


'''simple docstring'''







from ..utils import DummyObject, requires_backends






class 		lowerCamelCase    (				metaclass=snake_case_		):






						'''simple docstring'''







						__snake_case   =      ["torch", "transformers", "invisible_watermark"]







						def __init__(  self:	Optional[Any]      ,							*__UpperCamelCase:	Optional[int]      ,							**__UpperCamelCase:	List[str]     )							->	int:
										requires_backends(self      ,							["torch", "transformers", "invisible_watermark"]     )







						@classmethod
						def lowerCAmelCase__ (  cls:	int      ,							*__UpperCamelCase:	int      ,							**__UpperCamelCase:	List[Any]     )							->	Any:
										requires_backends(cls      ,							["torch", "transformers", "invisible_watermark"]     )







						@classmethod
						def lowerCAmelCase__ (  cls:	int      ,							*__UpperCamelCase:	Any      ,							**__UpperCamelCase:	Optional[Any]     )							->	List[Any]:
										requires_backends(cls      ,							["torch", "transformers", "invisible_watermark"]     )






class 		lowerCamelCase    (				metaclass=snake_case_		):






						'''simple docstring'''







						__snake_case   =      ["torch", "transformers", "invisible_watermark"]







						def __init__(  self:	List[str]      ,							*__UpperCamelCase:	Any      ,							**__UpperCamelCase:	Optional[Any]     )							->	Optional[Any]:
										requires_backends(self      ,							["torch", "transformers", "invisible_watermark"]     )







						@classmethod
						def lowerCAmelCase__ (  cls:	str      ,							*__UpperCamelCase:	Tuple      ,							**__UpperCamelCase:	Union[str, Any]     )							->	Any:
										requires_backends(cls      ,							["torch", "transformers", "invisible_watermark"]     )







						@classmethod
						def lowerCAmelCase__ (  cls:	Any      ,							*__UpperCamelCase:	Dict      ,							**__UpperCamelCase:	Optional[int]     )							->	int:
										requires_backends(cls      ,							["torch", "transformers", "invisible_watermark"]     )



'''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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


_SCREAMING_SNAKE_CASE       :   int        = logging.get_logger(__name__)






class 		_snake_case    (				snake_case_		):






						'''simple docstring'''







						__snake_case   =      ["pixel_values"]







						def __init__(  self:	Union[str, Any]      ,							__UpperCamelCase:	bool = True      ,							__UpperCamelCase:	Dict[str, int] = None      ,							__UpperCamelCase:	bool = True      ,							__UpperCamelCase:	Dict[str, int] = None      ,							__UpperCamelCase:	PILImageResampling = PILImageResampling.BICUBIC      ,							__UpperCamelCase:	bool = True      ,							__UpperCamelCase:	Union[int, float] = 1 / 255      ,							__UpperCamelCase:	bool = True      ,							__UpperCamelCase:	Optional[Union[float, List[float]]] = None      ,							__UpperCamelCase:	Optional[Union[float, List[float]]] = None      ,							**__UpperCamelCase:	Union[str, Any]      ,							)							->	None:
										super().__init__(**__UpperCamelCase     )
										__magic_name__				:					Optional[Any]						       =       crop_size if crop_size is not None else {"height": 256, "width": 256}
										__magic_name__				:					Optional[int]						       =       get_size_dict(__UpperCamelCase      ,							param_name="crop_size"     )
										__magic_name__				:					Union[str, Any]						       =       size if size is not None else {"height": 224, "width": 224}
										__magic_name__				:					Tuple						       =       get_size_dict(__UpperCamelCase     )

										__magic_name__				:					Tuple						       =       do_center_crop
										__magic_name__				:					int						       =       crop_size
										__magic_name__				:					Dict						       =       do_resize
										__magic_name__				:					Optional[Any]						       =       size
										__magic_name__				:					Optional[Any]						       =       resample
										__magic_name__				:					str						       =       do_rescale
										__magic_name__				:					int						       =       rescale_factor
										__magic_name__				:					Dict						       =       do_normalize
										__magic_name__				:					Any						       =       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
										__magic_name__				:					str						       =       image_std if image_std is not None else IMAGENET_DEFAULT_STD







						def lowerCAmelCase__ (  self:	str      ,							__UpperCamelCase:	np.ndarray      ,							__UpperCamelCase:	Dict[str, int]      ,							__UpperCamelCase:	Optional[int] = None      ,							__UpperCamelCase:	Optional[Union[str, ChannelDimension]] = None      ,							**__UpperCamelCase:	List[str]      ,							)							->	np.ndarray:
										__magic_name__				:					List[str]						       =       self.size if size is None else size
										__magic_name__				:					Dict						       =       get_size_dict(__UpperCamelCase     )
										__magic_name__				:					Dict						       =       get_size_dict(__UpperCamelCase      ,							param_name="crop_size"     )

