






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


A_	:	Union[str, Any]				=				logging.get_logger(__name__)


if is_vision_available():
   import PIL




class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       a       :	List[str]								=['''pixel_values''']





       def __init__(      self						,    _lowerCamelCase = True						,    _lowerCamelCase = None						,    _lowerCamelCase = PILImageResampling.BICUBIC						,    _lowerCamelCase = True						,    _lowerCamelCase = None						,    _lowerCamelCase = True						,    _lowerCamelCase = 1 / 2_5_5						,    _lowerCamelCase = True						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = True						,    **_lowerCamelCase						,    ):
         super().__init__(**_lowerCamelCase							)
         UpperCamelCase_:      str					   =       size if size is not None else {'shortest_edge': 2_2_4}
         UpperCamelCase_:      int					   =       get_size_dict(_lowerCamelCase						,    default_to_square=_lowerCamelCase							)
         UpperCamelCase_:      str					   =       crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
         UpperCamelCase_:      str					   =       get_size_dict(_lowerCamelCase							)

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





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase = PILImageResampling.BICUBIC						,    _lowerCamelCase = None						,    **_lowerCamelCase						,    ):
         UpperCamelCase_:      Optional[int]					   =       get_size_dict(_lowerCamelCase						,    default_to_square=_lowerCamelCase							)
         UpperCamelCase_:      Any					   =       get_resize_output_image_size(
             _lowerCamelCase						,    size=(size['height'], size['width'])						,    default_to_square=_lowerCamelCase							)
         return resize(_lowerCamelCase						,    size=_lowerCamelCase						,    resample=_lowerCamelCase						,    data_format=_lowerCamelCase						,    **_lowerCamelCase							)





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase = None						,    **_lowerCamelCase						,    ):
         UpperCamelCase_:      str					   =       get_size_dict(_lowerCamelCase							)
         return center_crop(_lowerCamelCase						,    size=(size['height'], size['width'])						,    data_format=_lowerCamelCase						,    **_lowerCamelCase							)





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase = None						,    **_lowerCamelCase						,    ):
         return rescale(_lowerCamelCase						,    scale=_lowerCamelCase						,    data_format=_lowerCamelCase						,    **_lowerCamelCase							)





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase = None						,    **_lowerCamelCase						,    ):
         return normalize(_lowerCamelCase						,    mean=_lowerCamelCase						,    std=_lowerCamelCase						,    data_format=_lowerCamelCase						,    **_lowerCamelCase							)





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = ChannelDimension.FIRST						,    **_lowerCamelCase						,    ):
         UpperCamelCase_:      str					   =       do_resize if do_resize is not None else self.do_resize
         UpperCamelCase_:      Optional[int]					   =       size if size is not None else self.size
         UpperCamelCase_:      Optional[int]					   =       get_size_dict(_lowerCamelCase						,    default_to_square=_lowerCamelCase							)
         UpperCamelCase_:      Union[str, Any]					   =       resample if resample is not None else self.resample
         UpperCamelCase_:      Optional[int]					   =       do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCamelCase_:      Union[str, Any]					   =       crop_size if crop_size is not None else self.crop_size
         UpperCamelCase_:      Tuple					   =       get_size_dict(_lowerCamelCase							)
         UpperCamelCase_:      Dict					   =       do_rescale if do_rescale is not None else self.do_rescale
         UpperCamelCase_:      Optional[int]					   =       rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCamelCase_:      Optional[Any]					   =       do_normalize if do_normalize is not None else self.do_normalize
         UpperCamelCase_:      List[Any]					   =       image_mean if image_mean is not None else self.image_mean
         UpperCamelCase_:      List[str]					   =       image_std if image_std is not None else self.image_std
         UpperCamelCase_:      str					   =       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         UpperCamelCase_:      Union[str, Any]					   =       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:
           UpperCamelCase_:      Optional[Any]					   =       [convert_to_rgb(_lowerCamelCase							) for image in images]

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

         if do_resize:
           UpperCamelCase_:      Optional[Any]					   =       [self.resize(image=_lowerCamelCase						,    size=_lowerCamelCase						,    resample=_lowerCamelCase							) for image in images]

         if do_center_crop:
           UpperCamelCase_:      Optional[Any]					   =       [self.center_crop(image=_lowerCamelCase						,    size=_lowerCamelCase							) for image in images]

         if do_rescale:
           UpperCamelCase_:      Optional[int]					   =       [self.rescale(image=_lowerCamelCase						,    scale=_lowerCamelCase							) for image in images]

