropout,
            attention_dropout=self.attention_dropout,
            initializer_range=self.initializer_range,
        )

    def create_and_check_model(self, config, pixel_values):
        model = Blip2VisionModel(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(pixel_values)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        image_size = (self.image_size, self.image_size)
        patch_size = (self.patch_size, self.patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, pixel_values = config_and_inputs
        inputs_dict = {"pixel_values": pixel_values}
        return config, inputs_dict

@require_torch
class Blip2VisionModelTest(ModelTesterMixin, unittest.TestCase):
    """
    Here we also overwrite some of the tests of test_modeling_common.py, as BLIP-2's vision encoder does not use input_ids, inputs_embeds,
    attention_mask and seq_length.
    """

    all_model_classes = (Blip2VisionModel,) if is_torch_available() else ()
    fx_compatible = False
    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False

    def setUp(self):
        self.model_tester = Blip2VisionModelTester(self)
        self.config_tester = ConfigTester(
            self, config_class=Blip2VisionConfig, has_text_modality=False, hidden_size=37
        )

    def test_config(self):
        self.config_tester.run_common_tests()

    @unittest.skip(reason="BLIP-2's vision encoder does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    def test_model_common_attributes(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
            x = model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(x, nn.Linear))

    def test_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.forward)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            arg_names = [*signature.parameters.keys()]

            expected_arg_names = ["pixel_values"]
            self.assertListEqual(arg_names[:1], expected_arg_names)

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_training(self):
        pass

    def test_training_gradient_checkpointing(self):
        pass

    @unittest.skip(
        reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
    )
    def test_training_gradient_checkpointing_use_reentrant(self):
        pass

    @unittest.skip(
        reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
    )
    def test_training_gradient_checkpointing_use_reentrant_false(self):
        pass

    @unittest.skip(reason="Blip2VisionModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_from_base(self):
        pass

    @unittest.skip(reason="Blip2VisionModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_to_base(self):
        pass

    @slow
    def test_model_from_pretrained(self):
        for model_name in BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = Blip2VisionModel.from_pretrained(model_name)
            self.assertIsNotNone(model)

class Blip2QFormerModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        seq_length=7,
        is_training=True,
        use_input_mask=True,
        use_labels=True,
        vocab_size=99,
        hidden_size=32,
        projection_dim=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        dropout=0.1,
        attention_dropout=0.1,
        max_position_embeddings=512,
        initializer_range=0.02,
        bos_token_id=0,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_input_mask = use_input_mask
        self.use_labels = use_labels
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.projection_dim = projection_dim
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.scope = scope
        self.bos_token_id = bos_token_id

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

        input_mask = None
        if self.use_input_mask:
            input_mask = random_attention_mask([self.batch_size, self.seq_length])

        if input_mask is not None:
            batch_size, seq_length = input_mask.shape
            rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
            for batch_idx, start_index in enumerate(rnd_start_indices):
                input_mask[batch_idx, :start_index] = 1
                input_mask[batch_idx, start_index:] = 0

        config = self.get_config()

        return config, input_ids, input_mask

    def get_config(self):
        return Blip2QFormerConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            projection_dim=self.projection_dim,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            intermediate_size=self.intermediate_size,
            dropout=self.dropout,
            attention_dropout=self.attention_dropout,
            max_position_embeddings=self.max_position_embeddings,
            initializer_range=self.initializer_range,
            bos_token_id=self.bos_token_id,
        )

# this class is based on `OPTModelTester` found in tests/models/opt/test_modeling_opt.py
class Blip2TextModelDecoderOnlyTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        seq_length=7,
        is_training=True,
        use_labels=False,
        vocab_size=99,
        hidden_size=16,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=4,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=20,
        eos_token_id=2,
        pad_token_id=1,
        bos_token_id=0,
        embed_dim=16,
        num_labels=3,
        word_embed_proj_dim=16,
        type_sequence_label_size=2,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_labels = use_labels
        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.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.max_position_embeddings = max_position_embeddings
        self.eos_token_id = eos_token_id
        self.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id
        self.embed_dim = embed_dim
        self.num_labels = num_labels
        self.type_sequence_label_size = type_sequence_label_size
        self.word_embed_proj_dim = word_embed_proj_dim
        self.is_encoder_decoder = False

    def prepare_config_and_inputs(self):
        config = self.get_config()

        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(3)
        input_ids[:, -1] = self.eos_token_id  # Eos Token

        attention_mask = input_ids.ne(self.pad_token_id)

        return config, input_ids, attention_mask

    def get_config(self):
        return CONFIG_MAPPING["opt"](
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            ffn_dim=self.intermediate_size,
            dropout=self.hidden_dropout_prob,
            attention_dropout=self.attention_probs_dropout_prob,
            max_position_embeddings=self.max_position_embeddings,
            eos_token_id=self.eos_token_id,
            bos_token_id=self.bos_token_id,
            pad_token_id=self.pad_token_id,
            embed_dim=self.embed_dim,
            is_encoder_decoder=False,
            word_embed_proj_dim=self.word_embed_proj_dim,
        )

# this model tester uses a decoder-only language model (OPT)
class Blip2ForConditionalGenerationDecoderOnlyModelTester:
    def __init__(
        self, parent, vision_kwargs=None, qformer_kwargs=None, text_kwargs=None, is_training=True, num_query_tokens=10
    ):
        if vision_kwargs is None:
            vision_kwargs = {}
        if qformer_kwargs is None:
            qformer_kwargs = {}
        if text_kwargs is None:
            text_kwargs = {}

        self.parent = parent
        self.vision_model_tester = Blip2VisionModelTester(parent, **vision_kwargs)
        self.qformer_model_tester = Blip2QFormerModelTester(parent, **qformer_kwargs)
        self.text_model_tester = Blip2TextModelDecoderOnlyTester(parent, **text_kwargs)
        self.is_training = is_training
        self.num_query_tokens = num_query_tokens

    def prepare_config_and_inputs(self):
        _, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
        _, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()

        config = self.get_config()

        return config, input_ids, attention_mask, pixel_values

    def get_config(self):
        return Blip2Config.from_vision_qformer_text_configs(
            vision_config=self.vision_model_tester.get_config(),
            qformer_config=self.qformer_model_tester.get_config(),
            text_config=self.text_model_tester.get_config(),
            num_query_tokens=self.num_query_tokens,
        )

    def create_and_check_for_conditional_generation(self, config, input_ids, attention_mask, pixel_values):
        model = Blip2ForConditionalGeneration(config).to(torch_device).eval()
        with torch.no_grad():
            result = model(pixel_values, input_ids, attention_mask)

        expected_seq_length = self.num_query_tokens + self.text_model_tester.seq_length
        self.parent.assertEqual(
            result.logits.shape,
            (self.vision_model_tester.batch_size, expected_seq_length, self.text_model_tester.vocab_size),
        )

