"""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,
    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


_A   					=    logging.get_logger(__name__)


if is_vision_available():
						import PIL

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		["pixel_values"]



					def __init__(						self			:				Optional[Any]   , _snake_case			:				bool = True   , _snake_case			:				Dict[str, int] = None   , _snake_case			:				PILImageResampling = PILImageResampling.BICUBIC   , _snake_case			:				bool = True   , _snake_case			:				Dict[str, int] = None   , _snake_case			:				bool = True   , _snake_case			:				Union[int, float] = 1 / 255   , _snake_case			:				bool = True   , _snake_case			:				Optional[Union[float, List[float]]] = None   , _snake_case			:				Optional[Union[float, List[float]]] = None   , _snake_case			:				bool = True   , **_snake_case			:				Any   , )							->       None:
									super().__init__(**_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				size if size is not None else {"shortest_edge": 224}
									SCREAMING_SNAKE_CASE__		  =				get_size_dict(_snake_case   , default_to_square=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				crop_size if crop_size is not None else {"height": 224, "width": 224}
									SCREAMING_SNAKE_CASE__		  =				get_size_dict(_snake_case   , default_to_square=_snake_case   , param_name="crop_size"		)

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



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				np.ndarray   , _snake_case			:				Dict[str, int]   , _snake_case			:				PILImageResampling = PILImageResampling.BICUBIC   , _snake_case			:				Optional[Union[str, ChannelDimension]] = None   , **_snake_case			:				int   , )							->       np.ndarray:
									SCREAMING_SNAKE_CASE__		  =				get_size_dict(_snake_case   , default_to_square=_snake_case		)
									if "shortest_edge" not in size:
													raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""		)
									SCREAMING_SNAKE_CASE__		  =				get_resize_output_image_size(_snake_case   , size=size["shortest_edge"]   , default_to_square=_snake_case		)
									return resize(_snake_case   , size=_snake_case   , resample=_snake_case   , data_format=_snake_case   , **_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				np.ndarray   , _snake_case			:				Dict[str, int]   , _snake_case			:				Optional[Union[str, ChannelDimension]] = None   , **_snake_case			:				Optional[Any]   , )							->       np.ndarray:
									SCREAMING_SNAKE_CASE__		  =				get_size_dict(_snake_case		)
									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(_snake_case   , size=(size["height"], size["width"])   , data_format=_snake_case   , **_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				np.ndarray   , _snake_case			:				Union[int, float]   , _snake_case			:				Optional[Union[str, ChannelDimension]] = None   , **_snake_case			:				List[Any]   , )							->       List[str]:
									return rescale(_snake_case   , scale=_snake_case   , data_format=_snake_case   , **_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				np.ndarray   , _snake_case			:				Union[float, List[float]]   , _snake_case			:				Union[float, List[float]]   , _snake_case			:				Optional[Union[str, ChannelDimension]] = None   , **_snake_case			:				Union[str, Any]   , )							->       np.ndarray:
									return normalize(_snake_case   , mean=_snake_case   , std=_snake_case   , data_format=_snake_case   , **_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				ImageInput   , _snake_case			:				bool = None   , _snake_case			:				Dict[str, int] = None   , _snake_case			:				PILImageResampling = None   , _snake_case			:				bool = None   , _snake_case			:				int = None   , _snake_case			:				bool = None   , _snake_case			:				float = None   , _snake_case			:				bool = None   , _snake_case			:				Optional[Union[float, List[float]]] = None   , _snake_case			:				Optional[Union[float, List[float]]] = None   , _snake_case			:				bool = None   , _snake_case			:				Optional[Union[str, TensorType]] = None   , _snake_case			:				Optional[ChannelDimension] = ChannelDimension.FIRST   , **_snake_case			:				str   , )							->       PIL.Image.Image:
									SCREAMING_SNAKE_CASE__		  =				do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE__		  =				size if size is not None else self.size
									SCREAMING_SNAKE_CASE__		  =				get_size_dict(_snake_case   , param_name="size"   , default_to_square=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE__		  =				do_center_crop if do_center_crop is not None else self.do_center_crop
									SCREAMING_SNAKE_CASE__		  =				crop_size if crop_size is not None else self.crop_size
									SCREAMING_SNAKE_CASE__		  =				get_size_dict(_snake_case   , param_name="crop_size"   , default_to_square=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE__		  =				rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE__		  =				do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE__		  =				image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE__		  =				image_std if image_std is not None else self.image_std
									SCREAMING_SNAKE_CASE__		  =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									SCREAMING_SNAKE_CASE__		  =				make_list_of_images(_snake_case		)

									if not valid_images(_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:
													SCREAMING_SNAKE_CASE__		  =				[convert_to_rgb(_snake_case		) for image in images]

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

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

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

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

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

									SCREAMING_SNAKE_CASE__		  =				[to_channel_dimension_format(_snake_case   , _snake_case		) for image in images]

									SCREAMING_SNAKE_CASE__		  =				{"pixel_values": images}
									return BatchFeature(data=_snake_case   , tensor_type=_snake_case		)





"""simple docstring"""






def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   int:

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

				SCREAMING_SNAKE_CASE__		  =				0
				SCREAMING_SNAKE_CASE__		  =				str(__UpperCAmelCase       )

				while len(__UpperCAmelCase       ) != 1:
								SCREAMING_SNAKE_CASE__		  =				[int(__UpperCAmelCase       ) for i in num_string]

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

								SCREAMING_SNAKE_CASE__		  =				str(__UpperCAmelCase       )

								steps += 1
				return steps




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   int:

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

				SCREAMING_SNAKE_CASE__		  =				0
				SCREAMING_SNAKE_CASE__		  =				str(__UpperCAmelCase       )

				while len(__UpperCAmelCase       ) != 1:
								SCREAMING_SNAKE_CASE__		  =				[int(__UpperCAmelCase       ) for i in num_string]

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

								SCREAMING_SNAKE_CASE__		  =				str(__UpperCAmelCase       )

								steps += 1
				return steps


if __name__ == "__main__":
						import doctest

						doctest.testmod()




