






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       __lowercase      (	unittest.TestCase  ):







					'''simple docstring'''




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


									"""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         =	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 		lowerCAmelCase_     (				self       :			int				):


									"""simple docstring"""



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





					def 		lowerCAmelCase_     (				self       :			Any				):


									"""simple docstring"""



									__A         =	self.prepare_config_and_inputs()
									__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
class       __lowercase      (	UpperCamelCase__			,      unittest.TestCase  ):







					'''simple docstring'''
					SCREAMING_SNAKE_CASE   						=				True

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




					def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


									"""simple docstring"""



									__A         =	FlaxRobertaModelTester(self				)





					@slow
					def 		lowerCAmelCase_     (				self       :			int				):


									"""simple docstring"""



									for model_class_name in self.all_model_classes:
													__A         =	model_class_name.from_pretrained("""roberta-base""" ,	from_pt=_a				)
													__A         =	model(np.ones((1, 1)				)				)
													self.assertIsNotNone(_a				)












from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import (
      ImageTextPipelineOutput,
      UniDiffuserPipeline,
  )
else:
  from .modeling_text_decoder import UniDiffuserTextDecoder
  from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
  from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline




