




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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


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




class 						__lowercase		(lowerCamelCase__							):




      """simple docstring"""





      _snake_case												=						['pixel_values']

      def __init__(				self    ,				A = True    ,				A = None    ,				A = PILImageResampling.BILINEAR    ,				A = True    ,				A = None    ,				A = True    ,				A = 1 / 2_5_5    ,				A = True    ,				A = None    ,				A = None    ,				**A    ,				)		->    List[Any]:
         super().__init__(**A     )
         snake_case				:    int           =	size if size is not None else {"""shortest_edge""": 2_5_6}
         snake_case				:    Dict           =	get_size_dict(A    ,				default_to_square=A     )
         snake_case				:    str           =	crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
         snake_case				:    Optional[int]           =	get_size_dict(A     )
         snake_case				:    Any           =	do_resize
         snake_case				:    Union[str, Any]           =	size
         snake_case				:    Any           =	resample
         snake_case				:    List[str]           =	do_center_crop
         snake_case				:    Union[str, Any]           =	crop_size
         snake_case				:    Union[str, Any]           =	do_rescale
         snake_case				:    Dict           =	rescale_factor
         snake_case				:    Optional[Any]           =	do_normalize
         snake_case				:    List[str]           =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         snake_case				:    str           =	image_std if image_std is not None else IMAGENET_STANDARD_STD

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A = PILImageResampling.BICUBIC    ,				A = None    ,				**A    ,				)		->    int:
         snake_case				:    Any           =	get_size_dict(A    ,				default_to_square=A     )
         if "shortest_edge" not in size:
            raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""     )
         snake_case				:    Union[str, Any]           =	get_resize_output_image_size(A    ,				size=size["""shortest_edge"""]    ,				default_to_square=A     )
         return resize(A    ,				size=A    ,				resample=A    ,				data_format=A    ,				**A     )

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A = None    ,				**A    ,				)		->    Any:
         snake_case				:    Union[str, Any]           =	get_size_dict(A     )
         return center_crop(A    ,				size=(size["""height"""], size["""width"""])    ,				data_format=A    ,				**A     )

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A = None    ,				**A     )		->    int:
         return rescale(A    ,				scale=A    ,				data_format=A    ,				**A     )

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A = None    ,				**A    ,				)		->    Tuple:
         return normalize(A    ,				mean=A    ,				std=A    ,				data_format=A    ,				**A     )

      def      UpperCAmelCase				(				self    ,				A    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = ChannelDimension.FIRST    ,				**A    ,				)		->    Optional[Any]:
         snake_case				:    str           =	do_resize if do_resize is not None else self.do_resize
         snake_case				:    Dict           =	size if size is not None else self.size
         snake_case				:    Optional[int]           =	get_size_dict(A    ,				default_to_square=A     )
         snake_case				:    Any           =	resample if resample is not None else self.resample
         snake_case				:    Optional[int]           =	do_center_crop if do_center_crop is not None else self.do_center_crop
         snake_case				:    Any           =	crop_size if crop_size is not None else self.crop_size
         snake_case				:    Optional[int]           =	get_size_dict(A     )
         snake_case				:    int           =	do_rescale if do_rescale is not None else self.do_rescale
         snake_case				:    Tuple           =	rescale_factor if rescale_factor is not None else self.rescale_factor
         snake_case				:    Optional[int]           =	do_normalize if do_normalize is not None else self.do_normalize
         snake_case				:    Union[str, Any]           =	image_mean if image_mean is not None else self.image_mean
         snake_case				:    Dict           =	image_std if image_std is not None else self.image_std

         snake_case				:    Optional[int]           =	make_list_of_images(A     )

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

         if do_resize and size is None:
            raise ValueError("""Size 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.
         snake_case				:    Tuple           =	[to_numpy_array(A     ) for image in images]

         if do_resize:
            snake_case				:    List[Any]           =	[self.resize(image=A    ,				size=A    ,				resample=A     ) for image in images]

         if do_center_crop:
            snake_case				:    Any           =	[self.center_crop(image=A    ,				size=A     ) for image in images]

         if do_rescale:
            snake_case				:    Dict           =	[self.rescale(image=A    ,				scale=A     ) for image in images]

         if do_normalize:
            snake_case				:    List[str]           =	[self.normalize(image=A    ,				mean=A    ,				std=A     ) for image in images]

         snake_case				:    Tuple           =	[to_channel_dimension_format(A    ,				A     ) for image in images]

         snake_case				:    Dict           =	{"""pixel_values""": images}
         return BatchFeature(data=A    ,				tensor_type=A     )







def 						SCREAMING_SNAKE_CASE__ (   )  ->       Dict:
   snake_case				:    Optional[int]           =	[]
   snake_case				:    Tuple           =	1

   while len(lowercase					) < 1E6:
      constant.append(str(lowercase					)					)
      i += 1

   snake_case				:    int           =	"""""".join(lowercase					)

   return (
       int(constant[0]					)
       * int(constant[9]					)
       * int(constant[99]					)
       * int(constant[999]					)
       * int(constant[9999]					)
       * int(constant[99999]					)
       * int(constant[999999]					)
   )


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





