



from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def       __lowercase  (					a__  )							->					str:
							return ConvertCommand(
							    args.model_type				,  args.tf_checkpoint				,  args.pytorch_dump_output				,  args.config				,  args.finetuning_task_name  )


lowerCAmelCase__						:	Tuple					='''
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			):

						'''simple docstring'''




						@staticmethod
						def 			_A      (       _A						):







													'''simple docstring'''

													__SCREAMING_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 ,				_A ,				_A ,				_A ,				_A ,				_A ,				*_A ,				):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   logging.get_logger('transformers-cli/converting'						)

													self._logger.info(f"""Loading model {model_type}"""						)
													__SCREAMING_SNAKE_CASE										=   model_type
													__SCREAMING_SNAKE_CASE										=   tf_checkpoint
													__SCREAMING_SNAKE_CASE										=   pytorch_dump_output
													__SCREAMING_SNAKE_CASE										=   config
													__SCREAMING_SNAKE_CASE										=   finetuning_task_name






						def 			_A      (       self						):







													'''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():
																											__SCREAMING_SNAKE_CASE										=   self._tf_checkpoint
																											__SCREAMING_SNAKE_CASE										=   ""
																				else:
																											__SCREAMING_SNAKE_CASE										=   self._tf_checkpoint
																											__SCREAMING_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]'						)














"""simple docstring"""
from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


lowerCamelCase_							      = logging.get_logger(__name__)







class      UpperCamelCase_			:
	__magic_name__       =							42
	__magic_name__       =							None





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     Optional[int]:
				raise NotImplementedError





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Union[str, Any]		)		->     Optional[Any]:
				raise NotImplementedError





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					Union[str, Any]		)		->     str:
				raise NotImplementedError





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Dict:
				if not self.is_available():
							raise RuntimeError(
							    f"""You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}."""		)





	@classmethod
	def 	_SCREAMING_SNAKE_CASE ( cls   :					Tuple		)		->     List[str]:
				return f"""`pip install {cls.pip_package or cls.name}`"""







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''optuna'''





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     List[str]:
				return is_optuna_available()





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Tuple		)		->     int:
				return run_hp_search_optuna(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					Dict		)		->     Optional[Any]:
				return default_hp_space_optuna(lowerCAmelCase_		)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''ray'''
	__magic_name__       =							'''\'ray[tune]\''''





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     Optional[int]:
				return is_ray_available()





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Tuple		)		->     List[str]:
				return run_hp_search_ray(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					Tuple		)		->     Optional[int]:
				return default_hp_space_ray(lowerCAmelCase_		)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''sigopt'''





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     List[Any]:
				return is_sigopt_available()





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					str		)		->     str:
				return run_hp_search_sigopt(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					Any		)		->     Any:
				return default_hp_space_sigopt(lowerCAmelCase_		)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''wandb'''





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     int:
				return is_wandb_available()





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Optional[Any]		)		->     Optional[Any]:
				return run_hp_search_wandb(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					List[str]		)		->     Any:
				return default_hp_space_wandb(lowerCAmelCase_		)





lowerCamelCase_							      = {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}

def 							snake_case (  ):
			UpperCAmelCase_	:					List[str]     	=   [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
			if len(A__		) > 0:
						UpperCAmelCase_	:					Optional[Any]     	=   available_backends[0].name
						if len(A__		) > 1:
									logger.info(
									    F"""{len(A__		)} hyperparameter search backends available. Using {name} as the default."""		)
						return name
			raise RuntimeError(
			    "No hyperparameter search backend available.\n"
			    + "\n".join(
			        F""" - To install {backend.name} run {backend.pip_install()}"""
			        for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()		)		)



