





import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
      import torch
      from torch import nn

      from transformers import ViTMSNForImageClassification, ViTMSNModel
      from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import ViTImageProcessor






class        snake_case_    :






  def __init__(							self			:					Tuple       ,  _snake_case			:					Tuple       ,  _snake_case			:					List[str]=13       ,  _snake_case			:					List[Any]=30       ,  _snake_case			:					str=2       ,  _snake_case			:					Dict=3       ,  _snake_case			:					List[Any]=True       ,  _snake_case			:					Dict=True       ,  _snake_case			:					List[str]=32       ,  _snake_case			:					Optional[Any]=5       ,  _snake_case			:					List[str]=4       ,  _snake_case			:					Tuple=37       ,  _snake_case			:					int="gelu"       ,  _snake_case			:					int=0.1       ,  _snake_case			:					int=0.1       ,  _snake_case			:					Tuple=10       ,  _snake_case			:					List[Any]=0.02       ,  _snake_case			:					Optional[int]=None       ,  )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Any      =       parent
   __lowerCAmelCase					:	List[str]      =       batch_size
   __lowerCAmelCase					:	Any      =       image_size
   __lowerCAmelCase					:	Optional[Any]      =       patch_size
   __lowerCAmelCase					:	Tuple      =       num_channels
   __lowerCAmelCase					:	Optional[int]      =       is_training
   __lowerCAmelCase					:	Any      =       use_labels
   __lowerCAmelCase					:	int      =       hidden_size
   __lowerCAmelCase					:	int      =       num_hidden_layers
   __lowerCAmelCase					:	int      =       num_attention_heads
   __lowerCAmelCase					:	int      =       intermediate_size
   __lowerCAmelCase					:	List[str]      =       hidden_act
   __lowerCAmelCase					:	Union[str, Any]      =       hidden_dropout_prob
   __lowerCAmelCase					:	Any      =       attention_probs_dropout_prob
   __lowerCAmelCase					:	str      =       type_sequence_label_size
   __lowerCAmelCase					:	str      =       initializer_range
   __lowerCAmelCase					:	Union[str, Any]      =       scope

   # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
   __lowerCAmelCase					:	Dict      =       (image_size // patch_size) ** 2
   __lowerCAmelCase					:	List[Any]      =       num_patches + 1






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






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

   __lowerCAmelCase					:	str      =       None
   if self.use_labels:
    __lowerCAmelCase					:	str      =       ids_tensor([self.batch_size]       ,  self.type_sequence_label_size      )

   __lowerCAmelCase					:	List[Any]      =       self.get_config()

   return config, pixel_values, labels






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






   '''simple docstring'''

   return ViTMSNConfig(
       image_size=self.image_size       ,  patch_size=self.patch_size       ,  num_channels=self.num_channels       ,  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       ,  initializer_range=self.initializer_range       ,  )






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






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       ViTMSNModel(config=lowercase__      )
   model.to(lowercase__      )
   model.eval()
   __lowerCAmelCase					:	Dict      =       model(lowercase__      )
   self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)      )






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






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       self.type_sequence_label_size
   __lowerCAmelCase					:	Dict      =       ViTMSNForImageClassification(lowercase__      )
   model.to(lowercase__      )
   model.eval()
   __lowerCAmelCase					:	List[Any]      =       model(lowercase__       ,  labels=lowercase__      )
   print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}"""      )
   print("""Labels: {labels}"""      )
   self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)      )

   # test greyscale images
   __lowerCAmelCase					:	Union[str, Any]      =       1
   __lowerCAmelCase					:	int      =       ViTMSNForImageClassification(lowercase__      )
   model.to(lowercase__      )
   model.eval()

   __lowerCAmelCase					:	Tuple      =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
   __lowerCAmelCase					:	Union[str, Any]      =       model(lowercase__      )
   self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)      )






