






import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    DebertaVaForMaskedLM,
							    DebertaVaForMultipleChoice,
							    DebertaVaForQuestionAnswering,
							    DebertaVaForSequenceClassification,
							    DebertaVaForTokenClassification,
							    DebertaVaModel,
							)
							from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST

class 				lowerCAmelCase_	(		__snake_case			):


				def __init__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase=1_3 ,						_lowerCAmelCase=7 ,						_lowerCAmelCase=True ,						_lowerCAmelCase=True ,						_lowerCAmelCase=True ,						_lowerCAmelCase=True ,						_lowerCAmelCase=9_9 ,						_lowerCAmelCase=3_2 ,						_lowerCAmelCase=5 ,						_lowerCAmelCase=4 ,						_lowerCAmelCase=3_7 ,						_lowerCAmelCase="gelu" ,						_lowerCAmelCase=0.1 ,						_lowerCAmelCase=0.1 ,						_lowerCAmelCase=5_1_2 ,						_lowerCAmelCase=1_6 ,						_lowerCAmelCase=2 ,						_lowerCAmelCase=0.02 ,						_lowerCAmelCase=False ,						_lowerCAmelCase=True ,						_lowerCAmelCase="None" ,						_lowerCAmelCase=3 ,						_lowerCAmelCase=4 ,						_lowerCAmelCase=None ,						):
											_lowercase							:   Any 							=   parent
											_lowercase							:   Optional[Any] 							=   batch_size
											_lowercase							:   Union[str, Any] 							=   seq_length
											_lowercase							:   List[Any] 							=   is_training
											_lowercase							:   Any 							=   use_input_mask
											_lowercase							:   Dict 							=   use_token_type_ids
											_lowercase							:   Tuple 							=   use_labels
											_lowercase							:   Union[str, Any] 							=   vocab_size
											_lowercase							:   Optional[Any] 							=   hidden_size
											_lowercase							:   Optional[int] 							=   num_hidden_layers
											_lowercase							:   List[str] 							=   num_attention_heads
											_lowercase							:   Tuple 							=   intermediate_size
											_lowercase							:   int 							=   hidden_act
											_lowercase							:   str 							=   hidden_dropout_prob
											_lowercase							:   Any 							=   attention_probs_dropout_prob
											_lowercase							:   Dict 							=   max_position_embeddings
											_lowercase							:   List[Any] 							=   type_vocab_size
											_lowercase							:   int 							=   type_sequence_label_size
											_lowercase							:   Dict 							=   initializer_range
											_lowercase							:   Dict 							=   num_labels
											_lowercase							:   Tuple 							=   num_choices
											_lowercase							:   Optional[int] 							=   relative_attention
											_lowercase							:   int 							=   position_biased_input
											_lowercase							:   int 							=   pos_att_type
											_lowercase							:   str 							=   scope


				def 						__a					(					self      ):
											_lowercase							:   Union[str, Any] 							=   ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size      )

											_lowercase							:   Optional[int] 							=   None
											if self.use_input_mask:
																		_lowercase							:   Tuple 							=   ids_tensor([self.batch_size, self.seq_length] ,						vocab_size=2      )

											_lowercase							:   str 							=   None
											if self.use_token_type_ids:
																		_lowercase							:   List[Any] 							=   ids_tensor([self.batch_size, self.seq_length] ,						self.type_vocab_size      )

											_lowercase							:   Optional[Any] 							=   None
											_lowercase							:   List[str] 							=   None
											_lowercase							:   Any 							=   None
											if self.use_labels:
																		_lowercase							:   Union[str, Any] 							=   ids_tensor([self.batch_size] ,						self.type_sequence_label_size      )
																		_lowercase							:   int 							=   ids_tensor([self.batch_size, self.seq_length] ,						self.num_labels      )
																		_lowercase							:   Union[str, Any] 							=   ids_tensor([self.batch_size] ,						self.num_choices      )

