def SCREAMING_SNAKE_CASE				(     lowercase_:	set		,						lowercase_:	set				):
       assert isinstance(lowercase_		,						lowercase_				), F"The input value of [set_a={set_a}] is not a set"
       assert isinstance(lowercase_		,						lowercase_				), F"The input value of [set_b={set_b}] is not a set"

       return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
 from doctest import testmod

 testmod()




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 SCREAMING_SNAKE_CASE				(     lowercase_:	Tuple		,						lowercase_:	Optional[int]=10		,						lowercase_:	Optional[Any]=10		,						lowercase_:	Optional[Any]=False		,						lowercase_:	Any=False				):
       _UpperCamelCase     :       List[Any]  						=  []
       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
              _UpperCamelCase     :       Tuple  						=  [Image.fromarray(np.moveaxis(lowercase_		,						0		,						-1				)				) for frame in video]

       if torchify:
              _UpperCamelCase     :       Union[str, Any]  						=  [torch.from_numpy(lowercase_				) for frame in video]

       return video


def SCREAMING_SNAKE_CASE				(     lowercase_:	List[str]		,						lowercase_:	Optional[Any]=False		,						lowercase_:	Tuple=False		,						lowercase_:	Union[str, Any]=False				):
       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       _UpperCamelCase     :       Any  						=  []
       for i in range(image_processor_tester.batch_size				):
              if equal_resolution:
                     _UpperCamelCase     :       str  						=  image_processor_tester.max_resolution
              else:
                     _UpperCamelCase						,      _UpperCamelCase     :       int  						=  np.random.choice(
                         np.arange(image_processor_tester.min_resolution		,						image_processor_tester.max_resolution				)		,						2				)
                     _UpperCamelCase     :       Optional[Any]  						=  prepare_video(
                         image_processor_tester=lowercase_		,						width=lowercase_		,						height=lowercase_		,						numpify=lowercase_		,						torchify=lowercase_		,						)
              video_inputs.append(lowercase_				)

       return video_inputs




class    __lowerCAmelCase  (       unittest.TestCase						):



 '''simple docstring'''

 def __init__(			self       :		Optional[Any]       ,			lowerCamelCase       :		Union[str, Any]       ,			lowerCamelCase       :		Optional[int]=7       ,			lowerCamelCase       :		List[Any]=3       ,			lowerCamelCase       :		str=4       ,			lowerCamelCase       :		Union[str, Any]=1_8       ,			lowerCamelCase       :		str=3_0       ,			lowerCamelCase       :		List[str]=4_0_0       ,			lowerCamelCase       :		Tuple=True       ,			lowerCamelCase       :		Union[str, Any]=None       ,			lowerCamelCase       :		List[str]=True       ,			lowerCamelCase       :		Optional[Any]=[0.5, 0.5, 0.5]       ,			lowerCamelCase       :		Any=[0.5, 0.5, 0.5]       ,			lowerCamelCase       :		str=True       ,			lowerCamelCase       :		Optional[Any]=None       ,			) ->							Union[str, Any]:
        _UpperCamelCase     :       Optional[int]  						=  size if size is not None else {'shortest_edge': 1_8}
        _UpperCamelCase     :       Dict  						=  crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}

        _UpperCamelCase     :       Any  						=  parent
        _UpperCamelCase     :       Optional[Any]  						=  batch_size
        _UpperCamelCase     :       List[Any]  						=  num_channels
        _UpperCamelCase     :       List[Any]  						=  num_frames
        _UpperCamelCase     :       Union[str, Any]  						=  image_size
        _UpperCamelCase     :       Dict  						=  min_resolution
        _UpperCamelCase     :       int  						=  max_resolution
        _UpperCamelCase     :       Union[str, Any]  						=  do_resize
        _UpperCamelCase     :       str  						=  size
        _UpperCamelCase     :       Dict  						=  do_normalize
        _UpperCamelCase     :       Union[str, Any]  						=  image_mean
        _UpperCamelCase     :       Optional[int]  						=  image_std
        _UpperCamelCase     :       Tuple  						=  do_center_crop
        _UpperCamelCase     :       Any  						=  crop_size

 def 					__snake_case					(			self       :		Tuple  ) ->							Optional[Any]:
        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    __lowerCAmelCase  (       a_,	unittest.TestCase						):



 '''simple docstring'''
 _lowerCamelCase						:	Any				    = TvltImageProcessor if is_vision_available() else None

 def 					__snake_case					(			self       :		int  ) ->							Optional[int]:
        _UpperCamelCase     :       Optional[Any]  						=  TvltImageProcessorTester(self  )

 @property
 def 					__snake_case					(			self       :		Optional[Any]  ) ->							Any:
        return self.image_processor_tester.prepare_image_processor_dict()

 def 					__snake_case					(			self       :		Union[str, Any]  ) ->							str:
        _UpperCamelCase     :       Tuple  						=  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_resize'  )  )
        self.assertTrue(hasattr(lowerCamelCase       ,			'do_center_crop'  )  )
        self.assertTrue(hasattr(lowerCamelCase       ,			'size'  )  )

 def 					__snake_case					(			self       :		Dict  ) ->							Optional[int]:
        # Initialize image_processor
        _UpperCamelCase     :       str  						=  self.image_processing_class(**self.image_processor_dict  )
        # create random PIL videos
        _UpperCamelCase     :       int  						=  prepare_video_inputs(self.image_processor_tester       ,			equal_resolution=lowerCamelCase  )
        for video in video_inputs:
               self.assertIsInstance(lowerCamelCase       ,			lowerCamelCase  )
               self.assertIsInstance(video[0]       ,			Image.Image  )

        # Test not batched input
        _UpperCamelCase     :       Dict  						=  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
        _UpperCamelCase     :       Any  						=  image_processor(lowerCamelCase       ,			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 					__snake_case					(			self       :		Union[str, Any]  ) ->							List[str]:
        # Initialize image_processor
        _UpperCamelCase     :       Optional[int]  						=  self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        _UpperCamelCase     :       int  						=  prepare_video_inputs(self.image_processor_tester       ,			equal_resolution=lowerCamelCase       ,			numpify=lowerCamelCase  )
        for video in video_inputs:
               self.assertIsInstance(lowerCamelCase       ,			lowerCamelCase  )
               self.assertIsInstance(video[0]       ,			np.ndarray  )

        # Test not batched input
        _UpperCamelCase     :       Optional[int]  						=  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
        _UpperCamelCase     :       int  						=  image_processor(lowerCamelCase       ,			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 					__snake_case					(			self       :		Optional[Any]  ) ->							Any:
        # Initialize image_processor
        _UpperCamelCase     :       int  						=  self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        _UpperCamelCase     :       Optional[Any]  						=  prepare_video_inputs(self.image_processor_tester       ,			equal_resolution=lowerCamelCase       ,			torchify=lowerCamelCase  )
        for video in video_inputs:
               self.assertIsInstance(lowerCamelCase       ,			lowerCamelCase  )
               self.assertIsInstance(video[0]       ,			torch.Tensor  )

        # Test not batched input
        _UpperCamelCase     :       Dict  						=  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
        _UpperCamelCase     :       Optional[int]  						=  image_processor(lowerCamelCase       ,			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'],
            )       ,			)



