





'''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       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase=13						,   lowerCamelCase=30						,   lowerCamelCase=2						,   lowerCamelCase=3						,   lowerCamelCase=True						,   lowerCamelCase=True						,   lowerCamelCase=32						,   lowerCamelCase=5						,   lowerCamelCase=4						,   lowerCamelCase=37						,   lowerCamelCase="gelu"						,   lowerCamelCase=0.1						,   lowerCamelCase=0.1						,   lowerCamelCase=10						,   lowerCamelCase=0.02						,   lowerCamelCase=None						,   ):
       _snake_case     		=				parent
       _snake_case     		=				batch_size
       _snake_case     		=				image_size
       _snake_case     		=				patch_size
       _snake_case     		=				num_channels
       _snake_case     		=				is_training
       _snake_case     		=				use_labels
       _snake_case     		=				hidden_size
       _snake_case     		=				num_hidden_layers
       _snake_case     		=				num_attention_heads
       _snake_case     		=				intermediate_size
       _snake_case     		=				hidden_act
       _snake_case     		=				hidden_dropout_prob
       _snake_case     		=				attention_probs_dropout_prob
       _snake_case     		=				type_sequence_label_size
       _snake_case     		=				initializer_range
       _snake_case     		=				scope

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

       _snake_case     		=				self.get_config()

       return config, pixel_values, labels

     def 	UpperCamelCase(					self	):
       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						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				ViTMSNModel(config=lowerCamelCase	)
       model.to(lowerCamelCase	)
       model.eval()
       _snake_case     		=				model(lowerCamelCase	)
       self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, self.seq_length, self.hidden_size)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				self.type_sequence_label_size
       _snake_case     		=				ViTMSNForImageClassification(lowerCamelCase	)
       model.to(lowerCamelCase	)
       model.eval()
       _snake_case     		=				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
       _snake_case     		=				1
       _snake_case     		=				ViTMSNForImageClassification(lowerCamelCase	)
       model.to(lowerCamelCase	)
       model.eval()

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






     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.prepare_config_and_inputs()
       _snake_case		,		_snake_case		,		_snake_case     		=				config_and_inputs
       _snake_case     		=				{"pixel_values": pixel_values}
       return config, inputs_dict







@require_torch
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    __UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Optional[Any]      =   (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
     UpperCAmelCase__       :						Dict      =   (
         {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
         if is_torch_available()
         else {}
     )

     UpperCAmelCase__       :						List[Any]      =   False
     UpperCAmelCase__       :						Optional[int]      =   False
     UpperCAmelCase__       :						Union[str, Any]      =   False
     UpperCAmelCase__       :						List[str]      =   False

     def 	UpperCamelCase(					self	):
       _snake_case     		=				ViTMSNModelTester(self	)
       _snake_case     		=				ConfigTester(self						,   config_class=lowerCamelCase						,   has_text_modality=lowerCamelCase						,   hidden_size=37	)

     def 	UpperCamelCase(					self	):
       self.config_tester.run_common_tests()

     @unittest.skip(reason="ViTMSN does not use inputs_embeds"	)
     def 	UpperCamelCase(					self	):
       pass

     def 	UpperCamelCase(					self	):
       _snake_case		,		_snake_case     		=				self.model_tester.prepare_config_and_inputs_for_common()

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

     def 	UpperCamelCase(					self	):
       _snake_case		,		_snake_case     		=				self.model_tester.prepare_config_and_inputs_for_common()

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

         _snake_case     		=				["pixel_values"]
         self.assertListEqual(arg_names[:1]						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCamelCase	)






     @slow
     def 	UpperCamelCase(					self	):
       for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _snake_case     		=				ViTMSNModel.from_pretrained(lowerCamelCase	)
         self.assertIsNotNone(lowerCamelCase	)







def 				snake_case_      (		):







  '''simple docstring'''



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




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



     '''simple docstring'''






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






     @slow
     def 	UpperCamelCase(					self	):
       torch.manual_seed(2	)
       _snake_case     		=				ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"	).to(lowerCamelCase	)

       _snake_case     		=				self.default_image_processor
       _snake_case     		=				prepare_img()
       _snake_case     		=				image_processor(images=lowerCamelCase						,   return_tensors="pt"	).to(lowerCamelCase	)

       # forward pass
       with torch.no_grad():
         _snake_case     		=				model(**lowerCamelCase	)

       # verify the logits
       _snake_case     		=				torch.Size((1, 1_000)	)
       self.assertEqual(outputs.logits.shape						,   lowerCamelCase	)

       _snake_case     		=				torch.tensor([-0.0803, -0.4454, -0.2375]	).to(lowerCamelCase	)

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









'''simple docstring'''



import cva
import numpy as np


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase	):

       if k in (0.04, 0.06):
         _snake_case     		=				k
         _snake_case     		=				window_size
       else:
         raise ValueError("invalid k value"	)

     def __str__(					self	):
       return str(self.k	)






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				cva.imread(lowerCamelCase						,   0	)
       _snake_case		,		_snake_case     		=				img.shape
       _snake_case     		=				[]
       _snake_case     		=				img.copy()
       _snake_case     		=				cva.cvtColor(lowerCamelCase						,   cva.COLOR_GRAY2RGB	)
       _snake_case		,		_snake_case     		=				np.gradient(lowerCamelCase	)
       _snake_case     		=				dx**2
       _snake_case     		=				dy**2
       _snake_case     		=				dx * dy
       _snake_case     		=				0.04
       _snake_case     		=				self.window_size // 2
       for y in range(lowerCamelCase						,   h - offset	):
         for x in range(lowerCamelCase						,   w - offset	):
           _snake_case     		=				ixx[
               y - offset : y + offset + 1, x - offset : x + offset + 1
           ].sum()
           _snake_case     		=				iyy[
               y - offset : y + offset + 1, x - offset : x + offset + 1
           ].sum()
           _snake_case     		=				ixy[
               y - offset : y + offset + 1, x - offset : x + offset + 1
           ].sum()

           _snake_case     		=				(wxx * wyy) - (wxy**2)
           _snake_case     		=				wxx + wyy
           _snake_case     		=				det - k * (trace**2)
           # Can change the value
           if r > 0.5:
             corner_list.append([x, y, r]	)
             color_img.itemset((y, x, 0)						,   0	)
             color_img.itemset((y, x, 1)						,   0	)
             color_img.itemset((y, x, 2)						,   255	)
       return color_img, corner_list


if __name__ == "__main__":
 __magic_name__   :						Tuple    =			HarrisCorner(0.04, 3)
 __magic_name__   ,    __magic_name__   :						Any    =			edge_detect.detect("""path_to_image""")
 cva.imwrite("""detect.png""", color_img)


