


import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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 (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
  )





class  _a							(    _UpperCAmelCase):




 def __init__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Any=13		, _SCREAMING_SNAKE_CASE						:		str=7		, _SCREAMING_SNAKE_CASE						:		List[Any]=True		, _SCREAMING_SNAKE_CASE						:		str=True		, _SCREAMING_SNAKE_CASE						:		str=False		, _SCREAMING_SNAKE_CASE						:		str=True		, _SCREAMING_SNAKE_CASE						:		Tuple=99		, _SCREAMING_SNAKE_CASE						:		Any=32		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=5		, _SCREAMING_SNAKE_CASE						:		List[str]=4		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=64		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]="gelu"		, _SCREAMING_SNAKE_CASE						:		Any=0.1		, _SCREAMING_SNAKE_CASE						:		List[Any]=0.1		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=512		, _SCREAMING_SNAKE_CASE						:		Any=16		, _SCREAMING_SNAKE_CASE						:		Any=2		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=0.02		, _SCREAMING_SNAKE_CASE						:		Optional[int]=3		, _SCREAMING_SNAKE_CASE						:		int=4		, _SCREAMING_SNAKE_CASE						:		Dict=None		, _SCREAMING_SNAKE_CASE						:		Tuple=2		, _SCREAMING_SNAKE_CASE						:		str=2		, _SCREAMING_SNAKE_CASE						:		Dict=2		, _SCREAMING_SNAKE_CASE						:		Any=2		, _SCREAMING_SNAKE_CASE						:		str=4		, _SCREAMING_SNAKE_CASE						:		Optional[int]=1		, )->	Optional[int]:
   lowerCAmelCase__       :      Tuple   					= parent
   lowerCAmelCase__       :      Optional[int]   					= batch_size
   lowerCAmelCase__       :      Optional[Any]   					= seq_length
   lowerCAmelCase__       :      int   					= is_training
   lowerCAmelCase__       :      Union[str, Any]   					= use_input_mask
   lowerCAmelCase__       :      Union[str, Any]   					= use_token_type_ids
   lowerCAmelCase__       :      Dict   					= use_labels
   lowerCAmelCase__       :      Union[str, Any]   					= vocab_size
   lowerCAmelCase__       :      Union[str, Any]   					= hidden_size
   lowerCAmelCase__       :      Tuple   					= num_hidden_layers
   lowerCAmelCase__       :      Any   					= num_attention_heads
   lowerCAmelCase__       :      int   					= intermediate_size
   lowerCAmelCase__       :      str   					= hidden_act
   lowerCAmelCase__       :      Optional[Any]   					= hidden_dropout_prob
   lowerCAmelCase__       :      str   					= attention_probs_dropout_prob
   lowerCAmelCase__       :      List[Any]   					= max_position_embeddings
   lowerCAmelCase__       :      Optional[Any]   					= type_vocab_size
   lowerCAmelCase__       :      int   					= type_sequence_label_size
   lowerCAmelCase__       :      Dict   					= initializer_range
   lowerCAmelCase__       :      Dict   					= num_labels
   lowerCAmelCase__       :      Tuple   					= num_choices
   lowerCAmelCase__       :      Optional[int]   					= scope
   lowerCAmelCase__       :      List[Any]   					= q_groups
   lowerCAmelCase__       :      Tuple   					= k_groups
   lowerCAmelCase__       :      Any   					= v_groups
   lowerCAmelCase__       :      List[str]   					= post_attention_groups
   lowerCAmelCase__       :      Tuple   					= intermediate_groups
   lowerCAmelCase__       :      int   					= output_groups




 def 							UpperCAmelCase__( self						:		Optional[Any]		)->	int:
   lowerCAmelCase__       :      Optional[Any]   					= ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size		)

   lowerCAmelCase__       :      Tuple   					= None
   if self.use_input_mask:
     lowerCAmelCase__       :      List[str]   					= random_attention_mask([self.batch_size, self.seq_length]		)

   lowerCAmelCase__       :      Optional[int]   					= None
   lowerCAmelCase__       :      List[Any]   					= None
   lowerCAmelCase__       :      Dict   					= None
   if self.use_labels:
     lowerCAmelCase__       :      Tuple   					= ids_tensor([self.batch_size]		, self.type_sequence_label_size		)
     lowerCAmelCase__       :      List[Any]   					= ids_tensor([self.batch_size, self.seq_length]		, self.num_labels		)
     lowerCAmelCase__       :      Tuple   					= ids_tensor([self.batch_size]		, self.num_choices		)

   lowerCAmelCase__       :      Dict   					= self.get_config()

