"""simple docstring"""






from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  List[Any]:
   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     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    @staticmethod
    def _snake_case			(   _UpperCAmelCase							):
       lowercase__:   List[str]					=      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    ,			):
       lowercase__:   str					=      logging.get_logger('''transformers-cli/converting'''							)

       self._logger.info(F"""Loading model {model_type}"""							)
       lowercase__:   List[str]					=      model_type
       lowercase__:   List[str]					=      tf_checkpoint
       lowercase__:   Tuple					=      pytorch_dump_output
       lowercase__:   Dict					=      config
       lowercase__:   Tuple					=      finetuning_task_name






    def _snake_case			(   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():
             lowercase__:   Any					=      self._tf_checkpoint
             lowercase__:   int					=      ''''''
          else:
             lowercase__:   Any					=      self._tf_checkpoint
             lowercase__:   Union[str, 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 json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


__A			   = logging.get_logger(__name__)

__A			   = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A			   = {
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

__A			   = {
    "yjernite/retribert-base-uncased": 5_1_2,
}


__A			   = {
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Dict						    = VOCAB_FILES_NAMES
    _UpperCAmelCase	:str						    = PRETRAINED_VOCAB_FILES_MAP
    _UpperCAmelCase	:Optional[int]						    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _UpperCAmelCase	:Optional[int]						    = PRETRAINED_INIT_CONFIGURATION
    _UpperCAmelCase	:str						    = RetriBertTokenizer
    _UpperCAmelCase	:List[str]						    = ["input_ids", "attention_mask"]






    def __init__(   self    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase="[UNK]"    ,			_UpperCAmelCase="[SEP]"    ,			_UpperCAmelCase="[PAD]"    ,			_UpperCAmelCase="[CLS]"    ,			_UpperCAmelCase="[MASK]"    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=None    ,			**_UpperCAmelCase    ,			):
       super().__init__(
           _UpperCAmelCase    ,			tokenizer_file=_UpperCAmelCase    ,			do_lower_case=_UpperCAmelCase    ,			unk_token=_UpperCAmelCase    ,			sep_token=_UpperCAmelCase    ,			pad_token=_UpperCAmelCase    ,			cls_token=_UpperCAmelCase    ,			mask_token=_UpperCAmelCase    ,			tokenize_chinese_chars=_UpperCAmelCase    ,			strip_accents=_UpperCAmelCase    ,			**_UpperCAmelCase    ,			)

       lowercase__:   Optional[Any]					=      json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
       if (
           normalizer_state.get('''lowercase'''    ,			_UpperCAmelCase							) != do_lower_case
           or normalizer_state.get('''strip_accents'''    ,			_UpperCAmelCase							) != strip_accents
           or normalizer_state.get('''handle_chinese_chars'''    ,			_UpperCAmelCase							) != tokenize_chinese_chars
       ):
          lowercase__:   str					=      getattr(_UpperCAmelCase    ,			normalizer_state.pop('''type'''							)							)
          lowercase__:   Optional[Any]					=      do_lower_case
          lowercase__:   Tuple					=      strip_accents
          lowercase__:   str					=      tokenize_chinese_chars
          lowercase__:   Union[str, Any]					=      normalizer_class(**_UpperCAmelCase							)

       lowercase__:   List[str]					=      do_lower_case






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=None							):
       lowercase__:   Union[str, Any]					=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
          output += token_ids_a + [self.sep_token_id]

       return output






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):
       lowercase__:   Optional[int]					=      [self.sep_token_id]
       lowercase__:   Union[str, 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 _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):
       lowercase__:   List[str]					=      self._tokenizer.model.save(_UpperCAmelCase    ,			name=_UpperCAmelCase							)
       return tuple(_UpperCAmelCase							)
