'''simple docstring'''




snake_case	=      {'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
snake_case	=      ['''a''', '''b''', '''c''', '''d''', '''e''']







def       A_ (			_lowerCamelCase      :				Dict	,	_lowerCamelCase      :				Optional[int]	,	_lowerCamelCase      :				int     ):
      _lowerCAmelCase		 = start
      # add current to visited
      visited.append(__A     )
      _lowerCAmelCase		 = edges[current]
      for neighbor in neighbors:
            # if neighbor not in visited, visit
            if neighbor not in visited:
                  _lowerCAmelCase		 = topological_sort(__A	,	__A	,	__A     )
                   # if all neighbors visited add current to sort
      sort.append(__A     )
      # if all vertices haven't been visited select a new one to visit
      if len(__A     ) != len(__A     ):
            for vertice in vertices:
                  if vertice not in visited:
                        _lowerCAmelCase		 = topological_sort(__A	,	__A	,	__A     )
                        # return sort
      return sort


if __name__ == "__main__":
 snake_case	=      topological_sort('''a''', [], [])
 print(sort)













'''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,
    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 TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)


def 				lowercase_					(     __A			:							Tuple      )							->   List[List[ImageInput]]:



 """simple docstring"""




 if isinstance(__A       ,   (list, tuple)      ) and isinstance(videos[0]       ,   (list, tuple)      ) and is_valid_image(videos[0][0]      ):
  return videos

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

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

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





class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      ['''pixel_values''']






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


       '''simple docstring'''

       super().__init__(**UpperCAmelCase		)
       lowercase       : Tuple						=size if size is not None else {'''shortest_edge''': 224}
       lowercase       : str						=get_size_dict(UpperCAmelCase  ,   default_to_square=UpperCAmelCase		)
       lowercase       : List[str]						=crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       lowercase       : List[Any]						=get_size_dict(UpperCAmelCase  ,   param_name='''crop_size'''		)

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






      def 					A__   (			self      :		Dict  ,   UpperCAmelCase      :		np.ndarray  ,   UpperCAmelCase      :		Dict[str, int]  ,   UpperCAmelCase      :		PILImageResampling = PILImageResampling.BILINEAR  ,   UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None  ,   **UpperCAmelCase      :		List[str]  ,   )	->	np.ndarray:


       '''simple docstring'''

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






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		np.ndarray  ,   UpperCAmelCase      :		Dict[str, int]  ,   UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None  ,   **UpperCAmelCase      :		Optional[Any]  ,   )	->	np.ndarray:


       '''simple docstring'''

       lowercase       : List[str]						=get_size_dict(UpperCAmelCase		)
       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(UpperCAmelCase  ,   size=(size['''height'''], size['''width'''])  ,   data_format=UpperCAmelCase  ,   **UpperCAmelCase		)






      def 					A__   (			self      :		int  ,   UpperCAmelCase      :		np.ndarray  ,   UpperCAmelCase      :		Union[int, float]  ,   UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None  ,   **UpperCAmelCase      :		Tuple  ,   )	->	List[Any]:


       '''simple docstring'''

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






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		np.ndarray  ,   UpperCAmelCase      :		Union[float, List[float]]  ,   UpperCAmelCase      :		Union[float, List[float]]  ,   UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None  ,   **UpperCAmelCase      :		int  ,   )	->	np.ndarray:


       '''simple docstring'''

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






      def 					A__   (			self      :		int  ,   UpperCAmelCase      :		ImageInput  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		PILImageResampling = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		float = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   UpperCAmelCase      :		Optional[ChannelDimension] = ChannelDimension.FIRST  ,   )	->	np.ndarray:


       '''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.'''		)

       # All transformations expect numpy arrays.
       lowercase       : Dict						=to_numpy_array(UpperCAmelCase		)

       if do_resize:
        lowercase       : Union[str, Any]						=self.resize(image=UpperCAmelCase  ,   size=UpperCAmelCase  ,   resample=UpperCAmelCase		)

       if do_center_crop:
        lowercase       : Optional[Any]						=self.center_crop(UpperCAmelCase  ,   size=UpperCAmelCase		)

       if do_rescale:
        lowercase       : Dict						=self.rescale(image=UpperCAmelCase  ,   scale=UpperCAmelCase		)

       if do_normalize:
        lowercase       : int						=self.normalize(image=UpperCAmelCase  ,   mean=UpperCAmelCase  ,   std=UpperCAmelCase		)

       lowercase       : Optional[Any]						=to_channel_dimension_format(UpperCAmelCase  ,   UpperCAmelCase		)
       return image






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		ImageInput  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		PILImageResampling = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		float = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   UpperCAmelCase      :		Optional[Union[str, TensorType]] = None  ,   UpperCAmelCase      :		ChannelDimension = ChannelDimension.FIRST  ,   **UpperCAmelCase      :		Optional[int]  ,   )	->	PIL.Image.Image:


       '''simple docstring'''

       lowercase       : Any						=do_resize if do_resize is not None else self.do_resize
       lowercase       : Union[str, Any]						=resample if resample is not None else self.resample
       lowercase       : Tuple						=do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase       : List[Any]						=do_rescale if do_rescale is not None else self.do_rescale
       lowercase       : str						=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       : List[str]						=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       : Optional[Any]						=size if size is not None else self.size
       lowercase       : Any						=get_size_dict(UpperCAmelCase  ,   default_to_square=UpperCAmelCase		)
       lowercase       : Union[str, Any]						=crop_size if crop_size is not None else self.crop_size
       lowercase       : Optional[int]						=get_size_dict(UpperCAmelCase  ,   param_name='''crop_size'''		)

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

       lowercase       : List[str]						=make_batched(UpperCAmelCase		)

       lowercase       : Union[str, Any]						=[
           [
               self._preprocess_image(
                   image=UpperCAmelCase  ,   do_resize=UpperCAmelCase  ,   size=UpperCAmelCase  ,   resample=UpperCAmelCase  ,   do_center_crop=UpperCAmelCase  ,   crop_size=UpperCAmelCase  ,   do_rescale=UpperCAmelCase  ,   rescale_factor=UpperCAmelCase  ,   do_normalize=UpperCAmelCase  ,   image_mean=UpperCAmelCase  ,   image_std=UpperCAmelCase  ,   data_format=UpperCAmelCase  ,   )
               for img in video
           ]
           for video in videos
       ]

       lowercase       : Dict						={'''pixel_values''': videos}
       return BatchFeature(data=UpperCAmelCase  ,   tensor_type=UpperCAmelCase		)



