





import unittest

from transformers import MraConfig, 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, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
      import torch

      from transformers import (
          MraForMaskedLM,
          MraForMultipleChoice,
          MraForQuestionAnswering,
          MraForSequenceClassification,
          MraForTokenClassification,
          MraModel,
      )
      from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST






class        snake_case_    :






  def __init__(							self			:					Union[str, Any]       ,  _snake_case			:					Optional[int]       ,  _snake_case			:					Optional[int]=2       ,  _snake_case			:					Union[str, Any]=8       ,  _snake_case			:					List[str]=True       ,  _snake_case			:					Dict=True       ,  _snake_case			:					Optional[Any]=True       ,  _snake_case			:					List[str]=True       ,  _snake_case			:					Tuple=99       ,  _snake_case			:					int=16       ,  _snake_case			:					Optional[int]=5       ,  _snake_case			:					int=2       ,  _snake_case			:					Tuple=36       ,  _snake_case			:					Optional[Any]="gelu"       ,  _snake_case			:					str=0.0       ,  _snake_case			:					Optional[int]=0.0       ,  _snake_case			:					Tuple=512       ,  _snake_case			:					str=16       ,  _snake_case			:					str=2       ,  _snake_case			:					int=0.02       ,  _snake_case			:					Optional[int]=3       ,  _snake_case			:					List[Any]=4       ,  _snake_case			:					Any=None       ,  )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       parent
   __lowerCAmelCase					:	Any      =       batch_size
   __lowerCAmelCase					:	Optional[int]      =       seq_length
   __lowerCAmelCase					:	List[str]      =       is_training
   __lowerCAmelCase					:	Dict      =       use_input_mask
   __lowerCAmelCase					:	Union[str, Any]      =       use_token_type_ids
   __lowerCAmelCase					:	Tuple      =       use_labels
   __lowerCAmelCase					:	Dict      =       vocab_size
   __lowerCAmelCase					:	Optional[int]      =       hidden_size
   __lowerCAmelCase					:	List[Any]      =       num_hidden_layers
   __lowerCAmelCase					:	Optional[Any]      =       num_attention_heads
   __lowerCAmelCase					:	str      =       intermediate_size
   __lowerCAmelCase					:	Dict      =       hidden_act
   __lowerCAmelCase					:	str      =       hidden_dropout_prob
   __lowerCAmelCase					:	Tuple      =       attention_probs_dropout_prob
   __lowerCAmelCase					:	Optional[Any]      =       max_position_embeddings
   __lowerCAmelCase					:	Tuple      =       type_vocab_size
   __lowerCAmelCase					:	int      =       type_sequence_label_size
   __lowerCAmelCase					:	List[Any]      =       initializer_range
   __lowerCAmelCase					:	List[str]      =       num_labels
   __lowerCAmelCase					:	List[str]      =       num_choices
   __lowerCAmelCase					:	Optional[Any]      =       scope






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size      )

   __lowerCAmelCase					:	Optional[Any]      =       None
   if self.use_input_mask:
    __lowerCAmelCase					:	List[Any]      =       random_attention_mask([self.batch_size, self.seq_length]      )

   __lowerCAmelCase					:	Tuple      =       None
   if self.use_token_type_ids:
    __lowerCAmelCase					:	Optional[Any]      =       ids_tensor([self.batch_size, self.seq_length]       ,  self.type_vocab_size      )

   __lowerCAmelCase					:	str      =       None
   __lowerCAmelCase					:	int      =       None
   __lowerCAmelCase					:	Any      =       None
   if self.use_labels:
    __lowerCAmelCase					:	int      =       ids_tensor([self.batch_size]       ,  self.type_sequence_label_size      )
    __lowerCAmelCase					:	Union[str, Any]      =       ids_tensor([self.batch_size, self.seq_length]       ,  self.num_labels      )
    __lowerCAmelCase					:	List[str]      =       ids_tensor([self.batch_size]       ,  self.num_choices      )

   __lowerCAmelCase					:	Dict      =       self.get_config()

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






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]      )->Optional[Any]:






