




"""simple docstring"""







import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def 							lowercase					(snake_case__   : int      ,       snake_case__   : List[Any]      ,       snake_case__   : Tuple )     ->  int:


    '''simple docstring'''






    lowerCAmelCase  =				("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

    lowerCAmelCase  =				(
        ("""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(snake_case__ ):
        os.makedirs(snake_case__ )

    lowerCAmelCase  =				model.state_dict()

    def to_tf_var_name(snake_case__   : int ):
        for patt, repl in iter(snake_case__ ):
            lowerCAmelCase  =				name.replace(snake_case__      ,       snake_case__ )
        return f'''bert/{name}'''

    def create_tf_var(snake_case__   : List[str]      ,       snake_case__   : int      ,       snake_case__   : List[Any] ):
        lowerCAmelCase  =				tf.dtypes.as_dtype(tensor.dtype )
        lowerCAmelCase  =				tf.get_variable(dtype=snake_case__      ,       shape=tensor.shape      ,       name=snake_case__      ,       initializer=tf.zeros_initializer() )
        session.run(tf.variables_initializer([tf_var] ) )
        session.run(snake_case__ )
        return tf_var

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

        lowerCAmelCase  =				tf.train.Saver(tf.trainable_variables() )
        saver.save(snake_case__      ,       os.path.join(snake_case__      ,       model_name.replace("""-"""      ,       """_""" ) + """.ckpt""" ) )





def 							lowercase					(snake_case__   : Union[str, Any]=None )     ->  List[str]:


    '''simple docstring'''






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

    lowerCAmelCase  =				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=snake_case__      ,       ckpt_dir=args.tf_cache_dir      ,       model_name=args.model_name )


if __name__ == "__main__":
  main()











"""simple docstring"""







from queue import PriorityQueue
from typing import Any

import numpy as np





def 							lowercase					(snake_case__   : dict      ,       snake_case__   : str      ,       snake_case__   : set      ,       snake_case__   : set      ,       snake_case__   : dict      ,       snake_case__   : dict      ,       snake_case__   : PriorityQueue      ,       snake_case__   : dict      ,       snake_case__   : float | int      ,       )     ->  float | int:


				'''simple docstring'''






				for nxt, d in graph[v]:
								if nxt in visited_forward:
												continue
								lowerCAmelCase  =				cst_fwd.get(snake_case__      ,       np.inf )
								lowerCAmelCase  =				cst_fwd[v] + d
								if new_cost_f < old_cost_f:
												queue.put((new_cost_f, nxt) )
												lowerCAmelCase  =				new_cost_f
												lowerCAmelCase  =				v
								if nxt in visited_backward:
												if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																lowerCAmelCase  =				cst_fwd[v] + d + cst_bwd[nxt]
				return shortest_distance





def 							lowercase					(snake_case__   : str      ,       snake_case__   : str      ,       snake_case__   : dict      ,       snake_case__   : dict )     ->  int:


				'''simple docstring'''






				lowerCAmelCase  =				-1

				lowerCAmelCase  =				set()
				lowerCAmelCase  =				set()
				lowerCAmelCase  =				{source: 0}
				lowerCAmelCase  =				{destination: 0}
				lowerCAmelCase  =				{source: None}
				lowerCAmelCase  =				{destination: None}
				lowerCAmelCase  =				PriorityQueue()
				lowerCAmelCase  =				PriorityQueue()

				lowerCAmelCase  =				np.inf

				queue_forward.put((0, source) )
				queue_backward.put((0, destination) )

				if source == destination:
								return 0

				while not queue_forward.empty() and not queue_backward.empty():
								lowerCAmelCase ,						lowerCAmelCase  =				queue_forward.get()
								visited_forward.add(snake_case__ )

								lowerCAmelCase ,						lowerCAmelCase  =				queue_backward.get()
								visited_backward.add(snake_case__ )

								lowerCAmelCase  =				pass_and_relaxation(
								    snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       )

								lowerCAmelCase  =				pass_and_relaxation(
								    snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       )

								if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
												break

				if shortest_distance != np.inf:
								lowerCAmelCase  =				shortest_distance
				return shortest_path_distance


a      =							{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
a      =							{
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
		import doctest

		doctest.testmod()





