



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 YolosImageProcessor







class 	_snake_case							(			unittest.TestCase      ):






   '''simple docstring'''






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


   def 	A__  (  self:			Dict				)     ->	List[str]:
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_pad": self.do_pad,
       }






   def 	A__  (  self:			Optional[Any]   ,lowerCamelCase_:			Dict   ,lowerCamelCase_:			int=False				)     ->	Dict:
       if not batched:
           UpperCAmelCase_			:    int   						= image_inputs[0]
           if isinstance(lowerCAmelCase__   ,Image.Image				):
               UpperCAmelCase_						,      UpperCAmelCase_			:    Union[str, Any]   						= image.size
           else:
               UpperCAmelCase_						,      UpperCAmelCase_			:    Optional[int]   						= image.shape[1], image.shape[2]
           if w < h:
               UpperCAmelCase_			:    Any   						= int(self.size["""shortest_edge"""] * h / w				)
               UpperCAmelCase_			:    Union[str, Any]   						= self.size["""shortest_edge"""]
           elif w > h:
               UpperCAmelCase_			:    Optional[Any]   						= self.size["""shortest_edge"""]
               UpperCAmelCase_			:    str   						= int(self.size["""shortest_edge"""] * w / h				)
           else:
               UpperCAmelCase_			:    Dict   						= self.size["""shortest_edge"""]
               UpperCAmelCase_			:    int   						= self.size["""shortest_edge"""]

       else:
           UpperCAmelCase_			:    List[Any]   						= []
           for image in image_inputs:
               UpperCAmelCase_						,      UpperCAmelCase_			:    Optional[int]   						= self.get_expected_values([image]				)
               expected_values.append((expected_height, expected_width)				)
           UpperCAmelCase_			:    List[Any]   						= max(lowerCAmelCase__   ,key=lambda lowerCamelCase_				: item[0]				)[0]
           UpperCAmelCase_			:    List[Any]   						= max(lowerCAmelCase__   ,key=lambda lowerCamelCase_				: item[1]				)[1]

       return expected_height, expected_width







@require_torch
@require_vision
class 	_snake_case							(			lowerCamelCase_      ,				unittest.TestCase      ):






   '''simple docstring'''






   A__	:      Optional[int]    =		YolosImageProcessor if is_vision_available() else None


   def 	A__  (  self:			int				)     ->	Optional[Any]:
       UpperCAmelCase_			:    Dict   						= YolosImageProcessingTester(self				)


   @property
   def 	A__  (  self:			Any				)     ->	Any:
       return self.image_processor_tester.prepare_image_processor_dict()


   def 	A__  (  self:			List[Any]				)     ->	Optional[int]:
       UpperCAmelCase_			:    Optional[Any]   						= self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(lowerCAmelCase__   ,"""image_mean"""				)				)
       self.assertTrue(hasattr(lowerCAmelCase__   ,"""image_std"""				)				)
       self.assertTrue(hasattr(lowerCAmelCase__   ,"""do_normalize"""				)				)
       self.assertTrue(hasattr(lowerCAmelCase__   ,"""do_resize"""				)				)
       self.assertTrue(hasattr(lowerCAmelCase__   ,"""size"""				)				)


   def 	A__  (  self:			Any				)     ->	Union[str, Any]:
       UpperCAmelCase_			:    Dict   						= 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   ,lowerCAmelCase__				)

       UpperCAmelCase_			:    List[Any]   						= self.image_processing_class.from_dict(
           self.image_processor_dict   ,size=42   ,max_size=84   ,pad_and_return_pixel_mask=lowerCAmelCase__				)
       self.assertEqual(image_processor.size   ,{"""shortest_edge""": 42, """longest_edge""": 84}				)
       self.assertEqual(image_processor.do_pad   ,lowerCAmelCase__				)


   def 	A__  (  self:			Dict				)     ->	Union[str, Any]:
       pass


   def 	A__  (  self:			int				)     ->	Optional[int]:
       # Initialize image_processing
       UpperCAmelCase_			:    List[Any]   						= self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       UpperCAmelCase_			:    Union[str, Any]   						= prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowerCAmelCase__				)
       for image in image_inputs:
           self.assertIsInstance(lowerCAmelCase__   ,Image.Image				)

       # Test not batched input
       UpperCAmelCase_			:    Optional[int]   						= image_processing(image_inputs[0]   ,return_tensors="""pt"""				).pixel_values

       UpperCAmelCase_						,      UpperCAmelCase_			:    Any   						= self.image_processor_tester.get_expected_values(lowerCAmelCase__				)

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

       # Test batched
       UpperCAmelCase_						,      UpperCAmelCase_			:    List[Any]   						= self.image_processor_tester.get_expected_values(lowerCAmelCase__   ,batched=lowerCAmelCase__				)

