




"""simple docstring"""




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class 							lowerCamelCase		( _UpperCAmelCase				):
   lowercase       :  Tuple      =				['image_processor', 'tokenizer']
   lowercase       :  Any      =				'AutoImageProcessor'
   lowercase       :  Optional[int]      =				'AutoTokenizer'







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      **SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   Optional[Any]          =							None
       if "feature_extractor" in kwargs:
           warnings.warn(
               """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
               """ instead."""    ,      SCREAMING_SNAKE_CASE_    ,      )
           UpperCamelCase				:   Optional[int]          =							kwargs.pop("""feature_extractor""" )

       UpperCamelCase				:   Union[str, Any]          =							image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
           raise ValueError("""You need to specify an `image_processor`.""" )
       if tokenizer is None:
           raise ValueError("""You need to specify a `tokenizer`.""" )

       super().__init__(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
       UpperCamelCase				:   Tuple          =							self.image_processor
       UpperCamelCase				:   List[Any]          =							False







   def __call__(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
       # For backward compatibility
       if self._in_target_context_manager:
           return self.current_processor(*SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )

       UpperCamelCase				:   List[str]          =							kwargs.pop("""images"""    ,      SCREAMING_SNAKE_CASE_ )
       UpperCamelCase				:   List[str]          =							kwargs.pop("""text"""    ,      SCREAMING_SNAKE_CASE_ )
       if len(SCREAMING_SNAKE_CASE_ ) > 0:
           UpperCamelCase				:   Dict          =							args[0]
           UpperCamelCase				:   Tuple          =							args[1:]

       if images is None and text is None:
           raise ValueError("""You need to specify either an `images` or `text` input to process.""" )

       if images is not None:
           UpperCamelCase				:   List[str]          =							self.image_processor(SCREAMING_SNAKE_CASE_    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
       if text is not None:
           UpperCamelCase				:   Dict          =							self.tokenizer(SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )

       if text is None:
           return inputs
       elif images is None:
           return encodings
       else:
           UpperCamelCase				:   Tuple          =							encodings["""input_ids"""]
           return inputs







   def       a_							(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
       return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )







   def       a_							(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
       return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )







   @contextmanager
   def       a_							(							self ):
       warnings.warn(
           """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
           """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
           """your images inputs, or in a separate call.""" )
       UpperCamelCase				:   int          =							True
       UpperCamelCase				:   Union[str, Any]          =							self.tokenizer
       yield
       UpperCamelCase				:   Dict          =							self.image_processor
       UpperCamelCase				:   Any          =							False







   @property
   def       a_							(							self ):
       warnings.warn(
           """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""    ,      SCREAMING_SNAKE_CASE_    ,      )
       return self.image_processor_class







   @property
   def       a_							(							self ):
       warnings.warn(
           """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    ,      SCREAMING_SNAKE_CASE_    ,      )
       return self.image_processor










"""simple docstring"""




import math
import sys
class 							lowerCamelCase		:







   def __init__(							self ):
       UpperCamelCase				:   Tuple          =							0
       UpperCamelCase				:   Optional[int]          =							[]
       UpperCamelCase				:   Optional[int]          =							{}  # To store the pos of node in array







   def       a_							(							self ):
       return self.cur_size == 0







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   List[str]          =							self.left(SCREAMING_SNAKE_CASE_ )
       UpperCamelCase				:   Optional[int]          =							self.right(SCREAMING_SNAKE_CASE_ )
       if lc < self.cur_size and self.array(SCREAMING_SNAKE_CASE_ )[0] < self.array(SCREAMING_SNAKE_CASE_ )[0]:
           UpperCamelCase				:   str          =							lc
       else:
           UpperCamelCase				:   Union[str, Any]          =							idx
       if rc < self.cur_size and self.array(SCREAMING_SNAKE_CASE_ )[0] < self.array(SCREAMING_SNAKE_CASE_ )[0]:
           UpperCamelCase				:   Optional[Any]          =							rc
       if smallest != idx:
           self.swap(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
           self.min_heapify(SCREAMING_SNAKE_CASE_ )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       # Inserts a node into the Priority Queue
       UpperCamelCase				:   List[Any]          =							self.cur_size
       self.cur_size += 1
       self.array.append((sys.maxsize, tup[1]) )
       self.decrease_key((sys.maxsize, tup[1])    ,      tup[0] )







   def       a_							(							self ):
       # Removes and returns the min element at top of priority queue
       UpperCamelCase				:   str          =							self.array[0][1]
       UpperCamelCase				:   Optional[int]          =							self.array[self.cur_size - 1]
       self.cur_size -= 1
       self.min_heapify(1 )
       del self.pos[min_node]
       return min_node







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       # returns the index of left child
       return 2 * i + 1







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       # returns the index of right child
       return 2 * i + 2







