import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
  import jieba

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


UpperCAmelCase_			    =							logging.get_logger(__name__)

UpperCAmelCase_			    =							{'''vocab_file''': '''vocab.txt'''}

UpperCAmelCase_			    =							{
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

UpperCAmelCase_			    =							{
    '''openbmb/cpm-ant-10b''': 1_0_2_4,
}




def       lowerCAmelCase_     (		lowercase:							List[str]		)    ->				List[str]:







 '''simple docstring'''
 _UpperCamelCase:     List[str]							= collections.OrderedDict()
 with open(lowercase  ,					'''r'''  ,					encoding='''utf-8'''		) as reader:
  _UpperCamelCase:     Dict							= reader.readlines()
 for index, token in enumerate(lowercase		):
  _UpperCamelCase:     Optional[int]							= token.rstrip('''\n'''		)
  _UpperCamelCase:     Optional[int]							= index
 return vocab




class  __magic_name__  (		__a    ):





   """simple docstring"""




   def __init__(	self					:  Tuple		,  _lowercase					:  List[Any]		,  _lowercase					:  Optional[Any]="<unk>"		,  _lowercase					:  Optional[Any]=200							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= vocab
    _UpperCamelCase:     Any							= unk_token
    _UpperCamelCase:     Optional[Any]							= max_input_chars_per_word



   def   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  Optional[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= list(_lowercase							)
    if len(_lowercase							) > self.max_input_chars_per_word:
     return [self.unk_token]

    _UpperCamelCase:     Optional[Any]							= 0
    _UpperCamelCase:     int							= []
    while start < len(_lowercase							):
     _UpperCamelCase:     Tuple							= len(_lowercase							)
     _UpperCamelCase:     Tuple							= None
     while start < end:
      _UpperCamelCase:     int							= ''''''.join(chars[start:end]							)
      if substr in self.vocab:
       _UpperCamelCase:     Tuple							= substr
       break
      end -= 1
     if cur_substr is None:
      sub_tokens.append(self.unk_token							)
      start += 1
     else:
      sub_tokens.append(_lowercase							)
      _UpperCamelCase:     Dict							= end

    return sub_tokens




class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    str  			=   VOCAB_FILES_NAMES
   lowerCAmelCase      :    List[Any]  			=   PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase      :    List[str]  			=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase      :    Union[str, Any]  			=   ['''input_ids''', '''attention_mask''']
   lowerCAmelCase      :    List[str]  			=   False



   def __init__(	self					:  List[Any]		,  _lowercase					:  Tuple		,  _lowercase					:  List[str]="<d>"		,  _lowercase					:  Any="</d>"		,  _lowercase					:  str="<s>"		,  _lowercase					:  Dict="</s>"		,  _lowercase					:  Optional[Any]="<pad>"		,  _lowercase					:  Optional[int]="<unk>"		,  _lowercase					:  Optional[Any]="</n>"		,  _lowercase					:  Any="</_>"		,  _lowercase					:  List[str]="left"		,  **_lowercase					:  str		,  ):



    """simple docstring"""



    requires_backends(self		,  ['''jieba''']							)
    super().__init__(
        bod_token=_lowercase		,  eod_token=_lowercase		,  bos_token=_lowercase		,  eos_token=_lowercase		,  pad_token=_lowercase		,  unk_token=_lowercase		,  line_token=_lowercase		,  space_token=_lowercase		,  padding_side=_lowercase		,  **_lowercase		,  )
    _UpperCamelCase:     Tuple							= bod_token
    _UpperCamelCase:     Tuple							= eod_token
    _UpperCamelCase:     Tuple							= load_vocab(_lowercase							)
    _UpperCamelCase:     Any							= self.encoder[space_token]
    _UpperCamelCase:     Optional[int]							= self.encoder[line_token]

    del self.encoder[space_token]
    del self.encoder[line_token]

    _UpperCamelCase:     List[Any]							= collections.OrderedDict(sorted(self.encoder.items()		,  key=lambda _lowercase							: x[1]							)							)
    _UpperCamelCase:     Optional[Any]							= {v: k for k, v in self.encoder.items()}

    _UpperCamelCase:     Any							= WordpieceTokenizer(vocab=self.encoder		,  unk_token=self.unk_token							)



   @property
   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    return self.encoder[self.bod_token]



   @property
   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    return self.encoder[self.eod_token]



   @property
   def   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    return self.encoder["\n"]



