


'''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


lowercase				=logging.get_logger(__name__)


if is_vision_available():
 import PIL






class    __magic_name__				(						lowerCAmelCase							):
       UpperCAmelCase		 =["pixel_values"]






       def __init__(     self    ,				snake_case = True    ,				snake_case = None    ,				snake_case = PILImageResampling.BICUBIC    ,				snake_case = True    ,				snake_case = None    ,				snake_case = True    ,				snake_case = 1 / 2_5_5    ,				snake_case = True    ,				snake_case = None    ,				snake_case = None    ,				snake_case = True    ,				**snake_case    ,				)		->    None:




          '''simple docstring'''
          super().__init__(**snake_case)
          _UpperCAmelCase  :  Optional[int]				       =size if size is not None else {'shortest_edge': 2_2_4}
          _UpperCAmelCase  :  List[Any]				       =get_size_dict(snake_case    ,				default_to_square=snake_case)
          _UpperCAmelCase  :  Optional[int]				       =crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
          _UpperCAmelCase  :  Dict				       =get_size_dict(snake_case    ,				default_to_square=snake_case    ,				param_name='crop_size')

          _UpperCAmelCase  :  int				       =do_resize
          _UpperCAmelCase  :  List[Any]				       =size
          _UpperCAmelCase  :  str				       =resample
          _UpperCAmelCase  :  str				       =do_center_crop
          _UpperCAmelCase  :  Optional[int]				       =crop_size
          _UpperCAmelCase  :  List[Any]				       =do_rescale
          _UpperCAmelCase  :  Optional[int]				       =rescale_factor
          _UpperCAmelCase  :  Tuple				       =do_normalize
          _UpperCAmelCase  :  Optional[int]				       =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          _UpperCAmelCase  :  int				       =image_std if image_std is not None else OPENAI_CLIP_STD
          _UpperCAmelCase  :  Tuple				       =do_convert_rgb






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case = PILImageResampling.BICUBIC    ,				snake_case = None    ,				**snake_case    ,				)		->    np.ndarray:




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






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case = None    ,				**snake_case    ,				)		->    np.ndarray:




          '''simple docstring'''
          _UpperCAmelCase  :  str				       =get_size_dict(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(snake_case    ,				size=(size['height'], size['width'])    ,				data_format=snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case = None    ,				**snake_case    ,				)		->    List[Any]:




          '''simple docstring'''
          return rescale(snake_case    ,				scale=snake_case    ,				data_format=snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case = None    ,				**snake_case    ,				)		->    np.ndarray:




          '''simple docstring'''
          return normalize(snake_case    ,				mean=snake_case    ,				std=snake_case    ,				data_format=snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = ChannelDimension.FIRST    ,				**snake_case    ,				)		->    PIL.Image.Image:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =do_resize if do_resize is not None else self.do_resize
          _UpperCAmelCase  :  int				       =size if size is not None else self.size
          _UpperCAmelCase  :  int				       =get_size_dict(snake_case    ,				param_name='size'    ,				default_to_square=snake_case)
          _UpperCAmelCase  :  List[Any]				       =resample if resample is not None else self.resample
          _UpperCAmelCase  :  List[Any]				       =do_center_crop if do_center_crop is not None else self.do_center_crop
          _UpperCAmelCase  :  Optional[int]				       =crop_size if crop_size is not None else self.crop_size
          _UpperCAmelCase  :  Tuple				       =get_size_dict(snake_case    ,				param_name='crop_size'    ,				default_to_square=snake_case)
          _UpperCAmelCase  :  List[Any]				       =do_rescale if do_rescale is not None else self.do_rescale
          _UpperCAmelCase  :  Dict				       =rescale_factor if rescale_factor is not None else self.rescale_factor
          _UpperCAmelCase  :  List[str]				       =do_normalize if do_normalize is not None else self.do_normalize
          _UpperCAmelCase  :  Tuple				       =image_mean if image_mean is not None else self.image_mean
          _UpperCAmelCase  :  int				       =image_std if image_std is not None else self.image_std
          _UpperCAmelCase  :  List[Any]				       =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          _UpperCAmelCase  :  str				       =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_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  :  Optional[int]				       =[convert_to_rgb(snake_case) for image in images]

          # All transformations expect numpy arrays.
          _UpperCAmelCase  :  Dict				       =[to_numpy_array(snake_case) for image in images]

          if do_resize:
             _UpperCAmelCase  :  List[str]				       =[self.resize(image=snake_case    ,				size=snake_case    ,				resample=snake_case) for image in images]

          if do_center_crop:
             _UpperCAmelCase  :  List[Any]				       =[self.center_crop(image=snake_case    ,				size=snake_case) for image in images]

          if do_rescale:
             _UpperCAmelCase  :  Union[str, Any]				       =[self.rescale(image=snake_case    ,				scale=snake_case) for image in images]

          if do_normalize:
             _UpperCAmelCase  :  Optional[Any]				       =[self.normalize(image=snake_case    ,				mean=snake_case    ,				std=snake_case) for image in images]

          _UpperCAmelCase  :  Union[str, Any]				       =[to_channel_dimension_format(snake_case    ,				snake_case) for image in images]

          _UpperCAmelCase  :  Any				       ={'pixel_values': images}
          return BatchFeature(data=snake_case    ,				tensor_type=snake_case)





'''simple docstring'''






import warnings
from functools import wraps
from typing import Callable





def lowerCamelCase__     (		__lowerCamelCase			:				Callable				):







   '''simple docstring'''




   @wraps(__lowerCamelCase				)
   def _inner_fn(*__lowerCamelCase			:				int			,       **__lowerCamelCase			:				Optional[Any]				):
      warnings.warn(
          (f"'{fn.__name__}' is experimental and might be subject to breaking changes in the future.")			,       __lowerCamelCase			,       )
      return fn(*__lowerCamelCase			,       **__lowerCamelCase				)

   return _inner_fn

