






"""simple docstring"""

import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
			import jax.numpy as jnp

			from transformers.models.albert.modeling_flax_albert import (
			    FlaxAlbertForMaskedLM,
			    FlaxAlbertForMultipleChoice,
			    FlaxAlbertForPreTraining,
			    FlaxAlbertForQuestionAnswering,
			    FlaxAlbertForSequenceClassification,
			    FlaxAlbertForTokenClassification,
			    FlaxAlbertModel,
			)

class 						A_							(      unittest.TestCase  ):







				def __init__(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						Tuple=1_3						,    __lowerCamelCase							:						Tuple=7						,    __lowerCamelCase							:						Dict=True						,    __lowerCamelCase							:						Any=True						,    __lowerCamelCase							:						Tuple=True						,    __lowerCamelCase							:						str=True						,    __lowerCamelCase							:						str=9_9						,    __lowerCamelCase							:						Tuple=3_2						,    __lowerCamelCase							:						Optional[Any]=5						,    __lowerCamelCase							:						str=4						,    __lowerCamelCase							:						Optional[Any]=3_7						,    __lowerCamelCase							:						List[str]="gelu"						,    __lowerCamelCase							:						List[str]=0.1						,    __lowerCamelCase							:						Optional[int]=0.1						,    __lowerCamelCase							:						Dict=5_1_2						,    __lowerCamelCase							:						Optional[int]=1_6						,    __lowerCamelCase							:						Optional[Any]=2						,    __lowerCamelCase							:						str=0.02						,    __lowerCamelCase							:						Any=4						,    )				->					List[str]:
										__magic_name__            =     parent
										__magic_name__            =     batch_size
										__magic_name__            =     seq_length
										__magic_name__            =     is_training
										__magic_name__            =     use_attention_mask
										__magic_name__            =     use_token_type_ids
										__magic_name__            =     use_labels
										__magic_name__            =     vocab_size
										__magic_name__            =     hidden_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     intermediate_size
										__magic_name__            =     hidden_act
										__magic_name__            =     hidden_dropout_prob
										__magic_name__            =     attention_probs_dropout_prob
										__magic_name__            =     max_position_embeddings
										__magic_name__            =     type_vocab_size
										__magic_name__            =     type_sequence_label_size
										__magic_name__            =     initializer_range
										__magic_name__            =     num_choices







				def     _snake_case					(							self							:						str	)				->					Dict:
										__magic_name__            =     ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size	)

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

										__magic_name__            =     None
										if self.use_token_type_ids:
																__magic_name__            =     ids_tensor([self.batch_size, self.seq_length]						,    self.type_vocab_size	)

										__magic_name__            =     AlbertConfig(
										    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							:						int	)				->					List[str]:
										__magic_name__            =     self.prepare_config_and_inputs()
										__magic_name__      ,    __magic_name__      ,    __magic_name__      ,    __magic_name__            =     config_and_inputs
										__magic_name__            =     {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
										return config, inputs_dict





@require_flax
class 						A_							(      snake_case_							,							unittest.TestCase  ):
				UpperCAmelCase__        =				(
				    (
				        FlaxAlbertModel,
				        FlaxAlbertForPreTraining,
				        FlaxAlbertForMaskedLM,
				        FlaxAlbertForMultipleChoice,
				        FlaxAlbertForQuestionAnswering,
				        FlaxAlbertForSequenceClassification,
				        FlaxAlbertForTokenClassification,
				        FlaxAlbertForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)







				def     _snake_case					(							self							:						Dict	)				->					Dict:
										__magic_name__            =     FlaxAlbertModelTester(self	)







				@slow
				def     _snake_case					(							self							:						Union[str, Any]	)				->					List[Any]:
										for model_class_name in self.all_model_classes:
																__magic_name__            =     model_class_name.from_pretrained("albert-base-v2"	)
																__magic_name__            =     model(np.ones((1, 1)	)	)
																self.assertIsNotNone(__lowerCamelCase	)





@require_flax
class 						A_							(      unittest.TestCase  ):







				@slow
				def     _snake_case					(							self							:						Tuple	)				->					int:
										__magic_name__            =     FlaxAlbertModel.from_pretrained("albert-base-v2"	)
										__magic_name__            =     np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]	)
										__magic_name__            =     np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
										__magic_name__            =     model(__lowerCamelCase						,    attention_mask=__lowerCamelCase	)[0]
										__magic_name__            =     (1, 1_1, 7_6_8)
										self.assertEqual(output.shape						,    __lowerCamelCase	)
										__magic_name__            =     np.array(
										    [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]	)

										self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]						,    __lowerCamelCase						,    atol=1e-4	)	)











"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:int = 2_0_0_0_0_0_0   ):

						'''simple docstring'''






						__magic_name__            =     [0 for i in range(n + 1   )]
						__magic_name__            =     1
						__magic_name__            =     1

						for i in range(2 ,						int(n**0.5   ) + 1   ):
												if primality_list[i] == 0:
																		for j in range(i * i ,						n + 1 ,						__lowerCamelCase   ):
																								__magic_name__            =     1
						__magic_name__            =     0
						for i in range(__lowerCamelCase   ):
												if primality_list[i] == 0:
																		sum_of_primes += i
						return sum_of_primes


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



