


"""simple docstring"""


from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFTransfoXLForSequenceClassification,
       TFTransfoXLLMHeadModel,
       TFTransfoXLModel,
   )



class   _a      :
    def __init__( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					):
          lowerCamelCase__           =		parent
          lowerCamelCase__           =		13
          lowerCamelCase__           =		7
          lowerCamelCase__           =		30
          lowerCamelCase__           =		self.seq_length + self.mem_len
          lowerCamelCase__           =		15
          lowerCamelCase__           =		True
          lowerCamelCase__           =		True
          lowerCamelCase__           =		99
          lowerCamelCase__           =		[10, 50, 80]
          lowerCamelCase__           =		32
          lowerCamelCase__           =		32
          lowerCamelCase__           =		4
          lowerCamelCase__           =		8
          lowerCamelCase__           =		1_28
          lowerCamelCase__           =		2
          lowerCamelCase__           =		2
          lowerCamelCase__           =		None
          lowerCamelCase__           =		1
          lowerCamelCase__           =		0
          lowerCamelCase__           =		3
          lowerCamelCase__           =		self.vocab_size - 1
          lowerCamelCase__           =		0.01
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size				)
          lowerCamelCase__           =		ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size				)

          lowerCamelCase__           =		None
          if self.use_labels:
                lowerCamelCase__           =		ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size				)

          lowerCamelCase__           =		TransfoXLConfig(
              vocab_size=self.vocab_size							,					mem_len=self.mem_len							,					clamp_len=self.clamp_len							,					cutoffs=self.cutoffs							,					d_model=self.hidden_size							,					d_embed=self.d_embed							,					n_head=self.num_attention_heads							,					d_head=self.d_head							,					d_inner=self.d_inner							,					div_val=self.div_val							,					n_layer=self.num_hidden_layers							,					eos_token_id=self.eos_token_id							,					pad_token_id=self.vocab_size - 1							,					init_range=self.init_range							,					num_labels=self.num_labels							,					)

          return (config, input_ids_a, input_ids_a, lm_labels)
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          random.seed(self.seed				)
          tf.random.set_seed(self.seed				)
    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]				):
          lowerCamelCase__           =		TFTransfoXLModel(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__    ,  lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__				).to_tuple()

          lowerCamelCase__           =		{'input_ids': input_ids_a, 'mems': mems_a}

          lowerCamelCase__    ,  lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__				).to_tuple()

          self.parent.assertEqual(hidden_states_a.shape							,					(self.batch_size, self.seq_length, self.hidden_size)				)
          self.parent.assertEqual(hidden_states_a.shape							,					(self.batch_size, self.seq_length, self.hidden_size)				)
          self.parent.assertListEqual(
              [mem.shape for mem in mems_a]							,					[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers							,					)
          self.parent.assertListEqual(
              [mem.shape for mem in mems_a]							,					[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers							,					)
    def      _UpperCamelCase       ( self							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Dict				):
          lowerCamelCase__           =		TFTransfoXLLMHeadModel(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__    ,  lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__				).to_tuple()

          lowerCamelCase__           =		{'input_ids': input_ids_a, 'labels': lm_labels}
          lowerCamelCase__    ,  lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__				).to_tuple()

          lowerCamelCase__    ,  lowerCamelCase__           =		model([input_ids_a, mems_a]				).to_tuple()

          lowerCamelCase__           =		{'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}

          lowerCamelCase__    ,  lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__				).to_tuple()

          self.parent.assertEqual(lm_logits_a.shape							,					(self.batch_size, self.seq_length, self.vocab_size)				)
          self.parent.assertListEqual(
              [mem.shape for mem in mems_a]							,					[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers							,					)

          self.parent.assertEqual(lm_logits_a.shape							,					(self.batch_size, self.seq_length, self.vocab_size)				)
          self.parent.assertListEqual(
              [mem.shape for mem in mems_a]							,					[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers							,					)
    def      _UpperCamelCase       ( self							:					Any							,					SCREAMING_SNAKE_CASE__							:					Any							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]				):
          lowerCamelCase__           =		TFTransfoXLForSequenceClassification(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__				)
          self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)				)


    def      _UpperCamelCase       ( self							:					List[Any]				):
          lowerCamelCase__           =		self.prepare_config_and_inputs()
          ((lowerCamelCase__)    ,  (lowerCamelCase__)    ,  (lowerCamelCase__)    ,  (lowerCamelCase__))           =		config_and_inputs
          lowerCamelCase__           =		{'input_ids': input_ids_a}
          return config, inputs_dict





