


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand



def 	_lowerCAmelCase  (							lowerCAmelCase_							:List[Any]							)->Any:
   '''simple docstring'''






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


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



class 			__lowerCAmelCase   (       A__    ):






  """simple docstring"""





  @staticmethod
  def     lowerCAmelCase__     (   _lowerCAmelCase				:				ArgumentParser     )      ->       Any:




     """simple docstring"""
     snake_case_    			=    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				:				int			, _lowerCAmelCase				:				str			, _lowerCAmelCase				:				str			, _lowerCAmelCase				:				str			, _lowerCAmelCase				:				str			, _lowerCAmelCase				:				str			, *_lowerCAmelCase				:				List[Any]			, )      ->       Dict:




     """simple docstring"""
     snake_case_    			=    logging.get_logger("transformers-cli/converting"     )

     self._logger.info(F'''Loading model {model_type}'''     )
     snake_case_    			=    model_type
     snake_case_    			=    tf_checkpoint
     snake_case_    			=    pytorch_dump_output
     snake_case_    			=    config
     snake_case_    			=    finetuning_task_name



  def     lowerCAmelCase__     (   self				:				int     )      ->       List[str]:




     """simple docstring"""
     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():
           snake_case_    			=    self._tf_checkpoint
           snake_case_    			=    ''''''
        else:
           snake_case_    			=    self._tf_checkpoint
           snake_case_    			=    ''''''
        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 inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version


def        _UpperCamelCase							(      *lowercase__    ,       lowercase__ = None    ,       lowercase__=True    ,       lowercase__=2      ):
						from .. import __version__

						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		take_from
						__SCREAMING_SNAKE_CASE						:     List[str]					=		()
						if not isinstance(args[0]    ,       lowercase__      ):
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		(args,)

						for attribute, version_name, message in args:
												if version.parse(version.parse(lowercase__      ).base_version      ) >= version.parse(lowercase__      ):
																		raise ValueError(
																		    F'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
																		    F''' version {__version__} is >= {version_name}'''      )

												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		None
												if isinstance(lowercase__    ,       lowercase__      ) and attribute in deprecated_kwargs:
																		values += (deprecated_kwargs.pop(lowercase__      ),)
																		__SCREAMING_SNAKE_CASE						:     List[Any]					=		F'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
												elif hasattr(lowercase__    ,       lowercase__      ):
																		values += (getattr(lowercase__    ,       lowercase__      ),)
																		__SCREAMING_SNAKE_CASE						:     List[str]					=		F'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
												elif deprecated_kwargs is None:
																		__SCREAMING_SNAKE_CASE						:     str					=		F'''`{attribute}` is deprecated and will be removed in version {version_name}.'''

												if warning is not None:
																		__SCREAMING_SNAKE_CASE						:     Any					=		warning + ''' ''' if standard_warn else ''''''
																		warnings.warn(warning + message    ,       lowercase__    ,       stacklevel=lowercase__      )

						if isinstance(lowercase__    ,       lowercase__      ) and len(lowercase__      ) > 0:
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		inspect.getouterframes(inspect.currentframe()      )[1]
												__SCREAMING_SNAKE_CASE						:     Dict					=		call_frame.filename
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		call_frame.lineno
												__SCREAMING_SNAKE_CASE						:     int					=		call_frame.function
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     int					=		next(iter(deprecated_kwargs.items()      )      )
												raise TypeError(F'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'''      )

						if len(lowercase__      ) == 0:
												return
						elif len(lowercase__      ) == 1:
												return values[0]
						return values
