



"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFDebertaVaForMaskedLM,
					    TFDebertaVaForQuestionAnswering,
					    TFDebertaVaForSequenceClassification,
					    TFDebertaVaForTokenClassification,
					    TFDebertaVaModel,
					)



class    _lowerCAmelCase    :





		"""simple docstring"""






		def __init__(					self							,    _lowercase							,    _lowercase=1_3							,    _lowercase=7							,    _lowercase=True							,    _lowercase=True							,    _lowercase=True							,    _lowercase=True							,    _lowercase=9_9							,    _lowercase=3_2							,    _lowercase=2							,    _lowercase=4							,    _lowercase=3_7							,    _lowercase="gelu"							,    _lowercase=0.1							,    _lowercase=0.1							,    _lowercase=5_1_2							,    _lowercase=1_6							,    _lowercase=2							,    _lowercase=0.02							,    _lowercase=False							,    _lowercase=True							,    _lowercase="None"							,    _lowercase=3							,    _lowercase=4							,    _lowercase=None							,    ) ->						Tuple:







								'''simple docstring'''

								snake_case_   : Union[str, Any]						=			parent
								snake_case_   : Tuple						=			batch_size
								snake_case_   : Tuple						=			seq_length
								snake_case_   : List[str]						=			is_training
								snake_case_   : Tuple						=			use_input_mask
								snake_case_   : Dict						=			use_token_type_ids
								snake_case_   : str						=			use_labels
								snake_case_   : List[str]						=			vocab_size
								snake_case_   : Tuple						=			hidden_size
								snake_case_   : Union[str, Any]						=			num_hidden_layers
								snake_case_   : List[Any]						=			num_attention_heads
								snake_case_   : Tuple						=			intermediate_size
								snake_case_   : Tuple						=			hidden_act
								snake_case_   : List[Any]						=			hidden_dropout_prob
								snake_case_   : Optional[Any]						=			attention_probs_dropout_prob
								snake_case_   : List[Any]						=			max_position_embeddings
								snake_case_   : Any						=			type_vocab_size
								snake_case_   : Any						=			type_sequence_label_size
								snake_case_   : Dict						=			initializer_range
								snake_case_   : str						=			num_labels
								snake_case_   : Tuple						=			num_choices
								snake_case_   : Dict						=			relative_attention
								snake_case_   : List[Any]						=			position_biased_input
								snake_case_   : Tuple						=			pos_att_type
								snake_case_   : Dict						=			scope





		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								snake_case_   : str						=			ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size   )

								snake_case_   : int						=			None
								if self.use_input_mask:
														snake_case_   : Optional[Any]						=			random_attention_mask([self.batch_size, self.seq_length]   )

								snake_case_   : Optional[Any]						=			None
								if self.use_token_type_ids:
														snake_case_   : Union[str, Any]						=			ids_tensor([self.batch_size, self.seq_length]							,    self.type_vocab_size   )

								snake_case_   : Optional[int]						=			None
								snake_case_   : int						=			None
								snake_case_   : Optional[Any]						=			None
								if self.use_labels:
														snake_case_   : Tuple						=			ids_tensor([self.batch_size]							,    self.type_sequence_label_size   )
														snake_case_   : Optional[int]						=			ids_tensor([self.batch_size, self.seq_length]							,    self.num_labels   )

								snake_case_   : Optional[int]						=			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							,    relative_attention=self.relative_attention							,    position_biased_input=self.position_biased_input							,    initializer_range=self.initializer_range							,    return_dict=_lowercase							,    )

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





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						str:







								'''simple docstring'''

								snake_case_   : int						=			TFDebertaVaModel(config=_lowercase   )
								snake_case_   : Optional[int]						=			{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

								snake_case_   : Any						=			[input_ids, input_mask]
								snake_case_   : str						=			model(_lowercase   )

								snake_case_   : str						=			model(_lowercase   )

								self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : int						=			TFDebertaVaForMaskedLM(config=_lowercase   )
								snake_case_   : Dict						=			{
								    """input_ids""": input_ids,
								    """attention_mask""": input_mask,
								    """token_type_ids""": token_type_ids,
								}
								snake_case_   : Tuple						=			model(_lowercase   )
								self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.vocab_size)   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						str:







