






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


__magic_name__   :  Dict        =							logging.get_logger(__name__)


if is_vision_available():
       import PIL





class 					A__     (     __snake_case ):

     '''simple docstring'''






     snake_case__					=					["""pixel_values"""]







     def __init__( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    Dict[str, int] = None   ,						_SCREAMING_SNAKE_CASE      :    PILImageResampling = PILImageResampling.BICUBIC   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    Dict[str, int] = None   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    Union[int, float] = 1 / 255   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[float, List[float]]] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[float, List[float]]] = None   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						**_SCREAMING_SNAKE_CASE      :    Optional[int]   ,						):






           """simple docstring"""




           super().__init__(**_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  size if size is not None else {'shortest_edge': 224}
           UpperCamelCase					       =  get_size_dict(_SCREAMING_SNAKE_CASE   ,						default_to_square=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  crop_size if crop_size is not None else {'height': 224, 'width': 224}
           UpperCamelCase					       =  get_size_dict(_SCREAMING_SNAKE_CASE   ,						default_to_square=_SCREAMING_SNAKE_CASE   ,						param_name='crop_size' )

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







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    np.ndarray   ,						_SCREAMING_SNAKE_CASE      :    Dict[str, int]   ,						_SCREAMING_SNAKE_CASE      :    PILImageResampling = PILImageResampling.BICUBIC   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, ChannelDimension]] = None   ,						**_SCREAMING_SNAKE_CASE      :    Union[str, Any]   ,						):






           """simple docstring"""




           UpperCamelCase					       =  get_size_dict(_SCREAMING_SNAKE_CASE   ,						default_to_square=_SCREAMING_SNAKE_CASE )
           if "shortest_edge" not in size:
                 raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
           UpperCamelCase					       =  get_resize_output_image_size(_SCREAMING_SNAKE_CASE   ,						size=size['shortest_edge']   ,						default_to_square=_SCREAMING_SNAKE_CASE )
           return resize(_SCREAMING_SNAKE_CASE   ,						size=_SCREAMING_SNAKE_CASE   ,						resample=_SCREAMING_SNAKE_CASE   ,						data_format=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    np.ndarray   ,						_SCREAMING_SNAKE_CASE      :    Dict[str, int]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, ChannelDimension]] = None   ,						**_SCREAMING_SNAKE_CASE      :    int   ,						):






           """simple docstring"""




           UpperCamelCase					       =  get_size_dict(_SCREAMING_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(_SCREAMING_SNAKE_CASE   ,						size=(size['height'], size['width'])   ,						data_format=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    int   ,						_SCREAMING_SNAKE_CASE      :    np.ndarray   ,						_SCREAMING_SNAKE_CASE      :    Union[int, float]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, ChannelDimension]] = None   ,						**_SCREAMING_SNAKE_CASE      :    List[str]   ,						):






           """simple docstring"""




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







     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    np.ndarray   ,						_SCREAMING_SNAKE_CASE      :    Union[float, List[float]]   ,						_SCREAMING_SNAKE_CASE      :    Union[float, List[float]]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, ChannelDimension]] = None   ,						**_SCREAMING_SNAKE_CASE      :    int   ,						):






           """simple docstring"""




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







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    ImageInput   ,						_SCREAMING_SNAKE_CASE      :    bool = None   ,						_SCREAMING_SNAKE_CASE      :    Dict[str, int] = None   ,						_SCREAMING_SNAKE_CASE      :    PILImageResampling = None   ,						_SCREAMING_SNAKE_CASE      :    bool = None   ,						_SCREAMING_SNAKE_CASE      :    int = None   ,						_SCREAMING_SNAKE_CASE      :    bool = None   ,						_SCREAMING_SNAKE_CASE      :    float = None   ,						_SCREAMING_SNAKE_CASE      :    bool = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[float, List[float]]] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[float, List[float]]] = None   ,						_SCREAMING_SNAKE_CASE      :    bool = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, TensorType]] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[ChannelDimension] = ChannelDimension.FIRST   ,						**_SCREAMING_SNAKE_CASE      :    List[Any]   ,						):






           """simple docstring"""




           UpperCamelCase					       =  do_resize if do_resize is not None else self.do_resize
           UpperCamelCase					       =  size if size is not None else self.size
           UpperCamelCase					       =  get_size_dict(_SCREAMING_SNAKE_CASE   ,						param_name='size'   ,						default_to_square=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  resample if resample is not None else self.resample
           UpperCamelCase					       =  do_center_crop if do_center_crop is not None else self.do_center_crop
           UpperCamelCase					       =  crop_size if crop_size is not None else self.crop_size
           UpperCamelCase					       =  get_size_dict(_SCREAMING_SNAKE_CASE   ,						param_name='crop_size'   ,						default_to_square=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase					       =  rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCamelCase					       =  do_normalize if do_normalize is not None else self.do_normalize
           UpperCamelCase					       =  image_mean if image_mean is not None else self.image_mean
           UpperCamelCase					       =  image_std if image_std is not None else self.image_std
           UpperCamelCase					       =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           UpperCamelCase					       =  make_list_of_images(_SCREAMING_SNAKE_CASE )

           if not valid_images(_SCREAMING_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:
                 UpperCamelCase					       =  [convert_to_rgb(_SCREAMING_SNAKE_CASE ) for image in images]

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

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

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

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

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

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

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











def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			bool:
      """simple docstring"""

      return numa ^ numa < 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()



