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 		_lowercase						(					unittest.TestCase       ):

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

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
  UpperCamelCase_					:						List[str] =					parent
  UpperCamelCase_					:						Union[str, Any] =					batch_size
  UpperCamelCase_					:						List[str] =					num_channels
  UpperCamelCase_					:						Optional[Any] =					min_resolution
  UpperCamelCase_					:						str =					max_resolution
  UpperCamelCase_					:						List[str] =					do_resize
  UpperCamelCase_					:						Dict =					size
  UpperCamelCase_					:						Any =					do_normalize
  UpperCamelCase_					:						Any =					image_mean
  UpperCamelCase_					:						Optional[Any] =					image_std
  UpperCamelCase_					:						Dict =					do_rescale
  UpperCamelCase_					:						Tuple =					rescale_factor
  UpperCamelCase_					:						Optional[int] =					do_pad

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Tuple:

  """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   ,      snake_case	:							str   ,      snake_case	:							Tuple=False			)   ->						Optional[int]:

  """simple docstring"""





  if not batched:
   UpperCamelCase_					:						str =					image_inputs[0]
   if isinstance(snake_case   ,      Image.Image			):
    UpperCamelCase_, UpperCamelCase_					:						str =					image.size
   else:
    UpperCamelCase_, UpperCamelCase_					:						Union[str, Any] =					image.shape[1], image.shape[2]
   if w < h:
    UpperCamelCase_					:						List[str] =					int(self.size['shortest_edge'] * h / w			)
    UpperCamelCase_					:						Any =					self.size['shortest_edge']
   elif w > h:
    UpperCamelCase_					:						str =					self.size['shortest_edge']
    UpperCamelCase_					:						Optional[int] =					int(self.size['shortest_edge'] * w / h			)
   else:
    UpperCamelCase_					:						Union[str, Any] =					self.size['shortest_edge']
    UpperCamelCase_					:						Tuple =					self.size['shortest_edge']

  else:
   UpperCamelCase_					:						Tuple =					[]
   for image in image_inputs:
    UpperCamelCase_, UpperCamelCase_					:						str =					self.get_expected_values([image]			)
    expected_values.append((expected_height, expected_width)			)
   UpperCamelCase_					:						Optional[Any] =					max(snake_case   ,      key=lambda snake_case			: item[0]			)[0]
   UpperCamelCase_					:						Union[str, Any] =					max(snake_case   ,      key=lambda snake_case			: item[1]			)[1]

  return expected_height, expected_width





@require_torch
@require_vision
class 		_lowercase						(					snake_case_						,      unittest.TestCase       ):
 lowercase				     =   DeformableDetrImageProcessor if is_vision_available() else None

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					DeformableDetrImageProcessingTester(self			)

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

  """simple docstring"""





  return self.image_processor_tester.prepare_image_processor_dict()

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					self.image_processing_class(**self.image_processor_dict			)
  self.assertTrue(hasattr(snake_case   ,      'image_mean'			)			)
  self.assertTrue(hasattr(snake_case   ,      'image_std'			)			)
  self.assertTrue(hasattr(snake_case   ,      'do_normalize'			)			)
  self.assertTrue(hasattr(snake_case   ,      'do_resize'			)			)
  self.assertTrue(hasattr(snake_case   ,      'do_rescale'			)			)
  self.assertTrue(hasattr(snake_case   ,      'do_pad'			)			)
  self.assertTrue(hasattr(snake_case   ,      'size'			)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						int =					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   ,      snake_case			)

  UpperCamelCase_					:						List[Any] =					self.image_processing_class.from_dict(
      self.image_processor_dict   ,      size=4_2   ,      max_size=8_4   ,      pad_and_return_pixel_mask=snake_case			)
  self.assertEqual(image_processor.size   ,      {'shortest_edge': 4_2, 'longest_edge': 8_4}			)
  self.assertEqual(image_processor.do_pad   ,      snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						int:

  """simple docstring"""





  pass

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

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.image_processing_class(**self.image_processor_dict			)
  # create random PIL images
  UpperCamelCase_					:						Optional[Any] =					prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=snake_case			)
  for image in image_inputs:
   self.assertIsInstance(snake_case   ,      Image.Image			)

