






"""simple docstring"""


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     : Any    ,		A     : Any    ,		A     : int=1_3    ,		A     : str=3_0    ,		A     : Optional[int]=2    ,		A     : Optional[Any]=3    ,		A     : List[Any]=True    ,		A     : Any=True    ,		A     : Dict=3_2    ,		A     : List[Any]=5    ,		A     : Dict=4    ,		A     : Any=3_7    ,		A     : Tuple="gelu"    ,		A     : List[str]=0.1    ,		A     : Optional[int]=0.1    ,		A     : List[str]=1_0    ,		A     : Dict=0.02    ,		A     : Optional[int]=None    ,		):


      '''simple docstring'''




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

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





    def     lowerCamelCase__				(							self     : int      ):


      '''simple docstring'''




      a					:      Dict    =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

      a					:      Tuple    =       None
      if self.use_labels:
        a					:      int    =       ids_tensor([self.batch_size]    ,		self.type_sequence_label_size      )

      a					:      Optional[Any]    =       self.get_config()

      return config, pixel_values, labels





    def     lowerCamelCase__				(							self     : Dict      ):


      '''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     lowerCamelCase__				(							self     : str    ,		A     : int    ,		A     : Any    ,		A     : Dict      ):


      '''simple docstring'''




      a					:      Optional[Any]    =       ViTMSNModel(config=A      )
      model.to(A      )
      model.eval()
      a					:      List[str]    =       model(A      )
      self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)      )





    def     lowerCamelCase__				(							self     : str    ,		A     : Dict    ,		A     : Union[str, Any]    ,		A     : List[str]      ):


      '''simple docstring'''




      a					:      int    =       self.type_sequence_label_size
      a					:      Dict    =       ViTMSNForImageClassification(A      )
      model.to(A      )
      model.eval()
      a					:      Dict    =       model(A    ,		labels=A      )
      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
      a					:      Union[str, Any]    =       1
      a					:      List[str]    =       ViTMSNForImageClassification(A      )
      model.to(A      )
      model.eval()

      a					:      int    =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
      a					:      Tuple    =       model(A      )
      self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.type_sequence_label_size)      )





    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      a					:      Optional[int]    =       self.prepare_config_and_inputs()
      a,					a,					a					:      int    =       config_and_inputs
      a					:      Optional[Any]    =       {'pixel_values': pixel_values}
      return config, inputs_dict






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

    __magic_name__   					=					False
    __magic_name__   					=					False
    __magic_name__   					=					False
    __magic_name__   					=					False





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      a					:      Union[str, Any]    =       ViTMSNModelTester(self      )
      a					:      Dict    =       ConfigTester(self    ,		config_class=A    ,		has_text_modality=A    ,		hidden_size=3_7      )





    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      self.config_tester.run_common_tests()





    @unittest.skip(reason='ViTMSN does not use inputs_embeds'      )
    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      pass





    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




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

      for model_class in self.all_model_classes:
        a					:      str    =       model_class(A      )
        self.assertIsInstance(model.get_input_embeddings()    ,		(nn.Module)      )
        a					:      List[str]    =       model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(A    ,		nn.Linear      )      )





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      a,					a					:      List[str]    =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a					:      str    =       model_class(A      )
        a					:      Any    =       inspect.signature(model.forward      )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        a					:      Dict    =       [*signature.parameters.keys()]

        a					:      Optional[int]    =       ['pixel_values']
        self.assertListEqual(arg_names[:1]    ,		A      )





    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      a					:      List[Any]    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A      )





    def     lowerCamelCase__				(							self     : Union[str, Any]      ):


      '''simple docstring'''




      a					:      Any    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*A      )





    @slow
    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a					:      Tuple    =       ViTMSNModel.from_pretrained(A      )
        self.assertIsNotNone(A      )




def       snake_case  ():







  '''simple docstring'''

  a					:      Any    =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
  return image






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





    @cached_property
    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




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





    @slow
    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




      torch.manual_seed(2      )
      a					:      List[str]    =       ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'      ).to(A      )

      a					:      Tuple    =       self.default_image_processor
      a					:      int    =       prepare_img()
      a					:      Optional[Any]    =       image_processor(images=A    ,		return_tensors='pt'      ).to(A      )

      # forward pass
      with torch.no_grad():
        a					:      str    =       model(**A      )

      # verify the logits
      a					:      str    =       torch.Size((1, 1_0_0_0)      )
      self.assertEqual(outputs.logits.shape    ,		A      )

      a					:      Tuple    =       torch.tensor([-0.08_03, -0.44_54, -0.23_75]      ).to(A      )

      self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,		A    ,		atol=1E-4      )      )












"""simple docstring"""


def       snake_case  (A_				:int				):







  '''simple docstring'''

  if isinstance(A_     ,      A_				):
    raise TypeError('\'float\' object cannot be interpreted as an integer'				)
  if isinstance(A_     ,      A_				):
    raise TypeError('\'str\' object cannot be interpreted as an integer'				)

  if num == 0:
    return "0b0"

  a					:      List[Any]    =       False

  if num < 0:
    a					:      Optional[int]    =       True
    a					:      Dict    =       -num

  a					:      list[int]    =       []
  while num > 0:
    binary.insert(0     ,      num % 2				)
    num >>= 1

  if negative:
    return "-0b" + "".join(str(A_				) for e in binary				)

  return "0b" + "".join(str(A_				) for e in binary				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()




