
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


UpperCamelCase_   	=	logging.get_logger(__name__)


if is_vision_available():
					import PIL

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Tuple   							=  ['pixel_values']







							def __init__(						self  :  Dict      ,				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__  :  int      ,				):
								"""simple docstring"""







								super().__init__(**snake_case__ )
								__lowerCAmelCase         =							size if size is not None else {"shortest_edge": 224}
								__lowerCAmelCase         =							get_size_dict(snake_case__      ,				default_to_square=snake_case__ )
								__lowerCAmelCase         =							crop_size if crop_size is not None else {"height": 224, "width": 224}
								__lowerCAmelCase         =							get_size_dict(snake_case__      ,				default_to_square=snake_case__      ,				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         =							do_normalize
								__lowerCAmelCase         =							image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								__lowerCAmelCase         =							image_std if image_std is not None else OPENAI_CLIP_STD
								__lowerCAmelCase         =							do_convert_rgb







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Dict[str, int]      ,				snake_case__  :  PILImageResampling = PILImageResampling.BICUBIC      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  List[Any]      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							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()}" )
								__lowerCAmelCase         =							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       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Dict[str, int]      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  int      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							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       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Union[int, float]      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  Optional[int]      ,				):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  str      ,				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__  :  str      ,				):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  Dict      ,				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__  :  int      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							do_resize if do_resize is not None else self.do_resize
								__lowerCAmelCase         =							size if size is not None else self.size
								__lowerCAmelCase         =							get_size_dict(snake_case__      ,				param_name="size"      ,				default_to_square=snake_case__ )
								__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         =							crop_size if crop_size is not None else self.crop_size
								__lowerCAmelCase         =							get_size_dict(snake_case__      ,				param_name="crop_size"      ,				default_to_square=snake_case__ )
								__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         =							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         =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								__lowerCAmelCase         =							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:
									__lowerCAmelCase         =							[convert_to_rgb(snake_case__ ) for image in images]

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

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

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

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

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

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

								__lowerCAmelCase         =							{"pixel_values": images}
								return BatchFeature(data=snake_case__      ,				tensor_type=snake_case__ )








from abc import ABC, abstractmethod
from argparse import ArgumentParser

class       a		(   __UpperCAmelCase					):







							@staticmethod
							@abstractmethod
							def       UpperCAmelCase__					(						snake_case__  :  ArgumentParser ):
								"""simple docstring"""







								raise NotImplementedError()







							@abstractmethod
							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								raise NotImplementedError()






