


"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

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 TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


UpperCAmelCase      		=			logging.get_logger(__name__)






def       lowerCamelCase					(a_						:Dict)     ->							List[List[ImageInput]]:


       if isinstance(__snake_case				,				(list, tuple)) and isinstance(videos[0]				,				(list, tuple)) and is_valid_image(videos[0][0]):
              return videos

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

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

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




class 					__magic_name__		(			_A     ):
       __A			:      Tuple        =   ["pixel_values"]






       def __init__(   self  :    Optional[Any]  ,     snake_case__  :    Dict = True  ,     snake_case__  :    str = None  ,     snake_case__  :    List[str] = PILImageResampling.BILINEAR  ,     snake_case__  :    str = True  ,     snake_case__  :    Tuple = None  ,     snake_case__  :    Dict = True  ,     snake_case__  :    Dict = 1 / 2_5_5  ,     snake_case__  :    str = True  ,     snake_case__  :    Union[str, Any] = None  ,     snake_case__  :    Dict = None  ,     **snake_case__  :    Optional[int]  ,     ):

              '''simple docstring'''






              super().__init__(**snake_case__				)
              lowercase		:Union[str, Any]       							=				size if size is not None else {"shortest_edge": 2_2_4}
              lowercase		:Any       							=				get_size_dict(snake_case__  ,     default_to_square=snake_case__				)
              lowercase		:Tuple       							=				crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
              lowercase		:Optional[Any]       							=				get_size_dict(snake_case__  ,     param_name='''crop_size'''				)

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






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    int  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    int = PILImageResampling.BILINEAR  ,     snake_case__  :    Optional[int] = None  ,     **snake_case__  :    Optional[int]  ,     ):

              '''simple docstring'''






              lowercase		:Any       							=				get_size_dict(snake_case__  ,     default_to_square=snake_case__				)
              if "shortest_edge" in size:
                     lowercase		:Any       							=				get_resize_output_image_size(snake_case__  ,     size['''shortest_edge''']  ,     default_to_square=snake_case__				)
              elif "height" in size and "width" in size:
                     lowercase		: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(snake_case__  ,     size=snake_case__  ,     resample=snake_case__  ,     data_format=snake_case__  ,     **snake_case__				)






       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    Tuple  ,     snake_case__  :    List[Any]  ,     snake_case__  :    Tuple = None  ,     **snake_case__  :    Tuple  ,     ):

              '''simple docstring'''






              lowercase		:Optional[Any]       							=				get_size_dict(snake_case__				)
              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(snake_case__  ,     size=(size['''height'''], size['''width'''])  ,     data_format=snake_case__  ,     **snake_case__				)






       def  __snake_case						(   self  :    Optional[int]  ,     snake_case__  :    Optional[int]  ,     snake_case__  :    Any  ,     snake_case__  :    Optional[int] = None  ,     **snake_case__  :    Optional[int]  ,     ):

              '''simple docstring'''






              return rescale(snake_case__  ,     scale=snake_case__  ,     data_format=snake_case__  ,     **snake_case__				)






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    List[str]  ,     snake_case__  :    List[str]  ,     snake_case__  :    List[str]  ,     snake_case__  :    List[Any] = None  ,     **snake_case__  :    Optional[Any]  ,     ):

              '''simple docstring'''






              return normalize(snake_case__  ,     mean=snake_case__  ,     std=snake_case__  ,     data_format=snake_case__  ,     **snake_case__				)






       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    Optional[int]  ,     snake_case__  :    List[str] = None  ,     snake_case__  :    List[Any] = None  ,     snake_case__  :    str = None  ,     snake_case__  :    int = None  ,     snake_case__  :    Union[str, Any] = None  ,     snake_case__  :    int = None  ,     snake_case__  :    List[Any] = None  ,     snake_case__  :    str = None  ,     snake_case__  :    Dict = None  ,     snake_case__  :    List[str] = None  ,     snake_case__  :    int = ChannelDimension.FIRST  ,     ):

