'''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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase__						:str          =			logging.get_logger(__name__)


if is_vision_available():
   import PIL







class 	SCREAMING_SNAKE_CASE	(      lowerCamelCase__  ):
       snake_case__    :     Optional[int]   		=			['pixel_values']




       def __init__(					self      : int	,    A__      : Dict = True	,    A__      : List[Any] = None	,    A__      : Tuple = PILImageResampling.BICUBIC	,    A__      : Dict = True	,    A__      : int = None	,    A__      : str = True	,    A__      : Dict = 1 / 255	,    A__      : Any = True	,    A__      : Tuple = None	,    A__      : Tuple = None	,    A__      : Optional[int] = True	,    **A__      : List[Any]	,    ):


            """simple docstring"""







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

            __lowerCamelCase						:       Optional[int]				   =				do_resize
            __lowerCamelCase						:       Any				   =				size
            __lowerCamelCase						:       int				   =				resample
            __lowerCamelCase						:       Dict				   =				do_center_crop
            __lowerCamelCase						:       str				   =				crop_size
            __lowerCamelCase						:       Dict				   =				do_rescale
            __lowerCamelCase						:       Optional[Any]				   =				rescale_factor
            __lowerCamelCase						:       Dict				   =				do_normalize
            __lowerCamelCase						:       Tuple				   =				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
            __lowerCamelCase						:       Union[str, Any]				   =				image_std if image_std is not None else OPENAI_CLIP_STD
            __lowerCamelCase						:       int				   =				do_convert_rgb




       def 					a_					(					self      : List[str]	,    A__      : int	,    A__      : Tuple	,    A__      : Union[str, Any] = PILImageResampling.BICUBIC	,    A__      : str = None	,    **A__      : Any	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				get_size_dict(__lowerCamelCase	,    default_to_square=__lowerCamelCase					)
            if "shortest_edge" not in size:
                 raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"					)
            __lowerCamelCase						:       Optional[int]				   =				get_resize_output_image_size(__lowerCamelCase	,    size=size["""shortest_edge"""]	,    default_to_square=__lowerCamelCase					)
            return resize(__lowerCamelCase	,    size=__lowerCamelCase	,    resample=__lowerCamelCase	,    data_format=__lowerCamelCase	,    **__lowerCamelCase					)




       def 					a_					(					self      : Tuple	,    A__      : List[str]	,    A__      : int	,    A__      : int = None	,    **A__      : str	,    ):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				get_size_dict(__lowerCamelCase					)
            if "height" not in size or "width" not in size:
                 raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}"					)
            return center_crop(__lowerCamelCase	,    size=(size["""height"""], size["""width"""])	,    data_format=__lowerCamelCase	,    **__lowerCamelCase					)




       def 					a_					(					self      : Tuple	,    A__      : Optional[Any]	,    A__      : List[str]	,    A__      : str = None	,    **A__      : Any	,    ):


            """simple docstring"""







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




       def 					a_					(					self      : Optional[int]	,    A__      : int	,    A__      : Optional[Any]	,    A__      : List[str]	,    A__      : str = None	,    **A__      : List[Any]	,    ):


            """simple docstring"""







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




       def 					a_					(					self      : Any	,    A__      : Dict	,    A__      : Any = None	,    A__      : List[str] = None	,    A__      : int = None	,    A__      : Optional[int] = None	,    A__      : str = None	,    A__      : Optional[Any] = None	,    A__      : List[str] = None	,    A__      : Union[str, Any] = None	,    A__      : str = None	,    A__      : Optional[int] = None	,    A__      : Optional[int] = None	,    A__      : List[str] = None	,    A__      : Any = ChannelDimension.FIRST	,    **A__      : Tuple	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				do_resize if do_resize is not None else self.do_resize
            __lowerCamelCase						:       Tuple				   =				size if size is not None else self.size
            __lowerCamelCase						:       Any				   =				get_size_dict(__lowerCamelCase	,    param_name="""size"""	,    default_to_square=__lowerCamelCase					)
            __lowerCamelCase						:       Tuple				   =				resample if resample is not None else self.resample
            __lowerCamelCase						:       Dict				   =				do_center_crop if do_center_crop is not None else self.do_center_crop
            __lowerCamelCase						:       List[Any]				   =				crop_size if crop_size is not None else self.crop_size
            __lowerCamelCase						:       str				   =				get_size_dict(__lowerCamelCase	,    param_name="""crop_size"""	,    default_to_square=__lowerCamelCase					)
            __lowerCamelCase						:       Dict				   =				do_rescale if do_rescale is not None else self.do_rescale
            __lowerCamelCase						:       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						:       Dict				   =				image_mean if image_mean is not None else self.image_mean
            __lowerCamelCase						:       int				   =				image_std if image_std is not None else self.image_std
            __lowerCamelCase						:       Dict				   =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