   return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




 def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	Union[str, Any]:
   return SqueezeBertConfig(
       embedding_size=self.hidden_size		, 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		, attention_probs_dropout_prob=self.hidden_dropout_prob		, attention_dropout=self.attention_probs_dropout_prob		, max_position_embeddings=self.max_position_embeddings		, initializer_range=self.initializer_range		, q_groups=self.q_groups		, k_groups=self.k_groups		, v_groups=self.v_groups		, post_attention_groups=self.post_attention_groups		, intermediate_groups=self.intermediate_groups		, output_groups=self.output_groups		, )




 def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Dict		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Any		, _SCREAMING_SNAKE_CASE						:		str		)->	int:
   lowerCAmelCase__       :      List[str]   					= SqueezeBertModel(config=SCREAMING_SNAKE_CASE_		)
   model.to(SCREAMING_SNAKE_CASE_		)
   model.eval()
   lowerCAmelCase__       :      Any   					= model(SCREAMING_SNAKE_CASE_		, SCREAMING_SNAKE_CASE_		)
   lowerCAmelCase__       :      Any   					= model(SCREAMING_SNAKE_CASE_		)
   self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size)		)




 def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		Dict		, _SCREAMING_SNAKE_CASE						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Tuple		)->	Optional[Any]:
   lowerCAmelCase__       :      Union[str, Any]   					= SqueezeBertForMaskedLM(config=SCREAMING_SNAKE_CASE_		)
   model.to(SCREAMING_SNAKE_CASE_		)
   model.eval()
   lowerCAmelCase__       :      List[Any]   					= model(SCREAMING_SNAKE_CASE_		, attention_mask=SCREAMING_SNAKE_CASE_		, labels=SCREAMING_SNAKE_CASE_		)
   self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.seq_length, self.vocab_size)		)




 def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Tuple		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		Any		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		str		)->	str:
   lowerCAmelCase__       :      List[Any]   					= SqueezeBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_		)
   model.to(SCREAMING_SNAKE_CASE_		)
   model.eval()
   lowerCAmelCase__       :      str   					= model(
       SCREAMING_SNAKE_CASE_		, attention_mask=SCREAMING_SNAKE_CASE_		, start_positions=SCREAMING_SNAKE_CASE_		, end_positions=SCREAMING_SNAKE_CASE_		)
   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 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		Dict		, _SCREAMING_SNAKE_CASE						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		)->	Union[str, Any]:
   lowerCAmelCase__       :      str   					= self.num_labels
   lowerCAmelCase__       :      Optional[Any]   					= SqueezeBertForSequenceClassification(SCREAMING_SNAKE_CASE_		)
   model.to(SCREAMING_SNAKE_CASE_		)
   model.eval()
   lowerCAmelCase__       :      Union[str, Any]   					= model(SCREAMING_SNAKE_CASE_		, attention_mask=SCREAMING_SNAKE_CASE_		, labels=SCREAMING_SNAKE_CASE_		)
   self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.num_labels)		)




 def 							UpperCAmelCase__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Tuple		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		Any		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	Any:
   lowerCAmelCase__       :      Any   					= self.num_labels
   lowerCAmelCase__       :      str   					= SqueezeBertForTokenClassification(config=SCREAMING_SNAKE_CASE_		)
   model.to(SCREAMING_SNAKE_CASE_		)
   model.eval()

   lowerCAmelCase__       :      Dict   					= model(SCREAMING_SNAKE_CASE_		, attention_mask=SCREAMING_SNAKE_CASE_		, labels=SCREAMING_SNAKE_CASE_		)
   self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.seq_length, self.num_labels)		)




 def 							UpperCAmelCase__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Dict		, _SCREAMING_SNAKE_CASE						:		List[str]		, _SCREAMING_SNAKE_CASE						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Dict		)->	Dict:
   lowerCAmelCase__       :      Optional[int]   					= self.num_choices
   lowerCAmelCase__       :      Tuple   					= SqueezeBertForMultipleChoice(config=SCREAMING_SNAKE_CASE_		)
   model.to(SCREAMING_SNAKE_CASE_		)
   model.eval()
   lowerCAmelCase__       :      Union[str, Any]   					= input_ids.unsqueeze(1		).expand(-1		, self.num_choices		, -1		).contiguous()
   lowerCAmelCase__       :      Union[str, Any]   					= input_mask.unsqueeze(1		).expand(-1		, self.num_choices		, -1		).contiguous()
   lowerCAmelCase__       :      Tuple   					= model(
       SCREAMING_SNAKE_CASE_		, attention_mask=SCREAMING_SNAKE_CASE_		, labels=SCREAMING_SNAKE_CASE_		, )
   self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.num_choices)		)




 def 							UpperCAmelCase__( self						:		Dict		)->	List[str]:
   lowerCAmelCase__       :      Optional[int]   					= self.prepare_config_and_inputs()
   (lowerCAmelCase__)       :      Optional[int]   					= config_and_inputs
   lowerCAmelCase__       :      Optional[int]   					= {"""input_ids""": input_ids, """attention_mask""": input_mask}
   return config, inputs_dict





