



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 							_a							(					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_=0.0_2,					SCREAMING_SNAKE_CASE_=4,					)    ->					List[Any]:
						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 			__snake_case    (self )    ->					Tuple:
						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 			__snake_case    (self )    ->					Optional[Any]:
						UpperCAmelCase_:						Union[str, Any]					=			self.prepare_config_and_inputs()
						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 			__snake_case    (self )    ->					Tuple:
						UpperCAmelCase_:						Optional[int]					=			self.prepare_config_and_inputs()
						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 							_a							(					UpperCAmelCase__      ,						unittest.TestCase ):
			A							=     True

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

			def 			__snake_case    (self )    ->					Any:
						UpperCAmelCase_:						List[str]					=			FlaxRobertaModelTester(self )

			@slow
			def 			__snake_case    (self )    ->					Dict:




						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() = }""")