										__magic_name__       ,     __magic_name__				:					Dict						       =       get_image_size(__UpperCamelCase     )
										__magic_name__				:					int						       =       min(__UpperCamelCase      ,							__UpperCamelCase     )
										__magic_name__				:					Dict						       =       (size["height"] / crop_size["height"]) * min_dim
										__magic_name__				:					str						       =       (size["width"] / crop_size["width"]) * min_dim
										return center_crop(__UpperCamelCase      ,							size=(cropped_height, cropped_width)      ,							data_format=__UpperCamelCase      ,							**__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	int      ,							__UpperCamelCase:	np.ndarray      ,							__UpperCamelCase:	Dict[str, int]      ,							__UpperCamelCase:	PILImageResampling = PIL.Image.BICUBIC      ,							__UpperCamelCase:	Optional[Union[str, ChannelDimension]] = None      ,							**__UpperCamelCase:	Any      ,							)							->	np.ndarray:
										__magic_name__				:					Optional[Any]						       =       get_size_dict(__UpperCamelCase     )
										if "height" not in size or "width" not in size:
														raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""     )
										return resize(
										    __UpperCamelCase      ,							size=(size["height"], size["width"])      ,							resample=__UpperCamelCase      ,							data_format=__UpperCamelCase      ,							**__UpperCamelCase     )







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







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







						def lowerCAmelCase__ (  self:	str      ,							__UpperCamelCase:	ImageInput      ,							__UpperCamelCase:	Optional[bool] = None      ,							__UpperCamelCase:	Optional[Dict[str, int]] = None      ,							__UpperCamelCase:	Optional[bool] = None      ,							__UpperCamelCase:	Optional[Dict[str, int]] = None      ,							__UpperCamelCase:	PILImageResampling = 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:	ChannelDimension = ChannelDimension.FIRST      ,							**__UpperCamelCase:	Dict      ,							)							->	PIL.Image.Image:
										__magic_name__				:					Optional[Any]						       =       do_center_crop if do_center_crop is not None else self.do_center_crop
										__magic_name__				:					Optional[Any]						       =       crop_size if crop_size is not None else self.crop_size
										__magic_name__				:					Tuple						       =       get_size_dict(__UpperCamelCase      ,							param_name="crop_size"     )
										__magic_name__				:					int						       =       do_resize if do_resize is not None else self.do_resize
										__magic_name__				:					Optional[Any]						       =       size if size is not None else self.size
										__magic_name__				:					Any						       =       get_size_dict(__UpperCamelCase     )
										__magic_name__				:					Optional[int]						       =       resample if resample is not None else self.resample
										__magic_name__				:					int						       =       do_rescale if do_rescale is not None else self.do_rescale
										__magic_name__				:					int						       =       rescale_factor if rescale_factor is not None else self.rescale_factor
										__magic_name__				:					Any						       =       do_normalize if do_normalize is not None else self.do_normalize
										__magic_name__				:					Dict						       =       image_mean if image_mean is not None else self.image_mean
										__magic_name__				:					Tuple						       =       image_std if image_std is not None else self.image_std

										__magic_name__				:					List[Any]						       =       make_list_of_images(__UpperCamelCase     )

										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_center_crop and crop_size is None:
														raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided."     )

										if do_resize and size is None:
														raise ValueError("Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True."     )

										# All transformations expect numpy arrays.
										__magic_name__				:					Optional[Any]						       =       [to_numpy_array(__UpperCamelCase     ) for image in images]

										if do_center_crop:
														__magic_name__				:					Union[str, Any]						       =       [self.center_crop(__UpperCamelCase      ,							__UpperCamelCase      ,							size=__UpperCamelCase     ) for image in images]

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

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

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

										__magic_name__				:					List[Any]						       =       [to_channel_dimension_format(__UpperCamelCase      ,							__UpperCamelCase     ) for image in images]

										__magic_name__				:					Optional[int]						       =       {"pixel_values": images}
										return BatchFeature(data=__UpperCamelCase      ,							tensor_type=__UpperCamelCase     )