



"""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 DetaImageProcessor


class       SCREAMING_SNAKE_CASE__       (							unittest.TestCase      ):

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







      """simple docstring"""







      lowercase_	    =       size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
      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     _UpperCAmelCase		( 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     _UpperCAmelCase		( self	: List[Any]       ,     lowerCAmelCase_	: int       ,     lowerCAmelCase_	: Dict=False):







      """simple docstring"""







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

      return expected_height, expected_width







@require_torch
@require_vision
class       SCREAMING_SNAKE_CASE__       (							lowercase__					,     unittest.TestCase      ):
    lowercase__          =      DetaImageProcessor if is_vision_available() else None

    def     _UpperCAmelCase		( self	: Optional[Any]):







      """simple docstring"""







      lowercase_	    =       DetaImageProcessingTester(self)

    @property
    def     _UpperCAmelCase		( self	: str):







      """simple docstring"""







      return self.image_processor_tester.prepare_image_processor_dict()

    def     _UpperCAmelCase		( self	: Dict):







      """simple docstring"""







      lowercase_	    =       self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(lowercase_       ,     """image_mean"""))
      self.assertTrue(hasattr(lowercase_       ,     """image_std"""))
      self.assertTrue(hasattr(lowercase_       ,     """do_normalize"""))
      self.assertTrue(hasattr(lowercase_       ,     """do_resize"""))
      self.assertTrue(hasattr(lowercase_       ,     """do_rescale"""))
      self.assertTrue(hasattr(lowercase_       ,     """do_pad"""))
      self.assertTrue(hasattr(lowercase_       ,     """size"""))

    def     _UpperCAmelCase		( self	: str):







      """simple docstring"""







      lowercase_	    =       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       ,     lowercase_)

    def     _UpperCAmelCase		( self	: int):







      """simple docstring"""







      pass

    def     _UpperCAmelCase		( self	: int):







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

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

      lowercase_	    =       self.image_processor_tester.get_expected_values(lowercase_)

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

      # Test batched
      lowercase_	    =       self.image_processor_tester.get_expected_values(lowercase_       ,     batched=lowercase_)

      lowercase_	    =       image_processing(lowercase_       ,     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	: List[str]):







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

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

      lowercase_	    =       self.image_processor_tester.get_expected_values(lowercase_)

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

      # Test batched
      lowercase_	    =       image_processing(lowercase_       ,     return_tensors="""pt""").pixel_values

      lowercase_	    =       self.image_processor_tester.get_expected_values(lowercase_       ,     batched=lowercase_)

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

    def     _UpperCAmelCase		( self	: Any):







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

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

      lowercase_	    =       self.image_processor_tester.get_expected_values(lowercase_)

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

      # Test batched
      lowercase_	    =       image_processing(lowercase_       ,     return_tensors="""pt""").pixel_values

      lowercase_	    =       self.image_processor_tester.get_expected_values(lowercase_       ,     batched=lowercase_)

      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	: Any):







      """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": 3_9_7_6_9, "annotations": target}

      # encode them
      lowercase_	    =       DetaImageProcessor()
      lowercase_	    =       image_processing(images=lowercase_       ,     annotations=lowercase_       ,     return_tensors="""pt""")

      # verify pixel values
      lowercase_	    =       torch.Size([1, 3, 8_0_0, 1_0_6_6])
      self.assertEqual(encoding["""pixel_values"""].shape       ,     lowercase_)

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

      # verify area
      lowercase_	    =       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"""]       ,     lowercase_))
      # verify boxes
      lowercase_	    =       torch.Size([6, 4])
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,     lowercase_)
      lowercase_	    =       torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,     lowercase_       ,     atol=1E-3))
      # verify image_id
      lowercase_	    =       torch.tensor([3_9_7_6_9])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,     lowercase_))
      # verify is_crowd
      lowercase_	    =       torch.tensor([0, 0, 0, 0, 0, 0])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,     lowercase_))
      # verify class_labels
      lowercase_	    =       torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,     lowercase_))
      # verify orig_size
      lowercase_	    =       torch.tensor([4_8_0, 6_4_0])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,     lowercase_))
      # verify size
      lowercase_	    =       torch.tensor([8_0_0, 1_0_6_6])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,     lowercase_))




    @slow
    def     _UpperCAmelCase		( self	: 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": 3_9_7_6_9, "segments_info": target}

      lowercase_	    =       pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""")

      # encode them
      lowercase_	    =       DetaImageProcessor(format="""coco_panoptic""")
      lowercase_	    =       image_processing(images=lowercase_       ,     annotations=lowercase_       ,     masks_path=lowercase_       ,     return_tensors="""pt""")