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       # returns the index of parent
       return math.floor(i / 2 )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
       # swaps array elements at indices i and j
       # update the pos{}
       UpperCamelCase				:   Dict          =							j
       UpperCamelCase				:   List[str]          =							i
       UpperCamelCase				:   Optional[int]          =							self.array[i]
       UpperCamelCase				:   Union[str, Any]          =							self.array[j]
       UpperCamelCase				:   Optional[int]          =							temp







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   Tuple          =							self.pos[tup[1]]
       # assuming the new_d is atmost old_d
       UpperCamelCase				:   Union[str, Any]          =							(new_d, tup[1])
       while idx > 0 and self.array[self.par(SCREAMING_SNAKE_CASE_ )][0] > self.array[idx][0]:
           self.swap(SCREAMING_SNAKE_CASE_    ,      self.par(SCREAMING_SNAKE_CASE_ ) )
           UpperCamelCase				:   Union[str, Any]          =							self.par(SCREAMING_SNAKE_CASE_ )







class 							lowerCamelCase		:







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   Any          =							{}  # To store graph: u -> (v,w)
       UpperCamelCase				:   int          =							num  # Number of nodes in graph
       # To store the distance from source vertex
       UpperCamelCase				:   Any          =							[0] * self.num_nodes
       UpperCamelCase				:   Union[str, Any]          =							[-1] * self.num_nodes  # To store the path







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
       #  Edge going from node u to v and v to u with weight w
       # u (w)-> v, v (w) -> u
       # Check if u already in graph
       if u in self.adjList:
           self.adjList[u].append((v, w) )
       else:
           UpperCamelCase				:   Optional[int]          =							[(v, w)]

       # Assuming undirected graph
       if v in self.adjList:
           self.adjList[v].append((u, w) )
       else:
           UpperCamelCase				:   List[Any]          =							[(u, w)]







   def       a_							(							self ):
       # u -> v(w)
       for u in self.adjList:
           print(SCREAMING_SNAKE_CASE_    ,      """->"""    ,      """ -> """.join(str(f'{v}({w})' ) for v, w in self.adjList[u] ) )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       # Flush old junk values in par[]
       UpperCamelCase				:   Tuple          =							[-1] * self.num_nodes
       # src is the source node
       UpperCamelCase				:   Dict          =							0
       UpperCamelCase				:   int          =							PriorityQueue()
       q.insert((0, src) )  # (dist from src, node)
       for u in self.adjList:
           if u != src:
               UpperCamelCase				:   Any          =							sys.maxsize  # Infinity
               UpperCamelCase				:   int          =							-1

       while not q.is_empty():
           UpperCamelCase				:   int          =							q.extract_min()  # Returns node with the min dist from source
           # Update the distance of all the neighbours of u and
           # if their prev dist was INFINITY then push them in Q
           for v, w in self.adjList[u]:
               UpperCamelCase				:   List[Any]          =							self.dist[u] + w
               if self.dist[v] > new_dist:
                   if self.dist[v] == sys.maxsize:
                       q.insert((new_dist, v) )
                   else:
                       q.decrease_key((self.dist[v], v)    ,      SCREAMING_SNAKE_CASE_ )
                   UpperCamelCase				:   Any          =							new_dist
                   UpperCamelCase				:   Tuple          =							u

        # Show the shortest distances from src
       self.show_distances(SCREAMING_SNAKE_CASE_ )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       print(f'Distance from node: {src}' )
       for u in range(self.num_nodes ):
           print(f'Node {u} has distance: {self.dist[u]}' )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
       # To show the shortest path from src to dest
       # WARNING: Use it *after* calling dijkstra
       UpperCamelCase				:   Tuple          =							[]
       UpperCamelCase				:   Optional[Any]          =							0
       UpperCamelCase				:   int          =							dest
       # Backtracking from dest to src
       while self.par[temp] != -1:
           path.append(SCREAMING_SNAKE_CASE_ )
           if temp != src:
               for v, w in self.adjList[temp]:
                   if v == self.par[temp]:
                       cost += w
                       break
           UpperCamelCase				:   int          =							self.par[temp]
       path.append(SCREAMING_SNAKE_CASE_ )
       path.reverse()

       print(f'----Path to reach {dest} from {src}----' )
       for u in path:
           print(f'{u}'    ,      end=""" """ )
           if u != dest:
               print("""-> """    ,      end="""""" )

       print("""\nTotal cost of path: """    ,      SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
  __A :		int      							=    Graph(9)
  graph.add_edge(0, 1, 4)
  graph.add_edge(0, 7, 8)
  graph.add_edge(1, 2, 8)
  graph.add_edge(1, 7, 11)
  graph.add_edge(2, 3, 7)
  graph.add_edge(2, 8, 2)
  graph.add_edge(2, 5, 4)
  graph.add_edge(3, 4, 9)
  graph.add_edge(3, 5, 14)
  graph.add_edge(4, 5, 10)
  graph.add_edge(5, 6, 2)
  graph.add_edge(6, 7, 1)
  graph.add_edge(6, 8, 6)
  graph.add_edge(7, 8, 7)
  graph.show_graph()
  graph.dijkstra(0)
  graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21




