


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand




def 	__lowercase		(  _UpperCamelCase   )			->Dict:







      """simple docstring"""

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


__a       =					'\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  __SCREAMING_SNAKE_CASE      (	__UpperCAmelCase      ):


      @staticmethod
      def 				__lowerCamelCase	(							SCREAMING_SNAKE_CASE__						):
            lowercase	:      Optional[int]          =						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							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  *SCREAMING_SNAKE_CASE__							,  ):
            lowercase	:      str          =						logging.get_logger('''transformers-cli/converting'''						)

            self._logger.info(f"""Loading model {model_type}"""						)
            lowercase	:      Union[str, Any]          =						model_type
            lowercase	:      Tuple          =						tf_checkpoint
            lowercase	:      str          =						pytorch_dump_output
            lowercase	:      Tuple          =						config
            lowercase	:      Tuple          =						finetuning_task_name






      def 				__lowerCamelCase	(							self						):
            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():
                        lowercase	:      Union[str, Any]          =						self._tf_checkpoint
                        lowercase	:      List[str]          =						''''''
                  else:
                        lowercase	:      Tuple          =						self._tf_checkpoint
                        lowercase	:      List[Any]          =						''''''
                  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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__  :  int						     =	logging.get_logger(__name__)

lowerCAmelCase__  :  str						     =	{
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = "xglm"
					snake_case__				    = ["past_key_values"]

					snake_case__				    = {
					    "num_attention_heads": "attention_heads",
					    "hidden_size": "d_model",
					    "num_hidden_layers": "num_layers",
					}







					def __init__(				self   :	Any    ,lowerCamelCase__   :	Any=256_008    ,lowerCamelCase__   :	Optional[Any]=2_048    ,lowerCamelCase__   :	List[str]=1_024    ,lowerCamelCase__   :	List[str]=4_096    ,lowerCamelCase__   :	Tuple=24    ,lowerCamelCase__   :	Optional[int]=16    ,lowerCamelCase__   :	int="gelu"    ,lowerCamelCase__   :	Dict=0.1    ,lowerCamelCase__   :	int=0.1    ,lowerCamelCase__   :	List[Any]=0.0    ,lowerCamelCase__   :	List[str]=0.0    ,lowerCamelCase__   :	Optional[Any]=0.0_2    ,lowerCamelCase__   :	List[str]=True    ,lowerCamelCase__   :	Optional[Any]=True    ,lowerCamelCase__   :	str=2    ,lowerCamelCase__   :	Dict=1    ,lowerCamelCase__   :	Optional[int]=0    ,lowerCamelCase__   :	Tuple=2    ,**lowerCamelCase__   :	List[Any]    ,):
									UpperCAmelCase__             =							vocab_size
									UpperCAmelCase__             =							max_position_embeddings
									UpperCAmelCase__             =							d_model
									UpperCAmelCase__             =							ffn_dim
									UpperCAmelCase__             =							num_layers
									UpperCAmelCase__             =							attention_heads
									UpperCAmelCase__             =							activation_function
									UpperCAmelCase__             =							dropout
									UpperCAmelCase__             =							attention_dropout
									UpperCAmelCase__             =							activation_dropout
									UpperCAmelCase__             =							layerdrop
									UpperCAmelCase__             =							init_std
									UpperCAmelCase__             =							scale_embedding  # scale factor will be sqrt(d_model) if True
									UpperCAmelCase__             =							use_cache

									super().__init__(
									    pad_token_id=lowerCamelCase__    ,bos_token_id=lowerCamelCase__    ,eos_token_id=lowerCamelCase__    ,decoder_start_token_id=lowerCamelCase__    ,**lowerCamelCase__    ,)



