





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def      A__       (    SCREAMING_SNAKE_CASE_   :      Namespace						)  ->  Optional[int]:






   """simple docstring"""







   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 			__UpperCamelCase    (							A__    ):





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





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							*_UpperCamelCase				,							):
        _UpperCAmelCase           =   logging.get_logger('''transformers-cli/converting'''					)

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





     def 		UpperCamelCase(     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(_UpperCamelCase					)

           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(_UpperCamelCase					)

           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(_UpperCamelCase					)

           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(_UpperCamelCase					)

           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(_UpperCamelCase					)

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

           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(_UpperCamelCase					)

           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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case      :							List[str]     	=    logging.get_logger(__name__)

_snake_case      :							int     	=    {
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  List[str]				   =					'''switch_transformers'''
						UpperCamelCase      :  Dict				   =					['''past_key_values''']
						UpperCamelCase      :  Union[str, Any]				   =					{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





						def __init__(			self							:int		,    lowerCAmelCase__							:str=32128		,    lowerCAmelCase__							:Union[str, Any]=768		,    lowerCAmelCase__							:Tuple=64		,    lowerCAmelCase__							:Any=2048		,    lowerCAmelCase__							:Optional[int]=64		,    lowerCAmelCase__							:Any=12		,    lowerCAmelCase__							:Any=3		,    lowerCAmelCase__							:Union[str, Any]=12		,    lowerCAmelCase__							:Tuple=3		,    lowerCAmelCase__							:str=12		,    lowerCAmelCase__							:Dict=8		,    lowerCAmelCase__							:Union[str, Any]=False		,    lowerCAmelCase__							:List[str]=0.01		,    lowerCAmelCase__							:Union[str, Any]="float32"		,    lowerCAmelCase__							:Optional[int]=False		,    lowerCAmelCase__							:Optional[Any]=32		,    lowerCAmelCase__							:int=128		,    lowerCAmelCase__							:Optional[Any]=0.1		,    lowerCAmelCase__							:Optional[Any]=1E-6		,    lowerCAmelCase__							:Any=0.0_01		,    lowerCAmelCase__							:Union[str, Any]=0.0_01		,    lowerCAmelCase__							:List[Any]=1.0		,    lowerCAmelCase__							:Optional[Any]="relu"		,    lowerCAmelCase__							:Optional[int]=True		,    lowerCAmelCase__							:List[Any]=False		,    lowerCAmelCase__							:int=True		,    lowerCAmelCase__							:int=0		,    lowerCAmelCase__							:Optional[Any]=1		,    **lowerCAmelCase__							:List[Any]		,    )  ->Dict:
										lowercase         =      vocab_size
										lowercase         =      d_model
										lowercase         =      d_kv
										lowercase         =      d_ff

										lowercase         =      num_sparse_encoder_layers

										lowercase         =      num_layers
										lowercase         =      (
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										lowercase         =      num_sparse_decoder_layers

										# This tells us, each how many encoder layer we'll have to set a sparse layer.
										if self.num_sparse_encoder_layers > 0:
														lowercase         =      self.num_layers // self.num_sparse_encoder_layers
										else:
														lowercase         =      self.num_layers  # HACK: this will create 0 sparse layers

										# This tells us, each how many encoder layer we'll have to set a sparse layer.
										if self.num_sparse_decoder_layers > 0:
														lowercase         =      self.num_decoder_layers // self.num_sparse_decoder_layers
										else:
														lowercase         =      self.num_decoder_layers  # HACK: this will create 0 sparse layers

										lowercase         =      num_heads
										lowercase         =      num_experts
										lowercase         =      expert_capacity
										lowercase         =      router_bias
										lowercase         =      router_jitter_noise
										if router_dtype not in ["float32", "float16", "bfloat16"]:
														raise ValueError(F'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'''     )
										lowercase         =      router_dtype

										lowercase         =      router_ignore_padding_tokens
										lowercase         =      relative_attention_num_buckets
										lowercase         =      relative_attention_max_distance

										lowercase         =      dropout_rate
										lowercase         =      layer_norm_epsilon
										lowercase         =      initializer_factor
										lowercase         =      feed_forward_proj
										lowercase         =      use_cache
										lowercase         =      add_router_probs

										lowercase         =      router_z_loss_coef
										lowercase         =      router_aux_loss_coef

										lowercase         =      self.feed_forward_proj.split("-"     )
										lowercase         =      act_info[-1]
										lowercase         =      act_info[0] == "gated"

										if len(lowerCAmelCase__     ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase__     ) > 2:
														raise ValueError(
														    F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
														    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
														    "'gated-gelu' or 'relu'"     )

										# for backwards compatibility
										if feed_forward_proj == "gated-gelu":
														lowercase         =      "gelu_new"

										super().__init__(
										    pad_token_id=lowerCAmelCase__		,    eos_token_id=lowerCAmelCase__		,    is_encoder_decoder=lowerCAmelCase__		,    **lowerCAmelCase__		,    )
