


import heapq as hq
import math
from collections.abc import Iterator






class 	__lowercase					:





      def __init__(		self    :       Optional[Any]       ,							__lowerCamelCase    :       Optional[int]							)     -> Dict:






         """simple docstring"""







         UpperCAmelCase      					=  str(id_							)
         UpperCAmelCase      					=  None
         UpperCAmelCase      					=  None
         UpperCAmelCase      					=  []
         UpperCAmelCase      					=  {}  # {vertex:distance}





      def __lt__(		self    :       int       ,							__lowerCamelCase    :       Any							)     -> Tuple:






         """simple docstring"""







         return self.key < other.key





      def __repr__(		self    :       int							)     -> List[str]:






         """simple docstring"""







         return self.id





      def 							_lowercase					(		self    :       int       ,							__lowerCamelCase    :       Dict							)     -> Dict:






         """simple docstring"""







         self.neighbors.append(__lowerCamelCase							)





      def 							_lowercase					(		self    :       List[Any]       ,							__lowerCamelCase    :       str       ,							__lowerCamelCase    :       str							)     -> List[str]:






         """simple docstring"""







         UpperCAmelCase      					=  weight

def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Union[str, Any]:
   # add the neighbors:
   graph[a - 1].add_neighbor(graph[b - 1] )
   graph[b - 1].add_neighbor(graph[a - 1] )
   # add the edges:
   graph[a - 1].add_edge(graph[b - 1]	,    lowerCAmelCase_ )
   graph[b - 1].add_edge(graph[a - 1]	,    lowerCAmelCase_ )
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->list:
   UpperCAmelCase      					=  []
   for u in graph:
      UpperCAmelCase      					=  math.inf
      UpperCAmelCase      					=  None
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  graph[:]
   while q:
      UpperCAmelCase      					=  min(lowerCAmelCase_ )
      q.remove(lowerCAmelCase_ )
      for v in u.neighbors:
         if (v in q) and (u.edges[v.id] < v.key):
            UpperCAmelCase      					=  u
            UpperCAmelCase      					=  u.edges[v.id]
   for i in range(1	,    len(lowerCAmelCase_ ) ):
      a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
   return a
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Iterator[tuple]:
   for u in graph:
      UpperCAmelCase      					=  math.inf
      UpperCAmelCase      					=  None
   UpperCAmelCase      					=  0

   UpperCAmelCase      					=  list(lowerCAmelCase_ )
   hq.heapify(lowerCAmelCase_ )

   while h:
      UpperCAmelCase      					=  hq.heappop(lowerCAmelCase_ )
      for v in u.neighbors:
         if (v in h) and (u.edges[v.id] < v.key):
            UpperCAmelCase      					=  u
            UpperCAmelCase      					=  u.edges[v.id]
            hq.heapify(lowerCAmelCase_ )

   for i in range(1	,    len(lowerCAmelCase_ ) ):
      yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def 	_UpperCamelCase					(    )    ->None:
   pass


if __name__ == "__main__":
 import doctest

 doctest.testmod()







import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[Any]:
   UpperCAmelCase      					=  ("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

   UpperCAmelCase      					=  (
       ("""layer.""", """layer_"""),
       ("""word_embeddings.weight""", """word_embeddings"""),
       ("""position_embeddings.weight""", """position_embeddings"""),
       ("""token_type_embeddings.weight""", """token_type_embeddings"""),
       (""".""", """/"""),
       ("""LayerNorm/weight""", """LayerNorm/gamma"""),
       ("""LayerNorm/bias""", """LayerNorm/beta"""),
       ("""weight""", """kernel"""),
   )

   if not os.path.isdir(lowerCAmelCase_ ):
      os.makedirs(lowerCAmelCase_ )

   UpperCAmelCase      					=  model.state_dict()

   def to_tf_var_name(lowerCAmelCase_ ):
      for patt, repl in iter(lowerCAmelCase_ ):
         UpperCAmelCase      					=  name.replace(lowerCAmelCase_	,    lowerCAmelCase_ )
      return F"""bert/{name}"""

   def create_tf_var(lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ ):
      UpperCAmelCase      					=  tf.dtypes.as_dtype(tensor.dtype )
      UpperCAmelCase      					=  tf.get_variable(dtype=lowerCAmelCase_	,    shape=tensor.shape	,    name=lowerCAmelCase_	,    initializer=tf.zeros_initializer() )
      session.run(tf.variables_initializer([tf_var] ) )
      session.run(lowerCAmelCase_ )
      return tf_var

   tf.reset_default_graph()
   with tf.Session() as session:
      for var_name in state_dict:
         UpperCAmelCase      					=  to_tf_var_name(lowerCAmelCase_ )
         UpperCAmelCase      					=  state_dict[var_name].numpy()
         if any(x in var_name for x in tensors_to_transpose ):
            UpperCAmelCase      					=  torch_tensor.T
         UpperCAmelCase      					=  create_tf_var(tensor=lowerCAmelCase_	,    name=lowerCAmelCase_	,    session=lowerCAmelCase_ )
         tf.keras.backend.set_value(lowerCAmelCase_	,    lowerCAmelCase_ )
         UpperCAmelCase      					=  session.run(lowerCAmelCase_ )
         print(F"""Successfully created {tf_name}: {np.allclose(lowerCAmelCase_	,    lowerCAmelCase_ )}""" )

      UpperCAmelCase      					=  tf.train.Saver(tf.trainable_variables() )
      saver.save(lowerCAmelCase_	,    os.path.join(lowerCAmelCase_	,    model_name.replace("""-"""	,    """_""" ) + """.ckpt""" ) )
def 	_UpperCamelCase					(    lowerCAmelCase_=None )    ->Union[str, Any]:
   UpperCAmelCase      					=  argparse.ArgumentParser()
   parser.add_argument("""--model_name"""	,    type=lowerCAmelCase_	,    required=lowerCAmelCase_	,    help="""model name e.g. bert-base-uncased""" )
   parser.add_argument(
       """--cache_dir"""	,    type=lowerCAmelCase_	,    default=lowerCAmelCase_	,    required=lowerCAmelCase_	,    help="""Directory containing pytorch model""" )
   parser.add_argument("""--pytorch_model_path"""	,    type=lowerCAmelCase_	,    required=lowerCAmelCase_	,    help="""/path/to/<pytorch-model-name>.bin""" )
   parser.add_argument("""--tf_cache_dir"""	,    type=lowerCAmelCase_	,    required=lowerCAmelCase_	,    help="""Directory in which to save tensorflow model""" )
   UpperCAmelCase      					=  parser.parse_args(lowerCAmelCase_ )

   UpperCAmelCase      					=  BertModel.from_pretrained(
       pretrained_model_name_or_path=args.model_name	,    state_dict=torch.load(args.pytorch_model_path )	,    cache_dir=args.cache_dir	,    )

   convert_pytorch_checkpoint_to_tf(model=lowerCAmelCase_	,    ckpt_dir=args.tf_cache_dir	,    model_name=args.model_name )


if __name__ == "__main__":
 main()



