






"""simple docstring"""


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  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase=1_0		,	__UpperCamelCase=1_0		,	__UpperCamelCase=False		,	__UpperCamelCase=False			):




  """simple docstring"""







  __A						      =			[]
  for i in range(image_processor_tester.num_frames			):
    video.append(np.random.randint(2_5_5		,	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
    __A						      =			[Image.fromarray(np.moveaxis(A__		,	0		,	-1			)			) for frame in video]

  if torchify:
    __A						      =			[torch.from_numpy(A__			) for frame in video]

  return video



def  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase=False		,	__UpperCamelCase=False		,	__UpperCamelCase=False			):




  """simple docstring"""







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

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

  return video_inputs


class  snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def __init__(     self			:					Tuple,	_lowerCamelCase			:					Optional[int],	_lowerCamelCase			:					List[Any]=7,	_lowerCamelCase			:					Any=3,	_lowerCamelCase			:					int=4,	_lowerCamelCase			:					Union[str, Any]=18,	_lowerCamelCase			:					Dict=30,	_lowerCamelCase			:					str=4_00,	_lowerCamelCase			:					List[str]=True,	_lowerCamelCase			:					Any=None,	_lowerCamelCase			:					List[str]=True,	_lowerCamelCase			:					Optional[int]=[0.5, 0.5, 0.5],	_lowerCamelCase			:					Union[str, Any]=[0.5, 0.5, 0.5],	_lowerCamelCase			:					str=True,	_lowerCamelCase			:					Optional[Any]=None,	):

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

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






   def     _SCREAMING_SNAKE_CASE    (     self			:					str  ):

     '''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  snake_case		(							_lowerCamelCase       ,     unittest.TestCase						):


   '''simple docstring'''

   A_						:     str									=			TvltImageProcessor if is_vision_available() else None


   def     _SCREAMING_SNAKE_CASE    (     self			:					Any  ):

     '''simple docstring'''
     __A						      =			TvltImageProcessorTester(self  )


   @property
   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str]  ):

     '''simple docstring'''
     return self.image_processor_tester.prepare_image_processor_dict()


   def     _SCREAMING_SNAKE_CASE    (     self			:					int  ):

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


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int]  ):

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

     # Test not batched input
     __A						      =			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
     __A						      =			image_processor(__UpperCamelCase,	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     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int]  ):

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

     # Test not batched input
     __A						      =			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
     __A						      =			image_processor(__UpperCamelCase,	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     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int]  ):

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

     # Test not batched input
     __A						      =			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
     __A						      =			image_processor(__UpperCamelCase,	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    (   __UpperCamelCase			):




  """simple docstring"""







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



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







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


if __name__ == "__main__":
       import doctest

       doctest.testmod()




