






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 						a    (					lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_=False	,     lowerCamelCase_=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__     				=    MODEL_CLASSES[model_type]

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

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

				if compare_with_pt_model:
								lowercase__     				=    tf_model(tf_model.dummy_inputs	,     training=lowerCamelCase_       )  # build the network

								lowercase__     				=    torch.load(lowerCamelCase_	,     map_location='''cpu'''       )
								lowercase__     				=    pt_model_class.from_pretrained(
								    pretrained_model_name_or_path=lowerCamelCase_	,     config=lowerCamelCase_	,     state_dict=lowerCamelCase_       )

								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(lowerCamelCase_	,     save_format='''h5'''       )






def 						a    (					lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_=None	,     lowerCamelCase_=None	,     lowerCamelCase_=False	,     lowerCamelCase_=False	,     lowerCamelCase_=False	,     lowerCamelCase_=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(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()       )}."""       )

								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(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
																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(lowerCamelCase_       )}: {model_shortcut_name} - model_type {model_type}"""       )
												print('''-''' * 100       )

												if config_shortcut_name in aws_config_map:
																lowercase__     				=    cached_file(lowerCamelCase_	,     lowerCamelCase_	,     force_download=not use_cached_models       )
												else:
																lowercase__     				=    config_shortcut_name

												if model_shortcut_name in aws_model_maps:
																lowercase__     				=    cached_file(lowerCamelCase_	,     lowerCamelCase_	,     force_download=not use_cached_models       )
												else:
																lowercase__     				=    model_shortcut_name

												if os.path.isfile(lowerCamelCase_       ):
																lowercase__     				=    "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[str]         =				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__			:    List[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,
							)












from functools import reduce

A__			:    Union[str, Any]         =				(
    '73167176531330624919225119674426574742355349194934'
    '96983520312774506326239578318016984801869478851843'
    '85861560789112949495459501737958331952853208805511'
    '12540698747158523863050715693290963295227443043557'
    '66896648950445244523161731856403098711121722383113'
    '62229893423380308135336276614282806444486645238749'
    '30358907296290491560440772390713810515859307960866'
    '70172427121883998797908792274921901699720888093776'
    '65727333001053367881220235421809751254540594752243'
    '52584907711670556013604839586446706324415722155397'
    '53697817977846174064955149290862569321978468622482'
    '83972241375657056057490261407972968652414535100474'
    '82166370484403199890008895243450658541227588666881'
    '16427171479924442928230863465674813919123162824586'
    '17866458359124566529476545682848912883142607690042'
    '24219022671055626321111109370544217506941658960408'
    '07198403850962455444362981230987879927244284909188'
    '84580156166097919133875499200524063689912560717606'
    '05886116467109405077541002256983155200055935729725'
    '71636269561882670428252483600823257530420752963450'
)






def 						a    (		lowerCamelCase_ = N       ):






				'''simple docstring'''
				return max(
				    # mypy cannot properly interpret reduce
				    int(reduce(lambda lowerCamelCase_	,     lowerCamelCase_       : str(int(lowerCamelCase_       ) * int(lowerCamelCase_       )       )	,     n[i : i + 13]       )       )
				    for i in range(len(lowerCamelCase_       ) - 12       )       )


if __name__ == "__main__":
							print(F"{solution() = }")