   '''simple docstring'''

   return MraConfig(
       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=__snake_case       ,  initializer_range=self.initializer_range       ,  )






  def 				UpperCAmelCase__						(							self			:					List[str]      )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       self.get_config()
   __lowerCAmelCase					:	Optional[Any]      =       300
   return config






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]      )->Union[str, Any]:






   '''simple docstring'''

   (
       __lowerCAmelCase     
   )					:	Optional[Any]      =       self.prepare_config_and_inputs()

   __lowerCAmelCase					:	Union[str, Any]      =       True
   __lowerCAmelCase					:	Tuple      =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
   __lowerCAmelCase					:	Dict      =       ids_tensor([self.batch_size, self.seq_length]       ,  vocab_size=2      )

   return (
       config,
       input_ids,
       token_type_ids,
       input_mask,
       sequence_labels,
       token_labels,
       choice_labels,
       encoder_hidden_states,
       encoder_attention_mask,
   )






  def 				UpperCAmelCase__						(							self			:					int       ,  _snake_case			:					int       ,  _snake_case			:					List[Any]       ,  _snake_case			:					Union[str, Any]       ,  _snake_case			:					List[Any]       ,  _snake_case			:					Union[str, Any]       ,  _snake_case			:					Dict       ,  _snake_case			:					Any      )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       MraModel(config=__snake_case      )
   model.to(__snake_case      )
   model.eval()
   __lowerCAmelCase					:	str      =       model(__snake_case       ,  attention_mask=__snake_case       ,  token_type_ids=__snake_case      )
   __lowerCAmelCase					:	List[str]      =       model(__snake_case       ,  token_type_ids=__snake_case      )
   __lowerCAmelCase					:	Union[str, Any]      =       model(__snake_case      )
   self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)      )






  def 				UpperCAmelCase__						(							self			:					List[str]       ,  _snake_case			:					Tuple       ,  _snake_case			:					List[str]       ,  _snake_case			:					str       ,  _snake_case			:					Optional[Any]       ,  _snake_case			:					Union[str, Any]       ,  _snake_case			:					Any       ,  _snake_case			:					Optional[Any]       ,  _snake_case			:					Optional[int]       ,  _snake_case			:					List[Any]       ,  )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       True
   __lowerCAmelCase					:	Optional[int]      =       MraModel(__snake_case      )
   model.to(__snake_case      )
   model.eval()
   __lowerCAmelCase					:	List[Any]      =       model(
       __snake_case       ,  attention_mask=__snake_case       ,  token_type_ids=__snake_case       ,  encoder_hidden_states=__snake_case       ,  encoder_attention_mask=__snake_case       ,  )
   __lowerCAmelCase					:	Any      =       model(
       __snake_case       ,  attention_mask=__snake_case       ,  token_type_ids=__snake_case       ,  encoder_hidden_states=__snake_case       ,  )
   __lowerCAmelCase					:	Optional[int]      =       model(__snake_case       ,  attention_mask=__snake_case       ,  token_type_ids=__snake_case      )
   self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)      )






  def 				UpperCAmelCase__						(							self			:					Optional[Any]       ,  _snake_case			:					int       ,  _snake_case			:					List[Any]       ,  _snake_case			:					Optional[int]       ,  _snake_case			:					Optional[Any]       ,  _snake_case			:					List[Any]       ,  _snake_case			:					Dict       ,  _snake_case			:					Optional[Any]      )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       MraForMaskedLM(config=__snake_case      )
   model.to(__snake_case      )
   model.eval()
   __lowerCAmelCase					:	List[str]      =       model(__snake_case       ,  attention_mask=__snake_case       ,  token_type_ids=__snake_case       ,  labels=__snake_case      )
   self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.vocab_size)      )