      # verify pixel values
      lowercase_	    =       torch.Size([1, 3, 8_0_0, 1_0_6_6])
      self.assertEqual(encoding["""pixel_values"""].shape       ,     lowercase_)

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

      # verify area
      lowercase_	    =       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"""]       ,     lowercase_))
      # verify boxes
      lowercase_	    =       torch.Size([6, 4])
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,     lowercase_)
      lowercase_	    =       torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,     lowercase_       ,     atol=1E-3))
      # verify image_id
      lowercase_	    =       torch.tensor([3_9_7_6_9])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,     lowercase_))
      # verify is_crowd
      lowercase_	    =       torch.tensor([0, 0, 0, 0, 0, 0])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,     lowercase_))
      # verify class_labels
      lowercase_	    =       torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,     lowercase_))
      # verify masks
      lowercase_	    =       8_2_2_8_7_3
      self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()       ,     lowercase_)
      # verify orig_size
      lowercase_	    =       torch.tensor([4_8_0, 6_4_0])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,     lowercase_))
      # verify size
      lowercase_	    =       torch.tensor([8_0_0, 1_0_6_6])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,     lowercase_))



"""simple docstring"""





import argparse
from collections import defaultdict







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					int			     =    f"""{file}_{class_name}_{test_name}"""
   done_test[_id] += 1

   with open(__lowerCamelCase,		"r"  ) as f:
      UpperCAmelCase_					:					List[Any]			     =    f.readlines()

   UpperCAmelCase_					:					int			     =    f"""class {class_name}("""
   UpperCAmelCase_					:					Optional[Any]			     =    f"""{4 * " "}def {test_name}("""
   UpperCAmelCase_					:					Optional[Any]			     =    f"""{8 * " "}{correct_line.split()[0]}"""
   UpperCAmelCase_					:					Tuple			     =    f"""{16 * " "}{correct_line.split()[0]}"""
   UpperCAmelCase_					:					int			     =    False
   UpperCAmelCase_					:					Union[str, Any]			     =    False
   UpperCAmelCase_					:					str			     =    False
   UpperCAmelCase_					:					Optional[Any]			     =    False
   UpperCAmelCase_					:					List[str]			     =    0
   UpperCAmelCase_					:					Optional[int]			     =    0

   UpperCAmelCase_					:					int			     =    []
   for line in lines:
      if line.startswith(__lowerCamelCase  ):
         UpperCAmelCase_					:					Tuple			     =    True
      elif in_class and line.startswith(__lowerCamelCase  ):
         UpperCAmelCase_					:					Optional[int]			     =    True
      elif in_class and in_func and (line.startswith(__lowerCamelCase  ) or line.startswith(__lowerCamelCase  )):
         UpperCAmelCase_					:					Any			     =    len(line.split(correct_line.split()[0]  )[0]  )
         count += 1

         if count == done_test[_id]:
            UpperCAmelCase_					:					Union[str, Any]			     =    True

      if in_class and in_func and in_line:
         if ")" not in line:
            continue
         else:
            UpperCAmelCase_					:					Any			     =    True

      if in_class and in_func and in_line and insert_line:
         new_lines.append(f"""{spaces * " "}{correct_line}"""  )
         UpperCAmelCase_					:					int			     =    False
      else:
         new_lines.append(__lowerCamelCase  )

   with open(__lowerCamelCase,		"w"  ) as f:
      for line in new_lines:
         f.write(__lowerCamelCase  )







def 	__a     (		__lowerCamelCase,		__lowerCamelCase=None  ):
   if fail is not None:
      with open(__lowerCamelCase,		"r"  ) as f:
         UpperCAmelCase_					:					Tuple			     =    {l.strip() for l in f.readlines()}
   else:
      UpperCAmelCase_					:					str			     =    None

   with open(__lowerCamelCase,		"r"  ) as f:
      UpperCAmelCase_					:					Optional[int]			     =    f.readlines()

   UpperCAmelCase_					:					Any			     =    defaultdict(__lowerCamelCase  )
   for line in correct_lines:
      UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Any			     =    line.split(";"  )
      if test_failures is None or "::".join([file, class_name, test_name]  ) in test_failures:
         overwrite_file(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )


if __name__ == "__main__":
    _a		       =							argparse.ArgumentParser()
    parser.add_argument('--correct_filename', help='filename of tests with expected result')
    parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
    _a		       =							parser.parse_args()

    main(args.correct_filename, args.fail_filename)


