


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 DetaImageProcessor
class 					__magic_name__		(			unittest.TestCase     ):






       def __init__(   self  :    str  ,     snake_case__  :    Dict  ,     snake_case__  :    Dict=7  ,     snake_case__  :    Dict=3  ,     snake_case__  :    List[str]=3_0  ,     snake_case__  :    List[str]=4_0_0  ,     snake_case__  :    Tuple=True  ,     snake_case__  :    Any=None  ,     snake_case__  :    str=True  ,     snake_case__  :    Optional[int]=[0.5, 0.5, 0.5]  ,     snake_case__  :    Union[str, Any]=[0.5, 0.5, 0.5]  ,     snake_case__  :    Optional[Any]=True  ,     snake_case__  :    Dict=1 / 2_5_5  ,     snake_case__  :    Union[str, Any]=True  ,     ):

              '''simple docstring'''






              lowercase		:Any       							=				size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
              lowercase		:Optional[Any]       							=				parent
              lowercase		:Dict       							=				batch_size
              lowercase		:Optional[Any]       							=				num_channels
              lowercase		:Union[str, Any]       							=				min_resolution
              lowercase		:List[Any]       							=				max_resolution
              lowercase		:Union[str, Any]       							=				do_resize
              lowercase		:Optional[Any]       							=				size
              lowercase		:Optional[Any]       							=				do_normalize
              lowercase		:Any       							=				image_mean
              lowercase		:Any       							=				image_std
              lowercase		:Any       							=				do_rescale
              lowercase		:Optional[Any]       							=				rescale_factor
              lowercase		:Dict       							=				do_pad






       def  __snake_case						(   self  :    Dict				):

              '''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  :    Tuple  ,     snake_case__  :    str  ,     snake_case__  :    Any=False				):

              '''simple docstring'''






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

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

              return expected_height, expected_width







@require_torch
@require_vision
class 					__magic_name__		(			__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      Any        =   DetaImageProcessor if is_vision_available() else None






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				DetaImageProcessingTester(self				)






       @property
       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              return self.image_processor_tester.prepare_image_processor_dict()






       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				self.image_processing_class(**self.image_processor_dict				)
              self.assertTrue(hasattr(snake_case__  ,     '''image_mean'''				)				)
              self.assertTrue(hasattr(snake_case__  ,     '''image_std'''				)				)
              self.assertTrue(hasattr(snake_case__  ,     '''do_normalize'''				)				)
              self.assertTrue(hasattr(snake_case__  ,     '''do_resize'''				)				)
              self.assertTrue(hasattr(snake_case__  ,     '''do_rescale'''				)				)
              self.assertTrue(hasattr(snake_case__  ,     '''do_pad'''				)				)
              self.assertTrue(hasattr(snake_case__  ,     '''size'''				)				)






       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              lowercase		:Dict       							=				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  ,     snake_case__				)






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              pass






       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              lowercase		:List[str]       							=				self.image_processing_class(**self.image_processor_dict				)
              # create random PIL images
              lowercase		:Optional[Any]       							=				prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=snake_case__				)
              for image in image_inputs:
                     self.assertIsInstance(snake_case__  ,     Image.Image				)

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

              lowercase		:Any       							=				self.image_processor_tester.get_expected_values(snake_case__				)

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

              # Test batched
              lowercase		:str       							=				self.image_processor_tester.get_expected_values(snake_case__  ,     batched=snake_case__				)

              lowercase		:Optional[Any]       							=				image_processing(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  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






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

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

              lowercase		:Optional[int]       							=				self.image_processor_tester.get_expected_values(snake_case__				)

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

              # Test batched
              lowercase		:Optional[int]       							=				image_processing(snake_case__  ,     return_tensors='''pt'''				).pixel_values

              lowercase		:List[str]       							=				self.image_processor_tester.get_expected_values(snake_case__  ,     batched=snake_case__				)

              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  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:int       							=				self.image_processing_class(**self.image_processor_dict				)
              # create random PyTorch tensors
              lowercase		:Union[str, Any]       							=				prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=snake_case__  ,     torchify=snake_case__				)
              for image in image_inputs:
                     self.assertIsInstance(snake_case__  ,     torch.Tensor				)

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

              lowercase		:Optional[Any]       							=				self.image_processor_tester.get_expected_values(snake_case__				)

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

              # Test batched
              lowercase		:Dict       							=				image_processing(snake_case__  ,     return_tensors='''pt'''				).pixel_values

              lowercase		:Optional[int]       							=				self.image_processor_tester.get_expected_values(snake_case__  ,     batched=snake_case__				)

              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  :    str				):

              '''simple docstring'''






              lowercase		: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:
                     lowercase		:Dict       							=				json.loads(f.read()				)

              lowercase		:Union[str, Any]       							=				{'''image_id''': 3_9_7_6_9, '''annotations''': target}

              # encode them
              lowercase		:Dict       							=				DetaImageProcessor()
              lowercase		:Union[str, Any]       							=				image_processing(images=snake_case__  ,     annotations=snake_case__  ,     return_tensors='''pt'''				)

              # verify pixel values
              lowercase		:Union[str, Any]       							=				torch.Size([1, 3, 8_0_0, 1_0_6_6]				)
              self.assertEqual(encoding['''pixel_values'''].shape  ,     snake_case__				)

              lowercase		:Optional[Any]       							=				torch.tensor([0.27_96, 0.31_38, 0.34_81]				)
              self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]  ,     snake_case__  ,     atol=1e-4				)				)

              # verify area
              lowercase		:Optional[Any]       							=				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''']  ,     snake_case__				)				)
              # verify boxes
              lowercase		:List[Any]       							=				torch.Size([6, 4]				)
              self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape  ,     snake_case__				)
              lowercase		:str       							=				torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]  ,     snake_case__  ,     atol=1e-3				)				)
              # verify image_id
              lowercase		:Union[str, Any]       							=				torch.tensor([3_9_7_6_9]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']  ,     snake_case__				)				)
              # verify is_crowd
              lowercase		:Optional[Any]       							=				torch.tensor([0, 0, 0, 0, 0, 0]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']  ,     snake_case__				)				)
              # verify class_labels
              lowercase		:Union[str, Any]       							=				torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']  ,     snake_case__				)				)
              # verify orig_size
              lowercase		:Union[str, Any]       							=				torch.tensor([4_8_0, 6_4_0]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']  ,     snake_case__				)				)
              # verify size
              lowercase		:Dict       							=				torch.tensor([8_0_0, 1_0_6_6]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']  ,     snake_case__				)				)







       @slow
       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              lowercase		: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:
                     lowercase		:List[str]       							=				json.loads(f.read()				)

              lowercase		:Optional[int]       							=				{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

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

              # encode them
              lowercase		:List[Any]       							=				DetaImageProcessor(format='''coco_panoptic'''				)
              lowercase		:List[Any]       							=				image_processing(images=snake_case__  ,     annotations=snake_case__  ,     masks_path=snake_case__  ,     return_tensors='''pt'''				)

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

              lowercase		:Dict       							=				torch.tensor([0.27_96, 0.31_38, 0.34_81]				)
              self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]  ,     snake_case__  ,     atol=1e-4				)				)

              # verify area
              lowercase		:Union[str, Any]       							=				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''']  ,     snake_case__				)				)
              # verify boxes
              lowercase		:Union[str, Any]       							=				torch.Size([6, 4]				)
              self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape  ,     snake_case__				)
              lowercase		:str       							=				torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]  ,     snake_case__  ,     atol=1e-3				)				)
              # verify image_id
              lowercase		:Tuple       							=				torch.tensor([3_9_7_6_9]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']  ,     snake_case__				)				)
              # verify is_crowd
              lowercase		:Tuple       							=				torch.tensor([0, 0, 0, 0, 0, 0]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']  ,     snake_case__				)				)
              # verify class_labels
              lowercase		:str       							=				torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']  ,     snake_case__				)				)
              # verify masks
              lowercase		:Dict       							=				8_2_2_8_7_3
              self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()  ,     snake_case__				)
              # verify orig_size
              lowercase		:List[str]       							=				torch.tensor([4_8_0, 6_4_0]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']  ,     snake_case__				)				)
              # verify size
              lowercase		:List[str]       							=				torch.tensor([8_0_0, 1_0_6_6]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']  ,     snake_case__				)				)





"""simple docstring"""



def       lowerCamelCase					(a_						:int)     ->							str:
       if number > 0:
              raise ValueError('''input must be a negative integer''')
       lowercase		:Any       							=				len(bin(a_)[3:])
       lowercase		:Any       							=				bin(abs(a_) - (1 << binary_number_length))[3:]
       lowercase		:Tuple       							=				(
           (
               '''1'''
               + '''0''' * (binary_number_length - len(a_))
               + twos_complement_number
           )
           if number < 0
           else '''0'''
       )
       return "0b" + twos_complement_number


if __name__ == "__main__":
 import doctest

 doctest.testmod()

