






"""simple docstring"""


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






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


__SCREAMING_SNAKE_CASE   =   "\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 						__UpperCamelCase     (     UpperCamelCase__						):
					@staticmethod
					def        UpperCAmelCase__ (							UpperCAmelCase							:	ArgumentParser   )	->       List[Any]:
											lowerCAmelCase   :List[str]						=			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=__snake_case     , required=__snake_case     , help='Model\'s type.'   )
											train_parser.add_argument(
											    '--tf_checkpoint'     , type=__snake_case     , required=__snake_case     , help='TensorFlow checkpoint path or folder.'   )
											train_parser.add_argument(
											    '--pytorch_dump_output'     , type=__snake_case     , required=__snake_case     , help='Path to the PyTorch saved model output.'   )
											train_parser.add_argument('--config'     , type=__snake_case     , default=''     , help='Configuration file path or folder.'   )
											train_parser.add_argument(
											    '--finetuning_task_name'     , type=__snake_case     , default=__snake_case     , help='Optional fine-tuning task name if the TF model was a finetuned model.'     , )
											train_parser.set_defaults(func=__snake_case   )
					def __init__(							self							:	Any     , UpperCAmelCase							:	str     , UpperCAmelCase							:	str     , UpperCAmelCase							:	str     , UpperCAmelCase							:	str     , UpperCAmelCase							:	str     , *UpperCAmelCase							:	Optional[Any]     , )	->       Any:
											lowerCAmelCase   :Union[str, Any]						=			logging.get_logger('transformers-cli/converting'   )

											self._logger.info(f"""Loading model {model_type}"""   )
											lowerCAmelCase   :List[Any]						=			model_type
											lowerCAmelCase   :List[str]						=			tf_checkpoint
											lowerCAmelCase   :Union[str, Any]						=			pytorch_dump_output
											lowerCAmelCase   :Tuple						=			config
											lowerCAmelCase   :Optional[int]						=			finetuning_task_name
					def        UpperCAmelCase__ (							self							:	int   )	->       Dict:

											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(__snake_case   )

																	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(__snake_case   )

																	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(__snake_case   )

																	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(__snake_case   )

																	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(__snake_case   )

																	if "ckpt" in self._tf_checkpoint.lower():
																							lowerCAmelCase   :Tuple						=			self._tf_checkpoint
																							lowerCAmelCase   :Tuple						=			""""""
																	else:
																							lowerCAmelCase   :List[Any]						=			self._tf_checkpoint
																							lowerCAmelCase   :List[str]						=			""""""
																	convert_transfo_xl_checkpoint_to_pytorch(
																	    __snake_case     , self._config     , self._pytorch_dump_output     , __snake_case   )
											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(__snake_case   )

																	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(__snake_case   )

																	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]'   )





from typing import List

from .keymap import KEYMAP, get_character




def 		lowerCAmelCase_ (						lowerCamelCase ):

				def decorator(lowerCamelCase ):
								__magic_name__						:						str										=getattr(lowerCamelCase						,  """handle_key"""						,  [] )
								handle += [key]
								setattr(lowerCamelCase						,  """handle_key"""						,  lowerCamelCase )
								return func

				return decorator




def 		lowerCAmelCase_ (						*lowerCamelCase ):

				def decorator(lowerCamelCase ):
								__magic_name__						:						Dict										=getattr(lowerCamelCase						,  """handle_key"""						,  [] )
								handle += keys
								setattr(lowerCamelCase						,  """handle_key"""						,  lowerCamelCase )
								return func

				return decorator




class 		__A   (      UpperCamelCase__      ):

						def __new__(		cls :Dict     ,			__snake_case :Optional[Any]     ,			__snake_case :Union[str, Any]     ,			__snake_case :List[str]			):
										'''simple docstring'''

										__magic_name__						:						int										=super().__new__(cls     ,			__snake_case     ,			__snake_case     ,			__snake_case			)
										if not hasattr(__snake_case     ,			"""key_handler"""			):
														setattr(__snake_case     ,			"""key_handler"""     ,			{}			)
										setattr(__snake_case     ,			"""handle_input"""     ,			KeyHandler.handle_input			)

										for value in attrs.values():
														__magic_name__						:						int										=getattr(__snake_case     ,			"""handle_key"""     ,			[]			)
														for key in handled_keys:
																		__magic_name__						:						List[str]										=value
										return new_cls


						@staticmethod
						def      A__    (		cls :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=get_character()
										if char != KEYMAP["undefined"]:
														__magic_name__						:						Optional[int]										=ord(__snake_case			)
										__magic_name__						:						int										=cls.key_handler.get(__snake_case			)
										if handler:
														__magic_name__						:						Dict										=char
														return handler(cls			)
										else:
														return None




def 		lowerCAmelCase_ (						cls ):
				return KeyHandler(cls.__name__						,  cls.__bases__						,  cls.__dict__.copy() )




