


'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
     import PIL

__magic_name__			=      logging.get_logger(__name__)




def      lowerCamelCase					(      lowerCamelCase  :      str):
      if isinstance(lowerCamelCase							,						(list, tuple)) and isinstance(videos[0]							,						(list, tuple)) and is_valid_image(videos[0][0]):
            return videos

      elif isinstance(lowerCamelCase							,						(list, tuple)) and is_valid_image(videos[0]):
            return [videos]

      elif is_valid_image(lowerCamelCase):
            return [[videos]]

      raise ValueError(F'Could not make batched video from {videos}')






class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""pixel_values"""]






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

        '''simple docstring'''




        super().__init__(**_a )
        A_						:  Union[str, Any]          =				size if size is not None else {"""shortest_edge""": 256}
        A_						:  Any          =				get_size_dict(_a	,default_to_square=_a )
        A_						:  List[str]          =				crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        A_						:  str          =				get_size_dict(_a	,param_name="""crop_size""" )

        A_						:  Any          =				do_resize
        A_						:  List[str]          =				size
        A_						:  Optional[int]          =				do_center_crop
        A_						:  Dict          =				crop_size
        A_						:  str          =				resample
        A_						:  Tuple          =				do_rescale
        A_						:  int          =				rescale_factor
        A_						:  Dict          =				offset
        A_						:  Optional[Any]          =				do_normalize
        A_						:  Optional[int]          =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        A_						:  List[str]          =				image_std if image_std is not None else IMAGENET_STANDARD_STD






  def        _a							(							self :       List[Any]	,_a :       np.ndarray	,_a :       Dict[str, int]	,_a :       PILImageResampling = PILImageResampling.BILINEAR	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       Dict	,):

        '''simple docstring'''




        A_						:  int          =				get_size_dict(_a	,default_to_square=_a )
        if "shortest_edge" in size:
              A_						:  Dict          =				get_resize_output_image_size(_a	,size["""shortest_edge"""]	,default_to_square=_a )
        elif "height" in size and "width" in size:
              A_						:  List[Any]          =				(size["""height"""], size["""width"""])
        else:
              raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}' )
        return resize(_a	,size=_a	,resample=_a	,data_format=_a	,**_a )






  def        _a							(							self :       Any	,_a :       np.ndarray	,_a :       Dict[str, int]	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       List[Any]	,):

        '''simple docstring'''




        A_						:  Tuple          =				get_size_dict(_a )
        if "height" not in size or "width" not in size:
              raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}' )
        return center_crop(_a	,size=(size["""height"""], size["""width"""])	,data_format=_a	,**_a )






  def        _a							(							self :       int	,_a :       np.ndarray	,_a :       Union[int, float]	,_a :       bool = True	,_a :       Optional[Union[str, ChannelDimension]] = None	,**_a :       List[str]	,):

        '''simple docstring'''




        A_						:  Optional[int]          =				image.astype(np.floataa )
        if offset:
              A_						:  Optional[int]          =				image - (scale / 2)
        return rescale(_a	,scale=_a	,data_format=_a	,**_a )






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

        '''simple docstring'''




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






  def        _a							(							self :       Tuple	,_a :       ImageInput	,_a :       bool = None	,_a :       Dict[str, int] = None	,_a :       PILImageResampling = None	,_a :       bool = None	,_a :       Dict[str, int] = None	,_a :       bool = None	,_a :       float = None	,_a :       bool = None	,_a :       bool = None	,_a :       Optional[Union[float, List[float]]] = None	,_a :       Optional[Union[float, List[float]]] = None	,_a :       Optional[ChannelDimension] = ChannelDimension.FIRST	,):

        '''simple docstring'''




        if do_resize and size is None or resample is None:
              raise ValueError("""Size and resample 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.""" )

        if offset and not do_rescale:
              raise ValueError("""For offset, do_rescale must also be set to True.""" )

        # All transformations expect numpy arrays.
        A_						:  Optional[int]          =				to_numpy_array(_a )

        if do_resize:
              A_						:  Tuple          =				self.resize(image=_a	,size=_a	,resample=_a )

        if do_center_crop:
              A_						:  Optional[int]          =				self.center_crop(_a	,size=_a )

        if do_rescale:
              A_						:  Any          =				self.rescale(image=_a	,scale=_a	,offset=_a )

        if do_normalize:
              A_						:  Tuple          =				self.normalize(image=_a	,mean=_a	,std=_a )

        A_						:  int          =				to_channel_dimension_format(_a	,_a )
        return image






  def        _a							(							self :       int	,_a :       ImageInput	,_a :       bool = None	,_a :       Dict[str, int] = None	,_a :       PILImageResampling = None	,_a :       bool = None	,_a :       Dict[str, int] = None	,_a :       bool = None	,_a :       float = None	,_a :       bool = None	,_a :       bool = None	,_a :       Optional[Union[float, List[float]]] = None	,_a :       Optional[Union[float, List[float]]] = None	,_a :       Optional[Union[str, TensorType]] = None	,_a :       ChannelDimension = ChannelDimension.FIRST	,**_a :       List[Any]	,):

        '''simple docstring'''




        A_						:  str          =				do_resize if do_resize is not None else self.do_resize
        A_						:  Tuple          =				resample if resample is not None else self.resample
        A_						:  Any          =				do_center_crop if do_center_crop is not None else self.do_center_crop
        A_						:  Dict          =				do_rescale if do_rescale is not None else self.do_rescale
        A_						:  List[str]          =				rescale_factor if rescale_factor is not None else self.rescale_factor
        A_						:  int          =				offset if offset is not None else self.offset
        A_						:  List[str]          =				do_normalize if do_normalize is not None else self.do_normalize
        A_						:  Union[str, Any]          =				image_mean if image_mean is not None else self.image_mean
        A_						:  int          =				image_std if image_std is not None else self.image_std

        A_						:  str          =				size if size is not None else self.size
        A_						:  List[Any]          =				get_size_dict(_a	,default_to_square=_a )
        A_						:  Tuple          =				crop_size if crop_size is not None else self.crop_size
        A_						:  List[str]          =				get_size_dict(_a	,param_name="""crop_size""" )

        if not valid_images(_a ):
              raise ValueError(
                  """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                  """torch.Tensor, tf.Tensor or jax.ndarray.""" )

        A_						:  List[Any]          =				make_batched(_a )

        A_						:  List[Any]          =				[
            [
                self._preprocess_image(
                    image=_a	,do_resize=_a	,size=_a	,resample=_a	,do_center_crop=_a	,crop_size=_a	,do_rescale=_a	,rescale_factor=_a	,offset=_a	,do_normalize=_a	,image_mean=_a	,image_std=_a	,data_format=_a	,)
                for img in video
            ]
            for video in videos
        ]

        A_						:  List[Any]          =				{"""pixel_values""": videos}
        return BatchFeature(data=_a	,tensor_type=_a )




'''simple docstring'''


import baseaa




def      lowerCamelCase					(      lowerCamelCase  :      str):
      return baseaa.aaaencode(string.encode("""utf-8"""))




def      lowerCamelCase					(      lowerCamelCase  :      bytes):
      return baseaa.aaadecode(lowerCamelCase).decode("""utf-8""")


if __name__ == "__main__":
     import doctest

     doctest.testmod()
