


'''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 center_crop, 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, is_vision_available, logging


if is_vision_available():
	import PIL


_UpperCAmelCase					:			str				     =					logging.get_logger(__name__)


class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		List[Any]						=						['pixel_values']




				def __init__(self							,      __lowercase = True							,      __lowercase = None							,      __lowercase = PIL.Image.NEAREST							,      __lowercase = False							,      __lowercase = None							,      __lowercase = 1 / 2_55							,      __lowercase = False							,      __lowercase = True							,      __lowercase = True							,      __lowercase = None							,      __lowercase = None							,      __lowercase = True							,      **__lowercase							,      ):
										super().__init__(**__lowercase		)
										__lowerCAmelCase											=					size if size is not None else {'''height''': 3_46, '''width''': 3_46}
										__lowerCAmelCase											=					get_size_dict(__lowercase		)
										__lowerCAmelCase											=					crop_size if crop_size is not None else {'''height''': 2_89, '''width''': 2_89}
										__lowerCAmelCase											=					get_size_dict(__lowercase							,      param_name='''crop_size'''		)

										__lowerCAmelCase											=					do_resize
										__lowerCAmelCase											=					size
										__lowerCAmelCase											=					resample
										__lowerCAmelCase											=					do_center_crop
										__lowerCAmelCase											=					crop_size
										__lowerCAmelCase											=					do_rescale
										__lowerCAmelCase											=					rescale_factor
										__lowerCAmelCase											=					rescale_offset
										__lowerCAmelCase											=					do_normalize
										__lowerCAmelCase											=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										__lowerCAmelCase											=					image_std if image_std is not None else IMAGENET_STANDARD_STD
										__lowerCAmelCase											=					include_top




				def        _snake_case	(self							,      __lowercase							,      __lowercase							,      __lowercase = PIL.Image.NEAREST							,      __lowercase = None							,      **__lowercase							,      ):
										__lowerCAmelCase											=					get_size_dict(__lowercase		)
										if "height" not in size or "width" not in size:
																raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""		)
										return resize(
										    __lowercase							,      size=(size['''height'''], size['''width'''])							,      resample=__lowercase							,      data_format=__lowercase							,      **__lowercase		)




				def        _snake_case	(self							,      __lowercase							,      __lowercase							,      __lowercase = None							,      **__lowercase							,      ):
										__lowerCAmelCase											=					get_size_dict(__lowercase		)
										if "height" not in size or "width" not in size:
																raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""		)
										return center_crop(__lowercase							,      size=(size['''height'''], size['''width'''])							,      data_format=__lowercase							,      **__lowercase		)




				def        _snake_case	(self							,      __lowercase							,      __lowercase							,      __lowercase = True							,      __lowercase = None							,      **__lowercase							,      ):
										if offset:
																__lowerCAmelCase											=					(image - 1_2_7.5) * scale
																if data_format is not None:
																						__lowerCAmelCase											=					to_channel_dimension_format(__lowercase							,      __lowercase		)
																__lowerCAmelCase											=					rescaled_image.astype(np.floataa		)
										else:
																__lowerCAmelCase											=					rescale(__lowercase							,      scale=__lowercase							,      data_format=__lowercase							,      **__lowercase		)
										return rescaled_image




				def        _snake_case	(self							,      __lowercase							,      __lowercase							,      __lowercase							,      __lowercase = None							,      **__lowercase							,      ):
										return normalize(__lowercase							,      mean=__lowercase							,      std=__lowercase							,      data_format=__lowercase							,      **__lowercase		)




				def        _snake_case	(self							,      __lowercase							,      __lowercase = None							,      __lowercase = None							,      __lowercase=None							,      __lowercase = None							,      __lowercase = None							,      __lowercase = None							,      __lowercase = None							,      __lowercase = None							,      __lowercase = None							,      __lowercase = None							,      __lowercase = None							,      __lowercase = None							,      __lowercase = None							,      __lowercase = ChannelDimension.FIRST							,      **__lowercase							,      ):
										__lowerCAmelCase											=					do_resize if do_resize is not None else self.do_resize
										__lowerCAmelCase											=					resample if resample is not None else self.resample
										__lowerCAmelCase											=					do_center_crop if do_center_crop is not None else self.do_center_crop
										__lowerCAmelCase											=					do_rescale if do_rescale is not None else self.do_rescale
										__lowerCAmelCase											=					rescale_factor if rescale_factor is not None else self.rescale_factor
										__lowerCAmelCase											=					rescale_offset if rescale_offset is not None else self.rescale_offset
										__lowerCAmelCase											=					do_normalize if do_normalize is not None else self.do_normalize
										__lowerCAmelCase											=					image_mean if image_mean is not None else self.image_mean
										__lowerCAmelCase											=					image_std if image_std is not None else self.image_std
										__lowerCAmelCase											=					include_top if include_top is not None else self.include_top

										__lowerCAmelCase											=					size if size is not None else self.size
										__lowerCAmelCase											=					get_size_dict(__lowercase		)
										__lowerCAmelCase											=					crop_size if crop_size is not None else self.crop_size
										__lowerCAmelCase											=					get_size_dict(__lowercase							,      param_name='''crop_size'''		)

										__lowerCAmelCase											=					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 or resample is None:
																raise ValueError('''Size and resample 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.
										__lowerCAmelCase											=					[to_numpy_array(__lowercase		) for image in images]

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

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

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

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

										if include_top:
																__lowerCAmelCase											=					[self.normalize(image=__lowercase							,      mean=[0, 0, 0]							,      std=__lowercase		) for image in images]

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

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





'''simple docstring'''

import math

def 				__magic_name__(    lowerCamelCase):
						__lowerCAmelCase											=					math.log(2,					1_0) * number
						__lowerCAmelCase											=					round((log_number - int(lowerCamelCase)),					1_5)

						return difference

def 				__magic_name__(    lowerCamelCase = 6_7_8_9_1_0):
						__lowerCAmelCase											=					9_0
						__lowerCAmelCase											=					0

						__lowerCAmelCase											=					math.log(1.23,					1_0)
						__lowerCAmelCase											=					math.log(1.24,					1_0)
						__lowerCAmelCase											=					0

						while position < number:
												__lowerCAmelCase											=					log_difference(lowerCamelCase)

												if difference >= upper_limit:
																		power_iterator += 9_3

												elif difference < lower_limit:
																		power_iterator += 1_9_6

												else:
																		__lowerCAmelCase											=					power_iterator
																		power_iterator += 1_9_6
																		position += 1

						return previous_power


if __name__ == "__main__":
	import doctest

	doctest.testmod()

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

