


import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
 import numpy as np
 import torch

 from . import (
     AlbertForPreTraining,
     BartForConditionalGeneration,
     BertForPreTraining,
     BertForQuestionAnswering,
     BertForSequenceClassification,
     CamembertForMaskedLM,
     CTRLLMHeadModel,
     DistilBertForMaskedLM,
     DistilBertForQuestionAnswering,
     DPRContextEncoder,
     DPRQuestionEncoder,
     DPRReader,
     ElectraForPreTraining,
     FlaubertWithLMHeadModel,
     GPTaLMHeadModel,
     LayoutLMForMaskedLM,
     LxmertForPreTraining,
     LxmertVisualFeatureEncoder,
     OpenAIGPTLMHeadModel,
     RobertaForMaskedLM,
     RobertaForSequenceClassification,
     TaForConditionalGeneration,
     TransfoXLLMHeadModel,
     XLMRobertaForMaskedLM,
     XLMWithLMHeadModel,
     XLNetLMHeadModel,
 )


logging.set_verbosity_info()

SCREAMING_SNAKE_CASE :   int							      =				{
    "bart": (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    "bert": (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "bert-large-uncased-whole-word-masking-finetuned-squad": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "bert-large-cased-whole-word-masking-finetuned-squad": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "bert-base-cased-finetuned-mrpc": (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "dpr": (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    "gpt2": (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "xlnet": (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "xlm": (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "xlm-roberta": (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "transfo-xl": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "openai-gpt": (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "roberta": (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "layoutlm": (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    "roberta-large-mnli": (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "camembert": (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "flaubert": (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "distilbert": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "distilbert-base-distilled-squad": (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "lxmert": (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "lxmert-visual-feature-encoder": (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "ctrl": (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "albert": (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "t5": (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "electra": (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "wav2vec2": (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_=False							,				lowerCamelCase_=True				)					->		Dict:
    if model_type not in MODEL_CLASSES:
        raise ValueError(F'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()				)}.'''				)

    _lowercase	,       _lowercase	,       _lowercase	,       _lowercase :							Optional[int]   	=	MODEL_CLASSES[model_type]

    # Initialise TF model
    if config_file in aws_config_map:
        _lowercase :							Union[str, Any]   	=	cached_file(lowerCamelCase_							,				lowerCamelCase_							,				force_download=not use_cached_models				)
    _lowercase :							List[str]   	=	config_class.from_json_file(lowerCamelCase_				)
    _lowercase :							List[str]   	=	True
    _lowercase :							str   	=	True
    print(F'''Building TensorFlow model from configuration: {config}'''				)
    _lowercase :							Any   	=	model_class(lowerCamelCase_				)

    # Load weights from tf checkpoint
    if pytorch_checkpoint_path in aws_config_map.keys():
        _lowercase :							Optional[int]   	=	cached_file(
            lowerCamelCase_							,				lowerCamelCase_							,				force_download=not use_cached_models				)
    # Load PyTorch checkpoint in tf2 model:
    _lowercase :							Optional[Any]   	=	load_pytorch_checkpoint_in_tfa_model(lowerCamelCase_							,				lowerCamelCase_				)

    if compare_with_pt_model:
        _lowercase :							Dict   	=	tf_model(tf_model.dummy_inputs							,				training=lowerCamelCase_				)  # build the network

        _lowercase :							List[str]   	=	torch.load(lowerCamelCase_							,				map_location='cpu'				)
        _lowercase :							Dict   	=	pt_model_class.from_pretrained(
            pretrained_model_name_or_path=lowerCamelCase_							,				config=lowerCamelCase_							,				state_dict=lowerCamelCase_				)

        with torch.no_grad():
            _lowercase :							int   	=	pt_model(**pt_model.dummy_inputs				)

        _lowercase :							Union[str, Any]   	=	pto[0].numpy()
        _lowercase :							int   	=	tfo[0].numpy()
        _lowercase :							str   	=	np.amax(np.abs(np_pt - np_tf				)				)
        print(F'''Max absolute difference between models outputs {diff}'''				)
        assert diff <= 2e-2, F'''Error, model absolute difference is >2e-2: {diff}'''

    # Save pytorch-model
    print(F'''Save TensorFlow model to {tf_dump_path}'''				)
    tf_model.save_weights(lowerCamelCase_							,				save_format='h5'				)



