


'''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 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


a__  : Dict				=	logging.get_logger(__name__)
class  lowercase_					(   a__       ):
    __UpperCAmelCase             =     ['pixel_values']


    def __init__(					self  , a = True  , a = None  , a = PILImageResampling.BILINEAR  , a = True  , a = 1 / 2_55  , a = True  , a = None  , a = None  , **a  , ):
          super().__init__(**a   )
          UpperCamelCase__  =    size if size is not None else {"height": 2_24, "width": 2_24}
          UpperCamelCase__  =    get_size_dict(a   )
          UpperCamelCase__  =    do_resize
          UpperCamelCase__  =    do_rescale
          UpperCamelCase__  =    do_normalize
          UpperCamelCase__  =    size
          UpperCamelCase__  =    resample
          UpperCamelCase__  =    rescale_factor
          UpperCamelCase__  =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCamelCase__  =    image_std if image_std is not None else IMAGENET_STANDARD_STD


    def        __a							(					self  , a  , a  , a = PILImageResampling.BILINEAR  , a = None  , **a  , ):
          UpperCamelCase__  =    get_size_dict(a   )
          if "height" not in size or "width" not in size:
                raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''   )
          return resize(
              a  , size=(size["height"], size["width"])  , resample=a  , data_format=a  , **a   )


    def        __a							(					self  , a  , a  , a = None  , **a   ):
          return rescale(a  , scale=a  , data_format=a  , **a   )


    def        __a							(					self  , a  , a  , a  , a = None  , **a  , ):
          return normalize(a  , mean=a  , std=a  , data_format=a  , **a   )


    def        __a							(					self  , a  , a = None  , a = None  , a = None  , a = None  , a = None  , a = None  , a = None  , a = None  , a = None  , a = ChannelDimension.FIRST  , **a  , ):
          UpperCamelCase__  =    do_resize if do_resize is not None else self.do_resize
          UpperCamelCase__  =    do_rescale if do_rescale is not None else self.do_rescale
          UpperCamelCase__  =    do_normalize if do_normalize is not None else self.do_normalize
          UpperCamelCase__  =    resample if resample is not None else self.resample
          UpperCamelCase__  =    rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCamelCase__  =    image_mean if image_mean is not None else self.image_mean
          UpperCamelCase__  =    image_std if image_std is not None else self.image_std

          UpperCamelCase__  =    size if size is not None else self.size
          UpperCamelCase__  =    get_size_dict(a   )

          UpperCamelCase__  =    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_rescale and rescale_factor is None:
                raise ValueError("Rescale factor must be specified if do_rescale is True."   )

          # All transformations expect numpy arrays.
          UpperCamelCase__  =    [to_numpy_array(a   ) for image in images]

          if do_resize:
                UpperCamelCase__  =    [self.resize(image=a  , size=a  , resample=a   ) for image in images]

          if do_rescale:
                UpperCamelCase__  =    [self.rescale(image=a  , scale=a   ) for image in images]

          if do_normalize:
                UpperCamelCase__  =    [self.normalize(image=a  , mean=a  , std=a   ) for image in images]

          UpperCamelCase__  =    [to_channel_dimension_format(a  , a   ) for image in images]

          UpperCamelCase__  =    {"pixel_values": images}
          return BatchFeature(data=a  , tensor_type=a   )









'''simple docstring'''







from itertools import permutations







def _UpperCamelCase	(					)							->		Optional[int]:


      '''simple docstring'''



      UpperCamelCase__  =    list(map("".join				,      permutations("0123456789"   )   )   )
      return result[999999]


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





