




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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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

lowerCamelCase	:	str				=   {'vocab_file': 'spiece.model'}

lowerCamelCase	:	Optional[int]				=   {
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

lowerCamelCase	:	Dict				=   {'bert_for_seq_generation': 5_1_2}




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      _snake_case												=						VOCAB_FILES_NAMES
      _snake_case												=						PRETRAINED_VOCAB_FILES_MAP
      _snake_case												=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case												=						[]
      _snake_case												=						["""input_ids""", """attention_mask"""]

      def __init__(				self    ,				A    ,				A="<s>"    ,				A="</s>"    ,				A="<unk>"    ,				A="<pad>"    ,				A="<::::>"    ,				A = None    ,				**A    ,				)		->    None:
         snake_case				:    List[str]           =	{} if sp_model_kwargs is None else sp_model_kwargs

         # Add extra_ids to the special token list
         super().__init__(
             bos_token=A    ,				eos_token=A    ,				unk_token=A    ,				pad_token=A    ,				sep_token=A    ,				sp_model_kwargs=self.sp_model_kwargs    ,				**A    ,				)

         snake_case				:    List[Any]           =	vocab_file

         snake_case				:    List[Any]           =	spm.SentencePieceProcessor(**self.sp_model_kwargs     )
         self.sp_model.Load(A     )

      @property
      def      UpperCAmelCase				(				self     )		->    List[str]:
         return self.sp_model.get_piece_size()

      def      UpperCAmelCase				(				self     )		->    List[Any]:
         snake_case				:    Any           =	{self.convert_ids_to_tokens(A     ): i for i in range(self.vocab_size     )}
         vocab.update(self.added_tokens_encoder     )
         return vocab

      def __getstate__(				self     )		->    Optional[int]:
         snake_case				:    Dict           =	self.__dict__.copy()
         snake_case				:    Optional[Any]           =	None
         return state

      def __setstate__(				self    ,				A     )		->    Dict:
         snake_case				:    int           =	d

         # for backward compatibility
         if not hasattr(self    ,				"""sp_model_kwargs"""     ):
            snake_case				:    Any           =	{}

         snake_case				:    Any           =	spm.SentencePieceProcessor(**self.sp_model_kwargs     )
         self.sp_model.Load(self.vocab_file     )

      def      UpperCAmelCase				(				self    ,				A     )		->    List[str]:
         return self.sp_model.encode(A    ,				out_type=A     )

      def      UpperCAmelCase				(				self    ,				A     )		->    Tuple:
         return self.sp_model.piece_to_id(A     )

      def      UpperCAmelCase				(				self    ,				A     )		->    str:
         snake_case				:    Optional[Any]           =	self.sp_model.IdToPiece(A     )
         return token

      def      UpperCAmelCase				(				self    ,				A     )		->    List[Any]:
         snake_case				:    List[Any]           =	[]
         snake_case				:    List[Any]           =	""""""
         for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
               out_string += self.sp_model.decode(A     ) + token
               snake_case				:    Optional[Any]           =	[]
            else:
               current_sub_tokens.append(A     )
         out_string += self.sp_model.decode(A     )
         return out_string.strip()

      def      UpperCAmelCase				(				self    ,				A    ,				A = None     )		->    Tuple[str]:
         if not os.path.isdir(A     ):
            logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""     )
            return
         snake_case				:    str           =	os.path.join(
             A    ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

         if os.path.abspath(self.vocab_file     ) != os.path.abspath(A     ) and os.path.isfile(self.vocab_file     ):
            copyfile(self.vocab_file    ,				A     )
         elif not os.path.isfile(self.vocab_file     ):
            with open(A    ,				"""wb"""     ) as fi:
               snake_case				:    Optional[int]           =	self.sp_model.serialized_model_proto()
               fi.write(A     )

         return (out_vocab_file,)











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase	:	str				=   {
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	List[Any]				=   ['RoFormerTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Dict				=   [
           'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RoFormerForCausalLM',
           'RoFormerForMaskedLM',
           'RoFormerForMultipleChoice',
           'RoFormerForQuestionAnswering',
           'RoFormerForSequenceClassification',
           'RoFormerForTokenClassification',
           'RoFormerLayer',
           'RoFormerModel',
           'RoFormerPreTrainedModel',
           'load_tf_weights_in_roformer',
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Any				=   [
           'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFRoFormerForCausalLM',
           'TFRoFormerForMaskedLM',
           'TFRoFormerForMultipleChoice',
           'TFRoFormerForQuestionAnswering',
           'TFRoFormerForSequenceClassification',
           'TFRoFormerForTokenClassification',
           'TFRoFormerLayer',
           'TFRoFormerModel',
           'TFRoFormerPreTrainedModel',
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Union[str, Any]				=   [
           'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'FlaxRoFormerForMaskedLM',
           'FlaxRoFormerForMultipleChoice',
           'FlaxRoFormerForQuestionAnswering',
           'FlaxRoFormerForSequenceClassification',
           'FlaxRoFormerForTokenClassification',
           'FlaxRoFormerModel',
           'FlaxRoFormerPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
       from .tokenization_roformer import RoFormerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roformer_fast import RoFormerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roformer import (
                  ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RoFormerForCausalLM,
                  RoFormerForMaskedLM,
                  RoFormerForMultipleChoice,
                  RoFormerForQuestionAnswering,
                  RoFormerForSequenceClassification,
                  RoFormerForTokenClassification,
                  RoFormerLayer,
                  RoFormerModel,
                  RoFormerPreTrainedModel,
                  load_tf_weights_in_roformer,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roformer import (
                  TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRoFormerForCausalLM,
                  TFRoFormerForMaskedLM,
                  TFRoFormerForMultipleChoice,
                  TFRoFormerForQuestionAnswering,
                  TFRoFormerForSequenceClassification,
                  TFRoFormerForTokenClassification,
                  TFRoFormerLayer,
                  TFRoFormerModel,
                  TFRoFormerPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roformer import (
                  FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaxRoFormerForMaskedLM,
                  FlaxRoFormerForMultipleChoice,
                  FlaxRoFormerForQuestionAnswering,
                  FlaxRoFormerForSequenceClassification,
                  FlaxRoFormerForTokenClassification,
                  FlaxRoFormerModel,
                  FlaxRoFormerPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase	:	Optional[int]				=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





