'''simple docstring'''






import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a										=					'src/transformers'
__a										=					'docs/source/en/tasks'






def    __UpperCAmelCase			(  a_:       Union[str, Any],					a_:       List[Any],					a_:       Any							):
   with open(snake_case_,					"r",					encoding="utf-8",					newline="\n"							) as f:
      _UpperCAmelCase							:	int							=   f.readlines()
   # Find the start prompt.
   _UpperCAmelCase							:	Any							=   0
   while not lines[start_index].startswith(snake_case_							):
      start_index += 1
   start_index += 1

   _UpperCAmelCase							:	Optional[Any]							=   start_index
   while not lines[end_index].startswith(snake_case_							):
      end_index += 1
   end_index -= 1

   while len(lines[start_index]							) <= 1:
      start_index += 1
   while len(lines[end_index]							) <= 1:
      end_index -= 1
   end_index += 1
   return "".join(lines[start_index:end_index]							), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
__a										=					direct_transformers_import(TRANSFORMERS_PATH)

__a										=					{
    'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
__a										=					{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}






def    __UpperCAmelCase			(  a_:       Tuple							):
   _UpperCAmelCase							:	Optional[int]							=   TASK_GUIDE_TO_MODELS[task_guide]
   _UpperCAmelCase							:	Optional[Any]							=   SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(snake_case_,					set()							)
   _UpperCAmelCase							:	List[str]							=   {
       code: name
       for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
       if (code in model_maping_names or code in special_model_types)
   }
   return ", ".join([f"""[{name}](../model_doc/{code})""" for code, name in model_names.items()]							) + "\n"






def    __UpperCAmelCase			(  a_:       int,					a_:       Optional[int]=False							):
   _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							:	List[str]							=   _find_text_in_file(
       filename=os.path.join(snake_case_,					snake_case_							),					start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->",					end_prompt="<!--End of the generated tip-->",					)

   _UpperCAmelCase							:	List[str]							=   get_model_list_for_task(snake_case_							)

   if current_list != new_list:
      if overwrite:
         with open(os.path.join(snake_case_,					snake_case_							),					"w",					encoding="utf-8",					newline="\n"							) as f:
            f.writelines(lines[:start_index] + [new_list] + lines[end_index:]							)
      else:
         raise ValueError(
             f"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
             " to fix this."							)


if __name__ == "__main__":
     __a										=					argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     __a										=					parser.parse_args()

     for task_guide in TASK_GUIDE_TO_MODELS.keys():
          check_model_list_for_task(task_guide, args.fix_and_overwrite)






import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin

@require_jieba
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				List[str]							=    CpmAntTokenizer
      A_						:				Optional[int]							=    False


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         super().setUp()

         __snake_case							       =   [
             '''<d>''',
             '''</d>''',
             '''<s>''',
             '''</s>''',
             '''</_>''',
             '''<unk>''',
             '''<pad>''',
             '''</n>''',
             '''我''',
             '''是''',
             '''C''',
             '''P''',
             '''M''',
             '''A''',
             '''n''',
             '''t''',
         ]
         __snake_case							       =   os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES['''vocab_file''']      )
         with open(self.vocab_file				,			'''w'''				,			encoding='''utf-8'''      ) as vocab_writer:
            vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]      )      )


      @tooslow
      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   CpmAntTokenizer.from_pretrained('''openbmb/cpm-ant-10b'''      )
         __snake_case							       =   '''今天天气真好！'''
         __snake_case							       =   ['''今天''', '''天气''', '''真''', '''好''', '''！''']
         __snake_case							       =   tokenizer.tokenize(a__      )
         self.assertListEqual(a__				,			a__      )
         __snake_case							       =   '''今天天气真好！'''
         __snake_case							       =   [tokenizer.bos_token] + tokens

         __snake_case							       =   [6, 9802, 1_4962, 2082, 831, 244]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a__      )				,			a__      )

         __snake_case							       =   tokenizer.decode(a__      )
         self.assertEqual(a__				,			a__      )