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_=None							,				lowerCamelCase_=None							,				lowerCamelCase_=False							,				lowerCamelCase_=False							,				lowerCamelCase_=False							,				lowerCamelCase_=False							,				)					->		Any:
    if args_model_type is None:
        _lowercase :							Union[str, Any]   	=	list(MODEL_CLASSES.keys()				)
    else:
        _lowercase :							Any   	=	[args_model_type]

    for j, model_type in enumerate(lowerCamelCase_							,				start=1				):
        print('=' * 100				)
        print(F''' Converting model type {j}/{len(lowerCamelCase_				)}: {model_type}'''				)
        print('=' * 100				)
        if model_type not in MODEL_CLASSES:
            raise ValueError(F'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()				)}.'''				)

        _lowercase	,       _lowercase	,       _lowercase	,       _lowercase	,       _lowercase :							List[str]   	=	MODEL_CLASSES[model_type]

        if model_shortcut_names_or_path is None:
            _lowercase :							List[Any]   	=	list(aws_model_maps.keys()				)
        if config_shortcut_names_or_path is None:
            _lowercase :							Any   	=	model_shortcut_names_or_path

        for i, (model_shortcut_name, config_shortcut_name) in enumerate(
            zip(lowerCamelCase_							,				lowerCamelCase_				)							,				start=1				):
            print('-' * 100				)
            if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                if not only_convert_finetuned_models:
                    print(F'''    Skipping finetuned checkpoint {model_shortcut_name}'''				)
                    continue
                _lowercase :							Optional[int]   	=	model_shortcut_name
            elif only_convert_finetuned_models:
                print(F'''    Skipping not finetuned checkpoint {model_shortcut_name}'''				)
                continue
            print(
                F'''    Converting checkpoint {i}/{len(lowerCamelCase_				)}: {model_shortcut_name} - model_type {model_type}'''				)
            print('-' * 100				)

            if config_shortcut_name in aws_config_map:
                _lowercase :							int   	=	cached_file(lowerCamelCase_							,				lowerCamelCase_							,				force_download=not use_cached_models				)
            else:
                _lowercase :							str   	=	config_shortcut_name

            if model_shortcut_name in aws_model_maps:
                _lowercase :							Any   	=	cached_file(lowerCamelCase_							,				lowerCamelCase_							,				force_download=not use_cached_models				)
            else:
                _lowercase :							Optional[Any]   	=	model_shortcut_name

            if os.path.isfile(lowerCamelCase_				):
                _lowercase :							int   	=	'converted_model'

            convert_pt_checkpoint_to_tf(
                model_type=lowerCamelCase_							,				pytorch_checkpoint_path=lowerCamelCase_							,				config_file=lowerCamelCase_							,				tf_dump_path=os.path.join(lowerCamelCase_							,				model_shortcut_name + '-tf_model.h5'				)							,				compare_with_pt_model=lowerCamelCase_							,				)
            if remove_cached_files:
                os.remove(lowerCamelCase_				)
                os.remove(lowerCamelCase_				)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE :   Optional[int]							      =				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_dump_path", default=None, type=str, required=True, help="Path to the output Tensorflow dump file."
 )
 parser.add_argument(
     "--model_type",
     default=None,
     type=str,
     help=(
         F"Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and "
         "convert all the models from AWS."
     ),
 )
 parser.add_argument(
     "--pytorch_checkpoint_path",
     default=None,
     type=str,
     help=(
         "Path to the PyTorch checkpoint path or shortcut name to download from AWS. "
         "If not given, will download and convert all the checkpoints from AWS."
     ),
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     help=(
         "The config json file corresponding to the pre-trained model. \n"
         "This specifies the model architecture. If not given and "
         "--pytorch_checkpoint_path is not given or is a shortcut name "
         "use the configuration associated to the shortcut name on the AWS"
     ),
 )
 parser.add_argument(
     "--compare_with_pt_model", action="store_true", help="Compare Tensorflow and PyTorch model predictions."
 )
 parser.add_argument(
     "--use_cached_models",
     action="store_true",
     help="Use cached models if possible instead of updating to latest checkpoint versions.",
 )
 parser.add_argument(
     "--remove_cached_files",
     action="store_true",
     help="Remove pytorch models after conversion (save memory when converting in batches).",
 )
 parser.add_argument("--only_convert_finetuned_models", action="store_true", help="Only convert finetuned models.")
 SCREAMING_SNAKE_CASE :   Optional[int]							      =				parser.parse_args()

 # if args.pytorch_checkpoint_path is not None:
 #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
 #                                 args.pytorch_checkpoint_path,
 #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
 #                                 args.tf_dump_path,
 #                                 compare_with_pt_model=args.compare_with_pt_model,
 #                                 use_cached_models=args.use_cached_models)
 # else:
 convert_all_pt_checkpoints_to_tf(
     args.model_type.lower() if args.model_type is not None else None,
     args.tf_dump_path,
     model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
     if args.pytorch_checkpoint_path is not None
     else None,
     config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
     compare_with_pt_model=args.compare_with_pt_model,
     use_cached_models=args.use_cached_models,
     remove_cached_files=args.remove_cached_files,
     only_convert_finetuned_models=args.only_convert_finetuned_models,
 )






import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
     MODEL_MAPPING,
     DeiTForImageClassification,
     DeiTForImageClassificationWithTeacher,
     DeiTForMaskedImageModeling,
     DeiTModel,
 )
 from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import DeiTImageProcessor






class _lowerCamelCase:
       def __init__(      self,   lowerCamelCase,   lowerCamelCase=13,   lowerCamelCase=30,   lowerCamelCase=2,   lowerCamelCase=3,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=32,   lowerCamelCase=5,   lowerCamelCase=4,   lowerCamelCase=37,   lowerCamelCase="gelu",   lowerCamelCase=0.1,   lowerCamelCase=0.1,   lowerCamelCase=10,   lowerCamelCase=0.0_2,   lowerCamelCase=3,   lowerCamelCase=None,   lowerCamelCase=2,   ) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Any   	=	parent
           _lowercase :							int   	=	batch_size
           _lowercase :							int   	=	image_size
           _lowercase :							str   	=	patch_size
           _lowercase :							int   	=	num_channels
           _lowercase :							Any   	=	is_training
           _lowercase :							Union[str, Any]   	=	use_labels
           _lowercase :							Dict   	=	hidden_size
           _lowercase :							List[str]   	=	num_hidden_layers
           _lowercase :							Optional[Any]   	=	num_attention_heads
           _lowercase :							Optional[int]   	=	intermediate_size
           _lowercase :							Tuple   	=	hidden_act
           _lowercase :							str   	=	hidden_dropout_prob
           _lowercase :							Optional[Any]   	=	attention_probs_dropout_prob
           _lowercase :							Tuple   	=	type_sequence_label_size
           _lowercase :							List[str]   	=	initializer_range
           _lowercase :							Any   	=	scope
           _lowercase :							Union[str, Any]   	=	encoder_stride

           # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
           _lowercase :							Union[str, Any]   	=	(image_size // patch_size) ** 2
           _lowercase :							Any   	=	num_patches + 2
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           _lowercase :							str   	=	None
           if self.use_labels:
               _lowercase :							List[Any]   	=	ids_tensor([self.batch_size],   self.type_sequence_label_size)

           _lowercase :							str   	=	self.get_config()

           return config, pixel_values, labels
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           return DeiTConfig(
               image_size=self.image_size,   patch_size=self.patch_size,   num_channels=self.num_channels,   hidden_size=self.hidden_size,   num_hidden_layers=self.num_hidden_layers,   num_attention_heads=self.num_attention_heads,   intermediate_size=self.intermediate_size,   hidden_act=self.hidden_act,   hidden_dropout_prob=self.hidden_dropout_prob,   attention_probs_dropout_prob=self.attention_probs_dropout_prob,   is_decoder=lowerCamelCase,   initializer_range=self.initializer_range,   encoder_stride=self.encoder_stride,   )
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Any   	=	DeiTModel(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Optional[int]   	=	model(lowerCamelCase)
           self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     int:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	DeiTForMaskedImageModeling(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							List[Any]   	=	model(lowerCamelCase)
           self.parent.assertEqual(
               result.reconstruction.shape,   (self.batch_size, self.num_channels, self.image_size, self.image_size))

           # test greyscale images
           _lowercase :							Any   	=	1
           _lowercase :							Optional[Any]   	=	DeiTForMaskedImageModeling(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()

           _lowercase :							List[str]   	=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           _lowercase :							Optional[int]   	=	model(lowerCamelCase)
           self.parent.assertEqual(result.reconstruction.shape,   (self.batch_size, 1, self.image_size, self.image_size))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Any:




           """simple docstring"""

           _lowercase :							str   	=	self.type_sequence_label_size
           _lowercase :							Dict   	=	DeiTForImageClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							List[Any]   	=	model(lowerCamelCase,   labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.type_sequence_label_size))

           # test greyscale images
           _lowercase :							Optional[Any]   	=	1
           _lowercase :							Optional[Any]   	=	DeiTForImageClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()

           _lowercase :							List[str]   	=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           _lowercase :							List[Any]   	=	model(lowerCamelCase,   labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.type_sequence_label_size))




       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.prepare_config_and_inputs()
           (
               (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       
           ) :							List[str]   	=	config_and_inputs
           _lowercase :							List[str]   	=	{'pixel_values': pixel_values}
           return config, inputs_dict






@require_torch
class _lowerCamelCase(		_a,				_a,				unittest.TestCase     ):
       lowercase_				:			Optional[Any]									=    (
           (
               DeiTModel,
               DeiTForImageClassification,
               DeiTForImageClassificationWithTeacher,
               DeiTForMaskedImageModeling,
           )
           if is_torch_available()
           else ()
       )
       lowercase_				:			Optional[Any]									=    (
           {
               """feature-extraction""": DeiTModel,
               """image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
           }
           if is_torch_available()
           else {}
       )

       lowercase_				:			Dict									=    False
       lowercase_				:			List[str]									=    False
       lowercase_				:			Union[str, Any]									=    False
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							int   	=	DeiTModelTester(self)
           _lowercase :							Optional[Any]   	=	ConfigTester(self,   config_class=lowerCamelCase,   has_text_modality=lowerCamelCase,   hidden_size=37)
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           self.config_tester.run_common_tests()
       @unittest.skip(reason='DeiT does not use inputs_embeds')
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           pass
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase	,       _lowercase :							int   	=	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               _lowercase :							int   	=	model_class(lowerCamelCase)
               self.assertIsInstance(model.get_input_embeddings(),   (nn.Module))
               _lowercase :							Union[str, Any]   	=	model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCamelCase,   nn.Linear))
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           _lowercase	,       _lowercase :							Any   	=	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               _lowercase :							Any   	=	model_class(lowerCamelCase)
               _lowercase :							Optional[Any]   	=	inspect.signature(model.forward)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _lowercase :							Union[str, Any]   	=	[*signature.parameters.keys()]

               _lowercase :							str   	=	['pixel_values']
               self.assertListEqual(arg_names[:1],   lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							int   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase)
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase=False) ->     Any:




           """simple docstring"""

           _lowercase :							Dict   	=	super()._prepare_for_class(lowerCamelCase,   lowerCamelCase,   return_labels=lowerCamelCase)

           if return_labels:
               if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                   del inputs_dict["labels"]

           return inputs_dict
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           if not self.model_tester.is_training:
               return

           _lowercase	,       _lowercase :							str   	=	self.model_tester.prepare_config_and_inputs_for_common()
           _lowercase :							Tuple   	=	True

           for model_class in self.all_model_classes:
               # DeiTForImageClassificationWithTeacher supports inference-only
               if (
                   model_class in get_values(lowerCamelCase)
                   or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
               ):
                   continue
               _lowercase :							Optional[int]   	=	model_class(lowerCamelCase)
               model.to(lowerCamelCase)
               model.train()
               _lowercase :							Optional[Any]   	=	self._prepare_for_class(lowerCamelCase,   lowerCamelCase,   return_labels=lowerCamelCase)
               _lowercase :							List[str]   	=	model(**lowerCamelCase).loss
               loss.backward()
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase	,       _lowercase :							Union[str, Any]   	=	self.model_tester.prepare_config_and_inputs_for_common()
           if not self.model_tester.is_training:
               return

