





'''simple docstring'''

import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
  from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase							:     Tuple		     =get_tests_dir("fixtures/test_sentencepiece.model")

__lowerCAmelCase							:     List[str]		     ={"target_lang": "fi", "source_lang": "en"}
__lowerCAmelCase							:     Tuple		     =">>zh<<"
__lowerCAmelCase							:     str		     ="Helsinki-NLP/"

if is_torch_available():
  __lowerCAmelCase							:     List[str]		     ="pt"
elif is_tf_available():
  __lowerCAmelCase							:     Any		     ="tf"
else:
  __lowerCAmelCase							:     Optional[int]		     ="jax"






@require_sentencepiece
class        UpperCAmelCase     (	UpperCamelCase__      ,	unittest.TestCase    ):
 __lowercase       		=    MarianTokenizer
 __lowercase       		=    False
 __lowercase       		=    True
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							int:
     super().setUp()
     A__    =      ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
     A__    =      dict(zip(lowercase_						,	range(len(lowercase_							)							)							)							)
     A__    =      Path(self.tmpdirname							)
     save_json(lowercase_						,	save_dir / VOCAB_FILES_NAMES["vocab"]							)
     save_json(lowercase_						,	save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"]							)
     if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
         copyfile(lowercase_						,	save_dir / VOCAB_FILES_NAMES["source_spm"]							)
         copyfile(lowercase_						,	save_dir / VOCAB_FILES_NAMES["target_spm"]							)

     A__    =      MarianTokenizer.from_pretrained(self.tmpdirname							)
     tokenizer.save_pretrained(self.tmpdirname							)
 def 					UpperCAmelCase_							(    self				:int						,	**lowercase_				:Any							)->							MarianTokenizer:
     return MarianTokenizer.from_pretrained(self.tmpdirname						,	**lowercase_							)
 def 					UpperCAmelCase_							(    self				:Dict						,	lowercase_				:List[str]							)->							Optional[int]:
     return (
         "This is a test",
         "This is a test",
     )
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Optional[int]:
     A__    =      "</s>"
     A__    =      0

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_							)						,	lowercase_							)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_							)						,	lowercase_							)
 def 					UpperCAmelCase_							(    self				:Dict							)->							List[str]:
     A__    =      list(self.get_tokenizer().get_vocab().keys()							)

     self.assertEqual(vocab_keys[0]						,	"</s>"							)
     self.assertEqual(vocab_keys[1]						,	"<unk>"							)
     self.assertEqual(vocab_keys[-1]						,	"<pad>"							)
     self.assertEqual(len(lowercase_							)						,	9							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Optional[Any]:
     self.assertEqual(self.get_tokenizer().vocab_size						,	9							)
 def 					UpperCAmelCase_							(    self				:Dict							)->							int:
     A__    =      MarianTokenizer.from_pretrained(F"{ORG_NAME}opus-mt-en-de"							)
     A__    =      en_de_tokenizer(["I am a small frog"]						,	return_tensors=lowercase_							)
     self.assertIsInstance(lowercase_						,	lowercase_							)
     A__    =      [38, 1_21, 14, 6_97, 3_88_48, 0]
     self.assertListEqual(lowercase_						,	batch.input_ids[0]							)

     A__    =      tempfile.mkdtemp()
     en_de_tokenizer.save_pretrained(lowercase_							)
     A__    =      [x.name for x in Path(lowercase_							).glob("*"							)]
     self.assertIn("source.spm"						,	lowercase_							)
     MarianTokenizer.from_pretrained(lowercase_							)
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Optional[int]:
     A__    =      self.get_tokenizer()

