





'''simple docstring'''







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


_lowercase       						=			logging.get_logger(__name__)

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

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

_lowercase       						=			{
    'openbmb/cpm-ant-10b': 1_024,
}

def    __UpperCamelCase	(	a    : Tuple     ) ->List[Any]:
 snake_case				=			collections.OrderedDict()
 with open(a					,   '''r'''					,   encoding='''utf-8'''     ) as reader:
  snake_case				=			reader.readlines()
 for index, token in enumerate(a     ):
  snake_case				=			token.rstrip('''\n'''     )
  snake_case				=			index
 return vocab
class 			_lowercase						(       __a					):







     def __init__( self    ,   A__    ,   A__="<unk>"    ,   A__=2_00    ):
      snake_case				=			vocab
      snake_case				=			unk_token
      snake_case				=			max_input_chars_per_word







     def     UpperCamelCase			( self    ,   A__    ):
      snake_case				=			list(A__    )
      if len(A__    ) > self.max_input_chars_per_word:
       return [self.unk_token]

      snake_case				=			0
      snake_case				=			[]
      while start < len(A__    ):
       snake_case				=			len(A__    )
       snake_case				=			None
       while start < end:
        snake_case				=			''''''.join(chars[start:end]    )
        if substr in self.vocab:
         snake_case				=			substr
         break
        end -= 1
       if cur_substr is None:
        sub_tokens.append(self.unk_token    )
        start += 1
       else:
        sub_tokens.append(A__    )
        snake_case				=			end

      return sub_tokens




class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					VOCAB_FILES_NAMES
     _UpperCAmelCase										=					PRETRAINED_VOCAB_FILES_MAP
     _UpperCAmelCase										=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCAmelCase										=					['''input_ids''', '''attention_mask''']
     _UpperCAmelCase										=					False







     def __init__( self    ,   A__    ,   A__="<d>"    ,   A__="</d>"    ,   A__="<s>"    ,   A__="</s>"    ,   A__="<pad>"    ,   A__="<unk>"    ,   A__="</n>"    ,   A__="</_>"    ,   A__="left"    ,   **A__    ,   ):
      requires_backends(self    ,   ['''jieba''']    )
      super().__init__(
          bod_token=A__    ,   eod_token=A__    ,   bos_token=A__    ,   eos_token=A__    ,   pad_token=A__    ,   unk_token=A__    ,   line_token=A__    ,   space_token=A__    ,   padding_side=A__    ,   **A__    ,   )
      snake_case				=			bod_token
      snake_case				=			eod_token
      snake_case				=			load_vocab(A__    )
      snake_case				=			self.encoder[space_token]
      snake_case				=			self.encoder[line_token]

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

      snake_case				=			collections.OrderedDict(sorted(self.encoder.items()    ,   key=lambda A__    : x[1]    )    )
      snake_case				=			{v: k for k, v in self.encoder.items()}

      snake_case				=			WordpieceTokenizer(vocab=self.encoder    ,   unk_token=self.unk_token    )







     @property
     def     UpperCamelCase			( self    ):
      return self.encoder[self.bod_token]







     @property
     def     UpperCamelCase			( self    ):
      return self.encoder[self.eod_token]







     @property
     def     UpperCamelCase			( self    ):
      return self.encoder["\n"]







     @property
     def     UpperCamelCase			( self    ):
      return len(self.encoder    )







     def     UpperCamelCase			( self    ):
      return dict(self.encoder    ,   **self.added_tokens_encoder    )







     def     UpperCamelCase			( self    ,   A__    ):
      snake_case				=			[]
      for x in jieba.cut(A__    ,   cut_all=A__    ):
       output_tokens.extend(self.wordpiece_tokenizer.tokenize(A__    )    )
      return output_tokens







     def     UpperCamelCase			( self    ,   A__    ,   **A__    ):
      snake_case				=			[i for i in token_ids if i >= 0]
      snake_case				=			[
          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(A__    ,   **A__    )







     def     UpperCamelCase			( self    ,   A__    ):
      return token in self.encoder







     def     UpperCamelCase			( self    ,   A__    ):
      return "".join(A__    )







     def     UpperCamelCase			( self    ,   A__    ):
      return self.encoder.get(A__    ,   self.encoder.get(self.unk_token    )    )







     def     UpperCamelCase			( self    ,   A__    ):
      return self.decoder.get(A__    ,   self.unk_token    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ):
      if os.path.isdir(A__    ):
       snake_case				=			os.path.join(
           A__    ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )
      else:
       snake_case				=			(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
      snake_case				=			0
      if " " in self.encoder:
       snake_case				=			self.encoder[''' ''']
       del self.encoder[" "]
      if "\n" in self.encoder:
       snake_case				=			self.encoder['''\n''']
       del self.encoder["\n"]
      snake_case				=			collections.OrderedDict(sorted(self.encoder.items()    ,   key=lambda A__    : x[1]    )    )
      with open(A__    ,   '''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!'''    )
         snake_case				=			token_index
        writer.write(token + '''\n'''    )
        index += 1
      return (vocab_file,)







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ):
      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     UpperCamelCase			( self    ,   A__    ,   A__ = None    ,   A__ = False    ):

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

      if token_ids_a is not None:
       return [1] + ([0] * len(A__    )) + [1] + ([0] * len(A__    ))
      return [1] + ([0] * len(A__    ))








'''simple docstring'''







_lowercase       						=			{
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}

