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:
  SCREAMING_SNAKE_CASE :	Optional[Any]							     =		None


SCREAMING_SNAKE_CASE :	Optional[int]							     =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :	int							     =		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE :	int							     =		{
    """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
SCREAMING_SNAKE_CASE :	Dict							     =		{
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			VOCAB_FILES_NAMES
  _SCREAMING_SNAKE_CASE    				=			PRETRAINED_VOCAB_FILES_MAP
  _SCREAMING_SNAKE_CASE    				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _SCREAMING_SNAKE_CASE    				=			["""input_ids""", """attention_mask"""]
  _SCREAMING_SNAKE_CASE    				=			TaTokenizer

  _SCREAMING_SNAKE_CASE    				=			[]

  def __init__(		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=None     ,							__SCREAMING_SNAKE_CASE :      Dict=None     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]="</s>"     ,							__SCREAMING_SNAKE_CASE :      List[Any]="<unk>"     ,							__SCREAMING_SNAKE_CASE :      Tuple="<pad>"     ,							__SCREAMING_SNAKE_CASE :      Tuple=1_00     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=None     ,							**__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							):
   # Add extra_ids to the special token list
   if extra_ids > 0 and additional_special_tokens is None:
    __a       =  [f"""<extra_id_{i}>""" for i in range(__SCREAMING_SNAKE_CASE       )]
   elif extra_ids > 0 and additional_special_tokens is not None:
    # Check that we have the right number of extra special tokens
    __a       =  len(set(filter(lambda __SCREAMING_SNAKE_CASE       : bool("extra_id_" in str(__SCREAMING_SNAKE_CASE       )       )     ,							__SCREAMING_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__(
       __SCREAMING_SNAKE_CASE     ,							tokenizer_file=__SCREAMING_SNAKE_CASE     ,							eos_token=__SCREAMING_SNAKE_CASE     ,							unk_token=__SCREAMING_SNAKE_CASE     ,							pad_token=__SCREAMING_SNAKE_CASE     ,							extra_ids=__SCREAMING_SNAKE_CASE     ,							additional_special_tokens=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE     ,							)

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

  @staticmethod
  def 		_UpperCAmelCase   (		__SCREAMING_SNAKE_CASE :      Any     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      Dict       ):
   if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
    __a       =  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."     ,							__SCREAMING_SNAKE_CASE     ,							)

   return max_model_length

  def 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Optional[str] = None       ):
   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(__SCREAMING_SNAKE_CASE       ):
    logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""       )
    return
   __a       =  os.path.join(
       __SCREAMING_SNAKE_CASE     ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

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

   return (out_vocab_file,)

  def 		_UpperCAmelCase   (		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      List[int]     ,							__SCREAMING_SNAKE_CASE :      Optional[List[int]] = None       ):
   __a       =  token_ids_a + [self.eos_token_id]
   if token_ids_a is None:
    return self.prefix_tokens + token_ids_a
   else:
    __a       =  token_ids_a + [self.eos_token_id]
    return self.prefix_tokens + token_ids_a + token_ids_a

  def 		_UpperCAmelCase   (		self :      int     ,							__SCREAMING_SNAKE_CASE :      List[int]     ,							__SCREAMING_SNAKE_CASE :      Optional[List[int]] = None       ):
   __a       =  [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 		_UpperCAmelCase   (		self :      List[Any]       ):
   return list(
       set(filter(lambda __SCREAMING_SNAKE_CASE       : bool(re.search(R"<extra_id_\d+>"     ,							__SCREAMING_SNAKE_CASE       )       ) is not None     ,							self.additional_special_tokens       )       )       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   return [self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE       ) for token in self.get_sentinel_tokens()]



from __future__ import annotations



def   __A		(			_A					):



 """simple docstring"""


 __a       =  [True] * limit
 __a       =  False
 __a       =  False
 __a       =  True

 for i in range(3							,				int(limit**0.5 + 1					)							,				2					):
  __a       =  i * 2
  while index < limit:
   __a       =  False
   __a       =  index + i

 __a       =  [2]

 for i in range(3							,				_A							,				2					):
  if is_prime[i]:
   primes.append(_A					)

 return primes



def   __A		(			_A = 100_0000					):



 """simple docstring"""


 __a       =  prime_sieve(_A					)
 __a       =  0
 __a       =  0

 for i in range(len(_A					)					):
  for j in range(i + length							,				len(_A					)					):
   __a       =  sum(primes[i:j]					)
   if sol >= ceiling:
    break

   if sol in primes:
    __a       =  j - i
    __a       =  sol

 return largest


if __name__ == "__main__":
  print(f'''{solution() = }''')


