

"""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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


A									=						logging.get_logger(__name__)

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    ['pixel_values']



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


         '''simple docstring'''





         super().__init__(**snake_case      )
         __magic_name__			:		Union[str, Any]        =     crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
         __magic_name__			:		Optional[Any]        =     get_size_dict(snake_case       ,       param_name='''crop_size'''      )
         __magic_name__			:		List[Any]        =     size if size is not None else {'''height''': 224, '''width''': 224}
         __magic_name__			:		int        =     get_size_dict(snake_case      )

         __magic_name__			:		Dict        =     do_center_crop
         __magic_name__			:		List[Any]        =     crop_size
         __magic_name__			:		Optional[Any]        =     do_resize
         __magic_name__			:		List[str]        =     size
         __magic_name__			:		Tuple        =     resample
         __magic_name__			:		List[str]        =     do_rescale
         __magic_name__			:		Dict        =     rescale_factor
         __magic_name__			:		List[Any]        =     do_normalize
         __magic_name__			:		Union[str, Any]        =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         __magic_name__			:		List[str]        =     image_std if image_std is not None else IMAGENET_DEFAULT_STD



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


         '''simple docstring'''





         __magic_name__			:		str        =     self.size if size is None else size
         __magic_name__			:		int        =     get_size_dict(snake_case      )
         __magic_name__			:		int        =     get_size_dict(snake_case       ,       param_name='''crop_size'''      )

         __magic_name__       ,							__magic_name__			:		Optional[Any]        =     get_image_size(snake_case      )
         __magic_name__			:		int        =     min(snake_case       ,       snake_case      )
         __magic_name__			:		Optional[int]        =     (size['''height'''] / crop_size['''height''']) * min_dim
         __magic_name__			:		Union[str, Any]        =     (size['''width'''] / crop_size['''width''']) * min_dim
         return center_crop(snake_case       ,       size=(cropped_height, cropped_width)       ,       data_format=snake_case       ,       **snake_case      )



  def 							_UpperCAmelCase      ( self					: str       ,       snake_case					: np.ndarray       ,       snake_case					: Dict[str, int]       ,       snake_case					: PILImageResampling = PIL.Image.BICUBIC       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: Optional[int]       ,       )	->			np.ndarray:


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     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 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: np.ndarray       ,       snake_case					: Union[int, float]       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: Optional[Any]       ,       )	->			int:


         '''simple docstring'''





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



  def 							_UpperCAmelCase      ( self					: Any       ,       snake_case					: np.ndarray       ,       snake_case					: Union[float, List[float]]       ,       snake_case					: Union[float, List[float]]       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: Any       ,       )	->			np.ndarray:


         '''simple docstring'''





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




  def 							_UpperCAmelCase      ( self					: Optional[Any]       ,       snake_case					: ImageInput       ,       snake_case					: Optional[bool] = None       ,       snake_case					: Optional[Dict[str, int]] = None       ,       snake_case					: Optional[bool] = None       ,       snake_case					: Optional[Dict[str, int]] = None       ,       snake_case					: PILImageResampling = None       ,       snake_case					: Optional[bool] = None       ,       snake_case					: Optional[float] = None       ,       snake_case					: Optional[bool] = None       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: Optional[Union[str, TensorType]] = None       ,       snake_case					: ChannelDimension = ChannelDimension.FIRST       ,       **snake_case					: Any       ,       )	->			PIL.Image.Image:


         '''simple docstring'''





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

         __magic_name__			:		Dict        =     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_center_crop and crop_size is None:
                raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.'''      )

         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.'''      )

         if do_normalize and (image_mean is None or image_std is None):
                raise ValueError('''Image mean and image standard deviation must be specified if do_normalize is True.'''      )

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

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

         if do_resize:
                __magic_name__			:		int        =     [self.resize(image=snake_case       ,       size=snake_case       ,       resample=snake_case      ) for image in images]

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

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

         __magic_name__			:		Optional[Any]        =     [to_channel_dimension_format(snake_case       ,       snake_case      ) for image in images]

         __magic_name__			:		List[str]        =     {'''pixel_values''': images}
         return BatchFeature(data=snake_case       ,       tensor_type=snake_case      )



"""simple docstring"""
def       UpperCamelCase_					(     lowerCamelCase							:					str   )						->     str:



       """simple docstring"""







       return " ".join([word[::-1] for word in input_str.split()]   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
