

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


a_     =			{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

a_     =			{
    'vocab_file': {
        'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'unc-nlp/lxmert-base-uncased': (
            'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json'
        ),
    },
}

a_     =			{
    'unc-nlp/lxmert-base-uncased': 512,
}

a_     =			{
    'unc-nlp/lxmert-base-uncased': {'do_lower_case': True},
}






class _UpperCamelCase							(							_SCREAMING_SNAKE_CASE      ):
			'''simple docstring'''







			lowerCamelCase__							=VOCAB_FILES_NAMES
			lowerCamelCase__							=PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__							=PRETRAINED_INIT_CONFIGURATION
			lowerCamelCase__							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__							=LxmertTokenizer
			def __init__(	self							:       str					,					a							:       Optional[Any]=None					,					a							:       Union[str, Any]=None					,					a							:       Tuple=True					,					a							:       Dict="[UNK]"					,					a							:       Dict="[SEP]"					,					a							:       List[str]="[PAD]"					,					a							:       Dict="[CLS]"					,					a							:       Optional[Any]="[MASK]"					,					a							:       int=True					,					a							:       Tuple=None					,					**a							:       int					,					)							->				str:

				"""simple docstring"""

				super().__init__(
				    _lowerCAmelCase					,					tokenizer_file=_lowerCAmelCase					,					do_lower_case=_lowerCAmelCase					,					unk_token=_lowerCAmelCase					,					sep_token=_lowerCAmelCase					,					pad_token=_lowerCAmelCase					,					cls_token=_lowerCAmelCase					,					mask_token=_lowerCAmelCase					,					tokenize_chinese_chars=_lowerCAmelCase					,					strip_accents=_lowerCAmelCase					,					**_lowerCAmelCase					,					)

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
				if (
				    normalizer_state.get("lowercase"					,					_lowerCAmelCase		) != do_lower_case
				    or normalizer_state.get("strip_accents"					,					_lowerCAmelCase		) != strip_accents
				    or normalizer_state.get("handle_chinese_chars"					,					_lowerCAmelCase		) != tokenize_chinese_chars
				):
					SCREAMING_SNAKE_CASE      :					str    =				getattr(_lowerCAmelCase					,					normalizer_state.pop("type"		)		)
					SCREAMING_SNAKE_CASE      :					Tuple    =				do_lower_case
					SCREAMING_SNAKE_CASE      :					Tuple    =				strip_accents
					SCREAMING_SNAKE_CASE      :					str    =				tokenize_chinese_chars
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				normalizer_class(**_lowerCAmelCase		)

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				do_lower_case
			def 							__UpperCamelCase       (	self							:       List[str]					,					a							:       str					,					a							:       Optional[Any]=None		)							->				Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Any    =				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

				if token_ids_a:
					output += token_ids_a + [self.sep_token_id]

				return output
			def 							__UpperCamelCase       (	self							:       List[str]					,					a							:       List[int]					,					a							:       Optional[List[int]] = None		)							->				Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				[self.sep_token_id]
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				[self.cls_token_id]
				if token_ids_a is None:
					return len(cls + token_ids_a + sep		) * [0]
				return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]






			def 							__UpperCamelCase       (	self							:       Union[str, Any]					,					a							:       str					,					a							:       Optional[str] = None		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self._tokenizer.model.save(_lowerCAmelCase					,					name=_lowerCAmelCase		)
				return tuple(_lowerCAmelCase		)





from __future__ import annotations

from math import gcd

def 					UpperCAmelCase_							(      __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.
            SCREAMING_SNAKE_CASE_							=			seed
            SCREAMING_SNAKE_CASE_							=			seed

            while True:
                  # At each iteration, the tortoise moves one step and the hare moves two.
                  SCREAMING_SNAKE_CASE_							=			rand_fn(__UpperCAmelCase			,			__UpperCAmelCase			,			__UpperCAmelCase							)
                  SCREAMING_SNAKE_CASE_							=			rand_fn(__UpperCAmelCase			,			__UpperCAmelCase			,			__UpperCAmelCase							)
                  SCREAMING_SNAKE_CASE_							=			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``.
                  SCREAMING_SNAKE_CASE_							=			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.
            SCREAMING_SNAKE_CASE_							=			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

   lowerCamelCase__ :   List[str]					=							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',
   )
   lowerCamelCase__ :   Tuple					=							parser.parse_args()

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