




"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a_				:    List[Any]										=     logging.get_logger(__name__)

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						Dict     		= ['pixel_values']
					def __init__(    self						,	__a = True						,	__a = None						,	__a = PILImageResampling.BICUBIC						,	__a = True						,	__a = True						,	__a = 1 / 255						,	__a = None						,	__a = True						,	__a = None						,	__a = None						,	**__a						,	):
								super().__init__(**__a     )
								__lowerCamelCase					:	str	       =				size if size is not None else {'height': 224, 'width': 224}
								__lowerCamelCase					:	Dict	       =				get_size_dict(__a     )
								__lowerCamelCase					:	Optional[int]	       =				crop_size if crop_size is not None else {'height': 224, 'width': 224}
								__lowerCamelCase					:	int	       =				get_size_dict(__a						,	default_to_square=__a						,	param_name='crop_size'     )

								__lowerCamelCase					:	Optional[Any]	       =				do_resize
								__lowerCamelCase					:	Any	       =				do_rescale
								__lowerCamelCase					:	str	       =				do_normalize
								__lowerCamelCase					:	Dict	       =				do_center_crop
								__lowerCamelCase					:	str	       =				crop_size
								__lowerCamelCase					:	Optional[Any]	       =				size
								__lowerCamelCase					:	Tuple	       =				resample
								__lowerCamelCase					:	List[str]	       =				rescale_factor
								__lowerCamelCase					:	List[str]	       =				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
								__lowerCamelCase					:	Optional[int]	       =				image_std if image_std is not None else IMAGENET_DEFAULT_STD
					def     snake_case_				(    self						,	__a						,	__a						,	__a = PILImageResampling.BILINEAR						,	__a = None						,	**__a						,	):
								__lowerCamelCase					:	List[Any]	       =				get_size_dict(__a     )

								if "shortest_edge" in size:
											__lowerCamelCase					:	Any	       =				get_resize_output_image_size(__a						,	size=size['shortest_edge']						,	default_to_square=__a     )
											# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
								elif "height" in size and "width" in size:
											__lowerCamelCase					:	Tuple	       =				(size['height'], size['width'])
								else:
											raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''     )
								return resize(__a						,	size=__a						,	resample=__a						,	data_format=__a						,	**__a     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a = None						,	**__a						,	):
								__lowerCamelCase					:	List[Any]	       =				get_size_dict(__a     )
								if "height" not in size or "width" not in size:
											raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''     )
								return center_crop(__a						,	size=(size['height'], size['width'])						,	data_format=__a						,	**__a     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a = None						,	**__a     ):
								return rescale(__a						,	scale=__a						,	data_format=__a						,	**__a     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a = None						,	**__a						,	):
								return normalize(__a						,	mean=__a						,	std=__a						,	data_format=__a						,	**__a     )





					def     snake_case_				(    self						,	__a						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = None						,	__a = ChannelDimension.FIRST						,	**__a						,	):
								__lowerCamelCase					:	Optional[Any]	       =				do_resize if do_resize is not None else self.do_resize
								__lowerCamelCase					:	Tuple	       =				do_rescale if do_rescale is not None else self.do_rescale
								__lowerCamelCase					:	List[str]	       =				do_normalize if do_normalize is not None else self.do_normalize
								__lowerCamelCase					:	str	       =				do_center_crop if do_center_crop is not None else self.do_center_crop
								__lowerCamelCase					:	str	       =				crop_size if crop_size is not None else self.crop_size
								__lowerCamelCase					:	int	       =				get_size_dict(__a						,	param_name='crop_size'						,	default_to_square=__a     )
								__lowerCamelCase					:	List[Any]	       =				resample if resample is not None else self.resample
								__lowerCamelCase					:	List[Any]	       =				rescale_factor if rescale_factor is not None else self.rescale_factor
								__lowerCamelCase					:	Dict	       =				image_mean if image_mean is not None else self.image_mean
								__lowerCamelCase					:	List[Any]	       =				image_std if image_std is not None else self.image_std

