





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Namespace   )   ->			Tuple:
    return ConvertCommand(
        args.model_type	,     args.tf_checkpoint	,     args.pytorch_dump_output	,     args.config	,     args.finetuning_task_name   )


UpperCAmelCase_  							=				'\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 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   @staticmethod
   def    UpperCamelCase__						(					__magic_name__							)				->   List[str]:






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




   def __init__(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	*__magic_name__,	)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   logging.get_logger('''transformers-cli/converting'''							)

       self._logger.info(f"Loading model {model_type}"							)
       UpperCamelCase__							:       Tuple      =   model_type
       UpperCamelCase__							:       str      =   tf_checkpoint
       UpperCamelCase__							:       List[Any]      =   pytorch_dump_output
       UpperCamelCase__							:       Optional[Any]      =   config
       UpperCamelCase__							:       Optional[int]      =   finetuning_task_name




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """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(__magic_name__							)

           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(__magic_name__							)

           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(__magic_name__							)

           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(__magic_name__							)

           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(__magic_name__							)

           if "ckpt" in self._tf_checkpoint.lower():
               UpperCamelCase__							:       int      =   self._tf_checkpoint
               UpperCamelCase__							:       int      =   ''''''
           else:
               UpperCamelCase__							:       str      =   self._tf_checkpoint
               UpperCamelCase__							:       str      =   ''''''
           convert_transfo_xl_checkpoint_to_pytorch(
               __magic_name__,	self._config,	self._pytorch_dump_output,	__magic_name__							)
       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(__magic_name__							)

           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(__magic_name__							)

           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 copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


UpperCAmelCase_  							=				logging.get_logger(__name__)






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Any     	=	"upernet"




   def __init__(					self,	__magic_name__=None,	__magic_name__=512,	__magic_name__=0.02,	__magic_name__=[1, 2, 3, 6],	__magic_name__=True,	__magic_name__=0.4,	__magic_name__=384,	__magic_name__=256,	__magic_name__=1,	__magic_name__=False,	__magic_name__=255,	**__magic_name__,	)				->   Dict:






       """simple docstring"""
       super().__init__(**__magic_name__							)

       if backbone_config is None:
           logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''							)
           UpperCamelCase__							:       str      =   CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']							)
       elif isinstance(__magic_name__,	__magic_name__							):
           UpperCamelCase__							:       Tuple      =   backbone_config.get('''model_type'''							)
           UpperCamelCase__							:       List[str]      =   CONFIG_MAPPING[backbone_model_type]
           UpperCamelCase__							:       Optional[int]      =   config_class.from_dict(__magic_name__							)

       UpperCamelCase__							:       Dict      =   backbone_config
       UpperCamelCase__							:       int      =   hidden_size
       UpperCamelCase__							:       int      =   initializer_range
       UpperCamelCase__							:       Dict      =   pool_scales
       UpperCamelCase__							:       Tuple      =   use_auxiliary_head
       UpperCamelCase__							:       Tuple      =   auxiliary_loss_weight
       UpperCamelCase__							:       Optional[int]      =   auxiliary_in_channels
       UpperCamelCase__							:       Optional[Any]      =   auxiliary_channels
       UpperCamelCase__							:       Any      =   auxiliary_num_convs
       UpperCamelCase__							:       Union[str, Any]      =   auxiliary_concat_input
       UpperCamelCase__							:       List[str]      =   loss_ignore_index




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   copy.deepcopy(self.__dict__							)
       UpperCamelCase__							:       int      =   self.backbone_config.to_dict()
       UpperCamelCase__							:       Any      =   self.__class__.model_type
       return output

