


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


__snake_case   							=						logging.get_logger(__name__)



class 	__lowerCamelCase			(_a						):
       _lowercase											=     ["""pixel_values"""]







       def __init__(			self: Tuple,A_: bool = True,A_: Optional[Dict[str, int]] = None,A_: PILImageResampling = PILImageResampling.BILINEAR,A_: bool = True,A_: Dict[str, int] = None,A_: bool = True,A_: Union[int, float] = 1 / 255,A_: bool = True,A_: Optional[Union[float, List[float]]] = None,A_: Optional[Union[float, List[float]]] = None,**A_: List[str],):






            '''simple docstring'''




            super().__init__(**A_					)
            __UpperCamelCase =			size if size is not None else {'shortest_edge': 256}
            __UpperCamelCase =			get_size_dict(A_,default_to_square=A_					)
            __UpperCamelCase =			crop_size if crop_size is not None else {'height': 224, 'width': 224}
            __UpperCamelCase =			get_size_dict(A_					)
            __UpperCamelCase =			do_resize
            __UpperCamelCase =			size
            __UpperCamelCase =			resample
            __UpperCamelCase =			do_center_crop
            __UpperCamelCase =			crop_size
            __UpperCamelCase =			do_rescale
            __UpperCamelCase =			rescale_factor
            __UpperCamelCase =			do_normalize
            __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 		snake_case_	(			self: Any,A_: np.ndarray,A_: Dict[str, int],A_: PILImageResampling = PILImageResampling.BICUBIC,A_: Optional[Union[str, ChannelDimension]] = None,**A_: Union[str, Any],):






            '''simple docstring'''




            __UpperCamelCase =			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()}'''					)
            __UpperCamelCase =			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 		snake_case_	(			self: int,A_: np.ndarray,A_: Dict[str, int],A_: Optional[Union[str, ChannelDimension]] = None,**A_: Optional[int],):






            '''simple docstring'''




            __UpperCamelCase =			get_size_dict(A_					)
            return center_crop(A_,size=(size['height'], size['width']),data_format=A_,**A_					)







       def 		snake_case_	(			self: Any,A_: np.ndarray,A_: float,A_: Optional[Union[str, ChannelDimension]] = None,**A_: Optional[int]					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Tuple,A_: np.ndarray,A_: Union[float, List[float]],A_: Union[float, List[float]],A_: Optional[Union[str, ChannelDimension]] = None,**A_: Optional[int],):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Any,A_: ImageInput,A_: Optional[bool] = None,A_: Dict[str, int] = None,A_: PILImageResampling = None,A_: bool = None,A_: Dict[str, int] = None,A_: Optional[bool] = None,A_: Optional[float] = None,A_: Optional[bool] = None,A_: Optional[Union[float, List[float]]] = None,A_: Optional[Union[float, List[float]]] = None,A_: Optional[Union[str, TensorType]] = None,A_: Union[str, ChannelDimension] = ChannelDimension.FIRST,**A_: Union[str, Any],):






            '''simple docstring'''




            __UpperCamelCase =			do_resize if do_resize is not None else self.do_resize
            __UpperCamelCase =			size if size is not None else self.size
            __UpperCamelCase =			get_size_dict(A_,default_to_square=A_					)
            __UpperCamelCase =			resample if resample is not None else self.resample
            __UpperCamelCase =			do_center_crop if do_center_crop is not None else self.do_center_crop
            __UpperCamelCase =			crop_size if crop_size is not None else self.crop_size
            __UpperCamelCase =			get_size_dict(A_					)
            __UpperCamelCase =			do_rescale if do_rescale is not None else self.do_rescale
            __UpperCamelCase =			rescale_factor if rescale_factor is not None else self.rescale_factor
            __UpperCamelCase =			do_normalize if do_normalize is not None else self.do_normalize
            __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 =			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.
            __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_center_crop:
                 __UpperCamelCase =			[self.center_crop(image=A_,size=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_					)






def 		_A		(		_lowercase )					-> int:


     """simple docstring"""


     assert column_title.isupper()
     __UpperCamelCase =			0
     __UpperCamelCase =			len(_lowercase ) - 1
     __UpperCamelCase =			0

     while index >= 0:
          __UpperCamelCase =			(ord(column_title[index] ) - 64) * pow(26					,  _lowercase )
          answer += value
          power += 1
          index -= 1

     return answer


if __name__ == "__main__":
    from doctest import testmod

    testmod()


