


import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


lowerCAmelCase       =							logging.getLogger(__name__)

lowerCAmelCase       =							'pytorch_model.bin'


@dataclasses.dataclass
class        _a							:
							_lowercase	:			str        = dataclasses.field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''}			)
							_lowercase	:			Optional[str]        = dataclasses.field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''}     ,	)


@dataclasses.dataclass
class        _a							:
							_lowercase	:			str        = dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''}			)
							_lowercase	:			str        = dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''}			)
							_lowercase	:			Optional[str]        = dataclasses.field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''A csv or a json file containing the validation data.'''}			)
							_lowercase	:			Optional[str]        = dataclasses.field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''The name of the task to train on.'''}     ,	)
							_lowercase	:			Optional[List[str]]        = dataclasses.field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''The list of labels for the task.'''}			)


@dataclasses.dataclass
class        _a							:
							_lowercase	:			str        = dataclasses.field(
							    metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}			)
							_lowercase	:			Optional[str]        = dataclasses.field(
							    default='''accuracy'''     ,	metadata={'''help''': '''The evaluation metric used for the task.'''}			)
							_lowercase	:			Optional[str]        = dataclasses.field(
							    default='''no'''     ,	metadata={
							        '''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
							    }     ,	)
							_lowercase	:			Optional[int]        = dataclasses.field(
							    default=10     ,	metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}     ,	)
							_lowercase	:			Optional[float]        = dataclasses.field(
							    default=0.0     ,	metadata={
							        '''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
							    }     ,	)
							_lowercase	:			Optional[bool]        = dataclasses.field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''}     ,	)
							_lowercase	:			Optional[bool]        = dataclasses.field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''}     ,	)
							_lowercase	:			Optional[bool]        = dataclasses.field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''}     ,	)
							_lowercase	:			Optional[float]        = dataclasses.field(
							    default=0.0     ,	metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''}     ,	)
							_lowercase	:			Optional[int]        = dataclasses.field(
							    default=100     ,	metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}     ,	)
							_lowercase	:			Optional[int]        = dataclasses.field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Random seed for initialization.'''}     ,	)
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      datasets.concatenate_datasets([infer_input, infer_output]						,				axis=1     )

		if args.do_filter_by_confidence:
				lowercase__            =      dataset.filter(lambda SCREAMING_SNAKE_CASE     : example["probability"] > args.confidence_threshold     )

		if args.do_filter_by_val_performance:
				assert eval_result >= 0.0 and eval_result <= 1.0
				lowercase__            =      int(eval_result * len(SCREAMING_SNAKE_CASE     )     )
				print(SCREAMING_SNAKE_CASE     )
				lowercase__            =      dataset.sort('''probability'''						,				reverse=SCREAMING_SNAKE_CASE     )
				lowercase__            =      dataset.select(range(SCREAMING_SNAKE_CASE     )     )

		lowercase__            =      dataset.remove_columns(['''label''', '''probability''']     )
		lowercase__            =      dataset.rename_column('''prediction'''						,				'''label'''     )
		lowercase__            =      dataset.map(lambda SCREAMING_SNAKE_CASE     : {"label": idalabel[example["label"]]}     )
		lowercase__            =      dataset.shuffle(seed=args.seed     )

		lowercase__            =      os.path.join(SCREAMING_SNAKE_CASE						,				f'train_pseudo.{args.data_file_extension}'     )
		if args.data_file_extension == "csv":
				dataset.to_csv(SCREAMING_SNAKE_CASE						,				index=SCREAMING_SNAKE_CASE     )
		else:
				dataset.to_json(SCREAMING_SNAKE_CASE     )
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				**SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      Accelerator()
		# Make one log on every process with the configuration for debugging.
		logging.basicConfig(
		    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''						,				datefmt='''%m/%d/%Y %H:%M:%S'''						,				level=logging.INFO						,				)
		logger.info(accelerator.state     )

		# Setup logging, we only want one process per machine to log things on the
		# screen. accelerator.is_local_main_process is only True for one process per
		# machine.
		logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR     )

		if accelerator.is_local_main_process:
				datasets.utils.logging.set_verbosity_warning()
				transformers.utils.logging.set_verbosity_info()
		else:
				datasets.utils.logging.set_verbosity_error()
				transformers.utils.logging.set_verbosity_error()

		lowercase__            =      STModelArguments(model_name_or_path=SCREAMING_SNAKE_CASE     )
		lowercase__            =      STDataArguments(train_file=SCREAMING_SNAKE_CASE						,				infer_file=SCREAMING_SNAKE_CASE     )
		lowercase__            =      STTrainingArguments(output_dir=SCREAMING_SNAKE_CASE     )
		lowercase__            =      argparse.Namespace()

		for arg_class in (model_args, data_args, training_args):
				for key, value in vars(SCREAMING_SNAKE_CASE     ).items():
						setattr(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )

		for key, value in kwargs.items():
				if hasattr(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):
						setattr(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )

    # Sanity checks
		lowercase__            =      {}
		lowercase__            =      None

		# You need to provide the training data and the data to predict on
		assert args.train_file is not None
		assert args.infer_file is not None
		lowercase__            =      args.train_file
		lowercase__            =      args.infer_file

		if args.evaluation_strategy != IntervalStrategy.NO.value:
				assert args.eval_file is not None
				lowercase__            =      args.eval_file

		for key in data_files:
				lowercase__            =      data_files[key].split('''.'''     )[-1]
				assert extension in ["csv", "json"], f'`{key}_file` should be a csv or a json file.'
				if args.data_file_extension is None:
						lowercase__            =      extension
				else:
						assert extension == args.data_file_extension, f'`{key}_file` should be a {args.data_file_extension} file`.'

		assert (
		    args.eval_metric in datasets.list_metrics()
		), f'{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'

		# If passed along, set the training seed now.
		if args.seed is not None:
				set_seed(args.seed     )

		logger.info('''Creating the initial data directory for self-training...'''     )
		lowercase__            =      f'{args.output_dir}/self-train_iter-{{}}'.format
		lowercase__            =      data_dir_format(0     )

		if accelerator.is_main_process:
				if args.output_dir is not None:
						os.makedirs(args.output_dir						,				exist_ok=SCREAMING_SNAKE_CASE     )
						os.makedirs(SCREAMING_SNAKE_CASE						,				exist_ok=SCREAMING_SNAKE_CASE     )
		accelerator.wait_for_everyone()

		lowercase__            =      None
		lowercase__            =      None
		lowercase__            =      0
		lowercase__            =      False
		# Show the progress bar
		lowercase__            =      tqdm(range(args.max_selftrain_iterations     )						,				disable=not accelerator.is_local_main_process     )

		# Self-train
		for iteration in range(0						,				int(args.max_selftrain_iterations     )     ):
				lowercase__            =      data_dir_format(SCREAMING_SNAKE_CASE     )
				assert os.path.exists(SCREAMING_SNAKE_CASE     )

				# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
				# iteration > 0
				lowercase__            =      os.path.join(SCREAMING_SNAKE_CASE						,				'''stage-1'''     )
				lowercase__            =      {
				    '''accelerator''': accelerator,
				    '''model_name_or_path''': args.model_name_or_path,
				    '''cache_dir''': args.cache_dir,
				    '''do_train''': True,
				    '''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
				    '''do_eval''': True if args.eval_file is not None else False,
				    '''eval_file''': data_files['''eval'''],
				    '''do_predict''': True,
				    '''infer_file''': data_files['''infer'''],
				    '''task_name''': args.task_name,
				    '''label_list''': args.label_list,
				    '''output_dir''': current_output_dir,
				    '''eval_metric''': args.eval_metric,
				    '''evaluation_strategy''': args.evaluation_strategy,
				    '''early_stopping_patience''': args.early_stopping_patience,
				    '''early_stopping_threshold''': args.early_stopping_threshold,
				    '''seed''': args.seed,
				}
				# Add additional training arguments
				for key, value in kwargs.items():
						if key not in arguments_dict and not hasattr(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):
								arguments_dict.update({key: value}     )

				lowercase__            =      os.path.join(SCREAMING_SNAKE_CASE						,				'''best-checkpoint'''						,				SCREAMING_SNAKE_CASE     )
				if os.path.exists(SCREAMING_SNAKE_CASE     ):
						logger.info(
						    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.'''						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				)
				else:
						logger.info('''***** Running self-training: iteration: %d, stage: 1 *****'''						,				SCREAMING_SNAKE_CASE     )
						finetune(**SCREAMING_SNAKE_CASE     )
						accelerator.wait_for_everyone()
						assert os.path.exists(SCREAMING_SNAKE_CASE     )
						logger.info('''Self-training job completed: iteration: %d, stage: 1.'''						,				SCREAMING_SNAKE_CASE     )

				if iteration > 0 and args.finetune_on_labeled_data:
						# Stage 2 (optional): fine-tuning on the original labeled data
						lowercase__            =      os.path.join(SCREAMING_SNAKE_CASE						,				'''best-checkpoint'''     )
						lowercase__            =      os.path.join(SCREAMING_SNAKE_CASE						,				'''stage-2'''     )
						# Update arguments_dict
						lowercase__            =      model_path
						lowercase__            =      data_files['''train''']
						lowercase__            =      current_output_dir

