

import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
      import jax.numpy as jnp

      from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
          FlaxRobertaPreLayerNormForCausalLM,
          FlaxRobertaPreLayerNormForMaskedLM,
          FlaxRobertaPreLayerNormForMultipleChoice,
          FlaxRobertaPreLayerNormForQuestionAnswering,
          FlaxRobertaPreLayerNormForSequenceClassification,
          FlaxRobertaPreLayerNormForTokenClassification,
          FlaxRobertaPreLayerNormModel,
      )

class    _lowercase				(					unittest.TestCase			):

   def __init__(				self	:    int			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    Optional[int]=1_3			,						lowerCamelCase__	:    str=7			,						lowerCamelCase__	:    Tuple=True			,						lowerCamelCase__	:    Dict=True			,						lowerCamelCase__	:    Dict=True			,						lowerCamelCase__	:    List[str]=True			,						lowerCamelCase__	:    int=9_9			,						lowerCamelCase__	:    int=3_2			,						lowerCamelCase__	:    Union[str, Any]=5			,						lowerCamelCase__	:    str=4			,						lowerCamelCase__	:    Dict=3_7			,						lowerCamelCase__	:    str="gelu"			,						lowerCamelCase__	:    Tuple=0.1			,						lowerCamelCase__	:    Optional[Any]=0.1			,						lowerCamelCase__	:    List[str]=5_1_2			,						lowerCamelCase__	:    Optional[int]=1_6			,						lowerCamelCase__	:    Dict=2			,						lowerCamelCase__	:    int=0.02			,						lowerCamelCase__	:    str=4			,						) ->			List[Any]:
         """simple docstring"""





         A_							       =		parent
         A_							       =		batch_size
         A_							       =		seq_length
         A_							       =		is_training
         A_							       =		use_attention_mask
         A_							       =		use_token_type_ids
         A_							       =		use_labels
         A_							       =		vocab_size
         A_							       =		hidden_size
         A_							       =		num_hidden_layers
         A_							       =		num_attention_heads
         A_							       =		intermediate_size
         A_							       =		hidden_act
         A_							       =		hidden_dropout_prob
         A_							       =		attention_probs_dropout_prob
         A_							       =		max_position_embeddings
         A_							       =		type_vocab_size
         A_							       =		type_sequence_label_size
         A_							       =		initializer_range
         A_							       =		num_choices

   def     UpperCamelCase					(				self	:    Tuple       ) ->			str:
         """simple docstring"""





         A_							       =		ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size       )

         A_							       =		None
         if self.use_attention_mask:
               A_							       =		random_attention_mask([self.batch_size, self.seq_length]       )

         A_							       =		None
         if self.use_token_type_ids:
               A_							       =		ids_tensor([self.batch_size, self.seq_length]			,						self.type_vocab_size       )

         A_							       =		RobertaPreLayerNormConfig(
             vocab_size=self.vocab_size			,						hidden_size=self.hidden_size			,						num_hidden_layers=self.num_hidden_layers			,						num_attention_heads=self.num_attention_heads			,						intermediate_size=self.intermediate_size			,						hidden_act=self.hidden_act			,						hidden_dropout_prob=self.hidden_dropout_prob			,						attention_probs_dropout_prob=self.attention_probs_dropout_prob			,						max_position_embeddings=self.max_position_embeddings			,						type_vocab_size=self.type_vocab_size			,						is_decoder=snake_case__			,						initializer_range=self.initializer_range			,						)

         return config, input_ids, token_type_ids, attention_mask

   def     UpperCamelCase					(				self	:    Tuple       ) ->			Dict:
         """simple docstring"""





         A_							       =		self.prepare_config_and_inputs()
         A_       ,A_       ,A_       ,A_							       =		config_and_inputs
         A_							       =		{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
         return config, inputs_dict





   def     UpperCamelCase					(				self	:    str       ) ->			Tuple:
         """simple docstring"""





         A_							       =		self.prepare_config_and_inputs()
         A_       ,A_       ,A_       ,A_							       =		config_and_inputs

         A_							       =		True
         A_							       =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
         A_							       =		ids_tensor([self.batch_size, self.seq_length]			,						vocab_size=2       )

         return (
             config,
             input_ids,
             token_type_ids,
             encoder_hidden_states,
             encoder_attention_mask,
         )



@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class    _lowercase				(					__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       List[str]  					=    True

   _lowercase     :       List[str]  					=    (
       (
           FlaxRobertaPreLayerNormModel,
           FlaxRobertaPreLayerNormForCausalLM,
           FlaxRobertaPreLayerNormForMaskedLM,
           FlaxRobertaPreLayerNormForSequenceClassification,
           FlaxRobertaPreLayerNormForTokenClassification,
           FlaxRobertaPreLayerNormForMultipleChoice,
           FlaxRobertaPreLayerNormForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )

   def     UpperCamelCase					(				self	:    str       ) ->			List[str]:
         """simple docstring"""





         A_							       =		FlaxRobertaPreLayerNormModelTester(self       )





   @slow
   def     UpperCamelCase					(				self	:    str       ) ->			Any:
         """simple docstring"""





         for model_class_name in self.all_model_classes:
               A_							       =		model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''			,						from_pt=snake_case__       )
               A_							       =		model(np.ones((1, 1)       )       )
               self.assertIsNotNone(snake_case__       )



@require_flax
class    _lowercase				(					unittest.TestCase			):

   @slow
   def     UpperCamelCase					(				self	:    Tuple       ) ->			str:
         """simple docstring"""





         A_							       =		FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''			,						from_pt=snake_case__       )

         A_							       =		np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]			,						dtype=jnp.intaa       )
         A_							       =		model(snake_case__       )[0]
         A_							       =		[1, 1_1, 5_0_2_6_5]
         self.assertEqual(list(output.shape       )			,						snake_case__       )
         # compare the actual values for a slice.
         A_							       =		np.array(
             [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]			,						dtype=np.floataa       )
         self.assertTrue(np.allclose(output[:, :3, :3]			,						snake_case__			,						atol=1e-4       )       )





   @slow
   def     UpperCamelCase					(				self	:    str       ) ->			Tuple:
         """simple docstring"""





         A_							       =		FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''			,						from_pt=snake_case__       )

         A_							       =		np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]			,						dtype=jnp.intaa       )
         A_							       =		model(snake_case__       )[0]
         # compare the actual values for a slice.
         A_							       =		np.array(
             [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]			,						dtype=np.floataa       )
         self.assertTrue(np.allclose(output[:, :3, :3]			,						snake_case__			,						atol=1e-4       )       )








from __future__ import annotations

from math import gcd


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	int = 2		,				_UpperCamelCase		:	int = 1		,				_UpperCamelCase		:	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(_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

      a_     :    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",
      )
      a_     :    Tuple     		=   parser.parse_args()

      a_     :    int     		=   pollard_rho(args.num, attempts=args.attempts)
      if divisor is None:
            print(F"""{args.num} is probably prime""")
      else:
            a_     :    Union[str, Any]     		=   args.num // divisor
            print(F"""{args.num} = {divisor} * {quotient}""")

