






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
   import PIL

A_		:		Union[str, Any]		     =			logging.get_logger(__name__)

def   snake_case (UpperCAmelCase__  )	->    List[List[ImageInput]]:
  if isinstance(UpperCAmelCase__     ,   (list, tuple)  ) and isinstance(videos[0]     ,   (list, tuple)  ) and is_valid_image(videos[0][0]  ):
    return videos

  elif isinstance(UpperCAmelCase__     ,   (list, tuple)  ) and is_valid_image(videos[0]  ):
    return [videos]

  elif is_valid_image(UpperCAmelCase__  ):
    return [[videos]]

  raise ValueError(F'''Could not make batched video from {videos}'''  )




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		List[str]									=['''pixel_values''']





       def __init__( self  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = PILImageResampling.BILINEAR  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = True  ,      _lowerCamelCase = 1 / 2_5_5  ,      _lowerCamelCase = True  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         super().__init__(**_lowerCamelCase							)
         UpperCamelCase_: Any   				=    size if size is not None else {'shortest_edge': 2_5_6}
         UpperCamelCase_: Optional[Any]   				=    get_size_dict(_lowerCamelCase  ,      default_to_square=_lowerCamelCase							)
         UpperCamelCase_: Optional[Any]   				=    crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
         UpperCamelCase_: Optional[int]   				=    get_size_dict(_lowerCamelCase  ,      param_name='crop_size'							)

         UpperCamelCase_: List[Any]   				=    do_resize
         UpperCamelCase_: str   				=    size
         UpperCamelCase_: Optional[Any]   				=    do_center_crop
         UpperCamelCase_: Optional[int]   				=    crop_size
         UpperCamelCase_: Any   				=    resample
         UpperCamelCase_: Tuple   				=    do_rescale
         UpperCamelCase_: Optional[Any]   				=    rescale_factor
         UpperCamelCase_: Union[str, Any]   				=    offset
         UpperCamelCase_: List[str]   				=    do_normalize
         UpperCamelCase_: Union[str, Any]   				=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCamelCase_: List[Any]   				=    image_std if image_std is not None else IMAGENET_STANDARD_STD





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = PILImageResampling.BILINEAR  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Union[str, Any]   				=    get_size_dict(_lowerCamelCase  ,      default_to_square=_lowerCamelCase							)
         if "shortest_edge" in size:
           UpperCamelCase_: str   				=    get_resize_output_image_size(_lowerCamelCase  ,      size['shortest_edge']  ,      default_to_square=_lowerCamelCase							)
         elif "height" in size and "width" in size:
           UpperCamelCase_: int   				=    (size['height'], size['width'])
         else:
           raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''							)
         return resize(_lowerCamelCase  ,      size=_lowerCamelCase  ,      resample=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[int]   				=    get_size_dict(_lowerCamelCase							)
         if "height" not in size or "width" not in size:
           raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''							)
         return center_crop(_lowerCamelCase  ,      size=(size['height'], size['width'])  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: int   				=    image.astype(np.floataa							)
         if offset:
           UpperCamelCase_: List[Any]   				=    image - (scale / 2)
         return rescale(_lowerCamelCase  ,      scale=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         return normalize(_lowerCamelCase  ,      mean=_lowerCamelCase  ,      std=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = ChannelDimension.FIRST  ,      ):
         if do_resize and size is None or resample is None:
           raise ValueError('Size and resample must be specified if do_resize is True.'							)

         if do_center_crop and crop_size is None:
           raise ValueError('Crop size must be specified if do_center_crop is True.'							)

         if do_rescale and rescale_factor is None:
           raise ValueError('Rescale factor must be specified if do_rescale is True.'							)

         if do_normalize and (image_mean is None or image_std is None):
           raise ValueError('Image mean and std must be specified if do_normalize is True.'							)

         if offset and not do_rescale:
           raise ValueError('For offset, do_rescale must also be set to True.'							)

