




from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


_lowerCamelCase      			=						logging.get_logger(__name__)



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	Tuple       		=  ['pixel_values']
     def __init__(     self       :      Tuple ,   __snake_case       :      bool = True ,   __snake_case       :      Union[int, float] = 1 / 2_55 ,   __snake_case       :      bool = True ,   __snake_case       :      int = 8 ,   **__snake_case       :      int ,   ):
         super().__init__(**__snake_case    )

         UpperCAmelCase_										=					do_rescale
         UpperCAmelCase_										=					rescale_factor
         UpperCAmelCase_										=					do_pad
         UpperCAmelCase_										=					pad_size
     def   lowerCamelCase_       (     self       :      Optional[int] ,   __snake_case       :      np.ndarray ,   __snake_case       :      float ,   __snake_case       :      Optional[Union[str, ChannelDimension]] = None ,   **__snake_case       :      Union[str, Any]    ):
         return rescale(__snake_case ,   scale=__snake_case ,   data_format=__snake_case ,   **__snake_case    )
     def   lowerCamelCase_       (     self       :      str ,   __snake_case       :      np.ndarray ,   __snake_case       :      int ,   __snake_case       :      Optional[Union[str, ChannelDimension]] = None    ):
         UpperCAmelCase_			,							UpperCAmelCase_										=					get_image_size(__snake_case    )
         UpperCAmelCase_										=					(old_height // size + 1) * size - old_height
         UpperCAmelCase_										=					(old_width // size + 1) * size - old_width

         return pad(__snake_case ,   ((0, pad_height), (0, pad_width)) ,   mode='''symmetric''' ,   data_format=__snake_case    )




     def   lowerCamelCase_       (     self       :      List[Any] ,   __snake_case       :      ImageInput ,   __snake_case       :      Optional[bool] = None ,   __snake_case       :      Optional[float] = None ,   __snake_case       :      Optional[bool] = None ,   __snake_case       :      Optional[int] = None ,   __snake_case       :      Optional[Union[str, TensorType]] = None ,   __snake_case       :      Union[str, ChannelDimension] = ChannelDimension.FIRST ,   **__snake_case       :      int ,   ):
         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_pad if do_pad is not None else self.do_pad
         UpperCAmelCase_										=					pad_size if pad_size is not None else self.pad_size

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

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

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

         if do_pad:
             UpperCAmelCase_										=					[self.pad(__snake_case ,   size=__snake_case    ) for image in images]

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

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











import numpy as np




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						np.array				)  ->							np.array:
    return (2 / (1 + np.exp(-2 * vector				))) - 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()





