


import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    NystromformerForMaskedLM,
						    NystromformerForMultipleChoice,
						    NystromformerForQuestionAnswering,
						    NystromformerForSequenceClassification,
						    NystromformerForTokenClassification,
						    NystromformerModel,
						)
						from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST







class 			snake_case_  :
					def __init__( self  :List[str]     ,__snake_case  :Optional[Any]     ,__snake_case  :List[Any]=13     ,__snake_case  :Any=7     ,__snake_case  :int=True     ,__snake_case  :List[str]=True     ,__snake_case  :Optional[int]=True     ,__snake_case  :Union[str, Any]=True     ,__snake_case  :Union[str, Any]=99     ,__snake_case  :Any=32     ,__snake_case  :int=5     ,__snake_case  :Union[str, Any]=4     ,__snake_case  :Tuple=37     ,__snake_case  :Union[str, Any]="gelu"     ,__snake_case  :Optional[Any]=0.1     ,__snake_case  :Optional[Any]=0.1     ,__snake_case  :int=5_12     ,__snake_case  :str=16     ,__snake_case  :List[Any]=2     ,__snake_case  :Any=0.02     ,__snake_case  :Optional[int]=3     ,__snake_case  :List[str]=4     ,__snake_case  :List[str]=None     ,)   ->       Dict:
												a__			 =	parent
												a__			 =	batch_size
												a__			 =	seq_length
												a__			 =	is_training
												a__			 =	use_input_mask
												a__			 =	use_token_type_ids
												a__			 =	use_labels
												a__			 =	vocab_size
												a__			 =	hidden_size
												a__			 =	num_hidden_layers
												a__			 =	num_attention_heads
												a__			 =	intermediate_size
												a__			 =	hidden_act
												a__			 =	hidden_dropout_prob
												a__			 =	attention_probs_dropout_prob
												a__			 =	max_position_embeddings
												a__			 =	type_vocab_size
												a__			 =	type_sequence_label_size
												a__			 =	initializer_range
												a__			 =	num_labels
												a__			 =	num_choices
												a__			 =	scope
					def 					lowerCamelCase__( self  :str )   ->       Union[str, Any]:
												a__			 =	ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size )

												a__			 =	None
												if self.use_input_mask:
																			a__			 =	random_attention_mask([self.batch_size, self.seq_length] )

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

												a__			 =	None
												a__			 =	None
												a__			 =	None
												if self.use_labels:
																			a__			 =	ids_tensor([self.batch_size]     ,self.type_sequence_label_size )
																			a__			 =	ids_tensor([self.batch_size, self.seq_length]     ,self.num_labels )
																			a__			 =	ids_tensor([self.batch_size]     ,self.num_choices )

												a__			 =	self.get_config()

