



'''simple docstring'''






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
UpperCamelCase__:   Any								=			logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")

UpperCamelCase__:   str								=			list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
UpperCamelCase__:   List[Any]								=			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class 				SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	lowerCamelCase__			 =       field(
	    default="""cifar10"""							,		metadata={"""help""": """Name of a dataset from the datasets package"""}			)
	lowerCamelCase__			 =       field(
	    default=UpperCamelCase__							,		metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}			)
	lowerCamelCase__			 =       field(
	    default=UpperCamelCase__							,		metadata={"""help""": """The column name of the images in the files. If not set, will try to use \'image\' or \'img\'."""}							,		)
	lowerCamelCase__			 =       field(default=UpperCamelCase__							,		metadata={"""help""": """A folder containing the training data."""}			)
	lowerCamelCase__			 =       field(default=UpperCamelCase__							,		metadata={"""help""": """A folder containing the validation data."""}			)
	lowerCamelCase__			 =       field(
	    default=0.15							,		metadata={"""help""": """Percent to split off of train for validation."""}			)
	lowerCamelCase__			 =       field(default=32							,		metadata={"""help""": """The size of the square patches to use for masking."""}			)
	lowerCamelCase__			 =       field(
	    default=0.6							,		metadata={"""help""": """Percentage of patches to mask."""}							,		)
	lowerCamelCase__			 =       field(
	    default=UpperCamelCase__							,		metadata={
	        """help""": (
	            """For debugging purposes or quicker training, truncate the number of training examples to this """
	            """value if set."""
	        )
	    }							,		)
	lowerCamelCase__			 =       field(
	    default=UpperCamelCase__							,		metadata={
	        """help""": (
	            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
	            """value if set."""
	        )
	    }							,		)







	def      A	(						self			:     Tuple		)			->		Tuple:
								UpperCAmelCase :       Tuple 							=		{}
								if self.train_dir is not None:
															UpperCAmelCase :       Optional[int] 							=		self.train_dir
								if self.validation_dir is not None:
															UpperCAmelCase :       Union[str, Any] 							=		self.validation_dir
								UpperCAmelCase :       Dict 							=		data_files if data_files else None

@dataclass
class 				SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	lowerCamelCase__			 =       field(
	    default=UpperCamelCase__							,		metadata={
	        """help""": (
	            """The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a """
	            """checkpoint identifier on the hub. """
	            """Don\'t set if you want to train a model from scratch."""
	        )
	    }							,		)
	lowerCamelCase__			 =       field(
	    default=UpperCamelCase__							,		metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(UpperCamelCase__			)}							,		)
	lowerCamelCase__			 =       field(
	    default=UpperCamelCase__							,		metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}			)
	lowerCamelCase__			 =       field(
	    default=UpperCamelCase__							,		metadata={
	        """help""": (
	            """Override some existing default config settings when a model is trained from scratch. Example: """
	            """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
	        )
	    }							,		)
	lowerCamelCase__			 =       field(
	    default=UpperCamelCase__							,		metadata={"""help""": """Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"""}							,		)
	lowerCamelCase__			 =       field(
	    default="""main"""							,		metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}							,		)
	lowerCamelCase__			 =       field(default=UpperCamelCase__							,		metadata={"""help""": """Name or path of preprocessor config."""}			)
	lowerCamelCase__			 =       field(
	    default=UpperCamelCase__							,		metadata={
	        """help""": (
	            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
	            """with private models)."""
	        )
	    }							,		)
	lowerCamelCase__			 =       field(
	    default=UpperCamelCase__							,		metadata={
	        """help""": (
	            """The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
	        )
	    }							,		)
	lowerCamelCase__			 =       field(
	    default=UpperCamelCase__							,		metadata={
	        """help""": (
	            """The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
	        )
	    }							,		)
	lowerCamelCase__			 =       field(
	    default=UpperCamelCase__							,		metadata={"""help""": """Stride to use for the encoder."""}							,		)

