





import argparse

import torch

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


logging.set_verbosity_info()

def   a_    ( lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			Dict,	lowerCAmelCase_      :			Optional[int]	):
   # Initialise PyTorch model
   __lowerCAmelCase								=	AlbertConfig.from_json_file(lowerCAmelCase_	)
   print(F"""Building PyTorch model from configuration: {config}"""	)
   __lowerCAmelCase								=	AlbertForPreTraining(lowerCAmelCase_	)

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

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


if __name__ == "__main__":
     _snake_case  :    Tuple						    =  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.'
     )
     _snake_case  :    List[Any]						    =  parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)








import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


_snake_case  :    Tuple						    =  importlib.util.find_spec('s3fs') is not None

if _has_safs:
     from .safilesystem import SaFileSystem  # noqa: F401

_snake_case  :    List[compression.BaseCompressedFileFileSystem]						    =  [
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
     if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
          warnings.warn(F"""A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.""")
     fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)

def   a_    ( lowerCAmelCase_      :			str	):
   if "://" in dataset_path:
      __lowerCAmelCase								=	dataset_path.split('://'	)[1]
   return dataset_path

def   a_    ( lowerCAmelCase_      :			fsspec.AbstractFileSystem	):
   if fs is not None and fs.protocol != "file":
      return True
   else:
      return False

def   a_    ( lowerCAmelCase_      :			fsspec.AbstractFileSystem,	lowerCAmelCase_      :			str,	lowerCAmelCase_      :			str	):
   __lowerCAmelCase								=	not is_remote_filesystem(lowerCAmelCase_	)
   if is_local:
      # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
      shutil.move(fs._strip_protocol(lowerCAmelCase_	),	fs._strip_protocol(lowerCAmelCase_	)	)
   else:
      fs.mv(lowerCAmelCase_,	lowerCAmelCase_,	recursive=lowerCAmelCase_	)




def   a_    ( ):
   if hasattr(fsspec.asyn,	'reset_lock'	):
      # for future fsspec>2022.05.0
      fsspec.asyn.reset_lock()
   else:
      __lowerCAmelCase								=	None
      __lowerCAmelCase								=	None
      __lowerCAmelCase								=	threading.Lock()

