





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
snake_case_				=			'src/transformers'
snake_case_				=			'docs/source/en/tasks'






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

   __snake_case							       =   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.
snake_case_				=			direct_transformers_import(TRANSFORMERS_PATH)

snake_case_				=			{
    '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`).
snake_case_				=			{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}






def lowerCamelCase__    (							snake_case_	:  Tuple       )      ->				Any:
   __snake_case							       =   TASK_GUIDE_TO_MODELS[task_guide]
   __snake_case							       =   SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(snake_case_       ,      set()       )
   __snake_case							       =   {
       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 lowerCamelCase__    (							snake_case_	:  int       ,      snake_case_	:  Optional[int]=False       )      ->				Optional[int]:
   __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   _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-->'''       ,      )

   __snake_case							       =   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__":
    snake_case_				=			argparse.ArgumentParser()
    parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
    snake_case_				=			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.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


snake_case_				=			get_tests_dir('fixtures/test_sentencepiece_bpe.model')

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				List[Any]							=    BartphoTokenizer
      A_						:				List[str]							=    False
      A_						:				Optional[Any]							=    True


      def    a (self       :      Tuple      ):





         """simple docstring"""



         super().setUp()

         __snake_case							       =   ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
         __snake_case							       =   dict(zip(a__				,			range(len(a__      )      )      )      )
         __snake_case							       =   {'''unk_token''': '''<unk>'''}

         __snake_case							       =   os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES['''monolingual_vocab_file''']      )
         with open(self.monolingual_vocab_file				,			'''w'''				,			encoding='''utf-8'''      ) as fp:
            for token in vocab_tokens:
               fp.write(f"""{token} {vocab_tokens[token]}\n"""      )

         __snake_case							       =   BartphoTokenizer(a__				,			self.monolingual_vocab_file				,			**self.special_tokens_map      )
         tokenizer.save_pretrained(self.tmpdirname      )


      def    a (self       :      str				,			**a__       :      str      ):





         """simple docstring"""



         kwargs.update(self.special_tokens_map      )
         return BartphoTokenizer.from_pretrained(self.tmpdirname				,			**a__      )


      def    a (self       :      str				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   '''This is a là test'''
         __snake_case							       =   '''This is a<unk><unk> test'''
         return input_text, output_text


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   BartphoTokenizer(a__				,			self.monolingual_vocab_file				,			**self.special_tokens_map      )
         __snake_case							       =   '''This is a là test'''
         __snake_case							       =   '''▁This ▁is ▁a ▁l à ▁t est'''.split()
         __snake_case							       =   tokenizer.tokenize(a__      )
         self.assertListEqual(a__				,			a__      )

         __snake_case							       =   tokens + [tokenizer.unk_token]
         __snake_case							       =   [4, 5, 6, 3, 3, 7, 8, 3]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a__      )				,			a__      )

