


"""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 	lowercase__				(					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      ,   )  ->  str:
      _lowerCamelCase	:					str   =		parent
      _lowerCamelCase	:					Any   =		batch_size
      _lowerCamelCase	:					List[str]   =		seq_length
      _lowerCamelCase	:					Union[str, Any]   =		is_training
      _lowerCamelCase	:					Any   =		use_attention_mask
      _lowerCamelCase	:					Optional[int]   =		use_token_type_ids
      _lowerCamelCase	:					Any   =		use_labels
      _lowerCamelCase	:					Optional[int]   =		vocab_size
      _lowerCamelCase	:					Dict   =		hidden_size
      _lowerCamelCase	:					int   =		num_hidden_layers
      _lowerCamelCase	:					Dict   =		num_attention_heads
      _lowerCamelCase	:					Optional[Any]   =		intermediate_size
      _lowerCamelCase	:					Any   =		hidden_act
      _lowerCamelCase	:					Optional[int]   =		hidden_dropout_prob
      _lowerCamelCase	:					int   =		attention_probs_dropout_prob
      _lowerCamelCase	:					List[str]   =		max_position_embeddings
      _lowerCamelCase	:					List[Any]   =		type_vocab_size
      _lowerCamelCase	:					Any   =		type_sequence_label_size
      _lowerCamelCase	:					Dict   =		initializer_range
      _lowerCamelCase	:					List[str]   =		num_choices





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      _lowerCamelCase	:					Optional[Any]   =		ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size)

      _lowerCamelCase	:					Dict   =		None
      if self.use_attention_mask:
         _lowerCamelCase	:					Union[str, Any]   =		random_attention_mask([self.batch_size, self.seq_length])

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

      _lowerCamelCase	:					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=SCREAMING_SNAKE_CASE      ,   initializer_range=self.initializer_range      ,   )

      return config, input_ids, token_type_ids, attention_mask





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Any   =		self.prepare_config_and_inputs()
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					int   =		config_and_inputs
      _lowerCamelCase	:					List[str]   =		{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
      return config, inputs_dict





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					List[Any]   =		self.prepare_config_and_inputs()
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					str   =		config_and_inputs

      _lowerCamelCase	:					List[str]   =		True
      _lowerCamelCase	:					Tuple   =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
      _lowerCamelCase	:					Tuple   =		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 	lowercase__				(					A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					True

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





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					Tuple   =		FlaxRobertaModelTester(self)





   @slow
   def 		UpperCamelCase_  (							self)  ->  Dict:
      for model_class_name in self.all_model_classes:
         _lowerCamelCase	:					Any   =		model_class_name.from_pretrained("""roberta-base"""      ,   from_pt=SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					int   =		model(np.ones((1, 1)))
         self.assertIsNotNone(SCREAMING_SNAKE_CASE)





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

def 				_snake_case      (   __snake_case							:      int   ):




   """simple docstring"""







   _lowerCamelCase	:					List[str]   =		[True] * max_number
   for i in range(2						, isqrt(max_number - 1   ) + 1   ):
      if is_prime[i]:
         for j in range(i**2						, __snake_case						, __snake_case   ):
            _lowerCamelCase	:					Optional[int]   =		False

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

def 				_snake_case      (   __snake_case							:      int = 800800						, __snake_case							:      int = 800800   ):




   """simple docstring"""







   _lowerCamelCase	:					Union[str, Any]   =		degree * loga(__snake_case   )
   _lowerCamelCase	:					Union[str, Any]   =		int(__snake_case   )
   _lowerCamelCase	:					Dict   =		calculate_prime_numbers(__snake_case   )

   _lowerCamelCase	:					Optional[int]   =		0
   _lowerCamelCase	:					Any   =		0
   _lowerCamelCase	:					Any   =		len(__snake_case   ) - 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() = }''')

