




"""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   a__			(       unittest.TestCase  ):





						def __init__(							self      :	List[str]     ,a__      :	Optional[int]     ,a__      :	int=13     ,a__      :	Union[str, Any]=7     ,a__      :	List[Any]=True     ,a__      :	Dict=True     ,a__      :	Dict=True     ,a__      :	List[Any]=True     ,a__      :	Union[str, Any]=99     ,a__      :	List[str]=32     ,a__      :	List[Any]=5     ,a__      :	Union[str, Any]=4     ,a__      :	Dict=37     ,a__      :	Optional[Any]="gelu"     ,a__      :	str=0.1     ,a__      :	Tuple=0.1     ,a__      :	str=512     ,a__      :	Dict=16     ,a__      :	Union[str, Any]=2     ,a__      :	str=0.02     ,a__      :	str=4     ,)						->       List[Any]:




											"""simple docstring"""







											_lowerCAmelCase:str									=  parent
											_lowerCAmelCase:Optional[int]									=  batch_size
											_lowerCAmelCase:List[str]									=  seq_length
											_lowerCAmelCase:Dict									=  is_training
											_lowerCAmelCase:Dict									=  use_attention_mask
											_lowerCAmelCase:Any									=  use_token_type_ids
											_lowerCAmelCase:Union[str, Any]									=  use_labels
											_lowerCAmelCase:Any									=  vocab_size
											_lowerCAmelCase:Tuple									=  hidden_size
											_lowerCAmelCase:Optional[Any]									=  num_hidden_layers
											_lowerCAmelCase:Optional[Any]									=  num_attention_heads
											_lowerCAmelCase:List[Any]									=  intermediate_size
											_lowerCAmelCase:List[str]									=  hidden_act
											_lowerCAmelCase:List[Any]									=  hidden_dropout_prob
											_lowerCAmelCase:Dict									=  attention_probs_dropout_prob
											_lowerCAmelCase:List[str]									=  max_position_embeddings
											_lowerCAmelCase:List[Any]									=  type_vocab_size
											_lowerCAmelCase:str									=  type_sequence_label_size
											_lowerCAmelCase:int									=  initializer_range
											_lowerCAmelCase:List[str]									=  num_choices





						def       __UpperCamelCase     (							self      :	int)						->       List[Any]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size)

											_lowerCAmelCase:List[Any]									=  None
											if self.use_attention_mask:
																_lowerCAmelCase:Optional[Any]									=  random_attention_mask([self.batch_size, self.seq_length])

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

											_lowerCAmelCase:Optional[Any]									=  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)						->       List[Any]:




											"""simple docstring"""







											_lowerCAmelCase:str									=  self.prepare_config_and_inputs()
											_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:List[str]									=  config_and_inputs
											_lowerCAmelCase:List[Any]									=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
											return config, inputs_dict







						def       __UpperCamelCase     (							self      :	List[Any])						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  self.prepare_config_and_inputs()
											_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:Optional[Any]									=  config_and_inputs

											_lowerCAmelCase:Optional[int]									=  True
											_lowerCAmelCase:Optional[int]									=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
											_lowerCAmelCase:List[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   a__			(       UpperCamelCase_					,	unittest.TestCase  ):
						snake_case__             = True

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





						def       __UpperCamelCase     (							self      :	Optional[int])						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:List[Any]									=  FlaxRobertaPreLayerNormModelTester(self)







						@slow
						def       __UpperCamelCase     (							self      :	str)						->       List[Any]:




											"""simple docstring"""







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




@require_flax
class   a__			(       unittest.TestCase  ):





						@slow
						def       __UpperCamelCase     (							self      :	List[Any])						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''     ,from_pt=a__)

											_lowerCAmelCase:Union[str, Any]									=  np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]     ,dtype=jnp.intaa)
											_lowerCAmelCase:List[Any]									=  model(a__)[0]
											_lowerCAmelCase:Optional[int]									=  [1, 11, 5_0265]
											self.assertEqual(list(output.shape)     ,a__)
											# compare the actual values for a slice.
											_lowerCAmelCase: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      :	Tuple)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''     ,from_pt=a__)

											_lowerCAmelCase:List[Any]									=  np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]     ,dtype=jnp.intaa)
											_lowerCAmelCase:str									=  model(a__)[0]
											# compare the actual values for a slice.
											_lowerCAmelCase:Optional[int]									=  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))












"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__						=			{
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__						=			['''ConvBertTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__						=			[
				    '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''ConvBertForMaskedLM''',
				    '''ConvBertForMultipleChoice''',
				    '''ConvBertForQuestionAnswering''',
				    '''ConvBertForSequenceClassification''',
				    '''ConvBertForTokenClassification''',
				    '''ConvBertLayer''',
				    '''ConvBertModel''',
				    '''ConvBertPreTrainedModel''',
				    '''load_tf_weights_in_convbert''',
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__						=			[
				    '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFConvBertForMaskedLM''',
				    '''TFConvBertForMultipleChoice''',
				    '''TFConvBertForQuestionAnswering''',
				    '''TFConvBertForSequenceClassification''',
				    '''TFConvBertForTokenClassification''',
				    '''TFConvBertLayer''',
				    '''TFConvBertModel''',
				    '''TFConvBertPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
				from .tokenization_convbert import ConvBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_convbert_fast import ConvBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_convbert import (
								    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ConvBertForMaskedLM,
								    ConvBertForMultipleChoice,
								    ConvBertForQuestionAnswering,
								    ConvBertForSequenceClassification,
								    ConvBertForTokenClassification,
								    ConvBertLayer,
								    ConvBertModel,
								    ConvBertPreTrainedModel,
								    load_tf_weights_in_convbert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_convbert import (
								    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFConvBertForMaskedLM,
								    TFConvBertForMultipleChoice,
								    TFConvBertForQuestionAnswering,
								    TFConvBertForSequenceClassification,
								    TFConvBertForTokenClassification,
								    TFConvBertLayer,
								    TFConvBertModel,
								    TFConvBertPreTrainedModel,
								)


else:
				import sys

				UpperCamelCase__						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






