


"""simple docstring"""







import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import DetrImageProcessor






class       __A				(       unittest.TestCase			):






 """simple docstring"""




 def __init__(		self ,    __A ,    __A=7 ,    __A=3 ,    __A=30 ,    __A=400 ,    __A=True ,    __A=None ,    __A=True ,    __A=1 / 255 ,    __A=True ,    __A=[0.5, 0.5, 0.5] ,    __A=[0.5, 0.5, 0.5] ,    __A=True ,    )					->     Any:
     # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
     a 		=size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
     a 		=parent
     a 		=batch_size
     a 		=num_channels
     a 		=min_resolution
     a 		=max_resolution
     a 		=do_resize
     a 		=size
     a 		=do_rescale
     a 		=rescale_factor
     a 		=do_normalize
     a 		=image_mean
     a 		=image_std
     a 		=do_pad


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Dict:
     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_rescale": self.do_rescale,
         "rescale_factor": self.rescale_factor,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_pad": self.do_pad,
     }





 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A=False						)					->     Union[str, Any]:
     if not batched:
         a 		=image_inputs[0]
         if isinstance(__A ,    Image.Image						):
             a 		=image.size
         else:
             a 		=image.shape[1], image.shape[2]
         if w < h:
             a 		=int(self.size['''shortest_edge'''] * h / w						)
             a 		=self.size["""shortest_edge"""]
         elif w > h:
             a 		=self.size["""shortest_edge"""]
             a 		=int(self.size['''shortest_edge'''] * w / h						)
         else:
             a 		=self.size["""shortest_edge"""]
             a 		=self.size["""shortest_edge"""]

     else:
         a 		=[]
         for image in image_inputs:
             a 		=self.get_expected_values([image]						)
             expected_values.append((expected_height, expected_width)						)
         a 		=max(__A ,    key=lambda __A						: item[0]						)[0]
         a 		=max(__A ,    key=lambda __A						: item[1]						)[1]

     return expected_height, expected_width






@require_torch
@require_vision
class       __A				(       _lowerCAmelCase,    unittest.TestCase			):






 """simple docstring"""




 __lowerCAmelCase 					=  DetrImageProcessor if is_vision_available() else None


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Union[str, Any]:
     a 		=DetrImageProcessingTester(self						)


 @property
 def       SCREAMING_SNAKE_CASE							(		self						)					->     Dict:
     return self.image_processor_tester.prepare_image_processor_dict()


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Any:
     a 		=self.image_processing_class(**self.image_processor_dict						)
     self.assertTrue(hasattr(__A ,    '''image_mean'''						)						)
     self.assertTrue(hasattr(__A ,    '''image_std'''						)						)
     self.assertTrue(hasattr(__A ,    '''do_normalize'''						)						)
     self.assertTrue(hasattr(__A ,    '''do_rescale'''						)						)
     self.assertTrue(hasattr(__A ,    '''rescale_factor'''						)						)
     self.assertTrue(hasattr(__A ,    '''do_resize'''						)						)
     self.assertTrue(hasattr(__A ,    '''size'''						)						)
     self.assertTrue(hasattr(__A ,    '''do_pad'''						)						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[int]:
     a 		=self.image_processing_class.from_dict(self.image_processor_dict						)
     self.assertEqual(image_processor.size ,    {'''shortest_edge''': 18, '''longest_edge''': 1333}						)
     self.assertEqual(image_processor.do_pad ,    __A						)

     a 		=self.image_processing_class.from_dict(
         self.image_processor_dict ,    size=42 ,    max_size=84 ,    pad_and_return_pixel_mask=__A						)
     self.assertEqual(image_processor.size ,    {'''shortest_edge''': 42, '''longest_edge''': 84}						)
     self.assertEqual(image_processor.do_pad ,    __A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[str]:
     pass


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Union[str, Any]:
     # Initialize image_processing
     a 		=self.image_processing_class(**self.image_processor_dict						)
     # create random PIL images
     a 		=prepare_image_inputs(self.image_processor_tester ,    equal_resolution=__A						)
     for image in image_inputs:
         self.assertIsInstance(__A ,    Image.Image						)

     # Test not batched input
     a 		=image_processing(image_inputs[0] ,    return_tensors='''pt'''						).pixel_values

     a 		=self.image_processor_tester.get_expected_values(__A						)

     self.assertEqual(
         encoded_images.shape ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,    )

     # Test batched
     a 		=self.image_processor_tester.get_expected_values(__A ,    batched=__A						)

     a 		=image_processing(__A ,    return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_images.shape ,    (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         ) ,    )


 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[Any]:
     # Initialize image_processing
     a 		=self.image_processing_class(**self.image_processor_dict						)
     # create random numpy tensors
     a 		=prepare_image_inputs(self.image_processor_tester ,    equal_resolution=__A ,    numpify=__A						)
     for image in image_inputs:
         self.assertIsInstance(__A ,    np.ndarray						)

     # Test not batched input
     a 		=image_processing(image_inputs[0] ,    return_tensors='''pt'''						).pixel_values

     a 		=self.image_processor_tester.get_expected_values(__A						)

     self.assertEqual(
         encoded_images.shape ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,    )

     # Test batched
     a 		=image_processing(__A ,    return_tensors='''pt'''						).pixel_values

     a 		=self.image_processor_tester.get_expected_values(__A ,    batched=__A						)

     self.assertEqual(
         encoded_images.shape ,    (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         ) ,    )


 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[str]:
     # Initialize image_processing
     a 		=self.image_processing_class(**self.image_processor_dict						)
     # create random PyTorch tensors
     a 		=prepare_image_inputs(self.image_processor_tester ,    equal_resolution=__A ,    torchify=__A						)
     for image in image_inputs:
         self.assertIsInstance(__A ,    torch.Tensor						)