       UpperCAmelCase_			:    List[str]   						= image_processing(lowerCAmelCase__   ,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 	A__  (  self:			Dict				)     ->	str:
       # Initialize image_processing
       UpperCAmelCase_			:    List[Any]   						= self.image_processing_class(**self.image_processor_dict				)
       # create random numpy tensors
       UpperCAmelCase_			:    Dict   						= prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowerCAmelCase__   ,numpify=lowerCAmelCase__				)
       for image in image_inputs:
           self.assertIsInstance(lowerCAmelCase__   ,np.ndarray				)

       # Test not batched input
       UpperCAmelCase_			:    Union[str, Any]   						= image_processing(image_inputs[0]   ,return_tensors="""pt"""				).pixel_values

       UpperCAmelCase_						,      UpperCAmelCase_			:    List[Any]   						= self.image_processor_tester.get_expected_values(lowerCAmelCase__				)

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

       # Test batched
       UpperCAmelCase_			:    int   						= image_processing(lowerCAmelCase__   ,return_tensors="""pt"""				).pixel_values

       UpperCAmelCase_						,      UpperCAmelCase_			:    List[Any]   						= self.image_processor_tester.get_expected_values(lowerCAmelCase__   ,batched=lowerCAmelCase__				)

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


   def 	A__  (  self:			Tuple				)     ->	Union[str, Any]:
       # Initialize image_processing
       UpperCAmelCase_			:    Any   						= self.image_processing_class(**self.image_processor_dict				)
       # create random PyTorch tensors
       UpperCAmelCase_			:    Optional[Any]   						= prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowerCAmelCase__   ,torchify=lowerCAmelCase__				)
       for image in image_inputs:
           self.assertIsInstance(lowerCAmelCase__   ,torch.Tensor				)

       # Test not batched input
       UpperCAmelCase_			:    Optional[int]   						= image_processing(image_inputs[0]   ,return_tensors="""pt"""				).pixel_values

       UpperCAmelCase_						,      UpperCAmelCase_			:    Optional[Any]   						= self.image_processor_tester.get_expected_values(lowerCAmelCase__				)

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

       # Test batched
       UpperCAmelCase_			:    Any   						= image_processing(lowerCAmelCase__   ,return_tensors="""pt"""				).pixel_values

       UpperCAmelCase_						,      UpperCAmelCase_			:    str   						= self.image_processor_tester.get_expected_values(lowerCAmelCase__   ,batched=lowerCAmelCase__				)

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


   def 	A__  (  self:			Optional[Any]				)     ->	Optional[int]:
       # Initialize image_processings
       UpperCAmelCase_			:    Union[str, Any]   						= self.image_processing_class(**self.image_processor_dict				)
       UpperCAmelCase_			:    List[str]   						= self.image_processing_class(do_resize=lowerCAmelCase__   ,do_normalize=lowerCAmelCase__   ,do_rescale=lowerCAmelCase__				)
       # create random PyTorch tensors
       UpperCAmelCase_			:    Union[str, Any]   						= prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowerCAmelCase__   ,torchify=lowerCAmelCase__				)
       for image in image_inputs:
           self.assertIsInstance(lowerCAmelCase__   ,torch.Tensor				)

       # Test whether the method "pad" and calling the image processor return the same tensors
       UpperCAmelCase_			:    Dict   						= image_processing_a.pad(lowerCAmelCase__   ,return_tensors="""pt"""				)
       UpperCAmelCase_			:    List[str]   						= image_processing_a(lowerCAmelCase__   ,return_tensors="""pt"""				)

       self.assertTrue(
           torch.allclose(encoded_images_with_method["""pixel_values"""]   ,encoded_images["""pixel_values"""]   ,atol=1e-4				)				)


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

       UpperCAmelCase_			:    List[str]   						= {"""image_id""": 39769, """annotations""": target}

       # encode them
       UpperCAmelCase_			:    Tuple   						= YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""				)
       UpperCAmelCase_			:    Dict   						= image_processing(images=lowerCAmelCase__   ,annotations=lowerCAmelCase__   ,return_tensors="""pt"""				)

       # verify pixel values
       UpperCAmelCase_			:    List[str]   						= torch.Size([1, 3, 800, 1066]				)
       self.assertEqual(encoding["""pixel_values"""].shape   ,lowerCAmelCase__				)

       UpperCAmelCase_			:    int   						= torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
       self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]   ,lowerCAmelCase__   ,atol=1e-4				)				)