						lowercase__            =      os.path.join(SCREAMING_SNAKE_CASE						,				'''best-checkpoint'''						,				SCREAMING_SNAKE_CASE     )
						if os.path.exists(SCREAMING_SNAKE_CASE     ):
								logger.info(
								    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'''						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				)
						else:
								logger.info('''***** Running self-training: iteration: %d, stage: 2 *****'''						,				SCREAMING_SNAKE_CASE     )
								finetune(**SCREAMING_SNAKE_CASE     )
								accelerator.wait_for_everyone()
								assert os.path.exists(SCREAMING_SNAKE_CASE     )
								logger.info('''Self-training job completed: iteration: %d, stage: 2.'''						,				SCREAMING_SNAKE_CASE     )

				lowercase__            =      iteration
				lowercase__            =      data_dir_format(iteration + 1     )

				lowercase__            =      AutoConfig.from_pretrained(os.path.join(SCREAMING_SNAKE_CASE						,				'''best-checkpoint'''     )     )
				lowercase__            =      config.idalabel
				lowercase__            =      os.path.join(SCREAMING_SNAKE_CASE						,				'''eval_results_best-checkpoint.json'''     )
				lowercase__            =      os.path.join(SCREAMING_SNAKE_CASE						,				'''test_results_best-checkpoint.json'''     )
				assert os.path.exists(SCREAMING_SNAKE_CASE     )

				with open(SCREAMING_SNAKE_CASE						,				'''r'''     ) as f:
						lowercase__            =      float(json.load(SCREAMING_SNAKE_CASE     )[args.eval_metric]     )
				lowercase__            =      os.path.join(SCREAMING_SNAKE_CASE						,				'''infer_output_best-checkpoint.csv'''     )
				assert os.path.exists(SCREAMING_SNAKE_CASE     )
				# Loading the dataset from local csv or json files.
				lowercase__            =      load_dataset(args.data_file_extension						,				data_files={'''data''': data_files['''infer''']}     )['''data''']
				lowercase__            =      load_dataset('''csv'''						,				data_files={'''data''': infer_output_file}     )['''data''']

				if accelerator.is_main_process:
						os.makedirs(SCREAMING_SNAKE_CASE						,				exist_ok=SCREAMING_SNAKE_CASE     )
						shutil.copy(SCREAMING_SNAKE_CASE						,				os.path.join(SCREAMING_SNAKE_CASE						,				f'eval_results_iter-{iteration}.json'     )     )
						if os.path.exists(SCREAMING_SNAKE_CASE     ):
								shutil.copy(SCREAMING_SNAKE_CASE						,				os.path.join(SCREAMING_SNAKE_CASE						,				f'test_results_iter-{iteration}.json'     )     )
						create_pseudo_labeled_data(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )
				accelerator.wait_for_everyone()

				lowercase__            =      os.path.join(SCREAMING_SNAKE_CASE						,				f'train_pseudo.{args.data_file_extension}'     )

				if args.evaluation_strategy != IntervalStrategy.NO.value:
						lowercase__            =      eval_result

						if best_iteration is None:
								lowercase__            =      new_iteration
								lowercase__            =      new_eval_result
						else:
								if new_eval_result - best_eval_result > args.early_stopping_threshold:
										lowercase__            =      new_iteration
										lowercase__            =      new_eval_result
										lowercase__            =      0
								else:
										if new_eval_result == best_eval_result:
												lowercase__            =      new_iteration
												lowercase__            =      new_eval_result
										early_stopping_patience_counter += 1

								if early_stopping_patience_counter >= args.early_stopping_patience:
										lowercase__            =      True

				progress_bar.update(1     )

				if should_training_stop:
						break

		if best_iteration is not None:
				# Save the best iteration
				logger.info('''Best iteration: %d'''						,				SCREAMING_SNAKE_CASE     )
				logger.info('''Best evaluation result: %s = %f'''						,				args.eval_metric						,				SCREAMING_SNAKE_CASE     )
				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						shutil.copy(
						    os.path.join(SCREAMING_SNAKE_CASE						,				f'eval_results_iter-{iteration}.json'     )						,				os.path.join(SCREAMING_SNAKE_CASE						,				'''eval_results_best-iteration.json'''     )						,				)
		else:
				# Assume that the last iteration is the best
				logger.info('''Best iteration: %d'''						,				args.max_selftrain_iterations - 1     )
				logger.info('''Best evaluation result: %s = %f'''						,				args.eval_metric						,				SCREAMING_SNAKE_CASE     )
				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						shutil.copy(
						    os.path.join(SCREAMING_SNAKE_CASE						,				f'eval_results_iter-{args.max_selftrain_iterations - 1}.json'     )						,				os.path.join(SCREAMING_SNAKE_CASE						,				'''eval_results_best-iteration.json'''     )						,				)






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,
			)


