



'''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 	lowerCAmelCase_:
  '''simple docstring'''




  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=0.0_2  ,__UpperCAmelCase=None  ,)   ->		int:
      lowerCAmelCase__			:  Union[str, Any]											= parent
      lowerCAmelCase__			:  Dict											= batch_size
      lowerCAmelCase__			:  List[Any]											= image_size
      lowerCAmelCase__			:  Tuple											= patch_size
      lowerCAmelCase__			:  List[Any]											= num_channels
      lowerCAmelCase__			:  Optional[Any]											= is_training
      lowerCAmelCase__			:  Tuple											= use_labels
      lowerCAmelCase__			:  Optional[int]											= hidden_size
      lowerCAmelCase__			:  str											= num_hidden_layers
      lowerCAmelCase__			:  List[str]											= num_attention_heads
      lowerCAmelCase__			:  Dict											= intermediate_size
      lowerCAmelCase__			:  str											= hidden_act
      lowerCAmelCase__			:  Dict											= hidden_dropout_prob
      lowerCAmelCase__			:  Optional[Any]											= attention_probs_dropout_prob
      lowerCAmelCase__			:  str											= type_sequence_label_size
      lowerCAmelCase__			:  List[Any]											= initializer_range
      lowerCAmelCase__			:  int											= scope

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




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  Dict											= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

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

      return config, pixel_values, labels




  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      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  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		List[Any]:
      lowerCAmelCase__			:  Dict											= ViTMSNModel(config=__UpperCAmelCase				)
      model.to(__UpperCAmelCase				)
      model.eval()
      lowerCAmelCase__			:  str											= model(__UpperCAmelCase				)
      self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		Dict:
      lowerCAmelCase__			:  List[str]											= self.type_sequence_label_size
      lowerCAmelCase__			:  Tuple											= ViTMSNForImageClassification(__UpperCAmelCase				)
      model.to(__UpperCAmelCase				)
      model.eval()
      lowerCAmelCase__			:  Union[str, Any]											= 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
      lowerCAmelCase__			:  int											= 1
      lowerCAmelCase__			:  List[Any]											= ViTMSNForImageClassification(__UpperCAmelCase				)
      model.to(__UpperCAmelCase				)
      model.eval()

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






  def 							UpperCAmelCase_     (   self				)   ->		int:
      lowerCAmelCase__			:  int											= self.prepare_config_and_inputs()
      lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__			:  Optional[Any]											= config_and_inputs
      lowerCAmelCase__			:  Union[str, Any]											= {"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_torch
class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				str           =				(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
  __lowercase							:				Optional[int]           =				(
      {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
      if is_torch_available()
      else {}
  )

  __lowercase							:				Optional[int]           =				False
  __lowercase							:				Union[str, Any]           =				False
  __lowercase							:				Any           =				False
  __lowercase							:				Dict           =				False




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      lowerCAmelCase__			:  int											= ViTMSNModelTester(self				)
      lowerCAmelCase__			:  int											= ConfigTester(self  ,config_class=__UpperCAmelCase  ,has_text_modality=__UpperCAmelCase  ,hidden_size=37				)




  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      self.config_tester.run_common_tests()




  @unittest.skip(reason="""ViTMSN does not use inputs_embeds"""				)
  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      pass




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      lowerCAmelCase__				,       lowerCAmelCase__			:  Any											= self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          lowerCAmelCase__			:  Dict											= model_class(__UpperCAmelCase				)
          self.assertIsInstance(model.get_input_embeddings()  ,(nn.Module)				)
          lowerCAmelCase__			:  List[Any]											= model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(__UpperCAmelCase  ,nn.Linear				)				)




  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      lowerCAmelCase__				,       lowerCAmelCase__			:  str											= self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          lowerCAmelCase__			:  Union[str, Any]											= model_class(__UpperCAmelCase				)
          lowerCAmelCase__			:  List[str]											= inspect.signature(model.forward				)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          lowerCAmelCase__			:  int											= [*signature.parameters.keys()]

          lowerCAmelCase__			:  List[Any]											= ["""pixel_values"""]
          self.assertListEqual(arg_names[:1]  ,__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      lowerCAmelCase__			:  Optional[Any]											= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      lowerCAmelCase__			:  List[Any]											= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase				)






  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase__			:  Optional[Any]											= ViTMSNModel.from_pretrained(__UpperCAmelCase				)
          self.assertIsNotNone(__UpperCAmelCase				)







def        _SCREAMING_SNAKE_CASE  (	):



    """simple docstring"""





    lowerCAmelCase__			:  Dict											= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
    return image






@require_torch
@require_vision
class 	lowerCAmelCase_(    unittest.TestCase					):
  '''simple docstring'''




  @cached_property
  def 							UpperCAmelCase_     (   self				)   ->		Any:
      return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small"""				) if is_vision_available() else None






  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Any:
      torch.manual_seed(2				)
      lowerCAmelCase__			:  Union[str, Any]											= ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small"""				).to(__UpperCAmelCase				)

      lowerCAmelCase__			:  str											= self.default_image_processor
      lowerCAmelCase__			:  Any											= prepare_img()
      lowerCAmelCase__			:  List[Any]											= image_processor(images=__UpperCAmelCase  ,return_tensors="""pt"""				).to(__UpperCAmelCase				)

      # forward pass
      with torch.no_grad():
          lowerCAmelCase__			:  int											= model(**__UpperCAmelCase				)

      # verify the logits
      lowerCAmelCase__			:  str											= torch.Size((1, 1000)				)
      self.assertEqual(outputs.logits.shape  ,__UpperCAmelCase				)

      lowerCAmelCase__			:  Union[str, Any]											= torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]				).to(__UpperCAmelCase				)

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







'''simple docstring'''





_lowerCAmelCase										=     '''Input must be a string of 8 numbers plus letter'''
_lowerCAmelCase										=     '''TRWAGMYFPDXBNJZSQVHLCKE'''
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    if not isinstance(UpperCamelCase							,   UpperCamelCase      ):
        lowerCAmelCase__			:  List[Any]											= f"""Expected string as input, found {type(UpperCamelCase      ).__name__}"""
        raise TypeError(UpperCamelCase      )

    lowerCAmelCase__			:  Union[str, Any]											= spanish_id.replace("""-"""							,   """"""      ).upper()
    if len(UpperCamelCase      ) != 9:
        raise ValueError(UpperCamelCase      )

    try:
        lowerCAmelCase__			:  Optional[int]											= int(spanish_id_clean[0:8]      )
        lowerCAmelCase__			:  int											= spanish_id_clean[8]
    except ValueError as ex:
        raise ValueError(UpperCamelCase      ) from ex

    if letter.isdigit():
        raise ValueError(UpperCamelCase      )

    return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
 import doctest

 doctest.testmod()


