


"""simple docstring"""





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def    _snake_case  (  _snake_case :	Namespace	):
      return ConvertCommand(
          args.model_type       ,      args.tf_checkpoint       ,      args.pytorch_dump_output       ,      args.config       ,      args.finetuning_task_name	)


snake_case__						:   Tuple			 = '''
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_(				a__	):






     @staticmethod
     def    lowerCamelCase__   (		UpperCamelCase_     :    ArgumentParser ):
           lowerCAmelCase    :		Dict				=      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     :    Optional[Any]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    str    , UpperCamelCase_     :    str    , UpperCamelCase_     :    str    , UpperCamelCase_     :    str    , *UpperCamelCase_     :    Union[str, Any]    , ):
           lowerCAmelCase    :		Union[str, Any]				=      logging.get_logger('''transformers-cli/converting''' )

           self._logger.info(F'''Loading model {model_type}''' )
           lowerCAmelCase    :		Optional[int]				=      model_type
           lowerCAmelCase    :		Optional[Any]				=      tf_checkpoint
           lowerCAmelCase    :		List[Any]				=      pytorch_dump_output
           lowerCAmelCase    :		Optional[int]				=      config
           lowerCAmelCase    :		Any				=      finetuning_task_name






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           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():
                       lowerCAmelCase    :		Union[str, Any]				=      self._tf_checkpoint
                       lowerCAmelCase    :		List[str]				=      ''''''
                 else:
                       lowerCAmelCase    :		Dict				=      self._tf_checkpoint
                       lowerCAmelCase    :		Any				=      ''''''
                 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]''' )






"""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_fnet import FNetTokenizer
else:
 snake_case__						:   str			 = None

snake_case__						:   Optional[Any]			 = logging.get_logger(__name__)
snake_case__						:   Optional[int]			 = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case__						:   Dict			 = {
    '''vocab_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/spiece.model''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json''',
    },
}

snake_case__						:   Any			 = {
    '''google/fnet-base''': 512,
    '''google/fnet-large''': 512,
}

snake_case__						:   Dict			 = '''▁'''



class 						snake_case_(				a__	):
     __UpperCamelCase     =    VOCAB_FILES_NAMES
     __UpperCamelCase     =    PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase     =    ['''input_ids''', '''token_type_ids''']
     __UpperCamelCase     =    FNetTokenizer






     def __init__(		self     :    Union[str, Any]    , UpperCamelCase_     :    Union[str, Any]=None    , UpperCamelCase_     :    Union[str, Any]=None    , UpperCamelCase_     :    Any=False    , UpperCamelCase_     :    Any=True    , UpperCamelCase_     :    Dict=True    , UpperCamelCase_     :    Tuple="<unk>"    , UpperCamelCase_     :    List[str]="[SEP]"    , UpperCamelCase_     :    List[Any]="<pad>"    , UpperCamelCase_     :    Union[str, Any]="[CLS]"    , UpperCamelCase_     :    int="[MASK]"    , **UpperCamelCase_     :    Optional[Any]    , ):
           # Mask token behave like a normal word, i.e. include the space before it and
           # is included in the raw text, there should be a match in a non-normalized sentence.
           lowerCAmelCase    :		int				=      (
               AddedToken(UpperCamelCase_    , lstrip=UpperCamelCase_    , rstrip=UpperCamelCase_    , normalized=UpperCamelCase_ )
               if isinstance(UpperCamelCase_    , UpperCamelCase_ )
               else mask_token
           )

           super().__init__(
               UpperCamelCase_    , tokenizer_file=UpperCamelCase_    , do_lower_case=UpperCamelCase_    , remove_space=UpperCamelCase_    , keep_accents=UpperCamelCase_    , unk_token=UpperCamelCase_    , sep_token=UpperCamelCase_    , pad_token=UpperCamelCase_    , cls_token=UpperCamelCase_    , mask_token=UpperCamelCase_    , **UpperCamelCase_    , )

           lowerCAmelCase    :		Optional[int]				=      do_lower_case
           lowerCAmelCase    :		str				=      remove_space
           lowerCAmelCase    :		Any				=      keep_accents
           lowerCAmelCase    :		int				=      vocab_file
           lowerCAmelCase    :		List[str]				=      False if not self.vocab_file else True






     def    lowerCamelCase__   (		self     :    List[Any]    , UpperCamelCase_     :    List[int]    , UpperCamelCase_     :    Optional[List[int]] = None ):
           lowerCAmelCase    :		Optional[int]				=      [self.sep_token_id]
           lowerCAmelCase    :		Optional[Any]				=      [self.cls_token_id]
           if token_ids_a is None:
                 return cls + token_ids_a + sep
           return cls + token_ids_a + sep + token_ids_a + sep






     def    lowerCamelCase__   (		self     :    List[str]    , UpperCamelCase_     :    List[int]    , UpperCamelCase_     :    Optional[List[int]] = None ):
           lowerCAmelCase    :		List[str]				=      [self.sep_token_id]
           lowerCAmelCase    :		Optional[Any]				=      [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 ) * [0] + len(token_ids_a + sep ) * [1]






     def    lowerCamelCase__   (		self     :    List[Any]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Optional[str] = None ):
           if not os.path.isdir(UpperCamelCase_ ):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
                 return
           lowerCAmelCase    :		str				=      os.path.join(
               UpperCamelCase_    , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

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

           return (out_vocab_file,)


