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:
   __lowerCamelCase    :				Union[str, Any]							     =	None


__lowerCamelCase    :				int							     =	logging.get_logger(__name__)

__lowerCamelCase    :				List[str]							     =	{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCamelCase    :				Union[str, 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
__lowerCamelCase    :				Any							     =	{
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							VOCAB_FILES_NAMES
   lowerCAmelCase_														=							PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase_														=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase_														=							["input_ids", "attention_mask"]
   lowerCAmelCase_														=							TaTokenizer

   lowerCAmelCase_														=							[]




   def __init__(					self    :       Any ,			_lowercase    :       Optional[Any]=None ,			_lowercase    :       Union[str, Any]=None ,			_lowercase    :       Any="</s>" ,			_lowercase    :       int="<unk>" ,			_lowercase    :       Any="<pad>" ,			_lowercase    :       List[Any]=1_00 ,			_lowercase    :       int=None ,			**_lowercase    :       Optional[Any] ,			):



     """simple docstring"""






     if extra_ids > 0 and additional_special_tokens is None:
       SCREAMING_SNAKE_CASE__    							=   [f"""<extra_id_{i}>""" for i in range(_lowercase   )]
     elif extra_ids > 0 and additional_special_tokens is not None:
       # Check that we have the right number of extra special tokens
       SCREAMING_SNAKE_CASE__    							=   len(set(filter(lambda _lowercase   : bool("""extra_id_""" in str(_lowercase   )   ) ,			_lowercase   )   )   )
       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__(
         _lowercase ,			tokenizer_file=_lowercase ,			eos_token=_lowercase ,			unk_token=_lowercase ,			pad_token=_lowercase ,			extra_ids=_lowercase ,			additional_special_tokens=_lowercase ,			**_lowercase ,			)

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




   @staticmethod
   def 			__a		(					_lowercase    :       str ,			_lowercase    :       List[str] ,			_lowercase    :       List[str]   ):



     """simple docstring"""






     if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
       SCREAMING_SNAKE_CASE__    							=   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.""" ,			_lowercase ,			)

     return max_model_length




   def 			__a		(					self    :       Tuple ,			_lowercase    :       str ,			_lowercase    :       Optional[str] = None   ):



     """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(_lowercase   ):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
       return
     SCREAMING_SNAKE_CASE__    							=   os.path.join(
         _lowercase ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

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

     return (out_vocab_file,)




   def 			__a		(					self    :       Tuple ,			_lowercase    :       List[int] ,			_lowercase    :       Optional[List[int]] = None   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
       return self.prefix_tokens + token_ids_a
     else:
       SCREAMING_SNAKE_CASE__    							=   token_ids_a + [self.eos_token_id]
       return self.prefix_tokens + token_ids_a + token_ids_a




   def 			__a		(					self    :       Any ,			_lowercase    :       List[int] ,			_lowercase    :       Optional[List[int]] = None   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   [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 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     return list(
         set(filter(lambda _lowercase   : bool(re.search(R"""<extra_id_\d+>""" ,			_lowercase   )   ) is not None ,			self.additional_special_tokens   )   )   )




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     return [self.convert_tokens_to_ids(_lowercase   ) for token in self.get_sentinel_tokens()]

from math import isqrt



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int			)							->      bool:







  """simple docstring"""





  return all(number % divisor != 0 for divisor in range(2				,  isqrt(__UpperCamelCase			) + 1			)			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int = 10**6			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   0
  SCREAMING_SNAKE_CASE__    							=   1
  SCREAMING_SNAKE_CASE__    							=   7
  while prime_candidate < max_prime:
    primes_count += is_prime(__UpperCamelCase			)

    cube_index += 1
    prime_candidate += 6 * cube_index

  return primes_count


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