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


if is_vision_available():
		import PIL


_lowercase											=			logging.get_logger(__name__)







class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      List[str] =      ['''pixel_values''']


						def __init__(	self       : int					,A_       : bool = True					,A_       : Dict[str, int] = None					,A_       : PILImageResampling = PIL.Image.NEAREST					,A_       : bool = False					,A_       : Dict[str, int] = None					,A_       : Union[int, float] = 1 / 255					,A_       : bool = False					,A_       : bool = True					,A_       : bool = True					,A_       : Optional[Union[float, List[float]]] = None					,A_       : Optional[Union[float, List[float]]] = None					,A_       : bool = True					,**A_       : Dict					,)       ->				None:
							super().__init__(**A_ )
							A  				=				size if size is not None else {'height': 346, 'width': 346}
							A  				=				get_size_dict(A_ )
							A  				=				crop_size if crop_size is not None else {'height': 289, 'width': 289}
							A  				=				get_size_dict(A_					,param_name='crop_size' )

							A  				=				do_resize
							A  				=				size
							A  				=				resample
							A  				=				do_center_crop
							A  				=				crop_size
							A  				=				do_rescale
							A  				=				rescale_factor
							A  				=				rescale_offset
							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
							A  				=				include_top


						def   _SCREAMING_SNAKE_CASE (	self       : str					,A_       : np.ndarray					,A_       : Dict[str, int]					,A_       : PILImageResampling = PIL.Image.NEAREST					,A_       : Optional[Union[str, ChannelDimension]] = None					,**A_       : Any					,)       ->				np.ndarray:
							A  				=				get_size_dict(A_ )
							if "height" not in size or "width" not in size:
								raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}' )
							return resize(
							    A_					,size=(size['height'], size['width'])					,resample=A_					,data_format=A_					,**A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : np.ndarray					,A_       : Dict[str, int]					,A_       : Optional[Union[str, ChannelDimension]] = None					,**A_       : str					,)       ->				np.ndarray:
							A  				=				get_size_dict(A_ )
							if "height" not in size or "width" not in size:
								raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}' )
							return center_crop(A_					,size=(size['height'], size['width'])					,data_format=A_					,**A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int]					,A_       : np.ndarray					,A_       : Union[int, float]					,A_       : bool = True					,A_       : Optional[Union[str, ChannelDimension]] = None					,**A_       : Optional[Any]					,)       ->				Optional[int]:
							if offset:
								A  				=				(image - 1_27.5) * scale
								if data_format is not None:
									A  				=				to_channel_dimension_format(A_					,A_ )
								A  				=				rescaled_image.astype(np.floataa )
							else:
								A  				=				rescale(A_					,scale=A_					,data_format=A_					,**A_ )
							return rescaled_image


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




						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int]					,A_       : ImageInput					,A_       : bool = None					,A_       : Dict[str, int] = None					,A_       : Optional[Any]=None					,A_       : bool = None					,A_       : Dict[str, int] = None					,A_       : bool = None					,A_       : float = None					,A_       : bool = None					,A_       : bool = None					,A_       : Optional[Union[float, List[float]]] = None					,A_       : Optional[Union[float, List[float]]] = None					,A_       : bool = None					,A_       : Optional[Union[str, TensorType]] = None					,A_       : ChannelDimension = ChannelDimension.FIRST					,**A_       : Any					,)       ->				PIL.Image.Image:
							A  				=				do_resize if do_resize is not None else self.do_resize
							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  				=				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  				=				rescale_offset if rescale_offset is not None else self.rescale_offset
							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  				=				include_top if include_top is not None else self.include_top

							A  				=				size if size is not None else self.size
							A  				=				get_size_dict(A_ )
							A  				=				crop_size if crop_size is not None else self.crop_size
							A  				=				get_size_dict(A_					,param_name='crop_size' )

							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 or resample is None:
								raise ValueError('Size and resample 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_					,offset=A_ ) for image in images]

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

							if include_top:
								A  				=				[self.normalize(image=A_					,mean=[0, 0, 0]					,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"""


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
		from .image_encoder import PaintByExampleImageEncoder
		from .pipeline_paint_by_example import PaintByExamplePipeline