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, input_ids, attention_mask, pixel_values = config_and_inputs
        inputs_dict = {
            "pixel_values": pixel_values,
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "labels": input_ids,
        }
        return config, inputs_dict

@require_torch
class Blip2ForConditionalGenerationDecoderOnlyTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (Blip2ForConditionalGeneration,) if is_torch_available() else ()
    fx_compatible = False
    test_head_masking = False
    test_pruning = False
    test_resize_embeddings = False
    test_attention_outputs = False
    test_torchscript = False

    def setUp(self):
        self.model_tester = Blip2ForConditionalGenerationDecoderOnlyModelTester(self)

    def test_for_conditional_generation(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_conditional_generation(*config_and_inputs)

    @unittest.skip(reason="Hidden_states is tested in individual model tests")
    def test_hidden_states_output(self):
        pass

    @unittest.skip(reason="Inputs_embeds is tested in individual model tests")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="Retain_grad is tested in individual model tests")
    def test_retain_grad_hidden_states_attentions(self):
        pass

    @unittest.skip(reason="Blip2Model does not have input/output embeddings")
    def test_model_common_attributes(self):
        pass

    @unittest.skip(reason="There's no base Blip2Model")
    def test_save_load_fast_init_from_base(self):
        pass

    @unittest.skip(reason="There's no base Blip2Model")
    def test_save_load_fast_init_to_base(self):
        pass

    def test_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.forward)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            arg_names = [*signature.parameters.keys()]

            expected_arg_names = ["pixel_values"]
            self.assertListEqual(arg_names[:1], expected_arg_names)

    def test_load_vision_qformer_text_config(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        # Save Blip2Config and check if we can load Blip2VisionConfig from it
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            vision_config = Blip2VisionConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())

        # Save Blip2Config and check if we can load Blip2QFormerConfig from it
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            qformer_config = Blip2QFormerConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.qformer_config.to_dict(), qformer_config.to_dict())

    @slow
    def test_model_from_pretrained(self):
        for model_name in BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST:
            model = Blip2ForConditionalGeneration.from_pretrained(model_name)
            self.assertIsNotNone(model)

# this class is based on `T5ModelTester` found in tests/models/t5/test_modeling_t5.py
class Blip2TextModelTester:
    def __init__(
        self,
        parent,
        vocab_size=99,
        batch_size=12,
        encoder_seq_length=7,
        decoder_seq_length=9,
        # For common tests
        is_training=True,
        use_attention_mask=True,
        use_labels=True,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        d_ff=37,
        relative_attention_num_buckets=8,
        dropout_rate=0.1,
        initializer_factor=0.002,
        eos_token_id=1,
        pad_token_id=0,
        decoder_start_token_id=0,
        scope=None,
        decoder_layers=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.encoder_seq_length = encoder_seq_length
        self.decoder_seq_length = decoder_seq_length
        # For common tests
        self.seq_length = self.decoder_seq_length
        self.is_training = is_training
        self.use_attention_mask = use_attention_mask
        self.use_labels = use_labels
        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.d_ff = d_ff
        self.relative_attention_num_buckets = relative_attention_num_buckets
        self.dropout_rate = dropout_rate
        self.initializer_factor = initializer_factor
        self.eos_token_id = eos_token_id
        self.pad_token_id = pad_token_id
        self.decoder_start_token_id = decoder_start_token_id
        self.scope = None
        self.decoder_layers = decoder_layers

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
        decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)

        attention_mask = None
        decoder_attention_mask = None
        if self.use_attention_mask:
            attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
            decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)

        lm_labels = None
        if self.use_labels:
            lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)

        config = self.get_config()

        return (
            config,
            input_ids,
            decoder_input_ids,
            attention_mask,
            decoder_attention_mask,
            lm_labels,
        )

    def get_config(self):
        return CONFIG_MAPPING["t5"](
            vocab_size=self.vocab_size,
            d_model=self.hidden_size,
            d_ff=self.d_ff,
            d_kv=self.hidden_size // self.num_attention_heads,
            num_layers=self.num_hidden_layers,
            num_decoder_layers=self.decoder_layers,
            num_heads=self.num_attention_heads,
            relative_attention_num_buckets=self.relative_attention_num_buckets,
            dropout_rate=self.dropout_rate,
            initializer_factor=self.initializer_factor,
            eos_token_id=self.eos_token_id,
            bos_token_id=self.pad_token_id,
            pad_token_id=self.pad_token_id,
            decoder_start_token_id=self.decoder_start_token_id,
        )

# this model tester uses an encoder-decoder language model (T5)
class Blip2ModelTester:
    def __init__(
        self, parent, vision_kwargs=None, qformer_kwargs=None, text_kwargs=None, is_training=True, num_query_tokens=10
    ):
        if vision_kwargs is None:
            vision_kwargs = {}
        if qformer_kwargs is None:
            qformer_kwargs = {}
        if text_kwargs is None:
            text_kwargs = {}

        self.parent = parent
        self.vision_model_tester = Blip2VisionModelTester(parent, **vision_kwargs)
        self.qformer_model_tester = Blip2QFormerModelTester(parent, **qformer_kwargs)
        self.text_model_tester = Blip2TextModelTester(parent, **text_kwargs)
        self.is_training = is_training
        self.num_query_tokens = num_query_tokens

    def prepare_config_and_inputs(self):
        _, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
        (
            _,
            input_ids,
            decoder_input_ids,
            attention_mask,
            decoder_attention_mask,
            lm_labels,
        ) = self.text_model_tester.prepare_config_and_inputs()

        config = self.get_config()

        return config, input_ids, attention_mask, pixel_values, decoder_input_ids, decoder_attention_mask, lm_labels

    def get_config(self):
        return Blip2Config.from_vision_qformer_text_configs(
            vision_config=self.vision_model_tester.get_config(),
            qformer_config=self.qformer_model_tester.get_config(),
            text_config=self.text_model_tester.get_config(),
            num_query_tokens=self.num_query_tokens,
        )

    def create_and_check_for_conditional_generation(
        self, config, input_ids, attention_mask, pixel_values, decoder_input_ids, decoder_attention_mask, labels
    ):
        model = Blip2ForConditionalGeneration(config).to(torch_device).eval()
        with torch.no_grad():
            result = model(pixel_values, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask)

        self.parent.assertEqual(
            result.logits.shape,
            (
                self.vision_model_tester.batch_size,
                self.text_model_tester.seq_length,
                self.text_model_tester.vocab_size,
            ),
        )

