

'''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_							=					{
    """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 (      __magic_name__	:    Tuple      ,   __magic_name__	:    int      ,   __magic_name__	:    Tuple      ,   __magic_name__	:    List[Any]      ,   __magic_name__	:    Dict=False      ,   __magic_name__	:    Dict=True	)							->     Dict:
						if model_type not in MODEL_CLASSES:
												raise ValueError(f'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()	)}.'''	)

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

						# Initialise TF model
						if config_file in aws_config_map:
												lowercase   :       Any								=cached_file(__magic_name__      ,   __magic_name__      ,   force_download=not use_cached_models	)
						lowercase   :       Optional[int]								=config_class.from_json_file(__magic_name__	)
						lowercase   :       Optional[Any]								=True
						lowercase   :       str								=True
						print(f'''Building TensorFlow model from configuration: {config}'''	)
						lowercase   :       Dict								=model_class(__magic_name__	)

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

						if compare_with_pt_model:
												lowercase   :       int								=tf_model(tf_model.dummy_inputs      ,   training=__magic_name__	)  # build the network

												lowercase   :       int								=torch.load(__magic_name__      ,   map_location='''cpu'''	)
												lowercase   :       Any								=pt_model_class.from_pretrained(
												    pretrained_model_name_or_path=__magic_name__      ,   config=__magic_name__      ,   state_dict=__magic_name__	)

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

												lowercase   :       int								=pto[0].numpy()
												lowercase   :       int								=tfo[0].numpy()
												lowercase   :       Dict								=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(__magic_name__      ,   save_format='''h5'''	)






def 							_lowerCAmelCase (      __magic_name__	:    int      ,   __magic_name__	:    List[str]      ,   __magic_name__	:    Any=None      ,   __magic_name__	:    Union[str, Any]=None      ,   __magic_name__	:    List[str]=False      ,   __magic_name__	:    Union[str, Any]=False      ,   __magic_name__	:    Tuple=False      ,   __magic_name__	:    str=False      ,   )							->     Tuple:
						if args_model_type is None:
												lowercase   :       Union[str, Any]								=list(MODEL_CLASSES.keys()	)
						else:
												lowercase   :       Optional[Any]								=[args_model_type]

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

												lowercase		,  lowercase		,  lowercase		,  lowercase		,  lowercase   :       str								=MODEL_CLASSES[model_type]

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

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

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

																		if model_shortcut_name in aws_model_maps:
																								lowercase   :       Optional[Any]								=cached_file(__magic_name__      ,   __magic_name__      ,   force_download=not use_cached_models	)
																		else:
																								lowercase   :       Optional[int]								=model_shortcut_name

																		if os.path.isfile(__magic_name__	):
																								lowercase   :       Tuple								='''converted_model'''

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


if __name__ == "__main__":
							UpperCamelCase_							=					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_							=					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,
							)








'''simple docstring'''





from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments






def 							_lowerCAmelCase (      )							->     List[Any]:
						lowercase   :       Tuple								=HfArgumentParser(__magic_name__	)
						lowercase   :       Union[str, Any]								=parser.parse_args_into_dataclasses()[0]
						lowercase   :       Any								=TensorFlowBenchmark(args=__magic_name__	)
						try:
												lowercase   :       List[Any]								=parser.parse_args_into_dataclasses()[0]
						except ValueError as e:
												lowercase   :       List[Any]								='''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
												lowercase   :       Any								=''' '''.join(str(__magic_name__	).split(''' '''	)[:-1]	)
												lowercase   :       Optional[Any]								=''''''
												lowercase   :       List[str]								=eval(str(__magic_name__	).split(''' '''	)[-1]	)
												lowercase   :       Optional[Any]								=[]
												for arg in depreciated_args:
																		# arg[2:] removes '--'
																		if arg[2:] in TensorFlowBenchmark.deprecated_args:
																								# arg[5:] removes '--no_'
																								full_error_msg += arg_error_msg.format(arg[5:]	)
																		else:
																								wrong_args.append(__magic_name__	)
												if len(__magic_name__	) > 0:
																		lowercase   :       int								=full_error_msg + begin_error_msg + str(__magic_name__	)
												raise ValueError(__magic_name__	)
						benchmark.run()


if __name__ == "__main__":
							main()