   @property
   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    return len(self.encoder							)



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    return dict(self.encoder		,  **self.added_tokens_encoder							)



   def   lowerCAmelCase						(	self					:  List[str]		,  _lowercase					:  List[str]							):



    """simple docstring"""



    _UpperCamelCase:     str							= []
    for x in jieba.cut(_lowercase		,  cut_all=_lowercase							):
     output_tokens.extend(self.wordpiece_tokenizer.tokenize(_lowercase							)							)
    return output_tokens



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  Tuple		,  **_lowercase					:  List[str]							):



    """simple docstring"""



    _UpperCamelCase:     str							= [i for i in token_ids if i >= 0]
    _UpperCamelCase:     Any							= [
        x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
    ]
    return super()._decode(_lowercase		,  **_lowercase							)



   def   lowerCAmelCase						(	self					:  Any		,  _lowercase					:  List[Any]							):



    """simple docstring"""



    return token in self.encoder



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  List[str]							):



    """simple docstring"""



    return "".join(_lowercase							)



   def   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  Optional[Any]							):



    """simple docstring"""



    return self.encoder.get(_lowercase		,  self.encoder.get(self.unk_token							)							)



   def   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  List[str]							):



    """simple docstring"""



    return self.decoder.get(_lowercase		,  self.unk_token							)



   def   lowerCAmelCase						(	self					:  List[str]		,  _lowercase					:  str		,  _lowercase					:  Optional[str] = None							):



    """simple docstring"""



    if os.path.isdir(_lowercase							):
     _UpperCamelCase:     int							= os.path.join(
         _lowercase		,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
    else:
     _UpperCamelCase:     List[Any]							= (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
    _UpperCamelCase:     Dict							= 0
    if " " in self.encoder:
     _UpperCamelCase:     Tuple							= self.encoder[''' ''']
     del self.encoder[" "]
    if "\n" in self.encoder:
     _UpperCamelCase:     Dict							= self.encoder['''\n''']
     del self.encoder["\n"]
    _UpperCamelCase:     List[str]							= collections.OrderedDict(sorted(self.encoder.items()		,  key=lambda _lowercase							: x[1]							)							)
    with open(_lowercase		,  '''w'''		,  encoding='''utf-8'''							) as writer:
     for token, token_index in self.encoder.items():
      if index != token_index:
       logger.warning(
           f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
           ''' Please check that the vocabulary is not corrupted!'''							)
       _UpperCamelCase:     str							= token_index
      writer.write(token + '''\n'''							)
      index += 1
    return (vocab_file,)



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  List[int]		,  _lowercase					:  List[int] = None							):



    """simple docstring"""



    if token_ids_a is None:
     return [self.bos_token_id] + token_ids_a
    return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  List[int]		,  _lowercase					:  Optional[List[int]] = None		,  _lowercase					:  bool = False							):



    """simple docstring"""



    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=_lowercase		,  token_ids_a=_lowercase		,  already_has_special_tokens=_lowercase							)

    if token_ids_a is not None:
     return [1] + ([0] * len(_lowercase							)) + [1] + ([0] * len(_lowercase							))
    return [1] + ([0] * len(_lowercase							))
from typing import TYPE_CHECKING

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


UpperCAmelCase_			    =							{
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							['''BlenderbotTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''BlenderbotForCausalLM''',
      '''BlenderbotForConditionalGeneration''',
      '''BlenderbotModel''',
      '''BlenderbotPreTrainedModel''',
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''TFBlenderbotForConditionalGeneration''',
      '''TFBlenderbotModel''',
      '''TFBlenderbotPreTrainedModel''',
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''FlaxBlenderbotForConditionalGeneration''',
      '''FlaxBlenderbotModel''',
      '''FlaxBlenderbotPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_blenderbot import (
      BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BlenderbotConfig,
      BlenderbotOnnxConfig,
  )
  from .tokenization_blenderbot import BlenderbotTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_blenderbot import (
        BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BlenderbotForCausalLM,
        BlenderbotForConditionalGeneration,
        BlenderbotModel,
        BlenderbotPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_blenderbot import (
        TFBlenderbotForConditionalGeneration,
        TFBlenderbotModel,
        TFBlenderbotPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_blenderbot import (
        FlaxBlenderbotForConditionalGeneration,
        FlaxBlenderbotModel,
        FlaxBlenderbotPreTrainedModel,
    )

else:
  import sys

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