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids,
            attention_mask,
            pixel_values,
            decoder_input_ids,
            decoder_attention_mask,
            labels,
        ) = config_and_inputs
        inputs_dict = {
            "pixel_values": pixel_values,
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "decoder_input_ids": decoder_input_ids,
            "decoder_attention_mask": decoder_attention_mask,
            "labels": labels,
        }
        return config, inputs_dict

@require_torch
class Blip2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (Blip2ForConditionalGeneration, Blip2Model) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "feature-extraction": Blip2Model,
            "image-to-text": Blip2ForConditionalGeneration,
            "visual-question-answering": Blip2ForConditionalGeneration,
        }
        if is_torch_available()
        else {}
    )
    fx_compatible = False
    test_head_masking = False
    test_pruning = False
    test_resize_embeddings = False
    test_attention_outputs = False
    test_torchscript = False

    def setUp(self):
        self.model_tester = Blip2ModelTester(self)

    def test_for_conditional_generation(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_conditional_generation(*config_and_inputs)

    @unittest.skip(reason="Hidden_states is tested in individual model tests")
    def test_hidden_states_output(self):
        pass

    @unittest.skip(reason="Inputs_embeds is tested in individual model tests")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="Retain_grad is tested in individual model tests")
    def test_retain_grad_hidden_states_attentions(self):
        pass

    @unittest.skip(reason="Blip2Model does not have input/output embeddings")
    def test_model_common_attributes(self):
        pass

    @unittest.skip(reason="There's no base Blip2Model")
    def test_save_load_fast_init_from_base(self):
        pass

    @unittest.skip(reason="There's no base Blip2Model")
    def test_save_load_fast_init_to_base(self):
        pass

    @unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.")
    def test_cpu_offload(self):
        pass

    def test_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.forward)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            arg_names = [*signature.parameters.keys()]

            expected_arg_names = ["pixel_values"]
            self.assertListEqual(arg_names[:1], expected_arg_names)

    def test_load_vision_qformer_text_config(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        # Save Blip2Config and check if we can load Blip2VisionConfig from it
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            vision_config = Blip2VisionConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())

        # Save Blip2Config and check if we can load Blip2QFormerConfig from it
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            qformer_config = Blip2QFormerConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.qformer_config.to_dict(), qformer_config.to_dict())

    @slow
    def test_model_from_pretrained(self):
        for model_name in BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST:
            model = Blip2ForConditionalGeneration.from_pretrained(model_name)
            self.assertIsNotNone(model)

    def test_get_text_features(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        inputs_dict = {
            "input_ids": torch.LongTensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]).to(torch_device),
            "attention_mask": torch.LongTensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]).to(torch_device),
            "decoder_input_ids": torch.LongTensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]).to(torch_device),
        }

        model = Blip2Model(config).to(torch_device)
        model.eval()
        text_features = model.get_text_features(**inputs_dict)
        self.assertEqual(text_features[0].shape, (1, 10, config.text_config.vocab_size))

    def test_get_image_features(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        keys_to_pop = ["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]

        for key in keys_to_pop:
            inputs_dict.pop(key)

        model = Blip2Model(config).to(torch_device)
        model.eval()
        image_features = model.get_image_features(**inputs_dict)
        self.assertEqual(
            image_features[0].shape,
            (
                self.model_tester.vision_model_tester.batch_size,
                self.model_tester.vision_model_tester.seq_length,
                config.vision_config.hidden_size,
            ),
        )

    def test_get_qformer_features(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        keys_to_pop = ["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]

        for key in keys_to_pop:
            inputs_dict.pop(key)

        model = Blip2Model(config).to(torch_device)
        model.eval()
        qformer_features = model.get_qformer_features(**inputs_dict)
        self.assertEqual(
            qformer_features[0].shape,
            (self.model_tester.vision_model_tester.batch_size, 10, config.vision_config.hidden_size),
        )

    # override from common to deal with nested configurations (`vision_config`, `text_config` and `qformer_config`)
    def test_initialization(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        configs_no_init = _config_zero_init(config)
        for key in ["vision_config", "qformer_config", "text_config"]:
            setattr(configs_no_init, key, _config_zero_init(getattr(configs_no_init, key)))
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            for name, param in model.named_parameters():
                if param.requires_grad:
                    self.assertIn(
                        ((param.data.mean() * 1e9).round() / 1e9).item(),
                        [0.0, 1.0],
                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                    )

# We will verify our results on an image of cute cats
def prepare_img():
    url = "https://huggingface.co/hf-internal-testing/blip-test-image/resolve/main/demo.jpg"
    image = Image.open(requests.get(url, stream=True).raw)
    return image

@require_vision
@require_torch
@slow
class Blip2ModelIntegrationTest(unittest.TestCase):
    def test_inference_opt(self):
        processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
        model = Blip2ForConditionalGeneration.from_pretrained(
            "Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16
        ).to(torch_device)

        # prepare image
        image = prepare_img()
        inputs = processor(images=image, return_tensors="pt").to(torch_device, dtype=torch.float16)

        predictions = model.generate(**inputs)
        generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()

        # Test output
        self.assertEqual(predictions[0].tolist(), [2, 102, 693, 2828, 15, 5, 4105, 19, 10, 2335, 50118])
        self.assertEqual("a woman sitting on the beach with a dog", generated_text)

        # image and context
        prompt = "Question: which city is this? Answer:"
        inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16)

        predictions = model.generate(**inputs)
        generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()

        # Test output
        self.assertEqual(
            predictions[0].tolist(),
            [2, 24, 18, 45, 10, 343, 6, 24, 18, 10, 4105, 50118],
        )
        self.assertEqual(generated_text, "it's not a city, it's a beach")

    def test_inference_opt_batched_beam_search(self):
        processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
        model = Blip2ForConditionalGeneration.from_pretrained(
            "Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16
        ).to(torch_device)

        # prepare image
        image = prepare_img()
        inputs = processor(images=[image, image], return_tensors="pt").to(torch_device, dtype=torch.float16)

        predictions = model.generate(**inputs, num_beams=2)

