


'''simple docstring'''

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    ,						UpperCamelCase    ,						UpperCamelCase=13    ,						UpperCamelCase=7    ,						UpperCamelCase=True    ,						UpperCamelCase=True    ,						UpperCamelCase=True    ,						UpperCamelCase=True    ,						UpperCamelCase=99    ,						UpperCamelCase=32    ,						UpperCamelCase=5    ,						UpperCamelCase=4    ,						UpperCamelCase=37    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=512    ,						UpperCamelCase=16    ,						UpperCamelCase=2    ,						UpperCamelCase=0.02    ,						UpperCamelCase=4    ,						):







			"""simple docstring"""
			lowerCamelCase_  				=						parent
			lowerCamelCase_  				=						batch_size
			lowerCamelCase_  				=						seq_length
			lowerCamelCase_  				=						is_training
			lowerCamelCase_  				=						use_attention_mask
			lowerCamelCase_  				=						use_token_type_ids
			lowerCamelCase_  				=						use_labels
			lowerCamelCase_  				=						vocab_size
			lowerCamelCase_  				=						hidden_size
			lowerCamelCase_  				=						num_hidden_layers
			lowerCamelCase_  				=						num_attention_heads
			lowerCamelCase_  				=						intermediate_size
			lowerCamelCase_  				=						hidden_act
			lowerCamelCase_  				=						hidden_dropout_prob
			lowerCamelCase_  				=						attention_probs_dropout_prob
			lowerCamelCase_  				=						max_position_embeddings
			lowerCamelCase_  				=						type_vocab_size
			lowerCamelCase_  				=						type_sequence_label_size
			lowerCamelCase_  				=						initializer_range
			lowerCamelCase_  				=						num_choices






		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_  				=						ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size  )

			lowerCamelCase_  				=						None
			if self.use_attention_mask:
				lowerCamelCase_  				=						random_attention_mask([self.batch_size, self.seq_length]  )

			lowerCamelCase_  				=						None
			if self.use_token_type_ids:
				lowerCamelCase_  				=						ids_tensor([self.batch_size, self.seq_length]    ,						self.type_vocab_size  )

			lowerCamelCase_  				=						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  ):







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







		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_  				=						self.prepare_config_and_inputs()
			lowerCamelCase_  				=						config_and_inputs

			lowerCamelCase_  				=						True
			lowerCamelCase_  				=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
			lowerCamelCase_  				=						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					(					__snake_case    ,		unittest.TestCase    ):





		"""simple docstring"""




		_lowerCamelCase						=						True

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






		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_  				=						FlaxRobertaModelTester(self  )







		@slow
		def 						snake_case	(					self  ):







			"""simple docstring"""

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







'''simple docstring'''

def __snake_case      (   UpperCAmelCase_		:		int = 1000	):
 return sum(2 * a * ((a - 1) // 2) for a in range(3      ,       n + 1	)	)


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



