






"""simple docstring"""





from __future__ import annotations

from math import gcd



def   a    (     __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.
												__magic_name__:	List[str]							=       seed
												__magic_name__:	int							=       seed

												while True:
																		# At each iteration, the tortoise moves one step and the hare moves two.
																		__magic_name__:	List[Any]							=       rand_fn(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )
																		__magic_name__:	Dict							=       rand_fn(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )
																		__magic_name__:	Tuple							=       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``.
																		__magic_name__:	Tuple							=       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.
												__magic_name__:	Dict							=       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							=  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							=  parser.parse_args()

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






"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


__UpperCamelCase    : List[Any]				 =						logging.get_logger(__name__)

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

__UpperCamelCase    : List[Any]				 =						{
    '''vocab_file''': {
        '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt''',
        '''distilbert-base-uncased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'''
        ),
        '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt''',
        '''distilbert-base-cased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'''
        ),
        '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt''',
        '''distilbert-base-multilingual-cased''': (
            '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json''',
        '''distilbert-base-uncased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json''',
        '''distilbert-base-cased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-german-cased''': (
            '''https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-multilingual-cased''': (
            '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'''
        ),
    },
}

__UpperCamelCase    : Union[str, Any]				 =						{
    '''distilbert-base-uncased''': 5_1_2,
    '''distilbert-base-uncased-distilled-squad''': 5_1_2,
    '''distilbert-base-cased''': 5_1_2,
    '''distilbert-base-cased-distilled-squad''': 5_1_2,
    '''distilbert-base-german-cased''': 5_1_2,
    '''distilbert-base-multilingual-cased''': 5_1_2,
}


__UpperCamelCase    : int				 =						{
    '''distilbert-base-uncased''': {'''do_lower_case''': True},
    '''distilbert-base-uncased-distilled-squad''': {'''do_lower_case''': True},
    '''distilbert-base-cased''': {'''do_lower_case''': False},
    '''distilbert-base-cased-distilled-squad''': {'''do_lower_case''': False},
    '''distilbert-base-german-cased''': {'''do_lower_case''': False},
    '''distilbert-base-multilingual-cased''': {'''do_lower_case''': False},
}







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			VOCAB_FILES_NAMES
					lowercase__												=			PRETRAINED_VOCAB_FILES_MAP
					lowercase__												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase__												=			PRETRAINED_INIT_CONFIGURATION
					lowercase__												=			["input_ids", "attention_mask"]
					lowercase__												=			DistilBertTokenizer







					def __init__(			self     :							int  ,lowercase_     :							List[str]=None  ,lowercase_     :							int=None  ,lowercase_     :							str=True  ,lowercase_     :							Union[str, Any]="[UNK]"  ,lowercase_     :							List[str]="[SEP]"  ,lowercase_     :							List[str]="[PAD]"  ,lowercase_     :							List[str]="[CLS]"  ,lowercase_     :							List[str]="[MASK]"  ,lowercase_     :							Tuple=True  ,lowercase_     :							Tuple=None  ,**lowercase_     :							str  ,):
						super().__init__(
						    lowercase_  ,tokenizer_file=lowercase_  ,do_lower_case=lowercase_  ,unk_token=lowercase_  ,sep_token=lowercase_  ,pad_token=lowercase_  ,cls_token=lowercase_  ,mask_token=lowercase_  ,tokenize_chinese_chars=lowercase_  ,strip_accents=lowercase_  ,**lowercase_  ,)

						lowerCAmelCase__      :		Tuple    							=      json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
						if (
						    normalizer_state.get('''lowercase'''  ,lowercase_						) != do_lower_case
						    or normalizer_state.get('''strip_accents'''  ,lowercase_						) != strip_accents
						    or normalizer_state.get('''handle_chinese_chars'''  ,lowercase_						) != tokenize_chinese_chars
						):
							lowerCAmelCase__      :		Union[str, Any]    							=      getattr(lowercase_  ,normalizer_state.pop('''type'''						)						)
							lowerCAmelCase__      :		Optional[int]    							=      do_lower_case
							lowerCAmelCase__      :		List[str]    							=      strip_accents
							lowerCAmelCase__      :		int    							=      tokenize_chinese_chars
							lowerCAmelCase__      :		Dict    							=      normalizer_class(**lowercase_						)

						lowerCAmelCase__      :		Tuple    							=      do_lower_case







					def 			__lowerCAmelCase					(			self     :							str  ,lowercase_     :							Tuple  ,lowercase_     :							Optional[Any]=None						):
						lowerCAmelCase__      :		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 			__lowerCAmelCase					(			self     :							int  ,lowercase_     :							List[int]  ,lowercase_     :							Optional[List[int]] = None						):
						lowerCAmelCase__      :		Dict    							=      [self.sep_token_id]
						lowerCAmelCase__      :		Dict    							=      [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 			__lowerCAmelCase					(			self     :							Any  ,lowercase_     :							str  ,lowercase_     :							Optional[str] = None						):
						lowerCAmelCase__      :		Any    							=      self._tokenizer.model.save(lowercase_  ,name=lowercase_						)
						return tuple(lowercase_						)