        # Test output (in this case, slightly different from greedy search)
        self.assertEqual(predictions[0].tolist(), [2, 102, 693, 2828, 15, 5, 4105, 19, 69, 2335, 50118])
        self.assertEqual(predictions[1].tolist(), [2, 102, 693, 2828, 15, 5, 4105, 19, 69, 2335, 50118])

    def test_inference_t5(self):
        processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
        model = Blip2ForConditionalGeneration.from_pretrained(
            "Salesforce/blip2-flan-t5-xl", torch_dtype=torch.float16
        ).to(torch_device)

        # prepare image
        image = prepare_img()
        inputs = processor(images=image, return_tensors="pt").to(torch_device, dtype=torch.float16)

        predictions = model.generate(**inputs)
        generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()

        # Test output
        self.assertEqual(predictions[0].tolist(), [0, 2335, 1556, 28, 1782, 30, 8, 2608, 1])
        self.assertEqual("woman playing with dog on the beach", generated_text)

        # image and context
        prompt = "Question: which city is this? Answer:"
        inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16)

        predictions = model.generate(**inputs)
        generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()

        # Test output
        self.assertEqual(
            predictions[0].tolist(),
            [0, 3, 7, 152, 67, 839, 1],
        )
        self.assertEqual(generated_text, "san diego")

    def test_inference_t5_batched_beam_search(self):
        processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
        model = Blip2ForConditionalGeneration.from_pretrained(
            "Salesforce/blip2-flan-t5-xl", torch_dtype=torch.float16
        ).to(torch_device)

        # prepare image
        image = prepare_img()
        inputs = processor(images=[image, image], return_tensors="pt").to(torch_device, dtype=torch.float16)

        predictions = model.generate(**inputs, num_beams=2)

        # Test output (in this case, slightly different from greedy search)
        self.assertEqual(predictions[0].tolist(), [0, 2335, 1556, 28, 1782, 30, 8, 2608, 1])
        self.assertEqual(predictions[1].tolist(), [0, 2335, 1556, 28, 1782, 30, 8, 2608, 1])

    @require_torch_multi_accelerator
    def test_inference_opt_multi_accelerator(self):
        processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
        model = Blip2ForConditionalGeneration.from_pretrained(
            "Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16, device_map="balanced"
        )

        # prepare image
        image = prepare_img()
        inputs = processor(images=image, return_tensors="pt").to(0, dtype=torch.float16)

        predictions = model.generate(**inputs)
        generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()

        # Test output
        self.assertEqual(predictions[0].tolist(), [2, 102, 693, 2828, 15, 5, 4105, 19, 10, 2335, 50118])
        self.assertEqual("a woman sitting on the beach with a dog", generated_text)

        # image and context
        prompt = "Question: which city is this? Answer:"
        inputs = processor(images=image, text=prompt, return_tensors="pt").to(0, dtype=torch.float16)

        predictions = model.generate(**inputs)
        generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()

        # Test output
        self.assertEqual(
            predictions[0].tolist(),
            [2, 24, 18, 45, 10, 343, 6, 24, 18, 10, 4105, 50118],
        )
        self.assertEqual(generated_text, "it's not a city, it's a beach")

    @require_torch_multi_accelerator
    def test_inference_t5_multi_accelerator(self):
        processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
        device_map = device_map = {
            "query_tokens": 0,
            "vision_model": 0,
            "language_model": 1,
            "language_projection": 0,
            "qformer": 0,
        }

        model = Blip2ForConditionalGeneration.from_pretrained(
            "Salesforce/blip2-flan-t5-xl", torch_dtype=torch.float16, device_map=device_map
        )

        # prepare image
        image = prepare_img()
        inputs = processor(images=image, return_tensors="pt").to(0, dtype=torch.float16)

        predictions = model.generate(**inputs)
        generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()

        # Test output
        self.assertEqual(predictions[0].tolist(), [0, 2335, 1556, 28, 1782, 30, 8, 2608, 1])
        self.assertEqual("woman playing with dog on the beach", generated_text)

        # image and context
        prompt = "Question: which city is this? Answer:"
        inputs = processor(images=image, text=prompt, return_tensors="pt").to(0, dtype=torch.float16)

        predictions = model.generate(**inputs)
        generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()

        # Test output
        self.assertEqual(
            predictions[0].tolist(),
            [0, 3, 7, 152, 67, 839, 1],
        )
        self.assertEqual(generated_text, "san diego")

Output: prepare_img

Output: Blip2ModelTest

Output: Blip2ModelTester


Input:
# coding=utf-8
# Copyright 2022 The HuggingFace Team 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 clone 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 gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeq2SeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_accelerate_available,
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)

def get_some_linear_layer(model):
    if model.config.model_type == "gpt2":
        return model.transformer.h[0].mlp.c_fc
    return model.transformer.h[0].mlp.dense_4h_to_h

if is_accelerate_available():
    from accelerate import PartialState
    from accelerate.logging import get_logger

    logger = get_logger(__name__)
    _ = PartialState()

if is_torch_available():
    import torch
    import torch.nn as nn

    class LoRALayer(nn.Module):
        """Wraps a linear layer with LoRA-like adapter - Used for testing purposes only"""

        def __init__(self, module: nn.Module, rank: int):
            super().__init__()
            self.module = module
            self.adapter = nn.Sequential(
                nn.Linear(module.in_features, rank, bias=False),
                nn.Linear(rank, module.out_features, bias=False),
            )
            small_std = (2.0 / (5 * min(module.in_features, module.out_features))) ** 0.5
            nn.init.normal_(self.adapter[0].weight, std=small_std)
            nn.init.zeros_(self.adapter[1].weight)
            self.adapter.to(module.weight.device)

        def forward(self, input, *args, **kwargs):
            return self.module(input, *args, **kwargs) + self.adapter(input)

@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class BaseMixedInt8Test(unittest.TestCase):
    # We keep the constants inside the init function and model loading inside setUp function

    # We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
    # Therefore here we use only bloom-1b3 to test our module
    model_name = "bigscience/bloom-1b7"

    # Constant values
    EXPECTED_RELATIVE_DIFFERENCE = (
        1.540025  # This was obtained on a Quadro RTX 8000 so the number might slightly change
    )

    input_text = "Hello my name is"
    EXPECTED_OUTPUTS = set()
    EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of the family.\n")
    MAX_NEW_TOKENS = 10

    def setUp(self):
        # Models and tokenizer
        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)

class MixedInt8Test(BaseMixedInt8Test):
    def setUp(self):
        super().setUp()

