



'''simple docstring'''



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 _UpperCamelCase		(     unittest.TestCase		):





				'''simple docstring'''





				def __init__(							self  :  Optional[Any]  ,       _lowerCAmelCase  :  List[Any]  ,       _lowerCAmelCase  :  int=1_3  ,       _lowerCAmelCase  :  Tuple=7  ,       _lowerCAmelCase  :  int=True  ,       _lowerCAmelCase  :  Dict=True  ,       _lowerCAmelCase  :  str=True  ,       _lowerCAmelCase  :  int=True  ,       _lowerCAmelCase  :  Dict=9_9  ,       _lowerCAmelCase  :  List[str]=3_2  ,       _lowerCAmelCase  :  int=5  ,       _lowerCAmelCase  :  Dict=4  ,       _lowerCAmelCase  :  Dict=3_7  ,       _lowerCAmelCase  :  int="gelu"  ,       _lowerCAmelCase  :  Optional[Any]=0.1  ,       _lowerCAmelCase  :  int=0.1  ,       _lowerCAmelCase  :  int=5_1_2  ,       _lowerCAmelCase  :  int=1_6  ,       _lowerCAmelCase  :  int=2  ,       _lowerCAmelCase  :  Tuple=0.02  ,       _lowerCAmelCase  :  int=4  ,       ):







											'''simple docstring'''

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

				def 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

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

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

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

											__lowercase       	=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=_lowerCAmelCase  ,       initializer_range=self.initializer_range  ,       )

											return config, input_ids, token_type_ids, attention_mask

				def 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

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

				def 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

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

											__lowercase       	=True
											__lowercase       	=floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
											__lowercase       	=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 _UpperCamelCase		(     A      ,       unittest.TestCase		):





				'''simple docstring'''





				lowerCAmelCase__					    =    True

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

				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

											__lowercase       	=FlaxRobertaPreLayerNormModelTester(self)

				@slow
				def 				__lowerCamelCase     (							self  :  Any):







											'''simple docstring'''

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





@require_flax
class _UpperCamelCase		(     unittest.TestCase		):





				'''simple docstring'''





				@slow
				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

											__lowercase       	=FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'  ,       from_pt=_lowerCAmelCase)

											__lowercase       	=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)
											__lowercase       	=model(_lowerCAmelCase)[0]
											__lowercase       	=[1, 1_1, 5_0_2_6_5]
											self.assertEqual(list(output.shape)  ,       _lowerCAmelCase)
											# compare the actual values for a slice.
											__lowercase       	=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]  ,       _lowerCAmelCase  ,       atol=1e-4))

				@slow
				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											__lowercase       	=FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'  ,       from_pt=_lowerCAmelCase)

											__lowercase       	=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)
											__lowercase       	=model(_lowerCAmelCase)[0]
											# compare the actual values for a slice.
											__lowercase       	=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]  ,       _lowerCAmelCase  ,       atol=1e-4))








'''simple docstring'''



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							if not isinstance(_lowerCAmelCase  ,   _lowerCAmelCase					):
														raise TypeError('only integers accepted as input'					)
							else:
														__lowercase       	=str(abs(_lowerCAmelCase					)					)
														__lowercase       	=[list(_lowerCAmelCase					) for char in range(len(_lowerCAmelCase					)					)]
														for index in range(len(_lowerCAmelCase					)					):
																					num_transpositions[index].pop(_lowerCAmelCase					)
														return max(
														    int(''.join(list(_lowerCAmelCase					)					)					) for transposition in num_transpositions					)


if __name__ == "__main__":
		__import__("""doctest""").testmod()