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

         UpperCamelCase_:      Optional[Any]					   =       [to_channel_dimension_format(_lowerCamelCase						,    _lowerCamelCase							) for image in images]

         UpperCamelCase_:      str					   =       {'pixel_values': images}
         return BatchFeature(data=_lowerCamelCase						,    tensor_type=_lowerCamelCase							)













from collections import deque

def 						snake_case      (UpperCAmelCase__						) ->       Any:
  return input(UpperCAmelCase__						).strip().split(' '						)

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       dict[int, list[int]]:
  UpperCamelCase_:      dict[int, list[int]]					   =       {}
  for i in range(UpperCAmelCase__						):
    UpperCamelCase_:      str					   =       []

  for e in range(UpperCAmelCase__						):
    UpperCamelCase_   ,UpperCamelCase_:      List[Any]					   =       (int(UpperCAmelCase__						) for i in _input(F'''Edge {e + 1}: <node1> <node2> '''						))
    graph[x].append(UpperCAmelCase__						)
  return graph

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       dict[int, list[int]]:
  UpperCamelCase_:      dict[int, list[int]]					   =       {}
  for i in range(UpperCAmelCase__						):
    UpperCamelCase_:      Tuple					   =       []

  for e in range(UpperCAmelCase__						):
    UpperCamelCase_   ,UpperCamelCase_:      str					   =       (int(UpperCAmelCase__						) for i in _input(F'''Edge {e + 1}: <node1> <node2> '''						))
    graph[x].append(UpperCAmelCase__						)
    graph[y].append(UpperCAmelCase__						)
  return graph

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       dict[int, list[tuple[int, int]]]:
  UpperCamelCase_:      dict[int, list[tuple[int, int]]]					   =       {}
  for i in range(UpperCAmelCase__						):
    UpperCamelCase_:      List[Any]					   =       []

  for e in range(UpperCAmelCase__						):
    UpperCamelCase_   ,UpperCamelCase_   ,UpperCamelCase_:      List[str]					   =       (int(UpperCAmelCase__						) 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__":
   A_			,				A_	:	str				=				(int(i) for i in _input('Number of nodes and edges: '))

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

   A_	:	List[str]				=				{
       1: initialize_unweighted_directed_graph,
       2: initialize_unweighted_undirected_graph,
       3: initialize_weighted_undirected_graph,
   }[graph_choice](n, m)

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       Union[str, Any]:
  UpperCamelCase_   ,UpperCamelCase_:      int					   =       {s}, [s]
  print(UpperCAmelCase__						)
  while _s:
    UpperCamelCase_:      Optional[Any]					   =       0
    for i in g[_s[-1]]:
      if i not in vis:
        _s.append(UpperCAmelCase__						)
        vis.add(UpperCAmelCase__						)
        UpperCamelCase_:      Any					   =       1
        print(UpperCAmelCase__						)
        break
    if not flag:
      _s.pop()

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       Dict:
  UpperCamelCase_   ,UpperCamelCase_:      int					   =       {s}, deque([s]						)
  print(UpperCAmelCase__						)
  while q:
    UpperCamelCase_:      Dict					   =       q.popleft()
    for v in g[u]:
      if v not in vis:
        vis.add(UpperCAmelCase__						)
        q.append(UpperCAmelCase__						)
        print(UpperCAmelCase__						)

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       int:
  UpperCamelCase_   ,UpperCamelCase_   ,UpperCamelCase_:      Any					   =       {s: 0}, set(), {s: 0}
  while True:
    if len(UpperCAmelCase__						) == len(UpperCAmelCase__						) - 1:
      break
    UpperCamelCase_:      str					   =       1_0_0_0_0_0
    for i in dist:
      if i not in known and dist[i] < mini:
        UpperCamelCase_:      Dict					   =       dist[i]
        UpperCamelCase_:      str					   =       i
    known.add(UpperCAmelCase__						)
    for v in g[u]:
      if v[0] not in known and dist[u] + v[1] < dist.get(v[0]			,					1_0_0_0_0_0						):
        UpperCamelCase_:      Optional[Any]					   =       dist[u] + v[1]
        UpperCamelCase_:      str					   =       u
  for i in dist:
    if i != s:
      print(dist[i]						)