  # Test not batched input
  UpperCamelCase_					:						List[Any] =					image_processing(image_inputs[0]   ,      return_tensors='pt'			).pixel_values

  UpperCamelCase_, UpperCamelCase_					:						Optional[int] =					self.image_processor_tester.get_expected_values(snake_case			)

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

  # Test batched
  UpperCamelCase_, UpperCamelCase_					:						int =					self.image_processor_tester.get_expected_values(snake_case   ,      batched=snake_case			)

  UpperCamelCase_					:						Optional[int] =					image_processing(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	:							Any			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.image_processing_class(**self.image_processor_dict			)
  # create random numpy tensors
  UpperCamelCase_					:						List[str] =					prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=snake_case   ,      numpify=snake_case			)
  for image in image_inputs:
   self.assertIsInstance(snake_case   ,      np.ndarray			)

  # Test not batched input
  UpperCamelCase_					:						List[Any] =					image_processing(image_inputs[0]   ,      return_tensors='pt'			).pixel_values

  UpperCamelCase_, UpperCamelCase_					:						Optional[int] =					self.image_processor_tester.get_expected_values(snake_case			)

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

  # Test batched
  UpperCamelCase_					:						List[str] =					image_processing(snake_case   ,      return_tensors='pt'			).pixel_values

  UpperCamelCase_, UpperCamelCase_					:						Tuple =					self.image_processor_tester.get_expected_values(snake_case   ,      batched=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	:							str			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					self.image_processing_class(**self.image_processor_dict			)
  # create random PyTorch tensors
  UpperCamelCase_					:						Any =					prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=snake_case   ,      torchify=snake_case			)
  for image in image_inputs:
   self.assertIsInstance(snake_case   ,      torch.Tensor			)

  # Test not batched input
  UpperCamelCase_					:						List[Any] =					image_processing(image_inputs[0]   ,      return_tensors='pt'			).pixel_values

  UpperCamelCase_, UpperCamelCase_					:						Tuple =					self.image_processor_tester.get_expected_values(snake_case			)

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

  # Test batched
  UpperCamelCase_					:						Union[str, Any] =					image_processing(snake_case   ,      return_tensors='pt'			).pixel_values

  UpperCamelCase_, UpperCamelCase_					:						List[str] =					self.image_processor_tester.get_expected_values(snake_case   ,      batched=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	:							Tuple			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
  with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'   ,      'r'			) as f:
   UpperCamelCase_					:						Union[str, Any] =					json.loads(f.read()			)

  UpperCamelCase_					:						Any =					{'image_id': 3_9_7_6_9, 'annotations': target}

  # encode them
  UpperCamelCase_					:						List[Any] =					DeformableDetrImageProcessor()
  UpperCamelCase_					:						Optional[Any] =					image_processing(images=snake_case   ,      annotations=snake_case   ,      return_tensors='pt'			)

  # verify pixel values
  UpperCamelCase_					:						int =					torch.Size([1, 3, 8_0_0, 1_0_6_6]			)
  self.assertEqual(encoding['pixel_values'].shape   ,      snake_case			)

  UpperCamelCase_					:						Dict =					torch.tensor([0.2796, 0.3138, 0.3481]			)
  self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,      snake_case   ,      atol=1e-4			)			)

  # verify area
  UpperCamelCase_					:						Any =					torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]			)
  self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,      snake_case			)			)
  # verify boxes
  UpperCamelCase_					:						Any =					torch.Size([6, 4]			)
  self.assertEqual(encoding['labels'][0]['boxes'].shape   ,      snake_case			)
  UpperCamelCase_					:						Any =					torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]			)
  self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,      snake_case   ,      atol=1e-3			)			)
  # verify image_id
  UpperCamelCase_					:						Tuple =					torch.tensor([3_9_7_6_9]			)
  self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,      snake_case			)			)
  # verify is_crowd
  UpperCamelCase_					:						int =					torch.tensor([0, 0, 0, 0, 0, 0]			)
  self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,      snake_case			)			)
  # verify class_labels
  UpperCamelCase_					:						Dict =					torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]			)
  self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,      snake_case			)			)
  # verify orig_size
  UpperCamelCase_					:						str =					torch.tensor([4_8_0, 6_4_0]			)
  self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,      snake_case			)			)
  # verify size
  UpperCamelCase_					:						int =					torch.tensor([8_0_0, 1_0_6_6]			)
  self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,      snake_case			)			)


