





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

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



def 						A_							(	_lowerCAmelCase	)		->					List[List[ImageInput]]:
 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    A__  (			__snake_case     ):
      _UpperCAmelCase      :Union[str, Any]		       =  ['pixel_values']





      def __init__(					self			,				A_ = True			,				A_ = None			,				A_ = PILImageResampling.BILINEAR			,				A_ = True			,				A_ = None			,				A_ = True			,				A_ = 1 / 255			,				A_ = True			,				A_ = True			,				A_ = None			,				A_ = None			,				**A_			,				):
       '''simple docstring'''




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

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





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_ = PILImageResampling.BILINEAR			,				A_ = None			,				**A_			,				):
       '''simple docstring'''




       UpperCamelCase      :   List[Any]			      =  get_size_dict(A_			,				default_to_square=A_  )
       if "shortest_edge" in size:
        UpperCamelCase      :   int			      =  get_resize_output_image_size(A_			,				size["shortest_edge"]			,				default_to_square=A_  )
       elif "height" in size and "width" in size:
        UpperCamelCase      :   Dict			      =  (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 			__UpperCamelCase(					self			,				A_			,				A_			,				A_ = None			,				**A_			,				):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  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 			__UpperCamelCase(					self			,				A_			,				A_			,				A_ = True			,				A_ = None			,				**A_			,				):
       '''simple docstring'''




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





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_			,				A_ = None			,				**A_			,				):
       '''simple docstring'''




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





      def 			__UpperCamelCase(					self			,				A_			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = 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.
       UpperCamelCase      :   Dict			      =  to_numpy_array(A_  )

       if do_resize:
        UpperCamelCase      :   List[Any]			      =  self.resize(image=A_			,				size=A_			,				resample=A_  )

       if do_center_crop:
        UpperCamelCase      :   Dict			      =  self.center_crop(A_			,				size=A_  )

       if do_rescale:
        UpperCamelCase      :   Dict			      =  self.rescale(image=A_			,				scale=A_			,				offset=A_  )

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

       UpperCamelCase      :   Dict			      =  to_channel_dimension_format(A_			,				A_  )
       return image





      def 			__UpperCamelCase(					self			,				A_			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = ChannelDimension.FIRST			,				**A_			,				):
       '''simple docstring'''




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

       UpperCamelCase      :   Union[str, Any]			      =  size if size is not None else self.size
       UpperCamelCase      :   str			      =  get_size_dict(A_			,				default_to_square=A_  )
       UpperCamelCase      :   Union[str, Any]			      =  crop_size if crop_size is not None else self.crop_size
       UpperCamelCase      :   Optional[int]			      =  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."  )

       UpperCamelCase      :   Tuple			      =  make_batched(A_  )

       UpperCamelCase      :   Union[str, 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
       ]

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







def 						A_							(	_lowerCAmelCase	)		->					bool:
 UpperCamelCase      :   List[Any]			      =  0
 for ch in input_str:
  UpperCamelCase      :   Optional[Any]			      =  ord(_lowerCAmelCase	)
  UpperCamelCase      :   Optional[Any]			      =  pow(2				,  _lowerCAmelCase	)

  # If we already turned on bit for current character's unicode
  if bitmap >> ch_unicode & 1 == 1:
   return False
  bitmap |= ch_bit_index_on
 return True


if __name__ == "__main__":
    import doctest

    doctest.testmod()