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






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       self.prepare_config_and_inputs()
   __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	Dict      =       config_and_inputs
   __lowerCAmelCase					:	List[Any]      =       {"""pixel_values""": pixel_values}
   return config, inputs_dict






@require_torch
class        snake_case_    (		lowerCamelCase__					,lowerCamelCase__					,unittest.TestCase						):
  A_      							=   (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
  A_      							=   (
      {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
      if is_torch_available()
      else {}
  )

  A_      							=   False
  A_      							=   False
  A_      							=   False
  A_      							=   False






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






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       ViTMSNModelTester(self      )
   __lowerCAmelCase					:	Dict      =       ConfigTester(self       ,  config_class=lowercase__       ,  has_text_modality=lowercase__       ,  hidden_size=37      )






  def 				UpperCAmelCase__						(							self			:					Any      )->str:






   '''simple docstring'''

   self.config_tester.run_common_tests()






  @unittest.skip(reason="""ViTMSN does not use inputs_embeds"""      )
  def 				UpperCAmelCase__						(							self			:					Any      )->int:






   '''simple docstring'''

   pass






  def 				UpperCAmelCase__						(							self			:					Any      )->Tuple:






   '''simple docstring'''

   __lowerCAmelCase ,					__lowerCAmelCase					:	Optional[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowerCAmelCase					:	Tuple      =       model_class(lowercase__      )
    self.assertIsInstance(model.get_input_embeddings()       ,  (nn.Module)      )
    __lowerCAmelCase					:	Tuple      =       model.get_output_embeddings()
    self.assertTrue(x is None or isinstance(lowercase__       ,  nn.Linear      )      )






  def 				UpperCAmelCase__						(							self			:					int      )->Any:






   '''simple docstring'''

   __lowerCAmelCase ,					__lowerCAmelCase					:	Optional[int]      =       self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowerCAmelCase					:	Optional[int]      =       model_class(lowercase__      )
    __lowerCAmelCase					:	Optional[Any]      =       inspect.signature(model.forward      )
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    __lowerCAmelCase					:	Any      =       [*signature.parameters.keys()]

    __lowerCAmelCase					:	Optional[int]      =       ["""pixel_values"""]
    self.assertListEqual(arg_names[:1]       ,  lowercase__      )






  def 				UpperCAmelCase__						(							self			:					Dict      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__      )






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






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*lowercase__      )






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






   '''simple docstring'''

   for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowerCAmelCase					:	int      =       ViTMSNModel.from_pretrained(lowercase__      )
    self.assertIsNotNone(lowercase__      )






def 	_SCREAMING_SNAKE_CASE		(      )      ->   Optional[int]:
 __lowerCAmelCase					:	int      =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
 return image







@require_torch
@require_vision
class        snake_case_    (		unittest.TestCase						):






  @cached_property
  def 				UpperCAmelCase__						(							self			:					str      )->Any:






   '''simple docstring'''

   return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small"""      ) if is_vision_available() else None






  @slow
  def 				UpperCAmelCase__						(							self			:					str      )->Tuple:






   '''simple docstring'''

   torch.manual_seed(2      )
   __lowerCAmelCase					:	str      =       ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small"""      ).to(lowercase__      )

   __lowerCAmelCase					:	str      =       self.default_image_processor
   __lowerCAmelCase					:	Union[str, Any]      =       prepare_img()
   __lowerCAmelCase					:	Union[str, Any]      =       image_processor(images=lowercase__       ,  return_tensors="""pt"""      ).to(lowercase__      )

   # forward pass
   with torch.no_grad():
    __lowerCAmelCase					:	Optional[Any]      =       model(**lowercase__      )

   # verify the logits
   __lowerCAmelCase					:	List[Any]      =       torch.Size((1, 1000)      )
   self.assertEqual(outputs.logits.shape       ,  lowercase__      )

   __lowerCAmelCase					:	str      =       torch.tensor([-0.0_803, -0.4_454, -0.2_375]      ).to(lowercase__      )

   self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,  lowercase__       ,  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() = }''')