





import os
from collections.abc import Iterator



def 				snake_case_(_UpperCamelCase = "."					)							->     Iterator[str]:



  """simple docstring"""






  for dir_path, dir_names, filenames in os.walk(_UpperCamelCase					):
    _snake_case						=							[d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
    for filename in filenames:
      if filename == "__init__.py":
        continue
      if os.path.splitext(_UpperCamelCase					)[1] in (".py", ".ipynb"):
        yield os.path.join(_UpperCamelCase			,	_UpperCamelCase					).lstrip('''./'''					)



def 				snake_case_(_UpperCamelCase					)							->     List[str]:



  """simple docstring"""






  return F"""{i * '  '}*""" if i else "\n##"



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     str:



  """simple docstring"""






  _snake_case						=							old_path.split(os.sep					)
  for i, new_part in enumerate(new_path.split(os.sep					)					):
    if (i + 1 > len(_UpperCamelCase					) or old_parts[i] != new_part) and new_part:
      print(F"""{md_prefix(_UpperCamelCase					)} {new_part.replace('_'			,	' '					).title()}"""					)
  return new_path



def 				snake_case_(_UpperCamelCase = "."					)							->     None:



  """simple docstring"""






  _snake_case						=							''''''
  for filepath in sorted(good_file_paths(_UpperCamelCase					)					):
    _snake_case,					_snake_case						=							os.path.split(_UpperCamelCase					)
    if filepath != old_path:
      _snake_case						=							print_path(_UpperCamelCase			,	_UpperCamelCase					)
    _snake_case						=							(filepath.count(os.sep					) + 1) if filepath else 0
    _snake_case						=							F"""{filepath}/{filename}""".replace(''' '''			,	'''%20'''					)
    _snake_case						=							os.path.splitext(filename.replace('''_'''			,	''' '''					).title()					)[0]
    print(F"""{md_prefix(_UpperCamelCase					)} [{filename}]({url})"""					)


if __name__ == "__main__":
 print_directory_md('''.''')




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Optional[Any]:



  """simple docstring"""






  _snake_case						=							AlbertConfig.from_json_file(_UpperCamelCase					)
  print(F"""Building PyTorch model from configuration: {config}"""					)
  _snake_case						=							AlbertForPreTraining(_UpperCamelCase					)

  # Load weights from tf checkpoint
  load_tf_weights_in_albert(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)

  # Save pytorch-model
  print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
  torch.save(model.state_dict()			,	_UpperCamelCase					)


if __name__ == "__main__":
 __A      		=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--albert_config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained ALBERT model. \n'''
         '''This specifies the model architecture.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 __A      		=     parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)

