
'''simple docstring'''
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


A__   :		List[Any] 		=logging.get_logger(__name__)







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       =['''pixel_values''']
 def __init__(   self			:    Optional[Any]				,   lowerCamelCase			:    bool = True				,   lowerCamelCase			:    Union[int, float] = 1 / 2_55				,   lowerCamelCase			:    bool = True				,   lowerCamelCase			:    int = 8				,   **lowerCamelCase			:    Optional[Any]				,   ):



   """simple docstring"""





   super().__init__(**lowerCamelCase       )

   __A		:     Optional[Any]												=		do_rescale
   __A		:     Union[str, Any]												=		rescale_factor
   __A		:     List[Any]												=		do_pad
   __A		:     Union[str, Any]												=		pad_size
 def 		lowercase_(   self			:    Any				,   lowerCamelCase			:    np.ndarray				,   lowerCamelCase			:    float				,   lowerCamelCase			:    Optional[Union[str, ChannelDimension]] = None				,   **lowerCamelCase			:    List[Any]       ):



   """simple docstring"""





   return rescale(lowerCamelCase				,   scale=lowerCamelCase				,   data_format=lowerCamelCase				,   **lowerCamelCase       )
 def 		lowercase_(   self			:    Tuple				,   lowerCamelCase			:    np.ndarray				,   lowerCamelCase			:    int				,   lowerCamelCase			:    Optional[Union[str, ChannelDimension]] = None       ):



   """simple docstring"""





   __A							,					__A		:     Dict												=		get_image_size(lowerCamelCase       )
   __A		:     List[Any]												=		(old_height // size + 1) * size - old_height
   __A		:     Any												=		(old_width // size + 1) * size - old_width

   return pad(lowerCamelCase				,   ((0, pad_height), (0, pad_width))				,   mode="""symmetric"""				,   data_format=lowerCamelCase       )






 def 		lowercase_(   self			:    str				,   lowerCamelCase			:    ImageInput				,   lowerCamelCase			:    Optional[bool] = None				,   lowerCamelCase			:    Optional[float] = None				,   lowerCamelCase			:    Optional[bool] = None				,   lowerCamelCase			:    Optional[int] = None				,   lowerCamelCase			:    Optional[Union[str, TensorType]] = None				,   lowerCamelCase			:    Union[str, ChannelDimension] = ChannelDimension.FIRST				,   **lowerCamelCase			:    Union[str, Any]				,   ):



   """simple docstring"""





   __A		:     Tuple												=		do_rescale if do_rescale is not None else self.do_rescale
   __A		:     int												=		rescale_factor if rescale_factor is not None else self.rescale_factor
   __A		:     List[str]												=		do_pad if do_pad is not None else self.do_pad
   __A		:     List[Any]												=		pad_size if pad_size is not None else self.pad_size

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

   # All transformations expect numpy arrays.
   __A		:     List[Any]												=		[to_numpy_array(lowerCamelCase       ) for image in images]

   if do_rescale:
     __A		:     Any												=		[self.rescale(image=lowerCamelCase				,   scale=lowerCamelCase       ) for image in images]

   if do_pad:
     __A		:     List[Any]												=		[self.pad(lowerCamelCase				,   size=lowerCamelCase       ) for image in images]

   __A		:     Any												=		[to_channel_dimension_format(lowerCamelCase				,   lowerCamelCase       ) for image in images]

   __A		:     List[str]												=		{"""pixel_values""": images}
   return BatchFeature(data=lowerCamelCase				,   tensor_type=lowerCamelCase       )





'''simple docstring'''
from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
     import accelerate







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Optional[int]:





  """simple docstring"""

  if not is_accelerate_available():
    return method
  __A		:     int												=		version.parse(accelerate.__version__ ).base_version
  if version.parse(__SCREAMING_SNAKE_CASE ) < version.parse("""0.17.0""" ):
    return method

  def wrapper(self						:  str			,     *__SCREAMING_SNAKE_CASE						:  Tuple			,     **__SCREAMING_SNAKE_CASE						:  Tuple ):
    if hasattr(self			,     """_hf_hook""" ) and hasattr(self._hf_hook			,     """pre_forward""" ):
      self._hf_hook.pre_forward(self )
    return method(self			,     *__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE )

  return wrapper



