





"""simple docstring"""




from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Namespace					) ->int:






	'''simple docstring'''






	return ConvertCommand(
	    args.model_type    ,       args.tf_checkpoint    ,       args.pytorch_dump_output    ,       args.config    ,       args.finetuning_task_name					)


a						: List[Any]     	=			'''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''







class        __UpperCamelCase				(						a__						):






							@staticmethod
							def     __a   (   lowerCAmelCase__      )	-> Dict:
								a      :		Optional[Any]	=				parser.add_parser(
								    "convert"      ,					help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."      ,					)
								train_parser.add_argument("--model_type"      ,					type=lowerCAmelCase__      ,					required=lowerCAmelCase__      ,					help="Model's type."      )
								train_parser.add_argument(
								    "--tf_checkpoint"      ,					type=lowerCAmelCase__      ,					required=lowerCAmelCase__      ,					help="TensorFlow checkpoint path or folder."      )
								train_parser.add_argument(
								    "--pytorch_dump_output"      ,					type=lowerCAmelCase__      ,					required=lowerCAmelCase__      ,					help="Path to the PyTorch saved model output."      )
								train_parser.add_argument("--config"      ,					type=lowerCAmelCase__      ,					default=""      ,					help="Configuration file path or folder."      )
								train_parser.add_argument(
								    "--finetuning_task_name"      ,					type=lowerCAmelCase__      ,					default=lowerCAmelCase__      ,					help="Optional fine-tuning task name if the TF model was a finetuned model."      ,					)
								train_parser.set_defaults(func=lowerCAmelCase__      )






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					*lowerCAmelCase__      ,					)	-> int:
								a      :		Optional[Any]	=				logging.get_logger("transformers-cli/converting"      )

								self._logger.info(f"""Loading model {model_type}"""      )
								a      :		int	=				model_type
								a      :		str	=				tf_checkpoint
								a      :		Union[str, Any]	=				pytorch_dump_output
								a      :		Dict	=				config
								a      :		Tuple	=				finetuning_task_name






							def     __a   (   self      )	-> List[str]:
								if self._model_type == "albert":
									try:
										from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
										    convert_tf_checkpoint_to_pytorch,
										)
									except ImportError:
										raise ImportError(lowerCAmelCase__      )

									convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,					self._config      ,					self._pytorch_dump_output      )
								elif self._model_type == "bert":
									try:
										from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
										    convert_tf_checkpoint_to_pytorch,
										)
									except ImportError:
										raise ImportError(lowerCAmelCase__      )

									convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,					self._config      ,					self._pytorch_dump_output      )
								elif self._model_type == "funnel":
									try:
										from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
										    convert_tf_checkpoint_to_pytorch,
										)
									except ImportError:
										raise ImportError(lowerCAmelCase__      )

									convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,					self._config      ,					self._pytorch_dump_output      )
								elif self._model_type == "t5":
									try:
										from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
									except ImportError:
										raise ImportError(lowerCAmelCase__      )

									convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,					self._config      ,					self._pytorch_dump_output      )
								elif self._model_type == "gpt":
									from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
									    convert_openai_checkpoint_to_pytorch,
									)

									convert_openai_checkpoint_to_pytorch(self._tf_checkpoint      ,					self._config      ,					self._pytorch_dump_output      )
								elif self._model_type == "transfo_xl":
									try:
										from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
										    convert_transfo_xl_checkpoint_to_pytorch,
										)
									except ImportError:
										raise ImportError(lowerCAmelCase__      )

									if "ckpt" in self._tf_checkpoint.lower():
										a      :		List[Any]	=				self._tf_checkpoint
										a      :		str	=				""
									else:
										a      :		Optional[Any]	=				self._tf_checkpoint
										a      :		Optional[Any]	=				""
									convert_transfo_xl_checkpoint_to_pytorch(
									    lowerCAmelCase__      ,					self._config      ,					self._pytorch_dump_output      ,					lowerCAmelCase__      )
								elif self._model_type == "gpt2":
									try:
										from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
										    convert_gpta_checkpoint_to_pytorch,
										)
									except ImportError:
										raise ImportError(lowerCAmelCase__      )

									convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint      ,					self._config      ,					self._pytorch_dump_output      )
								elif self._model_type == "xlnet":
									try:
										from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
										    convert_xlnet_checkpoint_to_pytorch,
										)
									except ImportError:
										raise ImportError(lowerCAmelCase__      )

									convert_xlnet_checkpoint_to_pytorch(
									    self._tf_checkpoint      ,					self._config      ,					self._pytorch_dump_output      ,					self._finetuning_task_name      )
								elif self._model_type == "xlm":
									from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
									    convert_xlm_checkpoint_to_pytorch,
									)

									convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint      ,					self._pytorch_dump_output      )
								elif self._model_type == "lxmert":
									from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
									    convert_lxmert_checkpoint_to_pytorch,
									)

									convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint      ,					self._pytorch_dump_output      )
								elif self._model_type == "rembert":
									from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
									    convert_rembert_tf_checkpoint_to_pytorch,
									)

									convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,					self._config      ,					self._pytorch_dump_output      )
								else:
									raise ValueError(
									    "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"      )









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

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


SCREAMING_SNAKE_CASE_						   =       importlib.util.find_spec("""s3fs""") is not None

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

SCREAMING_SNAKE_CASE_						   =       [
    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    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   str:


      '''simple docstring'''


      if "://" in dataset_path:
            SCREAMING_SNAKE_CASE		=						dataset_path.split("""://"""	)[1]
      return dataset_path



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   bool:


      '''simple docstring'''


      if fs is not None and fs.protocol != "file":
            return True
      else:
            return False



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Dict:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						not is_remote_filesystem(_SCREAMING_SNAKE_CASE	)
      if is_local:
            # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
            shutil.move(fs._strip_protocol(_SCREAMING_SNAKE_CASE	) ,						fs._strip_protocol(_SCREAMING_SNAKE_CASE	)	)
      else:
            fs.mv(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						recursive=_SCREAMING_SNAKE_CASE	)



def    __lowercase   (      ) ->   None:


      '''simple docstring'''


      if hasattr(fsspec.asyn ,						"""reset_lock"""	):
            # for future fsspec>2022.05.0
            fsspec.asyn.reset_lock()
      else:
            SCREAMING_SNAKE_CASE		=						None
            SCREAMING_SNAKE_CASE		=						None
            SCREAMING_SNAKE_CASE		=						threading.Lock()