        # Models and tokenizer
        self.model_fp16 = AutoModelForCausalLM.from_pretrained(
            self.model_name, torch_dtype=torch.float16, device_map="auto"
        )
        self.model_8bit = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")

    def tearDown(self):
        r"""
        TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
        avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
        """
        del self.model_fp16
        del self.model_8bit

        gc.collect()
        torch.cuda.empty_cache()

    def test_get_keys_to_not_convert_trust_remote_code(self):
        r"""
        Test the `get_keys_to_not_convert` function with `trust_remote_code` models.
        """
        from accelerate import init_empty_weights

        from transformers.integrations.bitsandbytes import get_keys_to_not_convert

        model_id = "mosaicml/mpt-7b"
        config = AutoConfig.from_pretrained(
            model_id, trust_remote_code=True, revision="ada218f9a93b5f1c6dce48a4cc9ff01fcba431e7"
        )
        with init_empty_weights():
            model = AutoModelForCausalLM.from_config(
                config, trust_remote_code=True, code_revision="ada218f9a93b5f1c6dce48a4cc9ff01fcba431e7"
            )
        self.assertEqual(get_keys_to_not_convert(model), ["transformer.wte"])

    def test_get_keys_to_not_convert(self):
        r"""
        Test the `get_keys_to_not_convert` function.
        """
        from accelerate import init_empty_weights

        from transformers import AutoModelForMaskedLM, Blip2ForConditionalGeneration, MptForCausalLM, OPTForCausalLM
        from transformers.integrations.bitsandbytes import get_keys_to_not_convert

        model_id = "mosaicml/mpt-7b"
        config = AutoConfig.from_pretrained(model_id, revision="72e5f594ce36f9cabfa2a9fd8f58b491eb467ee7")
        with init_empty_weights():
            model = MptForCausalLM(config)
        # The order of the keys does not matter, so we sort them before comparing, same for the other tests.
        self.assertEqual(get_keys_to_not_convert(model).sort(), ["lm_head", "transformer.wte"].sort())

        model_id = "Salesforce/blip2-opt-2.7b"
        config = AutoConfig.from_pretrained(model_id, revision="1ef7f63a8f0a144c13fdca8103eb7b4691c74cec")
        with init_empty_weights():
            model = Blip2ForConditionalGeneration(config)
        self.assertEqual(
            get_keys_to_not_convert(model).sort(),
            ["language_model.lm_head", "language_model.model.decoder.embed_tokens"].sort(),
        )

        model_id = "facebook/opt-350m"
        config = AutoConfig.from_pretrained(model_id, revision="cb32f77e905cccbca1d970436fb0f5e6b58ee3c5")
        with init_empty_weights():
            model = OPTForCausalLM(config)
        self.assertEqual(get_keys_to_not_convert(model).sort(), ["lm_head", "model.decoder.embed_tokens"].sort())

        model_id = "roberta-large"
        config = AutoConfig.from_pretrained(model_id, revision="716877d372b884cad6d419d828bac6c85b3b18d9")
        with init_empty_weights():
            model = AutoModelForMaskedLM.from_config(config)
        self.assertEqual(
            get_keys_to_not_convert(model).sort(),
            ["'roberta.embeddings.word_embeddings', 'lm_head', 'lm_head.decoder"].sort(),
        )

    def test_quantization_config_json_serialization(self):
        r"""
        A simple test to check if the quantization config is correctly serialized and deserialized
        """
        config = self.model_8bit.config

        self.assertTrue(hasattr(config, "quantization_config"))

        _ = config.to_dict()
        _ = config.to_diff_dict()

        _ = config.to_json_string()

    def test_original_dtype(self):
        r"""
        A simple test to check if the model succesfully stores the original dtype
        """
        self.assertTrue(hasattr(self.model_8bit.config, "_pre_quantization_dtype"))
        self.assertFalse(hasattr(self.model_fp16.config, "_pre_quantization_dtype"))
        self.assertTrue(self.model_8bit.config._pre_quantization_dtype == torch.float16)

    def test_memory_footprint(self):
        r"""
        A simple test to check if the model conversion has been done correctly by checking on the
        memory footprint of the converted model and the class type of the linear layers of the converted models
        """
        from bitsandbytes.nn import Int8Params

        mem_fp16 = self.model_fp16.get_memory_footprint()
        mem_8bit = self.model_8bit.get_memory_footprint()

        self.assertAlmostEqual(mem_fp16 / mem_8bit, self.EXPECTED_RELATIVE_DIFFERENCE)
        self.assertTrue(get_some_linear_layer(self.model_8bit).weight.__class__ == Int8Params)

    def test_linear_are_8bit(self):
        r"""
        A simple test to check if the model conversion has been done correctly by checking on the
        memory footprint of the converted model and the class type of the linear layers of the converted models
        """
        from transformers import T5PreTrainedModel

        self.model_fp16.get_memory_footprint()
        self.model_8bit.get_memory_footprint()

        for name, module in self.model_8bit.named_modules():
            if isinstance(module, torch.nn.Linear):
                if name not in ["lm_head"] + T5PreTrainedModel._keep_in_fp32_modules:
                    self.assertTrue(module.weight.dtype == torch.int8)

    def test_llm_skip(self):
        r"""
        A simple test to check if `llm_int8_skip_modules` works as expected
        """
        import bitsandbytes as bnb

        quantization_config = BitsAndBytesConfig(load_in_8bit=True, llm_int8_skip_modules=["classifier"])
        seq_classification_model = AutoModelForSequenceClassification.from_pretrained(
            "roberta-large-mnli", quantization_config=quantization_config
        )
        self.assertTrue(seq_classification_model.roberta.encoder.layer[0].output.dense.weight.dtype == torch.int8)
        self.assertTrue(
            isinstance(seq_classification_model.roberta.encoder.layer[0].output.dense, bnb.nn.Linear8bitLt)
        )

        self.assertTrue(isinstance(seq_classification_model.classifier.dense, nn.Linear))
        self.assertTrue(seq_classification_model.classifier.dense.weight.dtype != torch.int8)
        self.assertTrue(isinstance(seq_classification_model.classifier.out_proj, nn.Linear))
        self.assertTrue(seq_classification_model.classifier.out_proj != torch.int8)

    def test_generate_quality(self):
        r"""
        Test the generation quality of the quantized model and see that we are matching the expected output.
        Given that we are operating on small numbers + the testing model is relatively small, we might not get
        the same output across GPUs. So we'll generate few tokens (5-10) and check their output.
        """
        encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
        output_sequences = self.model_8bit.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)

        self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)

    def test_generate_quality_config(self):
        r"""
        Test that loading the model with the config is equivalent
        """
        bnb_config = BitsAndBytesConfig()
        bnb_config.load_in_8bit = True

