


'''simple docstring'''



import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowercase	:						int       =	logging.get_logger(__name__)

__lowercase	:						List[str]       =	{'vocab_file': 'vocab.json'}

__lowercase	:						List[Any]       =	{
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

__lowercase	:						Tuple       =	{'mgp-str': 27}





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    VOCAB_FILES_NAMES
	A_				=    PRETRAINED_VOCAB_FILES_MAP
	A_				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




	def __init__(   self						,				__a						,				__a="[GO]"						,				__a="[GO]"						,				__a="[s]"						,				__a="[GO]"						,				**__a							):

						'''simple docstring'''





						super().__init__(
						    unk_token=__a						,				bos_token=__a						,				eos_token=__a						,				pad_token=__a						,				**__a						,				)

						with open(__a						,				encoding='utf-8'							) as vocab_handle:
											__a			:       Dict					    =  json.load(__a							)
						__a			:       Union[str, Any]					    =  {v: k for k, v in self.vocab.items()}




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return len(self.vocab							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return dict(self.vocab						,				**self.added_tokens_encoder							)




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						__a			:       str					    =  []
						for s in text:
											char_tokens.extend(__a							)
						return char_tokens




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						return self.vocab.get(__a						,				self.vocab.get(self.unk_token							)							)




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						return self.decoder.get(__a							)





	def       __UpperCAmelCase							(   self						,				__a						,				__a = None							):

						'''simple docstring'''





						if not os.path.isdir(__a							):
											logger.error('Vocabulary path ({}) should be a directory'.format(__a							)							)
											return
						__a			:       Any					    =  os.path.join(
						    __a						,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

						with open(__a						,				'w'						,				encoding='utf-8'							) as f:
											f.write(json.dumps(self.vocab						,				indent=2						,				sort_keys=__a						,				ensure_ascii=__a							) + '\n'							)

						return (vocab_file,)








'''simple docstring'''






import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Union[str, Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = RemBertConfig.from_json_file(lowerCAmelCase_		)
       print("""Building PyTorch model from configuration: {}""".format(str(lowerCAmelCase_		)		)		)
       _UpperCAmelCase  :						Any			     = RemBertModel(lowerCAmelCase_		)

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)

       # Save pytorch-model
       print("""Save PyTorch model to {}""".format(lowerCAmelCase_		)		)
       torch.save(model.state_dict()       ,   lowerCAmelCase_		)


if __name__ == "__main__":
   A_							: Optional[Any]						 =	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(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT 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."""
   )
   A_							: Any						 =	parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



