

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				,       snake_case				,       snake_case=13				,       snake_case=7				,       snake_case=True				,       snake_case=True				,       snake_case=True				,       snake_case=True				,       snake_case=99				,       snake_case=32				,       snake_case=5				,       snake_case=4				,       snake_case=37				,       snake_case="gelu"				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=512				,       snake_case=16				,       snake_case=2				,       snake_case=0.02				,       snake_case=4				,       ):
											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	):
											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=snake_case				,       initializer_range=self.initializer_range				,       )

											return config, input_ids, token_type_ids, attention_mask




				def    a							( self	):
											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	):
											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 			lowercase    (	lowercase_     ,  unittest.TestCase      ):
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      True

				__SCREAMING_SNAKE_CASE :			str	      =      (
				    (
				        FlaxRobertaPreLayerNormModel,
				        FlaxRobertaPreLayerNormForCausalLM,
				        FlaxRobertaPreLayerNormForMaskedLM,
				        FlaxRobertaPreLayerNormForSequenceClassification,
				        FlaxRobertaPreLayerNormForTokenClassification,
				        FlaxRobertaPreLayerNormForMultipleChoice,
				        FlaxRobertaPreLayerNormForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)




				def    a							( self	):
											snake_case_          =   FlaxRobertaPreLayerNormModelTester(self	)




				@slow
				def    a							( self	):
											for model_class_name in self.all_model_classes:
																		snake_case_          =   model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'				,       from_pt=snake_case	)
																		snake_case_          =   model(np.ones((1, 1)	)	)
																		self.assertIsNotNone(snake_case	)







@require_flax
class 			lowercase    (	unittest.TestCase      ):




				@slow
				def    a							( self	):
											snake_case_          =   FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'				,       from_pt=snake_case	)

											snake_case_          =   np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]				,       dtype=jnp.intaa	)
											snake_case_          =   model(snake_case	)[0]
											snake_case_          =   [1, 11, 5_0265]
											self.assertEqual(list(output.shape	)				,       snake_case	)
											# compare the actual values for a slice.
											snake_case_          =   np.array(
											    [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]				,       dtype=np.floataa	)
											self.assertTrue(np.allclose(output[:, :3, :3]				,       snake_case				,       atol=1e-4	)	)




				@slow
				def    a							( self	):
											snake_case_          =   FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'				,       from_pt=snake_case	)

											snake_case_          =   np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]				,       dtype=jnp.intaa	)
											snake_case_          =   model(snake_case	)[0]
											# compare the actual values for a slice.
											snake_case_          =   np.array(
											    [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]				,       dtype=np.floataa	)
											self.assertTrue(np.allclose(output[:, :3, :3]				,       snake_case				,       atol=1e-4	)	)







from __future__ import annotations





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__ = None       ,							UpperCamelCase__ = None				):







							'''simple docstring'''






							if start is None:
														snake_case_          =   0

							if end is None:
														snake_case_          =   len(UpperCamelCase__				) - 1

							if start >= end:
														return

							snake_case_          =   (start + end) // 2

							slowsort(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)
							slowsort(UpperCamelCase__       ,							mid + 1       ,							UpperCamelCase__				)

							if sequence[end] < sequence[mid]:
														snake_case_    ,   snake_case_          =   sequence[mid], sequence[end]

							slowsort(UpperCamelCase__       ,							UpperCamelCase__       ,							end - 1				)


if __name__ == "__main__":
					from doctest import testmod

					testmod()




