


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import TvltImageProcessor






def 				UpperCamelCase      (			__magic_name__  :							Optional[int]					,					__magic_name__  :							List[Any]=10					,					__magic_name__  :							Optional[int]=10					,					__magic_name__  :							Dict=False					,					__magic_name__  :							Any=False  )     ->						str:
  """simple docstring"""




  lowercase__      =	[]
  for i in range(image_processor_tester.num_frames  ):
    video.append(np.random.randint(255					,					size=(image_processor_tester.num_channels, width, height)					,					dtype=np.uinta  )  )

  if not numpify and not torchify:
    # PIL expects the channel dimension as last dimension
    lowercase__      =	[Image.fromarray(np.moveaxis(_A					,					0					,					-1  )  ) for frame in video]

  if torchify:
    lowercase__      =	[torch.from_numpy(_A  ) for frame in video]

  return video






def 				UpperCamelCase      (			__magic_name__  :							List[str]					,					__magic_name__  :							Optional[int]=False					,					__magic_name__  :							Dict=False					,					__magic_name__  :							Optional[Any]=False  )     ->						Dict:
  """simple docstring"""




  assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

  lowercase__      =	[]
  for i in range(image_processor_tester.batch_size  ):
    if equal_resolution:
      lowercase__      =	lowercase__      =	image_processor_tester.max_resolution
    else:
      lowercase__    ,			lowercase__      =	np.random.choice(
          np.arange(image_processor_tester.min_resolution					,					image_processor_tester.max_resolution  )					,					2  )
      lowercase__      =	prepare_video(
          image_processor_tester=_A					,					width=_A					,					height=_A					,					numpify=_A					,					torchify=_A					,					)
    video_inputs.append(_A  )

  return video_inputs



class 							A					( unittest.TestCase							):
     '''simple docstring'''






     def __init__(self					:		Optional[Any]   ,      _UpperCAmelCase					:		List[str]   ,      _UpperCAmelCase					:		Union[str, Any]=7   ,      _UpperCAmelCase					:		str=3   ,      _UpperCAmelCase					:		List[str]=4   ,      _UpperCAmelCase					:		Optional[int]=18   ,      _UpperCAmelCase					:		List[Any]=30   ,      _UpperCAmelCase					:		str=400   ,      _UpperCAmelCase					:		Optional[int]=True   ,      _UpperCAmelCase					:		Tuple=None   ,      _UpperCAmelCase					:		int=True   ,      _UpperCAmelCase					:		int=[0.5, 0.5, 0.5]   ,      _UpperCAmelCase					:		List[str]=[0.5, 0.5, 0.5]   ,      _UpperCAmelCase					:		Union[str, Any]=True   ,      _UpperCAmelCase					:		Dict=None   ,      )		->     Optional[int]:




       """simple docstring"""


       lowercase__      =	size if size is not None else {"""shortest_edge""": 18}
       lowercase__      =	crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

       lowercase__      =	parent
       lowercase__      =	batch_size
       lowercase__      =	num_channels
       lowercase__      =	num_frames
       lowercase__      =	image_size
       lowercase__      =	min_resolution
       lowercase__      =	max_resolution
       lowercase__      =	do_resize
       lowercase__      =	size
       lowercase__      =	do_normalize
       lowercase__      =	image_mean
       lowercase__      =	image_std
       lowercase__      =	do_center_crop
       lowercase__      =	crop_size


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




       """simple docstring"""


       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
       }






@require_torch
@require_vision
class 							A					( lowerCamelCase__				,						unittest.TestCase							):
     '''simple docstring'''






     A__    =				TvltImageProcessor if is_vision_available() else None

     def 						lowerCamelCase__  (self					:		Union[str, Any]	)		->     Optional[Any]:




       """simple docstring"""


       lowercase__      =	TvltImageProcessorTester(self	)

     @property
     def 						lowerCamelCase__  (self					:		Dict	)		->     Optional[Any]:




       """simple docstring"""


       return self.image_processor_tester.prepare_image_processor_dict()

     def 						lowerCamelCase__  (self					:		Any	)		->     str:




