





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 DeformableDetrImageProcessor
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


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





     """simple docstring"""







     lowercase__			    =						size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
     lowercase__			    =						parent
     lowercase__			    =						batch_size
     lowercase__			    =						num_channels
     lowercase__			    =						min_resolution
     lowercase__			    =						max_resolution
     lowercase__			    =						do_resize
     lowercase__			    =						size
     lowercase__			    =						do_normalize
     lowercase__			    =						image_mean
     lowercase__			    =						image_std
     lowercase__			    =						do_rescale
     lowercase__			    =						rescale_factor
     lowercase__			    =						do_pad


   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     List[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  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any]    ,  a    :       int    ,  a    :       Tuple=False )->     List[Any]:





     """simple docstring"""







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

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

     return expected_height, expected_width






@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase    ,			unittest.TestCase   ):
   _UpperCamelCase					:	Optional[Any]				      = DeformableDetrImageProcessor if is_vision_available() else None


   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						DeformableDetrImageProcessingTester(self )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     Optional[Any]:





     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(a    ,  'image_mean' ) )
     self.assertTrue(hasattr(a    ,  'image_std' ) )
     self.assertTrue(hasattr(a    ,  'do_normalize' ) )
     self.assertTrue(hasattr(a    ,  'do_resize' ) )
     self.assertTrue(hasattr(a    ,  'do_rescale' ) )
     self.assertTrue(hasattr(a    ,  'do_pad' ) )
     self.assertTrue(hasattr(a    ,  'size' ) )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     int:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class.from_dict(self.image_processor_dict )
     self.assertEqual(image_processor.size    ,  {'shortest_edge': 18, 'longest_edge': 1_333} )
     self.assertEqual(image_processor.do_pad    ,  a )

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


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     List[str]:





     """simple docstring"""







     pass


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int] )->     Dict:





     """simple docstring"""







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

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

     lowercase__    ,					lowercase__			    =						self.image_processor_tester.get_expected_values(a )

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

     # Test batched
     lowercase__    ,					lowercase__			    =						self.image_processor_tester.get_expected_values(a    ,  batched=a )

     lowercase__			    =						image_processing(a    ,  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  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     Union[str, Any]:





     """simple docstring"""







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

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

     lowercase__    ,					lowercase__			    =						self.image_processor_tester.get_expected_values(a )

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

     # Test batched
     lowercase__			    =						image_processing(a    ,  return_tensors='pt' ).pixel_values

     lowercase__    ,					lowercase__			    =						self.image_processor_tester.get_expected_values(a    ,  batched=a )

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


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





     """simple docstring"""







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

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

     lowercase__    ,					lowercase__			    =						self.image_processor_tester.get_expected_values(a )

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

     # Test batched
     lowercase__			    =						image_processing(a    ,  return_tensors='pt' ).pixel_values

     lowercase__    ,					lowercase__			    =						self.image_processor_tester.get_expected_values(a    ,  batched=a )

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


   @slow
   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int] )->     Dict:





     """simple docstring"""







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

     lowercase__			    =						{'image_id': 39_769, 'annotations': target}

     # encode them
     lowercase__			    =						DeformableDetrImageProcessor()
     lowercase__			    =						image_processing(images=a    ,  annotations=a    ,  return_tensors='pt' )

     # verify pixel values
     lowercase__			    =						torch.Size([1, 3, 800, 1_066] )
     self.assertEqual(encoding['pixel_values'].shape    ,  a )

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

     # verify area
     lowercase__			    =						torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
     self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,  a ) )
     # verify boxes
     lowercase__			    =						torch.Size([6, 4] )
     self.assertEqual(encoding['labels'][0]['boxes'].shape    ,  a )
     lowercase__			    =						torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
     self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,  a    ,  atol=1E-3 ) )
     # verify image_id
     lowercase__			    =						torch.tensor([39_769] )
     self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,  a ) )
     # verify is_crowd
     lowercase__			    =						torch.tensor([0, 0, 0, 0, 0, 0] )
     self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,  a ) )
     # verify class_labels
     lowercase__			    =						torch.tensor([75, 75, 63, 65, 17, 17] )
     self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,  a ) )
     # verify orig_size
     lowercase__			    =						torch.tensor([480, 640] )
     self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,  a ) )
     # verify size
     lowercase__			    =						torch.tensor([800, 1_066] )
     self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,  a ) )


   @slow
   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Optional[Any]:





     """simple docstring"""







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

     lowercase__			    =						{'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

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

     # encode them
     lowercase__			    =						DeformableDetrImageProcessor(format='coco_panoptic' )
     lowercase__			    =						image_processing(images=a    ,  annotations=a    ,  masks_path=a    ,  return_tensors='pt' )

     # verify pixel values
     lowercase__			    =						torch.Size([1, 3, 800, 1_066] )
     self.assertEqual(encoding['pixel_values'].shape    ,  a )

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

     # verify area
     lowercase__			    =						torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
     self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,  a ) )
     # verify boxes
     lowercase__			    =						torch.Size([6, 4] )
     self.assertEqual(encoding['labels'][0]['boxes'].shape    ,  a )
     lowercase__			    =						torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
     self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,  a    ,  atol=1E-3 ) )
     # verify image_id
     lowercase__			    =						torch.tensor([39_769] )
     self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,  a ) )
     # verify is_crowd
     lowercase__			    =						torch.tensor([0, 0, 0, 0, 0, 0] )
     self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,  a ) )
     # verify class_labels
     lowercase__			    =						torch.tensor([17, 17, 63, 75, 75, 93] )
     self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,  a ) )
     # verify masks
     lowercase__			    =						822_873
     self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,  a )
     # verify orig_size
     lowercase__			    =						torch.tensor([480, 640] )
     self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,  a ) )
     # verify size
     lowercase__			    =						torch.tensor([800, 1_066] )
     self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,  a ) )









from PIL import Image




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Image:

  def brightness(_SCREAMING_SNAKE_CASE	) -> float:
    return 128 + level + (c - 128)

  if not -2_5_5.0 <= level <= 2_5_5.0:
    raise ValueError('level must be between -255.0 (black) and 255.0 (white)'	)
  return img.point(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      # Load image
      with Image.open("""image_data/lena.jpg""") as img:
            # Change brightness to 100
            lowercase_					     =	change_brightness(img, 100)
            brigt_img.save("""image_data/lena_brightness.png""", format="""png""")


