





import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def        _a						(    UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase     )				->				List[str]:







   """simple docstring"""
   lowerCamelCase__   :						Union[str, Any]					   =							('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

   lowerCamelCase__   :						List[str]					   =							(
       ('''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__   :						Union[str, Any]					   =							model.state_dict()

   def to_tf_var_name(UpperCAmelCase     ):
      for patt, repl in iter(__snake_case     ):
         lowerCamelCase__   :						List[Any]					   =							name.replace(__snake_case			,  __snake_case     )
      return f"bert/{name}"

   def create_tf_var(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase     ):
      lowerCamelCase__   :						List[str]					   =							tf.dtypes.as_dtype(tensor.dtype     )
      lowerCamelCase__   :						Dict					   =							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__   :						Optional[Any]					   =							to_tf_var_name(__snake_case     )
         lowerCamelCase__   :						str					   =							state_dict[var_name].numpy()
         if any(x in var_name for x in tensors_to_transpose     ):
            lowerCamelCase__   :						Any					   =							torch_tensor.T
         lowerCamelCase__   :						Any					   =							create_tf_var(tensor=__snake_case			,  name=__snake_case			,  session=__snake_case     )
         tf.keras.backend.set_value(__snake_case			,  __snake_case     )
         lowerCamelCase__   :						Union[str, Any]					   =							session.run(__snake_case     )
         print(f"Successfully created {tf_name}: {np.allclose(__snake_case			,  __snake_case     )}"     )

      lowerCamelCase__   :						str					   =							tf.train.Saver(tf.trainable_variables()     )
      saver.save(__snake_case			,  os.path.join(__snake_case			,  model_name.replace('''-'''			,  '''_'''     ) + '''.ckpt'''     )     )



def        _a						(    UpperCAmelCase=None     )				->				int:







   """simple docstring"""
   lowerCamelCase__   :						List[str]					   =							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__   :						Dict					   =							parser.parse_args(__snake_case     )

   lowerCamelCase__   :						str					   =							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()







from typing import Any
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)     ->			list:







 """simple docstring"""


 _validation(
     __snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)
 # Creates data structures and fill initial step
 _lowercase							      ={}
 _lowercase							      ={}
 for state in states_space:
  _lowercase							      =observations_space[0]
  _lowercase							      =(
      initial_probabilities[state] * emission_probabilities[state][observation]
  )
  _lowercase							      =None

 # Fills the data structure with the probabilities of
 # different transitions and pointers to previous states
 for o in range(1     ,						len(__snake_case						)						):
  _lowercase							      =observations_space[o]
  _lowercase							      =observations_space[o - 1]
  for state in states_space:
   # Calculates the argmax for probability function
   _lowercase							      =''''''
   _lowercase							      =-1
   for k_state in states_space:
    _lowercase							      =(
        probabilities[(k_state, prior_observation)]
        * transition_probabilities[k_state][state]
        * emission_probabilities[state][observation]
    )
    if probability > max_probability:
     _lowercase							      =probability
     _lowercase							      =k_state

            # Update probabilities and pointers dicts
   _lowercase							      =(
       probabilities[(arg_max, prior_observation)]
       * transition_probabilities[arg_max][state]
       * emission_probabilities[state][observation]
   )

   _lowercase							      =arg_max

    # The final observation
 _lowercase							      =observations_space[len(__snake_case						) - 1]

 # argmax for given final observation
 _lowercase							      =''''''
 _lowercase							      =-1
 for k_state in states_space:
  _lowercase							      =probabilities[(k_state, final_observation)]
  if probability > max_probability:
   _lowercase							      =probability
   _lowercase							      =k_state
 _lowercase							      =arg_max

 # Process pointers backwards
 _lowercase							      =last_state
 _lowercase							      =[]
 for o in range(len(__snake_case						) - 1     ,						-1     ,						-1						):
  result.append(__snake_case						)
  _lowercase							      =pointers[previous, observations_space[o]]
 result.reverse()

 return result
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)     ->			None:







 """simple docstring"""


 _validate_not_empty(
     __snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)
 _validate_lists(__snake_case     ,						__snake_case						)
 _validate_dicts(
     __snake_case     ,						__snake_case     ,						__snake_case						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)     ->			None:







 """simple docstring"""


 if not all(
     [
         observations_space,
         states_space,
         initial_probabilities,
         transition_probabilities,
         emission_probabilities,
     ]						):
  raise ValueError('''There\'s an empty parameter'''						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			None:







 """simple docstring"""


 _validate_list(__snake_case     ,						'''observations_space'''						)
 _validate_list(__snake_case     ,						'''states_space'''						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			None:







 """simple docstring"""


 if not isinstance(_object     ,						__snake_case						):
  _lowercase							      =F"{var_name} must be a list"
  raise ValueError(__snake_case						)
 else:
  for x in _object:
   if not isinstance(__snake_case     ,						__snake_case						):
    _lowercase							      =F"{var_name} must be a list of strings"
    raise ValueError(__snake_case						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						)     ->			None:







 """simple docstring"""


 _validate_dict(__snake_case     ,						'''initial_probabilities'''     ,						__snake_case						)
 _validate_nested_dict(__snake_case     ,						'''transition_probabilities'''						)
 _validate_nested_dict(__snake_case     ,						'''emission_probabilities'''						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			None:







 """simple docstring"""


 _validate_dict(_object     ,						__snake_case     ,						__snake_case						)
 for x in _object.values():
  _validate_dict(__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case = False						)     ->			None:







 """simple docstring"""


 if not isinstance(_object     ,						__snake_case						):
  _lowercase							      =F"{var_name} must be a dict"
  raise ValueError(__snake_case						)
 if not all(isinstance(__snake_case     ,						__snake_case						) for x in _object						):
  _lowercase							      =F"{var_name} all keys must be strings"
  raise ValueError(__snake_case						)
 if not all(isinstance(__snake_case     ,						__snake_case						) for x in _object.values()						):
  _lowercase							      ='''nested dictionary ''' if nested else ''''''
  _lowercase							      =F"{var_name} {nested_text}all values must be {value_type.__name__}"
  raise ValueError(__snake_case						)


if __name__ == "__main__":
  from doctest import testmod

  testmod()