												return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
					def 					lowerCamelCase__( self  :Optional[int] )   ->       List[str]:
												return NystromformerConfig(
												    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=_UpperCamelCase     ,initializer_range=self.initializer_range     ,)
					def 					lowerCamelCase__( self  :str     ,__snake_case  :Tuple     ,__snake_case  :List[Any]     ,__snake_case  :int     ,__snake_case  :int     ,__snake_case  :Optional[Any]     ,__snake_case  :List[str]     ,__snake_case  :int )   ->       Dict:
												a__			 =	NystromformerModel(config=_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()
												a__			 =	model(_UpperCamelCase     ,attention_mask=_UpperCamelCase     ,token_type_ids=_UpperCamelCase )
												a__			 =	model(_UpperCamelCase     ,token_type_ids=_UpperCamelCase )
												a__			 =	model(_UpperCamelCase )
												self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size) )
					def 					lowerCamelCase__( self  :List[str]     ,__snake_case  :int     ,__snake_case  :Tuple     ,__snake_case  :List[str]     ,__snake_case  :Dict     ,__snake_case  :Optional[Any]     ,__snake_case  :Dict     ,__snake_case  :Dict )   ->       Tuple:
												a__			 =	NystromformerForMaskedLM(config=_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()
												a__			 =	model(_UpperCamelCase     ,attention_mask=_UpperCamelCase     ,token_type_ids=_UpperCamelCase     ,labels=_UpperCamelCase )
												self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.vocab_size) )
					def 					lowerCamelCase__( self  :Dict     ,__snake_case  :str     ,__snake_case  :Union[str, Any]     ,__snake_case  :Optional[Any]     ,__snake_case  :Any     ,__snake_case  :Optional[int]     ,__snake_case  :Union[str, Any]     ,__snake_case  :int )   ->       Tuple:
												a__			 =	NystromformerForQuestionAnswering(config=_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()
												a__			 =	model(
												    _UpperCamelCase     ,attention_mask=_UpperCamelCase     ,token_type_ids=_UpperCamelCase     ,start_positions=_UpperCamelCase     ,end_positions=_UpperCamelCase     ,)
												self.parent.assertEqual(result.start_logits.shape     ,(self.batch_size, self.seq_length) )
												self.parent.assertEqual(result.end_logits.shape     ,(self.batch_size, self.seq_length) )
					def 					lowerCamelCase__( self  :Union[str, Any]     ,__snake_case  :Union[str, Any]     ,__snake_case  :List[Any]     ,__snake_case  :List[str]     ,__snake_case  :Dict     ,__snake_case  :str     ,__snake_case  :Optional[int]     ,__snake_case  :Any )   ->       Optional[Any]:
												a__			 =	self.num_labels
												a__			 =	NystromformerForSequenceClassification(_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()
												a__			 =	model(_UpperCamelCase     ,attention_mask=_UpperCamelCase     ,token_type_ids=_UpperCamelCase     ,labels=_UpperCamelCase )
												self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels) )
					def 					lowerCamelCase__( self  :List[Any]     ,__snake_case  :Optional[Any]     ,__snake_case  :int     ,__snake_case  :Any     ,__snake_case  :Union[str, Any]     ,__snake_case  :Optional[Any]     ,__snake_case  :Optional[int]     ,__snake_case  :List[Any] )   ->       List[Any]:
												a__			 =	self.num_labels
												a__			 =	NystromformerForTokenClassification(config=_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()
												a__			 =	model(_UpperCamelCase     ,attention_mask=_UpperCamelCase     ,token_type_ids=_UpperCamelCase     ,labels=_UpperCamelCase )
												self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.num_labels) )
					def 					lowerCamelCase__( self  :Union[str, Any]     ,__snake_case  :Optional[int]     ,__snake_case  :str     ,__snake_case  :Optional[int]     ,__snake_case  :int     ,__snake_case  :Dict     ,__snake_case  :Dict     ,__snake_case  :Optional[Any] )   ->       Optional[int]:
												a__			 =	self.num_choices
												a__			 =	NystromformerForMultipleChoice(config=_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()
												a__			 =	input_ids.unsqueeze(1 ).expand(-1     ,self.num_choices     ,-1 ).contiguous()
												a__			 =	token_type_ids.unsqueeze(1 ).expand(-1     ,self.num_choices     ,-1 ).contiguous()
												a__			 =	input_mask.unsqueeze(1 ).expand(-1     ,self.num_choices     ,-1 ).contiguous()
												a__			 =	model(
												    _UpperCamelCase     ,attention_mask=_UpperCamelCase     ,token_type_ids=_UpperCamelCase     ,labels=_UpperCamelCase     ,)
												self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_choices) )



					def 					lowerCamelCase__( self  :Optional[Any] )   ->       Optional[int]:
												a__			 =	self.prepare_config_and_inputs()
												(
												    (
												    a__      
												) ,   (
												    a__      
												) ,   (
												    a__      
												) ,   (
												    a__      
												) ,   (
												    a__      
												) ,   (
												    a__      
												) ,   (
												    a__      
												) ,         
												)			 =	config_and_inputs
												a__			 =	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
												return config, inputs_dict







