






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 					A__     (     unittest.TestCase ):

     '''simple docstring'''






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






           """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   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """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      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    List[str]=False ):






           """simple docstring"""




           if not batched:
                 UpperCamelCase					       =  image_inputs[0]
                 if isinstance(_SCREAMING_SNAKE_CASE   ,						Image.Image ):
                       UpperCamelCase       ,  UpperCamelCase					       =  image.size
                 else:
                       UpperCamelCase       ,  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       ,  UpperCamelCase					       =  self.get_expected_values([image] )
                       expected_values.append((expected_height, expected_width) )
                 UpperCamelCase					       =  max(_SCREAMING_SNAKE_CASE   ,						key=lambda _SCREAMING_SNAKE_CASE : item[0] )[0]
                 UpperCamelCase					       =  max(_SCREAMING_SNAKE_CASE   ,						key=lambda _SCREAMING_SNAKE_CASE : item[1] )[1]

           return expected_height, expected_width





@require_torch
@require_vision
class 					A__     (     __snake_case   ,    unittest.TestCase ):

     '''simple docstring'''






     snake_case__					=					ConditionalDetrImageProcessor if is_vision_available() else None







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  ConditionalDetrImageProcessingTester(self )







     @property
     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           return self.image_processor_tester.prepare_image_processor_dict()







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  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   _SCREAMING_SNAKE_CASE     ( self      :    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   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  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   _SCREAMING_SNAKE_CASE     ( self      :    List[str] ):






           """simple docstring"""




           pass







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """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=_SCREAMING_SNAKE_CASE )
           for image in image_inputs:
                 self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						Image.Image )

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

           UpperCamelCase       ,  UpperCamelCase					       =  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
           UpperCamelCase       ,  UpperCamelCase					       =  self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE   ,						batched=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  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   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """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=_SCREAMING_SNAKE_CASE   ,						numpify=_SCREAMING_SNAKE_CASE )
           for image in image_inputs:
                 self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						np.ndarray )

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

           UpperCamelCase       ,  UpperCamelCase					       =  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
           UpperCamelCase					       =  image_processing(_SCREAMING_SNAKE_CASE   ,						return_tensors='pt' ).pixel_values

           UpperCamelCase       ,  UpperCamelCase					       =  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   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """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=_SCREAMING_SNAKE_CASE   ,						torchify=_SCREAMING_SNAKE_CASE )
           for image in image_inputs:
                 self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						torch.Tensor )

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

           UpperCamelCase       ,  UpperCamelCase					       =  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
           UpperCamelCase					       =  image_processing(_SCREAMING_SNAKE_CASE   ,						return_tensors='pt' ).pixel_values

           UpperCamelCase       ,  UpperCamelCase					       =  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,
               )   ,						)







     @slow
     def   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """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': 3_9769, 'annotations': target}

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

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

           UpperCamelCase					       =  torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
           self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,						_SCREAMING_SNAKE_CASE   ,						atol=1E-4 ) )

           # verify area
           UpperCamelCase					       =  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']   ,						_SCREAMING_SNAKE_CASE ) )
           # verify boxes
           UpperCamelCase					       =  torch.Size([6, 4] )
           self.assertEqual(encoding['labels'][0]['boxes'].shape   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,						_SCREAMING_SNAKE_CASE   ,						atol=1E-3 ) )
           # verify image_id
           UpperCamelCase					       =  torch.tensor([3_9769] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,						_SCREAMING_SNAKE_CASE ) )
           # verify is_crowd
           UpperCamelCase					       =  torch.tensor([0, 0, 0, 0, 0, 0] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,						_SCREAMING_SNAKE_CASE ) )
           # verify class_labels
           UpperCamelCase					       =  torch.tensor([75, 75, 63, 65, 17, 17] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,						_SCREAMING_SNAKE_CASE ) )
           # verify orig_size
           UpperCamelCase					       =  torch.tensor([480, 640] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,						_SCREAMING_SNAKE_CASE ) )
           # verify size
           UpperCamelCase					       =  torch.tensor([800, 1066] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,						_SCREAMING_SNAKE_CASE ) )







     @slow
     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """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': 3_9769, 'segments_info': target}

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

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

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

           UpperCamelCase					       =  torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
           self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,						_SCREAMING_SNAKE_CASE   ,						atol=1E-4 ) )

           # verify area
           UpperCamelCase					       =  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']   ,						_SCREAMING_SNAKE_CASE ) )
           # verify boxes
           UpperCamelCase					       =  torch.Size([6, 4] )
           self.assertEqual(encoding['labels'][0]['boxes'].shape   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,						_SCREAMING_SNAKE_CASE   ,						atol=1E-3 ) )
           # verify image_id
           UpperCamelCase					       =  torch.tensor([3_9769] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,						_SCREAMING_SNAKE_CASE ) )
           # verify is_crowd
           UpperCamelCase					       =  torch.tensor([0, 0, 0, 0, 0, 0] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,						_SCREAMING_SNAKE_CASE ) )
           # verify class_labels
           UpperCamelCase					       =  torch.tensor([17, 17, 63, 75, 75, 93] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,						_SCREAMING_SNAKE_CASE ) )
           # verify masks
           UpperCamelCase					       =  82_2873
           self.assertEqual(encoding['labels'][0]['masks'].sum().item()   ,						_SCREAMING_SNAKE_CASE )
           # verify orig_size
           UpperCamelCase					       =  torch.tensor([480, 640] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,						_SCREAMING_SNAKE_CASE ) )
           # verify size
           UpperCamelCase					       =  torch.tensor([800, 1066] )
           self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,						_SCREAMING_SNAKE_CASE ) )











def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			bool:
      """simple docstring"""

      return numa ^ numa < 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()