       """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_center_crop"""	)	)
       self.assertTrue(hasattr(lowercase_   ,      """size"""	)	)

     def 						lowerCamelCase__  (self					:		Union[str, Any]	)		->     Tuple:




       """simple docstring"""


       lowercase__      =	self.image_processing_class(**self.image_processor_dict	)
       # create random PIL videos
       lowercase__      =	prepare_video_inputs(self.image_processor_tester   ,      equal_resolution=lowercase_	)
       for video in video_inputs:
         self.assertIsInstance(lowercase_   ,      lowercase_	)
         self.assertIsInstance(video[0]   ,      Image.Image	)

       # Test not batched input
       lowercase__      =	image_processor(video_inputs[0]   ,      return_tensors="""pt"""	).pixel_values
       self.assertEqual(
           encoded_videos.shape   ,      (
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )   ,      )

       # Test batched
       lowercase__      =	image_processor(lowercase_   ,      return_tensors="""pt"""	).pixel_values
       self.assertEqual(
           encoded_videos.shape   ,      (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )   ,      )

     def 						lowerCamelCase__  (self					:		Tuple	)		->     int:




       """simple docstring"""


       lowercase__      =	self.image_processing_class(**self.image_processor_dict	)
       # create random numpy tensors
       lowercase__      =	prepare_video_inputs(self.image_processor_tester   ,      equal_resolution=lowercase_   ,      numpify=lowercase_	)
       for video in video_inputs:
         self.assertIsInstance(lowercase_   ,      lowercase_	)
         self.assertIsInstance(video[0]   ,      np.ndarray	)

       # Test not batched input
       lowercase__      =	image_processor(video_inputs[0]   ,      return_tensors="""pt"""	).pixel_values
       self.assertEqual(
           encoded_videos.shape   ,      (
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )   ,      )

       # Test batched
       lowercase__      =	image_processor(lowercase_   ,      return_tensors="""pt"""	).pixel_values
       self.assertEqual(
           encoded_videos.shape   ,      (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )   ,      )


     def 						lowerCamelCase__  (self					:		Tuple	)		->     int:




       """simple docstring"""


       lowercase__      =	self.image_processing_class(**self.image_processor_dict	)
       # create random PyTorch tensors
       lowercase__      =	prepare_video_inputs(self.image_processor_tester   ,      equal_resolution=lowercase_   ,      torchify=lowercase_	)
       for video in video_inputs:
         self.assertIsInstance(lowercase_   ,      lowercase_	)
         self.assertIsInstance(video[0]   ,      torch.Tensor	)

       # Test not batched input
       lowercase__      =	image_processor(video_inputs[0]   ,      return_tensors="""pt"""	).pixel_values
       self.assertEqual(
           encoded_videos.shape   ,      (
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )   ,      )

       # Test batched
       lowercase__      =	image_processor(lowercase_   ,      return_tensors="""pt"""	).pixel_values
       self.assertEqual(
           encoded_videos.shape   ,      (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )   ,      )





from __future__ import annotations






def 				UpperCamelCase      (			__magic_name__  :							list[list[int]]					,					__magic_name__  :							list[list[int]]  )     ->						list[int]:
		"""simple docstring"""




		assert len(__magic_name__  ) == len(__magic_name__  )

		lowercase__      =	len(__magic_name__  )
		lowercase__      =	list(range(__magic_name__  )  )
		lowercase__      =	[-1] * n  # who the donor has donated to
		lowercase__      =	[-1] * n  # who the recipient has received from
		lowercase__      =	[0] * n

		while unmatched_donors:
				lowercase__      =	unmatched_donors[0]
				lowercase__      =	donor_pref[donor]
				lowercase__      =	donor_preference[num_donations[donor]]
				num_donations[donor] += 1
				lowercase__      =	recipient_pref[recipient]
				lowercase__      =	rec_record[recipient]

				if prev_donor != -1:
						if rec_preference.index(__magic_name__  ) > rec_preference.index(__magic_name__  ):
								lowercase__      =	donor
								lowercase__      =	recipient
								unmatched_donors.append(__magic_name__  )
								unmatched_donors.remove(__magic_name__  )
				else:
						lowercase__      =	donor
						lowercase__      =	recipient
						unmatched_donors.remove(__magic_name__  )
		return donor_record