  def 				UpperCAmelCase__						(							self			:					Tuple       ,  _snake_case			:					Union[str, Any]       ,  _snake_case			:					str       ,  _snake_case			:					Tuple       ,  _snake_case			:					str       ,  _snake_case			:					Tuple       ,  _snake_case			:					Optional[int]       ,  _snake_case			:					int      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       MraForQuestionAnswering(config=__snake_case      )
   model.to(__snake_case      )
   model.eval()
   __lowerCAmelCase					:	Optional[int]      =       model(
       __snake_case       ,  attention_mask=__snake_case       ,  token_type_ids=__snake_case       ,  start_positions=__snake_case       ,  end_positions=__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			:					Dict       ,  _snake_case			:					Tuple       ,  _snake_case			:					List[Any]       ,  _snake_case			:					List[Any]       ,  _snake_case			:					Optional[Any]       ,  _snake_case			:					Any       ,  _snake_case			:					List[str]       ,  _snake_case			:					str      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       self.num_labels
   __lowerCAmelCase					:	Dict      =       MraForSequenceClassification(__snake_case      )
   model.to(__snake_case      )
   model.eval()
   __lowerCAmelCase					:	Any      =       model(__snake_case       ,  attention_mask=__snake_case       ,  token_type_ids=__snake_case       ,  labels=__snake_case      )
   self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)      )






  def 				UpperCAmelCase__						(							self			:					str       ,  _snake_case			:					Dict       ,  _snake_case			:					Optional[Any]       ,  _snake_case			:					int       ,  _snake_case			:					List[Any]       ,  _snake_case			:					Any       ,  _snake_case			:					List[Any]       ,  _snake_case			:					int      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       self.num_labels
   __lowerCAmelCase					:	Tuple      =       MraForTokenClassification(config=__snake_case      )
   model.to(__snake_case      )
   model.eval()
   __lowerCAmelCase					:	List[Any]      =       model(__snake_case       ,  attention_mask=__snake_case       ,  token_type_ids=__snake_case       ,  labels=__snake_case      )
   self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.num_labels)      )






  def 				UpperCAmelCase__						(							self			:					Any       ,  _snake_case			:					Any       ,  _snake_case			:					str       ,  _snake_case			:					Dict       ,  _snake_case			:					List[Any]       ,  _snake_case			:					Any       ,  _snake_case			:					Any       ,  _snake_case			:					str      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       self.num_choices
   __lowerCAmelCase					:	int      =       MraForMultipleChoice(config=__snake_case      )
   model.to(__snake_case      )
   model.eval()
   __lowerCAmelCase					:	Tuple      =       input_ids.unsqueeze(1      ).expand(-1       ,  self.num_choices       ,  -1      ).contiguous()
   __lowerCAmelCase					:	Optional[int]      =       token_type_ids.unsqueeze(1      ).expand(-1       ,  self.num_choices       ,  -1      ).contiguous()
   __lowerCAmelCase					:	Optional[Any]      =       input_mask.unsqueeze(1      ).expand(-1       ,  self.num_choices       ,  -1      ).contiguous()
   __lowerCAmelCase					:	int      =       model(
       __snake_case       ,  attention_mask=__snake_case       ,  token_type_ids=__snake_case       ,  labels=__snake_case       ,  )
   self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_choices)      )






  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->int:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       self.prepare_config_and_inputs()
   (
       __lowerCAmelCase     
   )					:	Union[str, Any]      =       config_and_inputs
   __lowerCAmelCase					:	List[str]      =       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
   return config, inputs_dict






@require_torch
class        snake_case_    (		lowerCamelCase__					,unittest.TestCase						):
  A_      							=   (
      (
          MraModel,
          MraForMaskedLM,
          MraForMultipleChoice,
          MraForQuestionAnswering,
          MraForSequenceClassification,
          MraForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  A_      							=   False
  A_      							=   False
  A_      							=   False
  A_      							=   False

  A_      							=   ()






  def 				UpperCAmelCase__						(							self			:					Any      )->Optional[int]:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       MraModelTester(self      )
   __lowerCAmelCase					:	str      =       ConfigTester(self       ,  config_class=__snake_case       ,  hidden_size=37      )






