






from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import (
							    TFConvBertForMaskedLM,
							    TFConvBertForMultipleChoice,
							    TFConvBertForQuestionAnswering,
							    TFConvBertForSequenceClassification,
							    TFConvBertForTokenClassification,
							    TFConvBertModel,
							)
class  _UpperCAmelCase   :

				"""simple docstring"""




				def __init__(	self		:       Tuple,    lowerCamelCase		:       List[str],    lowerCamelCase		:       Any=13,    lowerCamelCase		:       Any=7,    lowerCamelCase		:       List[Any]=True,    lowerCamelCase		:       List[str]=True,    lowerCamelCase		:       List[Any]=True,    lowerCamelCase		:       str=True,    lowerCamelCase		:       Union[str, Any]=99,    lowerCamelCase		:       List[str]=32,    lowerCamelCase		:       int=2,    lowerCamelCase		:       Union[str, Any]=4,    lowerCamelCase		:       Tuple=37,    lowerCamelCase		:       int="gelu",    lowerCamelCase		:       Tuple=0.1,    lowerCamelCase		:       Tuple=0.1,    lowerCamelCase		:       Dict=512,    lowerCamelCase		:       Optional[Any]=16,    lowerCamelCase		:       str=2,    lowerCamelCase		:       List[str]=0.02,    lowerCamelCase		:       int=3,    lowerCamelCase		:       Any=4,    lowerCamelCase		:       Tuple=None,    ):

								'''simple docstring'''
								lowercase__  					=     parent
								lowercase__  					=     13
								lowercase__  					=     7
								lowercase__  					=     True
								lowercase__  					=     True
								lowercase__  					=     True
								lowercase__  					=     True
								lowercase__  					=     99
								lowercase__  					=     384
								lowercase__  					=     2
								lowercase__  					=     4
								lowercase__  					=     37
								lowercase__  					=     '''gelu'''
								lowercase__  					=     0.1
								lowercase__  					=     0.1
								lowercase__  					=     512
								lowercase__  					=     16
								lowercase__  					=     2
								lowercase__  					=     0.02
								lowercase__  					=     3
								lowercase__  					=     4
								lowercase__  					=     128
								lowercase__  					=     2
								lowercase__  					=     9
								lowercase__  					=     1
								lowercase__  					=     None




				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								lowercase__  					=     ids_tensor([self.batch_size, self.seq_length],    self.vocab_size    )

								lowercase__  					=     None
								if self.use_input_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__  					=     None
								lowercase__  					=     None
								lowercase__  					=     None
								if self.use_labels:
												lowercase__  					=     ids_tensor([self.batch_size],    self.type_sequence_label_size    )
												lowercase__  					=     ids_tensor([self.batch_size, self.seq_length],    self.num_labels    )
												lowercase__  					=     ids_tensor([self.batch_size],    self.num_choices    )

								lowercase__  					=     ConvBertConfig(
								    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,    initializer_range=self.initializer_range,    return_dict=lowerCamelCase,    )

								return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       Tuple,    lowerCamelCase		:       Optional[Any],    lowerCamelCase		:       Any,    lowerCamelCase		:       Dict,    lowerCamelCase		:       Union[str, Any],    lowerCamelCase		:       Optional[Any],    lowerCamelCase		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     TFConvBertModel(config=lowerCamelCase    )
								lowercase__  					=     {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

								lowercase__  					=     [input_ids, input_mask]
								lowercase__  					=     model(lowerCamelCase    )

								lowercase__  					=     model(lowerCamelCase    )

								self.parent.assertEqual(result.last_hidden_state.shape,    (self.batch_size, self.seq_length, self.hidden_size)    )




				def 							lowercase__  (	self		:       List[Any],    lowerCamelCase		:       str,    lowerCamelCase		:       List[str],    lowerCamelCase		:       Optional[int],    lowerCamelCase		:       Any,    lowerCamelCase		:       Any,    lowerCamelCase		:       Union[str, Any],    lowerCamelCase		:       int    ):

