


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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowercase_								=    logging.get_logger(__name__)


if is_vision_available():
		import PIL




class 					_UpperCamelCase (      lowerCamelCase__	):







							'''simple docstring'''
							_A  				=  ["pixel_values"]







							def __init__(		self  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				bool = True		,				SCREAMING_SNAKE_CASE_  :				Dict[str, int] = None		,				SCREAMING_SNAKE_CASE_  :				PILImageResampling = PILImageResampling.BICUBIC		,				SCREAMING_SNAKE_CASE_  :				bool = True		,				SCREAMING_SNAKE_CASE_  :				Dict[str, int] = None		,				SCREAMING_SNAKE_CASE_  :				bool = True		,				SCREAMING_SNAKE_CASE_  :				Union[int, float] = 1 / 2_5_5		,				SCREAMING_SNAKE_CASE_  :				bool = True		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[float, List[float]]] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[float, List[float]]] = None		,				SCREAMING_SNAKE_CASE_  :				bool = True		,				**SCREAMING_SNAKE_CASE_  :				Optional[Any]		,				):
													super().__init__(**SCREAMING_SNAKE_CASE_		)
													_a     =			size if size is not None else {'shortest_edge': 2_2_4}
													_a     =			get_size_dict(SCREAMING_SNAKE_CASE_		,				default_to_square=SCREAMING_SNAKE_CASE_		)
													_a     =			crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
													_a     =			get_size_dict(SCREAMING_SNAKE_CASE_		)

													_a     =			do_resize
													_a     =			size
													_a     =			resample
													_a     =			do_center_crop
													_a     =			crop_size
													_a     =			do_rescale
													_a     =			rescale_factor
													_a     =			do_normalize
													_a     =			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
													_a     =			image_std if image_std is not None else OPENAI_CLIP_STD
													_a     =			do_convert_rgb







							def 		_UpperCAmelCase	(		self  :				str		,				SCREAMING_SNAKE_CASE_  :				np.ndarray		,				SCREAMING_SNAKE_CASE_  :				Dict[str, int]		,				SCREAMING_SNAKE_CASE_  :				PILImageResampling = PILImageResampling.BICUBIC		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[str, ChannelDimension]] = None		,				**SCREAMING_SNAKE_CASE_  :				int		,				):
													_a     =			get_size_dict(SCREAMING_SNAKE_CASE_		,				default_to_square=SCREAMING_SNAKE_CASE_		)
													_a     =			get_resize_output_image_size(
													    SCREAMING_SNAKE_CASE_		,				size=(size['height'], size['width'])		,				default_to_square=SCREAMING_SNAKE_CASE_		)
													return resize(SCREAMING_SNAKE_CASE_		,				size=SCREAMING_SNAKE_CASE_		,				resample=SCREAMING_SNAKE_CASE_		,				data_format=SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)







							def 		_UpperCAmelCase	(		self  :				Any		,				SCREAMING_SNAKE_CASE_  :				np.ndarray		,				SCREAMING_SNAKE_CASE_  :				Dict[str, int]		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[str, ChannelDimension]] = None		,				**SCREAMING_SNAKE_CASE_  :				Dict		,				):
													_a     =			get_size_dict(SCREAMING_SNAKE_CASE_		)
													return center_crop(SCREAMING_SNAKE_CASE_		,				size=(size['height'], size['width'])		,				data_format=SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)







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







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







							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				ImageInput		,				SCREAMING_SNAKE_CASE_  :				bool = None		,				SCREAMING_SNAKE_CASE_  :				Dict[str, int] = None		,				SCREAMING_SNAKE_CASE_  :				PILImageResampling = None		,				SCREAMING_SNAKE_CASE_  :				bool = None		,				SCREAMING_SNAKE_CASE_  :				int = None		,				SCREAMING_SNAKE_CASE_  :				bool = None		,				SCREAMING_SNAKE_CASE_  :				float = None		,				SCREAMING_SNAKE_CASE_  :				bool = None		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[float, List[float]]] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[float, List[float]]] = None		,				SCREAMING_SNAKE_CASE_  :				bool = None		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[str, TensorType]] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[ChannelDimension] = ChannelDimension.FIRST		,				**SCREAMING_SNAKE_CASE_  :				Any		,				):
													_a     =			do_resize if do_resize is not None else self.do_resize
													_a     =			size if size is not None else self.size
													_a     =			get_size_dict(SCREAMING_SNAKE_CASE_		,				default_to_square=SCREAMING_SNAKE_CASE_		)
													_a     =			resample if resample is not None else self.resample
													_a     =			do_center_crop if do_center_crop is not None else self.do_center_crop
													_a     =			crop_size if crop_size is not None else self.crop_size
													_a     =			get_size_dict(SCREAMING_SNAKE_CASE_		)
													_a     =			do_rescale if do_rescale is not None else self.do_rescale
													_a     =			rescale_factor if rescale_factor is not None else self.rescale_factor
													_a     =			do_normalize if do_normalize is not None else self.do_normalize
													_a     =			image_mean if image_mean is not None else self.image_mean
													_a     =			image_std if image_std is not None else self.image_std
													_a     =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

													_a     =			make_list_of_images(SCREAMING_SNAKE_CASE_		)

													if not valid_images(SCREAMING_SNAKE_CASE_		):
																			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.'		)

													# PIL RGBA images are converted to RGB
													if do_convert_rgb:
																			_a     =			[convert_to_rgb(SCREAMING_SNAKE_CASE_		) for image in images]

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

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

													if do_center_crop:
																			_a     =			[self.center_crop(image=SCREAMING_SNAKE_CASE_		,				size=SCREAMING_SNAKE_CASE_		) for image in images]

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

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

													_a     =			[to_channel_dimension_format(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		) for image in images]

													_a     =			{'pixel_values': images}
													return BatchFeature(data=SCREAMING_SNAKE_CASE_		,				tensor_type=SCREAMING_SNAKE_CASE_		)






import math


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  float:
						_a     =			math.log(2							,     10				) * number
						_a     =			round((log_number - int(_UpperCAmelCase				))							,     15				)

						return difference


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase = 678910				)    ->  int:
						_a     =			90
						_a     =			0

						_a     =			math.log(1.23							,     10				)
						_a     =			math.log(1.24							,     10				)
						_a     =			0

						while position < number:
												_a     =			log_difference(_UpperCAmelCase				)

												if difference >= upper_limit:
																		power_iterator += 93

												elif difference < lower_limit:
																		power_iterator += 196

												else:
																		_a     =			power_iterator
																		power_iterator += 196
																		position += 1

						return previous_power


if __name__ == "__main__":
		import doctest

		doctest.testmod()

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