								'''simple docstring'''

								snake_case_   : Any						=			self.num_labels
								snake_case_   : Any						=			TFDebertaVaForSequenceClassification(config=_lowercase   )
								snake_case_   : Tuple						=			{
								    """input_ids""": input_ids,
								    """attention_mask""": input_mask,
								    """token_type_ids""": token_type_ids,
								}

								snake_case_   : int						=			model(_lowercase   )
								self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						str:







								'''simple docstring'''

								snake_case_   : int						=			self.num_labels
								snake_case_   : Union[str, Any]						=			TFDebertaVaForTokenClassification(config=_lowercase   )
								snake_case_   : Optional[Any]						=			{
								    """input_ids""": input_ids,
								    """attention_mask""": input_mask,
								    """token_type_ids""": token_type_ids,
								}
								snake_case_   : Any						=			model(_lowercase   )
								self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.num_labels)   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						Any:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			TFDebertaVaForQuestionAnswering(config=_lowercase   )
								snake_case_   : Optional[int]						=			{
								    """input_ids""": input_ids,
								    """attention_mask""": input_mask,
								    """token_type_ids""": token_type_ids,
								}

								snake_case_   : Optional[Any]						=			model(_lowercase   )
								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    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : Tuple						=			self.prepare_config_and_inputs()
								(
								    (
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		
								)   : str						=			config_and_inputs
								snake_case_   : List[str]						=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
								return config, inputs_dict




@require_tf
class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__  ,				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       (
		    (
		        TFDebertaVaModel,
		        TFDebertaVaForMaskedLM,
		        TFDebertaVaForQuestionAnswering,
		        TFDebertaVaForSequenceClassification,
		        TFDebertaVaForTokenClassification,
		    )
		    if is_tf_available()
		    else ()
		)
		_lowerCamelCase			  =       (
		    {
		        '''feature-extraction''': TFDebertaVaModel,
		        '''fill-mask''': TFDebertaVaForMaskedLM,
		        '''question-answering''': TFDebertaVaForQuestionAnswering,
		        '''text-classification''': TFDebertaVaForSequenceClassification,
		        '''token-classification''': TFDebertaVaForTokenClassification,
		        '''zero-shot''': TFDebertaVaForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)

		_lowerCamelCase			  =       False
		_lowerCamelCase			  =       False





		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_   : int						=			TFDebertaVaModelTester(self   )
								snake_case_   : int						=			ConfigTester(self							,    config_class=_lowercase							,    hidden_size=3_7   )





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								self.config_tester.run_common_tests()





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : str						=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						Optional[Any]:







								'''simple docstring'''

								snake_case_   : Tuple						=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : int						=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : Union[str, Any]						=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*_lowercase   )





		@slow
		def    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""   )
								self.assertIsNotNone(_lowercase   )




@require_tf
class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		@unittest.skip(reason="""Model not available yet"""   )
		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								pass





		@slow
		def    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : int						=			TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""   )
								snake_case_   : Any						=			tf.constant([[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]]   )
								snake_case_   : List[str]						=			tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
								snake_case_   : Optional[int]						=			model(_lowercase							,    attention_mask=_lowercase   )[0]

								snake_case_   : Any						=			tf.constant(
								    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]   )
								tf.debugging.assert_near(output[:, 1:4, 1:4]							,    _lowercase							,    atol=1E-4   )






"""simple docstring"""





def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			bool = False    ):