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






   '''simple docstring'''

   self.config_tester.run_common_tests()






  def 				UpperCAmelCase__						(							self			:					List[str]      )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*__snake_case      )






  def 				UpperCAmelCase__						(							self			:					Any      )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       self.model_tester.prepare_config_and_inputs()
   for type in ["absolute", "relative_key", "relative_key_query"]:
    __lowerCAmelCase					:	Dict      =       type
    self.model_tester.create_and_check_model(*__snake_case      )






  def 				UpperCAmelCase__						(							self			:					List[Any]      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*__snake_case      )






  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_multiple_choice(*__snake_case      )






  def 				UpperCAmelCase__						(							self			:					List[Any]      )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*__snake_case      )






  def 				UpperCAmelCase__						(							self			:					Tuple      )->Tuple:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*__snake_case      )






  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*__snake_case      )






  @slow
  def 				UpperCAmelCase__						(							self			:					int      )->Union[str, Any]:






   '''simple docstring'''

   for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowerCAmelCase					:	Dict      =       MraModel.from_pretrained(__snake_case      )
    self.assertIsNotNone(__snake_case      )






  @unittest.skip(reason="""MRA does not output attentions"""      )
  def 				UpperCAmelCase__						(							self			:					Union[str, Any]      )->Optional[int]:






   '''simple docstring'''

   return






@require_torch
class        snake_case_    (		unittest.TestCase						):






  @slow
  def 				UpperCAmelCase__						(							self			:					Union[str, Any]      )->Tuple:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       MraModel.from_pretrained("""uw-madison/mra-base-512-4"""      )
   __lowerCAmelCase					:	List[str]      =       torch.arange(256      ).unsqueeze(0      )

   with torch.no_grad():
    __lowerCAmelCase					:	Optional[int]      =       model(__snake_case      )[0]

   __lowerCAmelCase					:	Any      =       torch.Size((1, 256, 768)      )
   self.assertEqual(output.shape       ,  __snake_case      )

   __lowerCAmelCase					:	str      =       torch.tensor(
       [[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]]      )

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






  @slow
  def 				UpperCAmelCase__						(							self			:					Optional[int]      )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4"""      )
   __lowerCAmelCase					:	Optional[int]      =       torch.arange(256      ).unsqueeze(0      )

   with torch.no_grad():
    __lowerCAmelCase					:	Dict      =       model(__snake_case      )[0]

   __lowerCAmelCase					:	Union[str, Any]      =       50265

   __lowerCAmelCase					:	Dict      =       torch.Size((1, 256, vocab_size)      )
   self.assertEqual(output.shape       ,  __snake_case      )

   __lowerCAmelCase					:	Dict      =       torch.tensor(
       [[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]]      )

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






  @slow
  def 				UpperCAmelCase__						(							self			:					Any      )->int:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3"""      )
   __lowerCAmelCase					:	Optional[int]      =       torch.arange(4096      ).unsqueeze(0      )

   with torch.no_grad():
    __lowerCAmelCase					:	Tuple      =       model(__snake_case      )[0]

   __lowerCAmelCase					:	List[Any]      =       50265

   __lowerCAmelCase					:	str      =       torch.Size((1, 4096, vocab_size)      )
   self.assertEqual(output.shape       ,  __snake_case      )

   __lowerCAmelCase					:	int      =       torch.tensor(
       [[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]]      )

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






from math import isqrt






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   )      ->   list[int]:
 __lowerCAmelCase					:	Tuple      =       [True] * max_number
 for i in range(2   ,   isqrt(max_number - 1   ) + 1   ):
  if is_prime[i]:
   for j in range(i**2   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
    __lowerCAmelCase					:	Tuple      =       False

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






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int = 10**8   )      ->   int:
 __lowerCAmelCase					:	int      =       calculate_prime_numbers(max_number // 2   )

 __lowerCAmelCase					:	List[Any]      =       0
 __lowerCAmelCase					:	List[str]      =       0
 __lowerCAmelCase					:	List[Any]      =       len(SCREAMING_SNAKE_CASE   ) - 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() = }''')