class 				SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(						self			:     Optional[int]		,			__snake_case			:     Optional[Any]=192		,			__snake_case			:     Optional[int]=32		,			__snake_case			:     int=4		,			__snake_case			:     List[Any]=0.6		)			->		Tuple:
								UpperCAmelCase :       List[str] 							=		input_size
								UpperCAmelCase :       Any 							=		mask_patch_size
								UpperCAmelCase :       Dict 							=		model_patch_size
								UpperCAmelCase :       Optional[Any] 							=		mask_ratio

								if self.input_size % self.mask_patch_size != 0:
															raise ValueError('''Input size must be divisible by mask patch size'''		)
								if self.mask_patch_size % self.model_patch_size != 0:
															raise ValueError('''Mask patch size must be divisible by model patch size'''		)

								UpperCAmelCase :       Tuple 							=		self.input_size // self.mask_patch_size
								UpperCAmelCase :       List[Any] 							=		self.mask_patch_size // self.model_patch_size

								UpperCAmelCase :       Tuple 							=		self.rand_size**2
								UpperCAmelCase :       Union[str, Any] 							=		int(np.ceil(self.token_count * self.mask_ratio		)		)







	def __call__(						self			:     Dict		)			->		Union[str, Any]:
								UpperCAmelCase :       List[Any] 							=		np.random.permutation(self.token_count		)[: self.mask_count]
								UpperCAmelCase :       int 							=		np.zeros(self.token_count		,			dtype=UpperCamelCase_		)
								UpperCAmelCase :       List[Any] 							=		1

								UpperCAmelCase :       List[str] 							=		mask.reshape((self.rand_size, self.rand_size)		)
								UpperCAmelCase :       List[str] 							=		mask.repeat(self.scale		,			axis=0		).repeat(self.scale		,			axis=1		)

								return torch.tensor(mask.flatten()		)


def  snake_case_     (					_lowerCAmelCase  :						Dict  )      ->   Union[str, Any]:
							UpperCAmelCase :       List[Any] 							=		torch.stack([example['''pixel_values'''] for example in examples]  )
							UpperCAmelCase :       Dict 							=		torch.stack([example['''mask'''] for example in examples]  )
							return {"pixel_values": pixel_values, "bool_masked_pos": mask}


def  snake_case_     (					)      ->   Optional[int]:
							UpperCAmelCase :       Union[str, Any] 							=		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
							if len(sys.argv  ) == 2 and sys.argv[1].endswith('''.json'''  ):
														# If we pass only one argument to the script and it's the path to a json file,
														# let's parse it to get our arguments.
														UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Any 							=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
							else:
														UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		parser.parse_args_into_dataclasses()

							# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
							# information sent is the one passed as arguments along with your Python/PyTorch versions.
							send_example_telemetry('''run_mim'''   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )

							# Setup logging
							logging.basicConfig(
							    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''   ,      datefmt='''%m/%d/%Y %H:%M:%S'''   ,      handlers=[logging.StreamHandler(sys.stdout  )]   ,      )

							if training_args.should_log:
														# The default of training_args.log_level is passive, so we set log level at info here to have that default.
														transformers.utils.logging.set_verbosity_info()

							UpperCAmelCase :       Tuple 							=		training_args.get_process_log_level()
							logger.setLevel(_lowerCAmelCase  )
							transformers.utils.logging.set_verbosity(_lowerCAmelCase  )
							transformers.utils.logging.enable_default_handler()
							transformers.utils.logging.enable_explicit_format()

							# Log on each process the small summary:
							logger.warning(
							    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
							    + f"""distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}"""  )
							logger.info(f"""Training/evaluation parameters {training_args}"""  )

							# Detecting last checkpoint.
							UpperCAmelCase :       str 							=		None
							if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
														UpperCAmelCase :       List[str] 							=		get_last_checkpoint(training_args.output_dir  )
														if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
																					raise ValueError(
																					    f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
																					    '''Use --overwrite_output_dir to overcome.'''  )
														elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
																					logger.info(
																					    f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
																					    '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''  )

    # Initialize our dataset.
							UpperCAmelCase :       Optional[Any] 							=		load_dataset(
							    data_args.dataset_name   ,      data_args.dataset_config_name   ,      data_files=data_args.data_files   ,      cache_dir=model_args.cache_dir   ,      use_auth_token=True if model_args.use_auth_token else None   ,      )

