


from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml





class 					A							(    yaml.SafeLoader					):


   def       lowercase_				(self					:						Tuple	,  __UpperCAmelCase					:						str		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  [self.constructed_objects[key_node] for key_node, _ in node.value]
         UpperCAmelCase__								=  [tuple(__UpperCAmelCase		) if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		) else key for key in keys]
         UpperCAmelCase__								=  Counter(__UpperCAmelCase		)
         UpperCAmelCase__								=  [key for key in counter if counter[key] > 1]
         if duplicate_keys:
               raise TypeError(f"""Got duplicate yaml keys: {duplicate_keys}"""		)





   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						Any=False		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  super().construct_mapping(__UpperCAmelCase	,  deep=__UpperCAmelCase		)
         self._check_no_duplicates_on_constructed_node(__UpperCAmelCase		)
         return mapping




def        lowerCAmelCase_			(					__A							)					->    Tuple[Optional[str], str]:







      '''simple docstring'''


      UpperCAmelCase__								=  list(readme_content.splitlines()							)
      if full_content and full_content[0] == "---" and "---" in full_content[1:]:
            UpperCAmelCase__								=  full_content[1:].index("---"							) + 1
            UpperCAmelCase__								=  "\n".join(full_content[1:sep_idx]							)
            return yamlblock, "\n".join(full_content[sep_idx + 1 :]							)

      return None, "\n".join(__A							)





class 					A							(    UpperCAmelCase_					):
   # class attributes
   __UpperCAmelCase			:			Optional[Any]              =      {'train_eval_index'}  # train-eval-index in the YAML metadata


   @classmethod
   def       lowercase_				(cls					:						List[str]	,  __UpperCAmelCase					:						Path		)						->    "DatasetMetadata":
         """simple docstring"""



         with open(__UpperCAmelCase	,  encoding="utf-8"		) as readme_file:
               UpperCAmelCase__		,				UpperCAmelCase__								=  _split_yaml_from_readme(readme_file.read()		)
         if yaml_string is not None:
               return cls.from_yaml_string(__UpperCAmelCase		)
         else:
               return cls()


   def       lowercase_				(self					:						int	,  __UpperCAmelCase					:						Path		)						->    str:
         """simple docstring"""



         if path.exists():
               with open(__UpperCAmelCase	,  encoding="utf-8"		) as readme_file:
                     UpperCAmelCase__								=  readme_file.read()
         else:
               UpperCAmelCase__								=  None
         UpperCAmelCase__								=  self._to_readme(__UpperCAmelCase		)
         with open(__UpperCAmelCase	,  "w"	,  encoding="utf-8"		) as readme_file:
               readme_file.write(__UpperCAmelCase		)


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						Optional[str] = None		)						->    str:
         """simple docstring"""



         if readme_content is not None:
               UpperCAmelCase__		,				UpperCAmelCase__								=  _split_yaml_from_readme(__UpperCAmelCase		)
               UpperCAmelCase__								=  "---\n" + self.to_yaml_string() + "---\n" + content
         else:
               UpperCAmelCase__								=  "---\n" + self.to_yaml_string() + "---\n"
         return full_content


   @classmethod
   def       lowercase_				(cls					:						Optional[int]	,  __UpperCAmelCase					:						str		)						->    "DatasetMetadata":
         """simple docstring"""



         UpperCAmelCase__								=  yaml.load(__UpperCAmelCase	,  Loader=_NoDuplicateSafeLoader		) or {}

         # Convert the YAML keys to DatasetMetadata fields
         UpperCAmelCase__								=  {
             (key.replace("-"	,  "_"		) if key.replace("-"	,  "_"		) in cls._FIELDS_WITH_DASHES else key): value
             for key, value in metadata_dict.items()
         }
         return cls(**__UpperCAmelCase		)





   def       lowercase_				(self					:						List[str]		)						->    str:
         """simple docstring"""



         return yaml.safe_dump(
             {
                 (key.replace("_"	,  "-"		) if key in self._FIELDS_WITH_DASHES else key): value
                 for key, value in self.items()
             }	,  sort_keys=__UpperCAmelCase	,  allow_unicode=__UpperCAmelCase	,  encoding="utf-8"	,  ).decode("utf-8"		)




UpperCamelCase__    							= {
    'image-classification': [],
    'translation': [],
    'image-segmentation': [],
    'fill-mask': [],
    'automatic-speech-recognition': [],
    'token-classification': [],
    'sentence-similarity': [],
    'audio-classification': [],
    'question-answering': [],
    'summarization': [],
    'zero-shot-classification': [],
    'table-to-text': [],
    'feature-extraction': [],
    'other': [],
    'multiple-choice': [],
    'text-classification': [],
    'text-to-image': [],
    'text2text-generation': [],
    'zero-shot-image-classification': [],
    'tabular-classification': [],
    'tabular-regression': [],
    'image-to-image': [],
    'tabular-to-text': [],
    'unconditional-image-generation': [],
    'text-retrieval': [],
    'text-to-speech': [],
    'object-detection': [],
    'audio-to-audio': [],
    'text-generation': [],
    'conversational': [],
    'table-question-answering': [],
    'visual-question-answering': [],
    'image-to-text': [],
    'reinforcement-learning': [],
    'voice-activity-detection': [],
    'time-series-forecasting': [],
    'document-question-answering': [],
}


if __name__ == "__main__":
      from argparse import ArgumentParser

      UpperCamelCase__    							= ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
      ap.add_argument('readme_filepath')
      UpperCamelCase__    							= ap.parse_args()

