





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  A__    :




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase=13					,  lowercase=30					,  lowercase=2					,  lowercase=3					,  lowercase=True					,  lowercase=True					,  lowercase=32					,  lowercase=5					,  lowercase=4					,  lowercase=37					,  lowercase="gelu"					,  lowercase=0.1					,  lowercase=0.1					,  lowercase=10					,  lowercase=0.02					,  lowercase=None					,  )			->						Union[str, Any]:







          '''simple docstring'''

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

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

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	Tuple =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          a__						:	Tuple =				None
          if self.use_labels:
                 a__						:	List[Any] =				ids_tensor([self.batch_size]					,  self.type_sequence_label_size)

          a__						:	List[str] =				self.get_config()

          return config, pixel_values, labels

   def 							__lowercase		(					self)			->						Optional[int]:







          '''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 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						List[str]:







          '''simple docstring'''

          a__						:	int =				ViTMSNModel(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	int =				model(lowercase)
          self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						Tuple:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.type_sequence_label_size
          a__						:	List[str] =				ViTMSNForImageClassification(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	int =				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
          a__						:	List[str] =				1
          a__						:	Optional[int] =				ViTMSNForImageClassification(lowercase)
          model.to(lowercase)
          model.eval()

          a__						:	Dict =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
          a__						:	Dict =				model(lowercase)
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.type_sequence_label_size))

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

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





@require_torch
class  A__    (							__UpperCAmelCase ,  __UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Any         =  (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
   __A   :				Tuple         =  (
       {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
       if is_torch_available()
       else {}
   )

   __A   :				List[str]         =  False
   __A   :				Optional[Any]         =  False
   __A   :				Union[str, Any]         =  False
   __A   :				Any         =  False

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







          '''simple docstring'''

          a__						:	Optional[int] =				ViTMSNModelTester(self)
          a__						:	Union[str, Any] =				ConfigTester(self					,  config_class=lowercase					,  has_text_modality=lowercase					,  hidden_size=37)

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







          '''simple docstring'''

          self.config_tester.run_common_tests()

   @unittest.skip(reason='ViTMSN does not use inputs_embeds')
   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          pass

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__ ,							a__						:	Tuple =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__						:	Union[str, Any] =				model_class(lowercase)
                 self.assertIsInstance(model.get_input_embeddings()					,  (nn.Module))
                 a__						:	str =				model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(lowercase					,  nn.Linear))

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

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

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

                 a__						:	Optional[Any] =				['pixel_values']
                 self.assertListEqual(arg_names[:1]					,  lowercase)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	List[str] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Any =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase)

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







          '''simple docstring'''

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



def A_	(		)  ->		Dict:
       a__						:	List[str] =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       return image





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




   """simple docstring"""



   @cached_property
   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

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

   @slow
   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

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

          a__						:	Any =				self.default_image_processor
          a__						:	List[Any] =				prepare_img()
          a__						:	Optional[Any] =				image_processor(images=lowercase					,  return_tensors='pt').to(lowercase)

          # forward pass
          with torch.no_grad():
                 a__						:	Tuple =				model(**lowercase)

          # verify the logits
          a__						:	Union[str, Any] =				torch.Size((1, 1000))
          self.assertEqual(outputs.logits.shape					,  lowercase)

          a__						:	Any =				torch.tensor([-0.08_03, -0.44_54, -0.23_75]).to(lowercase)

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










"""simple docstring"""





from typing import Any






class      a						:






    def __init__(       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		List[Any]:
      lowerCamelCase_         =						data
      lowerCamelCase_         =						None






class      a						:






    def __init__(       self     :							Union[str, Any]					)     ->		List[Any]:
      lowerCamelCase_         =						None






    def    UpperCamelCase       (       self     :							Dict					)     ->		Optional[int]:
      lowerCamelCase_         =						self.head
      while temp is not None:
        print(temp.data       ,						end=' '					)
        lowerCamelCase_         =						temp.next
      print()






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		Union[str, Any]:
      lowerCamelCase_         =						Node(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						self.head
      lowerCamelCase_         =						new_node






    def    UpperCamelCase       (       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							int					)     ->		Union[str, Any]:
      if node_data_a == node_data_a:
        return
      else:
        lowerCamelCase_         =						self.head
        while node_a is not None and node_a.data != node_data_a:
          lowerCamelCase_         =						node_a.next

        lowerCamelCase_         =						self.head
        while node_a is not None and node_a.data != node_data_a:
          lowerCamelCase_         =						node_a.next

        if node_a is None or node_a is None:
          return

        lowerCamelCase_     ,	lowerCamelCase_         =						node_a.data, node_a.data


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				Optional[int]					    =						LinkedList()
  for i in range(5, 0, -1):
    ll.push(i)

  ll.print_list()

  ll.swap_nodes(1, 4)
  print('''After swapping''')
  ll.print_list()




