


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 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			str					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			Optional[Any]=13					,      __lowerCamelCase							:			List[str]=30					,      __lowerCamelCase							:			List[Any]=2					,      __lowerCamelCase							:			List[Any]=3					,      __lowerCamelCase							:			List[Any]=True					,      __lowerCamelCase							:			Dict=True					,      __lowerCamelCase							:			List[Any]=32					,      __lowerCamelCase							:			List[str]=5					,      __lowerCamelCase							:			Dict=4					,      __lowerCamelCase							:			Any=37					,      __lowerCamelCase							:			Dict="gelu"					,      __lowerCamelCase							:			List[str]=0.1					,      __lowerCamelCase							:			str=0.1					,      __lowerCamelCase							:			Any=10					,      __lowerCamelCase							:			Tuple=0.0_2					,      __lowerCamelCase							:			Union[str, Any]=None					,      ):


             '''simple docstring'''



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

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





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



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

             lowerCamelCase__		:				Optional[int]    =  None
             if self.use_labels:
                   lowerCamelCase__		:				Union[str, Any]    =  ids_tensor([self.batch_size]					,      self.type_sequence_label_size       )

             lowerCamelCase__		:				Any    =  self.get_config()

             return config, pixel_values, labels





       def 						lowerCAmelCase     (   self							:			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 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



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





       def 						lowerCAmelCase     (   self							:			List[str]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  self.type_sequence_label_size
             lowerCamelCase__		:				Dict    =  ViTMSNForImageClassification(__lowerCamelCase       )
             model.to(__lowerCamelCase       )
             model.eval()
             lowerCamelCase__		:				Optional[int]    =  model(__lowerCamelCase					,      labels=__lowerCamelCase       )
             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__		:				Dict    =  ViTMSNForImageClassification(__lowerCamelCase       )
             model.to(__lowerCamelCase       )
             model.eval()

             lowerCamelCase__		:				List[str]    =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
             lowerCamelCase__		:				str    =  model(__lowerCamelCase       )
             self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)       )





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  self.prepare_config_and_inputs()
             lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				str    =  config_and_inputs
             lowerCamelCase__		:				List[Any]    =  {"pixel_values": pixel_values}
             return config, inputs_dict







@require_torch
class 			_lowercase (							lowercase__							,  lowercase__							,  unittest.TestCase):


       """simple docstring"""




       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 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  ViTMSNModelTester(self       )
             lowerCamelCase__		:				int    =  ConfigTester(self					,      config_class=__lowerCamelCase					,      has_text_modality=__lowerCamelCase					,      hidden_size=37       )





       def 						lowerCAmelCase     (   self							:			Union[str, 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							:			Optional[int]       ):


             '''simple docstring'''



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

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





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__    ,   lowerCamelCase__		:				List[Any]    =  self.model_tester.prepare_config_and_inputs_for_common()

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

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





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase       )





       @slow
       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   lowerCamelCase__		:				Optional[int]    =  ViTMSNModel.from_pretrained(__lowerCamelCase       )
                   self.assertIsNotNone(__lowerCamelCase       )



def       lowercase_		(		):

      """simple docstring"""




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







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


       """simple docstring"""




       @cached_property
       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



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





       @slow
       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             torch.manual_seed(2       )
             lowerCamelCase__		:				List[str]    =  ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"       ).to(__lowerCamelCase       )

             lowerCamelCase__		:				str    =  self.default_image_processor
             lowerCamelCase__		:				Optional[Any]    =  prepare_img()
             lowerCamelCase__		:				Dict    =  image_processor(images=__lowerCamelCase					,      return_tensors="pt"       ).to(__lowerCamelCase       )

             # forward pass
             with torch.no_grad():
                   lowerCamelCase__		:				Dict    =  model(**__lowerCamelCase       )

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

             lowerCamelCase__		:				str    =  torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]       ).to(__lowerCamelCase       )

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






from PIL import Image

def       lowercase_		(		_A			:    Image						):

      """simple docstring"""




      lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  image.size
      lowerCamelCase__		:				Dict    =  0
      lowerCamelCase__		:				Dict    =  image.load()
      for i in range(_A						):
            for j in range(_A						):
                  lowerCamelCase__		:				List[str]    =  pixels[j, i]
                  mean += pixel
      mean //= width * height

      for j in range(_A						):
            for i in range(_A						):
                  lowerCamelCase__		:				Union[str, Any]    =  255 if pixels[i, j] > mean else 0
      return image


if __name__ == "__main__":
    A      :    int		=					mean_threshold(Image.open("path_to_image").convert("L"))
    image.save("output_image_path")


