






'''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


UpperCamelCase__ =			logging.get_logger(__name__)


if is_vision_available():
      import PIL


class        _UpperCAmelCase						(		__a			):
    __lowerCamelCase:   Tuple		       =    ["pixel_values"]




    def __init__(			self  :       Any  ,     a  :       Any = True  ,     a  :       Optional[Any] = None  ,     a  :       List[str] = PILImageResampling.BICUBIC  ,     a  :       Optional[Any] = True  ,     a  :       str = None  ,     a  :       Optional[int] = True  ,     a  :       Optional[int] = 1 / 2_5_5  ,     a  :       Tuple = True  ,     a  :       Tuple = None  ,     a  :       List[str] = None  ,     a  :       Tuple = True  ,     **a  :       List[Any]  ,     ):






       '''simple docstring'''






       super().__init__(**lowerCAmelCase_			)
       lowercase_	:    List[Any]           =     size if size is not None else {"shortest_edge": 2_2_4}
       lowercase_	:    Dict           =     get_size_dict(lowerCAmelCase_  ,     default_to_square=lowerCAmelCase_			)
       lowercase_	:    Union[str, Any]           =     crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
       lowercase_	:    Union[str, Any]           =     get_size_dict(lowerCAmelCase_  ,     default_to_square=lowerCAmelCase_  ,     param_name="crop_size"			)

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




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Any  ,     a  :       Union[str, Any]  ,     a  :       int = PILImageResampling.BICUBIC  ,     a  :       Tuple = None  ,     **a  :       Optional[int]  ,     ):






       '''simple docstring'''






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




    def 				lowerCAmelCase__				(			self  :       Optional[Any]  ,     a  :       Optional[int]  ,     a  :       Optional[Any]  ,     a  :       str = None  ,     **a  :       List[str]  ,     ):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     get_size_dict(lowerCAmelCase_			)
       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(lowerCAmelCase_  ,     size=(size["height"], size["width"])  ,     data_format=lowerCAmelCase_  ,     **lowerCAmelCase_			)




    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       Optional[int]  ,     a  :       Dict  ,     a  :       List[str] = None  ,     **a  :       Tuple  ,     ):






       '''simple docstring'''






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




    def 				lowerCAmelCase__				(			self  :       Optional[Any]  ,     a  :       str  ,     a  :       Union[str, Any]  ,     a  :       int  ,     a  :       Optional[Any] = None  ,     **a  :       Any  ,     ):






       '''simple docstring'''






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





    def 				lowerCAmelCase__				(			self  :       str  ,     a  :       List[Any]  ,     a  :       str = None  ,     a  :       str = None  ,     a  :       Union[str, Any] = None  ,     a  :       Union[str, Any] = None  ,     a  :       Union[str, Any] = None  ,     a  :       Any = None  ,     a  :       int = None  ,     a  :       List[Any] = None  ,     a  :       Tuple = None  ,     a  :       Any = None  ,     a  :       Optional[Any] = None  ,     a  :       List[Any] = None  ,     a  :       Optional[int] = ChannelDimension.FIRST  ,     **a  :       int  ,     ):






       '''simple docstring'''






       lowercase_	:    Dict           =     do_resize if do_resize is not None else self.do_resize
       lowercase_	:    str           =     size if size is not None else self.size
       lowercase_	:    str           =     get_size_dict(lowerCAmelCase_  ,     param_name="size"  ,     default_to_square=lowerCAmelCase_			)
       lowercase_	:    Union[str, Any]           =     resample if resample is not None else self.resample
       lowercase_	:    Any           =     do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase_	:    Any           =     crop_size if crop_size is not None else self.crop_size
       lowercase_	:    List[Any]           =     get_size_dict(lowerCAmelCase_  ,     param_name="crop_size"  ,     default_to_square=lowerCAmelCase_			)
       lowercase_	:    Optional[int]           =     do_rescale if do_rescale is not None else self.do_rescale
       lowercase_	:    List[Any]           =     rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase_	:    List[str]           =     do_normalize if do_normalize is not None else self.do_normalize
       lowercase_	:    Dict           =     image_mean if image_mean is not None else self.image_mean
       lowercase_	:    Optional[int]           =     image_std if image_std is not None else self.image_std
       lowercase_	:    List[str]           =     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       lowercase_	:    Optional[Any]           =     make_list_of_images(lowerCAmelCase_			)

       if not valid_images(lowerCAmelCase_			):
          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:
          lowercase_	:    Union[str, Any]           =     [convert_to_rgb(lowerCAmelCase_			) for image in images]

       # All transformations expect numpy arrays.
       lowercase_	:    Union[str, Any]           =     [to_numpy_array(lowerCAmelCase_			) for image in images]

       if do_resize:
          lowercase_	:    Any           =     [self.resize(image=lowerCAmelCase_  ,     size=lowerCAmelCase_  ,     resample=lowerCAmelCase_			) for image in images]

       if do_center_crop:
          lowercase_	:    Tuple           =     [self.center_crop(image=lowerCAmelCase_  ,     size=lowerCAmelCase_			) for image in images]

       if do_rescale:
          lowercase_	:    List[str]           =     [self.rescale(image=lowerCAmelCase_  ,     scale=lowerCAmelCase_			) for image in images]

       if do_normalize:
          lowercase_	:    List[str]           =     [self.normalize(image=lowerCAmelCase_  ,     mean=lowerCAmelCase_  ,     std=lowerCAmelCase_			) for image in images]

       lowercase_	:    int           =     [to_channel_dimension_format(lowerCAmelCase_  ,     lowerCAmelCase_			) for image in images]

       lowercase_	:    Any           =     {"pixel_values": images}
       return BatchFeature(data=lowerCAmelCase_  ,     tensor_type=lowerCAmelCase_			)












'''simple docstring'''
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   if number < 0:
      raise ValueError("number must not be negative"							)
   return number & (number - 1) == 0


if __name__ == "__main__":
 import doctest

 doctest.testmod()




