




'''simple docstring'''


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

class     A					:







							def __init__(						self :						List[Any]							,    lowerCAmelCase_ :						Dict      )    ->							Union[str, Any]:


														"""simple docstring"""


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







							def __lt__(						self :						Optional[Any]							,    lowerCAmelCase_ :						Any      )    ->							List[str]:


														"""simple docstring"""


														return self.key < other.key







							def __repr__(						self :						Optional[int]      )    ->							Tuple:


														"""simple docstring"""


														return self.id







							def      __lowerCAmelCase    (						self :						Any							,    lowerCAmelCase_ :						Union[str, Any]      )    ->							List[str]:


														"""simple docstring"""


														self.neighbors.append(lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						Optional[int]							,    lowerCAmelCase_ :						Optional[Any]      )    ->							Optional[int]:


														"""simple docstring"""


														_a			  =       weight



def 	snake_case_					(UpperCamelCase :						Tuple				,		UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Any				,		UpperCamelCase :						Union[str, Any]   ):





							'''simple docstring'''


							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]				,		UpperCamelCase   )
							graph[b - 1].add_edge(graph[a - 1]				,		UpperCamelCase   )
def 	snake_case_					(UpperCamelCase :						list				,		UpperCamelCase :						Vertex   ):





							'''simple docstring'''


							_a			  =       []
							for u in graph:
														_a			  =       math.inf
														_a			  =       None
							_a			  =       0
							_a			  =       graph[:]
							while q:
														_a			  =       min(UpperCamelCase   )
														q.remove(UpperCamelCase   )
														for v in u.neighbors:
																					if (v in q) and (u.edges[v.id] < v.key):
																												_a			  =       u
																												_a			  =       u.edges[v.id]
							for i in range(1				,		len(UpperCamelCase   )   ):
														a.append((int(graph[i].id   ) + 1, int(graph[i].pi.id   ) + 1)   )
							return a
def 	snake_case_					(UpperCamelCase :						list				,		UpperCamelCase :						Vertex   ):





							'''simple docstring'''


							for u in graph:
														_a			  =       math.inf
														_a			  =       None
							_a			  =       0

							_a			  =       list(UpperCamelCase   )
							hq.heapify(UpperCamelCase   )

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

							for i in range(1				,		len(UpperCamelCase   )   ):
														yield (int(graph[i].id   ) + 1, int(graph[i].pi.id   ) + 1)
def 	snake_case_					():





							'''simple docstring'''




if __name__ == "__main__":
				import doctest

				doctest.testmod()







'''simple docstring'''


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def 	snake_case_					(UpperCamelCase :						BertModel				,		UpperCamelCase :						str				,		UpperCamelCase :						str   ):





							'''simple docstring'''


							_a			  =       ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

							_a			  =       (
							    ('''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(UpperCamelCase   ):
														os.makedirs(UpperCamelCase   )

							_a			  =       model.state_dict()

							def to_tf_var_name(UpperCamelCase :						str   ):
														for patt, repl in iter(UpperCamelCase   ):
																					_a			  =       name.replace(UpperCamelCase				,		UpperCamelCase   )
														return f'bert/{name}'

							def create_tf_var(UpperCamelCase :						np.ndarray				,		UpperCamelCase :						str				,		UpperCamelCase :						tf.Session   ):
														_a			  =       tf.dtypes.as_dtype(tensor.dtype   )
														_a			  =       tf.get_variable(dtype=UpperCamelCase				,		shape=tensor.shape				,		name=UpperCamelCase				,		initializer=tf.zeros_initializer()   )
														session.run(tf.variables_initializer([tf_var]   )   )
														session.run(UpperCamelCase   )
														return tf_var

							tf.reset_default_graph()
							with tf.Session() as session:
														for var_name in state_dict:
																					_a			  =       to_tf_var_name(UpperCamelCase   )
																					_a			  =       state_dict[var_name].numpy()
																					if any(x in var_name for x in tensors_to_transpose   ):
																												_a			  =       torch_tensor.T
																					_a			  =       create_tf_var(tensor=UpperCamelCase				,		name=UpperCamelCase				,		session=UpperCamelCase   )
																					tf.keras.backend.set_value(UpperCamelCase				,		UpperCamelCase   )
																					_a			  =       session.run(UpperCamelCase   )
																					print(f'Successfully created {tf_name}: {np.allclose(UpperCamelCase				,		UpperCamelCase   )}'   )

														_a			  =       tf.train.Saver(tf.trainable_variables()   )
														saver.save(UpperCamelCase				,		os.path.join(UpperCamelCase				,		model_name.replace('''-'''				,		'''_'''   ) + '''.ckpt'''   )   )
def 	snake_case_					(UpperCamelCase :						Tuple=None   ):





							'''simple docstring'''


							_a			  =       argparse.ArgumentParser()
							parser.add_argument('''--model_name'''				,		type=UpperCamelCase				,		required=UpperCamelCase				,		help='''model name e.g. bert-base-uncased'''   )
							parser.add_argument(
							    '''--cache_dir'''				,		type=UpperCamelCase				,		default=UpperCamelCase				,		required=UpperCamelCase				,		help='''Directory containing pytorch model'''   )
							parser.add_argument('''--pytorch_model_path'''				,		type=UpperCamelCase				,		required=UpperCamelCase				,		help='''/path/to/<pytorch-model-name>.bin'''   )
							parser.add_argument('''--tf_cache_dir'''				,		type=UpperCamelCase				,		required=UpperCamelCase				,		help='''Directory in which to save tensorflow model'''   )
							_a			  =       parser.parse_args(UpperCamelCase   )

							_a			  =       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=UpperCamelCase				,		ckpt_dir=args.tf_cache_dir				,		model_name=args.model_name   )


if __name__ == "__main__":
				main()

