

'''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()

lowerCAmelCase__    :							Dict					    =    {
    """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							(					__lowerCAmelCase		:   List[Any] ,				__lowerCAmelCase		:   Optional[Any] ,				__lowerCAmelCase		:   int ,				__lowerCAmelCase		:   Dict ,				__lowerCAmelCase		:   Tuple=False ,				__lowerCAmelCase		:   Optional[int]=True  ):

		"""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__    :							Optional[int]							    =	MODEL_CLASSES[model_type]

		# Initialise TF model
		if config_file in aws_config_map:
				snake_case__    :							Tuple							    =	cached_file(__a ,				__a ,				force_download=not use_cached_models  )
		snake_case__    :							Union[str, Any]							    =	config_class.from_json_file(__a  )
		snake_case__    :							str							    =	True
		snake_case__    :							Tuple							    =	True
		print(F"""Building TensorFlow model from configuration: {config}"""  )
		snake_case__    :							Dict							    =	model_class(__a  )

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

		if compare_with_pt_model:
				snake_case__    :							List[str]							    =	tf_model(tf_model.dummy_inputs ,				training=__a  )  # build the network

				snake_case__    :							Optional[int]							    =	torch.load(__a ,				map_location='''cpu'''  )
				snake_case__    :							Optional[Any]							    =	pt_model_class.from_pretrained(
				    pretrained_model_name_or_path=__a ,				config=__a ,				state_dict=__a  )

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

				snake_case__    :							Any							    =	pto[0].numpy()
				snake_case__    :							Optional[Any]							    =	tfo[0].numpy()
				snake_case__    :							List[str]							    =	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(__a ,				save_format='''h5'''  )




def 						_a							(					__lowerCAmelCase		:   Tuple ,				__lowerCAmelCase		:   Tuple ,				__lowerCAmelCase		:   Tuple=None ,				__lowerCAmelCase		:   Optional[int]=None ,				__lowerCAmelCase		:   int=False ,				__lowerCAmelCase		:   str=False ,				__lowerCAmelCase		:   Union[str, Any]=False ,				__lowerCAmelCase		:   Any=False ,				):

		"""simple docstring"""

		if args_model_type is None:
				snake_case__    :							List[str]							    =	list(MODEL_CLASSES.keys()  )
		else:
				snake_case__    :							Optional[int]							    =	[args_model_type]

		for j, model_type in enumerate(__a ,				start=1  ):
				print('''=''' * 1_00  )
				print(F""" Converting model type {j}/{len(__a  )}: {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()  )}."""  )

				snake_case__    :							Tuple							    =	MODEL_CLASSES[model_type]

				if model_shortcut_names_or_path is None:
						snake_case__    :							List[Any]							    =	list(aws_model_maps.keys()  )
				if config_shortcut_names_or_path is None:
						snake_case__    :							Union[str, Any]							    =	model_shortcut_names_or_path

				for i, (model_shortcut_name, config_shortcut_name) in enumerate(
				    zip(__a ,				__a  ) ,				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
								snake_case__    :							Any							    =	model_shortcut_name
						elif only_convert_finetuned_models:
								print(F"""    Skipping not finetuned checkpoint {model_shortcut_name}"""  )
								continue
						print(
						    F"""    Converting checkpoint {i}/{len(__a  )}: {model_shortcut_name} - model_type {model_type}"""  )
						print('''-''' * 1_00  )

						if config_shortcut_name in aws_config_map:
								snake_case__    :							str							    =	cached_file(__a ,				__a ,				force_download=not use_cached_models  )
						else:
								snake_case__    :							List[str]							    =	config_shortcut_name

						if model_shortcut_name in aws_model_maps:
								snake_case__    :							int							    =	cached_file(__a ,				__a ,				force_download=not use_cached_models  )
						else:
								snake_case__    :							int							    =	model_shortcut_name

						if os.path.isfile(__a  ):
								snake_case__    :							Tuple							    =	'converted_model'

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


if __name__ == "__main__":
					lowerCAmelCase__    :							Tuple					    =    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__    :							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,
					)








"""simple docstring"""







# Lint as: python3
import itertools
import os
import re


a_							=    re.compile(r"""([A-Z]+)([A-Z][a-z])""")
a_							=    re.compile(r"""([a-z\d])([A-Z])""")

a_							=    re.compile(r"""(?<!_)_(?!_)""")
a_							=    re.compile(r"""(_{2,})""")

a_							=    r"""^\w+(\.\w+)*$"""

a_							=    r"""<>:/\|?*"""

def     UpperCAmelCase_					(				__a   :			Optional[int]						):




			'''simple docstring'''



			_lowerCamelCase					:					str	       =    _uppercase_uppercase_re.sub(r'\1_\2'      ,							__a						)
			_lowerCamelCase					:					Tuple	       =    _lowercase_uppercase_re.sub(r'\1_\2'      ,							__a						)
			return name.lower()

def     UpperCAmelCase_					(				__a   :			Optional[int]						):




			'''simple docstring'''



			_lowerCamelCase					:					Dict	       =    _single_underscore_re.split(__a						)
			_lowerCamelCase					:					Tuple	       =    [_multiple_underscores_re.split(__a						) for n in name]
			return "".join(n.capitalize() for n in itertools.chain.from_iterable(__a						) if n != ''						)

def     UpperCAmelCase_					(				__a   :			List[Any]						):




			'''simple docstring'''



			if os.path.basename(__a						) != name:
						raise ValueError(f"Should be a dataset name, not a path: {name}"						)
			return camelcase_to_snakecase(__a						)

def     UpperCAmelCase_					(				__a   :			Union[str, Any]      ,							__a   :			Optional[int]						):




			'''simple docstring'''



			if os.path.basename(__a						) != name:
						raise ValueError(f"Should be a dataset name, not a path: {name}"						)
			if not re.match(_split_re      ,							__a						):
						raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'."						)
			return f"{filename_prefix_for_name(__a						)}-{split}"

def     UpperCAmelCase_					(				__a   :			Any      ,							__a   :			Union[str, Any]      ,							__a   :			List[Any]      ,							__a   :			List[str]=None						):




			'''simple docstring'''



			_lowerCamelCase					:					List[Any]	       =    filename_prefix_for_split(__a      ,							__a						)
			if filetype_suffix:
						prefix += f".{filetype_suffix}"
			_lowerCamelCase					:					List[str]	       =    os.path.join(__a      ,							__a						)
			return f"{filepath}*"



def     UpperCAmelCase_					(				__a   :			str      ,							__a   :			List[Any]      ,							__a   :			List[str]      ,							__a   :			Tuple=None      ,							__a   :			Tuple=None						):




			'''simple docstring'''



			_lowerCamelCase					:					Tuple	       =    filename_prefix_for_split(__a      ,							__a						)
			_lowerCamelCase					:					List[str]	       =    os.path.join(__a      ,							__a						)

			if shard_lengths:
						_lowerCamelCase					:					Union[str, Any]	       =    len(__a						)
						_lowerCamelCase					:					str	       =    [f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(__a						)]
						if filetype_suffix:
									_lowerCamelCase					:					int	       =    [filename + f".{filetype_suffix}" for filename in filenames]
						return filenames
			else:
						_lowerCamelCase					:					int	       =    prefix
						if filetype_suffix:
									filename += f".{filetype_suffix}"
						return [filename]





