


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()

A							:					Optional[Any]     						=     {
    '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 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase=False      ,	_lowerCAmelCase=True )						->	Dict:


						'''simple docstring'''






						if model_type not in MODEL_CLASSES:
												raise ValueError(F'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys() )}.''' )

						__snake_case				=		MODEL_CLASSES[model_type]

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

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

						if compare_with_pt_model:
												__snake_case				=		tf_model(tf_model.dummy_inputs      ,	training=_lowerCAmelCase )  # build the network

												__snake_case				=		torch.load(_lowerCAmelCase      ,	map_location="cpu" )
												__snake_case				=		pt_model_class.from_pretrained(
												    pretrained_model_name_or_path=_lowerCAmelCase      ,	config=_lowerCAmelCase      ,	state_dict=_lowerCAmelCase )

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

												__snake_case				=		pto[0].numpy()
												__snake_case				=		tfo[0].numpy()
												__snake_case				=		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 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase=None      ,	_lowerCAmelCase=None      ,	_lowerCAmelCase=False      ,	_lowerCAmelCase=False      ,	_lowerCAmelCase=False      ,	_lowerCAmelCase=False      ,	)						->	Tuple:


						'''simple docstring'''






						if args_model_type is None:
												__snake_case				=		list(MODEL_CLASSES.keys() )
						else:
												__snake_case				=		[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() )}.''' )

												__snake_case				=		MODEL_CLASSES[model_type]

												if model_shortcut_names_or_path is None:
																		__snake_case				=		list(aws_model_maps.keys() )
												if config_shortcut_names_or_path is None:
																		__snake_case				=		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
																								__snake_case				=		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:
																								__snake_case				=		cached_file(_lowerCAmelCase      ,	_lowerCAmelCase      ,	force_download=not use_cached_models )
																		else:
																								__snake_case				=		config_shortcut_name

																		if model_shortcut_name in aws_model_maps:
																								__snake_case				=		cached_file(_lowerCAmelCase      ,	_lowerCAmelCase      ,	force_download=not use_cached_models )
																		else:
																								__snake_case				=		model_shortcut_name

																		if os.path.isfile(_lowerCAmelCase ):
																								__snake_case				=		'''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__":
	A							:					List[Any]     						=     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.')
	A							:					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,
	)









def 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






						__snake_case				=		set()

						# Replace all the whitespace in our sentence
						__snake_case				=		input_str.replace(" "      ,	"" )
						for alpha in input_str:
												if "a" <= alpha.lower() <= "z":
																		frequency.add(alpha.lower() )
						return len(_lowerCAmelCase ) == 26





def 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






						__snake_case				=		[False] * 26
						for char in input_str:
												if char.islower():
																		__snake_case				=		True
												elif char.isupper():
																		__snake_case				=		True
						return all(_lowerCAmelCase )





def 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






						return len({char for char in input_str.lower() if char.isalpha()} ) == 26





def 	_lowerCAmelCase       (    )						->	None:


						'''simple docstring'''






						from timeit import timeit

						__snake_case				=		"from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
						print(timeit("is_pangram()"      ,	setup=_lowerCAmelCase ) )
						print(timeit("is_pangram_faster()"      ,	setup=_lowerCAmelCase ) )
						print(timeit("is_pangram_fastest()"      ,	setup=_lowerCAmelCase ) )
						# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
						# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()





