

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 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





    def __init__(				self						,       a						,       a=13						,       a=7						,       a=True						,       a=True						,       a=True						,       a=True						,       a=99						,       a=32						,       a=5						,       a=4						,       a=37						,       a="gelu"						,       a=0.1						,       a=0.1						,       a=512						,       a=16						,       a=2						,       a=0.02						,       a=4						,       ):
         lowercase__   :       List[Any]				=	parent
         lowercase__   :       Optional[int]				=	batch_size
         lowercase__   :       Dict				=	seq_length
         lowercase__   :       Union[str, Any]				=	is_training
         lowercase__   :       Optional[int]				=	use_attention_mask
         lowercase__   :       int				=	use_token_type_ids
         lowercase__   :       Any				=	use_labels
         lowercase__   :       Optional[Any]				=	vocab_size
         lowercase__   :       Optional[Any]				=	hidden_size
         lowercase__   :       Tuple				=	num_hidden_layers
         lowercase__   :       Dict				=	num_attention_heads
         lowercase__   :       Union[str, Any]				=	intermediate_size
         lowercase__   :       Union[str, Any]				=	hidden_act
         lowercase__   :       Optional[int]				=	hidden_dropout_prob
         lowercase__   :       Dict				=	attention_probs_dropout_prob
         lowercase__   :       Any				=	max_position_embeddings
         lowercase__   :       Dict				=	type_vocab_size
         lowercase__   :       Optional[int]				=	type_sequence_label_size
         lowercase__   :       List[Any]				=	initializer_range
         lowercase__   :       Optional[Any]				=	num_choices





    def 				snake_case_    (				self):
         lowercase__   :       Optional[Any]				=	ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size)

         lowercase__   :       List[str]				=	None
         if self.use_attention_mask:
              lowercase__   :       Tuple				=	random_attention_mask([self.batch_size, self.seq_length])

         lowercase__   :       int				=	None
         if self.use_token_type_ids:
              lowercase__   :       Optional[Any]				=	ids_tensor([self.batch_size, self.seq_length]						,       self.type_vocab_size)

         lowercase__   :       Optional[int]				=	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 				snake_case_    (				self):
         lowercase__   :       Any				=	self.prepare_config_and_inputs()
         lowercase__   :       Optional[int]				=	config_and_inputs
         lowercase__   :       Optional[Any]				=	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
         return config, inputs_dict





    def 				snake_case_    (				self):
         lowercase__   :       Optional[int]				=	self.prepare_config_and_inputs()
         lowercase__   :       Optional[Any]				=	config_and_inputs

         lowercase__   :       str				=	True
         lowercase__   :       Optional[Any]				=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
         lowercase__   :       Union[str, 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 				SCREAMING_SNAKE_CASE__ (__snake_case		,	unittest.TestCase					):
    __lowerCamelCase						:	int														=					True

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





    def 				snake_case_    (				self):
         lowercase__   :       str				=	FlaxRobertaModelTester(self)





    @slow
    def 				snake_case_    (				self):
         for model_class_name in self.all_model_classes:
              lowercase__   :       Optional[int]				=	model_class_name.from_pretrained('roberta-base'						,       from_pt=lowerCamelCase_)
              lowercase__   :       Union[str, Any]				=	model(np.ones((1, 1)))
              self.assertIsNotNone(lowerCamelCase_)




import math
import sys

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     if number != int(SCREAMING_SNAKE_CASE_      ):
          raise ValueError('the value of input must be a natural number'      )
     if number < 0:
          raise ValueError('the value of input must not be a negative number'      )
     if number == 0:
          return 1
     lowercase__   :       Tuple				=	[-1] * (number + 1)
     lowercase__   :       Tuple				=	0
     for i in range(1     ,					number + 1      ):
          lowercase__   :       Tuple				=	sys.maxsize
          lowercase__   :       str				=	int(math.sqrt(SCREAMING_SNAKE_CASE_      )      )
          for j in range(1     ,					root + 1      ):
               lowercase__   :       List[Any]				=	1 + answers[i - (j**2)]
               lowercase__   :       str				=	min(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
          lowercase__   :       List[str]				=	answer
     return answers[number]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