      UpperCamelCase__    							= Path(args.readme_filepath)
      UpperCamelCase__    							= DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_pegasus import PegasusTokenizer
else:
      UpperCamelCase__    							= None


UpperCamelCase__    							= logging.get_logger(__name__)


UpperCamelCase__    							= '▁'

UpperCamelCase__    							= {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__    							= {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

UpperCamelCase__    							= {
    'google/pegasus-xsum': 5_1_2,
}





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			str              =      VOCAB_FILES_NAMES
   __UpperCAmelCase			:			Any              =      PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase			:			List[Any]              =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase			:			Union[str, Any]              =      PegasusTokenizer
   __UpperCAmelCase			:			Any              =      ['input_ids', 'attention_mask']


   def __init__(self					:						Optional[int]	,  __UpperCAmelCase					:						List[str]=None	,  __UpperCAmelCase					:						Any=None	,  __UpperCAmelCase					:						Union[str, Any]="<pad>"	,  __UpperCAmelCase					:						List[str]="</s>"	,  __UpperCAmelCase					:						Union[str, Any]="<unk>"	,  __UpperCAmelCase					:						int="<mask_2>"	,  __UpperCAmelCase					:						Optional[Any]="<mask_1>"	,  __UpperCAmelCase					:						Union[str, Any]=None	,  __UpperCAmelCase					:						str=1_0_3	,  **__UpperCAmelCase					:						str	,  )						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  offset

         if additional_special_tokens is not None:
               if not isinstance(__UpperCAmelCase	,  __UpperCAmelCase		):
                     raise TypeError(
                         f"""additional_special_tokens should be of type {type(__UpperCAmelCase		)}, but is"""
                         f""" {type(__UpperCAmelCase		)}"""		)

               UpperCAmelCase__								=  (
                   ([mask_token_sent] + additional_special_tokens)
                   if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                   else additional_special_tokens
               )
               # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
               additional_special_tokens_extended += [
                   f"""<unk_{i}>""" for i in range(len(__UpperCAmelCase		)	,  self.offset - 1		)
               ]

               if len(set(__UpperCAmelCase		)		) != len(__UpperCAmelCase		):
                     raise ValueError(
                         "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                         f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""		)
               UpperCAmelCase__								=  additional_special_tokens_extended
         else:
               UpperCAmelCase__								=  [mask_token_sent] if mask_token_sent is not None else []
               additional_special_tokens += [f"""<unk_{i}>""" for i in range(2	,  self.offset		)]

         super().__init__(
             __UpperCAmelCase	,  tokenizer_file=__UpperCAmelCase	,  pad_token=__UpperCAmelCase	,  eos_token=__UpperCAmelCase	,  unk_token=__UpperCAmelCase	,  mask_token=__UpperCAmelCase	,  mask_token_sent=__UpperCAmelCase	,  offset=__UpperCAmelCase	,  additional_special_tokens=__UpperCAmelCase	,  **__UpperCAmelCase	,  )
         UpperCAmelCase__								=  vocab_file
         UpperCAmelCase__								=  False if not self.vocab_file else True


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Tuple		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  set(self.all_special_ids		)  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id		)  # <unk> is only sometimes special

         if all_special_ids != set(range(len(self.additional_special_tokens		) + 3		)		):
               raise ValueError(
                   "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
                   f""" {len(self.additional_special_tokens		)} additional_special_tokens, but got {all_special_ids}"""		)

         return [1 if x in all_special_ids else 0 for x in seq]


   def       lowercase_				(self					:						Union[str, Any]	,  __UpperCAmelCase					:						List	,  __UpperCAmelCase					:						Optional[List] = None	,  __UpperCAmelCase					:						bool = False		)						->    List[int]:
         """simple docstring"""



         if already_has_special_tokens:
               return self._special_token_mask(__UpperCAmelCase		)
         elif token_ids_a is None:
               return self._special_token_mask(__UpperCAmelCase		) + [1]
         else:
               return self._special_token_mask(token_ids_a + token_ids_a		) + [1]


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						List[str]	,  __UpperCAmelCase					:						Optional[Any]=None		)						->    List[int]:
         """simple docstring"""



         if token_ids_a is None:
               return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]





   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						Optional[str] = None		)						->    Tuple[str]:
         """simple docstring"""



         if not self.can_save_slow_tokenizer:
               raise ValueError(
                   "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                   "tokenizer."		)

         if not os.path.isdir(__UpperCAmelCase		):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
               return
         UpperCAmelCase__								=  os.path.join(
             __UpperCAmelCase	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

         if os.path.abspath(self.vocab_file		) != os.path.abspath(__UpperCAmelCase		):
               copyfile(self.vocab_file	,  __UpperCAmelCase		)

         return (out_vocab_file,)



