



import math
import sys






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     Optional[int]       ) -> List[str]:
    __lowerCamelCase	:	Optional[int]   	=			0
    __lowerCamelCase	:	Any   	=			[]
    __lowerCamelCase	:	Dict   	=			{}  # To store the pos of node in array




  def 						_lowercase					(			self				:     Tuple       ) -> Dict:
    return self.cur_size == 0




  def 						_lowercase					(			self				:     Optional[Any]      ,     _a				:     Any       ) -> List[str]:
    __lowerCamelCase	:	List[Any]   	=			self.left(lowercase_       )
    __lowerCamelCase	:	List[str]   	=			self.right(lowercase_       )
    if lc < self.cur_size and self.array(lowercase_       )[0] < self.array(lowercase_       )[0]:
      __lowerCamelCase	:	int   	=			lc
    else:
      __lowerCamelCase	:	Dict   	=			idx
    if rc < self.cur_size and self.array(lowercase_       )[0] < self.array(lowercase_       )[0]:
      __lowerCamelCase	:	str   	=			rc
    if smallest != idx:
      self.swap(lowercase_      ,     lowercase_       )
      self.min_heapify(lowercase_       )




  def 						_lowercase					(			self				:     Union[str, Any]      ,     _a				:     Optional[int]       ) -> List[str]:
    # Inserts a node into the Priority Queue
    __lowerCamelCase	:	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 						_lowercase					(			self				:     Dict       ) -> int:
    # Removes and returns the min element at top of priority queue
    __lowerCamelCase	:	Optional[int]   	=			self.array[0][1]
    __lowerCamelCase	:	Dict   	=			self.array[self.cur_size - 1]
    self.cur_size -= 1
    self.min_heapify(1       )
    del self.pos[min_node]
    return min_node




  def 						_lowercase					(			self				:     Union[str, Any]      ,     _a				:     str       ) -> Optional[int]:
    # returns the index of left child
    return 2 * i + 1




  def 						_lowercase					(			self				:     str      ,     _a				:     Dict       ) -> List[Any]:
    # returns the index of right child
    return 2 * i + 2




  def 						_lowercase					(			self				:     str      ,     _a				:     List[Any]       ) -> List[Any]:
    # returns the index of parent
    return math.floor(i / 2       )




  def 						_lowercase					(			self				:     Union[str, Any]      ,     _a				:     List[str]      ,     _a				:     List[Any]       ) -> List[Any]:
    # swaps array elements at indices i and j
    # update the pos{}
    __lowerCamelCase	:	Any   	=			j
    __lowerCamelCase	:	Tuple   	=			i
    __lowerCamelCase	:	Optional[int]   	=			self.array[i]
    __lowerCamelCase	:	List[Any]   	=			self.array[j]
    __lowerCamelCase	:	Tuple   	=			temp




  def 						_lowercase					(			self				:     str      ,     _a				:     List[Any]      ,     _a				:     Optional[Any]       ) -> int:
    __lowerCamelCase	:	Any   	=			self.pos[tup[1]]
    # assuming the new_d is atmost old_d
    __lowerCamelCase	:	Union[str, Any]   	=			(new_d, tup[1])
    while idx > 0 and self.array[self.par(lowercase_       )][0] > self.array[idx][0]:
      self.swap(lowercase_      ,     self.par(lowercase_       )       )
      __lowerCamelCase	:	Union[str, Any]   	=			self.par(lowercase_       )






class  lowerCamelCase_   :
  """simple docstring"""




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




  def 						_lowercase					(			self				:     Union[str, Any]      ,     _a				:     str      ,     _a				:     Optional[Any]      ,     _a				:     Tuple       ) -> Any:
    #  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:
      __lowerCamelCase	:	Union[str, Any]   	=			[(v, w)]

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




  def 						_lowercase					(			self				:     List[str]       ) -> Optional[Any]:
    # u -> v(w)
    for u in self.adjList:
      print(lowercase_      ,     '->'      ,     ' -> '.join(str(f'{v}({w})'       ) for v, w in self.adjList[u]       )       )




  def 						_lowercase					(			self				:     Dict      ,     _a				:     List[str]       ) -> int:
    # Flush old junk values in par[]
    __lowerCamelCase	:	int   	=			[-1] * self.num_nodes
    # src is the source node
    __lowerCamelCase	:	Optional[Any]   	=			0
    __lowerCamelCase	:	List[str]   	=			PriorityQueue()
    q.insert((0, src)       )  # (dist from src, node)
    for u in self.adjList:
      if u != src:
        __lowerCamelCase	:	List[Any]   	=			sys.maxsize  # Infinity
        __lowerCamelCase	:	int   	=			-1

    while not q.is_empty():
      __lowerCamelCase	:	str   	=			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]:
        __lowerCamelCase	:	str   	=			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)      ,     lowercase_       )
          __lowerCamelCase	:	List[str]   	=			new_dist
          __lowerCamelCase	:	str   	=			u

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




  def 						_lowercase					(			self				:     Optional[int]      ,     _a				:     Dict       ) -> Any:
    print(f'Distance from node: {src}'       )
    for u in range(self.num_nodes       ):
      print(f'Node {u} has distance: {self.dist[u]}'       )




  def 						_lowercase					(			self				:     Union[str, Any]      ,     _a				:     Tuple      ,     _a				:     Tuple       ) -> List[Any]:
    # To show the shortest path from src to dest
    # WARNING: Use it *after* calling dijkstra
    __lowerCamelCase	:	List[Any]   	=			[]
    __lowerCamelCase	:	str   	=			0
    __lowerCamelCase	:	List[Any]   	=			dest
    # Backtracking from dest to src
    while self.par[temp] != -1:
      path.append(lowercase_       )
      if temp != src:
        for v, w in self.adjList[temp]:
          if v == self.par[temp]:
            cost += w
            break
      __lowerCamelCase	:	int   	=			self.par[temp]
    path.append(lowercase_       )
    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: '      ,     lowercase_       )


if __name__ == "__main__":
   _UpperCamelCase							    =						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











'''simple docstring'''





import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				List[Any]:
  # Initialize PyTorch model
  __lowerCamelCase	:	Union[str, Any]   	=			CanineConfig()
  __lowerCamelCase	:	Optional[Any]   	=			CanineModel(_lowerCAmelCase	)
  model.eval()

  print(F'Building PyTorch model from configuration: {config}'	)

  # Load weights from tf checkpoint
  load_tf_weights_in_canine(_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)

  # Save pytorch-model (weights and configuration)
  print(F'Save PyTorch model to {pytorch_dump_path}'	)
  model.save_pretrained(_lowerCAmelCase	)

  # Save tokenizer files
  __lowerCamelCase	:	str   	=			CanineTokenizer()
  print(F'Save tokenizer files to {pytorch_dump_path}'	)
  tokenizer.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
   _UpperCamelCase							    =						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path',
       default=None,
       type=str,
       required=True,
       help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
   )
   parser.add_argument(
       '--pytorch_dump_path',
       default=None,
       type=str,
       required=True,
       help='Path to a folder where the PyTorch model will be placed.',
   )
   _UpperCamelCase							    =						parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