							# If we don't have a validation split, split off a percentage of train as validation.
							UpperCAmelCase :       Optional[int] 							=		None if '''validation''' in ds.keys() else data_args.train_val_split
							if isinstance(data_args.train_val_split   ,      _lowerCAmelCase  ) and data_args.train_val_split > 0.0:
														UpperCAmelCase :       Dict 							=		ds['''train'''].train_test_split(data_args.train_val_split  )
														UpperCAmelCase :       Optional[Any] 							=		split['''train''']
														UpperCAmelCase :       Tuple 							=		split['''test''']

							# Create config
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							UpperCAmelCase :       Optional[int] 							=		{
							    '''cache_dir''': model_args.cache_dir,
							    '''revision''': model_args.model_revision,
							    '''use_auth_token''': True if model_args.use_auth_token else None,
							}
							if model_args.config_name_or_path:
														UpperCAmelCase :       List[str] 							=		AutoConfig.from_pretrained(model_args.config_name_or_path   ,      **_lowerCAmelCase  )
							elif model_args.model_name_or_path:
														UpperCAmelCase :       Optional[Any] 							=		AutoConfig.from_pretrained(model_args.model_name_or_path   ,      **_lowerCAmelCase  )
							else:
														UpperCAmelCase :       Any 							=		CONFIG_MAPPING[model_args.model_type]()
														logger.warning('''You are instantiating a new config instance from scratch.'''  )
														if model_args.config_overrides is not None:
																					logger.info(f"""Overriding config: {model_args.config_overrides}"""  )
																					config.update_from_string(model_args.config_overrides  )
																					logger.info(f"""New config: {config}"""  )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
							if hasattr(_lowerCAmelCase   ,      '''decoder_type'''  ):
														UpperCAmelCase :       Optional[Any] 							=		'''simmim'''

							# adapt config
							UpperCAmelCase :       str 							=		model_args.image_size if model_args.image_size is not None else config.image_size
							UpperCAmelCase :       str 							=		model_args.patch_size if model_args.patch_size is not None else config.patch_size
							UpperCAmelCase :       Union[str, Any] 							=		(
							    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
							)

							config.update(
							    {
							        '''image_size''': model_args.image_size,
							        '''patch_size''': model_args.patch_size,
							        '''encoder_stride''': model_args.encoder_stride,
							    }  )

							# create image processor
							if model_args.image_processor_name:
														UpperCAmelCase :       List[Any] 							=		AutoImageProcessor.from_pretrained(model_args.image_processor_name   ,      **_lowerCAmelCase  )
							elif model_args.model_name_or_path:
														UpperCAmelCase :       Tuple 							=		AutoImageProcessor.from_pretrained(model_args.model_name_or_path   ,      **_lowerCAmelCase  )
							else:
														UpperCAmelCase :       Optional[int] 							=		{
														    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
														}
														UpperCAmelCase :       Optional[Any] 							=		IMAGE_PROCESSOR_TYPES[model_args.model_type]()

							# create model
							if model_args.model_name_or_path:
														UpperCAmelCase :       Optional[int] 							=		AutoModelForMaskedImageModeling.from_pretrained(
														    model_args.model_name_or_path   ,      from_tf=bool('''.ckpt''' in model_args.model_name_or_path  )   ,      config=_lowerCAmelCase   ,      cache_dir=model_args.cache_dir   ,      revision=model_args.model_revision   ,      use_auth_token=True if model_args.use_auth_token else None   ,      )
							else:
														logger.info('''Training new model from scratch'''  )
														UpperCAmelCase :       Tuple 							=		AutoModelForMaskedImageModeling.from_config(_lowerCAmelCase  )

							if training_args.do_train:
														UpperCAmelCase :       Union[str, Any] 							=		ds['''train'''].column_names
							else:
														UpperCAmelCase :       Union[str, Any] 							=		ds['''validation'''].column_names

							if data_args.image_column_name is not None:
														UpperCAmelCase :       Dict 							=		data_args.image_column_name
							elif "image" in column_names:
														UpperCAmelCase :       str 							=		'''image'''
							elif "img" in column_names:
														UpperCAmelCase :       Dict 							=		'''img'''
							else:
														UpperCAmelCase :       Union[str, Any] 							=		column_names[0]

