"""simple docstring"""



from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand



def 					_lowerCamelCase(					a							):
       return ConvertCommand(
           args.model_type ,  args.tf_checkpoint ,  args.pytorch_dump_output ,  args.config ,  args.finetuning_task_name							)


SCREAMING_SNAKE_CASE__: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 				snake_case__     (   snake_case_   ):


 @staticmethod
 def 		a__ (				lowerCamelCase		):
        __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=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  ,						):
        __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 		a__ (				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():
                      __a     				=  self._tf_checkpoint
                      __a     				=  ""
               else:
                      __a     				=  self._tf_checkpoint
                      __a     				=  ""
               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"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_barthez import BarthezTokenizer
else:
   SCREAMING_SNAKE_CASE__:Optional[Any]			=	None

SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:int			=	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE__:Dict			=	{
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

SCREAMING_SNAKE_CASE__:List[str]			=	"""▁"""






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			str           =		VOCAB_FILES_NAMES
 _snake_case   :			Optional[Any]           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			str           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			Dict           =		["""input_ids""", """attention_mask"""]
 _snake_case   :			str           =		BarthezTokenizer


 def __init__(				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase="<pad>"  ,						lowerCamelCase="<mask>"  ,						**lowerCamelCase  ,						):
        # Mask token behave like a normal word, i.e. include the space before it
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else mask_token

        super().__init__(
            lowerCamelCase  ,						tokenizer_file=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						unk_token=lowerCamelCase  ,						sep_token=lowerCamelCase  ,						cls_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						mask_token=lowerCamelCase  ,						**lowerCamelCase  ,						)

        __a     				=  vocab_file
        __a     				=  False if not self.vocab_file else True


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):

        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __a     				=  [self.cls_token_id]
        __a     				=  [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        __a     				=  [self.sep_token_id]
        __a     				=  [self.cls_token_id]

        if token_ids_a is None:
               return len(cls + token_ids_a + sep		) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if not self.can_save_slow_tokenizer:
               raise ValueError(
                   "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                   "tokenizer."		)

        if not os.path.isdir(lowerCamelCase		):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
               return
        __a     				=  os.path.join(
            lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

        if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCamelCase		):
               copyfile(self.vocab_file  ,						lowerCamelCase		)

        return (out_vocab_file,)