								__lowerCamelCase					:	Optional[int]	       =				size if size is not None else self.size
								__lowerCamelCase					:	Optional[Any]	       =				get_size_dict(__a     )

								if not is_batched(__a     ):
											__lowerCamelCase					:	Tuple	       =				[images]

								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.'     )

								# All transformations expect numpy arrays.
								__lowerCamelCase					:	Union[str, Any]	       =				[to_numpy_array(__a     ) for image in images]

								if do_resize:
											__lowerCamelCase					:	List[Any]	       =				[self.resize(image=__a						,	size=__a						,	resample=__a     ) for image in images]

								if do_center_crop:
											__lowerCamelCase					:	Optional[Any]	       =				[self.center_crop(image=__a						,	size=__a     ) for image in images]

								if do_rescale:
											__lowerCamelCase					:	int	       =				[self.rescale(image=__a						,	scale=__a     ) for image in images]

								if do_normalize:
											__lowerCamelCase					:	Any	       =				[self.normalize(image=__a						,	mean=__a						,	std=__a     ) for image in images]

								__lowerCamelCase					:	Dict	       =				[to_channel_dimension_format(__a						,	__a     ) for image in images]

								__lowerCamelCase					:	Union[str, Any]	       =				{'pixel_values': images}
								return BatchFeature(data=__a						,	tensor_type=__a     )









"""simple docstring"""
a_				:    Dict										=     {
    '''meter''': '''m''',
    '''kilometer''': '''km''',
    '''megametre''': '''Mm''',
    '''gigametre''': '''Gm''',
    '''terametre''': '''Tm''',
    '''petametre''': '''Pm''',
    '''exametre''': '''Em''',
    '''zettametre''': '''Zm''',
    '''yottametre''': '''Ym''',
}
# Exponent of the factor(meter)
a_				:    Optional[int]										=     {
    '''m''': 0,
    '''km''': 3,
    '''Mm''': 6,
    '''Gm''': 9,
    '''Tm''': 12,
    '''Pm''': 15,
    '''Em''': 18,
    '''Zm''': 21,
    '''Ym''': 24,
}





def 						UpperCAmelCase				(			A__:    float							,				A__:    str							,				A__:    str     )  ->     float:
			__lowerCamelCase					:	List[Any]	       =				from_type.lower().strip('s'     )
			__lowerCamelCase					:	Optional[int]	       =				to_type.lower().strip('s'     )

			__lowerCamelCase					:	int	       =				UNIT_SYMBOL.get(A__							,				A__     )
			__lowerCamelCase					:	Dict	       =				UNIT_SYMBOL.get(A__							,				A__     )

			if from_sanitized not in METRIC_CONVERSION:
						__lowerCamelCase					:	Dict	       =				(
						    f'''Invalid \'from_type\' value: {from_type!r}.\n'''
						    f'''Conversion abbreviations are: {", ".join(A__     )}'''
						)
						raise ValueError(A__     )
			if to_sanitized not in METRIC_CONVERSION:
						__lowerCamelCase					:	Any	       =				(
						    f'''Invalid \'to_type\' value: {to_type!r}.\n'''
						    f'''Conversion abbreviations are: {", ".join(A__     )}'''
						)
						raise ValueError(A__     )
			__lowerCamelCase					:	Any	       =				METRIC_CONVERSION[from_sanitized]
			__lowerCamelCase					:	Tuple	       =				METRIC_CONVERSION[to_sanitized]
			__lowerCamelCase					:	Any	       =				1

			if from_exponent > to_exponent:
						__lowerCamelCase					:	Union[str, Any]	       =				from_exponent - to_exponent
			else:
						__lowerCamelCase					:	List[Any]	       =				-(to_exponent - from_exponent)

			return value * pow(10							,				A__     )


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

					testmod()