								'''simple docstring'''
								lowercase__  					=     TFConvBertForMaskedLM(config=lowerCamelCase    )
								lowercase__  					=     {
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								    '''token_type_ids''': token_type_ids,
								}
								lowercase__  					=     model(lowerCamelCase    )
								self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.seq_length, self.vocab_size)    )




				def 							lowercase__  (	self		:       List[Any],    lowerCamelCase		:       Any,    lowerCamelCase		:       Optional[int],    lowerCamelCase		:       Optional[Any],    lowerCamelCase		:       int,    lowerCamelCase		:       Union[str, Any],    lowerCamelCase		:       List[Any],    lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     self.num_labels
								lowercase__  					=     TFConvBertForSequenceClassification(config=lowerCamelCase    )
								lowercase__  					=     {
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								    '''token_type_ids''': token_type_ids,
								}

								lowercase__  					=     model(lowerCamelCase    )
								self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.num_labels)    )




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       int,    lowerCamelCase		:       Any,    lowerCamelCase		:       int,    lowerCamelCase		:       int,    lowerCamelCase		:       int,    lowerCamelCase		:       Dict,    lowerCamelCase		:       str    ):

								'''simple docstring'''
								lowercase__  					=     self.num_choices
								lowercase__  					=     TFConvBertForMultipleChoice(config=lowerCamelCase    )
								lowercase__  					=     tf.tile(tf.expand_dims(lowerCamelCase,    1    ),    (1, self.num_choices, 1)    )
								lowercase__  					=     tf.tile(tf.expand_dims(lowerCamelCase,    1    ),    (1, self.num_choices, 1)    )
								lowercase__  					=     tf.tile(tf.expand_dims(lowerCamelCase,    1    ),    (1, self.num_choices, 1)    )
								lowercase__  					=     {
								    '''input_ids''': multiple_choice_inputs_ids,
								    '''attention_mask''': multiple_choice_input_mask,
								    '''token_type_ids''': multiple_choice_token_type_ids,
								}
								lowercase__  					=     model(lowerCamelCase    )
								self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.num_choices)    )




				def 							lowercase__  (	self		:       str,    lowerCamelCase		:       List[Any],    lowerCamelCase		:       List[str],    lowerCamelCase		:       Union[str, Any],    lowerCamelCase		:       Dict,    lowerCamelCase		:       List[Any],    lowerCamelCase		:       Union[str, Any],    lowerCamelCase		:       str    ):

								'''simple docstring'''
								lowercase__  					=     self.num_labels
								lowercase__  					=     TFConvBertForTokenClassification(config=lowerCamelCase    )
								lowercase__  					=     {
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								    '''token_type_ids''': token_type_ids,
								}
								lowercase__  					=     model(lowerCamelCase    )
								self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.seq_length, self.num_labels)    )




				def 							lowercase__  (	self		:       List[str],    lowerCamelCase		:       int,    lowerCamelCase		:       Optional[int],    lowerCamelCase		:       Optional[int],    lowerCamelCase		:       Tuple,    lowerCamelCase		:       Optional[Any],    lowerCamelCase		:       Dict,    lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     TFConvBertForQuestionAnswering(config=lowerCamelCase    )
								lowercase__  					=     {
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								    '''token_type_ids''': token_type_ids,
								}

								lowercase__  					=     model(lowerCamelCase    )
								self.parent.assertEqual(result.start_logits.shape,    (self.batch_size, self.seq_length)    )
								self.parent.assertEqual(result.end_logits.shape,    (self.batch_size, self.seq_length)    )




				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     self.prepare_config_and_inputs()
								(
								    (
								    lowercase__
								)					,		(
								    lowercase__
								)					,		(
								    lowercase__
								)					,		(
								    lowercase__
								)					,		(
								    lowercase__
								)					,		(
								    lowercase__
								)					,		(
								    lowercase__
								)					,		
								)  					=     config_and_inputs
								lowercase__  					=     {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
								return config, inputs_dict



@require_tf
class  _UpperCAmelCase   (			A__						,A__						,unittest.TestCase      ):

