


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  lowerCAmelCase__(    snake_case__    ,    unittest.TestCase						):






    '''simple docstring'''





    A_  :     Optional[Any]			= RoFormerTokenizer
    A_  :     int			= RoFormerTokenizerFast
    A_  :     Dict			= True
    A_  :     Optional[Any]			= True





    def 					_lowerCamelCase (	self				:   Union[str, Any]	):





       '''simple docstring'''
       super().setUp()





    def 					_lowerCamelCase (	self				:   int       ,   **__snake_case				:   Any	):





       '''simple docstring'''
       return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''       ,   **__snake_case	)





    def 					_lowerCamelCase (	self				:   List[str]       ,   **__snake_case				:   str	):





       '''simple docstring'''
       return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''       ,   **__snake_case	)





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   '''永和服装饰品有限公司,今天天气非常好'''
       UpperCAmelCase_							:      List[str]						=   '''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
       return input_text, output_text





    def 					_lowerCamelCase (	self				:   List[Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   self.get_tokenizer()
       UpperCAmelCase_		,      UpperCAmelCase_							:      List[Any]						=   self.get_chinese_input_output_texts()
       UpperCAmelCase_							:      int						=   tokenizer.tokenize(__snake_case	)

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

       UpperCAmelCase_							:      Any						=   tokens + [tokenizer.unk_token]
       UpperCAmelCase_							:      Union[str, Any]						=   [22_943, 21_332, 34_431, 45_904, 117, 306, 1_231, 1_231, 2_653, 33_994, 1_266, 100]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case	)       ,   __snake_case	)





    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   self.get_rust_tokenizer()
       UpperCAmelCase_		,      UpperCAmelCase_							:      Union[str, Any]						=   self.get_chinese_input_output_texts()
       UpperCAmelCase_							:      List[str]						=   tokenizer.tokenize(__snake_case	)
       self.assertListEqual(__snake_case       ,   output_text.split()	)
       UpperCAmelCase_							:      str						=   tokens + [tokenizer.unk_token]
       UpperCAmelCase_							:      List[str]						=   [22_943, 21_332, 34_431, 45_904, 117, 306, 1_231, 1_231, 2_653, 33_994, 1_266, 100]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case	)       ,   __snake_case	)





    def 					_lowerCamelCase (	self				:   List[str]	):





       '''simple docstring'''
       pass





    def 					_lowerCamelCase (	self				:   Optional[Any]	):





       '''simple docstring'''
       pass





    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       pass



import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def      snake_case_						( __lowercase			,       __lowercase       ):
   # Load checkpoint
   UpperCAmelCase_							:      Tuple						=   torch.load(__lowercase			,       map_location='''cpu'''       )

   UpperCAmelCase_							:      Optional[int]						=   chkpt['''model''']

   # We have the base model one level deeper than the original XLM repository
   UpperCAmelCase_							:      str						=   {}
   for k, v in state_dict.items():
      if "pred_layer" in k:
         UpperCAmelCase_							:      Tuple						=   v
      else:
         UpperCAmelCase_							:      Union[str, Any]						=   v

   UpperCAmelCase_							:      int						=   chkpt['''params''']
   UpperCAmelCase_							:      Union[str, Any]						=   {n: v for n, v in config.items() if not isinstance(__lowercase			,       (torch.FloatTensor, numpy.ndarray)       )}

   UpperCAmelCase_							:      int						=   chkpt['''dico_word2id''']
   UpperCAmelCase_							:      List[Any]						=   {s + '''</w>''' if s.find('''@@'''       ) == -1 and i > 1_3 else s.replace('''@@'''			,       ''''''       ): i for s, i in vocab.items()}

   # Save pytorch-model
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + CONFIG_NAME
   UpperCAmelCase_							:      Dict						=   pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

   print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''       )
   torch.save(__lowercase			,       __lowercase       )

   print(F'''Save configuration file to {pytorch_config_dump_path}'''       )
   with open(__lowercase			,       '''w'''			,       encoding='''utf-8'''       ) as f:
      f.write(json.dumps(__lowercase			,       indent=2       ) + '''\n'''       )

   print(F'''Save vocab file to {pytorch_config_dump_path}'''       )
   with open(__lowercase			,       '''w'''			,       encoding='''utf-8'''       ) as f:
      f.write(json.dumps(__lowercase			,       indent=2       ) + '''\n'''       )


if __name__ == "__main__":
       __UpperCamelCase				:  str      					=							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--xlm_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       __UpperCamelCase				:  Dict      					=							parser.parse_args()
       convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)