






"""simple docstring"""





from typing import Optional, Tuple

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


UpperCamelCase_						:					Optional[Any]      				=			logging.get_logger(__name__)

UpperCamelCase_						:					str      				=			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase_						:					Tuple      				=			{
    """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"""},
}

UpperCamelCase_						:					Union[str, Any]      				=			{
    """openai-gpt""": 512,
}


class   _lowercase			(  _snake_case    ):
      _a :  int										= VOCAB_FILES_NAMES
      _a :  Tuple										= PRETRAINED_VOCAB_FILES_MAP
      _a :  Dict										= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _a :  Dict										= ['''input_ids''', '''attention_mask''']
      _a :  Optional[Any]										= OpenAIGPTTokenizer


      def __init__(     self					:						Optional[int]				, a					:						Any=None				, a					:						Any=None				, a					:						List[str]=None				, a					:						str="<unk>"				, **a					:						List[str]    ):





            """simple docstring"""







            super().__init__(lowercase__				, lowercase__				, tokenizer_file=lowercase__				, unk_token=lowercase__				, **lowercase__    )


      @property
      def _UpperCamelCase    (     self					:						Dict    ):





            """simple docstring"""







            return True







      def _UpperCamelCase    (     self					:						List[Any]				, a					:						str				, a					:						Optional[str] = None    ):





            """simple docstring"""







            __snake_case   :     Dict	      =self._tokenizer.model.save(lowercase__				, name=lowercase__    )
            return tuple(lowercase__    )







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       RoFormerConfig.from_json_file(_UpperCAmelCase				)
					print(f"""Building PyTorch model from configuration: {config}"""				)
					lowercase						   =       RoFormerForMaskedLM(_UpperCAmelCase				)

					# Load weights from tf checkpoint
					load_tf_weights_in_roformer(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				)

					# Save pytorch-model
					print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
					torch.save(model.state_dict()       ,			_UpperCAmelCase       ,			_use_new_zipfile_serialization=_UpperCAmelCase				)


if __name__ == "__main__":
		__magic_name__       =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--bert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained BERT model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		__magic_name__       =     parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