@require_torch
class  _a							(    _UpperCAmelCase					,						_UpperCAmelCase					,						unittest.TestCase):
 _a  :  Dict				   =							(
     (
         SqueezeBertModel,
         SqueezeBertForMaskedLM,
         SqueezeBertForMultipleChoice,
         SqueezeBertForQuestionAnswering,
         SqueezeBertForSequenceClassification,
         SqueezeBertForTokenClassification,
     )
     if is_torch_available()
     else None
 )
 _a  :  Dict				   =							(
     {
         'feature-extraction': SqueezeBertModel,
         'fill-mask': SqueezeBertForMaskedLM,
         'question-answering': SqueezeBertForQuestionAnswering,
         'text-classification': SqueezeBertForSequenceClassification,
         'token-classification': SqueezeBertForTokenClassification,
         'zero-shot': SqueezeBertForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 _a  :  Dict				   =							False
 _a  :  str				   =							True
 _a  :  str				   =							False




 def 							UpperCAmelCase__( self						:		List[str]		)->	List[str]:
   lowerCAmelCase__       :      Any   					= SqueezeBertModelTester(self		)
   lowerCAmelCase__       :      List[Any]   					= ConfigTester(self		, config_class=SCREAMING_SNAKE_CASE_		, dim=37		)




 def 							UpperCAmelCase__( self						:		List[str]		)->	Optional[int]:
   self.config_tester.run_common_tests()




 def 							UpperCAmelCase__( self						:		int		)->	List[str]:
   lowerCAmelCase__       :      Tuple   					= self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_squeezebert_model(*SCREAMING_SNAKE_CASE_		)




 def 							UpperCAmelCase__( self						:		Tuple		)->	List[str]:
   lowerCAmelCase__       :      Any   					= self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_squeezebert_for_masked_lm(*SCREAMING_SNAKE_CASE_		)




 def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	str:
   lowerCAmelCase__       :      Tuple   					= self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_squeezebert_for_question_answering(*SCREAMING_SNAKE_CASE_		)




 def 							UpperCAmelCase__( self						:		int		)->	Optional[int]:
   lowerCAmelCase__       :      List[Any]   					= self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_squeezebert_for_sequence_classification(*SCREAMING_SNAKE_CASE_		)




 def 							UpperCAmelCase__( self						:		List[str]		)->	Optional[Any]:
   lowerCAmelCase__       :      str   					= self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_squeezebert_for_token_classification(*SCREAMING_SNAKE_CASE_		)




 def 							UpperCAmelCase__( self						:		List[str]		)->	Union[str, Any]:
   lowerCAmelCase__       :      List[str]   					= self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_squeezebert_for_multiple_choice(*SCREAMING_SNAKE_CASE_		)




 @slow
 def 							UpperCAmelCase__( self						:		Dict		)->	Dict:
   for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowerCAmelCase__       :      Optional[Any]   					= SqueezeBertModel.from_pretrained(SCREAMING_SNAKE_CASE_		)
     self.assertIsNotNone(SCREAMING_SNAKE_CASE_		)





@require_sentencepiece
@require_tokenizers
@require_torch
class  _a							(    unittest.TestCase):




 @slow
 def 							UpperCAmelCase__( self						:		Dict		)->	List[str]:
   lowerCAmelCase__       :      Optional[Any]   					= SqueezeBertForSequenceClassification.from_pretrained('''squeezebert/squeezebert-mnli'''		)

   lowerCAmelCase__       :      Dict   					= torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]]		)
   lowerCAmelCase__       :      List[str]   					= model(SCREAMING_SNAKE_CASE_		)[0]
   lowerCAmelCase__       :      Optional[Any]   					= torch.Size((1, 3)		)
   self.assertEqual(output.shape		, SCREAMING_SNAKE_CASE_		)
   lowerCAmelCase__       :      Optional[int]   					= torch.tensor([[0.6401, -0.0349, -0.6041]]		)
   self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_		, SCREAMING_SNAKE_CASE_		, atol=1E-4		)		)








from math import isqrt






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Dict   					= [True] * max_number
		for i in range(2     ,       isqrt(max_number - 1	) + 1	):
				if is_prime[i]:
						for j in range(i**2     ,       _a     ,       _a	):
								lowerCAmelCase__       :      int   					= False

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






def 					lowerCamelCase_   (	_a = 10**8	):




		"""simple docstring"""






		lowerCAmelCase__       :      Any   					= calculate_prime_numbers(max_number // 2	)

		lowerCAmelCase__       :      Tuple   					= 0
		lowerCAmelCase__       :      List[Any]   					= 0
		lowerCAmelCase__       :      Optional[int]   					= len(_a	) - 1
		while left <= right:
				while prime_numbers[left] * prime_numbers[right] >= max_number:
						right -= 1
				semiprimes_count += right - left + 1
				left += 1

		return semiprimes_count


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




