



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

									return config, input_ids, token_type_ids, attention_mask





			def 		snake_case__			(				self	:       int   )						->					Union[str, Any]:
									"""simple docstring"""







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







			def 		snake_case__			(				self	:       Union[str, Any]   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				self.prepare_config_and_inputs()
									__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
class 			_lowerCAmelCase   ( _UpperCAmelCase    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  Tuple      =			True

			lowercase__      :  List[str]      =			(
			    (
			        FlaxRobertaModel,
			        FlaxRobertaForCausalLM,
			        FlaxRobertaForMaskedLM,
			        FlaxRobertaForSequenceClassification,
			        FlaxRobertaForTokenClassification,
			        FlaxRobertaForMultipleChoice,
			        FlaxRobertaForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)





			def 		snake_case__			(				self	:       Dict   )						->					str:
									"""simple docstring"""







									__lowercase					=				FlaxRobertaModelTester(self   )







			@slow
			def 		snake_case__			(				self	:       Any   )						->					Tuple:
									"""simple docstring"""







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










def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      bool:
						__lowercase					=				len(lowercase__   )
						__lowercase					=				[[False] * (required_sum + 1) for _ in range(arr_len + 1   )]

						# for each arr value, a sum of zero(0) can be formed by not taking any element
						# hence True/1
						for i in range(arr_len + 1   ):
												__lowercase					=				True

						# sum is not zero and set is empty then false
						for i in range(1						,       required_sum + 1   ):
												__lowercase					=				False

						for i in range(1						,       arr_len + 1   ):
												for j in range(1						,       required_sum + 1   ):
																		if arr[i - 1] > j:
																								__lowercase					=				subset[i - 1][j]
																		if arr[i - 1] <= j:
																								__lowercase					=				subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

						return subset[arr_len][required_sum]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