        model_8bit_from_config = AutoModelForCausalLM.from_pretrained(
            self.model_name, quantization_config=bnb_config, device_map="auto"
        )

        encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
        output_sequences = model_8bit_from_config.generate(
            input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10
        )

        self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)

    def test_raise_if_config_and_load_in_8bit(self):
        r"""
        Test that loading the model with the config and `load_in_8bit` raises an error
        """
        bnb_config = BitsAndBytesConfig()

        with self.assertRaises(ValueError):
            _ = AutoModelForCausalLM.from_pretrained(
                self.model_name,
                quantization_config=bnb_config,
                load_in_8bit=True,
                device_map="auto",
                llm_int8_enable_fp32_cpu_offload=True,
            )

    def test_device_and_dtype_assignment(self):
        r"""
        Test whether trying to cast (or assigning a device to) a model after converting it in 8-bit will throw an error.
        Checks also if other models are casted correctly.
        """
        with self.assertRaises(ValueError):
            # Tries with `str`
            self.model_8bit.to("cpu")

        with self.assertRaises(ValueError):
            # Tries with a `dtype``
            self.model_8bit.to(torch.float16)

        with self.assertRaises(ValueError):
            # Tries with a `device`
            self.model_8bit.to(torch.device("cuda:0"))

        with self.assertRaises(ValueError):
            # Tries with a `device`
            self.model_8bit.float()

        with self.assertRaises(ValueError):
            # Tries with a `device`
            self.model_8bit.half()

        # Test if we did not break anything
        encoded_input = self.tokenizer(self.input_text, return_tensors="pt")

        self.model_fp16 = self.model_fp16.to(torch.float32)
        _ = self.model_fp16.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)

        # Check this does not throw an error
        _ = self.model_fp16.to("cpu")

        # Check this does not throw an error
        _ = self.model_fp16.half()

        # Check this does not throw an error
        _ = self.model_fp16.float()

    def test_fp32_int8_conversion(self):
        r"""
        Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly.
        """
        model = AutoModelForSeq2SeqLM.from_pretrained("t5-small", load_in_8bit=True, device_map="auto")
        self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.float32)

    def test_int8_serialization(self):
        r"""
        Test whether it is possible to serialize a model in 8-bit.
        """
        from bitsandbytes.nn import Int8Params

        with tempfile.TemporaryDirectory() as tmpdirname:
            self.model_8bit.save_pretrained(tmpdirname)

            # check that the file `quantization_config` is present
            config = AutoConfig.from_pretrained(tmpdirname)
            self.assertTrue(hasattr(config, "quantization_config"))

            model_from_saved = AutoModelForCausalLM.from_pretrained(tmpdirname, load_in_8bit=True, device_map="auto")

            linear = get_some_linear_layer(model_from_saved)
            self.assertTrue(linear.weight.__class__ == Int8Params)
            self.assertTrue(hasattr(linear.weight, "SCB"))

            # generate
            encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
            output_sequences = model_from_saved.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)

        self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)

    def test_int8_serialization_regression(self):
        r"""
        Test whether it is possible to serialize a model in 8-bit - using not safetensors
        """
        from bitsandbytes.nn import Int8Params

        with tempfile.TemporaryDirectory() as tmpdirname:
            self.model_8bit.save_pretrained(tmpdirname, safe_serialization=False)

            # check that the file `quantization_config` is present
            config = AutoConfig.from_pretrained(tmpdirname)
            self.assertTrue(hasattr(config, "quantization_config"))

            model_from_saved = AutoModelForCausalLM.from_pretrained(tmpdirname, load_in_8bit=True, device_map="auto")

            linear = get_some_linear_layer(model_from_saved)
            self.assertTrue(linear.weight.__class__ == Int8Params)
            self.assertTrue(hasattr(linear.weight, "SCB"))

            # generate
            encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
            output_sequences = model_from_saved.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)

        self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)

    def test_int8_serialization_sharded(self):
        r"""
        Test whether it is possible to serialize a model in 8-bit - sharded version.
        """
        from bitsandbytes.nn import Int8Params

        with tempfile.TemporaryDirectory() as tmpdirname:
            self.model_8bit.save_pretrained(tmpdirname, max_shard_size="200MB")

            # check that the file `quantization_config` is present
            config = AutoConfig.from_pretrained(tmpdirname)
            self.assertTrue(hasattr(config, "quantization_config"))

            model_from_saved = AutoModelForCausalLM.from_pretrained(tmpdirname)

            linear = get_some_linear_layer(model_from_saved)
            self.assertTrue(linear.weight.__class__ == Int8Params)
            self.assertTrue(hasattr(linear.weight, "SCB"))

            # generate
            encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
            output_sequences = model_from_saved.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)

            self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)

    def test_int8_from_pretrained(self):
        r"""
        Test whether loading a 8bit model from the Hub works as expected
        """
        from bitsandbytes.nn import Int8Params

        model_id = "ybelkada/bloom-1b7-8bit"

        model = AutoModelForCausalLM.from_pretrained(model_id)

        linear = get_some_linear_layer(model)
        self.assertTrue(linear.weight.__class__ == Int8Params)
        self.assertTrue(hasattr(linear.weight, "SCB"))

        # generate
        encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
        output_sequences = model.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)

        self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)

@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class MixedInt8T5Test(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.model_name = "t5-small"
        cls.dense_act_model_name = "google/flan-t5-small"  # flan-t5 uses dense-act instead of dense-relu-dense
        cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name)
        cls.input_text = "Translate in German: Hello, my dog is cute"

    def tearDown(self):
        r"""
        TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
        avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
        """
        gc.collect()
        torch.cuda.empty_cache()

    def test_inference_without_keep_in_fp32(self):
        r"""
        Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly.
        `flan-t5-small` uses `T5DenseGatedActDense` whereas `t5-small` uses `T5DenseReluDense`. We need to test
        both cases.
        """
        from transformers import T5ForConditionalGeneration

        modules = T5ForConditionalGeneration._keep_in_fp32_modules
        T5ForConditionalGeneration._keep_in_fp32_modules = None

        # test with `t5-small`
        model = T5ForConditionalGeneration.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
        encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
        _ = model.generate(**encoded_input)

