
'''simple docstring'''
import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_rjieba
@require_tokenizers
class  __A				( _SCREAMING_SNAKE_CASE    ,						unittest.TestCase			):
 lowerCamelCase				       =RoFormerTokenizer
 lowerCamelCase				       =RoFormerTokenizerFast
 lowerCamelCase				       =True
 lowerCamelCase				       =True
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   super().setUp()
 def 		lowercase_(   self			:    Dict				,   **lowerCamelCase			:    Optional[Any]       ):



   """simple docstring"""





   return self.tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base"""				,   **lowerCamelCase       )
 def 		lowercase_(   self			:    Optional[Any]				,   **lowerCamelCase			:    List[Any]       ):



   """simple docstring"""





   return self.rust_tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base"""				,   **lowerCamelCase       )
 def 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   __A		:     Tuple												=		"""永和服装饰品有限公司,今天天气非常好"""
   __A		:     List[Any]												=		"""永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"""
   return input_text, output_text
 def 		lowercase_(   self			:    List[Any]       ):



   """simple docstring"""





   __A		:     Dict												=		self.get_tokenizer()
   __A		:     List[Any]												=		self.get_chinese_input_output_texts()
   __A		:     Tuple												=		tokenizer.tokenize(lowerCamelCase       )

   self.assertListEqual(lowerCamelCase				,   output_text.split()       )

   __A		:     Dict												=		tokens + [tokenizer.unk_token]
   __A		:     str												=		[2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase       )				,   lowerCamelCase       )
 def 		lowercase_(   self			:    Optional[int]       ):



   """simple docstring"""





   __A		:     int												=		self.get_rust_tokenizer()
   __A		:     str												=		self.get_chinese_input_output_texts()
   __A		:     List[str]												=		tokenizer.tokenize(lowerCamelCase       )
   self.assertListEqual(lowerCamelCase				,   output_text.split()       )
   __A		:     Optional[Any]												=		tokens + [tokenizer.unk_token]
   __A		:     Any												=		[2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase       )				,   lowerCamelCase       )
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   pass
 def 		lowercase_(   self			:    Optional[int]       ):



   """simple docstring"""





   pass






 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   pass





'''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        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       List[Any]:





  """simple docstring"""

  __A		:     Optional[int]												=		RemBertConfig.from_json_file(__SCREAMING_SNAKE_CASE )
  print("""Building PyTorch model from configuration: {}""".format(str(__SCREAMING_SNAKE_CASE ) ) )
  __A		:     str												=		RemBertModel(__SCREAMING_SNAKE_CASE )

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

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


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__   :		Dict 		=parser.parse_args()
     convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