         # All transformations expect numpy arrays.
         UpperCamelCase_: Optional[Any]   				=    to_numpy_array(_lowerCamelCase							)

         if do_resize:
           UpperCamelCase_: Dict   				=    self.resize(image=_lowerCamelCase  ,      size=_lowerCamelCase  ,      resample=_lowerCamelCase							)

         if do_center_crop:
           UpperCamelCase_: Any   				=    self.center_crop(_lowerCamelCase  ,      size=_lowerCamelCase							)

         if do_rescale:
           UpperCamelCase_: Optional[int]   				=    self.rescale(image=_lowerCamelCase  ,      scale=_lowerCamelCase  ,      offset=_lowerCamelCase							)

         if do_normalize:
           UpperCamelCase_: Optional[Any]   				=    self.normalize(image=_lowerCamelCase  ,      mean=_lowerCamelCase  ,      std=_lowerCamelCase							)

         UpperCamelCase_: List[str]   				=    to_channel_dimension_format(_lowerCamelCase  ,      _lowerCamelCase							)
         return image





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = ChannelDimension.FIRST  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Union[str, Any]   				=    do_resize if do_resize is not None else self.do_resize
         UpperCamelCase_: Any   				=    resample if resample is not None else self.resample
         UpperCamelCase_: int   				=    do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCamelCase_: Optional[Any]   				=    do_rescale if do_rescale is not None else self.do_rescale
         UpperCamelCase_: Union[str, Any]   				=    rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCamelCase_: int   				=    offset if offset is not None else self.offset
         UpperCamelCase_: Union[str, Any]   				=    do_normalize if do_normalize is not None else self.do_normalize
         UpperCamelCase_: Any   				=    image_mean if image_mean is not None else self.image_mean
         UpperCamelCase_: List[str]   				=    image_std if image_std is not None else self.image_std

         UpperCamelCase_: Optional[int]   				=    size if size is not None else self.size
         UpperCamelCase_: Tuple   				=    get_size_dict(_lowerCamelCase  ,      default_to_square=_lowerCamelCase							)
         UpperCamelCase_: str   				=    crop_size if crop_size is not None else self.crop_size
         UpperCamelCase_: Dict   				=    get_size_dict(_lowerCamelCase  ,      param_name='crop_size'							)

         if not valid_images(_lowerCamelCase							):
           raise ValueError(
               'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
               'torch.Tensor, tf.Tensor or jax.ndarray.'							)

         UpperCamelCase_: Tuple   				=    make_batched(_lowerCamelCase							)

         UpperCamelCase_: Dict   				=    [
             [
                 self._preprocess_image(
                     image=_lowerCamelCase  ,      do_resize=_lowerCamelCase  ,      size=_lowerCamelCase  ,      resample=_lowerCamelCase  ,      do_center_crop=_lowerCamelCase  ,      crop_size=_lowerCamelCase  ,      do_rescale=_lowerCamelCase  ,      rescale_factor=_lowerCamelCase  ,      offset=_lowerCamelCase  ,      do_normalize=_lowerCamelCase  ,      image_mean=_lowerCamelCase  ,      image_std=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      )
                 for img in video
             ]
             for video in videos
         ]

         UpperCamelCase_: Optional[Any]   				=    {'pixel_values': videos}
         return BatchFeature(data=_lowerCamelCase  ,      tensor_type=_lowerCamelCase							)







def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  while b:
    UpperCamelCase_				,UpperCamelCase_: int   				=    b, a % b
  return a

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  return a if b == 0 else euclidean_gcd_recursive(UpperCAmelCase__     ,   a % b  )

def   snake_case ()	->    int:
  print(F'''euclidean_gcd(3, 5) = {euclidean_gcd(3     ,   5  )}'''  )
  print(F'''euclidean_gcd(5, 3) = {euclidean_gcd(5     ,   3  )}'''  )
  print(F'''euclidean_gcd(1, 3) = {euclidean_gcd(1     ,   3  )}'''  )
  print(F'''euclidean_gcd(3, 6) = {euclidean_gcd(3     ,   6  )}'''  )
  print(F'''euclidean_gcd(6, 3) = {euclidean_gcd(6     ,   3  )}'''  )

  print(F'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3     ,   5  )}'''  )
  print(F'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5     ,   3  )}'''  )
  print(F'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1     ,   3  )}'''  )
  print(F'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3     ,   6  )}'''  )
  print(F'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6     ,   3  )}'''  )


if __name__ == "__main__":
   main()