
"""simple docstring"""





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()

UpperCamelCase_       :    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 				A_  (__a					, __a					, __a					, __a					, __a=False					, __a=True					):

					'''simple docstring'''





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

					A_								=    MODEL_CLASSES[model_type]

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

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

					if compare_with_pt_model:
										A_								=    tf_model(tf_model.dummy_inputs					, training=lowerCAmelCase__					)  # build the network

										A_								=    torch.load(lowerCAmelCase__					, map_location="cpu"					)
										A_								=    pt_model_class.from_pretrained(
										    pretrained_model_name_or_path=lowerCAmelCase__					, config=lowerCAmelCase__					, state_dict=lowerCAmelCase__					)

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

										A_								=    pto[0].numpy()
										A_								=    tfo[0].numpy()
										A_								=    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 				A_  (__a					, __a					, __a=None					, __a=None					, __a=False					, __a=False					, __a=False					, __a=False					, ):

					'''simple docstring'''





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

										A_								=    MODEL_CLASSES[model_type]

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

															if model_shortcut_name in aws_model_maps:
																				A_								=    cached_file(lowerCAmelCase__					, lowerCAmelCase__					, force_download=not use_cached_models					)
															else:
																				A_								=    model_shortcut_name

															if os.path.isfile(lowerCAmelCase__					):
																				A_								=    '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__":
		UpperCamelCase_       :    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.''')
		UpperCamelCase_       :    str						      =			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 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'						)

		lowerCamelCase_	:     int 						=      str(bin(lowerCAmelCase__						)						)
		binary_number += "0" * shift_amount
		return binary_number


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'						)

		lowerCamelCase_	:     Union[str, Any] 						=      str(bin(lowerCAmelCase__						)						)[2:]
		if shift_amount >= len(lowerCAmelCase__						):
				return "0b0"
		lowerCamelCase_	:     List[str] 						=      binary_number[: len(lowerCAmelCase__						) - shift_amount]
		return "0b" + shifted_binary_number


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		if number >= 0:  # Get binary representation of positive number
				lowerCamelCase_	:     List[Any] 						=      '0' + str(bin(lowerCAmelCase__						)						).strip('-'						)[2:]
		else:  # Get binary (2's complement) representation of negative number
				lowerCamelCase_	:     Any 						=      len(bin(lowerCAmelCase__						)[3:]						)  # Find 2's complement of number
				lowerCamelCase_	:     List[str] 						=      bin(abs(lowerCAmelCase__						) - (1 << binary_number_length)						)[3:]
				lowerCamelCase_	:     List[str] 						=      (
				    '1' + '0' * (binary_number_length - len(lowerCAmelCase__						)) + binary_number
				)

		if shift_amount >= len(lowerCAmelCase__						):
				return "0b" + binary_number[0] * len(lowerCAmelCase__						)
		return (
		    "0b"
		    + binary_number[0] * shift_amount
		    + binary_number[: len(lowerCAmelCase__						) - shift_amount]
		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
