

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 ,   lowercase__ ,   lowercase__=7 ,   lowercase__=3 ,   lowercase__=3_0 ,   lowercase__=4_0_0 ,   lowercase__=True ,   lowercase__=None ,   lowercase__=True ,   lowercase__=[0.5, 0.5, 0.5] ,   lowercase__=[0.5, 0.5, 0.5] ,   lowercase__=True ,   lowercase__=1 / 2_5_5 ,   lowercase__=True ,   ):







             '''simple docstring'''



             __A    =size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
             __A    =parent
             __A    =batch_size
             __A    =num_channels
             __A    =min_resolution
             __A    =max_resolution
             __A    =do_resize
             __A    =size
             __A    =do_normalize
             __A    =image_mean
             __A    =image_std
             __A    =do_rescale
             __A    =rescale_factor
             __A    =do_pad

       def     __UpperCamelCase     (							self     ):







             '''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     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__=False     ):







             '''simple docstring'''



             if not batched:
                   __A    =image_inputs[0]
                   if isinstance(lowerCamelCase_ ,   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(lowerCamelCase_ ,   key=lambda lowercase__     : item[0]     )[0]
                   __A    =max(lowerCamelCase_ ,   key=lambda lowercase__     : item[1]     )[1]

             return expected_height, expected_width





@require_torch
@require_vision
class        lowerCAmelCase__  (					_UpperCamelCase				,							unittest.TestCase			):



       '''simple docstring'''







       lowercase_						=    YolosImageProcessor if is_vision_available() else None

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =YolosImageProcessingTester(self     )

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self.image_processor_tester.prepare_image_processor_dict()

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =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     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.image_processing_class.from_dict(self.image_processor_dict     )
             self.assertEqual(image_processor.size ,   {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}     )
             self.assertEqual(image_processor.do_pad ,   lowerCamelCase_     )

             __A    =self.image_processing_class.from_dict(
                 self.image_processor_dict ,   size=4_2 ,   max_size=8_4 ,   pad_and_return_pixel_mask=lowerCamelCase_     )
             self.assertEqual(image_processor.size ,   {'''shortest_edge''': 4_2, '''longest_edge''': 8_4}     )
             self.assertEqual(image_processor.do_pad ,   lowerCamelCase_     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             pass

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

             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(lowerCamelCase_ ,   batched=lowerCamelCase_     )

             __A    =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     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.image_processing_class(**self.image_processor_dict     )
             # create random numpy tensors
             __A    =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
             __A    =image_processing(image_inputs[0] ,   return_tensors='''pt'''     ).pixel_values

             __A    =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
             __A    =image_processing(lowerCamelCase_ ,   return_tensors='''pt'''     ).pixel_values

             __A    =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     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.image_processing_class(**self.image_processor_dict     )
             # create random PyTorch tensors
             __A    =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
             __A    =image_processing(image_inputs[0] ,   return_tensors='''pt'''     ).pixel_values

             __A    =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
             __A    =image_processing(lowerCamelCase_ ,   return_tensors='''pt'''     ).pixel_values

             __A    =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     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.image_processing_class(**self.image_processor_dict     )
             __A    =self.image_processing_class(do_resize=lowerCamelCase_ ,   do_normalize=lowerCamelCase_ ,   do_rescale=lowerCamelCase_     )
             # create random PyTorch tensors
             __A    =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
             __A    =image_processing_a.pad(lowerCamelCase_ ,   return_tensors='''pt'''     )
             __A    =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     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __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_9_7_6_9, 'annotations': target}

             # encode them
             __A    =YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''     )
             __A    =image_processing(images=lowerCamelCase_ ,   annotations=lowerCamelCase_ ,   return_tensors='''pt'''     )

             # verify pixel values
             __A    =torch.Size([1, 3, 8_0_0, 1_0_6_6]     )
             self.assertEqual(encoding['''pixel_values'''].shape ,   lowerCamelCase_     )

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

             # verify area
             __A    =torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] ,   lowerCamelCase_     )     )
             # verify boxes
             __A    =torch.Size([6, 4]     )
             self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,   lowerCamelCase_     )
             __A    =torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,   lowerCamelCase_ ,   atol=1E-3     )     )
             # verify image_id
             __A    =torch.tensor([3_9_7_6_9]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,   lowerCamelCase_     )     )
             # verify is_crowd
             __A    =torch.tensor([0, 0, 0, 0, 0, 0]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,   lowerCamelCase_     )     )
             # verify class_labels
             __A    =torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,   lowerCamelCase_     )     )
             # verify orig_size
             __A    =torch.tensor([4_8_0, 6_4_0]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,   lowerCamelCase_     )     )
             # verify size
             __A    =torch.tensor([8_0_0, 1_0_6_6]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,   lowerCamelCase_     )     )


       @slow
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __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_9_7_6_9, 'segments_info': target}

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

             # encode them
             __A    =YolosImageProcessor(format='''coco_panoptic'''     )
             __A    =image_processing(images=lowerCamelCase_ ,   annotations=lowerCamelCase_ ,   masks_path=lowerCamelCase_ ,   return_tensors='''pt'''     )

             # verify pixel values
             __A    =torch.Size([1, 3, 8_0_0, 1_0_6_6]     )
             self.assertEqual(encoding['''pixel_values'''].shape ,   lowerCamelCase_     )

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

             # verify area
             __A    =torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] ,   lowerCamelCase_     )     )
             # verify boxes
             __A    =torch.Size([6, 4]     )
             self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,   lowerCamelCase_     )
             __A    =torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,   lowerCamelCase_ ,   atol=1E-3     )     )
             # verify image_id
             __A    =torch.tensor([3_9_7_6_9]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,   lowerCamelCase_     )     )
             # verify is_crowd
             __A    =torch.tensor([0, 0, 0, 0, 0, 0]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,   lowerCamelCase_     )     )
             # verify class_labels
             __A    =torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,   lowerCamelCase_     )     )
             # verify masks
             __A    =8_2_2_8_7_3
             self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() ,   lowerCamelCase_     )
             # verify orig_size
             __A    =torch.tensor([4_8_0, 6_4_0]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,   lowerCamelCase_     )     )
             # verify size
             __A    =torch.tensor([8_0_0, 1_0_6_6]     )
             self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,   lowerCamelCase_     )     )









def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : int			)   ->							Optional[int]:




		'''simple docstring'''
		if b == 0:
				return 1
		if (b % 2) == 0:
				return actual_power(_lowercase   ,    int(b / 2			)			) * actual_power(_lowercase   ,    int(b / 2			)			)
		else:
				return a * actual_power(_lowercase   ,    int(b / 2			)			) * actual_power(_lowercase   ,    int(b / 2			)			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : int			)   ->							float:




		'''simple docstring'''
		if b < 0:
				return 1 / actual_power(_lowercase   ,    _lowercase			)
		return actual_power(_lowercase   ,    _lowercase			)


if __name__ == "__main__":
			print(power(-2, -3))


