






"""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        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							def __init__(   self:     List[str]				,		__A:     Optional[Any]				,		__A:     List[Any]=13				,		__A:     Optional[int]=7				,		__A:     Any=True				,		__A:     str=True				,		__A:     Any=True				,		__A:     str=True				,		__A:     Optional[Any]=99				,		__A:     Union[str, Any]=32				,		__A:     str=5				,		__A:     Any=4				,		__A:     List[str]=37				,		__A:     Union[str, Any]="gelu"				,		__A:     str=0.1				,		__A:     Tuple=0.1				,		__A:     Optional[Any]=512				,		__A:     Union[str, Any]=16				,		__A:     str=2				,		__A:     Any=0.0_2				,		__A:     Tuple=4				,		):

												'''simple docstring'''

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




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

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

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

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

												a__          =  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  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

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





							def  lowercase		(   self:     int    ):

												'''simple docstring'''

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

												a__          =  True
												a__          =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
												a__          =  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						(  __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =	True

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




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormModelTester(self    )





							@slow
							def  lowercase		(   self:     int    ):

												'''simple docstring'''

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







@require_flax
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@slow
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )

												a__          =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				,		dtype=jnp.intaa    )
												a__          =  model(__A    )[0]
												a__          =  [1, 11, 50265]
												self.assertEqual(list(output.shape    )				,		__A    )
												# compare the actual values for a slice.
												a__          =  np.array(
												    [[[40.4880, 18.0199, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 10.7085], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]				,		dtype=np.floataa    )
												self.assertTrue(np.allclose(output[:, :3, :3]				,		__A				,		atol=1e-4    )    )





							@slow
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )

												a__          =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				,		dtype=jnp.intaa    )
												a__          =  model(__A    )[0]
												# compare the actual values for a slice.
												a__          =  np.array(
												    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]				,		dtype=np.floataa    )
												self.assertTrue(np.allclose(output[:, :3, :3]				,		__A				,		atol=1e-4    )    )










"""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        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							def __init__(   self:     List[str]				,		__A:     Optional[Any]				,		__A:     List[Any]=13				,		__A:     Optional[int]=7				,		__A:     Any=True				,		__A:     str=True				,		__A:     Any=True				,		__A:     str=True				,		__A:     Optional[Any]=99				,		__A:     Union[str, Any]=32				,		__A:     str=5				,		__A:     Any=4				,		__A:     List[str]=37				,		__A:     Union[str, Any]="gelu"				,		__A:     str=0.1				,		__A:     Tuple=0.1				,		__A:     Optional[Any]=512				,		__A:     Union[str, Any]=16				,		__A:     str=2				,		__A:     Any=0.0_2				,		__A:     Tuple=4				,		):

												'''simple docstring'''

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




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

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

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

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

												a__          =  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  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

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





							def  lowercase		(   self:     int    ):

												'''simple docstring'''

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

												a__          =  True
												a__          =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
												a__          =  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						(  __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =True

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




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormModelTester(self    )





							@slow
							def  lowercase		(   self:     int    ):

												'''simple docstring'''

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







@require_flax
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@slow
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )

												a__          =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				,		dtype=jnp.intaa    )
												a__          =  model(__A    )[0]
												a__          =  [1, 11, 50265]
												self.assertEqual(list(output.shape    )				,		__A    )
												# compare the actual values for a slice.
												a__          =  np.array(
												    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]				,		dtype=np.floataa    )
												self.assertTrue(np.allclose(output[:, :3, :3]				,		__A				,		atol=1e-4    )    )





							@slow
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )

												a__          =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				,		dtype=jnp.intaa    )
												a__          =  model(__A    )[0]
												# compare the actual values for a slice.
												a__          =  np.array(
												    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]				,		dtype=np.floataa    )
												self.assertTrue(np.allclose(output[:, :3, :3]				,		__A				,		atol=1e-4    )    )