				"""simple docstring"""




				lowercase__											=			(
				    (
				        TFConvBertModel,
				        TFConvBertForMaskedLM,
				        TFConvBertForQuestionAnswering,
				        TFConvBertForSequenceClassification,
				        TFConvBertForTokenClassification,
				        TFConvBertForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				lowercase__											=			(
				    {
				        """feature-extraction""": TFConvBertModel,
				        """fill-mask""": TFConvBertForMaskedLM,
				        """question-answering""": TFConvBertForQuestionAnswering,
				        """text-classification""": TFConvBertForSequenceClassification,
				        """token-classification""": TFConvBertForTokenClassification,
				        """zero-shot""": TFConvBertForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				lowercase__											=			False
				lowercase__											=			False
				lowercase__											=			False




				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								lowercase__  					=     TFConvBertModelTester(self    )
								lowercase__  					=     ConfigTester(self,    config_class=lowerCamelCase,    hidden_size=37    )




				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								self.config_tester.run_common_tests()




				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase    )




				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase    )




				def 							lowercase__  (	self		:       Tuple    ):

								'''simple docstring'''
								lowercase__  					=     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase    )




				def 							lowercase__  (	self		:       Optional[Any]    ):

								'''simple docstring'''
								lowercase__  					=     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*lowerCamelCase    )




				def 							lowercase__  (	self		:       Optional[Any]    ):

								'''simple docstring'''
								lowercase__  					=     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase    )




				def 							lowercase__  (	self		:       Optional[Any]    ):

								'''simple docstring'''
								lowercase__  					=     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*lowerCamelCase    )




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

								'''simple docstring'''
								lowercase__					,		lowercase__  					=     self.model_tester.prepare_config_and_inputs_for_common()
								lowercase__  					=     True
								lowercase__  					=     True

								if hasattr(lowerCamelCase,    '''use_cache'''    ):
												lowercase__  					=     True

								lowercase__  					=     getattr(self.model_tester,    '''encoder_seq_length''',    self.model_tester.seq_length    )
								lowercase__  					=     getattr(self.model_tester,    '''key_length''',    lowerCamelCase    )

								for model_class in self.all_model_classes:
												lowercase__  					=     self._prepare_for_class(lowerCamelCase,    lowerCamelCase    )
												lowercase__  					=     model_class(lowerCamelCase    )
												lowercase__  					=     len(model(lowerCamelCase    )    )

												with tempfile.TemporaryDirectory() as tmpdirname:
																model.save_pretrained(lowerCamelCase,    saved_model=lowerCamelCase    )
																lowercase__  					=     os.path.join(lowerCamelCase,    '''saved_model''',    '''1'''    )
																lowercase__  					=     tf.keras.models.load_model(lowerCamelCase    )
																lowercase__  					=     model(lowerCamelCase    )

																if self.is_encoder_decoder:
																				lowercase__  					=     outputs['''encoder_hidden_states''']
																				lowercase__  					=     outputs['''encoder_attentions''']
																else:
																				lowercase__  					=     outputs['''hidden_states''']
																				lowercase__  					=     outputs['''attentions''']

																self.assertEqual(len(lowerCamelCase    ),    lowerCamelCase    )

																lowercase__  					=     getattr(
																    self.model_tester,    '''expected_num_hidden_layers''',    self.model_tester.num_hidden_layers + 1    )

																self.assertEqual(len(lowerCamelCase    ),    lowerCamelCase    )
																self.assertListEqual(
																    list(output_hidden_states[0].shape[-2:]    ),    [self.model_tester.seq_length, self.model_tester.hidden_size],    )

																self.assertEqual(len(lowerCamelCase    ),    self.model_tester.num_hidden_layers    )
																self.assertListEqual(
																    list(output_attentions[0].shape[-3:]    ),    [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length],    )




				@slow
				def 							lowercase__  (	self		:       Any    ):

								'''simple docstring'''
								lowercase__  					=     TFConvBertModel.from_pretrained('''YituTech/conv-bert-base'''    )
								self.assertIsNotNone(lowerCamelCase    )