@require_torch
class 			snake_case_  (A__      ,		A__      ,		unittest.TestCase ):
					UpperCAmelCase__   :       Tuple        =     (
					    (
					        NystromformerModel,
					        NystromformerForMaskedLM,
					        NystromformerForMultipleChoice,
					        NystromformerForQuestionAnswering,
					        NystromformerForSequenceClassification,
					        NystromformerForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					UpperCAmelCase__   :       Optional[Any]        =     (
					    {
					        """feature-extraction""": NystromformerModel,
					        """fill-mask""": NystromformerForMaskedLM,
					        """question-answering""": NystromformerForQuestionAnswering,
					        """text-classification""": NystromformerForSequenceClassification,
					        """token-classification""": NystromformerForTokenClassification,
					        """zero-shot""": NystromformerForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					UpperCAmelCase__   :       Any        =     False
					UpperCAmelCase__   :       Optional[Any]        =     False
					def 					lowerCamelCase__( self  :Optional[Any] )   ->       Any:
												a__			 =	NystromformerModelTester(self )
												a__			 =	ConfigTester(self     ,config_class=_UpperCamelCase     ,hidden_size=37 )
					def 					lowerCamelCase__( self  :Tuple )   ->       Dict:
												self.config_tester.run_common_tests()
					def 					lowerCamelCase__( self  :List[Any] )   ->       Optional[int]:
												a__			 =	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_UpperCamelCase )
					def 					lowerCamelCase__( self  :List[str] )   ->       int:
												a__			 =	self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																			a__			 =	type
																			self.model_tester.create_and_check_model(*_UpperCamelCase )
					def 					lowerCamelCase__( self  :Optional[int] )   ->       Dict:
												a__			 =	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase )
					def 					lowerCamelCase__( self  :Any )   ->       Tuple:
												a__			 =	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_multiple_choice(*_UpperCamelCase )
					def 					lowerCamelCase__( self  :Optional[Any] )   ->       Dict:
												a__			 =	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase )
					def 					lowerCamelCase__( self  :List[str] )   ->       Optional[Any]:
												a__			 =	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase )
					def 					lowerCamelCase__( self  :Union[str, Any] )   ->       Any:
												a__			 =	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase )



					@slow
					def 					lowerCamelCase__( self  :Union[str, Any] )   ->       str:
												for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			a__			 =	NystromformerModel.from_pretrained(_UpperCamelCase )
																			self.assertIsNotNone(_UpperCamelCase )







@require_torch
class 			snake_case_  (unittest.TestCase ):
					@slow
					def 					lowerCamelCase__( self  :str )   ->       Optional[int]:
												a__			 =	NystromformerModel.from_pretrained('uw-madison/nystromformer-512' )
												a__			 =	torch.tensor([[0, 1, 2, 3, 4, 5]] )

												with torch.no_grad():
																			a__			 =	model(_UpperCamelCase )[0]

												a__			 =	torch.Size((1, 6, 7_68) )
												self.assertEqual(output.shape     ,_UpperCamelCase )

												a__			 =	torch.tensor(
												    [[[-0.45_32, -0.09_36, 0.51_37], [-0.26_76, 0.06_28, 0.61_86], [-0.36_29, -0.17_26, 0.47_16]]] )

												self.assertTrue(torch.allclose(output[:, :3, :3]     ,_UpperCamelCase     ,atol=1E-4 ) )



					@slow
					def 					lowerCamelCase__( self  :int )   ->       Optional[Any]:
												a__			 =	'the [MASK] of Belgium is Brussels'

												a__			 =	AutoTokenizer.from_pretrained('uw-madison/nystromformer-512' )
												a__			 =	NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512' )

												a__			 =	tokenizer(_UpperCamelCase     ,return_tensors='pt' )

												with torch.no_grad():
																			a__			 =	model(encoding.input_ids ).logits

												a__			 =	token_logits[:, 2, :].argmax(-1 )[0]

												self.assertEqual(tokenizer.decode(_UpperCamelCase )     ,'capital' )











def      A__       (    SCREAMING_SNAKE_CASE_   :      int = 2_00_00_00						)  ->  int:






   """simple docstring"""







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

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


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