

'''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 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, logging


lowerCamelCase				:   Union[str, Any]      				=  logging.get_logger(__name__)







class 							A__					( snake_case__						):
  A__					=       ['pixel_values']





  def __init__(	self   :			Dict							,      _a   :			int = True							,      _a   :			Union[str, Any] = None							,      _a   :			str = PILImageResampling.BILINEAR							,      _a   :			List[str] = True							,      _a   :			Dict = 1 / 255							,      _a   :			List[Any] = True							,      _a   :			Dict = None							,      _a   :			Tuple = None							,      **_a   :			Union[str, Any]							,      )			->					int:





       '''simple docstring'''






       super().__init__(**__snake_case				)
       _SCREAMING_SNAKE_CASE												=size if size is not None else {'height': 224, 'width': 224}
       _SCREAMING_SNAKE_CASE												=get_size_dict(__snake_case				)
       _SCREAMING_SNAKE_CASE												=do_resize
       _SCREAMING_SNAKE_CASE												=do_rescale
       _SCREAMING_SNAKE_CASE												=do_normalize
       _SCREAMING_SNAKE_CASE												=size
       _SCREAMING_SNAKE_CASE												=resample
       _SCREAMING_SNAKE_CASE												=rescale_factor
       _SCREAMING_SNAKE_CASE												=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       _SCREAMING_SNAKE_CASE												=image_std if image_std is not None else IMAGENET_STANDARD_STD





  def 		A						(	self   :			Optional[Any]							,      _a   :			List[str]							,      _a   :			Dict							,      _a   :			Optional[Any] = PILImageResampling.BILINEAR							,      _a   :			Union[str, Any] = None							,      **_a   :			str							,      )			->					List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=get_size_dict(__snake_case				)
       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(
           __snake_case							,      size=(size['height'], size['width'])							,      resample=__snake_case							,      data_format=__snake_case							,      **__snake_case				)





  def 		A						(	self   :			Optional[Any]							,      _a   :			Optional[Any]							,      _a   :			Optional[int]							,      _a   :			Tuple = None							,      **_a   :			str				)			->					List[str]:





       '''simple docstring'''






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





  def 		A						(	self   :			Optional[Any]							,      _a   :			Tuple							,      _a   :			Tuple							,      _a   :			str							,      _a   :			List[str] = None							,      **_a   :			int							,      )			->					Dict:





       '''simple docstring'''






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





  def 		A						(	self   :			List[str]							,      _a   :			Tuple							,      _a   :			Tuple = None							,      _a   :			Tuple = None							,      _a   :			Optional[Any] = None							,      _a   :			str = None							,      _a   :			Optional[Any] = None							,      _a   :			Tuple = None							,      _a   :			Optional[Any] = None							,      _a   :			str = None							,      _a   :			int = None							,      _a   :			int = ChannelDimension.FIRST							,      **_a   :			List[Any]							,      )			->					Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=do_resize if do_resize is not None else self.do_resize
       _SCREAMING_SNAKE_CASE												=do_rescale if do_rescale is not None else self.do_rescale
       _SCREAMING_SNAKE_CASE												=do_normalize if do_normalize is not None else self.do_normalize
       _SCREAMING_SNAKE_CASE												=resample if resample is not None else self.resample
       _SCREAMING_SNAKE_CASE												=rescale_factor if rescale_factor is not None else self.rescale_factor
       _SCREAMING_SNAKE_CASE												=image_mean if image_mean is not None else self.image_mean
       _SCREAMING_SNAKE_CASE												=image_std if image_std is not None else self.image_std

       _SCREAMING_SNAKE_CASE												=size if size is not None else self.size
       _SCREAMING_SNAKE_CASE												=get_size_dict(__snake_case				)

       _SCREAMING_SNAKE_CASE												=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_rescale and rescale_factor is None:
            raise ValueError('Rescale factor must be specified if do_rescale is True.'				)

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

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

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

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

       _SCREAMING_SNAKE_CASE												=[to_channel_dimension_format(__snake_case							,      __snake_case				) for image in images]

       _SCREAMING_SNAKE_CASE												={'pixel_values': images}
       return BatchFeature(data=__snake_case							,      tensor_type=__snake_case				)



'''simple docstring'''
from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


lowerCamelCase				:   Dict      				=  datasets.utils.logging.get_logger(__name__)







class 							A__					( folder_based_builder.FolderBasedBuilderConfig						):
  A__					=       None
  A__					=       None







class 							A__					( folder_based_builder.FolderBasedBuilder						):
  A__					=       datasets.Image()
  A__					=       'image'
  A__					=       ImageFolderConfig
  A__					=       42  # definition at the bottom of the script
  A__					=       ImageClassification(image_column='image'  ,     label_column='label'						)

lowerCamelCase				:   Optional[int]      				=  [
    ".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",
]
lowerCamelCase				:   Any      				=  IMAGE_EXTENSIONS