        # test with `flan-t5-small`
        model = T5ForConditionalGeneration.from_pretrained(
            self.dense_act_model_name, load_in_8bit=True, device_map="auto"
        )
        encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
        _ = model.generate(**encoded_input)
        T5ForConditionalGeneration._keep_in_fp32_modules = modules

    def test_inference_with_keep_in_fp32(self):
        r"""
        Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly.
        `flan-t5-small` uses `T5DenseGatedActDense` whereas `t5-small` uses `T5DenseReluDense`. We need to test
        both cases.
        """
        import bitsandbytes as bnb

        from transformers import T5ForConditionalGeneration

        # test with `t5-small`
        model = T5ForConditionalGeneration.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")

        # there was a bug with decoders - this test checks that it is fixed
        self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q, bnb.nn.Linear8bitLt))

        encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
        _ = model.generate(**encoded_input)

        # test with `flan-t5-small`
        model = T5ForConditionalGeneration.from_pretrained(
            self.dense_act_model_name, load_in_8bit=True, device_map="auto"
        )
        encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
        _ = model.generate(**encoded_input)

    def test_inference_with_keep_in_fp32_serialized(self):
        r"""
        Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly on
        a serialized model.
        `flan-t5-small` uses `T5DenseGatedActDense` whereas `t5-small` uses `T5DenseReluDense`. We need to test
        both cases.
        """
        import bitsandbytes as bnb

        from transformers import T5ForConditionalGeneration

        # test with `t5-small`
        model = T5ForConditionalGeneration.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")

        with tempfile.TemporaryDirectory() as tmp_dir:
            model.save_pretrained(tmp_dir)

            model = T5ForConditionalGeneration.from_pretrained(tmp_dir)

            # there was a bug with decoders - this test checks that it is fixed
            self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q, bnb.nn.Linear8bitLt))

            encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
            _ = model.generate(**encoded_input)

            # test with `flan-t5-small`
            model = T5ForConditionalGeneration.from_pretrained(
                self.dense_act_model_name, load_in_8bit=True, device_map="auto"
            )
            encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
            _ = model.generate(**encoded_input)

class MixedInt8ModelClassesTest(BaseMixedInt8Test):
    def setUp(self):
        super().setUp()
        # model_name
        self.model_name = "bigscience/bloom-560m"
        self.seq_to_seq_name = "t5-small"

        # Different types of model

        self.base_model = AutoModel.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
        # Sequence classification model
        self.sequence_model = AutoModelForSequenceClassification.from_pretrained(
            self.model_name, load_in_8bit=True, device_map="auto"
        )
        # CausalLM model
        self.model_8bit = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
        # Seq2seq model
        self.seq_to_seq_model = AutoModelForSeq2SeqLM.from_pretrained(
            self.seq_to_seq_name, load_in_8bit=True, device_map="auto"
        )

    def tearDown(self):
        r"""
        TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
        avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
        """
        del self.base_model
        del self.sequence_model
        del self.model_8bit
        del self.seq_to_seq_model

        gc.collect()
        torch.cuda.empty_cache()

    def test_correct_head_class(self):
        r"""
        A simple test to check if the last modules for some classes (AutoModelForCausalLM or SequenceClassification)
        are kept in their native class.
        """
        from bitsandbytes.nn import Int8Params

        # last param of a base model should be a linear8bit module
        self.assertTrue(self.base_model.h[-1].mlp.dense_4h_to_h.weight.__class__ == Int8Params)

        # Other heads should be nn.Parameter
        self.assertTrue(self.model_8bit.lm_head.weight.__class__ == torch.nn.Parameter)
        self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter)
        self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter)

class MixedInt8TestPipeline(BaseMixedInt8Test):
    def setUp(self):
        super().setUp()

    def tearDown(self):
        r"""
        TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
        avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
        """
        del self.pipe

        gc.collect()
        torch.cuda.empty_cache()

    def test_pipeline(self):
        r"""
        The aim of this test is to verify that the mixed int8 is compatible with `pipeline` from transformers. Since
        we used pipline for inference speed benchmarking we want to make sure that this feature does not break anything
        on pipline.
        """
        # self._clear_cuda_cache()
        self.pipe = pipeline(
            "text-generation",
            model=self.model_name,
            model_kwargs={"device_map": "auto", "load_in_8bit": True},
            max_new_tokens=self.MAX_NEW_TOKENS,
        )

        # Real second forward pass
        pipeline_output = self.pipe(self.input_text)
        self.assertIn(pipeline_output[0]["generated_text"], self.EXPECTED_OUTPUTS)

@require_torch_multi_gpu
class MixedInt8TestMultiGpu(BaseMixedInt8Test):
    def setUp(self):
        super().setUp()

    def test_multi_gpu_loading(self):
        r"""
        This tests that the model has been loaded and can be used correctly on a multi-GPU setup.
        Let's just try to load a model on 2 GPUs and see if it works. The model we test has ~2GB of total, 3GB should suffice
        """

        model_parallel = AutoModelForCausalLM.from_pretrained(
            self.model_name, load_in_8bit=True, device_map="balanced"
        )

        # Check correct device map
        self.assertEqual(set(model_parallel.hf_device_map.values()), {0, 1})

        # Check that inference pass works on the model
        encoded_input = self.tokenizer(self.input_text, return_tensors="pt")

        # Second real batch
        output_parallel = model_parallel.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
        self.assertIn(self.tokenizer.decode(output_parallel[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)

@require_torch_multi_gpu
class MixedInt8TestCpuGpu(BaseMixedInt8Test):
    def setUp(self):
        super().setUp()

    def check_inference_correctness(self, model):
        # Check that inference pass works on the model
        encoded_input = self.tokenizer(self.input_text, return_tensors="pt")

        # Check the exactness of the results
        output_parallel = model.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)

        # Get the generation
        output_text = self.tokenizer.decode(output_parallel[0], skip_special_tokens=True)
        self.assertIn(output_text, self.EXPECTED_OUTPUTS)

    def test_cpu_gpu_loading_random_device_map(self):
        r"""
        A test to check is dispatching a model on cpu & gpu works correctly using a random `device_map`.
        """
        device_map = {
            "transformer.word_embeddings": 0,
            "transformer.word_embeddings_layernorm": 0,
            "lm_head": 0,
            "transformer.h.0": "cpu",
            "transformer.h.1": "cpu",
            "transformer.h.2": 0,
            "transformer.h.3": 0,
            "transformer.h.4": 0,
            "transformer.h.5": 0,
            "transformer.h.6": 0,
            "transformer.h.7": 0,
            "transformer.h.8": 0,
            "transformer.h.9": 1,
            "transformer.h.10": 0,
            "transformer.h.11": 1,
            "transformer.h.12": 0,
            "transformer.h.13": 0,
            "transformer.h.14": 1,
            "transformer.h.15": 0,
            "transformer.h.16": 0,
            "transformer.h.17": 1,
            "transformer.h.18": 1,
            "transformer.h.19": 0,
            "transformer.h.20": 1,
            "transformer.h.21": 1,
            "transformer.h.22": 0,
            "transformer.h.23": 0,
            "transformer.ln_f": 1,
        }

        bnb_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True, load_in_8bit=True)

        model_8bit = AutoModelForCausalLM.from_pretrained(
            self.model_name,
            device_map=device_map,
            quantization_config=bnb_config,
        )

