



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 				__snake_case						(     unittest.TestCase						):




							def __init__(       self     :       int				,				A_     :       List[str]				,				A_     :       Dict=1_3				,				A_     :       Dict=7				,				A_     :       int=True				,				A_     :       Optional[int]=True				,				A_     :       List[str]=True				,				A_     :       Union[str, Any]=True				,				A_     :       Any=9_9				,				A_     :       List[str]=3_2				,				A_     :       int=5				,				A_     :       str=4				,				A_     :       Any=3_7				,				A_     :       Dict="gelu"				,				A_     :       Any=0.1				,				A_     :       Any=0.1				,				A_     :       Tuple=5_1_2				,				A_     :       Optional[int]=1_6				,				A_     :       str=2				,				A_     :       Optional[int]=0.02				,				A_     :       Optional[int]=4				,				):
									lowerCAmelCase_			:     List[Any]									= parent
									lowerCAmelCase_			:     Tuple									= batch_size
									lowerCAmelCase_			:     int									= seq_length
									lowerCAmelCase_			:     str									= is_training
									lowerCAmelCase_			:     Optional[Any]									= use_attention_mask
									lowerCAmelCase_			:     Optional[int]									= use_token_type_ids
									lowerCAmelCase_			:     str									= use_labels
									lowerCAmelCase_			:     Dict									= vocab_size
									lowerCAmelCase_			:     Union[str, Any]									= hidden_size
									lowerCAmelCase_			:     Tuple									= num_hidden_layers
									lowerCAmelCase_			:     str									= num_attention_heads
									lowerCAmelCase_			:     Any									= intermediate_size
									lowerCAmelCase_			:     Any									= hidden_act
									lowerCAmelCase_			:     Dict									= hidden_dropout_prob
									lowerCAmelCase_			:     Dict									= attention_probs_dropout_prob
									lowerCAmelCase_			:     int									= max_position_embeddings
									lowerCAmelCase_			:     List[Any]									= type_vocab_size
									lowerCAmelCase_			:     List[str]									= type_sequence_label_size
									lowerCAmelCase_			:     List[str]									= initializer_range
									lowerCAmelCase_			:     Any									= num_choices




							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_			:     List[Any]									= ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size)

									lowerCAmelCase_			:     Dict									= None
									if self.use_attention_mask:
											lowerCAmelCase_			:     Optional[int]									= random_attention_mask([self.batch_size, self.seq_length])

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

									lowerCAmelCase_			:     Optional[int]									= 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=A_				,				initializer_range=self.initializer_range				,				)

									return config, input_ids, token_type_ids, attention_mask




							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_			:     str									= self.prepare_config_and_inputs()
									lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Tuple									= config_and_inputs
									lowerCAmelCase_			:     List[str]									= {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
									return config, inputs_dict







							def    UpperCAmelCase__			(       self     :       Optional[int]):
									lowerCAmelCase_			:     Any									= self.prepare_config_and_inputs()
									lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     str									= config_and_inputs

									lowerCAmelCase_			:     Tuple									= True
									lowerCAmelCase_			:     Union[str, Any]									= floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
									lowerCAmelCase_			:     Optional[Any]									= 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 				__snake_case						(     UpperCamelCase_						,unittest.TestCase						):
							_a			      = True

							_a			      = (
							    (
							        FlaxRobertaPreLayerNormModel,
							        FlaxRobertaPreLayerNormForCausalLM,
							        FlaxRobertaPreLayerNormForMaskedLM,
							        FlaxRobertaPreLayerNormForSequenceClassification,
							        FlaxRobertaPreLayerNormForTokenClassification,
							        FlaxRobertaPreLayerNormForMultipleChoice,
							        FlaxRobertaPreLayerNormForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)




							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     Optional[int]									= FlaxRobertaPreLayerNormModelTester(self)







							@slow
							def    UpperCAmelCase__			(       self     :       str):
									for model_class_name in self.all_model_classes:
											lowerCAmelCase_			:     Tuple									= model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,				from_pt=A_)
											lowerCAmelCase_			:     int									= model(np.ones((1, 1)))
											self.assertIsNotNone(A_)





@require_flax
class 				__snake_case						(     unittest.TestCase						):




							@slow
							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     str									= FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,				from_pt=A_)

									lowerCAmelCase_			:     Dict									= 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)
									lowerCAmelCase_			:     Union[str, Any]									= model(A_)[0]
									lowerCAmelCase_			:     Any									= [1, 1_1, 5_0_2_6_5]
									self.assertEqual(list(output.shape)				,				A_)
									# compare the actual values for a slice.
									lowerCAmelCase_			:     Union[str, Any]									= 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]				,				A_				,				atol=1e-4))







							@slow
							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     List[str]									= FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,				from_pt=A_)

									lowerCAmelCase_			:     str									= 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)
									lowerCAmelCase_			:     List[Any]									= model(A_)[0]
									# compare the actual values for a slice.
									lowerCAmelCase_			:     Any									= 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]				,				A_				,				atol=1e-4))












def  _SCREAMING_SNAKE_CASE				(   a					,						a = 0  )      ->      list:
					__A	:						int       =				length or len(a  )
					__A	:						str       =				False
					for i in range(length - 1  ):
										if list_data[i] > list_data[i + 1]:
															__A    ,				__A	:						Optional[int]       =				list_data[i + 1], list_data[i]
															__A	:						Union[str, Any]       =				True

					return list_data if not swapped else bubble_sort(a					,						length - 1  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




