





'''simple docstring'''

from typing import Dict, List, Optional, Tuple, 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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
  import torch


__lowerCAmelCase							:     Tuple		     =logging.get_logger(__name__)






class        UpperCAmelCase     (	UpperCamelCase__    ):
 __lowercase       		=    ["""pixel_values"""]
 def __init__(    self				:Optional[int]						,	lowercase_				:bool = True						,	lowercase_				:Optional[Dict[str, int]] = None						,	lowercase_				:PILImageResampling = PILImageResampling.BILINEAR						,	lowercase_				:bool = True						,	lowercase_				:Dict[str, int] = None						,	lowercase_				:bool = True						,	lowercase_				:Union[int, float] = 1 / 2_55						,	lowercase_				:bool = True						,	lowercase_				:Optional[Union[float, List[float]]] = None						,	lowercase_				:Optional[Union[float, List[float]]] = None						,	**lowercase_				:List[str]						,	)->							None:
     super().__init__(**lowercase_							)
     A__    =      size if size is not None else {"shortest_edge": 2_56}
     A__    =      get_size_dict(lowercase_						,	default_to_square=lowercase_							)
     A__    =      crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
     A__    =      get_size_dict(lowercase_						,	param_name="crop_size"							)
     A__    =      do_resize
     A__    =      size
     A__    =      resample
     A__    =      do_center_crop
     A__    =      crop_size
     A__    =      do_rescale
     A__    =      rescale_factor
     A__    =      do_normalize
     A__    =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     A__    =      image_std if image_std is not None else IMAGENET_STANDARD_STD
 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:np.ndarray						,	lowercase_				:Dict[str, int]						,	lowercase_				:PILImageResampling = PILImageResampling.BICUBIC						,	lowercase_				:Optional[Union[str, ChannelDimension]] = None						,	**lowercase_				:Union[str, Any]						,	)->							np.ndarray:
     A__    =      get_size_dict(lowercase_						,	default_to_square=lowercase_							)
     if "shortest_edge" not in size:
         raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"							)
     A__    =      get_resize_output_image_size(lowercase_						,	size=size["shortest_edge"]						,	default_to_square=lowercase_							)
     return resize(lowercase_						,	size=lowercase_						,	resample=lowercase_						,	data_format=lowercase_						,	**lowercase_							)
 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:np.ndarray						,	lowercase_				:Dict[str, int]						,	lowercase_				:Optional[Union[str, ChannelDimension]] = None						,	**lowercase_				:Dict						,	)->							np.ndarray:
     A__    =      get_size_dict(lowercase_							)
     if "height" not in size or "width" not in size:
         raise ValueError(F"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"							)
     return center_crop(lowercase_						,	size=(size["height"], size["width"])						,	data_format=lowercase_						,	**lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:np.ndarray						,	lowercase_				:float						,	lowercase_				:Optional[Union[str, ChannelDimension]] = None						,	**lowercase_				:List[str]							)->							np.ndarray:
     return rescale(lowercase_						,	scale=lowercase_						,	data_format=lowercase_						,	**lowercase_							)
 def 					UpperCAmelCase_							(    self				:Any						,	lowercase_				:np.ndarray						,	lowercase_				:Union[float, List[float]]						,	lowercase_				:Union[float, List[float]]						,	lowercase_				:Optional[Union[str, ChannelDimension]] = None						,	**lowercase_				:int						,	)->							np.ndarray:
     return normalize(lowercase_						,	mean=lowercase_						,	std=lowercase_						,	data_format=lowercase_						,	**lowercase_							)
 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:ImageInput						,	lowercase_				:Optional[bool] = None						,	lowercase_				:Dict[str, int] = None						,	lowercase_				:PILImageResampling = None						,	lowercase_				:bool = None						,	lowercase_				:Dict[str, int] = None						,	lowercase_				:Optional[bool] = None						,	lowercase_				:Optional[float] = None						,	lowercase_				:Optional[bool] = None						,	lowercase_				:Optional[Union[float, List[float]]] = None						,	lowercase_				:Optional[Union[float, List[float]]] = None						,	lowercase_				:Optional[Union[str, TensorType]] = None						,	lowercase_				:Union[str, ChannelDimension] = ChannelDimension.FIRST						,	**lowercase_				:str						,	)->							Tuple:
     A__    =      do_resize if do_resize is not None else self.do_resize
     A__    =      size if size is not None else self.size
     A__    =      get_size_dict(lowercase_						,	default_to_square=lowercase_							)
     A__    =      resample if resample is not None else self.resample
     A__    =      do_center_crop if do_center_crop is not None else self.do_center_crop
     A__    =      crop_size if crop_size is not None else self.crop_size
     A__    =      get_size_dict(lowercase_						,	param_name="crop_size"							)
     A__    =      do_rescale if do_rescale is not None else self.do_rescale
     A__    =      rescale_factor if rescale_factor is not None else self.rescale_factor
     A__    =      do_normalize if do_normalize is not None else self.do_normalize
     A__    =      image_mean if image_mean is not None else self.image_mean
     A__    =      image_std if image_std is not None else self.image_std