						'''simple docstring'''






						if n == 2:
												return True
						if not n % 2 or n < 2:
												return False
						if n > 5 and n % 1_0 not in (1, 3, 7, 9):  # can quickly check last digit
												return False
						if n > 3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1 and not allow_probable:
												raise ValueError(
												    """Warning: upper bound of deterministic test is exceeded. """
												    """Pass allow_probable=True to allow probabilistic test. """
												    """A return value of True indicates a probable prime."""    )
						# array bounds provided by analysis
						snake_case_   : List[Any]						=			[
						    2_0_4_7,
						    1_3_7_3_6_5_3,
						    2_5_3_2_6_0_0_1,
						    3_2_1_5_0_3_1_7_5_1,
						    2_1_5_2_3_0_2_8_9_8_7_4_7,
						    3_4_7_4_7_4_9_6_6_0_3_8_3,
						    3_4_1_5_5_0_0_7_1_7_2_8_3_2_1,
						    1,
						    3_8_2_5_1_2_3_0_5_6_5_4_6_4_1_3_0_5_1,
						    1,
						    1,
						    3_1_8_6_6_5_8_5_7_8_3_4_0_3_1_1_5_1_1_6_7_4_6_1,
						    3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1,
						]

						snake_case_   : Dict						=			[2, 3, 5, 7, 1_1, 1_3, 1_7, 1_9, 2_3, 2_9, 3_1, 3_7, 4_1]
						for idx, _p in enumerate(__UpperCamelCase					, 1    ):
												if n < _p:
																		# then we have our last prime to check
																		snake_case_   : Optional[int]						=			primes[:idx]
																		break
						snake_case_			,		snake_case_   : Tuple						=			n - 1, 0
						# break up n -1 into a power of 2 (s) and
						# remaining odd component
						# essentially, solve for d * 2 ** s == n - 1
						while d % 2 == 0:
												d //= 2
												s += 1
						for prime in plist:
												snake_case_   : List[str]						=			False
												for r in range(__UpperCamelCase    ):
																		snake_case_   : int						=			pow(__UpperCamelCase					, d * 2**r					, __UpperCamelCase    )
																		# see article for analysis explanation for m
																		if (r == 0 and m == 1) or ((m + 1) % n == 0):
																								snake_case_   : Optional[Any]						=			True
																								# this loop will not determine compositeness
																								break
												if pr:
																		continue
												# if pr is False, then the above loop never evaluated to true,
												# and the n MUST be composite
												return False
						return True
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						assert not miller_rabin(5_6_1    )
						assert miller_rabin(5_6_3    )
						# 2047

						assert not miller_rabin(8_3_8_2_0_1    )
						assert miller_rabin(8_3_8_2_0_7    )
						# 1_373_653

						assert not miller_rabin(1_7_3_1_6_0_0_1    )
						assert miller_rabin(1_7_3_1_6_0_1_7    )
						# 25_326_001

						assert not miller_rabin(3_0_7_8_3_8_6_6_4_1    )
						assert miller_rabin(3_0_7_8_3_8_6_6_5_3    )
						# 3_215_031_751

						assert not miller_rabin(1_7_1_3_0_4_5_5_7_4_8_0_1    )
						assert miller_rabin(1_7_1_3_0_4_5_5_7_4_8_1_9    )
						# 2_152_302_898_747

						assert not miller_rabin(2_7_7_9_7_9_9_7_2_8_3_0_7    )
						assert miller_rabin(2_7_7_9_7_9_9_7_2_8_3_2_7    )
						# 3_474_749_660_383

						assert not miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_4_4_1    )
						assert miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_5_2_7    )
						# 341_550_071_728_321

						assert not miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_5_1    )
						assert miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_9_1    )
						# 3_825_123_056_546_413_051

						assert not miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_8_6_7    )
						assert miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_9_5_1    )
						# 318_665_857_834_031_151_167_461

						assert not miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_3_3    )
						assert miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_5_9    )
						# 3_317_044_064_679_887_385_961_981
						# upper limit for probabilistic test


if __name__ == "__main__":
					test_miller_rabin()

