

"""simple docstring"""




import os
import re
import warnings
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_ta import TaTokenizer
else:
     UpperCamelCase__     :			Union[str, Any]	  =			None


UpperCamelCase__     :			Any	  =			logging.get_logger(__name__)

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

UpperCamelCase__     :			Optional[Any]	  =			{
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
UpperCamelCase__     :			List[Any]	  =			{
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}


class 		_A       (			_a					):
      __a								=	VOCAB_FILES_NAMES
      __a								=	PRETRAINED_VOCAB_FILES_MAP
      __a								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __a								=	["""input_ids""", """attention_mask"""]
      __a								=	TaTokenizer

      __a								=	[]





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__="</s>"    ,						SCREAMING_SNAKE_CASE__="<unk>"    ,						SCREAMING_SNAKE_CASE__="<pad>"    ,						SCREAMING_SNAKE_CASE__=100    ,						SCREAMING_SNAKE_CASE__=None    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					Dict:
         # Add extra_ids to the special token list
         if extra_ids > 0 and additional_special_tokens is None:
            lowerCamelCase__						=			[f'<extra_id_{i}>' for i in range(snake_case_		)]
         elif extra_ids > 0 and additional_special_tokens is not None:
            # Check that we have the right number of extra special tokens
            lowerCamelCase__						=			len(set(filter(lambda SCREAMING_SNAKE_CASE__		: bool("extra_id_" in str(snake_case_		)		)    ,						snake_case_		)		)		)
            if extra_tokens != extra_ids:
               raise ValueError(
                   f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                   " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
                   " tokens"		)

         super().__init__(
             snake_case_    ,						tokenizer_file=snake_case_    ,						eos_token=snake_case_    ,						unk_token=snake_case_    ,						pad_token=snake_case_    ,						extra_ids=snake_case_    ,						additional_special_tokens=snake_case_    ,						**snake_case_    ,						)

         lowerCamelCase__						=			vocab_file
         lowerCamelCase__						=			False if not self.vocab_file else True
         lowerCamelCase__						=			extra_ids





      @staticmethod
      def 					_lowerCamelCase  (  SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					int:
         if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
            lowerCamelCase__						=			TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
            if init_max_model_length is not None and init_max_model_length != max_model_length:
               return init_max_model_length
            elif init_max_model_length is None:
               warnings.warn(
                   "This tokenizer was incorrectly instantiated with a model max length of"
                   f' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
                   " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
                   " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
                   f' {pretrained_model_name_or_path} automatically truncating your input to'
                   f' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
                   f' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
                   " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
                   " instantiate this tokenizer with `model_max_length` set to your preferred value."    ,						snake_case_    ,						)

         return max_model_length





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					Tuple[str]:
         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(snake_case_		):
            logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
            return
         lowerCamelCase__						=			os.path.join(
             snake_case_    ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

         if os.path.abspath(self.vocab_file		) != os.path.abspath(snake_case_		):
            copyfile(self.vocab_file    ,						snake_case_		)
            logger.info(f'Copy vocab file to {out_vocab_file}'		)

         return (out_vocab_file,)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					List[int]:
         lowerCamelCase__						=			token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
            return self.prefix_tokens + token_ids_a
         else:
            lowerCamelCase__						=			token_ids_a + [self.eos_token_id]
            return self.prefix_tokens + token_ids_a + token_ids_a





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					List[int]:
         lowerCamelCase__						=			[self.eos_token_id]

         if token_ids_a is None:
            return len(token_ids_a + eos		) * [0]
         return len(token_ids_a + eos + token_ids_a + eos		) * [0]





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         return list(
             set(filter(lambda SCREAMING_SNAKE_CASE__		: bool(re.search(r"<extra_id_\d+>"    ,						snake_case_		)		) is not None    ,						self.additional_special_tokens		)		)		)





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         return [self.convert_tokens_to_ids(snake_case_		) for token in self.get_sentinel_tokens()]







"""simple docstring"""




from __future__ import annotations

import bisect



def 	UpperCAmelCase__   (				A__	,    A__	,    A__ = 0	,    A__ = -1   )      ->      int:




   """simple docstring"""
   if hi < 0:
      lowerCamelCase__						=			len(A__   )

   while lo < hi:
      lowerCamelCase__						=			lo + (hi - lo) // 2
      if sorted_collection[mid] < item:
         lowerCamelCase__						=			mid + 1
      else:
         lowerCamelCase__						=			mid

   return lo



def 	UpperCAmelCase__   (				A__	,    A__	,    A__ = 0	,    A__ = -1   )      ->      int:




   """simple docstring"""
   if hi < 0:
      lowerCamelCase__						=			len(A__   )

   while lo < hi:
      lowerCamelCase__						=			lo + (hi - lo) // 2
      if sorted_collection[mid] <= item:
         lowerCamelCase__						=			mid + 1
      else:
         lowerCamelCase__						=			mid

   return lo



def 	UpperCAmelCase__   (				A__	,    A__	,    A__ = 0	,    A__ = -1   )      ->      None:




   """simple docstring"""
   sorted_collection.insert(bisect_left(A__	,    A__	,    A__	,    A__   )	,    A__   )



def 	UpperCAmelCase__   (				A__	,    A__	,    A__ = 0	,    A__ = -1   )      ->      None:




   """simple docstring"""
   sorted_collection.insert(bisect_right(A__	,    A__	,    A__	,    A__   )	,    A__   )



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      int | None:




   """simple docstring"""
   lowerCamelCase__						=			0
   lowerCamelCase__						=			len(A__   ) - 1

   while left <= right:
      lowerCamelCase__						=			left + (right - left) // 2
      lowerCamelCase__						=			sorted_collection[midpoint]
      if current_item == item:
         return midpoint
      elif item < current_item:
         lowerCamelCase__						=			midpoint - 1
      else:
         lowerCamelCase__						=			midpoint + 1
   return None



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      int | None:




   """simple docstring"""
   lowerCamelCase__						=			bisect.bisect_left(A__	,    A__   )
   if index != len(A__   ) and sorted_collection[index] == item:
      return index
   return None



def 	UpperCAmelCase__   (				A__	,    A__	,    A__	,    A__   )      ->      int | None:




   """simple docstring"""
   if right < left:
      return None

   lowerCamelCase__						=			left + (right - left) // 2

   if sorted_collection[midpoint] == item:
      return midpoint
   elif sorted_collection[midpoint] > item:
      return binary_search_by_recursion(A__	,    A__	,    A__	,    midpoint - 1   )
   else:
      return binary_search_by_recursion(A__	,    A__	,    midpoint + 1	,    A__   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			input('''Enter numbers separated by comma:\n''').strip()
     SCREAMING_SNAKE_CASE_     :			int	  =			sorted(int(item) for item in user_input.split(''','''))
     SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			int(input('''Enter a single number to be found in the list:\n'''))
     SCREAMING_SNAKE_CASE_     :			Dict	  =			binary_search(collection, target)
     if result is None:
          print(F'''{target} was not found in {collection}.''')
     else:
          print(F'''{target} was found at position {result} in {collection}.''')




