

'''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 					A__      :



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__=13		,				UpperCamelCase__=30		,				UpperCamelCase__=2		,				UpperCamelCase__=3		,				UpperCamelCase__=True		,				UpperCamelCase__=True		,				UpperCamelCase__=32		,				UpperCamelCase__=5		,				UpperCamelCase__=4		,				UpperCamelCase__=37		,				UpperCamelCase__="gelu"		,				UpperCamelCase__=0.1		,				UpperCamelCase__=0.1		,				UpperCamelCase__=10		,				UpperCamelCase__=0.02		,				UpperCamelCase__=None		,				)						->      Dict:





           '''simple docstring'''
           A_						=      parent
           A_						=      batch_size
           A_						=      image_size
           A_						=      patch_size
           A_						=      num_channels
           A_						=      is_training
           A_						=      use_labels
           A_						=      hidden_size
           A_						=      num_hidden_layers
           A_						=      num_attention_heads
           A_						=      intermediate_size
           A_						=      hidden_act
           A_						=      hidden_dropout_prob
           A_						=      attention_probs_dropout_prob
           A_						=      type_sequence_label_size
           A_						=      initializer_range
           A_						=      scope

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



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





           '''simple docstring'''
           A_						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

           A_						=      self.get_config()

           return config, pixel_values, labels



    def 					snake_case_			(					self       )						->      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 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Optional[int]:





           '''simple docstring'''
           A_						=      ViTMSNModel(config=UpperCamelCase__       )
           model.to(UpperCamelCase__       )
           model.eval()
           A_						=      model(UpperCamelCase__       )
           self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)       )



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      int:





           '''simple docstring'''
           A_						=      self.type_sequence_label_size
           A_						=      ViTMSNForImageClassification(UpperCamelCase__       )
           model.to(UpperCamelCase__       )
           model.eval()
           A_						=      model(UpperCamelCase__		,				labels=UpperCamelCase__       )
           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_						=      1
           A_						=      ViTMSNForImageClassification(UpperCamelCase__       )
           model.to(UpperCamelCase__       )
           model.eval()

           A_						=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
           A_						=      model(UpperCamelCase__       )
           self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)       )







    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           A_						=      self.prepare_config_and_inputs()
           A_					,			A_					,			A_						=      config_and_inputs
           A_						=      {"""pixel_values""": pixel_values}
           return config, inputs_dict


@require_torch
class 					A__      (      _snake_case						,						_snake_case						,						unittest.TestCase			):
    lowercase						=		(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
    lowercase						=		(
        {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
        if is_torch_available()
        else {}
    )

    lowercase						=		False
    lowercase						=		False
    lowercase						=		False
    lowercase						=		False



    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           A_						=      ViTMSNModelTester(self       )
           A_						=      ConfigTester(self		,				config_class=UpperCamelCase__		,				has_text_modality=UpperCamelCase__		,				hidden_size=37       )



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





           '''simple docstring'''
           self.config_tester.run_common_tests()



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





           '''simple docstring'''
           pass



    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           A_					,			A_						=      self.model_tester.prepare_config_and_inputs_for_common()

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



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





           '''simple docstring'''
           A_					,			A_						=      self.model_tester.prepare_config_and_inputs_for_common()

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

                  A_						=      ["""pixel_values"""]
                  self.assertListEqual(arg_names[:1]		,				UpperCamelCase__       )



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





           '''simple docstring'''
           A_						=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase__       )



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





           '''simple docstring'''
           A_						=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__       )







    @slow
    def 					snake_case_			(					self       )						->      Tuple:





           '''simple docstring'''
           for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_						=      ViTMSNModel.from_pretrained(UpperCamelCase__       )
                  self.assertIsNotNone(UpperCamelCase__       )

def       UpperCAmelCase__    (      )			->			Optional[Any]:
       A_						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
       return image







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



    @cached_property
    def 					snake_case_			(					self       )						->      Tuple:





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







    @slow
    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           torch.manual_seed(2       )
           A_						=      ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small"""       ).to(UpperCamelCase__       )

           A_						=      self.default_image_processor
           A_						=      prepare_img()
           A_						=      image_processor(images=UpperCamelCase__		,				return_tensors="""pt"""       ).to(UpperCamelCase__       )

           # forward pass
           with torch.no_grad():
                  A_						=      model(**UpperCamelCase__       )

           # verify the logits
           A_						=      torch.Size((1, 1000)       )
           self.assertEqual(outputs.logits.shape		,				UpperCamelCase__       )

           A_						=      torch.tensor([-0.0803, -0.4454, -0.2375]       ).to(UpperCamelCase__       )

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



'''simple docstring'''







def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			bool:
       if num < 0:
              return False

       A_						=      num
       A_						=      0
       while num > 0:
              A_						=      rev_num * 10 + (num % 10)
              num //= 10

       return num_copy == rev_num


if __name__ == "__main__":
      import doctest

      doctest.testmod()
