



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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
	import PIL

if is_torch_available():
	import torch


__snake_case		:Optional[Any]   	=  logging.get_logger(__name__)

class 					_A (			__UpperCAmelCase					):
							UpperCamelCase__						:					List[Any]		      =		['''pixel_values''']



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


											'''simple docstring'''

											super().__init__(**__SCREAMING_SNAKE_CASE)
											__a		 =       size if size is not None else {'''shortest_edge''': 224}
											__a		 =       get_size_dict(__SCREAMING_SNAKE_CASE  ,	default_to_square=__SCREAMING_SNAKE_CASE)
											__a		 =       crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
											__a		 =       get_size_dict(__SCREAMING_SNAKE_CASE  ,	param_name='''crop_size''')

											__a		 =       do_resize
											__a		 =       size
											__a		 =       resample
											__a		 =       do_rescale
											__a		 =       rescale_factor
											__a		 =       do_center_crop
											__a		 =       crop_size
											__a		 =       do_flip_channel_order



							def 	_lowerCamelCase    (	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  np.ndarray  ,	__SCREAMING_SNAKE_CASE							:  Dict[str, int]  ,	__SCREAMING_SNAKE_CASE							:  PILImageResampling = PIL.Image.BILINEAR  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[str, ChannelDimension]] = None  ,	**__SCREAMING_SNAKE_CASE							:  Tuple  ,	):


											'''simple docstring'''

											__a		 =       get_size_dict(__SCREAMING_SNAKE_CASE  ,	default_to_square=__SCREAMING_SNAKE_CASE)
											if "shortest_edge" not in size:
															raise ValueError(F'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}')
											__a		 =       get_resize_output_image_size(__SCREAMING_SNAKE_CASE  ,	size=size['''shortest_edge''']  ,	default_to_square=__SCREAMING_SNAKE_CASE)
											return resize(__SCREAMING_SNAKE_CASE  ,	size=__SCREAMING_SNAKE_CASE  ,	resample=__SCREAMING_SNAKE_CASE  ,	data_format=__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE)



							def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  np.ndarray  ,	__SCREAMING_SNAKE_CASE							:  Dict[str, int]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[str, ChannelDimension]] = None  ,	**__SCREAMING_SNAKE_CASE							:  Any  ,	):


											'''simple docstring'''

											__a		 =       get_size_dict(__SCREAMING_SNAKE_CASE)
											if "height" not in size or "width" not in size:
															raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}')
											return center_crop(__SCREAMING_SNAKE_CASE  ,	size=(size['''height'''], size['''width'''])  ,	data_format=__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE)



							def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  np.ndarray  ,	__SCREAMING_SNAKE_CASE							:  Union[int, float]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[str, ChannelDimension]] = None  ,	**__SCREAMING_SNAKE_CASE							:  List[Any]  ,	):


											'''simple docstring'''

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



							def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  np.ndarray  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[str, ChannelDimension]] = None):


											'''simple docstring'''

											return flip_channel_order(__SCREAMING_SNAKE_CASE  ,	data_format=__SCREAMING_SNAKE_CASE)



							def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  ImageInput  ,	__SCREAMING_SNAKE_CASE							:  bool = None  ,	__SCREAMING_SNAKE_CASE							:  Dict[str, int] = None  ,	__SCREAMING_SNAKE_CASE							:  PILImageResampling = None  ,	__SCREAMING_SNAKE_CASE							:  bool = None  ,	__SCREAMING_SNAKE_CASE							:  float = None  ,	__SCREAMING_SNAKE_CASE							:  bool = None  ,	__SCREAMING_SNAKE_CASE							:  Dict[str, int] = None  ,	__SCREAMING_SNAKE_CASE							:  bool = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[str, TensorType]] = None  ,	__SCREAMING_SNAKE_CASE							:  ChannelDimension = ChannelDimension.FIRST  ,	**__SCREAMING_SNAKE_CASE							:  Dict  ,	):


											'''simple docstring'''

											__a		 =       do_resize if do_resize is not None else self.do_resize
											__a		 =       resample if resample is not None else self.resample
											__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_center_crop if do_center_crop is not None else self.do_center_crop
											__a		 =       (
											    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
											)

											__a		 =       size if size is not None else self.size
											__a		 =       get_size_dict(__SCREAMING_SNAKE_CASE  ,	default_to_square=__SCREAMING_SNAKE_CASE)
											__a		 =       crop_size if crop_size is not None else self.crop_size
											__a		 =       get_size_dict(__SCREAMING_SNAKE_CASE  ,	param_name='''crop_size''')

											__a		 =       make_list_of_images(__SCREAMING_SNAKE_CASE)

											if not valid_images(__SCREAMING_SNAKE_CASE):
															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_rescale and rescale_factor is None:
															raise ValueError('''Rescale factor must be specified if do_rescale is True.''')

											if do_center_crop and crop_size is None:
															raise ValueError('''Crop size must be specified if do_center_crop is True.''')

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

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

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

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

											# the pretrained checkpoints assume images are BGR, not RGB
											if do_flip_channel_order:
															__a		 =       [self.flip_channel_order(image=__SCREAMING_SNAKE_CASE) for image in images]

											__a		 =       [to_channel_dimension_format(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE) for image in images]

											__a		 =       {'''pixel_values''': images}
											return BatchFeature(data=__SCREAMING_SNAKE_CASE  ,	tensor_type=__SCREAMING_SNAKE_CASE)





							def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  List[Tuple] = None):


											'''simple docstring'''

											__a		 =       outputs.logits

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

															if is_torch_tensor(__SCREAMING_SNAKE_CASE):
																			__a		 =       target_sizes.numpy()

															__a		 =       []

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

											return semantic_segmentation










__snake_case		: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],
]





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    # Return True if there is node that has not iterated.
    __a		 =       [False] * len(_UpperCAmelCase       )
    __a		 =       [s]
    __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(_UpperCAmelCase       )
                __a		 =       True
                __a		 =       u

    return visited[t]





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       [-1] * (len(_UpperCAmelCase       ))
    __a		 =       0
    __a		 =       []
    __a		 =       [i[:] for i in graph]  # Record original cut, copy.
    while bfs(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
        __a		 =       float('''Inf'''       )
        __a		 =       sink

        while s != source:
            # Find the minimum value in select path
            __a		 =       min(_UpperCAmelCase     ,				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]

    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))





