

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







		def __init__(    self      ,      lowercase      ,      lowercase=13      ,      lowercase=7      ,      lowercase=True      ,      lowercase=True      ,      lowercase=True      ,      lowercase=True      ,      lowercase=99      ,      lowercase=32      ,      lowercase=5      ,      lowercase=4      ,      lowercase=37      ,      lowercase="gelu"      ,      lowercase=0.1      ,      lowercase=0.1      ,      lowercase=512      ,      lowercase=16      ,      lowercase=2      ,      lowercase=0.02      ,      lowercase=4      ,      )  ->						Dict:
						'''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      UpperCamelCase		(    self       )  ->						Dict:
						'''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=lowercase_      ,      initializer_range=self.initializer_range      ,      )

						return config, input_ids, token_type_ids, attention_mask


		def      UpperCamelCase		(    self       )  ->						Any:
						'''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      UpperCamelCase		(    self       )  ->						Tuple:
						'''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 					a__						(					snake_case__							,	unittest.TestCase			):
		"""simple docstring"""







		__lowerCamelCase        =       True

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


		def      UpperCamelCase		(    self       )  ->						Tuple:
						'''simple docstring'''






						A__        =			FlaxRobertaModelTester(self       )


		@slow
		def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
						'''simple docstring'''






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




def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int      )			->      int:




				'''simple docstring'''





				if divisor % 5 == 0 or divisor % 2 == 0:
								return 0
				A__        =			1
				A__        =			1
				while repunit:
								A__        =			(1_0 * repunit + 1) % divisor
								repunit_index += 1
				return repunit_index



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int = 1_0_0_0_0_0_0      )			->      int:




				'''simple docstring'''





				A__        =			limit - 1
				if divisor % 2 == 0:
								divisor += 1
				while least_divisible_repunit(SCREAMING_SNAKE_CASE_      ) <= limit:
								divisor += 2
				return divisor


if __name__ == "__main__":
	print(f"""{solution() = }""")

