
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):




	'''simple docstring'''




	def __init__(					self		:Optional[Any]     ,   a		:int     ,   a		:Union[str, Any]=1_3     ,   a		:List[str]=7     ,   a		:int=True     ,   a		:Optional[Any]=True     ,   a		:Union[str, Any]=True     ,   a		:Dict=True     ,   a		:int=9_9     ,   a		:Tuple=3_2     ,   a		:List[str]=5     ,   a		:List[str]=4     ,   a		:Any=3_7     ,   a		:Optional[int]="gelu"     ,   a		:Tuple=0.1     ,   a		:List[Any]=0.1     ,   a		:List[Any]=5_1_2     ,   a		:List[Any]=1_6     ,   a		:int=2     ,   a		:Any=0.02     ,   a		:Tuple=4     ,   )		-> List[Any]:
								__UpperCamelCase	:   List[Any]			   =					parent
								__UpperCamelCase	:   Dict			   =					batch_size
								__UpperCamelCase	:   Tuple			   =					seq_length
								__UpperCamelCase	:   Union[str, Any]			   =					is_training
								__UpperCamelCase	:   List[Any]			   =					use_attention_mask
								__UpperCamelCase	:   Optional[int]			   =					use_token_type_ids
								__UpperCamelCase	:   Any			   =					use_labels
								__UpperCamelCase	:   int			   =					vocab_size
								__UpperCamelCase	:   Dict			   =					hidden_size
								__UpperCamelCase	:   List[Any]			   =					num_hidden_layers
								__UpperCamelCase	:   Union[str, Any]			   =					num_attention_heads
								__UpperCamelCase	:   List[Any]			   =					intermediate_size
								__UpperCamelCase	:   str			   =					hidden_act
								__UpperCamelCase	:   str			   =					hidden_dropout_prob
								__UpperCamelCase	:   int			   =					attention_probs_dropout_prob
								__UpperCamelCase	:   str			   =					max_position_embeddings
								__UpperCamelCase	:   Optional[Any]			   =					type_vocab_size
								__UpperCamelCase	:   Any			   =					type_sequence_label_size
								__UpperCamelCase	:   Optional[Any]			   =					initializer_range
								__UpperCamelCase	:   int			   =					num_choices







	def    _lowerCamelCase				(					self		:Dict						)		-> Union[str, Any]:
								__UpperCamelCase	:   str			   =					ids_tensor([self.batch_size, self.seq_length]     ,   self.vocab_size						)

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

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

								__UpperCamelCase	:   Union[str, Any]			   =					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						)		-> Optional[int]:
								__UpperCamelCase	:   str			   =					self.prepare_config_and_inputs()
								__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase	:   Tuple			   =					config_and_inputs
								__UpperCamelCase	:   Tuple			   =					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
								return config, inputs_dict







	def    _lowerCamelCase				(					self		:Tuple						)		-> Dict:
								__UpperCamelCase	:   str			   =					self.prepare_config_and_inputs()
								__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase	:   Tuple			   =					config_and_inputs

								__UpperCamelCase	:   int			   =					True
								__UpperCamelCase	:   Tuple			   =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
								__UpperCamelCase	:   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
class  lowerCamelCase__							(				__lowercase     ,						unittest.TestCase):




	'''simple docstring'''




	_A								=     True

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







	def    _lowerCamelCase				(					self		:str						)		-> Optional[Any]:
								__UpperCamelCase	:   Union[str, Any]			   =					FlaxRobertaModelTester(self						)







	@slow
	def    _lowerCamelCase				(					self		:str						)		-> str:






								for model_class_name in self.all_model_classes:
															__UpperCamelCase	:   Optional[int]			   =					model_class_name.from_pretrained("roberta-base"     ,   from_pt=A_						)
															__UpperCamelCase	:   int			   =					model(np.ones((1, 1)						)						)
															self.assertIsNotNone(A_						)





def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       int	,							lowercase	:       int     ):


      '''simple docstring'''



      return int((input_a, input_a).count(1     ) != 0     )

def 			_SCREAMING_SNAKE_CASE			(							):


      '''simple docstring'''



      assert or_gate(0	,							0     ) == 0
      assert or_gate(0	,							1     ) == 1
      assert or_gate(1	,							0     ) == 1
      assert or_gate(1	,							1     ) == 1


if __name__ == "__main__":
 print(or_gate(0, 1))
 print(or_gate(1, 0))
 print(or_gate(0, 0))
 print(or_gate(1, 1))