           _lowercase :							Dict   	=	False
           _lowercase :							Optional[int]   	=	True

           for model_class in self.all_model_classes:
               if model_class in get_values(lowerCamelCase) or not model_class.supports_gradient_checkpointing:
                   continue
               # DeiTForImageClassificationWithTeacher supports inference-only
               if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                   continue
               _lowercase :							str   	=	model_class(lowerCamelCase)
               model.gradient_checkpointing_enable()
               model.to(lowerCamelCase)
               model.train()
               _lowercase :							Union[str, Any]   	=	self._prepare_for_class(lowerCamelCase,   lowerCamelCase,   return_labels=lowerCamelCase)
               _lowercase :							List[Any]   	=	model(**lowerCamelCase).loss
               loss.backward()
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase	,       _lowercase :							Optional[Any]   	=	self.model_tester.prepare_config_and_inputs_for_common()

           _lowercase :							int   	=	[
               {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
               {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
               {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
           ]

           for model_class in self.all_model_classes:
               if (
                   model_class
                   not in [
                       *get_values(lowerCamelCase),
                       *get_values(lowerCamelCase),
                   ]
                   or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
               ):
                   continue

               for problem_type in problem_types:
                   with self.subTest(msg=F'''Testing {model_class} with {problem_type["title"]}'''):
                       _lowercase :							List[Any]   	=	problem_type['title']
                       _lowercase :							str   	=	problem_type['num_labels']

                       _lowercase :							Optional[int]   	=	model_class(lowerCamelCase)
                       model.to(lowerCamelCase)
                       model.train()

                       _lowercase :							Tuple   	=	self._prepare_for_class(lowerCamelCase,   lowerCamelCase,   return_labels=lowerCamelCase)

                       if problem_type["num_labels"] > 1:
                           _lowercase :							Dict   	=	inputs['labels'].unsqueeze(1).repeat(1,   problem_type['num_labels'])

                       _lowercase :							Optional[int]   	=	inputs['labels'].to(problem_type['dtype'])

                       # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                       # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                       # they have the same size." which is a symptom something in wrong for the regression problem.
                       # See https://github.com/huggingface/transformers/issues/11780
                       with warnings.catch_warnings(record=lowerCamelCase) as warning_list:
                           _lowercase :							Dict   	=	model(**lowerCamelCase).loss
                       for w in warning_list:
                           if "Using a target size that is different to the input size" in str(w.message):
                               raise ValueError(
                                   F'''Something is going wrong in the regression problem: intercepted {w.message}''')

                       loss.backward()




       @slow
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase :							Tuple   	=	DeiTModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)



def        UpperCamelCase_(   )					->		List[str]:
    _lowercase :							Optional[Any]   	=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
    return image






@require_torch
@require_vision
class _lowerCamelCase(		unittest.TestCase     ):
       @cached_property
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           return (
               DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224')
               if is_vision_available()
               else None
           )
       @slow
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224').to(
               lowerCamelCase)

           _lowercase :							List[str]   	=	self.default_image_processor
           _lowercase :							List[str]   	=	prepare_img()
           _lowercase :							Tuple   	=	image_processor(images=lowerCamelCase,   return_tensors='pt').to(lowerCamelCase)

           # forward pass
           with torch.no_grad():
               _lowercase :							int   	=	model(**lowerCamelCase)

           # verify the logits
           _lowercase :							Any   	=	torch.Size((1, 10_00))
           self.assertEqual(outputs.logits.shape,   lowerCamelCase)

           _lowercase :							Union[str, Any]   	=	torch.tensor([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]).to(lowerCamelCase)

           self.assertTrue(torch.allclose(outputs.logits[0, :3],   lowerCamelCase,   atol=1E-4))




       @slow
       @require_accelerate
       @require_torch_gpu
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	DeiTModel.from_pretrained(
               'facebook/deit-base-distilled-patch16-224',   torch_dtype=torch.floataa,   device_map='auto')
           _lowercase :							Union[str, Any]   	=	self.default_image_processor

           _lowercase :							Union[str, Any]   	=	prepare_img()
           _lowercase :							int   	=	image_processor(images=lowerCamelCase,   return_tensors='pt')
           _lowercase :							Union[str, Any]   	=	inputs.pixel_values.to(lowerCamelCase)

           # forward pass to make sure inference works in fp16
           with torch.no_grad():
               _lowercase :							Optional[int]   	=	model(lowerCamelCase)


