


'''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 YolosImageProcessor


class 			__lowerCamelCase			(	unittest.TestCase	):
				"""simple docstring"""


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


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

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

											return expected_height, expected_width




@require_torch
@require_vision
class 			__lowerCamelCase			(	a_    , unittest.TestCase	):
				"""simple docstring"""
				a													=   YolosImageProcessor if is_vision_available() else None


				def        A   (  self      :							Optional[int]):
											_A					:     Optional[int]     = YolosImageProcessingTester(self)


				@property
				def        A   (  self      :							Optional[Any]):
											return self.image_processor_tester.prepare_image_processor_dict()


				def        A   (  self      :							Any):
											_A					:     Tuple     = self.image_processing_class(**self.image_processor_dict)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,							'image_mean'))
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,							'image_std'))
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,							'do_normalize'))
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,							'do_resize'))
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,							'size'))


				def        A   (  self      :							Optional[Any]):
											_A					:     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				,							SCREAMING_SNAKE_CASE)

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


				def        A   (  self      :							Optional[int]):
											pass


				def        A   (  self      :							int):
											# Initialize image_processing
											_A					:     str     = self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											_A					:     List[Any]     = prepare_image_inputs(self.image_processor_tester				,							equal_resolution=SCREAMING_SNAKE_CASE)
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE				,							Image.Image)

											# Test not batched input
											_A					:     Optional[Any]     = image_processing(image_inputs[0]				,							return_tensors='pt').pixel_values

											_A					:     List[Any]     = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE)

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

											# Test batched
											_A					:     Optional[Any]     = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE				,							batched=SCREAMING_SNAKE_CASE)

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

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

											_A					:     Tuple     = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE)

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

											# Test batched
											_A					:     str     = image_processing(SCREAMING_SNAKE_CASE				,							return_tensors='pt').pixel_values

											_A					:     List[str]     = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE				,							batched=SCREAMING_SNAKE_CASE)

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


				def        A   (  self      :							int):
											# Initialize image_processing
											_A					:     int     = self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											_A					:     Tuple     = prepare_image_inputs(self.image_processor_tester				,							equal_resolution=SCREAMING_SNAKE_CASE				,							torchify=SCREAMING_SNAKE_CASE)
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE				,							torch.Tensor)

											# Test not batched input
											_A					:     List[str]     = image_processing(image_inputs[0]				,							return_tensors='pt').pixel_values

											_A					:     str     = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE)

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

											# Test batched
											_A					:     Optional[Any]     = image_processing(SCREAMING_SNAKE_CASE				,							return_tensors='pt').pixel_values

											_A					:     List[str]     = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE				,							batched=SCREAMING_SNAKE_CASE)

											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[int]):
											# Initialize image_processings
											_A					:     Any     = self.image_processing_class(**self.image_processor_dict)
											_A					:     Any     = self.image_processing_class(do_resize=SCREAMING_SNAKE_CASE				,							do_normalize=SCREAMING_SNAKE_CASE				,							do_rescale=SCREAMING_SNAKE_CASE)
											# create random PyTorch tensors
											_A					:     Optional[int]     = prepare_image_inputs(self.image_processor_tester				,							equal_resolution=SCREAMING_SNAKE_CASE				,							torchify=SCREAMING_SNAKE_CASE)
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE				,							torch.Tensor)

											# Test whether the method "pad" and calling the image processor return the same tensors
											_A					:     List[Any]     = image_processing_a.pad(SCREAMING_SNAKE_CASE				,							return_tensors='pt')
											_A					:     Any     = image_processing_a(SCREAMING_SNAKE_CASE				,							return_tensors='pt')

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


				@slow
				def        A   (  self      :							int):
											# prepare image and target
											_A					:     Union[str, Any]     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
											with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'				,							'r') as f:
																		_A					:     List[str]     = json.loads(f.read())

											_A					:     Optional[int]     = {'image_id': 39769, 'annotations': target}

											# encode them
											_A					:     Any     = YolosImageProcessor.from_pretrained('hustvl/yolos-small')
											_A					:     List[Any]     = image_processing(images=SCREAMING_SNAKE_CASE				,							annotations=SCREAMING_SNAKE_CASE				,							return_tensors='pt')

