





"""simple docstring"""





import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Optional[Any]:





  """simple docstring"""


  lowerCAmelCase__     :Optional[Any]											=				('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

  lowerCAmelCase__     :int											=				(
      ('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(_SCREAMING_SNAKE_CASE     ):
    os.makedirs(_SCREAMING_SNAKE_CASE     )

  lowerCAmelCase__     :Optional[int]											=				model.state_dict()

  def to_tf_var_name(_SCREAMING_SNAKE_CASE     ):
    for patt, repl in iter(_SCREAMING_SNAKE_CASE     ):
      lowerCAmelCase__     :List[Any]											=				name.replace(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
    return F"bert/{name}"

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

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

    lowerCAmelCase__     :Tuple											=				tf.train.Saver(tf.trainable_variables()     )
    saver.save(_SCREAMING_SNAKE_CASE	,  os.path.join(_SCREAMING_SNAKE_CASE	,  model_name.replace('-'	,  '_'     ) + '.ckpt'     )     )
def 					__A  (_SCREAMING_SNAKE_CASE=None     )   ->Union[str, Any]:





  """simple docstring"""


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

  lowerCAmelCase__     :Union[str, Any]											=				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=_SCREAMING_SNAKE_CASE	,  ckpt_dir=args.tf_cache_dir	,  model_name=args.model_name     )


if __name__ == "__main__":
  main()






from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm





def        __UpperCAmelCase						(							lowerCamelCase_ :     np.ndarray  ,							lowerCamelCase_ :     np.ndarray  )       ->  float:







			"""simple docstring"""





			return math.sqrt(sum(pow(a - b  ,							2  ) for a, b in zip(lowerCamelCase_  ,							lowerCamelCase_  )  )  )





def        __UpperCAmelCase						(							lowerCamelCase_ :     np.ndarray  ,							lowerCamelCase_ :     np.ndarray  )       ->  list[list[list[float] | float]]:







			"""simple docstring"""





			if dataset.ndim != value_array.ndim:
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      (
						    'Wrong input data\'s dimensions... '
						    F'dataset : {dataset.ndim}, value_array : {value_array.ndim}'
						)
						raise ValueError(lowerCamelCase_  )

			try:
						if dataset.shape[1] != value_array.shape[1]:
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      (
									    'Wrong input data\'s shape... '
									    F'dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'
									)
									raise ValueError(lowerCamelCase_  )
			except IndexError:
						if dataset.ndim != value_array.ndim:
									raise TypeError('Wrong shape'  )

			if dataset.dtype != value_array.dtype:
						SCREAMING_SNAKE_CASE_   : Tuple					  =      (
						    'Input data have different datatype... '
						    F'dataset : {dataset.dtype}, value_array : {value_array.dtype}'
						)
						raise TypeError(lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      []

			for value in value_array:
						SCREAMING_SNAKE_CASE_   : str					  =      euclidean(lowerCamelCase_  ,							dataset[0]  )
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      dataset[0].tolist()

						for dataset_value in dataset[1:]:
									SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      euclidean(lowerCamelCase_  ,							lowerCamelCase_  )

									if dist > temp_dist:
												SCREAMING_SNAKE_CASE_   : Optional[int]					  =      temp_dist
												SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      dataset_value.tolist()

						answer.append([vector, dist]  )

			return answer





def        __UpperCAmelCase						(							lowerCamelCase_ :     np.ndarray  ,							lowerCamelCase_ :     np.ndarray  )       ->  float:







			"""simple docstring"""





			return np.dot(lowerCamelCase_  ,							lowerCamelCase_  ) / (norm(lowerCamelCase_  ) * norm(lowerCamelCase_  ))


if __name__ == "__main__":
						import doctest

						doctest.testmod()



