
"""simple docstring"""


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def    _snake_case					(		snake_case__ :  BertModel						,   snake_case__ :  str						,   snake_case__ :  str  ):
	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(snake_case__  ):
		os.makedirs(snake_case__  )

	A     = model.state_dict()

	def to_tf_var_name(snake_case__ :  str  ):
		for patt, repl in iter(snake_case__  ):
			A     = name.replace(snake_case__						,   snake_case__  )
		return F'bert/{name}'

	def create_tf_var(snake_case__ :  np.ndarray						,   snake_case__ :  str						,   snake_case__ :  tf.Session  ):
		A     = tf.dtypes.as_dtype(tensor.dtype  )
		A     = 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:
			A     = to_tf_var_name(snake_case__  )
			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=snake_case__						,   name=snake_case__						,   session=snake_case__  )
			tf.keras.backend.set_value(snake_case__						,   snake_case__  )
			A     = session.run(snake_case__  )
			print(F'Successfully created {tf_name}: {np.allclose(snake_case__						,   snake_case__  )}'  )

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






def    _snake_case					(		snake_case__ :  Tuple=None  ):
	A     = 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'  )
	A     = parser.parse_args(snake_case__  )

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


if __name__ == "__main__":
		main()






"""simple docstring"""





import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


__A											=						{
    """<""": operator.lt,
    """<=""": operator.le,
    """==""": operator.eq,
    """!=""": operator.ne,
    """>=""": operator.ge,
    """>""": operator.gt,
}
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Union[str, Any]:





  """simple docstring"""


  if got_ver is None or want_ver is None:
    raise ValueError(
        F"Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"
        F" reinstalling {pkg}."     )
  if not ops[op](version.parse(_SCREAMING_SNAKE_CASE     )	,  version.parse(_SCREAMING_SNAKE_CASE     )     ):
    raise ImportError(
        F"{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"     )
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE = None     )   ->None:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				F"\n{hint}" if hint is not None else ''

  # non-versioned check
  if re.match(r'^[\w_\-\d]+$'	,  _SCREAMING_SNAKE_CASE     ):
    lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :Any											=				requirement, None, None
  else:
    lowerCAmelCase__     :List[str]											=				re.findall(r'^([^!=<>\s]+)([\s!=<>]{1,2}.+)'	,  _SCREAMING_SNAKE_CASE     )
    if not match:
      raise ValueError(
          'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'
          F" got {requirement}"     )
    lowerCAmelCase__				,							lowerCAmelCase__     :Union[str, Any]											=				match[0]
    lowerCAmelCase__     :List[Any]											=				want_full.split(','     )  # there could be multiple requirements
    lowerCAmelCase__     :Any											=				{}
    for w in want_range:
      lowerCAmelCase__     :Tuple											=				re.findall(r'^([\s!=<>]{1,2})(.+)'	,  _SCREAMING_SNAKE_CASE     )
      if not match:
        raise ValueError(
            'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'
            F" but got {requirement}"     )
      lowerCAmelCase__				,							lowerCAmelCase__     :int											=				match[0]
      lowerCAmelCase__     :str											=				want_ver
      if op not in ops:
        raise ValueError(F"{requirement}: need one of {list(ops.keys()     )}, but got {op}"     )

    # special case
  if pkg == "python":
    lowerCAmelCase__     :Any											=				'.'.join([str(_SCREAMING_SNAKE_CASE     ) for x in sys.version_info[:3]]     )
    for op, want_ver in wanted.items():
      _compare_versions(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
    return

  # check if any version is installed
  try:
    lowerCAmelCase__     :List[Any]											=				importlib.metadata.version(_SCREAMING_SNAKE_CASE     )
  except importlib.metadata.PackageNotFoundError:
    raise importlib.metadata.PackageNotFoundError(
        F"The '{requirement}' distribution was not found and is required by this application. {hint}"     )

  # check that the right version is installed if version number or a range was provided
  if want_ver is not None:
    for op, want_ver in wanted.items():
      _compare_versions(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )




def 					__A  (_SCREAMING_SNAKE_CASE     )   ->List[Any]:





  """simple docstring"""


  lowerCAmelCase__     :Optional[Any]											=				'Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'
  return require_version(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