            __lowerCamelCase						:       Optional[int]				   =				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_resize and size is None:
                 raise ValueError("""Size 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."""					)

            # PIL RGBA images are converted to RGB
            if do_convert_rgb:
                 __lowerCamelCase						:       Union[str, Any]				   =				[convert_to_rgb(__lowerCamelCase					) for image in images]

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

            if do_resize:
                 __lowerCamelCase						:       str				   =				[self.resize(image=__lowerCamelCase	,    size=__lowerCamelCase	,    resample=__lowerCamelCase					) for image in images]

            if do_center_crop:
                 __lowerCamelCase						:       Dict				   =				[self.center_crop(image=__lowerCamelCase	,    size=__lowerCamelCase					) for image in images]

            if do_rescale:
                 __lowerCamelCase						:       Union[str, Any]				   =				[self.rescale(image=__lowerCamelCase	,    scale=__lowerCamelCase					) for image in images]

            if do_normalize:
                 __lowerCamelCase						:       Optional[Any]				   =				[self.normalize(image=__lowerCamelCase	,    mean=__lowerCamelCase	,    std=__lowerCamelCase					) for image in images]

            __lowerCamelCase						:       List[Any]				   =				[to_channel_dimension_format(__lowerCamelCase	,    __lowerCamelCase					) for image in images]

            __lowerCamelCase						:       Optional[int]				   =				{'''pixel_values''': images}
            return BatchFeature(data=__lowerCamelCase	,    tensor_type=__lowerCamelCase					)











'''simple docstring'''

def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase     )   ->     Union[str, Any]:


     """simple docstring"""
     # Return True if there is node that has not iterated.
     __lowerCamelCase						:       Optional[Any]				   =				[False] * len(_lowercase     )
     __lowerCamelCase						:       Dict				   =				[]
     queue.append(_lowercase     )
     __lowerCamelCase						:       Optional[int]				   =				True

     while queue:
          __lowerCamelCase						:       Optional[Any]				   =				queue.pop(0     )
          for ind in range(len(graph[u]     )     ):
               if visited[ind] is False and graph[u][ind] > 0:
                    queue.append(_lowercase     )
                    __lowerCamelCase						:       List[str]				   =				True
                    __lowerCamelCase						:       str				   =				u

     return visited[t]
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     # This array is filled by BFS and to store path
     __lowerCamelCase						:       Any				   =				[-1] * (len(_lowercase     ))
     __lowerCamelCase						:       Optional[int]				   =				0
     while bfs(_lowercase,						_lowercase,						_lowercase,						_lowercase     ):
          __lowerCamelCase						:       List[Any]				   =				float("""Inf"""     )
          __lowerCamelCase						:       Union[str, Any]				   =				sink

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

          max_flow += path_flow
          __lowerCamelCase						:       str				   =				sink

          while v != source:
               __lowerCamelCase						:       List[Any]				   =				parent[v]
               graph[u][v] -= path_flow
               graph[v][u] += path_flow
               __lowerCamelCase						:       Dict				   =				parent[v]
     return max_flow


UpperCAmelCase__						:str          =			[
    [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],
]

UpperCAmelCase__       ,				UpperCAmelCase__						:Optional[Any]          =			0, 5
print(ford_fulkerson(graph, source, sink))



