





"""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 ConditionalDetrImageProcessor






class        snake_case_    (		unittest.TestCase						):






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






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
   __lowerCAmelCase					:	List[Any]      =       parent
   __lowerCAmelCase					:	Dict      =       batch_size
   __lowerCAmelCase					:	Any      =       num_channels
   __lowerCAmelCase					:	str      =       min_resolution
   __lowerCAmelCase					:	Dict      =       max_resolution
   __lowerCAmelCase					:	Union[str, Any]      =       do_resize
   __lowerCAmelCase					:	List[str]      =       size
   __lowerCAmelCase					:	Tuple      =       do_normalize
   __lowerCAmelCase					:	Dict      =       image_mean
   __lowerCAmelCase					:	Union[str, Any]      =       image_std
   __lowerCAmelCase					:	List[Any]      =       do_rescale
   __lowerCAmelCase					:	Union[str, Any]      =       rescale_factor
   __lowerCAmelCase					:	List[Any]      =       do_pad






  def 				UpperCAmelCase__						(							self			:					Dict      )->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			:					str       ,  _snake_case			:					Optional[int]       ,  _snake_case			:					str=False      )->Union[str, Any]:






   '''simple docstring'''

   if not batched:
    __lowerCAmelCase					:	List[Any]      =       image_inputs[0]
    if isinstance(snake_case_       ,  Image.Image      ):
     __lowerCAmelCase					:	Dict      =       image.size
    else:
     __lowerCAmelCase					:	Optional[int]      =       image.shape[1], image.shape[2]
    if w < h:
     __lowerCAmelCase					:	List[str]      =       int(self.size["""shortest_edge"""] * h / w      )
     __lowerCAmelCase					:	Dict      =       self.size["""shortest_edge"""]
    elif w > h:
     __lowerCAmelCase					:	Any      =       self.size["""shortest_edge"""]
     __lowerCAmelCase					:	int      =       int(self.size["""shortest_edge"""] * w / h      )
    else:
     __lowerCAmelCase					:	List[Any]      =       self.size["""shortest_edge"""]
     __lowerCAmelCase					:	str      =       self.size["""shortest_edge"""]

   else:
    __lowerCAmelCase					:	Optional[Any]      =       []
    for image in image_inputs:
     __lowerCAmelCase					:	int      =       self.get_expected_values([image]      )
     expected_values.append((expected_height, expected_width)      )
    __lowerCAmelCase					:	Dict      =       max(snake_case_       ,  key=lambda _snake_case      : item[0]      )[0]
    __lowerCAmelCase					:	Tuple      =       max(snake_case_       ,  key=lambda _snake_case      : item[1]      )[1]

   return expected_height, expected_width






@require_torch
@require_vision
class        snake_case_    (		_a					,unittest.TestCase						):
  A_      							=   ConditionalDetrImageProcessor if is_vision_available() else None






  def 				UpperCAmelCase__						(							self			:					Any      )->Optional[int]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       ConditionalDetrImageProcessingTester(self      )






  @property
  def 				UpperCAmelCase__						(							self			:					str      )->Union[str, Any]:






   '''simple docstring'''

   return self.image_processor_tester.prepare_image_processor_dict()






  def 				UpperCAmelCase__						(							self			:					List[Any]      )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       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_       ,  """size"""      )      )






  def 				UpperCAmelCase__						(							self			:					int      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       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       ,  snake_case_      )

   __lowerCAmelCase					:	str      =       self.image_processing_class.from_dict(
       self.image_processor_dict       ,  size=42       ,  max_size=84       ,  pad_and_return_pixel_mask=snake_case_      )
   self.assertEqual(image_processor.size       ,  {"""shortest_edge""": 42, """longest_edge""": 84}      )
   self.assertEqual(image_processor.do_pad       ,  snake_case_      )






  def 				UpperCAmelCase__						(							self			:					Any      )->Any:






   '''simple docstring'''

   pass






  def 				UpperCAmelCase__						(							self			:					Any      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       self.image_processing_class(**self.image_processor_dict      )
   # create random PIL images
   __lowerCAmelCase					:	Tuple      =       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
   __lowerCAmelCase					:	Any      =       image_processing(image_inputs[0]       ,  return_tensors="""pt"""      ).pixel_values