       # verify area
       UpperCAmelCase_			:    Any   						= torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]				)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]   ,lowerCAmelCase__				)				)
       # verify boxes
       UpperCAmelCase_			:    str   						= torch.Size([6, 4]				)
       self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape   ,lowerCAmelCase__				)
       UpperCAmelCase_			:    Any   						= torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]				)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]   ,lowerCAmelCase__   ,atol=1e-3				)				)
       # verify image_id
       UpperCAmelCase_			:    List[str]   						= torch.tensor([39769]				)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]   ,lowerCAmelCase__				)				)
       # verify is_crowd
       UpperCAmelCase_			:    Tuple   						= torch.tensor([0, 0, 0, 0, 0, 0]				)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]   ,lowerCAmelCase__				)				)
       # verify class_labels
       UpperCAmelCase_			:    Optional[int]   						= torch.tensor([75, 75, 63, 65, 17, 17]				)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]   ,lowerCAmelCase__				)				)
       # verify orig_size
       UpperCAmelCase_			:    Tuple   						= torch.tensor([480, 640]				)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]   ,lowerCAmelCase__				)				)
       # verify size
       UpperCAmelCase_			:    int   						= torch.tensor([800, 1066]				)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]   ,lowerCAmelCase__				)				)






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

       UpperCAmelCase_			:    int   						= {"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

       UpperCAmelCase_			:    Optional[Any]   						= pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""				)

       # encode them
       UpperCAmelCase_			:    str   						= YolosImageProcessor(format="""coco_panoptic"""				)
       UpperCAmelCase_			:    Optional[int]   						= image_processing(images=lowerCAmelCase__   ,annotations=lowerCAmelCase__   ,masks_path=lowerCAmelCase__   ,return_tensors="""pt"""				)

       # verify pixel values
       UpperCAmelCase_			:    str   						= torch.Size([1, 3, 800, 1066]				)
       self.assertEqual(encoding["""pixel_values"""].shape   ,lowerCAmelCase__				)

       UpperCAmelCase_			:    List[Any]   						= torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
       self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]   ,lowerCAmelCase__   ,atol=1e-4				)				)

       # verify area
       UpperCAmelCase_			:    Optional[int]   						= torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]				)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]   ,lowerCAmelCase__				)				)
       # verify boxes
       UpperCAmelCase_			:    Tuple   						= torch.Size([6, 4]				)
       self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape   ,lowerCAmelCase__				)
       UpperCAmelCase_			:    int   						= torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]				)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]   ,lowerCAmelCase__   ,atol=1e-3				)				)
       # verify image_id
       UpperCAmelCase_			:    List[str]   						= torch.tensor([39769]				)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]   ,lowerCAmelCase__				)				)
       # verify is_crowd
       UpperCAmelCase_			:    List[Any]   						= torch.tensor([0, 0, 0, 0, 0, 0]				)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]   ,lowerCAmelCase__				)				)
       # verify class_labels
       UpperCAmelCase_			:    str   						= torch.tensor([17, 17, 63, 75, 75, 93]				)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]   ,lowerCAmelCase__				)				)
       # verify masks
       UpperCAmelCase_			:    Optional[int]   						= 822873
       self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()   ,lowerCAmelCase__				)
       # verify orig_size
       UpperCAmelCase_			:    List[Any]   						= torch.tensor([480, 640]				)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]   ,lowerCAmelCase__				)				)
       # verify size
       UpperCAmelCase_			:    str   						= torch.tensor([800, 1066]				)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]   ,lowerCAmelCase__				)				)




"""simple docstring"""




__UpperCamelCase						=      0  # The first color of the flag.
__UpperCamelCase						=      1  # The second color of the flag.
__UpperCamelCase						=      2  # The third color of the flag.
__UpperCamelCase						=      (red, white, blue)


def     lowercase			(SCREAMING_SNAKE_CASE_		:				list  )   ->						list:
						if not sequence:
												return []
						if len(SCREAMING_SNAKE_CASE_  ) == 1:
												return list(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					0
						SCREAMING_SNAKE_CASE													=					len(SCREAMING_SNAKE_CASE_  ) - 1
						SCREAMING_SNAKE_CASE													=					0
						while mid <= high:
												if sequence[mid] == colors[0]:
																		SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					sequence[mid], sequence[low]
																		low += 1
																		mid += 1
												elif sequence[mid] == colors[1]:
																		mid += 1
												elif sequence[mid] == colors[2]:
																		SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					sequence[high], sequence[mid]
																		high -= 1
												else:
																		SCREAMING_SNAKE_CASE													=					F'The elements inside the sequence must contains only {colors} values'
																		raise ValueError(SCREAMING_SNAKE_CASE_  )
						return sequence


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	__UpperCamelCase						=      input('''Enter numbers separated by commas:\n''').strip()
	__UpperCamelCase						=      [int(item.strip()) for item in user_input.split(''',''')]
	print(f'''{dutch_national_flag_sort(unsorted)}''')


