
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


lowercase   =	logging.get_logger(__name__)





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     ['''pixel_values''']

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

												lowerCamelCase_					=       do_resize
												lowerCamelCase_					=       do_rescale
												lowerCamelCase_					=       do_normalize
												lowerCamelCase_					=       do_center_crop
												lowerCamelCase_					=       crop_size
												lowerCamelCase_					=       size
												lowerCamelCase_					=       resample
												lowerCamelCase_					=       rescale_factor
												lowerCamelCase_					=       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
												lowerCamelCase_					=       image_std if image_std is not None else IMAGENET_DEFAULT_STD

					def  lowercase__   (						self       :  str ,       __UpperCamelCase       :  np.ndarray ,       __UpperCamelCase       :  Dict[str, int] ,       __UpperCamelCase       :  PILImageResampling = PILImageResampling.BILINEAR ,       __UpperCamelCase       :  Optional[Union[str, ChannelDimension]] = None ,       **__UpperCamelCase       :  Union[str, Any] ,       ):
												lowerCamelCase_					=       get_size_dict(__UpperCamelCase )

												if "shortest_edge" in size:
																			lowerCamelCase_					=       get_resize_output_image_size(__UpperCamelCase ,       size=size["""shortest_edge"""] ,       default_to_square=__UpperCamelCase )
																			# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
												elif "height" in size and "width" in size:
																			lowerCamelCase_					=       (size["""height"""], size["""width"""])
												else:
																			raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''' )
												return resize(__UpperCamelCase ,       size=__UpperCamelCase ,       resample=__UpperCamelCase ,       data_format=__UpperCamelCase ,       **__UpperCamelCase )

					def  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  np.ndarray ,       __UpperCamelCase       :  Dict[str, int] ,       __UpperCamelCase       :  Optional[Union[str, ChannelDimension]] = None ,       **__UpperCamelCase       :  Tuple ,       ):
												lowerCamelCase_					=       get_size_dict(__UpperCamelCase )
												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(__UpperCamelCase ,       size=(size["""height"""], size["""width"""]) ,       data_format=__UpperCamelCase ,       **__UpperCamelCase )

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

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



					def  lowercase__   (						self       :  List[str] ,       __UpperCamelCase       :  ImageInput ,       __UpperCamelCase       :  Optional[bool] = None ,       __UpperCamelCase       :  Dict[str, int] = None ,       __UpperCamelCase       :  PILImageResampling = None ,       __UpperCamelCase       :  bool = None ,       __UpperCamelCase       :  int = None ,       __UpperCamelCase       :  Optional[bool] = None ,       __UpperCamelCase       :  Optional[float] = None ,       __UpperCamelCase       :  Optional[bool] = None ,       __UpperCamelCase       :  Optional[Union[float, List[float]]] = None ,       __UpperCamelCase       :  Optional[Union[float, List[float]]] = None ,       __UpperCamelCase       :  Optional[Union[str, TensorType]] = None ,       __UpperCamelCase       :  Union[str, ChannelDimension] = ChannelDimension.FIRST ,       **__UpperCamelCase       :  int ,       ):
												lowerCamelCase_					=       do_resize if do_resize is not None else self.do_resize
												lowerCamelCase_					=       do_rescale if do_rescale is not None else self.do_rescale
												lowerCamelCase_					=       do_normalize if do_normalize is not None else self.do_normalize
												lowerCamelCase_					=       do_center_crop if do_center_crop is not None else self.do_center_crop
												lowerCamelCase_					=       crop_size if crop_size is not None else self.crop_size
												lowerCamelCase_					=       get_size_dict(__UpperCamelCase ,       param_name="""crop_size""" ,       default_to_square=__UpperCamelCase )
												lowerCamelCase_					=       resample if resample is not None else self.resample
												lowerCamelCase_					=       rescale_factor if rescale_factor is not None else self.rescale_factor
												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_					=       size if size is not None else self.size
												lowerCamelCase_					=       get_size_dict(__UpperCamelCase )

												if not is_batched(__UpperCamelCase ):
																			lowerCamelCase_					=       [images]

												if not valid_images(__UpperCamelCase ):
																			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_					=       [to_numpy_array(__UpperCamelCase ) for image in images]

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

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

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

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

												lowerCamelCase_					=       [to_channel_dimension_format(__UpperCamelCase ,       __UpperCamelCase ) for image in images]

												lowerCamelCase_					=       {"""pixel_values""": images}
												return BatchFeature(data=__UpperCamelCase ,       tensor_type=__UpperCamelCase )







def   __lowerCAmelCase    (				UpperCAmelCase__					:   int							)	->     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"""							)

							lowerCamelCase_					=       0
							lowerCamelCase_					=       str(UpperCAmelCase__							)

							while len(UpperCAmelCase__							) != 1:
														lowerCamelCase_					=       [int(UpperCAmelCase__							) for i in num_string]

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

														lowerCamelCase_					=       str(UpperCAmelCase__							)

														steps += 1
							return steps



def   __lowerCAmelCase    (				UpperCAmelCase__					:   int							)	->     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"""							)

							lowerCamelCase_					=       0
							lowerCamelCase_					=       str(UpperCAmelCase__							)

							while len(UpperCAmelCase__							) != 1:
														lowerCamelCase_					=       [int(UpperCAmelCase__							) for i in num_string]

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

														lowerCamelCase_					=       str(UpperCAmelCase__							)

														steps += 1
							return steps


if __name__ == "__main__":
		import doctest

		doctest.testmod()