   __lowerCAmelCase					:	Any      =       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
   __lowerCAmelCase					:	Optional[Any]      =       self.image_processor_tester.get_expected_values(snake_case_       ,  batched=snake_case_      )

   __lowerCAmelCase					:	Optional[Any]      =       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 				UpperCAmelCase__						(							self			:					Dict      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       self.image_processing_class(**self.image_processor_dict      )
   # create random numpy tensors
   __lowerCAmelCase					:	Optional[int]      =       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
   __lowerCAmelCase					:	Union[str, Any]      =       image_processing(image_inputs[0]       ,  return_tensors="""pt"""      ).pixel_values

   __lowerCAmelCase					:	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
   __lowerCAmelCase					:	Optional[int]      =       image_processing(snake_case_       ,  return_tensors="""pt"""      ).pixel_values

   __lowerCAmelCase					:	Any      =       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 				UpperCAmelCase__						(							self			:					str      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       self.image_processing_class(**self.image_processor_dict      )
   # create random PyTorch tensors
   __lowerCAmelCase					:	Union[str, 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
   __lowerCAmelCase					:	Dict      =       image_processing(image_inputs[0]       ,  return_tensors="""pt"""      ).pixel_values

   __lowerCAmelCase					:	Any      =       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
   __lowerCAmelCase					:	Union[str, Any]      =       image_processing(snake_case_       ,  return_tensors="""pt"""      ).pixel_values

   __lowerCAmelCase					:	int      =       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 				UpperCAmelCase__						(							self			:					Optional[int]      )->Union[str, Any]:






   '''simple docstring'''

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

   __lowerCAmelCase					:	str      =       {"""image_id""": 39769, """annotations""": target}

   # encode them
   __lowerCAmelCase					:	Any      =       ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""      )
   __lowerCAmelCase					:	Union[str, Any]      =       image_processing(images=snake_case_       ,  annotations=snake_case_       ,  return_tensors="""pt"""      )

   # verify pixel values
   __lowerCAmelCase					:	str      =       torch.Size([1, 3, 800, 1066]      )
   self.assertEqual(encoding["""pixel_values"""].shape       ,  snake_case_      )

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

   # verify area
   __lowerCAmelCase					:	Optional[Any]      =       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"""]       ,  snake_case_      )      )
   # verify boxes
   __lowerCAmelCase					:	int      =       torch.Size([6, 4]      )
   self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,  snake_case_      )
   __lowerCAmelCase					:	List[Any]      =       torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]      )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,  snake_case_       ,  atol=1E-3      )      )
   # verify image_id
   __lowerCAmelCase					:	int      =       torch.tensor([39769]      )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,  snake_case_      )      )
   # verify is_crowd
   __lowerCAmelCase					:	Any      =       torch.tensor([0, 0, 0, 0, 0, 0]      )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,  snake_case_      )      )
   # verify class_labels
   __lowerCAmelCase					:	Union[str, Any]      =       torch.tensor([75, 75, 63, 65, 17, 17]      )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,  snake_case_      )      )
   # verify orig_size
   __lowerCAmelCase					:	str      =       torch.tensor([480, 640]      )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,  snake_case_      )      )
   # verify size
   __lowerCAmelCase					:	List[str]      =       torch.tensor([800, 1066]      )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,  snake_case_      )      )






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






   '''simple docstring'''

   __lowerCAmelCase					:	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:
    __lowerCAmelCase					:	Tuple      =       json.loads(f.read()      )

   __lowerCAmelCase					:	Dict      =       {"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

   __lowerCAmelCase					:	Optional[int]      =       pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""      )

   # encode them
   __lowerCAmelCase					:	Tuple      =       ConditionalDetrImageProcessor(format="""coco_panoptic"""      )
   __lowerCAmelCase					:	Union[str, Any]      =       image_processing(images=snake_case_       ,  annotations=snake_case_       ,  masks_path=snake_case_       ,  return_tensors="""pt"""      )

   # verify pixel values
   __lowerCAmelCase					:	Optional[int]      =       torch.Size([1, 3, 800, 1066]      )
   self.assertEqual(encoding["""pixel_values"""].shape       ,  snake_case_      )

