





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def      _A       (		SCREAMING_SNAKE_CASE     :   Namespace						):





  """simple docstring"""



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


UpperCAmelCase	:	Optional[int]		     =       """
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 				__lowerCAmelCase       (    UpperCamelCase__):







      @staticmethod
      def    _lowercase  (    lowerCAmelCase__							)   ->						Any:



        '''simple docstring'''




        a__	:					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		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      *lowerCAmelCase__		,      )   ->						Optional[int]:



        '''simple docstring'''




        a__	:					Optional[int]										=logging.get_logger("transformers-cli/converting"							)

        self._logger.info(F'''Loading model {model_type}'''							)
        a__	:					Optional[int]										=model_type
        a__	:					Union[str, Any]										=tf_checkpoint
        a__	:					Optional[Any]										=pytorch_dump_output
        a__	:					Dict										=config
        a__	:					Tuple										=finetuning_task_name







      def    _lowercase  (    self							)   ->						Union[str, 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(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():
            a__	:					Tuple										=self._tf_checkpoint
            a__	:					str										=""
          else:
            a__	:					Union[str, Any]										=self._tf_checkpoint
            a__	:					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]"							)













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


UpperCAmelCase	:	Optional[Any]		     =       logging.get_logger(__name__)

UpperCAmelCase	:	Optional[int]		     =       {
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					List[Any]						=			"""canine"""







      def __init__(    self		,      lowerCAmelCase__=7_6_8		,      lowerCAmelCase__=1_2		,      lowerCAmelCase__=1_2		,      lowerCAmelCase__=3_0_7_2		,      lowerCAmelCase__="gelu"		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__=1_6_3_8_4		,      lowerCAmelCase__=1_6		,      lowerCAmelCase__=0.02		,      lowerCAmelCase__=1E-12		,      lowerCAmelCase__=0		,      lowerCAmelCase__=0XE0_00		,      lowerCAmelCase__=0XE0_01		,      lowerCAmelCase__=4		,      lowerCAmelCase__=4		,      lowerCAmelCase__=8		,      lowerCAmelCase__=1_6_3_8_4		,      lowerCAmelCase__=1_2_8		,      **lowerCAmelCase__		,      )   ->						Dict:



        '''simple docstring'''




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

        a__	:					Optional[int]										=max_position_embeddings
        a__	:					str										=hidden_size
        a__	:					Optional[Any]										=num_hidden_layers
        a__	:					Tuple										=num_attention_heads
        a__	:					Optional[Any]										=intermediate_size
        a__	:					Optional[int]										=hidden_act
        a__	:					List[Any]										=hidden_dropout_prob
        a__	:					Union[str, Any]										=attention_probs_dropout_prob
        a__	:					Optional[Any]										=initializer_range
        a__	:					Union[str, Any]										=type_vocab_size
        a__	:					Optional[int]										=layer_norm_eps

        # Character config:
        a__	:					int										=downsampling_rate
        a__	:					Optional[Any]										=upsampling_kernel_size
        a__	:					Union[str, Any]										=num_hash_functions
        a__	:					Any										=num_hash_buckets
        a__	:					int										=local_transformer_stride