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__=None			,					UpperCAmelCase__=None						) ->       Optional[Any]:
  if q is None:
    UpperCamelCase_:      Optional[Any]					   =       [1]
  if ind is None:
    UpperCamelCase_:      Union[str, Any]					   =       [0] * (len(UpperCAmelCase__						) + 1)  # SInce oth Index is ignored
    for u in g:
      for v in g[u]:
        ind[v] += 1
    UpperCamelCase_:      Optional[int]					   =       deque()
    for i in g:
      if ind[i] == 0:
        q.append(UpperCAmelCase__						)
  if len(UpperCAmelCase__						) == 0:
    return
  UpperCamelCase_:      Dict					   =       q.popleft()
  print(UpperCAmelCase__						)
  for w in g[v]:
    ind[w] -= 1
    if ind[w] == 0:
      q.append(UpperCAmelCase__						)
  topo(UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__						)

def 						snake_case      () ->       str:
  UpperCamelCase_:      Dict					   =       input().strip()
  UpperCamelCase_:      List[str]					   =       []
  for _ in range(UpperCAmelCase__						):
    a.append(map(UpperCAmelCase__			,					input().strip().split()						)						)
  return a, n

def 						snake_case      (UpperCAmelCase__						) ->       Union[str, Any]:
  ((UpperCamelCase_)   ,(UpperCamelCase_)):      Dict					   =       a_and_n
  UpperCamelCase_:      List[str]					   =       list(UpperCAmelCase__						)
  UpperCamelCase_:      int					   =       [[0] * n for i in range(UpperCAmelCase__						)]
  for k in range(UpperCAmelCase__						):
    for i in range(UpperCAmelCase__						):
      for j in range(UpperCAmelCase__						):
        if dist[i][j] > dist[i][k] + dist[k][j]:
          UpperCamelCase_:      Union[str, Any]					   =       dist[i][k] + dist[k][j]
          UpperCamelCase_:      Optional[int]					   =       k
  print(UpperCAmelCase__						)

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       str:
  UpperCamelCase_   ,UpperCamelCase_   ,UpperCamelCase_:      Tuple					   =       {s: 0}, set(), {s: 0}
  while True:
    if len(UpperCAmelCase__						) == len(UpperCAmelCase__						) - 1:
      break
    UpperCamelCase_:      List[str]					   =       1_0_0_0_0_0
    for i in dist:
      if i not in known and dist[i] < mini:
        UpperCamelCase_:      Tuple					   =       dist[i]
        UpperCamelCase_:      Optional[Any]					   =       i
    known.add(UpperCAmelCase__						)
    for v in g[u]:
      if v[0] not in known and v[1] < dist.get(v[0]			,					1_0_0_0_0_0						):
        UpperCamelCase_:      Dict					   =       v[1]
        UpperCamelCase_:      Optional[Any]					   =       u
  return dist

def 						snake_case      () ->       Optional[int]:
  UpperCamelCase_   ,UpperCamelCase_:      Tuple					   =       map(UpperCAmelCase__			,					input().split(' '						)						)
  UpperCamelCase_:      List[str]					   =       []
  for _ in range(UpperCAmelCase__						):
    edges.append(map(UpperCAmelCase__			,					input().split(' '						)						)						)
  return edges, n

def 						snake_case      (UpperCAmelCase__						) ->       Optional[Any]:
  # Sort edges on the basis of distance
  ((UpperCamelCase_)   ,(UpperCamelCase_)):      Optional[Any]					   =       e_and_n
  e.sort(reverse=UpperCAmelCase__			,					key=lambda UpperCAmelCase__						: x[2]						)
  UpperCamelCase_:      str					   =       [{i} for i in range(1			,					n + 1						)]
  while True:
    if len(UpperCAmelCase__						) == 1:
      break
    print(UpperCAmelCase__						)
    UpperCamelCase_:      Any					   =       e.pop()
    for i in range(len(UpperCAmelCase__						)						):
      if x[0] in s[i]:
        break
    for j in range(len(UpperCAmelCase__						)						):
      if x[1] in s[j]:
        if i == j:
          break
        s[j].update(s[i]						)
        s.pop(UpperCAmelCase__						)
        break

def 						snake_case      (UpperCAmelCase__						) ->       Dict:
  UpperCamelCase_:      Tuple					   =       []
  for node in graph:
    if not graph[node]:
      isolated.append(UpperCAmelCase__						)
  return isolated