




'''simple docstring'''






import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()

def       __lowerCAmelCase (   snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__						):
			# Initialise PyTorch model
			__UpperCamelCase				:      Tuple									=       BertConfig.from_pretrained(
			    "bert-large-cased" ,				vocab_size=snake_case__ ,				max_position_embeddings=512 ,				is_decoder=snake_case__ ,				add_cross_attention=snake_case__ ,				)
			__UpperCamelCase				:      str									=       bert_config.to_dict()
			del bert_config_dict["type_vocab_size"]
			__UpperCamelCase				:      int									=       BertGenerationConfig(**snake_case__						)
			if is_encoder:
						__UpperCamelCase				:      Optional[Any]									=       BertGenerationEncoder(snake_case__						)
			else:
						__UpperCamelCase				:      Any									=       BertGenerationDecoder(snake_case__						)
			print(F"Building PyTorch model from configuration: {config}"						)

			# Load weights from tf checkpoint
			load_tf_weights_in_bert_generation(
			    snake_case__ ,				snake_case__ ,				model_class="bert" ,				is_encoder_named_decoder=snake_case__ ,				is_encoder=snake_case__ ,				)

			# Save pytorch-model
			print(F"Save PyTorch model and config to {pytorch_dump_path}"						)
			model.save_pretrained(snake_case__						)


if __name__ == "__main__":
				_lowerCAmelCase				     = argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--tf_hub_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
				)
				parser.add_argument(
				    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
				)
				parser.add_argument(
				    '''--is_encoder_named_decoder''',
				    action='''store_true''',
				    help='''If decoder has to be renamed to encoder in PyTorch model.''',
				)
				parser.add_argument('''--is_encoder''', action='''store_true''', help='''If model is an encoder.''')
				parser.add_argument('''--vocab_size''', default=50358, type=int, help='''Vocab size of model''')
				_lowerCAmelCase				     = parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.tf_hub_path,
				    args.pytorch_dump_path,
				    args.is_encoder_named_decoder,
				    args.vocab_size,
				    is_encoder=args.is_encoder,
				)









'''simple docstring'''






from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


_lowerCAmelCase				     = logging.get_logger(__name__)

_lowerCAmelCase				     = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCAmelCase				     = {
    '''vocab_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/vocab.json'''},
    '''merges_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/merges.txt'''},
    '''tokenizer_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'''},
}

_lowerCAmelCase				     = {
    '''openai-gpt''': 512,
}



class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    VOCAB_FILES_NAMES
				A				     =    PRETRAINED_VOCAB_FILES_MAP
				A				     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A				     =    ["input_ids", "attention_mask"]
				A				     =    OpenAIGPTTokenizer
				def __init__(self			,  _UpperCAmelCase=None			,  _UpperCAmelCase=None			,  _UpperCAmelCase=None			,  _UpperCAmelCase="<unk>"			,  **_UpperCAmelCase      )     ->	str:
							super().__init__(_UpperCAmelCase			,  _UpperCAmelCase			,  tokenizer_file=_UpperCAmelCase			,  unk_token=_UpperCAmelCase			,  **_UpperCAmelCase      )
				@property
				def 	a_	(self      )     ->	Dict:
							return True





				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase = None      )     ->	Tuple[str]:
							__UpperCamelCase				:      Optional[Any]									=       self._tokenizer.model.save(_UpperCAmelCase			,  name=_UpperCAmelCase      )
							return tuple(_UpperCAmelCase      )



