



"""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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A    =			logging.get_logger(__name__)






class      UpperCAmelCase__		(      UpperCamelCase_     ):
		lowerCAmelCase_       :	Optional[int] 							=     ["""pixel_values"""]



		def __init__( self  :    Any      ,							snake_case  :    int = True      ,							snake_case  :    Any = None      ,							snake_case  :    Optional[int] = PILImageResampling.BILINEAR      ,							snake_case  :    List[Any] = True      ,							snake_case  :    Union[str, Any] = None      ,							snake_case  :    Optional[Any] = True      ,							snake_case  :    int = 1 / 255      ,							snake_case  :    Tuple = True      ,							snake_case  :    Dict = None      ,							snake_case  :    List[Any] = None      ,							**snake_case  :    int      ,							)			->						None:






									'''simple docstring'''

									super().__init__(**_a				)
									A         =					size if size is not None else {"""shortest_edge""": 256}
									A         =					get_size_dict(_a      ,							default_to_square=_a				)
									A         =					crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
									A         =					get_size_dict(_a				)
									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 IMAGENET_STANDARD_MEAN
									A         =					image_std if image_std is not None else IMAGENET_STANDARD_STD



		def 					A_		( self  :    List[str]      ,							snake_case  :    int      ,							snake_case  :    int      ,							snake_case  :    Optional[Any] = PILImageResampling.BICUBIC      ,							snake_case  :    Optional[Any] = None      ,							**snake_case  :    List[Any]      ,							)			->						np.ndarray:






									'''simple docstring'''

									A         =					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()}"""				)
									A         =					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 					A_		( self  :    Any      ,							snake_case  :    Tuple      ,							snake_case  :    Tuple      ,							snake_case  :    Optional[int] = None      ,							**snake_case  :    Optional[int]      ,							)			->						np.ndarray:






									'''simple docstring'''

									A         =					get_size_dict(_a				)
									return center_crop(_a      ,							size=(size['height'], size['width'])      ,							data_format=_a      ,							**_a				)



		def 					A_		( self  :    Tuple      ,							snake_case  :    Optional[int]      ,							snake_case  :    List[Any]      ,							snake_case  :    Tuple = None      ,							**snake_case  :    List[Any]				)			->						np.ndarray:






									'''simple docstring'''

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



		def 					A_		( self  :    Tuple      ,							snake_case  :    Optional[int]      ,							snake_case  :    Dict      ,							snake_case  :    str      ,							snake_case  :    Tuple = None      ,							**snake_case  :    str      ,							)			->						np.ndarray:






									'''simple docstring'''

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



		def 					A_		( self  :    Optional[int]      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    Optional[Any] = None      ,							snake_case  :    Tuple = None      ,							snake_case  :    List[Any] = None      ,							snake_case  :    str = None      ,							snake_case  :    Union[str, Any] = None      ,							snake_case  :    str = None      ,							snake_case  :    Optional[Any] = None      ,							snake_case  :    Tuple = None      ,							snake_case  :    List[Any] = None      ,							snake_case  :    Optional[Any] = None      ,							snake_case  :    Optional[int] = None      ,							snake_case  :    int = ChannelDimension.FIRST      ,							**snake_case  :    Optional[int]      ,							)			->						Optional[int]:






									'''simple docstring'''

									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(_a      ,							default_to_square=_a				)
									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(_a				)
									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         =					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.
									A         =					[to_numpy_array(_a				) for image in images]

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

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

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

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

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

									A         =					{"""pixel_values""": images}
									return BatchFeature(data=_a      ,							tensor_type=_a				)








"""simple docstring"""







def   lowerCAmelCase__			(     lowerCamelCase__			) ->							int:

							if not isinstance(lowerCamelCase__				,      lowerCamelCase__			):
														raise ValueError('multiplicative_persistence() only accepts integral values'			)
							if num < 0:
														raise ValueError('multiplicative_persistence() does not accept negative values'			)

							A         =					0
							A         =					str(lowerCamelCase__			)

							while len(lowerCamelCase__			) != 1:
														A         =					[int(lowerCamelCase__			) for i in num_string]

														A         =					1
														for i in range(0				,      len(lowerCamelCase__			)			):
																					total *= numbers[i]

														A         =					str(lowerCamelCase__			)

														steps += 1
							return steps

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							int:

							if not isinstance(lowerCamelCase__				,      lowerCamelCase__			):
														raise ValueError('additive_persistence() only accepts integral values'			)
							if num < 0:
														raise ValueError('additive_persistence() does not accept negative values'			)

							A         =					0
							A         =					str(lowerCamelCase__			)

							while len(lowerCamelCase__			) != 1:
														A         =					[int(lowerCamelCase__			) for i in num_string]

														A         =					0
														for i in range(0				,      len(lowerCamelCase__			)			):
																					total += numbers[i]

														A         =					str(lowerCamelCase__			)

														steps += 1
							return steps


if __name__ == "__main__":
							import doctest

							doctest.testmod()



