




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        =    {
    "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_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Optional[Any]=False ,     UpperCamelCase__				:      str=True      )     ->       List[Any]:





				"""simple docstring"""


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

				__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    MODEL_CLASSES[model_type]

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

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

				if compare_with_pt_model:
								__lowerCamelCase		       =    tf_model(tf_model.dummy_inputs ,     training=UpperCamelCase__      )  # build the network

								__lowerCamelCase		       =    torch.load(UpperCamelCase__ ,     map_location='cpu'      )
								__lowerCamelCase		       =    pt_model_class.from_pretrained(
								    pretrained_model_name_or_path=UpperCamelCase__ ,     config=UpperCamelCase__ ,     state_dict=UpperCamelCase__      )

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

								__lowerCamelCase		       =    pto[0].numpy()
								__lowerCamelCase		       =    tfo[0].numpy()
								__lowerCamelCase		       =    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(UpperCamelCase__ ,     save_format='h5'      )







def 			lowerCamelCase_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      Union[str, Any] ,     UpperCamelCase__				:      Optional[int]=None ,     UpperCamelCase__				:      Any=None ,     UpperCamelCase__				:      Union[str, Any]=False ,     UpperCamelCase__				:      Tuple=False ,     UpperCamelCase__				:      Tuple=False ,     UpperCamelCase__				:      Tuple=False ,     )     ->       Optional[int]:





				"""simple docstring"""


				if args_model_type is None:
								__lowerCamelCase		       =    list(MODEL_CLASSES.keys()      )
				else:
								__lowerCamelCase		       =    [args_model_type]

				for j, model_type in enumerate(UpperCamelCase__ ,     start=1      ):
								print('=' * 100      )
								print(F""" Converting model type {j}/{len(UpperCamelCase__      )}: {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()      )}."""      )

								__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    MODEL_CLASSES[model_type]

								if model_shortcut_names_or_path is None:
												__lowerCamelCase		       =    list(aws_model_maps.keys()      )
								if config_shortcut_names_or_path is None:
												__lowerCamelCase		       =    model_shortcut_names_or_path

								for i, (model_shortcut_name, config_shortcut_name) in enumerate(
								    zip(UpperCamelCase__ ,     UpperCamelCase__      ) ,     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
																__lowerCamelCase		       =    model_shortcut_name
												elif only_convert_finetuned_models:
																print(F"""    Skipping not finetuned checkpoint {model_shortcut_name}"""      )
																continue
												print(
												    F"""    Converting checkpoint {i}/{len(UpperCamelCase__      )}: {model_shortcut_name} - model_type {model_type}"""      )
												print('-' * 100      )

												if config_shortcut_name in aws_config_map:
																__lowerCamelCase		       =    cached_file(UpperCamelCase__ ,     UpperCamelCase__ ,     force_download=not use_cached_models      )
												else:
																__lowerCamelCase		       =    config_shortcut_name

												if model_shortcut_name in aws_model_maps:
																__lowerCamelCase		       =    cached_file(UpperCamelCase__ ,     UpperCamelCase__ ,     force_download=not use_cached_models      )
												else:
																__lowerCamelCase		       =    model_shortcut_name

												if os.path.isfile(UpperCamelCase__      ):
																__lowerCamelCase		       =    'converted_model'

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


if __name__ == "__main__":
					__A        =    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        =    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 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''')     )
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      credit_card_number
		lowercase__            =      0
		lowercase__            =      len(SCREAMING_SNAKE_CASE     ) - 2
		for i in range(SCREAMING_SNAKE_CASE						,				-1						,				-2     ):
				#  double the value of every second digit
				lowercase__            =      int(cc_number[i]     )
				digit *= 2
				# If doubling of a number results in a two digit number
				# i.e greater than 9(e.g., 6 × 2 = 12),
				# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
				# to get a single digit number.
				if digit > 9:
						digit %= 10
						digit += 1
				lowercase__            =      cc_number[:i] + str(SCREAMING_SNAKE_CASE     ) + cc_number[i + 1 :]
				total += digit

		# Sum up the remaining digits
		for i in range(len(SCREAMING_SNAKE_CASE     ) - 1						,				-1						,				-2     ):
				total += int(cc_number[i]     )

		return total % 10 == 0
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      f'{credit_card_number} is an invalid credit card number because'
		if not credit_card_number.isdigit():
				print(f'{error_message} it has nonnumerical characters.'     )
				return False

		if not 13 <= len(SCREAMING_SNAKE_CASE     ) <= 16:
				print(f'{error_message} of its length.'     )
				return False

		if not validate_initial_digits(SCREAMING_SNAKE_CASE     ):
				print(f'{error_message} of its first two digits.'     )
				return False

		if not luhn_validation(SCREAMING_SNAKE_CASE     ):
				print(f'{error_message} it fails the Luhn check.'     )
				return False

		print(f'{credit_card_number} is a valid credit card number.'     )
		return True


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			validate_credit_card_number('4111111111111111')
			validate_credit_card_number('32323')


