



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 		UpperCAmelCase						(						unittest.TestCase				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase=7       ,     _lowerCAmelCase=3       ,     _lowerCAmelCase=30       ,     _lowerCAmelCase=400       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=[0.5, 0.5, 0.5]       ,     _lowerCAmelCase=[0.5, 0.5, 0.5]       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=1 / 255       ,     _lowerCAmelCase=True       ,     ):
         _lowerCAmelCase 			=  size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1_333}
         _lowerCAmelCase 			=  parent
         _lowerCAmelCase 			=  batch_size
         _lowerCAmelCase 			=  num_channels
         _lowerCAmelCase 			=  min_resolution
         _lowerCAmelCase 			=  max_resolution
         _lowerCAmelCase 			=  do_resize
         _lowerCAmelCase 			=  size
         _lowerCAmelCase 			=  do_normalize
         _lowerCAmelCase 			=  image_mean
         _lowerCAmelCase 			=  image_std
         _lowerCAmelCase 			=  do_rescale
         _lowerCAmelCase 			=  rescale_factor
         _lowerCAmelCase 			=  do_pad





   def 	__lowerCAmelCase							(	self			):
         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 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase=False			):


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

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

         return expected_height, expected_width






@require_torch
@require_vision
class 		UpperCAmelCase						(						UpperCamelCase_       ,unittest.TestCase				):
   SCREAMING_SNAKE_CASE__							=     ConditionalDetrImageProcessor if is_vision_available() else None





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  ConditionalDetrImageProcessingTester(self			)





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.image_processor_tester.prepare_image_processor_dict()





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  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       ,     '''size'''			)			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  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			)

         _lowerCAmelCase 			=  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 	__lowerCAmelCase							(	self			):
         pass





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

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

         _lowerCAmelCase 			=  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
         _lowerCAmelCase 			=  self.image_processor_tester.get_expected_values(_a       ,     batched=_a			)

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

         _lowerCAmelCase 			=  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
         _lowerCAmelCase 			=  image_processing(_a       ,     return_tensors='''pt'''			).pixel_values

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

         _lowerCAmelCase 			=  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
         _lowerCAmelCase 			=  image_processing(_a       ,     return_tensors='''pt'''			).pixel_values

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

         _lowerCAmelCase 			=  {"""image_id""": 39_769, """annotations""": target}

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

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

         _lowerCAmelCase 			=  torch.tensor([0.2_796, 0.3_138, 0.3_481]			)
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,     _a       ,     atol=1E-4			)			)

         # verify area
         _lowerCAmelCase 			=  torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,     _a			)			)
         # verify boxes
         _lowerCAmelCase 			=  torch.Size([6, 4]			)
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,     _a			)
         _lowerCAmelCase 			=  torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,     _a       ,     atol=1E-3			)			)
         # verify image_id
         _lowerCAmelCase 			=  torch.tensor([39_769]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,     _a			)			)
         # verify is_crowd
         _lowerCAmelCase 			=  torch.tensor([0, 0, 0, 0, 0, 0]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,     _a			)			)
         # verify class_labels
         _lowerCAmelCase 			=  torch.tensor([75, 75, 63, 65, 17, 17]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,     _a			)			)
         # verify orig_size
         _lowerCAmelCase 			=  torch.tensor([480, 640]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,     _a			)			)
         # verify size
         _lowerCAmelCase 			=  torch.tensor([800, 1_066]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,     _a			)			)





   @slow
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
         with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''       ,     '''r'''			) as f:
               _lowerCAmelCase 			=  json.loads(f.read()			)

         _lowerCAmelCase 			=  {"""file_name""": """000000039769.png""", """image_id""": 39_769, """segments_info""": target}

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

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

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

         _lowerCAmelCase 			=  torch.tensor([0.2_796, 0.3_138, 0.3_481]			)
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,     _a       ,     atol=1E-4			)			)

         # verify area
         _lowerCAmelCase 			=  torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,     _a			)			)
         # verify boxes
         _lowerCAmelCase 			=  torch.Size([6, 4]			)
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,     _a			)
         _lowerCAmelCase 			=  torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,     _a       ,     atol=1E-3			)			)
         # verify image_id
         _lowerCAmelCase 			=  torch.tensor([39_769]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,     _a			)			)
         # verify is_crowd
         _lowerCAmelCase 			=  torch.tensor([0, 0, 0, 0, 0, 0]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,     _a			)			)
         # verify class_labels
         _lowerCAmelCase 			=  torch.tensor([17, 17, 63, 75, 75, 93]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,     _a			)			)
         # verify masks
         _lowerCAmelCase 			=  822_873
         self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()       ,     _a			)
         # verify orig_size
         _lowerCAmelCase 			=  torch.tensor([480, 640]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,     _a			)			)
         # verify size
         _lowerCAmelCase 			=  torch.tensor([800, 1_066]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,     _a			)			)











import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available
def 							UpperCAmelCase__		(				*_SCREAMING_SNAKE_CASE   :	Tuple				)->List[Any]:
      if not isinstance(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				):
            _lowerCAmelCase 			=  list(_SCREAMING_SNAKE_CASE				)
      for i in range(len(_SCREAMING_SNAKE_CASE				)				):
            _lowerCAmelCase 			=  None
      gc.collect()
      if is_xpu_available():
            torch.xpu.empty_cache()
      elif is_npu_available():
            torch.npu.empty_cache()
      else:
            torch.cuda.empty_cache()
      return objects
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Exception				)->bool:
      _lowerCAmelCase 			=  [
          '''CUDA out of memory.''',  # CUDA OOM
          '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
          '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
      ]
      if isinstance(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				) and len(exception.args				) == 1:
            return any(err in exception.args[0] for err in _statements				)
      return False


def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	callable = None     ,				_SCREAMING_SNAKE_CASE   :	int = 1_2_8				)->Optional[int]:
      if function is None:
            return functools.partial(_SCREAMING_SNAKE_CASE     ,				starting_batch_size=_SCREAMING_SNAKE_CASE				)

      _lowerCAmelCase 			=  starting_batch_size

      def decorator(*_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				**_SCREAMING_SNAKE_CASE   :	Optional[Any]				):
            nonlocal batch_size
            gc.collect()
            if is_xpu_available():
                  torch.xpu.empty_cache()
            elif is_npu_available():
                  torch.npu.empty_cache()
            else:
                  torch.cuda.empty_cache()
            _lowerCAmelCase 			=  list(inspect.signature(_SCREAMING_SNAKE_CASE				).parameters.keys()				)
            # Guard against user error
            if len(_SCREAMING_SNAKE_CASE				) < (len(_SCREAMING_SNAKE_CASE				) + 1):
                  _lowerCAmelCase 			=  ''', '''.join([f'''{arg}={value}''' for arg, value in zip(params[1:]     ,				args[1:]				)]				)
                  raise TypeError(
                      f'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
                      f'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`'''				)
            while True:
                  if batch_size == 0:
                        raise RuntimeError('''No executable batch size found, reached zero.'''				)
                  try:
                        return function(_SCREAMING_SNAKE_CASE     ,				*_SCREAMING_SNAKE_CASE     ,				**_SCREAMING_SNAKE_CASE				)
                  except Exception as e:
                        if should_reduce_batch_size(_SCREAMING_SNAKE_CASE				):
                              gc.collect()
                              if is_xpu_available():
                                    torch.xpu.empty_cache()
                              elif is_npu_available():
                                    torch.npu.empty_cache()
                              else:
                                    torch.cuda.empty_cache()
                              batch_size //= 2
                        else:
                              raise

      return decorator