




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 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



				def __init__(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  Optional[Any]=13      ,   __lowerCamelCase       :  Tuple=7      ,   __lowerCamelCase       :  Union[str, Any]=True      ,   __lowerCamelCase       :  Optional[int]=True      ,   __lowerCamelCase       :  Dict=True      ,   __lowerCamelCase       :  Tuple=True      ,   __lowerCamelCase       :  List[str]=99      ,   __lowerCamelCase       :  List[Any]=32      ,   __lowerCamelCase       :  Optional[int]=5      ,   __lowerCamelCase       :  Tuple=4      ,   __lowerCamelCase       :  Any=37      ,   __lowerCamelCase       :  Tuple="gelu"      ,   __lowerCamelCase       :  int=0.1      ,   __lowerCamelCase       :  Any=0.1      ,   __lowerCamelCase       :  Optional[int]=5_12      ,   __lowerCamelCase       :  Optional[Any]=16      ,   __lowerCamelCase       :  List[str]=2      ,   __lowerCamelCase       :  List[Any]=0.02      ,   __lowerCamelCase       :  Dict=4      ,   )      ->  Union[str, Any]:
							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 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Union[str, Any]:
							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=__lowerCamelCase      ,   initializer_range=self.initializer_range      ,   )

							return config, input_ids, token_type_ids, attention_mask





				def 							__UpperCAmelCase							(   self       :  str							)      ->  Any:
							a					=    self.prepare_config_and_inputs()
							a					,	a					,	a					,	a					=    config_and_inputs
							a					=    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
							return config, inputs_dict





				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  List[Any]:
							a					=    self.prepare_config_and_inputs()
							a					,	a					,	a					,	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 					snake_case__							(_UpperCamelCase					, unittest.TestCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Union[str, Any]											=      True

				SCREAMING_SNAKE_CASE_ :							str											=      (
				    (
				        FlaxRobertaModel,
				        FlaxRobertaForCausalLM,
				        FlaxRobertaForMaskedLM,
				        FlaxRobertaForSequenceClassification,
				        FlaxRobertaForTokenClassification,
				        FlaxRobertaForMultipleChoice,
				        FlaxRobertaForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  Dict:
							a					=    FlaxRobertaModelTester(self							)





				@slow
				def 							__UpperCAmelCase							(   self       :  int							)      ->  Union[str, Any]:
							for model_class_name in self.all_model_classes:
										a					=    model_class_name.from_pretrained("roberta-base"      ,   from_pt=__lowerCamelCase							)
										a					=    model(np.ones((1, 1)							)							)
										self.assertIsNotNone(__lowerCamelCase							)












__lowerCAmelCase	:       List[Any]     =	{str(digit): digit**5 for digit in range(10)}




def     __magic_name__       (				A	:      int     ):






			'''simple docstring'''







			return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A     )     )




def     __magic_name__       (				):






			'''simple docstring'''







			return sum(
			    number
			    for number in range(1000,						1000000     )
			    if number == digits_fifth_powers_sum(A     )     )


if __name__ == "__main__":
		print(solution())






