




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


lowerCAmelCase_					    = logging.get_logger(__name__)



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	List[List[ImageInput]]:

  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  _lowerCAmelCase     (  _UpperCAmelCase	):
      A__		     =		['pixel_values']

      def __init__(   self      ,				__UpperCAmelCase = True      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = PILImageResampling.BILINEAR      ,				__UpperCAmelCase = True      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = True      ,				__UpperCAmelCase = 1 / 255      ,				__UpperCAmelCase = True      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				**__UpperCAmelCase      ,				):
        super().__init__(**__UpperCamelCase		)
        lowerCAmelCase__				:  Optional[Any]		       =     size if size is not None else {'''shortest_edge''': 224}
        lowerCAmelCase__				:  List[Any]		       =     get_size_dict(__UpperCamelCase      ,				default_to_square=__UpperCamelCase		)
        lowerCAmelCase__				:  Dict		       =     crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        lowerCAmelCase__				:  str		       =     get_size_dict(__UpperCamelCase      ,				param_name='''crop_size'''		)

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

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase = PILImageResampling.BILINEAR      ,				__UpperCAmelCase = None      ,				**__UpperCAmelCase      ,				):
        lowerCAmelCase__				:  Dict		       =     get_size_dict(__UpperCamelCase      ,				default_to_square=__UpperCamelCase		)
        if "shortest_edge" in size:
          lowerCAmelCase__				:  str		       =     get_resize_output_image_size(__UpperCamelCase      ,				size['''shortest_edge''']      ,				default_to_square=__UpperCamelCase		)
        elif "height" in size and "width" in size:
          lowerCAmelCase__				:  Optional[int]		       =     (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 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None      ,				**__UpperCAmelCase      ,				):
        lowerCAmelCase__				:  Dict		       =     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 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None      ,				**__UpperCAmelCase      ,				):
        return rescale(__UpperCamelCase      ,				scale=__UpperCamelCase      ,				data_format=__UpperCamelCase      ,				**__UpperCamelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None      ,				**__UpperCAmelCase      ,				):
        return normalize(__UpperCamelCase      ,				mean=__UpperCamelCase      ,				std=__UpperCamelCase      ,				data_format=__UpperCamelCase      ,				**__UpperCamelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = ChannelDimension.FIRST      ,				):






        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.
        lowerCAmelCase__				:  List[Any]		       =     to_numpy_array(__UpperCamelCase		)

        if do_resize:
          lowerCAmelCase__				:  Optional[Any]		       =     self.resize(image=__UpperCamelCase      ,				size=__UpperCamelCase      ,				resample=__UpperCamelCase		)

        if do_center_crop:
          lowerCAmelCase__				:  Any		       =     self.center_crop(__UpperCamelCase      ,				size=__UpperCamelCase		)

        if do_rescale:
          lowerCAmelCase__				:  str		       =     self.rescale(image=__UpperCamelCase      ,				scale=__UpperCamelCase		)

        if do_normalize:
          lowerCAmelCase__				:  Any		       =     self.normalize(image=__UpperCamelCase      ,				mean=__UpperCamelCase      ,				std=__UpperCamelCase		)

        lowerCAmelCase__				:  List[str]		       =     to_channel_dimension_format(__UpperCamelCase      ,				__UpperCamelCase		)
        return image







      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = ChannelDimension.FIRST      ,				**__UpperCAmelCase      ,				):
        lowerCAmelCase__				:  List[Any]		       =     do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase__				:  Tuple		       =     resample if resample is not None else self.resample
        lowerCAmelCase__				:  Optional[Any]		       =     do_center_crop if do_center_crop is not None else self.do_center_crop
        lowerCAmelCase__				:  Optional[Any]		       =     do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase__				:  Optional[int]		       =     rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase__				:  List[Any]		       =     do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase__				:  Optional[int]		       =     image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase__				:  Union[str, Any]		       =     image_std if image_std is not None else self.image_std

        lowerCAmelCase__				:  Tuple		       =     size if size is not None else self.size
        lowerCAmelCase__				:  Optional[int]		       =     get_size_dict(__UpperCamelCase      ,				default_to_square=__UpperCamelCase		)
        lowerCAmelCase__				:  Optional[Any]		       =     crop_size if crop_size is not None else self.crop_size
        lowerCAmelCase__				:  Dict		       =     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.'''		)

        lowerCAmelCase__				:  List[str]		       =     make_batched(__UpperCamelCase		)

        lowerCAmelCase__				:  Optional[int]		       =     [
            [
                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
        ]

        lowerCAmelCase__				:  Dict		       =     {'''pixel_values''': videos}
        return BatchFeature(data=__UpperCamelCase      ,				tensor_type=__UpperCamelCase		)







lowerCAmelCase_					    = [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	int:
  # Return True if there is node that has not iterated.
  lowerCAmelCase__				:  List[str]		       =     [False] * len(UpperCamelCase      )
  lowerCAmelCase__				:  int		       =     [s]
  lowerCAmelCase__				:  Dict		       =     True

  while queue:
    lowerCAmelCase__				:  Union[str, Any]		       =     queue.pop(0      )
    for ind in range(len(graph[u]      )      ):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(UpperCamelCase      )
        lowerCAmelCase__				:  List[str]		       =     True
        lowerCAmelCase__				:  int		       =     u

  return visited[t]



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Optional[Any]:
  lowerCAmelCase__				:  Dict		       =     [-1] * (len(UpperCamelCase      ))
  lowerCAmelCase__				:  Dict		       =     0
  lowerCAmelCase__				:  List[str]		       =     []
  lowerCAmelCase__				:  Tuple		       =     [i[:] for i in graph]  # Record original cut, copy.
  while bfs(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      ):
    lowerCAmelCase__				:  Optional[Any]		       =     float('''Inf'''      )
    lowerCAmelCase__				:  Optional[Any]		       =     sink

    while s != source:
      # Find the minimum value in select path
      lowerCAmelCase__				:  Optional[Any]		       =     min(UpperCamelCase	,      graph[parent[s]][s]      )
      lowerCAmelCase__				:  Union[str, Any]		       =     parent[s]

    max_flow += path_flow
    lowerCAmelCase__				:  List[str]		       =     sink

    while v != source:
      lowerCAmelCase__				:  Union[str, Any]		       =     parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      lowerCAmelCase__				:  Dict		       =     parent[v]

  for i in range(len(UpperCamelCase      )      ):
    for j in range(len(graph[0]      )      ):
      if graph[i][j] == 0 and temp[i][j] > 0:
        res.append((i, j)      )

  return res


if __name__ == "__main__":
     print(mincut(test_graph, source=0, sink=5))