											_lowercase							:   Optional[Any] 							=   self.get_config()

											return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


				def 						__a					(					self      ):
											return DebertaVaConfig(
											    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 ,						initializer_range=self.initializer_range ,						relative_attention=self.relative_attention ,						position_biased_input=self.position_biased_input ,						pos_att_type=self.pos_att_type ,						)


				def 						__a					(					self ,						_lowerCAmelCase      ):
											self.parent.assertListEqual(list(result.loss.size()      ) ,						[]      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   Optional[Any] 							=   DebertaVaModel(config=_lowerCAmelCase      )
											model.to(_lowerCAmelCase      )
											model.eval()
											_lowercase							:   Tuple 							=   model(_lowerCAmelCase ,						attention_mask=_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase      )[0]
											_lowercase							:   int 							=   model(_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase      )[0]
											_lowercase							:   Tuple 							=   model(_lowerCAmelCase      )[0]

											self.parent.assertListEqual(list(sequence_output.size()      ) ,						[self.batch_size, self.seq_length, self.hidden_size]      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   Optional[Any] 							=   DebertaVaForMaskedLM(config=_lowerCAmelCase      )
											model.to(_lowerCAmelCase      )
											model.eval()
											_lowercase							:   str 							=   model(_lowerCAmelCase ,						attention_mask=_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase ,						labels=_lowerCAmelCase      )

											self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.vocab_size)      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   Dict 							=   self.num_labels
											_lowercase							:   Optional[int] 							=   DebertaVaForSequenceClassification(_lowerCAmelCase      )
											model.to(_lowerCAmelCase      )
											model.eval()
											_lowercase							:   Optional[int] 							=   model(_lowerCAmelCase ,						attention_mask=_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase ,						labels=_lowerCAmelCase      )
											self.parent.assertListEqual(list(result.logits.size()      ) ,						[self.batch_size, self.num_labels]      )
											self.check_loss_output(_lowerCAmelCase      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   Any 							=   self.num_labels
											_lowercase							:   List[str] 							=   DebertaVaForTokenClassification(config=_lowerCAmelCase      )
											model.to(_lowerCAmelCase      )
											model.eval()
											_lowercase							:   int 							=   model(_lowerCAmelCase ,						attention_mask=_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase ,						labels=_lowerCAmelCase      )
											self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.num_labels)      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   Union[str, Any] 							=   DebertaVaForQuestionAnswering(config=_lowerCAmelCase      )
											model.to(_lowerCAmelCase      )
											model.eval()
											_lowercase							:   Optional[int] 							=   model(
											    _lowerCAmelCase ,						attention_mask=_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase ,						start_positions=_lowerCAmelCase ,						end_positions=_lowerCAmelCase ,						)
											self.parent.assertEqual(result.start_logits.shape ,						(self.batch_size, self.seq_length)      )
											self.parent.assertEqual(result.end_logits.shape ,						(self.batch_size, self.seq_length)      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   Tuple 							=   DebertaVaForMultipleChoice(config=_lowerCAmelCase      )
											model.to(_lowerCAmelCase      )
											model.eval()
											_lowercase							:   Any 							=   input_ids.unsqueeze(1      ).expand(-1 ,						self.num_choices ,						-1      ).contiguous()
											_lowercase							:   List[str] 							=   token_type_ids.unsqueeze(1      ).expand(-1 ,						self.num_choices ,						-1      ).contiguous()
											_lowercase							:   int 							=   input_mask.unsqueeze(1      ).expand(-1 ,						self.num_choices ,						-1      ).contiguous()
											_lowercase							:   int 							=   model(
											    _lowerCAmelCase ,						attention_mask=_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase ,						labels=_lowerCAmelCase ,						)
											self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_choices)      )


				def 						__a					(					self      ):
											_lowercase							:   Dict 							=   self.prepare_config_and_inputs()
											(
											    (
											    _lowercase
											)							,							(
											    _lowercase
											)							,							(
											    _lowercase
											)							,							(
											    _lowercase
											)							,							(
											    _lowercase
											)							,							(
											    _lowercase
											)							,							(
											    _lowercase
											)							,							
											)							:   int 							=   config_and_inputs
											_lowercase							:   Any 							=   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
											return config, inputs_dict




