



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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


lowercase_  =   logging.get_logger(__name__)


if is_vision_available():
							import PIL
class  _snake_case	(    a_):
				UpperCamelCase__ :    Union[str, Any]   						=["""pixel_values"""]




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

							lowercase__         = size if size is not None else {'height': 2560, 'width': 1920}
							if isinstance(__lowercase,    (tuple, list)  ):
										# The previous feature extractor size parameter was in (width, height) format
										lowercase__         = size[::-1]
							lowercase__         = get_size_dict(__lowercase  )

							lowercase__         = do_resize
							lowercase__         = size
							lowercase__         = resample
							lowercase__         = do_thumbnail
							lowercase__         = do_align_long_axis
							lowercase__         = do_pad
							lowercase__         = do_rescale
							lowercase__         = rescale_factor
							lowercase__         = do_normalize
							lowercase__         = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowercase__         = image_std if image_std is not None else IMAGENET_STANDARD_STD




				def 							A__					(  self    :  Optional[int],    __lowercase    :  np.ndarray,    __lowercase    :  Dict[str, int],    __lowercase    :  Optional[Union[str, ChannelDimension]] = None  ):
							lowercase__         = get_image_size(__lowercase  )
							lowercase__         = size['height'], size['width']

							if (output_width < output_height and input_width > input_height) or (
							    output_width > output_height and input_width < input_height
							):
										lowercase__         = np.rotaa(__lowercase,    3  )

							if data_format is not None:
										lowercase__         = to_channel_dimension_format(__lowercase,    __lowercase  )

							return image




				def 							A__					(  self    :  Optional[int],    __lowercase    :  np.ndarray,    __lowercase    :  Dict[str, int],    __lowercase    :  bool = False,    __lowercase    :  Optional[Union[str, ChannelDimension]] = None,    ):
							lowercase__         = size['height'], size['width']
							lowercase__         = get_image_size(__lowercase  )

							lowercase__         = output_width - input_width
							lowercase__         = output_height - input_height

							if random_padding:
										lowercase__         = np.random.randint(low=0,    high=delta_height + 1  )
										lowercase__         = np.random.randint(low=0,    high=delta_width + 1  )
							else:
										lowercase__         = delta_height // 2
										lowercase__         = delta_width // 2

							lowercase__         = delta_height - pad_top
							lowercase__         = delta_width - pad_left

							lowercase__         = ((pad_top, pad_bottom), (pad_left, pad_right))
							return pad(__lowercase,    __lowercase,    data_format=__lowercase  )




				def 							A__					(  self    :  Dict,    *__lowercase    :  Dict,    **__lowercase    :  Dict  ):
							logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."  )
							return self.pad_image(*__lowercase,    **__lowercase  )




				def 							A__					(  self    :  Optional[Any],    __lowercase    :  np.ndarray,    __lowercase    :  Dict[str, int],    __lowercase    :  PILImageResampling = PILImageResampling.BICUBIC,    __lowercase    :  Optional[Union[str, ChannelDimension]] = None,    **__lowercase    :  Optional[int],    ):
							lowercase__         = get_image_size(__lowercase  )
							lowercase__         = size['height'], size['width']

							# We always resize to the smallest of either the input or output size.
							lowercase__         = min(__lowercase,    __lowercase  )
							lowercase__         = min(__lowercase,    __lowercase  )

							if height == input_height and width == input_width:
										return image

							if input_height > input_width:
										lowercase__         = int(input_width * height / input_height  )
							elif input_width > input_height:
										lowercase__         = int(input_height * width / input_width  )

							return resize(
							    __lowercase,    size=(height, width),    resample=__lowercase,    reducing_gap=2.0,    data_format=__lowercase,    **__lowercase  )




				def 							A__					(  self    :  Optional[int],    __lowercase    :  np.ndarray,    __lowercase    :  Dict[str, int],    __lowercase    :  PILImageResampling = PILImageResampling.BICUBIC,    __lowercase    :  Optional[Union[str, ChannelDimension]] = None,    **__lowercase    :  str,    ):
							lowercase__         = get_size_dict(__lowercase  )
							lowercase__         = min(size["height"],    size["width"]  )
							lowercase__         = get_resize_output_image_size(__lowercase,    size=__lowercase,    default_to_square=__lowercase  )
							lowercase__         = resize(__lowercase,    size=__lowercase,    resample=__lowercase,    data_format=__lowercase,    **__lowercase  )
							return resized_image