 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Tuple:

  """simple docstring"""





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

  UpperCamelCase_					:						Any =					{'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

  UpperCamelCase_					:						Any =					pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'			)

  # encode them
  UpperCamelCase_					:						str =					DeformableDetrImageProcessor(format='coco_panoptic'			)
  UpperCamelCase_					:						Any =					image_processing(images=snake_case   ,      annotations=snake_case   ,      masks_path=snake_case   ,      return_tensors='pt'			)

  # verify pixel values
  UpperCamelCase_					:						Dict =					torch.Size([1, 3, 8_0_0, 1_0_6_6]			)
  self.assertEqual(encoding['pixel_values'].shape   ,      snake_case			)

  UpperCamelCase_					:						Optional[Any] =					torch.tensor([0.2796, 0.3138, 0.3481]			)
  self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,      snake_case   ,      atol=1e-4			)			)

  # verify area
  UpperCamelCase_					:						List[Any] =					torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]			)
  self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,      snake_case			)			)
  # verify boxes
  UpperCamelCase_					:						Optional[Any] =					torch.Size([6, 4]			)
  self.assertEqual(encoding['labels'][0]['boxes'].shape   ,      snake_case			)
  UpperCamelCase_					:						Optional[int] =					torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]			)
  self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,      snake_case   ,      atol=1e-3			)			)
  # verify image_id
  UpperCamelCase_					:						List[Any] =					torch.tensor([3_9_7_6_9]			)
  self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,      snake_case			)			)
  # verify is_crowd
  UpperCamelCase_					:						Union[str, Any] =					torch.tensor([0, 0, 0, 0, 0, 0]			)
  self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,      snake_case			)			)
  # verify class_labels
  UpperCamelCase_					:						List[str] =					torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]			)
  self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,      snake_case			)			)
  # verify masks
  UpperCamelCase_					:						Union[str, Any] =					8_2_2_8_7_3
  self.assertEqual(encoding['labels'][0]['masks'].sum().item()   ,      snake_case			)
  # verify orig_size
  UpperCamelCase_					:						List[Any] =					torch.tensor([4_8_0, 6_4_0]			)
  self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,      snake_case			)			)
  # verify size
  UpperCamelCase_					:						Any =					torch.tensor([8_0_0, 1_0_6_6]			)
  self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,      snake_case			)			)




from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments





def    __lowercase     (						):
 UpperCamelCase_					:						Optional[Any] =					HfArgumentParser(lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					parser.parse_args_into_dataclasses()[0]
 UpperCamelCase_					:						Dict =					TensorFlowBenchmark(args=lowerCamelCase	)
 try:
  UpperCamelCase_					:						Any =					parser.parse_args_into_dataclasses()[0]
 except ValueError as e:
  UpperCamelCase_					:						Any =					'Arg --no_{0} is no longer used, please use --no-{0} instead.'
  UpperCamelCase_					:						Optional[int] =					' '.join(str(lowerCamelCase	).split(' '	)[:-1]	)
  UpperCamelCase_					:						Any =					''
  UpperCamelCase_					:						Any =					eval(str(lowerCamelCase	).split(' '	)[-1]	)
  UpperCamelCase_					:						List[Any] =					[]
  for arg in depreciated_args:
   # arg[2:] removes '--'
   if arg[2:] in TensorFlowBenchmark.deprecated_args:
    # arg[5:] removes '--no_'
    full_error_msg += arg_error_msg.format(arg[5:]	)
   else:
    wrong_args.append(lowerCamelCase	)
  if len(lowerCamelCase	) > 0:
   UpperCamelCase_					:						List[str] =					full_error_msg + begin_error_msg + str(lowerCamelCase	)
  raise ValueError(lowerCamelCase	)
 benchmark.run()


if __name__ == "__main__":
   main()