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

   # verify area
   __lowerCAmelCase					:	Union[str, Any]      =       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"""]       ,  snake_case_      )      )
   # verify boxes
   __lowerCAmelCase					:	int      =       torch.Size([6, 4]      )
   self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,  snake_case_      )
   __lowerCAmelCase					:	Tuple      =       torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]      )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,  snake_case_       ,  atol=1E-3      )      )
   # verify image_id
   __lowerCAmelCase					:	List[str]      =       torch.tensor([39769]      )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,  snake_case_      )      )
   # verify is_crowd
   __lowerCAmelCase					:	Optional[Any]      =       torch.tensor([0, 0, 0, 0, 0, 0]      )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,  snake_case_      )      )
   # verify class_labels
   __lowerCAmelCase					:	int      =       torch.tensor([17, 17, 63, 75, 75, 93]      )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,  snake_case_      )      )
   # verify masks
   __lowerCAmelCase					:	Optional[Any]      =       822873
   self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()       ,  snake_case_      )
   # verify orig_size
   __lowerCAmelCase					:	Optional[Any]      =       torch.tensor([480, 640]      )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,  snake_case_      )      )
   # verify size
   __lowerCAmelCase					:	Optional[Any]      =       torch.tensor([800, 1066]      )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,  snake_case_      )      )






def 	_SCREAMING_SNAKE_CASE		(      )      ->   list[list[int]]:
 return [list(range(1_000 - i   ,   -1_000 - i   ,   -1   )   ) for i in range(1_000   )]


_UpperCAmelCase             =							generate_large_matrix()
_UpperCAmelCase             =							(
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int]]   )      ->   None:
 assert all(row == sorted(SCREAMING_SNAKE_CASE   ,   reverse=SCREAMING_SNAKE_CASE   ) for row in grid   )
 assert all(list(SCREAMING_SNAKE_CASE   ) == sorted(SCREAMING_SNAKE_CASE   ,   reverse=SCREAMING_SNAKE_CASE   ) for col in zip(*SCREAMING_SNAKE_CASE   )   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[int]   )      ->   int:
 __lowerCAmelCase					:	Optional[int]      =       0
 __lowerCAmelCase					:	Tuple      =       len(SCREAMING_SNAKE_CASE   ) - 1

 # Edge cases such as no values or all numbers are negative.
 if not array or array[0] < 0:
  return 0

 while right + 1 > left:
  __lowerCAmelCase					:	Any      =       (left + right) // 2
  __lowerCAmelCase					:	Optional[int]      =       array[mid]

  # Num must be negative and the index must be greater than or equal to 0.
  if num < 0 and array[mid - 1] >= 0:
   return mid

  if num >= 0:
   __lowerCAmelCase					:	Dict      =       mid + 1
  else:
   __lowerCAmelCase					:	str      =       mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
 return len(SCREAMING_SNAKE_CASE   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int]]   )      ->   int:
 __lowerCAmelCase					:	Any      =       0
 __lowerCAmelCase					:	Union[str, Any]      =       len(grid[0]   )

 for i in range(len(SCREAMING_SNAKE_CASE   )   ):
  __lowerCAmelCase					:	str      =       find_negative_index(grid[i][:bound]   )
  total += bound
 return (len(SCREAMING_SNAKE_CASE   ) * len(grid[0]   )) - total






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int]]   )      ->   int:
 return len([number for row in grid for number in row if number < 0]   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int]]   )      ->   int:
 __lowerCAmelCase					:	List[str]      =       0
 for row in grid:
  for i, number in enumerate(SCREAMING_SNAKE_CASE   ):
   if number < 0:
    total += len(SCREAMING_SNAKE_CASE   ) - i
    break
 return total






def 	_SCREAMING_SNAKE_CASE		(      )      ->   None:
 from timeit import timeit

 print("""Running benchmarks"""   )
 __lowerCAmelCase					:	Tuple      =       (
     """from __main__ import count_negatives_binary_search, """
     """count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
 )
 for func in (
     "count_negatives_binary_search",  # took 0.7727 seconds
     "count_negatives_brute_force_with_break",  # took 4.6505 seconds
     "count_negatives_brute_force",  # took 12.8160 seconds
 ):
  __lowerCAmelCase					:	Optional[int]      =       timeit(F'''{func}(grid=grid)'''   ,   setup=SCREAMING_SNAKE_CASE   ,   number=500   )
  print(F'''{func}() took {time:0.4f} seconds'''   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()