				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								lowercase__					,		lowercase__  					=     self.model_tester.prepare_config_and_inputs_for_common()
								lowercase__  					=     True
								lowercase__  					=     getattr(self.model_tester,    '''decoder_seq_length''',    self.model_tester.seq_length    )
								lowercase__  					=     getattr(self.model_tester,    '''encoder_seq_length''',    self.model_tester.seq_length    )
								lowercase__  					=     getattr(self.model_tester,    '''key_length''',    lowerCamelCase    )
								lowercase__  					=     getattr(self.model_tester,    '''key_length''',    lowerCamelCase    )

								def check_decoder_attentions_output(lowerCamelCase		:       Union[str, Any]    ):
												lowercase__  					=     len(lowerCamelCase    )
												self.assertEqual(out_len % 2,    0    )
												lowercase__  					=     outputs.decoder_attentions
												self.assertEqual(len(lowerCamelCase    ),    self.model_tester.num_hidden_layers    )
												self.assertListEqual(
												    list(decoder_attentions[0].shape[-3:]    ),    [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length],    )

								def check_encoder_attentions_output(lowerCamelCase		:       Optional[int]    ):
												lowercase__  					=     [
												    t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
												]
												self.assertEqual(len(lowerCamelCase    ),    self.model_tester.num_hidden_layers    )
												self.assertListEqual(
												    list(attentions[0].shape[-3:]    ),    [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length],    )

								for model_class in self.all_model_classes:
												lowercase__  					=     True
												lowercase__  					=     False
												lowercase__  					=     model_class(lowerCamelCase    )
												lowercase__  					=     model(self._prepare_for_class(lowerCamelCase,    lowerCamelCase    )    )
												lowercase__  					=     len(lowerCamelCase    )
												self.assertEqual(config.output_hidden_states,    lowerCamelCase    )
												check_encoder_attentions_output(lowerCamelCase    )

												if self.is_encoder_decoder:
																lowercase__  					=     model_class(lowerCamelCase    )
																lowercase__  					=     model(self._prepare_for_class(lowerCamelCase,    lowerCamelCase    )    )
																self.assertEqual(config.output_hidden_states,    lowerCamelCase    )
																check_decoder_attentions_output(lowerCamelCase    )

												# Check that output attentions can also be changed via the config
												del inputs_dict["output_attentions"]
												lowercase__  					=     True
												lowercase__  					=     model_class(lowerCamelCase    )
												lowercase__  					=     model(self._prepare_for_class(lowerCamelCase,    lowerCamelCase    )    )
												self.assertEqual(config.output_hidden_states,    lowerCamelCase    )
												check_encoder_attentions_output(lowerCamelCase    )

												# Check attention is always last and order is fine
												lowercase__  					=     True
												lowercase__  					=     True
												lowercase__  					=     model_class(lowerCamelCase    )
												lowercase__  					=     model(self._prepare_for_class(lowerCamelCase,    lowerCamelCase    )    )

												self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1),    len(lowerCamelCase    )    )
												self.assertEqual(model.config.output_hidden_states,    lowerCamelCase    )
												check_encoder_attentions_output(lowerCamelCase    )



@require_tf
class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




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

								'''simple docstring'''
								lowercase__  					=     TFConvBertModel.from_pretrained('''YituTech/conv-bert-base'''    )
								lowercase__  					=     tf.constant([[0, 1, 2, 3, 4, 5]]    )
								lowercase__  					=     model(lowerCamelCase    )[0]

								lowercase__  					=     [1, 6, 768]
								self.assertEqual(output.shape,    lowerCamelCase    )

								lowercase__  					=     tf.constant(
								    [
								        [
								            [-0.03475493, -0.4686034, -0.30638832],
								            [0.22637248, -0.26988646, -0.7423424],
								            [0.10324868, -0.45013508, -0.58280784],
								        ]
								    ]    )
								tf.debugging.assert_near(output[:, :3, :3],    lowerCamelCase,    atol=1E-4    )












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

								'''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 							lowercase__  (	self		:       List[str]    ):

								'''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 							lowercase__  (	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"""




				lowercase__											=			True

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




				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								lowercase__  					=     FlaxRobertaPreLayerNormModelTester(self    )




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

								'''simple docstring'''
								lowercase__  					=     FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''',    from_pt=lowerCamelCase    )

								lowercase__  					=     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]],    dtype=jnp.intaa    )
								lowercase__  					=     model(lowerCamelCase    )[0]
								lowercase__  					=     [1, 11, 50_265]
								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 							lowercase__  (	self		:       Any    ):

								'''simple docstring'''
								lowercase__  					=     FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''',    from_pt=lowerCamelCase    )

								lowercase__  					=     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 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    )    )




