

from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Namespace      )							->      str:





    '''simple docstring'''






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


lowercase_       		=  "\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  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @staticmethod
    def 						snake_case__     (      lowercase_ :       ArgumentParser		)->    List[Any]:





        '''simple docstring'''







        A__          =   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=lowercase_,required=lowercase_,help='Model\'s type.'		)
        train_parser.add_argument(
            '--tf_checkpoint',type=lowercase_,required=lowercase_,help='TensorFlow checkpoint path or folder.'		)
        train_parser.add_argument(
            '--pytorch_dump_output',type=lowercase_,required=lowercase_,help='Path to the PyTorch saved model output.'		)
        train_parser.add_argument('--config',type=lowercase_,default='',help='Configuration file path or folder.'		)
        train_parser.add_argument(
            '--finetuning_task_name',type=lowercase_,default=lowercase_,help='Optional fine-tuning task name if the TF model was a finetuned model.',)
        train_parser.set_defaults(func=lowercase_		)


    def __init__(      self :       Tuple,lowercase_ :       str,lowercase_ :       str,lowercase_ :       str,lowercase_ :       str,lowercase_ :       str,*lowercase_ :       Dict,)->    Dict:





        '''simple docstring'''







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

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


    def 						snake_case__     (      self :       List[str]		)->    Optional[Any]:





        '''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(lowercase_		)

            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(lowercase_		)

            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(lowercase_		)

            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(lowercase_		)

            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(lowercase_		)

            if "ckpt" in self._tf_checkpoint.lower():
                A__          =   self._tf_checkpoint
                A__          =   ''
            else:
                A__          =   self._tf_checkpoint
                A__          =   ''
            convert_transfo_xl_checkpoint_to_pytorch(
                lowercase_,self._config,self._pytorch_dump_output,lowercase_		)
        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(lowercase_		)

            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(lowercase_		)

            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]'		)




class  A						:







    """simple docstring"""





    def __init__(      self :       Any,lowercase_ :       Tuple,lowercase_ :       Any,lowercase_ :       List[str]		)->    List[Any]:





        '''simple docstring'''







        A__          =   name
        A__          =   value
        A__          =   weight


    def __repr__(      self :       int		)->    Tuple:





        '''simple docstring'''







        return F'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'


    def 						snake_case__     (      self :       Any		)->    str:





        '''simple docstring'''







        return self.value


    def 						snake_case__     (      self :       Any		)->    Tuple:





        '''simple docstring'''







        return self.name


    def 						snake_case__     (      self :       Any		)->    Dict:





        '''simple docstring'''







        return self.weight


    def 						snake_case__     (      self :       Union[str, Any]		)->    Optional[Any]:





        '''simple docstring'''







        return self.value / self.weight




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	List[Any]      )							->      List[Any]:





    '''simple docstring'''






    A__          =   []
    for i in range(len(SCREAMING_SNAKE_CASE__      )      ):
        menu.append(Things(name[i]    ,			value[i]    ,			weight[i]      )      )
    return menu
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	int      )							->      Any:





    '''simple docstring'''






    A__          =   sorted(SCREAMING_SNAKE_CASE__    ,			key=SCREAMING_SNAKE_CASE__    ,			reverse=SCREAMING_SNAKE_CASE__      )
    A__          =   []
    A__      ,						A__          =   0.0, 0.0
    for i in range(len(SCREAMING_SNAKE_CASE__      )      ):
        if (total_cost + items_copy[i].get_weight()) <= max_cost:
            result.append(items_copy[i]      )
            total_cost += items_copy[i].get_weight()
            total_value += items_copy[i].get_value()
    return (result, total_value)
def 						_snake_case(    )							->      Any:





    '''simple docstring'''








if __name__ == "__main__":
     import doctest

     doctest.testmod()

