





'''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 ConditionalDetrImageProcessor
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def __init__(							self       :		Optional[Any]	,			lowerCAmelCase__       :		List[str]	,			lowerCAmelCase__       :		Union[str, Any]=7	,			lowerCAmelCase__       :		int=3	,			lowerCAmelCase__       :		List[Any]=30	,			lowerCAmelCase__       :		str=400	,			lowerCAmelCase__       :		Union[str, Any]=True	,			lowerCAmelCase__       :		int=None	,			lowerCAmelCase__       :		List[Any]=True	,			lowerCAmelCase__       :		List[str]=[0.5, 0.5, 0.5]	,			lowerCAmelCase__       :		Optional[int]=[0.5, 0.5, 0.5]	,			lowerCAmelCase__       :		Optional[Any]=True	,			lowerCAmelCase__       :		Union[str, Any]=1 / 255	,			lowerCAmelCase__       :		Union[str, Any]=True	,			)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
            _UpperCamelCase       =	parent
            _UpperCamelCase       =	batch_size
            _UpperCamelCase       =	num_channels
            _UpperCamelCase       =	min_resolution
            _UpperCamelCase       =	max_resolution
            _UpperCamelCase       =	do_resize
            _UpperCamelCase       =	size
            _UpperCamelCase       =	do_normalize
            _UpperCamelCase       =	image_mean
            _UpperCamelCase       =	image_std
            _UpperCamelCase       =	do_rescale
            _UpperCamelCase       =	rescale_factor
            _UpperCamelCase       =	do_pad





       def snake_case__						(							self       :		int					)   ->					str:

            '''simple docstring'''




            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 snake_case__						(							self       :		str	,			lowerCAmelCase__       :		Optional[Any]	,			lowerCAmelCase__       :		int=False					)   ->					List[Any]:

            '''simple docstring'''




            if not batched:
                 _UpperCamelCase       =	image_inputs[0]
                 if isinstance(__lowercase	,			Image.Image					):
                      _UpperCamelCase       =	image.size
                 else:
                      _UpperCamelCase       =	image.shape[1], image.shape[2]
                 if w < h:
                      _UpperCamelCase       =	int(self.size['''shortest_edge'''] * h / w					)
                      _UpperCamelCase       =	self.size['''shortest_edge''']
                 elif w > h:
                      _UpperCamelCase       =	self.size['''shortest_edge''']
                      _UpperCamelCase       =	int(self.size['''shortest_edge'''] * w / h					)
                 else:
                      _UpperCamelCase       =	self.size['''shortest_edge''']
                      _UpperCamelCase       =	self.size['''shortest_edge''']

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

            return expected_height, expected_width

@require_torch
@require_vision
class     __lowerCAmelCase					(   __lowerCamelCase						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Any						    =   ConditionalDetrImageProcessor if is_vision_available() else None





       def snake_case__						(							self       :		Tuple					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	ConditionalDetrImageProcessingTester(self					)





       @property
       def snake_case__						(							self       :		int					)   ->					Tuple:

            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()





       def snake_case__						(							self       :		List[str]					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	self.image_processing_class(**self.image_processor_dict					)
            self.assertTrue(hasattr(__lowercase	,			'''image_mean'''					)					)
            self.assertTrue(hasattr(__lowercase	,			'''image_std'''					)					)
            self.assertTrue(hasattr(__lowercase	,			'''do_normalize'''					)					)
            self.assertTrue(hasattr(__lowercase	,			'''do_resize'''					)					)
            self.assertTrue(hasattr(__lowercase	,			'''size'''					)					)





       def snake_case__						(							self       :		List[str]					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	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	,			__lowercase					)

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





       def snake_case__						(							self       :		List[str]					)   ->					Dict:

            '''simple docstring'''




            pass





       def snake_case__						(							self       :		List[Any]					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	self.image_processing_class(**self.image_processor_dict					)
            # create random PIL images
            _UpperCamelCase       =	prepare_image_inputs(self.image_processor_tester	,			equal_resolution=__lowercase					)
            for image in image_inputs:
                 self.assertIsInstance(__lowercase	,			Image.Image					)

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

            _UpperCamelCase       =	self.image_processor_tester.get_expected_values(__lowercase					)

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

            # Test batched
            _UpperCamelCase       =	self.image_processor_tester.get_expected_values(__lowercase	,			batched=__lowercase					)

            _UpperCamelCase       =	image_processing(__lowercase	,			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 snake_case__						(							self       :		Tuple					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	self.image_processing_class(**self.image_processor_dict					)
            # create random numpy tensors
            _UpperCamelCase       =	prepare_image_inputs(self.image_processor_tester	,			equal_resolution=__lowercase	,			numpify=__lowercase					)
            for image in image_inputs:
                 self.assertIsInstance(__lowercase	,			np.ndarray					)

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

            _UpperCamelCase       =	self.image_processor_tester.get_expected_values(__lowercase					)

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

            # Test batched
            _UpperCamelCase       =	image_processing(__lowercase	,			return_tensors='''pt'''					).pixel_values

            _UpperCamelCase       =	self.image_processor_tester.get_expected_values(__lowercase	,			batched=__lowercase					)

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





       def snake_case__						(							self       :		Any					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	self.image_processing_class(**self.image_processor_dict					)
            # create random PyTorch tensors
            _UpperCamelCase       =	prepare_image_inputs(self.image_processor_tester	,			equal_resolution=__lowercase	,			torchify=__lowercase					)
            for image in image_inputs:
                 self.assertIsInstance(__lowercase	,			torch.Tensor					)

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

            _UpperCamelCase       =	self.image_processor_tester.get_expected_values(__lowercase					)

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

            # Test batched
            _UpperCamelCase       =	image_processing(__lowercase	,			return_tensors='''pt'''					).pixel_values

            _UpperCamelCase       =	self.image_processor_tester.get_expected_values(__lowercase	,			batched=__lowercase					)

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





       @slow
       def snake_case__						(							self       :		int					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
            with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''	,			'''r'''					) as f:
                 _UpperCamelCase       =	json.loads(f.read()					)

            _UpperCamelCase       =	{'''image_id''': 39769, '''annotations''': target}

            # encode them
            _UpperCamelCase       =	ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''					)
            _UpperCamelCase       =	image_processing(images=__lowercase	,			annotations=__lowercase	,			return_tensors='''pt'''					)

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

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

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







       @slow
       def snake_case__						(							self       :		Union[str, Any]					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
            with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''	,			'''r'''					) as f:
                 _UpperCamelCase       =	json.loads(f.read()					)

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

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

            # encode them
            _UpperCamelCase       =	ConditionalDetrImageProcessor(format='''coco_panoptic'''					)
            _UpperCamelCase       =	image_processing(images=__lowercase	,			annotations=__lowercase	,			masks_path=__lowercase	,			return_tensors='''pt'''					)

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

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

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










'''simple docstring'''

import math







def  a__    (    lowercase :					float, lowercase :					float				)		->		float:





     """simple docstring"""

     if initial_intensity < 0:
          raise ValueError('''The value of intensity cannot be negative'''				)
          # handling of negative values of initial intensity
     if angle < 0 or angle > 360:
          raise ValueError('''In Malus Law, the angle is in the range 0-360 degrees'''				)
          # handling of values out of allowed range
     return initial_intensity * (math.cos(math.radians(lowercase				)				) ** 2)


if __name__ == "__main__":
   import doctest

   doctest.testmod(name='malus_law')