											# verify pixel values
											_A					:     Optional[int]     = torch.Size([1, 3, 800, 1066])
											self.assertEqual(encoding['pixel_values'].shape				,							SCREAMING_SNAKE_CASE)

											_A					:     Tuple     = torch.tensor([0.2796, 0.3138, 0.3481])
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]				,							SCREAMING_SNAKE_CASE				,							atol=1e-4))

											# verify area
											_A					:     str     = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']				,							SCREAMING_SNAKE_CASE))
											# verify boxes
											_A					:     List[str]     = torch.Size([6, 4])
											self.assertEqual(encoding['labels'][0]['boxes'].shape				,							SCREAMING_SNAKE_CASE)
											_A					:     Optional[int]     = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,							SCREAMING_SNAKE_CASE				,							atol=1e-3))
											# verify image_id
											_A					:     List[Any]     = torch.tensor([39769])
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,							SCREAMING_SNAKE_CASE))
											# verify is_crowd
											_A					:     str     = torch.tensor([0, 0, 0, 0, 0, 0])
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,							SCREAMING_SNAKE_CASE))
											# verify class_labels
											_A					:     Any     = torch.tensor([75, 75, 63, 65, 17, 17])
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,							SCREAMING_SNAKE_CASE))
											# verify orig_size
											_A					:     str     = torch.tensor([480, 640])
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,							SCREAMING_SNAKE_CASE))
											# verify size
											_A					:     List[str]     = torch.tensor([800, 1066])
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,							SCREAMING_SNAKE_CASE))


				@slow
				def        A   (  self      :							str):
											# prepare image, target and masks_path
											_A					:     List[str]     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
											with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'				,							'r') as f:
																		_A					:     List[str]     = json.loads(f.read())

											_A					:     int     = {'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

											_A					:     List[str]     = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic')

											# encode them
											_A					:     str     = YolosImageProcessor(format='coco_panoptic')
											_A					:     List[Any]     = image_processing(images=SCREAMING_SNAKE_CASE				,							annotations=SCREAMING_SNAKE_CASE				,							masks_path=SCREAMING_SNAKE_CASE				,							return_tensors='pt')

											# verify pixel values
											_A					:     Dict     = torch.Size([1, 3, 800, 1066])
											self.assertEqual(encoding['pixel_values'].shape				,							SCREAMING_SNAKE_CASE)

											_A					:     Dict     = torch.tensor([0.2796, 0.3138, 0.3481])
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]				,							SCREAMING_SNAKE_CASE				,							atol=1e-4))

											# verify area
											_A					:     List[str]     = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']				,							SCREAMING_SNAKE_CASE))
											# verify boxes
											_A					:     Optional[Any]     = torch.Size([6, 4])
											self.assertEqual(encoding['labels'][0]['boxes'].shape				,							SCREAMING_SNAKE_CASE)
											_A					:     str     = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,							SCREAMING_SNAKE_CASE				,							atol=1e-3))
											# verify image_id
											_A					:     Optional[Any]     = torch.tensor([39769])
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,							SCREAMING_SNAKE_CASE))
											# verify is_crowd
											_A					:     Optional[int]     = torch.tensor([0, 0, 0, 0, 0, 0])
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,							SCREAMING_SNAKE_CASE))
											# verify class_labels
											_A					:     int     = torch.tensor([17, 17, 63, 75, 75, 93])
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,							SCREAMING_SNAKE_CASE))
											# verify masks
											_A					:     Optional[Any]     = 822873
											self.assertEqual(encoding['labels'][0]['masks'].sum().item()				,							SCREAMING_SNAKE_CASE)
											# verify orig_size
											_A					:     Dict     = torch.tensor([480, 640])
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,							SCREAMING_SNAKE_CASE))
											# verify size
											_A					:     Optional[Any]     = torch.tensor([800, 1066])
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,							SCREAMING_SNAKE_CASE))










'''simple docstring'''







def 				lowerCAmelCase__	(    lowerCamelCase					:      int = 10       ):
       if not isinstance(lowerCamelCase  ,lowerCamelCase       ) or n < 0:
              raise ValueError('Invalid input'       )
       _A					:     Optional[Any]     = 10**n
       _A					:     List[str]     = 28433 * (pow(2  ,7830457  ,lowerCamelCase       )) + 1
       return str(number % modulus       )


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   print(f"""{solution(10) = }""")