     A__    =      make_list_of_images(lowercase_							)

     if not valid_images(lowercase_							):
         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."							)

     # All transformations expect numpy arrays.
     A__    =      [to_numpy_array(lowercase_							) for image in images]

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

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

     if do_rescale:
         A__    =      [self.rescale(image=lowercase_						,	scale=lowercase_							) for image in images]

     if do_normalize:
         A__    =      [self.normalize(image=lowercase_						,	mean=lowercase_						,	std=lowercase_							) for image in images]

     A__    =      [to_channel_dimension_format(lowercase_						,	lowercase_							) for image in images]

     A__    =      {"pixel_values": images}
     return BatchFeature(data=lowercase_						,	tensor_type=lowercase_							)



 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:List[Any]						,	lowercase_				:List[Tuple] = None							)->							List[str]:
     A__    =      outputs.logits

     # Resize logits and compute semantic segmentation maps
     if target_sizes is not None:
         if len(lowercase_							) != len(lowercase_							):
             raise ValueError(
                 "Make sure that you pass in as many target sizes as the batch dimension of the logits"							)

         if is_torch_tensor(lowercase_							):
             A__    =      target_sizes.numpy()

         A__    =      []

         for idx in range(len(lowercase_							)							):
             A__    =      torch.nn.functional.interpolate(
                 logits[idx].unsqueeze(dim=0							)						,	size=target_sizes[idx]						,	mode="bilinear"						,	align_corners=lowercase_							)
             A__    =      resized_logits[0].argmax(dim=0							)
             semantic_segmentation.append(lowercase_							)
     else:
         A__    =      logits.argmax(dim=1							)
         A__    =      [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

     return semantic_segmentation










'''simple docstring'''

def 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : List[str]						,	_lowerCamelCase : List[str]						,	_lowerCamelCase : Tuple	):
    # Return True if there is node that has not iterated.
    A__    =      [False] * len(_lowerCamelCase	)
    A__    =      []
    queue.append(_lowerCamelCase	)
    A__    =      True

    while queue:
        A__    =      queue.pop(0	)
        for ind in range(len(graph[u]	)	):
            if visited[ind] is False and graph[u][ind] > 0:
                queue.append(_lowerCamelCase	)
                A__    =      True
                A__    =      u

    return visited[t]





def 						UpperCamelCase    (							_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[int]	):
    # This array is filled by BFS and to store path
    A__    =      [-1] * (len(_lowerCamelCase	))
    A__    =      0
    while bfs(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	):
        A__    =      float("Inf"	)
        A__    =      sink

        while s != source:
            # Find the minimum value in select path
            A__    =      min(_lowerCamelCase						,	graph[parent[s]][s]	)
            A__    =      parent[s]

        max_flow += path_flow
        A__    =      sink

        while v != source:
            A__    =      parent[v]
            graph[u][v] -= path_flow
            graph[v][u] += path_flow
            A__    =      parent[v]
    return max_flow


__lowerCAmelCase							:     Any		     =[
    [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],
]

__lowerCAmelCase     ,					__lowerCAmelCase							:     int		     =0, 5



print(ford_fulkerson(graph, source, sink))