							# transformations as done in original SimMIM paper
							# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
							UpperCAmelCase :       Optional[Any] 							=		Compose(
							    [
							        Lambda(lambda _lowerCAmelCase  : img.convert('''RGB'''  ) if img.mode != "RGB" else img  ),
							        RandomResizedCrop(model_args.image_size   ,      scale=(0.6_7, 1.0)   ,      ratio=(3.0 / 4.0, 4.0 / 3.0)  ),
							        RandomHorizontalFlip(),
							        ToTensor(),
							        Normalize(mean=image_processor.image_mean   ,      std=image_processor.image_std  ),
							    ]  )

							# create mask generator
							UpperCAmelCase :       Any 							=		MaskGenerator(
							    input_size=model_args.image_size   ,      mask_patch_size=data_args.mask_patch_size   ,      model_patch_size=model_args.patch_size   ,      mask_ratio=data_args.mask_ratio   ,      )

							def preprocess_images(_lowerCAmelCase  :						Optional[Any]  ):
														UpperCAmelCase :       Dict 							=		[transforms(_lowerCAmelCase  ) for image in examples[image_column_name]]
														UpperCAmelCase :       Union[str, Any] 							=		[mask_generator() for i in range(len(examples[image_column_name]  )  )]

														return examples

							if training_args.do_train:
														if "train" not in ds:
																					raise ValueError('''--do_train requires a train dataset'''  )
														if data_args.max_train_samples is not None:
																					UpperCAmelCase :       Any 							=		ds['''train'''].shuffle(seed=training_args.seed  ).select(range(data_args.max_train_samples  )  )
														# Set the training transforms
														ds["train"].set_transform(_lowerCAmelCase  )

							if training_args.do_eval:
														if "validation" not in ds:
																					raise ValueError('''--do_eval requires a validation dataset'''  )
														if data_args.max_eval_samples is not None:
																					UpperCAmelCase :       Dict 							=		(
																					    ds['''validation'''].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
																					)
														# Set the validation transforms
														ds["validation"].set_transform(_lowerCAmelCase  )

							# Initialize our trainer
							UpperCAmelCase :       Dict 							=		Trainer(
							    model=_lowerCAmelCase   ,      args=_lowerCAmelCase   ,      train_dataset=ds['''train'''] if training_args.do_train else None   ,      eval_dataset=ds['''validation'''] if training_args.do_eval else None   ,      tokenizer=_lowerCAmelCase   ,      data_collator=_lowerCAmelCase   ,      )

							# Training
							if training_args.do_train:
														UpperCAmelCase :       int 							=		None
														if training_args.resume_from_checkpoint is not None:
																					UpperCAmelCase :       Dict 							=		training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					UpperCAmelCase :       Any 							=		last_checkpoint
														UpperCAmelCase :       List[str] 							=		trainer.train(resume_from_checkpoint=_lowerCAmelCase  )
														trainer.save_model()
														trainer.log_metrics('''train'''   ,      train_result.metrics  )
														trainer.save_metrics('''train'''   ,      train_result.metrics  )
														trainer.save_state()

							# Evaluation
							if training_args.do_eval:
														UpperCAmelCase :       Tuple 							=		trainer.evaluate()
														trainer.log_metrics('''eval'''   ,      _lowerCAmelCase  )
														trainer.save_metrics('''eval'''   ,      _lowerCAmelCase  )

							# Write model card and (optionally) push to hub
							UpperCAmelCase :       Union[str, Any] 							=		{
							    '''finetuned_from''': model_args.model_name_or_path,
							    '''tasks''': '''masked-image-modeling''',
							    '''dataset''': data_args.dataset_name,
							    '''tags''': ['''masked-image-modeling'''],
							}
							if training_args.push_to_hub:
														trainer.push_to_hub(**_lowerCAmelCase  )
							else:
														trainer.create_model_card(**_lowerCAmelCase  )


if __name__ == "__main__":
						main()