				def 							A__					(  self    :  Dict,    __lowercase    :  np.ndarray,    __lowercase    :  Union[int, float],    __lowercase    :  Optional[Union[str, ChannelDimension]] = None,    **__lowercase    :  List[Any],    ):
							return rescale(__lowercase,    scale=__lowercase,    data_format=__lowercase,    **__lowercase  )




				def 							A__					(  self    :  List[str],    __lowercase    :  np.ndarray,    __lowercase    :  Union[float, List[float]],    __lowercase    :  Union[float, List[float]],    __lowercase    :  Optional[Union[str, ChannelDimension]] = None,    **__lowercase    :  Optional[int],    ):
							return normalize(__lowercase,    mean=__lowercase,    std=__lowercase,    data_format=__lowercase,    **__lowercase  )




				def 							A__					(  self    :  Dict,    __lowercase    :  ImageInput,    __lowercase    :  bool = None,    __lowercase    :  Dict[str, int] = None,    __lowercase    :  PILImageResampling = None,    __lowercase    :  bool = None,    __lowercase    :  bool = None,    __lowercase    :  bool = None,    __lowercase    :  bool = False,    __lowercase    :  bool = None,    __lowercase    :  float = None,    __lowercase    :  bool = None,    __lowercase    :  Optional[Union[float, List[float]]] = None,    __lowercase    :  Optional[Union[float, List[float]]] = None,    __lowercase    :  Optional[Union[str, TensorType]] = None,    __lowercase    :  Optional[ChannelDimension] = ChannelDimension.FIRST,    **__lowercase    :  Union[str, Any],    ):
							lowercase__         = do_resize if do_resize is not None else self.do_resize
							lowercase__         = size if size is not None else self.size
							if isinstance(__lowercase,    (tuple, list)  ):
										# Previous feature extractor had size in (width, height) format
										lowercase__         = size[::-1]
							lowercase__         = get_size_dict(__lowercase  )
							lowercase__         = resample if resample is not None else self.resample
							lowercase__         = do_thumbnail if do_thumbnail is not None else self.do_thumbnail
							lowercase__         = do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
							lowercase__         = do_pad if do_pad is not None else self.do_pad
							lowercase__         = do_rescale if do_rescale is not None else self.do_rescale
							lowercase__         = rescale_factor if rescale_factor is not None else self.rescale_factor
							lowercase__         = do_normalize if do_normalize is not None else self.do_normalize
							lowercase__         = image_mean if image_mean is not None else self.image_mean
							lowercase__         = image_std if image_std is not None else self.image_std

							lowercase__         = make_list_of_images(__lowercase  )

							if not valid_images(__lowercase  ):
										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."  )

							if do_pad and size is None:
										raise ValueError("Size must be specified if do_pad 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__         = [to_numpy_array(__lowercase  ) for image in images]

							if do_align_long_axis:
										lowercase__         = [self.align_long_axis(__lowercase,    size=__lowercase  ) for image in images]

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

							if do_thumbnail:
										lowercase__         = [self.thumbnail(image=__lowercase,    size=__lowercase  ) for image in images]

							if do_pad:
										lowercase__         = [self.pad_image(image=__lowercase,    size=__lowercase,    random_padding=__lowercase  ) for image in images]

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

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

							lowercase__         = [to_channel_dimension_format(__lowercase,    __lowercase  ) for image in images]

							lowercase__         = {'pixel_values': images}
							return BatchFeature(data=__lowercase,    tensor_type=__lowercase  )








'''simple docstring'''







import math






def       lowerCAmelCase__       (				lowerCamelCase     :			int   ):
       _A     :	int      =					math.log(2  ,10   ) * number
       _A     :	List[Any]      =					round((log_number - int(lowerCamelCase   ))  ,15   )

       return difference






def       lowerCAmelCase__       (				lowerCamelCase     :			int = 678910   ):
       _A     :	List[str]      =					90
       _A     :	List[Any]      =					0

       _A     :	List[str]      =					math.log(1.23  ,10   )
       _A     :	Any      =					math.log(1.24  ,10   )
       _A     :	Union[str, Any]      =					0

       while position < number:
              _A     :	Dict      =					log_difference(lowerCamelCase   )

              if difference >= upper_limit:
                     power_iterator += 93

              elif difference < lower_limit:
                     power_iterator += 196

              else:
                     _A     :	str      =					power_iterator
                     power_iterator += 196
                     position += 1

       return previous_power


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(f"""{solution() = }""")






