






'''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      :       Tuple      =	logging.get_logger(__name__)


if is_vision_available():
       import PIL






class 					a__    (			lowerCamelCase						):
     '''simple docstring'''







     _snake_case             =  ['pixel_values']






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

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






     def     _UpperCamelCase   (self    :	Union[str, Any]				,				lowercase__    :	np.ndarray				,				lowercase__    :	Dict[str, int]				,				lowercase__    :	PILImageResampling = PILImageResampling.BICUBIC				,				lowercase__    :	Optional[Union[str, ChannelDimension]] = None				,				**lowercase__    :	Tuple				,				)  ->       np.ndarray:
           __A		   =      get_size_dict(lowercase__				,				default_to_square=lowercase__					)
           __A		   =      get_resize_output_image_size(
               lowercase__				,				size=(size["height"], size["width"])				,				default_to_square=lowercase__					)
           return resize(lowercase__				,				size=lowercase__				,				resample=lowercase__				,				data_format=lowercase__				,				**lowercase__					)






     def     _UpperCamelCase   (self    :	Any				,				lowercase__    :	np.ndarray				,				lowercase__    :	Dict[str, int]				,				lowercase__    :	Optional[Union[str, ChannelDimension]] = None				,				**lowercase__    :	Any				,				)  ->       np.ndarray:
           __A		   =      get_size_dict(lowercase__					)
           return center_crop(lowercase__				,				size=(size["height"], size["width"])				,				data_format=lowercase__				,				**lowercase__					)






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






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






     def     _UpperCamelCase   (self    :	str				,				lowercase__    :	ImageInput				,				lowercase__    :	bool = None				,				lowercase__    :	Dict[str, int] = None				,				lowercase__    :	PILImageResampling = None				,				lowercase__    :	bool = None				,				lowercase__    :	int = None				,				lowercase__    :	bool = None				,				lowercase__    :	float = None				,				lowercase__    :	bool = None				,				lowercase__    :	Optional[Union[float, List[float]]] = None				,				lowercase__    :	Optional[Union[float, List[float]]] = None				,				lowercase__    :	bool = None				,				lowercase__    :	Optional[Union[str, TensorType]] = None				,				lowercase__    :	Optional[ChannelDimension] = ChannelDimension.FIRST				,				**lowercase__    :	Union[str, Any]				,				)  ->       PIL.Image.Image:
           __A		   =      do_resize if do_resize is not None else self.do_resize
           __A		   =      size if size is not None else self.size
           __A		   =      get_size_dict(lowercase__				,				default_to_square=lowercase__					)
           __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		   =      crop_size if crop_size is not None else self.crop_size
           __A		   =      get_size_dict(lowercase__					)
           __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		   =      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		   =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           __A		   =      make_list_of_images(lowercase__					)

           if not valid_images(lowercase__					):
                 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:
                 __A		   =      [convert_to_rgb(lowercase__					) for image in images]

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

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

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

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

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

           __A		   =      [to_channel_dimension_format(lowercase__				,				lowercase__					) for image in images]

           __A		   =      {"pixel_values": images}
           return BatchFeature(data=lowercase__				,				tensor_type=lowercase__					)










'''simple docstring'''







from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


__A      :       Tuple      =	datasets.utils.logging.get_logger(__name__)






class 					a__    (			folder_based_builder.FolderBasedBuilderConfig						):
     '''simple docstring'''







     _snake_case             =  None
     _snake_case             =  None







class 					a__    (			folder_based_builder.FolderBasedBuilder						):
     '''simple docstring'''







     _snake_case             =  datasets.Image()
     _snake_case             =  'image'
     _snake_case             =  ImageFolderConfig
     _snake_case             =  42  # definition at the bottom of the script
     _snake_case             =  ImageClassification(image_column='image' ,						label_column='label'						)







__A      :       Any      =	[
    '.blp',
    '.bmp',
    '.dib',
    '.bufr',
    '.cur',
    '.pcx',
    '.dcx',
    '.dds',
    '.ps',
    '.eps',
    '.fit',
    '.fits',
    '.fli',
    '.flc',
    '.ftc',
    '.ftu',
    '.gbr',
    '.gif',
    '.grib',
    '.h5',
    '.hdf',
    '.png',
    '.apng',
    '.jp2',
    '.j2k',
    '.jpc',
    '.jpf',
    '.jpx',
    '.j2c',
    '.icns',
    '.ico',
    '.im',
    '.iim',
    '.tif',
    '.tiff',
    '.jfif',
    '.jpe',
    '.jpg',
    '.jpeg',
    '.mpg',
    '.mpeg',
    '.msp',
    '.pcd',
    '.pxr',
    '.pbm',
    '.pgm',
    '.ppm',
    '.pnm',
    '.psd',
    '.bw',
    '.rgb',
    '.rgba',
    '.sgi',
    '.ras',
    '.tga',
    '.icb',
    '.vda',
    '.vst',
    '.webp',
    '.wmf',
    '.emf',
    '.xbm',
    '.xpm',
]
__A      :       List[Any]      =	IMAGE_EXTENSIONS