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()

lowerCAmelCase       =							{
    '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 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=False						,				SCREAMING_SNAKE_CASE=True     ):


		"""simple docstring"""




		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__            =      MODEL_CLASSES[model_type]

		# Initialise TF model
		if config_file in aws_config_map:
				lowercase__            =      cached_file(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				force_download=not use_cached_models     )
		lowercase__            =      config_class.from_json_file(SCREAMING_SNAKE_CASE     )
		lowercase__            =      True
		lowercase__            =      True
		print(f'Building TensorFlow model from configuration: {config}'     )
		lowercase__            =      model_class(SCREAMING_SNAKE_CASE     )

		# Load weights from tf checkpoint
		if pytorch_checkpoint_path in aws_config_map.keys():
				lowercase__            =      cached_file(
				    SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				force_download=not use_cached_models     )
		# Load PyTorch checkpoint in tf2 model:
		lowercase__            =      load_pytorch_checkpoint_in_tfa_model(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )

		if compare_with_pt_model:
				lowercase__            =      tf_model(tf_model.dummy_inputs						,				training=SCREAMING_SNAKE_CASE     )  # build the network

				lowercase__            =      torch.load(SCREAMING_SNAKE_CASE						,				map_location='''cpu'''     )
				lowercase__            =      pt_model_class.from_pretrained(
				    pretrained_model_name_or_path=SCREAMING_SNAKE_CASE						,				config=SCREAMING_SNAKE_CASE						,				state_dict=SCREAMING_SNAKE_CASE     )

				with torch.no_grad():
						lowercase__            =      pt_model(**pt_model.dummy_inputs     )

				lowercase__            =      pto[0].numpy()
				lowercase__            =      tfo[0].numpy()
				lowercase__            =      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(SCREAMING_SNAKE_CASE						,				save_format='''h5'''     )
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=None						,				SCREAMING_SNAKE_CASE=None						,				SCREAMING_SNAKE_CASE=False						,				SCREAMING_SNAKE_CASE=False						,				SCREAMING_SNAKE_CASE=False						,				SCREAMING_SNAKE_CASE=False						,				):


		"""simple docstring"""




		if args_model_type is None:
				lowercase__            =      list(MODEL_CLASSES.keys()     )
		else:
				lowercase__            =      [args_model_type]

		for j, model_type in enumerate(SCREAMING_SNAKE_CASE						,				start=1     ):
				print('''=''' * 1_00     )
				print(f' Converting model type {j}/{len(SCREAMING_SNAKE_CASE     )}: {model_type}'     )
				print('''=''' * 1_00     )
				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__            =      MODEL_CLASSES[model_type]

				if model_shortcut_names_or_path is None:
						lowercase__            =      list(aws_model_maps.keys()     )
				if config_shortcut_names_or_path is None:
						lowercase__            =      model_shortcut_names_or_path

				for i, (model_shortcut_name, config_shortcut_name) in enumerate(
				    zip(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )						,				start=1     ):
						print('''-''' * 1_00     )
						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__            =      model_shortcut_name
						elif only_convert_finetuned_models:
								print(f'    Skipping not finetuned checkpoint {model_shortcut_name}'     )
								continue
						print(
						    f'    Converting checkpoint {i}/{len(SCREAMING_SNAKE_CASE     )}: {model_shortcut_name} - model_type {model_type}'     )
						print('''-''' * 1_00     )

						if config_shortcut_name in aws_config_map:
								lowercase__            =      cached_file(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				force_download=not use_cached_models     )
						else:
								lowercase__            =      config_shortcut_name

						if model_shortcut_name in aws_model_maps:
								lowercase__            =      cached_file(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				force_download=not use_cached_models     )
						else:
								lowercase__            =      model_shortcut_name

						if os.path.isfile(SCREAMING_SNAKE_CASE     ):
								lowercase__            =      '''converted_model'''

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


if __name__ == "__main__":
			lowerCAmelCase       =							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.')
			lowerCAmelCase       =							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,
			)