     A__    =      tok(
         ["I am a small frog" * 10_00, "I am a small frog"]						,	padding=lowercase_						,	truncation=lowercase_						,	return_tensors=lowercase_							)
     self.assertIsInstance(lowercase_						,	lowercase_							)
     self.assertEqual(batch.input_ids.shape						,	(2, 5_12)							)
 def 					UpperCAmelCase_							(    self				:Dict							)->							Union[str, Any]:
     A__    =      self.get_tokenizer()
     A__    =      tok(["I am a tiny frog", "I am a small frog"]						,	padding=lowercase_						,	return_tensors=lowercase_							)
     self.assertIsInstance(lowercase_						,	lowercase_							)
     self.assertEqual(batch_smaller.input_ids.shape						,	(2, 10)							)
 @slow
 def 					UpperCAmelCase_							(    self				:int							)->							Tuple:
     # fmt: off
     A__    =      {"input_ids": [[4_34_95, 4_62, 20, 4_21_64, 13_69, 52, 4_64, 1_32, 17_03, 4_92, 13, 74_91, 3_89_99, 6, 8, 4_64, 1_32, 17_03, 4_92, 13, 46_69, 3_78_67, 13, 75_25, 27, 15_93, 9_88, 13, 3_39_72, 70_29, 6, 20, 82_51, 3_83, 2, 2_70, 58_66, 37_88, 2, 23_53, 82_51, 1_23_38, 2, 1_39_58, 3_87, 2, 36_29, 69_53, 1_88, 29_00, 2, 1_39_58, 80_11, 1_15_01, 23, 84_60, 40_73, 3_40_09, 20, 4_35, 1_14_39, 27, 8, 84_60, 40_73, 60_04, 20, 99_88, 3_75, 27, 33, 2_66, 19_45, 10_76, 13_50, 3_78_67, 32_88, 5, 5_77, 10_76, 43_74, 8, 50_82, 5, 2_64_53, 2_57, 5_56, 4_03, 2, 2_42, 1_32, 3_83, 3_16, 4_92, 8, 1_07_67, 6, 3_16, 3_04, 42_39, 3, 0], [1_48, 1_57_22, 19, 18_39, 12, 13_50, 13, 2_23_27, 50_82, 54_18, 4_75_67, 3_59_38, 59, 3_18, 1_95_52, 1_08, 21_83, 54, 1_49_76, 48_35, 32, 5_47, 11_14, 8, 3_15, 24_17, 5, 92, 1_90_88, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00], [36, 63_95, 1_25_70, 3_91_47, 1_15_97, 6, 2_66, 4, 4_54_05, 72_96, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=lowercase_						,	model_name="Helsinki-NLP/opus-mt-en-de"						,	revision="1a8c2263da11e68e50938f97e10cd57820bd504c"						,	decode_kwargs={"use_source_tokenizer": True}						,	)



 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Tuple:
     A__    =      MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs"							)

     A__    =      "Tämä on testi"
     A__    =      "This is a test"

     A__    =      [76, 7, 20_47, 2]
     A__    =      [69, 12, 11, 9_40, 2]

     A__    =      tokenizer(lowercase_							).input_ids
     self.assertListEqual(lowercase_						,	lowercase_							)

     A__    =      tokenizer(text_target=lowercase_							).input_ids
     self.assertListEqual(lowercase_						,	lowercase_							)

     A__    =      tokenizer.decode(lowercase_						,	skip_special_tokens=lowercase_							)
     self.assertEqual(lowercase_						,	lowercase_							)










'''simple docstring'''

from __future__ import annotations

from math import gcd





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : int = 2						,	_lowerCamelCase : int = 1						,	_lowerCamelCase : int = 3						,	):
    # 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(_lowerCamelCase : int						,	_lowerCamelCase : int						,	_lowerCamelCase : int	) -> int:
        return (pow(_lowerCamelCase						,	2	) + step) % modulus

    for _ in range(_lowerCamelCase	):
        # These track the position within the cycle detection logic.
        A__    =      seed
        A__    =      seed

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

            # 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``.
            A__    =      gcd(hare - tortoise						,	_lowerCamelCase	)

            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.
        A__    =      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							:     Optional[int]		     =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							:     Optional[int]		     =parser.parse_args()

  __lowerCAmelCase							:     Dict		     =pollard_rho(args.num, attempts=args.attempts)
  if divisor is None:
    print(f"""{args.num} is probably prime""")
  else:
    __lowerCAmelCase							:     Optional[Any]		     =args.num // divisor
    print(f"""{args.num} = {divisor} * {quotient}""")



