





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      							=   logging.get_logger(__name__)


_lowerCamelCase      							=   '▁'

_lowerCamelCase      							=   {'vocab_file': 'spiece.model'}

_lowerCamelCase      							=   {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

_lowerCamelCase      							=   {
    'google/reformer-crime-and-punishment': 52_4288,
}







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







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


      def __init__(		self   ,				a__   ,				a__="</s>"   ,				a__="<unk>"   ,				a__=[]   ,				a__ = None   ,				**a__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		{} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 eos_token=a__   ,				unk_token=a__   ,				additional_special_tokens=a__   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**a__   ,				)

             _lowerCamelCase				:  Union[str, Any]						=		vocab_file
             _lowerCamelCase				:  Optional[Any]						=		spm.SentencePieceProcessor(**self.sp_model_kwargs)
             self.sp_model.Load(a__)


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return self.sp_model.get_piece_size()


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		{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):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		self.__dict__.copy()
             _lowerCamelCase				:  Tuple						=		None
             return state


      def __setstate__(		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  int						=		d

             # for backward compatibility
             if not hasattr(self   ,				'''sp_model_kwargs'''):
                    _lowerCamelCase				:  str						=		{}

             _lowerCamelCase				:  Tuple						=		spm.SentencePieceProcessor(**self.sp_model_kwargs)
             self.sp_model.Load(self.vocab_file)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return self.sp_model.encode(a__   ,				out_type=a__)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return self.sp_model.piece_to_id(a__)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             if index < self.sp_model.get_piece_size():
                    _lowerCamelCase				:  Tuple						=		self.sp_model.IdToPiece(a__)
             return token


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		[]
             _lowerCamelCase				:  Tuple						=		''''''
             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
                           _lowerCamelCase				:  List[Any]						=		[]
                    else:
                           current_sub_tokens.append(a__)
             out_string += self.sp_model.decode(a__)
             return out_string.strip()



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



             """simple docstring"""





             if not os.path.isdir(a__):
                    logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
                    return
             _lowerCamelCase				:  Union[str, Any]						=		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:
                           _lowerCamelCase				:  str						=		self.sp_model.serialized_model_proto()
                           fi.write(a__)

             return (out_vocab_file,)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase      							=   {
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase      							=   [
           'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CLIPSegModel',
           'CLIPSegPreTrainedModel',
           'CLIPSegTextModel',
           'CLIPSegVisionModel',
           'CLIPSegForImageSegmentation',
       ]

if TYPE_CHECKING:
       from .configuration_clipseg import (
           CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
           CLIPSegConfig,
           CLIPSegTextConfig,
           CLIPSegVisionConfig,
       )
       from .processing_clipseg import CLIPSegProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_clipseg import (
                  CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CLIPSegForImageSegmentation,
                  CLIPSegModel,
                  CLIPSegPreTrainedModel,
                  CLIPSegTextModel,
                  CLIPSegVisionModel,
              )

else:
       import sys

       _lowerCamelCase      							=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

