


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 						_UpperCAmelCase      (     unittest.TestCase							):






		def __init__(						self				:     List[Any]   ,  UpperCAmelCase				:     str   ,  UpperCAmelCase				:     int=13   ,  UpperCAmelCase				:     Dict=7   ,  UpperCAmelCase				:     Any=True   ,  UpperCAmelCase				:     int=True   ,  UpperCAmelCase				:     List[str]=True   ,  UpperCAmelCase				:     int=True   ,  UpperCAmelCase				:     List[str]=99   ,  UpperCAmelCase				:     Tuple=32   ,  UpperCAmelCase				:     Dict=5   ,  UpperCAmelCase				:     Any=4   ,  UpperCAmelCase				:     Any=37   ,  UpperCAmelCase				:     Optional[int]="gelu"   ,  UpperCAmelCase				:     Optional[int]=0.1   ,  UpperCAmelCase				:     Optional[Any]=0.1   ,  UpperCAmelCase				:     Tuple=5_12   ,  UpperCAmelCase				:     List[Any]=16   ,  UpperCAmelCase				:     Union[str, Any]=2   ,  UpperCAmelCase				:     Any=0.02   ,  UpperCAmelCase				:     Optional[int]=4   ,  ):
				SCREAMING_SNAKE_CASE_				:int				     =						parent
				SCREAMING_SNAKE_CASE_				:str				     =						batch_size
				SCREAMING_SNAKE_CASE_				:List[str]				     =						seq_length
				SCREAMING_SNAKE_CASE_				:int				     =						is_training
				SCREAMING_SNAKE_CASE_				:int				     =						use_attention_mask
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						use_token_type_ids
				SCREAMING_SNAKE_CASE_				:str				     =						use_labels
				SCREAMING_SNAKE_CASE_				:List[str]				     =						vocab_size
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						hidden_size
				SCREAMING_SNAKE_CASE_				:int				     =						num_hidden_layers
				SCREAMING_SNAKE_CASE_				:str				     =						num_attention_heads
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						intermediate_size
				SCREAMING_SNAKE_CASE_				:Any				     =						hidden_act
				SCREAMING_SNAKE_CASE_				:Any				     =						hidden_dropout_prob
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE_				:Tuple				     =						max_position_embeddings
				SCREAMING_SNAKE_CASE_				:str				     =						type_vocab_size
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						type_sequence_label_size
				SCREAMING_SNAKE_CASE_				:Dict				     =						initializer_range
				SCREAMING_SNAKE_CASE_				:str				     =						num_choices






		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						ids_tensor([self.batch_size, self.seq_length]   ,  self.vocab_size)

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

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

				SCREAMING_SNAKE_CASE_				:List[str]				     =						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=UpperCAmelCase   ,  initializer_range=self.initializer_range   ,  )

				return config, input_ids, token_type_ids, attention_mask






		def _snake_case		(						self				:     Optional[Any]):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Dict				     =						config_and_inputs
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
				return config, inputs_dict







		def _snake_case		(						self				:     Optional[int]):
				SCREAMING_SNAKE_CASE_				:int				     =						self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						config_and_inputs

				SCREAMING_SNAKE_CASE_				:Tuple				     =						True
				SCREAMING_SNAKE_CASE_				:str				     =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
				SCREAMING_SNAKE_CASE_				: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 						_UpperCAmelCase      (     lowercase		,		unittest.TestCase							):
		lowerCamelCase_	:			int       			=  True

		lowerCamelCase_	:			Optional[Any]       			=  (
		    (
		        FlaxRobertaModel,
		        FlaxRobertaForCausalLM,
		        FlaxRobertaForMaskedLM,
		        FlaxRobertaForSequenceClassification,
		        FlaxRobertaForTokenClassification,
		        FlaxRobertaForMultipleChoice,
		        FlaxRobertaForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)






		def _snake_case		(						self				:     List[Any]):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						FlaxRobertaModelTester(self)







		@slow
		def _snake_case		(						self				:     Union[str, Any]):
				for model_class_name in self.all_model_classes:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						model_class_name.from_pretrained("roberta-base"   ,  from_pt=UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						model(np.ones((1, 1)))
						self.assertIsNotNone(UpperCAmelCase)










def     lowercase	(    a = 50 ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						[1] * (length + 1)

		for row_length in range(3     , length + 1 ):
				for block_length in range(3     , row_length + 1 ):
						for block_start in range(row_length - block_length ):
								ways_number[row_length] += ways_number[
								    row_length - block_start - block_length - 1
								]

						ways_number[row_length] += 1

		return ways_number[length]


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






