


import unittest

import numpy as np

from transformers import RobertaConfig, 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():
		from transformers.models.roberta.modeling_flax_roberta import (
		    FlaxRobertaForCausalLM,
		    FlaxRobertaForMaskedLM,
		    FlaxRobertaForMultipleChoice,
		    FlaxRobertaForQuestionAnswering,
		    FlaxRobertaForSequenceClassification,
		    FlaxRobertaForTokenClassification,
		    FlaxRobertaModel,
		)


class 					lowerCAmelCase_ (       unittest.TestCase):




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






									"""simple docstring"""






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




						def  _snake_case					(	self						:   Optional[int]						)      ->int:






									"""simple docstring"""






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

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

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

									a__ :Tuple						  =    RobertaConfig(
									    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  _snake_case					(	self						:   Optional[int]						)      ->Optional[int]:






									"""simple docstring"""






									a__ :Any						  =    self.prepare_config_and_inputs()
									a__ :int						  =    config_and_inputs
									a__ :Tuple						  =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
									return config, inputs_dict






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






									"""simple docstring"""






									a__ :Optional[Any]						  =    self.prepare_config_and_inputs()
									a__ :str						  =    config_and_inputs

									a__ :Optional[Any]						  =    True
									a__ :str						  =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
									a__ :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
class 					lowerCAmelCase_ (       _a			,unittest.TestCase):
						lowerCamelCase_         =     True

						lowerCamelCase_         =     (
						    (
						        FlaxRobertaModel,
						        FlaxRobertaForCausalLM,
						        FlaxRobertaForMaskedLM,
						        FlaxRobertaForSequenceClassification,
						        FlaxRobertaForTokenClassification,
						        FlaxRobertaForMultipleChoice,
						        FlaxRobertaForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)




						def  _snake_case					(	self						:   List[str]						)      ->List[Any]:






									"""simple docstring"""






									a__ :List[str]						  =    FlaxRobertaModelTester(self						)






						@slow
						def  _snake_case					(	self						:   Tuple						)      ->Tuple:






									"""simple docstring"""






									for model_class_name in self.all_model_classes:
												a__ :Optional[Any]						  =    model_class_name.from_pretrained("roberta-base"   ,     from_pt=__A						)
												a__ :str						  =    model(np.ones((1, 1)						)						)
												self.assertIsNotNone(__A						)






from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *


