



"""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       SCREAMING_SNAKE_CASE__  (					unittest.TestCase ):

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=13      ,     _SCREAMING_SNAKE_CASE=7      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=99      ,     _SCREAMING_SNAKE_CASE=32      ,     _SCREAMING_SNAKE_CASE=5      ,     _SCREAMING_SNAKE_CASE=4      ,     _SCREAMING_SNAKE_CASE=37      ,     _SCREAMING_SNAKE_CASE="gelu"      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE=512      ,     _SCREAMING_SNAKE_CASE=16      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=0.02      ,     _SCREAMING_SNAKE_CASE=4      ,     )  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	parent
    UpperCAmelCase	:					List[Any]	=	batch_size
    UpperCAmelCase	:					Optional[Any]	=	seq_length
    UpperCAmelCase	:					Any	=	is_training
    UpperCAmelCase	:					Optional[Any]	=	use_attention_mask
    UpperCAmelCase	:					Optional[int]	=	use_token_type_ids
    UpperCAmelCase	:					List[str]	=	use_labels
    UpperCAmelCase	:					List[Any]	=	vocab_size
    UpperCAmelCase	:					int	=	hidden_size
    UpperCAmelCase	:					Any	=	num_hidden_layers
    UpperCAmelCase	:					Tuple	=	num_attention_heads
    UpperCAmelCase	:					Optional[int]	=	intermediate_size
    UpperCAmelCase	:					str	=	hidden_act
    UpperCAmelCase	:					int	=	hidden_dropout_prob
    UpperCAmelCase	:					Dict	=	attention_probs_dropout_prob
    UpperCAmelCase	:					Optional[int]	=	max_position_embeddings
    UpperCAmelCase	:					Any	=	type_vocab_size
    UpperCAmelCase	:					List[str]	=	type_sequence_label_size
    UpperCAmelCase	:					Any	=	initializer_range
    UpperCAmelCase	:					Union[str, Any]	=	num_choices

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	ids_tensor([self.batch_size, self.seq_length]      ,     self.vocab_size						)

    UpperCAmelCase	:					str	=	None
    if self.use_attention_mask:
     UpperCAmelCase	:					Optional[Any]	=	random_attention_mask([self.batch_size, self.seq_length]						)

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

    UpperCAmelCase	:					Optional[Any]	=	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=_SCREAMING_SNAKE_CASE      ,     initializer_range=self.initializer_range      ,     )

    return config, input_ids, token_type_ids, attention_mask

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	self.prepare_config_and_inputs()
    UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					Tuple	=	config_and_inputs
    UpperCAmelCase	:					str	=	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
    return config, inputs_dict




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	self.prepare_config_and_inputs()
    UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					int	=	config_and_inputs

    UpperCAmelCase	:					Tuple	=	True
    UpperCAmelCase	:					Optional[Any]	=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
    UpperCAmelCase	:					str	=	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__  (					UpperCAmelCase__      ,			unittest.TestCase ):
   __lowerCAmelCase  :   List[str]						=						True

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

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	FlaxRobertaModelTester(self						)




   @slow
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




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





"""simple docstring"""
from math import isqrt, loga

def       _snake_case						(						UpperCamelCase						:		int						):
 UpperCAmelCase	:					Any	=	[True] * max_number
 for i in range(2			,		isqrt(max_number - 1						) + 1						):
  if is_prime[i]:
   for j in range(i**2			,		UpperCamelCase			,		UpperCamelCase						):
    UpperCAmelCase	:					str	=	False

 return [i for i in range(2			,		UpperCamelCase						) if is_prime[i]]

def       _snake_case						(						UpperCamelCase						:		int = 800800			,		UpperCamelCase						:		int = 800800						):
 UpperCAmelCase	:					Union[str, Any]	=	degree * loga(UpperCamelCase						)
 UpperCAmelCase	:					int	=	int(UpperCamelCase						)
 UpperCAmelCase	:					Union[str, Any]	=	calculate_prime_numbers(UpperCamelCase						)

 UpperCAmelCase	:					Dict	=	0
 UpperCAmelCase	:					Optional[int]	=	0
 UpperCAmelCase	:					Dict	=	len(UpperCamelCase						) - 1
 while left < right:
  while (
      prime_numbers[right] * loga(prime_numbers[left]						)
      + prime_numbers[left] * loga(prime_numbers[right]						)
      > upper_bound
  ):
   right -= 1
  hybrid_integers_count += right - left
  left += 1

 return hybrid_integers_count


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