              '''simple docstring'''






              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.'''				)

              # All transformations expect numpy arrays.
              lowercase		:Any       							=				to_numpy_array(snake_case__				)

              if do_resize:
                     lowercase		:str       							=				self.resize(image=snake_case__  ,     size=snake_case__  ,     resample=snake_case__				)

              if do_center_crop:
                     lowercase		:Optional[int]       							=				self.center_crop(snake_case__  ,     size=snake_case__				)

              if do_rescale:
                     lowercase		:int       							=				self.rescale(image=snake_case__  ,     scale=snake_case__				)

              if do_normalize:
                     lowercase		:Any       							=				self.normalize(image=snake_case__  ,     mean=snake_case__  ,     std=snake_case__				)

              lowercase		:Optional[Any]       							=				to_channel_dimension_format(snake_case__  ,     snake_case__				)
              return image







       def  __snake_case						(   self  :    Tuple  ,     snake_case__  :    Dict  ,     snake_case__  :    Union[str, Any] = None  ,     snake_case__  :    List[Any] = None  ,     snake_case__  :    Union[str, Any] = None  ,     snake_case__  :    int = None  ,     snake_case__  :    Dict = None  ,     snake_case__  :    Any = None  ,     snake_case__  :    Optional[int] = None  ,     snake_case__  :    str = None  ,     snake_case__  :    Dict = None  ,     snake_case__  :    List[str] = None  ,     snake_case__  :    List[str] = None  ,     snake_case__  :    int = ChannelDimension.FIRST  ,     **snake_case__  :    Optional[int]  ,     ):

              '''simple docstring'''






              lowercase		:Any       							=				do_resize if do_resize is not None else self.do_resize
              lowercase		:Dict       							=				resample if resample is not None else self.resample
              lowercase		:Dict       							=				do_center_crop if do_center_crop is not None else self.do_center_crop
              lowercase		:List[Any]       							=				do_rescale if do_rescale is not None else self.do_rescale
              lowercase		:Optional[int]       							=				rescale_factor if rescale_factor is not None else self.rescale_factor
              lowercase		:Union[str, Any]       							=				do_normalize if do_normalize is not None else self.do_normalize
              lowercase		:Dict       							=				image_mean if image_mean is not None else self.image_mean
              lowercase		:Optional[int]       							=				image_std if image_std is not None else self.image_std

              lowercase		:str       							=				size if size is not None else self.size
              lowercase		:Union[str, Any]       							=				get_size_dict(snake_case__  ,     default_to_square=snake_case__				)
              lowercase		:Dict       							=				crop_size if crop_size is not None else self.crop_size
              lowercase		:Tuple       							=				get_size_dict(snake_case__  ,     param_name='''crop_size'''				)

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

              lowercase		:int       							=				make_batched(snake_case__				)

              lowercase		:int       							=				[
                  [
                      self._preprocess_image(
                          image=snake_case__  ,     do_resize=snake_case__  ,     size=snake_case__  ,     resample=snake_case__  ,     do_center_crop=snake_case__  ,     crop_size=snake_case__  ,     do_rescale=snake_case__  ,     rescale_factor=snake_case__  ,     do_normalize=snake_case__  ,     image_mean=snake_case__  ,     image_std=snake_case__  ,     data_format=snake_case__  ,     )
                      for img in video
                  ]
                  for video in videos
              ]

              lowercase		:List[str]       							=				{"pixel_values": videos}
              return BatchFeature(data=snake_case__  ,     tensor_type=snake_case__				)





"""simple docstring"""



UpperCAmelCase      		=			{str(digit): digit**5 for digit in range(10)}






def       lowerCamelCase					(a_						:int)     ->							int:
       return sum(DIGITS_FIFTH_POWER[digit] for digit in str(a_))






def       lowerCamelCase					()     ->							int:
       return sum(
           number
           for number in range(1000				,				100_0000)
           if number == digits_fifth_powers_sum(a_))


if __name__ == "__main__":
 print(solution())

