
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 						A_			(				snake_case				: List[str]			,   snake_case				: Tuple=10			,   snake_case				: Tuple=10			,   snake_case				: Union[str, Any]=False			,   snake_case				: int=False			)   ->     Tuple:



						'''simple docstring'''

						__UpperCamelCase  	=      []
						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  	=      [Image.fromarray(np.moveaxis(snake_case			,   0			,   -1			)			) for frame in video]

						if torchify:
												__UpperCamelCase  	=      [torch.from_numpy(snake_case			) for frame in video]

						return video




def 						A_			(				snake_case				: List[Any]			,   snake_case				: int=False			,   snake_case				: Optional[Any]=False			,   snake_case				: Any=False			)   ->     Any:



						'''simple docstring'''

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

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

						return video_inputs






class 							SCREAMING_SNAKE_CASE__     (				unittest.TestCase     ):

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=7  ,						SCREAMING_SNAKE_CASE_=3  ,						SCREAMING_SNAKE_CASE_=4  ,						SCREAMING_SNAKE_CASE_=18  ,						SCREAMING_SNAKE_CASE_=30  ,						SCREAMING_SNAKE_CASE_=400  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]  ,						SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=None  ,						)-> Dict:





													'''simple docstring'''






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

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



							def      A__		(	self      )-> 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 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		unittest.TestCase     ):

							"""simple docstring"""






							_snake_case  		=	TvltImageProcessor if is_vision_available() else None



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      TvltImageProcessorTester(self      )



							@property
							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													return self.image_processor_tester.prepare_image_processor_dict()



							def      A__		(	self      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      self.image_processing_class(**self.image_processor_dict      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''image_mean'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''image_std'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''do_normalize'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''do_resize'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''do_center_crop'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''size'''      )      )



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





													'''simple docstring'''






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

													# Test not batched input
													__UpperCamelCase  	=      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  	=      image_processor(SCREAMING_SNAKE_CASE_  ,						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      A__		(	self      )-> Dict:





													'''simple docstring'''






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

													# Test not batched input
													__UpperCamelCase  	=      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  	=      image_processor(SCREAMING_SNAKE_CASE_  ,						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      A__		(	self      )-> Union[str, Any]:





													'''simple docstring'''






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

													# Test not batched input
													__UpperCamelCase  	=      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  	=      image_processor(SCREAMING_SNAKE_CASE_  ,						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'''],
													    )  ,						)











"""simple docstring"""






def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							bool:






       """simple docstring"""




       if not isinstance(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				):
              raise ValueError('Input series is not valid, valid series - [2, 4, 8]'				)
       if len(SCREAMING_SNAKE_CASE				) == 0:
              raise ValueError('Input list must be a non empty list'				)
       if len(SCREAMING_SNAKE_CASE				) == 1:
              return True
       try:
              _UpperCAmelCase			     =  series[1] / series[0]
              for index in range(len(SCREAMING_SNAKE_CASE				) - 1				):
                     if series[index + 1] / series[index] != common_ratio:
                            return False
       except ZeroDivisionError:
              return False
       return True

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							float:






       """simple docstring"""




       if not isinstance(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				):
              raise ValueError('Input series is not valid, valid series - [2, 4, 8]'				)
       if len(SCREAMING_SNAKE_CASE				) == 0:
              raise ValueError('Input list must be a non empty list'				)
       _UpperCAmelCase			     =  1
       for value in series:
              answer *= value
       return pow(SCREAMING_SNAKE_CASE,1 / len(SCREAMING_SNAKE_CASE				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