     # Test not batched input
     a 		=image_processing(image_inputs[0] ,    return_tensors='''pt'''						).pixel_values

     a 		=self.image_processor_tester.get_expected_values(__A						)

     self.assertEqual(
         encoded_images.shape ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,    )

     # Test batched
     a 		=image_processing(__A ,    return_tensors='''pt'''						).pixel_values

     a 		=self.image_processor_tester.get_expected_values(__A ,    batched=__A						)

     self.assertEqual(
         encoded_images.shape ,    (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         ) ,    )


 @slow
 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[Any]:
     # prepare image and target
     a 		=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
     with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' ,    '''r'''						) as f:
         a 		=json.loads(f.read()						)

     a 		={"""image_id""": 3_9769, """annotations""": target}

     # encode them
     a 		=DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''						)
     a 		=image_processing(images=__A ,    annotations=__A ,    return_tensors='''pt'''						)

     # verify pixel values
     a 		=torch.Size([1, 3, 800, 1066]						)
     self.assertEqual(encoding['''pixel_values'''].shape ,    __A						)

     a 		=torch.tensor([0.2_796, 0.3_138, 0.3_481]						)
     self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] ,    __A ,    atol=1E-4						)						)

     # verify area
     a 		=torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]						)
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] ,    __A						)						)
     # verify boxes
     a 		=torch.Size([6, 4]						)
     self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,    __A						)
     a 		=torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]						)
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,    __A ,    atol=1E-3						)						)
     # verify image_id
     a 		=torch.tensor([3_9769]						)
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,    __A						)						)
     # verify is_crowd
     a 		=torch.tensor([0, 0, 0, 0, 0, 0]						)
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,    __A						)						)
     # verify class_labels
     a 		=torch.tensor([75, 75, 63, 65, 17, 17]						)
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,    __A						)						)
     # verify orig_size
     a 		=torch.tensor([480, 640]						)
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,    __A						)						)
     # verify size
     a 		=torch.tensor([800, 1066]						)
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,    __A						)						)





 @slow
 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[int]:
     # prepare image, target and masks_path
     a 		=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
     with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' ,    '''r'''						) as f:
         a 		=json.loads(f.read()						)

     a 		={"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

     a 		=pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''						)

     # encode them
     a 		=DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''						)
     a 		=image_processing(images=__A ,    annotations=__A ,    masks_path=__A ,    return_tensors='''pt'''						)

     # verify pixel values
     a 		=torch.Size([1, 3, 800, 1066]						)
     self.assertEqual(encoding['''pixel_values'''].shape ,    __A						)

     a 		=torch.tensor([0.2_796, 0.3_138, 0.3_481]						)
     self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] ,    __A ,    atol=1E-4						)						)

     # verify area
     a 		=torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]						)
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] ,    __A						)						)
     # verify boxes
     a 		=torch.Size([6, 4]						)
     self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,    __A						)
     a 		=torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]						)
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,    __A ,    atol=1E-3						)						)
     # verify image_id
     a 		=torch.tensor([3_9769]						)
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,    __A						)						)
     # verify is_crowd
     a 		=torch.tensor([0, 0, 0, 0, 0, 0]						)
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,    __A						)						)
     # verify class_labels
     a 		=torch.tensor([17, 17, 63, 75, 75, 93]						)
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,    __A						)						)
     # verify masks
     a 		=82_2873
     self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() ,    __A						)
     # verify orig_size
     a 		=torch.tensor([480, 640]						)
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,    __A						)						)
     # verify size
     a 		=torch.tensor([800, 1066]						)
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,    __A						)						)




"""simple docstring"""
import math
class 				lowerCamelCase  :






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							Tuple						,     snake_case:							list[list[float]]						,     snake_case:							list[int]						)   ->	int:
											snake_case_ :Any						=			0.0
											snake_case_ :Tuple						=			0.0
											for i in range(len(snake_case						)						):
																	da += math.pow((sample[i] - weights[0][i])						,     2						)
																	da += math.pow((sample[i] - weights[1][i])						,     2						)
																	return 0 if da > da else 1
											return 0






					def    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							list[list[int | float]]						,     snake_case:							list[int]						,     snake_case:							int						,     snake_case:							float						)   ->	list[list[int | float]]:
											for i in range(len(snake_case						)						):
																	weights[j][i] += alpha * (sample[i] - weights[j][i])
											return weights

def    A_  (			):







						'''simple docstring'''


						snake_case_ :Dict						=			[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

						# weight initialization ( n, C )
						snake_case_ :List[Any]						=			[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

						# training
						snake_case_ :Optional[Any]						=			SelfOrganizingMap()
						snake_case_ :Dict						=			3
						snake_case_ :Dict						=			0.5

						for _ in range(_lowercase	):
												for j in range(len(_lowercase	)	):
																		# training sample
																		snake_case_ :List[Any]						=			training_samples[j]

																		# Compute the winning vector
																		snake_case_ :Optional[int]						=			self_organizing_map.get_winner(_lowercase,					_lowercase	)

																		# Update the winning vector
																		snake_case_ :List[str]						=			self_organizing_map.update(_lowercase,					_lowercase,					_lowercase,					_lowercase	)

    # classify test sample
						snake_case_ :str						=			[0, 0, 0, 1]
						snake_case_ :List[Any]						=			self_organizing_map.get_winner(_lowercase,					_lowercase	)

						# results
						print(f"""Clusters that the test sample belongs to : {winner}"""	)
						print(f"""Weights that have been trained : {weights}"""	)


# running the main() function
if __name__ == "__main__":
						main()