        # Check that the model has been correctly set on device 0, 1, and `cpu`.
        self.assertEqual(set(model_8bit.hf_device_map.values()), {0, 1, "cpu"})

        self.check_inference_correctness(model_8bit)

    def test_cpu_gpu_loading_custom_device_map(self):
        r"""
        A test to check is dispatching a model on cpu & gpu works correctly using a custom `device_map`.
        This time the device map is more organized than the test above and uses the abstraction
        `transformer.h` to encapsulate all the decoder layers.
        """
        device_map = {
            "transformer.word_embeddings": "cpu",
            "transformer.word_embeddings_layernorm": "cpu",
            "lm_head": "cpu",
            "transformer.h": 0,
            "transformer.ln_f": 1,
        }
        bnb_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True, load_in_8bit=True)

        # Load model
        model_8bit = AutoModelForCausalLM.from_pretrained(
            self.model_name,
            device_map=device_map,
            quantization_config=bnb_config,
        )

        # Check that the model has been correctly set on device 0, 1, and `cpu`.
        self.assertEqual(set(model_8bit.hf_device_map.values()), {0, 1, "cpu"})

        self.check_inference_correctness(model_8bit)

    def test_cpu_gpu_disk_loading_custom_device_map(self):
        r"""
        A test to check is dispatching a model on cpu & gpu works correctly using a custom `device_map`.
        This time we also add `disk` on the device_map.
        """
        device_map = {
            "transformer.word_embeddings": 0,
            "transformer.word_embeddings_layernorm": "cpu",
            "lm_head": 0,
            "transformer.h": 1,
            "transformer.ln_f": "disk",
        }
        bnb_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True, load_in_8bit=True)
        with tempfile.TemporaryDirectory() as tmpdirname:
            # Load model
            model_8bit = AutoModelForCausalLM.from_pretrained(
                self.model_name,
                device_map=device_map,
                quantization_config=bnb_config,
                offload_folder=tmpdirname,
            )

            # Check that the model has been correctly set on device 0, 1, and `cpu`.
            self.assertEqual(set(model_8bit.hf_device_map.values()), {0, 1, "cpu", "disk"})

            self.check_inference_correctness(model_8bit)

    def test_cpu_gpu_disk_loading_custom_device_map_kwargs(self):
        r"""
        A test to check is dispatching a model on cpu & gpu works correctly using a custom `device_map`.
        This time we also add `disk` on the device_map - using the kwargs directly instead of the quantization config
        """
        device_map = {
            "transformer.word_embeddings": 0,
            "transformer.word_embeddings_layernorm": "cpu",
            "lm_head": 0,
            "transformer.h": 1,
            "transformer.ln_f": "disk",
        }
        with tempfile.TemporaryDirectory() as tmpdirname:
            # Load model
            model_8bit = AutoModelForCausalLM.from_pretrained(
                self.model_name,
                device_map=device_map,
                load_in_8bit=True,
                llm_int8_enable_fp32_cpu_offload=True,
                offload_folder=tmpdirname,
            )

            # Check that the model has been correctly set on device 0, 1, and `cpu`.
            self.assertEqual(set(model_8bit.hf_device_map.values()), {0, 1, "cpu", "disk"})

            self.check_inference_correctness(model_8bit)

class MixedInt8TestTraining(BaseMixedInt8Test):
    def setUp(self):
        self.model_name = "facebook/opt-350m"
        super().setUp()

    def test_training(self):
        if version.parse(importlib.metadata.version("bitsandbytes")) < version.parse("0.37.0"):
            return

        # Step 1: freeze all parameters
        model = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_8bit=True)

        self.assertEqual(set(model.hf_device_map.values()), {torch.cuda.current_device()})

        for param in model.parameters():
            param.requires_grad = False  # freeze the model - train adapters later
            if param.ndim == 1:
                # cast the small parameters (e.g. layernorm) to fp32 for stability
                param.data = param.data.to(torch.float32)

        # Step 2: add adapters
        for _, module in model.named_modules():
            if "OPTAttention" in repr(type(module)):
                module.q_proj = LoRALayer(module.q_proj, rank=16)
                module.k_proj = LoRALayer(module.k_proj, rank=16)
                module.v_proj = LoRALayer(module.v_proj, rank=16)

        # Step 3: dummy batch
        batch = self.tokenizer("Test batch ", return_tensors="pt").to(0)

        # Step 4: Check if the gradient is not None
        with torch.cuda.amp.autocast():
            out = model.forward(**batch)
            out.logits.norm().backward()

        for module in model.modules():
            if isinstance(module, LoRALayer):
                self.assertTrue(module.adapter[1].weight.grad is not None)
                self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0)
            elif isinstance(module, nn.Embedding):
                self.assertTrue(module.weight.grad is None)

class MixedInt8GPT2Test(MixedInt8Test):
    model_name = "gpt2-xl"
    EXPECTED_RELATIVE_DIFFERENCE = 1.8720077507258357
    EXPECTED_OUTPUTS = set()
    EXPECTED_OUTPUTS.add("Hello my name is John Doe, and I'm a big fan of")
    EXPECTED_OUTPUTS.add("Hello my name is John Doe, and I'm a fan of the")

    def test_int8_from_pretrained(self):
        r"""
        Test whether loading a 8bit model from the Hub works as expected
        """
        from bitsandbytes.nn import Int8Params

        model_id = "ybelkada/gpt2-xl-8bit"

        model = AutoModelForCausalLM.from_pretrained(model_id)

        linear = get_some_linear_layer(model)
        self.assertTrue(linear.weight.__class__ == Int8Params)
        self.assertTrue(hasattr(linear.weight, "SCB"))

        # generate
        encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
        output_sequences = model.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)

        self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)

Output: get_some_linear_layer

Output: LoRALayer

Output: BaseMixedInt8Test

Output: MixedInt8T5Test

Output: MixedInt8TestCpuGpu

Output: MixedInt8GPT2Test

Output: MixedInt8Test