
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   SCREAMING_SNAKE_CASE__	(      unittest.TestCase   ):





	'''simple docstring'''



	def __init__(    self      :       List[Any]     ,    lowercase      :       Dict     ,    lowercase      :       Dict=13     ,    lowercase      :       Optional[int]=7     ,    lowercase      :       Optional[Any]=True     ,    lowercase      :       Union[str, Any]=True     ,    lowercase      :       Optional[Any]=True     ,    lowercase      :       Optional[Any]=True     ,    lowercase      :       List[Any]=99     ,    lowercase      :       Any=32     ,    lowercase      :       List[str]=5     ,    lowercase      :       Union[str, Any]=4     ,    lowercase      :       Union[str, Any]=37     ,    lowercase      :       Optional[int]="gelu"     ,    lowercase      :       Tuple=0.1     ,    lowercase      :       List[str]=0.1     ,    lowercase      :       Optional[Any]=512     ,    lowercase      :       int=16     ,    lowercase      :       str=2     ,    lowercase      :       int=0.02     ,    lowercase      :       int=4     ,    ):


				'''simple docstring'''


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



	def 					A				(    self      :       Dict      ):


				'''simple docstring'''


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

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

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

				_snake_case            =     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=lowercase     ,    initializer_range=self.initializer_range     ,    )

				return config, input_ids, token_type_ids, attention_mask



	def 					A				(    self      :       List[str]      ):


				'''simple docstring'''


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





	def 					A				(    self      :       Optional[int]      ):


				'''simple docstring'''


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

				_snake_case            =     True
				_snake_case            =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
				_snake_case            =     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   SCREAMING_SNAKE_CASE__	(      A__				,unittest.TestCase   ):





	'''simple docstring'''



	_UpperCAmelCase			:						Tuple         =   True

	_UpperCAmelCase			:						Optional[int]         =   (
	    (
	        FlaxRobertaPreLayerNormModel,
	        FlaxRobertaPreLayerNormForCausalLM,
	        FlaxRobertaPreLayerNormForMaskedLM,
	        FlaxRobertaPreLayerNormForSequenceClassification,
	        FlaxRobertaPreLayerNormForTokenClassification,
	        FlaxRobertaPreLayerNormForMultipleChoice,
	        FlaxRobertaPreLayerNormForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)



	def 					A				(    self      :       int      ):


				'''simple docstring'''


				_snake_case            =     FlaxRobertaPreLayerNormModelTester(self      )





	@slow
	def 					A				(    self      :       List[Any]      ):


				'''simple docstring'''


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




@require_flax
class   SCREAMING_SNAKE_CASE__	(      unittest.TestCase   ):





	'''simple docstring'''



	@slow
	def 					A				(    self      :       Union[str, Any]      ):


				'''simple docstring'''


				_snake_case            =     FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'     ,    from_pt=lowercase      )

				_snake_case            =     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]     ,    dtype=jnp.intaa      )
				_snake_case            =     model(lowercase      )[0]
				_snake_case            =     [1, 11, 50_265]
				self.assertEqual(list(output.shape      )     ,    lowercase      )
				# compare the actual values for a slice.
				_snake_case            =     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]     ,    lowercase     ,    atol=1E-4      )      )





	@slow
	def 					A				(    self      :       Any      ):


				'''simple docstring'''


				_snake_case            =     FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'     ,    from_pt=lowercase      )

				_snake_case            =     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]     ,    dtype=jnp.intaa      )
				_snake_case            =     model(lowercase      )[0]
				# compare the actual values for a slice.
				_snake_case            =     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]     ,    lowercase     ,    atol=1E-4      )      )







def     a     (		lowerCamelCase_							):






				'''simple docstring'''

				if p < 2:
								raise ValueError('''p should not be less than 2!'''							)
				elif p == 2:
								return True

				lowercase__  					=     4
				lowercase__  					=     (1 << p) - 1
				for _ in range(p - 2							):
								lowercase__  					=     ((s * s) - 2) % m
				return s == 0


if __name__ == "__main__":
							print(lucas_lehmer_test(7))
							print(lucas_lehmer_test(11))