@require_tf
class   _a      (      SCREAMING_SNAKE_CASE_		,		SCREAMING_SNAKE_CASE_		,		unittest.TestCase ):
    a_    :				Dict             =    (
        (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
    )
    a_    :				int             =    () if is_tf_available() else ()
    a_    :				List[Any]             =    (
        {
            'feature-extraction': TFTransfoXLModel,
            'text-classification': TFTransfoXLForSequenceClassification,
            'text-generation': TFTransfoXLLMHeadModel,
            'zero-shot': TFTransfoXLForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
    a_    :				Optional[Any]             =    False
    a_    :				Dict             =    False
    a_    :				Any             =    False
    a_    :				Any             =    False
    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Tuple				):
          if pipeline_test_casse_name == "TextGenerationPipelineTests":
                # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
                # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
                # tokenizer.
                return True

          return False
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		TFTransfoXLModelTester(self				)
          lowerCamelCase__           =		ConfigTester(self							,					config_class=SCREAMING_SNAKE_CASE__							,					d_embed=37				)
    def      _UpperCamelCase       ( self							:					Any				):
          self.config_tester.run_common_tests()
    def      _UpperCamelCase       ( self							:					List[str]				):
          self.model_tester.set_seed()
          lowerCamelCase__           =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_transfo_xl_model(*SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          self.model_tester.set_seed()
          lowerCamelCase__           =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_transfo_xl_lm_head(*SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					List[str]				):
          lowerCamelCase__           =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					int				):
          lowerCamelCase__    ,  lowerCamelCase__           =		self.model_tester.prepare_config_and_inputs_for_common()
          lowerCamelCase__           =		[TFTransfoXLForSequenceClassification]

          for model_class in self.all_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)
                assert isinstance(model.get_input_embeddings()							,					tf.keras.layers.Layer				)
                if model_class in list_other_models_with_output_ebd:
                      lowerCamelCase__           =		model.get_output_embeddings()
                      assert isinstance(SCREAMING_SNAKE_CASE__							,					tf.keras.layers.Layer				)
                      lowerCamelCase__           =		model.get_bias()
                      assert name is None
                else:
                      lowerCamelCase__           =		model.get_output_embeddings()
                      assert x is None
                      lowerCamelCase__           =		model.get_bias()
                      assert name is None
    def      _UpperCamelCase       ( self							:					int				):
          # TODO JP: Make TransfoXL XLA compliant
          pass
    @slow
    def      _UpperCamelCase       ( self							:					str				):
          for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                lowerCamelCase__           =		TFTransfoXLModel.from_pretrained(SCREAMING_SNAKE_CASE__				)
                self.assertIsNotNone(SCREAMING_SNAKE_CASE__				)


    @unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.'				)
    def      _UpperCamelCase       ( self							:					Dict				):
          pass





@require_tf
class   _a      (      unittest.TestCase ):
    @unittest.skip('Skip test until #12651 is resolved.'				)
    @slow
    def      _UpperCamelCase       ( self							:					Dict				):
          lowerCamelCase__           =		TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103'				)
          # fmt: off
          lowerCamelCase__           =		tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]]							,					dtype=tf.intaa				)  # noqa: E231
          # fmt: on
          #  In 1991 , the remains of Russian Tsar Nicholas II and his family
          #  ( except for Alexei and Maria ) are discovered .
          #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
          #  remainder of the story . 1883 Western Siberia ,
          #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
          #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
          #  father initially slaps him for making such an accusation , Rasputin watches as the
          #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
          #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
          #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

          # fmt: off
          lowerCamelCase__           =		[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0]  # noqa: E231
          # fmt: on
          #  In 1991, the remains of Russian Tsar Nicholas II and his family (
          #  except for Alexei and Maria ) are discovered. The voice of young son,
          #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
          #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
          #  and a group of men to perform magic. Rasputin has a vision and
          #  denounces one of the men as a horse thief. Although his father initially
          #  slaps him for making such an accusation, Rasputin watches as the man
          #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
          #  of the Virgin Mary, prompting him to become a priest.
          #  Rasputin quickly becomes famous, with people, even a bishop, begging for
          #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
          # Nicholas II and his family were discovered. The voice of <unk> young son,
          # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

          lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__							,					max_length=2_00							,					do_sample=SCREAMING_SNAKE_CASE__				)
          self.assertListEqual(output_ids[0].numpy().tolist()							,					SCREAMING_SNAKE_CASE__				)







"""simple docstring"""


from __future__ import annotations

from math import gcd



def   snake_case						(				_a:     int		,   _a:     int = 2		,   _a:     int = 1		,   _a:     int = 3		,   )->		int | None:
      '''simple docstring'''


      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(_a:     int		,   _a:     int		,   _a:     int							) -> int:
            return (pow(_a		,   2							) + step) % modulus

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

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

                  # 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``.
                  lowerCamelCase__           =		gcd(hare - tortoise		,   _a							)

                  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.
            lowerCamelCase__           =		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

   _snake_case          =							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",
   )
   _snake_case          =							parser.parse_args()

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



