

from importlib import import_module

from .logging import get_logger


__A      		=     get_logger(__name__)



class   lowercase_   :



      def __init__(      self				:    List[Any]      , A__				:    Any      , A__				:    str=None		)   ->					str:
        _snake_case						=							attrs or []
        if module is not None:
          for key in module.__dict__:
            if key in attrs or not key.startswith('''__'''		):
              setattr(self      , A__      , getattr(A__      , A__		)		)
        _snake_case						=							module._original_module if isinstance(A__      , _PatchedModuleObj		) else module



class   lowercase_   :
      UpperCamelCase_      :		Tuple										=    []



      def __init__(      self				:    Any      , A__				:    Optional[int]      , A__				:    str      , A__				:    Optional[Any]      , A__				:    int=None		)   ->					Tuple:
        _snake_case						=							obj
        _snake_case						=							target
        _snake_case						=							new
        _snake_case						=							target.split('''.'''		)[0]
        _snake_case						=							{}
        _snake_case						=							attrs or []



      def __enter__(      self				:    List[str]		)   ->					int:
        *_snake_case,					_snake_case						=							self.target.split('''.'''		)

        # Patch modules:
        # it's used to patch attributes of submodules like "os.path.join";
        # in this case we need to patch "os" and "os.path"

        for i in range(len(A__		)		):
          try:
            _snake_case						=							import_module('''.'''.join(submodules[: i + 1]		)		)
          except ModuleNotFoundError:
            continue
          # We iterate over all the globals in self.obj in case we find "os" or "os.path"
          for attr in self.obj.__dir__():
            _snake_case						=							getattr(self.obj      , A__		)
            # We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
            # This allows to patch renamed modules like "from os import path as ospath".
            if obj_attr is submodule or (
                (isinstance(A__      , _PatchedModuleObj		) and obj_attr._original_module is submodule)
            ):
              _snake_case						=							obj_attr
              # patch at top level
              setattr(self.obj      , A__      , _PatchedModuleObj(A__      , attrs=self.attrs		)		)
              _snake_case						=							getattr(self.obj      , A__		)
              # construct lower levels patches
              for key in submodules[i + 1 :]:
                setattr(A__      , A__      , _PatchedModuleObj(getattr(A__      , A__      , A__		)      , attrs=self.attrs		)		)
                _snake_case						=							getattr(A__      , A__		)
              # finally set the target attribute
              setattr(A__      , A__      , self.new		)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

        if submodules:  # if it's an attribute of a submodule like "os.path.join"
          try:
            _snake_case						=							getattr(import_module('''.'''.join(A__		)		)      , A__		)
          except (AttributeError, ModuleNotFoundError):
            return
          # We iterate over all the globals in self.obj in case we find "os.path.join"
          for attr in self.obj.__dir__():
            # We don't check for the name of the global, but rather if its value *is* "os.path.join".
            # This allows to patch renamed attributes like "from os.path import join as pjoin".
            if getattr(self.obj      , A__		) is attr_value:
              _snake_case						=							getattr(self.obj      , A__		)
              setattr(self.obj      , A__      , self.new		)
        elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
          _snake_case						=							globals()['''__builtins__'''][target_attr]
          setattr(self.obj      , A__      , self.new		)
        else:
          raise RuntimeError(f"""Tried to patch attribute {target_attr} instead of a submodule."""		)



      def __exit__(      self				:    Tuple      , *A__				:    Dict		)   ->					Dict:
        for attr in list(self.original		):
          setattr(self.obj      , A__      , self.original.pop(A__		)		)



      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					Union[str, Any]:
        self.__enter__()
        self._active_patches.append(self		)







      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					int:
        try:
          self._active_patches.remove(self		)
        except ValueError:
          # If the patch hasn't been started this will fail
          return None

        return self.__exit__()




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)

