
"""simple docstring"""
from collections import deque


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			str:


     '''simple docstring'''
     return input(lowercase_ ).strip().split(""" """ )


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			dict[int, list[int]]:


     '''simple docstring'''
     __a:						dict[int, list[int]]		    =					{}
     for i in range(lowercase_ ):
          __a:						Union[str, Any]		    =					[]

     for e in range(lowercase_ ):
          __a:						int		    =					(int(lowercase_ ) for i in _input(f"""Edge {e + 1}: <node1> <node2> """ ))
          graph[x].append(lowercase_ )
     return graph


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			dict[int, list[int]]:


     '''simple docstring'''
     __a:						dict[int, list[int]]		    =					{}
     for i in range(lowercase_ ):
          __a:						Union[str, Any]		    =					[]

     for e in range(lowercase_ ):
          __a:						Tuple		    =					(int(lowercase_ ) for i in _input(f"""Edge {e + 1}: <node1> <node2> """ ))
          graph[x].append(lowercase_ )
          graph[y].append(lowercase_ )
     return graph


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			dict[int, list[tuple[int, int]]]:


     '''simple docstring'''
     __a:						dict[int, list[tuple[int, int]]]		    =					{}
     for i in range(lowercase_ ):
          __a:						Dict		    =					[]

     for e in range(lowercase_ ):
          __a:						str		    =					(int(lowercase_ ) for i in _input(f"""Edge {e + 1}: <node1> <node2> <weight> """ ))
          graph[x].append((y, w) )
          graph[y].append((x, w) )
     return graph


if __name__ == "__main__":
   lowercase,lowercase		:						Optional[int] =					(int(i) for i in _input('Number of nodes and edges: '))

   lowercase		:						Optional[int] =					int(
       _input(
           'Press 1 or 2 or 3 \n'
           '1. Unweighted directed \n'
           '2. Unweighted undirected \n'
           '3. Weighted undirected \n'
       )[0]
   )

   lowercase		:						int =					{
       1: initialize_unweighted_directed_graph,
       2: initialize_unweighted_undirected_graph,
       3: initialize_weighted_undirected_graph,
   }[graph_choice](n, m)


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			Dict:


     '''simple docstring'''
     __a:						Optional[Any]		    =					{s}, [s]
     print(lowercase_ )
     while _s:
          __a:						Optional[int]		    =					0
          for i in g[_s[-1]]:
               if i not in vis:
                    _s.append(lowercase_ )
                    vis.add(lowercase_ )
                    __a:						Union[str, Any]		    =					1
                    print(lowercase_ )
                    break
          if not flag:
               _s.pop()


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			str:


     '''simple docstring'''
     __a:						List[str]		    =					{s}, deque([s] )
     print(lowercase_ )
     while q:
          __a:						Any		    =					q.popleft()
          for v in g[u]:
               if v not in vis:
                    vis.add(lowercase_ )
                    q.append(lowercase_ )
                    print(lowercase_ )


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			List[Any]:


     '''simple docstring'''
     __a:						Optional[int]		    =					{s: 0}, set(), {s: 0}
     while True:
          if len(lowercase_ ) == len(lowercase_ ) - 1:
               break
          __a:						Optional[Any]		    =					10_00_00
          for i in dist:
               if i not in known and dist[i] < mini:
                    __a:						Optional[Any]		    =					dist[i]
                    __a:						Dict		    =					i
          known.add(lowercase_ )
          for v in g[u]:
               if v[0] not in known and dist[u] + v[1] < dist.get(v[0]	,				10_00_00 ):
                    __a:						List[str]		    =					dist[u] + v[1]
                    __a:						List[Any]		    =					u
     for i in dist:
          if i != s:
               print(dist[i] )


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_=None	,				lowerCamelCase_=None )     ->			Dict:


     '''simple docstring'''
     if q is None:
          __a:						Optional[Any]		    =					[1]
     if ind is None:
          __a:						int		    =					[0] * (len(lowercase_ ) + 1)  # SInce oth Index is ignored
          for u in g:
               for v in g[u]:
                    ind[v] += 1
          __a:						Optional[Any]		    =					deque()
          for i in g:
               if ind[i] == 0:
                    q.append(lowercase_ )
     if len(lowercase_ ) == 0:
          return
     __a:						str		    =					q.popleft()
     print(lowercase_ )
     for w in g[v]:
          ind[w] -= 1
          if ind[w] == 0:
               q.append(lowercase_ )
     topo(lowercase_	,				lowercase_	,				lowercase_ )


def     lowerCamelCase_    (	)     ->			Any:


     '''simple docstring'''
     __a:						List[Any]		    =					input().strip()
     __a:						Dict		    =					[]
     for _ in range(lowercase_ ):
          a.append(map(lowercase_	,				input().strip().split() ) )
     return a, n


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			int:


     '''simple docstring'''
     (__a):						Any		    =					a_and_n
     __a:						List[Any]		    =					list(lowercase_ )
     __a:						str		    =					[[0] * n for i in range(lowercase_ )]
     for k in range(lowercase_ ):
          for i in range(lowercase_ ):
               for j in range(lowercase_ ):
                    if dist[i][j] > dist[i][k] + dist[k][j]:
                         __a:						Tuple		    =					dist[i][k] + dist[k][j]
                         __a:						str		    =					k
     print(lowercase_ )


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			int:


     '''simple docstring'''
     __a:						Union[str, Any]		    =					{s: 0}, set(), {s: 0}
     while True:
          if len(lowercase_ ) == len(lowercase_ ) - 1:
               break
          __a:						Union[str, Any]		    =					10_00_00
          for i in dist:
               if i not in known and dist[i] < mini:
                    __a:						Optional[Any]		    =					dist[i]
                    __a:						List[str]		    =					i
          known.add(lowercase_ )
          for v in g[u]:
               if v[0] not in known and v[1] < dist.get(v[0]	,				10_00_00 ):
                    __a:						Dict		    =					v[1]
                    __a:						Optional[Any]		    =					u
     return dist


def     lowerCamelCase_    (	)     ->			Tuple:


     '''simple docstring'''
     __a:						List[Any]		    =					map(lowercase_	,				input().split(""" """ ) )
     __a:						List[Any]		    =					[]
     for _ in range(lowercase_ ):
          edges.append(map(lowercase_	,				input().split(""" """ ) ) )
     return edges, n


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			str:


     '''simple docstring'''
     # Sort edges on the basis of distance
     (__a):						List[str]		    =					e_and_n
     e.sort(reverse=lowercase_	,				key=lambda lowerCamelCase_ : x[2] )
     __a:						List[Any]		    =					[{i} for i in range(1	,				n + 1 )]
     while True:
          if len(lowercase_ ) == 1:
               break
          print(lowercase_ )
          __a:						Any		    =					e.pop()
          for i in range(len(lowercase_ ) ):
               if x[0] in s[i]:
                    break
          for j in range(len(lowercase_ ) ):
               if x[1] in s[j]:
                    if i == j:
                         break
                    s[j].update(s[i] )
                    s.pop(lowercase_ )
                    break




def     lowerCamelCase_    (	lowerCamelCase_ )     ->			int:


     '''simple docstring'''
     __a:						List[Any]		    =					[]
     for node in graph:
          if not graph[node]:
               isolated.append(lowercase_ )
     return isolated


"""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 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, logging


lowercase		:						Tuple =					logging.get_logger(__name__)




class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						['''pixel_values''']






							def __init__(					self						:			Union[str, Any]						, snake_case__						:			bool = True						, snake_case__						:			Optional[Dict[str, int]] = None						, snake_case__						:			PILImageResampling = PILImageResampling.BILINEAR						, snake_case__						:			bool = True						, snake_case__						:			Union[int, float] = 1 / 2_5_5						, snake_case__						:			bool = True						, snake_case__						:			Optional[Union[float, List[float]]] = None						, snake_case__						:			Optional[Union[float, List[float]]] = None						, **snake_case__						:			Optional[int]						, ):






												"""simple docstring"""



												super().__init__(**snake_case__    )
												__a:						Dict		    =					size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
												__a:						Optional[Any]		    =					get_size_dict(snake_case__    )
												__a:						Union[str, Any]		    =					do_resize
												__a:						str		    =					do_rescale
												__a:						Tuple		    =					do_normalize
												__a:						Dict		    =					size
												__a:						str		    =					resample
												__a:						Any		    =					rescale_factor
												__a:						Optional[int]		    =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												__a:						int		    =					image_std if image_std is not None else IMAGENET_STANDARD_STD






							def 							__lowercase  (					self						:			Optional[Any]						, snake_case__						:			np.ndarray						, snake_case__						:			Dict[str, int]						, snake_case__						:			PILImageResampling = PILImageResampling.BILINEAR						, snake_case__						:			Optional[Union[str, ChannelDimension]] = None						, **snake_case__						:			List[str]						, ):






												"""simple docstring"""



												__a:						int		    =					get_size_dict(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 resize(
												    snake_case__						, size=(size["""height"""], size["""width"""])						, resample=snake_case__						, data_format=snake_case__						, **snake_case__    )






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






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			Dict						, snake_case__						:			np.ndarray						, snake_case__						:			Union[float, List[float]]						, snake_case__						:			Union[float, List[float]]						, snake_case__						:			Optional[Union[str, ChannelDimension]] = None						, **snake_case__						:			Union[str, Any]						, ):






												"""simple docstring"""



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






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






												"""simple docstring"""



												__a:						Any		    =					do_resize if do_resize is not None else self.do_resize
												__a:						Any		    =					do_rescale if do_rescale is not None else self.do_rescale
												__a:						int		    =					do_normalize if do_normalize is not None else self.do_normalize
												__a:						Any		    =					resample if resample is not None else self.resample
												__a:						Any		    =					rescale_factor if rescale_factor is not None else self.rescale_factor
												__a:						Dict		    =					image_mean if image_mean is not None else self.image_mean
												__a:						List[str]		    =					image_std if image_std is not None else self.image_std

												__a:						int		    =					size if size is not None else self.size
												__a:						Any		    =					get_size_dict(snake_case__    )

												__a:						List[str]		    =					make_list_of_images(snake_case__    )

												if not valid_images(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."""    )

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

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

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

												if do_normalize:
																	__a:						Dict		    =					[self.normalize(image=snake_case__						, mean=snake_case__						, std=snake_case__    ) for image in images]

												__a:						int		    =					[to_channel_dimension_format(snake_case__						, snake_case__    ) for image in images]

												__a:						str		    =					{"""pixel_values""": images}
												return BatchFeature(data=snake_case__						, tensor_type=snake_case__    )