@require_torch
class 				lowerCAmelCase_	(		__snake_case   ,		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       Union[str, Any]							       = (
				    (
				        DebertaVaModel,
				        DebertaVaForMaskedLM,
				        DebertaVaForSequenceClassification,
				        DebertaVaForTokenClassification,
				        DebertaVaForQuestionAnswering,
				        DebertaVaForMultipleChoice,
				    )
				    if is_torch_available()
				    else ()
				)
				_UpperCamelCase					:       Optional[int]							       = (
				    {
				        "feature-extraction": DebertaVaModel,
				        "fill-mask": DebertaVaForMaskedLM,
				        "question-answering": DebertaVaForQuestionAnswering,
				        "text-classification": DebertaVaForSequenceClassification,
				        "token-classification": DebertaVaForTokenClassification,
				        "zero-shot": DebertaVaForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)

				_UpperCamelCase					:       List[Any]							       = True
				_UpperCamelCase					:       List[str]							       = False
				_UpperCamelCase					:       Any							       = False
				_UpperCamelCase					:       Dict							       = False
				_UpperCamelCase					:       Optional[Any]							       = False


				def 						__a					(					self      ):
											_lowercase							:   Any 							=   DebertaVaModelTester(self      )
											_lowercase							:   Optional[int] 							=   ConfigTester(self ,						config_class=_lowerCAmelCase ,						hidden_size=3_7      )


				def 						__a					(					self      ):
											self.config_tester.run_common_tests()


				def 						__a					(					self      ):
											_lowercase							:   Optional[int] 							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_model(*_lowerCAmelCase      )


				def 						__a					(					self      ):
											_lowercase							:   Tuple 							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_sequence_classification(*_lowerCAmelCase      )


				def 						__a					(					self      ):
											_lowercase							:   Tuple 							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_masked_lm(*_lowerCAmelCase      )


				def 						__a					(					self      ):
											_lowercase							:   str 							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_question_answering(*_lowerCAmelCase      )


				def 						__a					(					self      ):
											_lowercase							:   Dict 							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_token_classification(*_lowerCAmelCase      )


				def 						__a					(					self      ):
											_lowercase							:   Any 							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_multiple_choice(*_lowerCAmelCase      )


				@slow
				def 						__a					(					self      ):
											for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		_lowercase							:   Any 							=   DebertaVaModel.from_pretrained(_lowerCAmelCase      )
																		self.assertIsNotNone(_lowerCAmelCase      )




@require_torch
@require_sentencepiece
@require_tokenizers
class 				lowerCAmelCase_	(		unittest.TestCase			):


				@unittest.skip(reason='Model not available yet'      )
				def 						__a					(					self      ):
											pass


				@slow
				def 						__a					(					self      ):
											_lowercase							:   int 							=   DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge'      )

											_lowercase							:   Optional[Any] 							=   torch.tensor([[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]]      )
											_lowercase							:   List[str] 							=   torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
											with torch.no_grad():
																		_lowercase							:   Any 							=   model(_lowerCAmelCase ,						attention_mask=_lowerCAmelCase      )[0]
											# compare the actual values for a slice.
											_lowercase							:   Optional[int] 							=   torch.tensor(
											    [[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]]      )
											self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,						_lowerCAmelCase ,						atol=1E-4      ) ,						F"""{output[:, 1:4, 1:4]}"""      )









'''simple docstring'''

from __future__ import annotations

from math import gcd



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_ = 2		,     lowerCAmelCase_ = 1		,     lowerCAmelCase_ = 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(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    ) -> int:
				return (pow(lowerCAmelCase_		,     2    ) + step) % modulus

		for _ in range(lowerCAmelCase_    ):
				# These track the position within the cycle detection logic.
				_a	: Optional[int]								=				seed
				_a	: str								=				seed

				while True:
						# At each iteration, the tortoise moves one step and the hare moves two.
						_a	: str								=				rand_fn(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )
						_a	: Dict								=				rand_fn(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )
						_a	: Tuple								=				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	: Optional[Any]								=				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	: Union[str, Any]								=				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}""")




