





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_  							=				None


UpperCAmelCase_  							=				logging.get_logger(__name__)

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

UpperCAmelCase_  							=				{
    '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_  							=				{
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Tuple     	=	VOCAB_FILES_NAMES
   a		:							int     	=	PRETRAINED_VOCAB_FILES_MAP
   a		:							Tuple     	=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a		:							List[Any]     	=	["input_ids", "attention_mask"]
   a		:							Tuple     	=	TaTokenizer

   a		:							List[int]     	=	[]




   def __init__(					self,	__magic_name__=None,	__magic_name__=None,	__magic_name__="</s>",	__magic_name__="<unk>",	__magic_name__="<pad>",	__magic_name__=100,	__magic_name__=None,	**__magic_name__,	)				->   int:






       """simple docstring"""
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
           UpperCamelCase__							:       Optional[int]      =   [f"<extra_id_{i}>" for i in range(__magic_name__							)]
       elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra special tokens
           UpperCamelCase__							:       Optional[Any]      =   len(set(filter(lambda __magic_name__							: bool('''extra_id_''' in str(__magic_name__							)							),	__magic_name__							)							)							)
           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__(
           __magic_name__,	tokenizer_file=__magic_name__,	eos_token=__magic_name__,	unk_token=__magic_name__,	pad_token=__magic_name__,	extra_ids=__magic_name__,	additional_special_tokens=__magic_name__,	**__magic_name__,	)

       UpperCamelCase__							:       List[Any]      =   vocab_file
       UpperCamelCase__							:       int      =   False if not self.vocab_file else True
       UpperCamelCase__							:       Tuple      =   extra_ids




   @staticmethod
   def    UpperCamelCase__						(					__magic_name__,	__magic_name__,	__magic_name__							)				->   List[Any]:






       """simple docstring"""
       if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
           UpperCamelCase__							:       Optional[Any]      =   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.''',	__magic_name__,	)

       return max_model_length




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = 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(__magic_name__							):
           logger.error(f"Vocabulary path ({save_directory}) should be a directory"							)
           return
       UpperCamelCase__							:       Union[str, Any]      =   os.path.join(
           __magic_name__,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

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

       return (out_vocab_file,)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   List[int]:






       """simple docstring"""
       UpperCamelCase__							:       int      =   token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
           return self.prefix_tokens + token_ids_a
       else:
           UpperCamelCase__							:       Any      =   token_ids_a + [self.eos_token_id]
           return self.prefix_tokens + token_ids_a + token_ids_a




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   List[int]:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   [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							)				->   Optional[Any]:






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




   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       return [self.convert_tokens_to_ids(__magic_name__							) for token in self.get_sentinel_tokens()]








from __future__ import annotations

from math import gcd




def 	lowerCAmelCase_					(						__UpperCAmelCase:    int	,     __UpperCAmelCase:    int = 2	,     __UpperCAmelCase:    int = 1	,     __UpperCAmelCase:    int = 3	,     )   ->			int | None:
    # A value less than 2 can cause an infinite loop in the algorithm.
    if num < 2:
        raise ValueError('''The input value cannot be less than 2'''   )

    # Because of the relationship between ``f(f(x))`` and ``f(x)``, this
    # algorithm struggles to find factors that are divisible by two.
    # As a workaround, we specifically check for two and even inputs.
    #   See: https://math.stackexchange.com/a/2856214/165820
    if num > 2 and num % 2 == 0:
        return 2

    # Pollard's Rho algorithm requires a function that returns pseudorandom
    # values between 0 <= X < ``num``.  It doesn't need to be random in the
    # sense that the output value is cryptographically secure or difficult
    # to calculate, it only needs to be random in the sense that all output
    # values should be equally likely to appear.
    # For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
    # However, the success of Pollard's algorithm isn't guaranteed and is
    # determined in part by the initial seed and the chosen random function.
    # To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
    # where ``C`` is a value that we can modify between each attempt.
    def rand_fn(__UpperCAmelCase:    int	,     __UpperCAmelCase:    int	,     __UpperCAmelCase:    int   ) -> int:
        return (pow(__UpperCAmelCase	,     2   ) + step) % modulus

    for _ in range(__UpperCAmelCase   ):
        # These track the position within the cycle detection logic.
        UpperCamelCase__							:       List[Any]      =   seed
        UpperCamelCase__							:       List[str]      =   seed

        while True:
            # At each iteration, the tortoise moves one step and the hare moves two.
            UpperCamelCase__							:       Optional[int]      =   rand_fn(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )
            UpperCamelCase__							:       List[str]      =   rand_fn(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )
            UpperCamelCase__							:       Optional[int]      =   rand_fn(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )

            # At some point both the tortoise and the hare will enter a cycle whose
            # length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
            # the tortoise and hare will end up on the same value modulo ``p``.
            # We can detect when this happens because the position difference between
            # the tortoise and the hare will share a common divisor with ``num``.
            UpperCamelCase__							:       Optional[int]      =   gcd(hare - tortoise	,     __UpperCAmelCase   )

            if divisor == 1:
                # No common divisor yet, just keep searching.
                continue
            else:
                # We found a common divisor!
                if divisor == num:
                    # Unfortunately, the divisor is ``num`` itself and is useless.
                    break
                else:
                    # The divisor is a nontrivial factor of ``num``!
                    return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
        UpperCamelCase__							:       List[str]      =   hare

        # The new step value for the random function can just be incremented.
        # At first the results will be similar to what the old function would
        # have produced, but the value will quickly diverge after a bit.
        step += 1

    # We haven't found a divisor within the requested number of attempts.
    # We were unlucky or ``num`` itself is actually prime.
    return None


if __name__ == "__main__":
  import argparse

  UpperCAmelCase_  							=				argparse.ArgumentParser()
  parser.add_argument(
      'num',
      type=int,
      help='The value to find a divisor of',
  )
  parser.add_argument(
      '--attempts',
      type=int,
      default=3,
      help='The number of attempts before giving up',
  )
  UpperCAmelCase_  							=				parser.parse_args()

  UpperCAmelCase_  							=				pollard_rho(args.num, attempts=args.attempts)
  if divisor is None:
    print(F'''{args.num} is probably prime''')
  else:
    UpperCAmelCase_  							=				args.num // divisor
    print(F'''{args.num} = {divisor} * {quotient}''')

