

"""simple docstring"""


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 DetrImageProcessor





class lowerCamelCase__   (				unittest.TestCase    ):



    '''simple docstring'''




    def __init__( self ,lowerCamelCase_ ,lowerCamelCase_=7 ,lowerCamelCase_=3 ,lowerCamelCase_=3_0 ,lowerCamelCase_=4_0_0 ,lowerCamelCase_=True ,lowerCamelCase_=None ,lowerCamelCase_=True ,lowerCamelCase_=1 / 2_5_5 ,lowerCamelCase_=True ,lowerCamelCase_=[0.5, 0.5, 0.5] ,lowerCamelCase_=[0.5, 0.5, 0.5] ,lowerCamelCase_=True ,)    ->      Union[str, Any]:
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          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_rescale
          A				  =		rescale_factor
          A				  =		do_normalize
          A				  =		image_mean
          A				  =		image_std
          A				  =		do_pad




    def UpperCamelCase__		( self    )    ->      Dict:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_rescale": self.do_rescale,
              "rescale_factor": self.rescale_factor,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_pad": self.do_pad,
          }




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_=False    )    ->      Tuple:
          if not batched:
                A				  =		image_inputs[0]
                if isinstance(lowerCamelCase_ ,Image.Image    ):
                      A  ,			A				  =		image.size
                else:
                      A  ,			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  ,			A				  =		self.get_expected_values([image]    )
                      expected_values.append((expected_height, expected_width)    )
                A				  =		max(lowerCamelCase_ ,key=lambda lowerCamelCase_    : item[0]    )[0]
                A				  =		max(lowerCamelCase_ ,key=lambda lowerCamelCase_    : item[1]    )[1]

          return expected_height, expected_width





@require_torch
@require_vision
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE     ,   unittest.TestCase    ):



    '''simple docstring'''




    _lowerCamelCase					=   DetrImageProcessor if is_vision_available() else None




    def UpperCamelCase__		( self    )    ->      Optional[Any]:
          A				  =		DetrImageProcessingTester(self    )




    @property
    def UpperCamelCase__		( self    )    ->      Tuple:
          return self.image_processor_tester.prepare_image_processor_dict()




    def UpperCamelCase__		( self    )    ->      Any:
          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_rescale"""    )    )
          self.assertTrue(hasattr(lowerCamelCase_ ,"""rescale_factor"""    )    )
          self.assertTrue(hasattr(lowerCamelCase_ ,"""do_resize"""    )    )
          self.assertTrue(hasattr(lowerCamelCase_ ,"""size"""    )    )
          self.assertTrue(hasattr(lowerCamelCase_ ,"""do_pad"""    )    )




    def UpperCamelCase__		( self    )    ->      Union[str, Any]:
          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    )    ->      Union[str, Any]:
          pass




    def UpperCamelCase__		( self    )    ->      str:
          # Initialize image_processing
          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  ,			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  ,			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    )    ->      Optional[Any]:
          # Initialize image_processing
          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  ,			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  ,			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    )    ->      List[str]:
          # Initialize image_processing
          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  ,			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  ,			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,
              ) ,)




    @slow
    def UpperCamelCase__		( self    )    ->      str:
          # prepare image and target
          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				  =		DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""    )
          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([0.27_96, 0.31_38, 0.34_81]    )
          self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] ,lowerCamelCase_ ,atol=1E-4    )    )

          # verify area
          A				  =		torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]    )
          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([0.55_03, 0.27_65, 0.06_04, 0.22_15]    )
          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    )    ->      Union[str, Any]:
          # prepare image, target and masks_path
          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				  =		DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-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([0.27_96, 0.31_38, 0.34_81]    )
          self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] ,lowerCamelCase_ ,atol=1E-4    )    )

          # verify area
          A				  =		torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]    )
          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([0.26_25, 0.54_37, 0.46_88, 0.86_25]    )
          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_    )    )







"""simple docstring"""


import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available



def   _A					(	*_a	:						int				):



      """simple docstring"""






      if not isinstance(_a     ,   _a				):
            A				  =		list(_a				)
      for i in range(len(_a				)				):
            A				  =		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   _A					(	_a	:						Exception				):



      """simple docstring"""






      A				  =		[
          """CUDA out of memory.""",  # CUDA OOM
          """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
          """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
      ]
      if isinstance(_a     ,   _a				) and len(exception.args				) == 1:
            return any(err in exception.args[0] for err in _statements				)
      return False





def   _A					(	_a	:						callable = None     ,   _a	:						int = 1_2_8				):



      """simple docstring"""






      if function is None:
            return functools.partial(_a     ,   starting_batch_size=_a				)

      A				  =		starting_batch_size

      def decorator(*_a	:						Union[str, Any]     ,   **_a	:						List[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()
            A				  =		list(inspect.signature(_a				).parameters.keys()				)
            # Guard against user error
            if len(_a				) < (len(_a				) + 1):
                  A				  =		""", """.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(_a     ,   *_a     ,   **_a				)
                  except Exception as e:
                        if should_reduce_batch_size(_a				):
                              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




