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_024,
}

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




       '''simple docstring'''






       _A=	collections.OrderedDict()
       with open(lowerCAmelCase_       ,					'r'       ,					encoding='utf-8'   ) as reader:
              _A=	reader.readlines()
       for index, token in enumerate(lowerCAmelCase_   ):
              _A=	token.rstrip('\n'   )
              _A=	index
       return vocab





class      lowerCAmelCase						(					_a			):



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__="<unk>"				,      lowerCAmelCase__=200       ):
            _A=	vocab
            _A=	unk_token
            _A=	max_input_chars_per_word





     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A=	list(lowerCAmelCase__       )
            if len(lowerCAmelCase__       ) > self.max_input_chars_per_word:
                   return [self.unk_token]

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

            return sub_tokens





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Union[str, Any]		=VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE     :     Tuple		=PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE     :     Union[str, Any]		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE     :     List[Any]		=["""input_ids""", """attention_mask"""]
     _SCREAMING_SNAKE_CASE     :     List[str]		=False



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

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

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

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



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



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



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



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



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



     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A=	[]
            for x in jieba.cut(lowerCAmelCase__				,      cut_all=lowerCAmelCase__       ):
                   output_tokens.extend(self.wordpiece_tokenizer.tokenize(lowerCAmelCase__       )       )
            return output_tokens



     def 			a__  (					self				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            _A=	[i for i in token_ids if i >= 0]
            _A=	[
                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(lowerCAmelCase__				,      **lowerCAmelCase__       )



     def 			a__  (					self				,      lowerCAmelCase__       ):
            return token in self.encoder



     def 			a__  (					self				,      lowerCAmelCase__       ):
            return "".join(lowerCAmelCase__       )



     def 			a__  (					self				,      lowerCAmelCase__       ):
            return self.encoder.get(lowerCAmelCase__				,      self.encoder.get(self.unk_token       )       )



     def 			a__  (					self				,      lowerCAmelCase__       ):
            return self.decoder.get(lowerCAmelCase__				,      self.unk_token       )



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



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__ = 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 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = False       ):

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

            if token_ids_a is not None:
                   return [1] + ([0] * len(lowerCAmelCase__       )) + [1] + ([0] * len(lowerCAmelCase__       ))
            return [1] + ([0] * len(lowerCAmelCase__       ))
UpperCAmelCase_								=							{
    '''Pillow''': '''Pillow<10.0.0''',
    '''accelerate''': '''accelerate>=0.20.3''',
    '''av''': '''av==9.2.0''',
    '''beautifulsoup4''': '''beautifulsoup4''',
    '''black''': '''black~=23.1''',
    '''codecarbon''': '''codecarbon==1.2.0''',
    '''cookiecutter''': '''cookiecutter==1.7.3''',
    '''dataclasses''': '''dataclasses''',
    '''datasets''': '''datasets!=2.5.0''',
    '''decord''': '''decord==0.6.0''',
    '''deepspeed''': '''deepspeed>=0.9.3''',
    '''diffusers''': '''diffusers''',
    '''dill''': '''dill<0.3.5''',
    '''evaluate''': '''evaluate>=0.2.0''',
    '''fairscale''': '''fairscale>0.3''',
    '''faiss-cpu''': '''faiss-cpu''',
    '''fastapi''': '''fastapi''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1,<=0.7.0''',
    '''ftfy''': '''ftfy''',
    '''fugashi''': '''fugashi>=1.0''',
    '''GitPython''': '''GitPython<3.1.19''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.14.1,<1.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''ipadic''': '''ipadic>=1.0.0,<2.0''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2,<=0.4.13''',
    '''jaxlib''': '''jaxlib>=0.1.65,<=0.4.13''',
    '''jieba''': '''jieba''',
    '''kenlm''': '''kenlm''',
    '''keras-nlp''': '''keras-nlp>=0.3.1''',
    '''librosa''': '''librosa''',
    '''nltk''': '''nltk''',
    '''natten''': '''natten>=0.14.6''',
    '''numpy''': '''numpy>=1.17''',
    '''onnxconverter-common''': '''onnxconverter-common''',
    '''onnxruntime-tools''': '''onnxruntime-tools>=1.4.2''',
    '''onnxruntime''': '''onnxruntime>=1.4.0''',
    '''opencv-python''': '''opencv-python''',
    '''optuna''': '''optuna''',
    '''optax''': '''optax>=0.0.8,<=0.1.4''',
    '''packaging''': '''packaging>=20.0''',
    '''parameterized''': '''parameterized''',
    '''phonemizer''': '''phonemizer''',
    '''protobuf''': '''protobuf''',
    '''psutil''': '''psutil''',
    '''pyyaml''': '''pyyaml>=5.1''',
    '''pydantic''': '''pydantic<2''',
    '''pytest''': '''pytest>=7.2.0''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''python''': '''python>=3.8.0''',
    '''ray[tune]''': '''ray[tune]''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''rhoknp''': '''rhoknp>=1.1.0,<1.3.1''',
    '''rjieba''': '''rjieba''',
    '''rouge-score''': '''rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1''',
    '''ruff''': '''ruff>=0.0.241,<=0.0.259''',
    '''sacrebleu''': '''sacrebleu>=1.4.12,<2.0.0''',
    '''sacremoses''': '''sacremoses''',
    '''safetensors''': '''safetensors>=0.3.1''',
    '''sagemaker''': '''sagemaker>=2.31.0''',
    '''scikit-learn''': '''scikit-learn''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''sigopt''': '''sigopt''',
    '''starlette''': '''starlette''',
    '''sudachipy''': '''sudachipy>=0.6.6''',
    '''sudachidict_core''': '''sudachidict_core>=20220729''',
    '''tensorflow-cpu''': '''tensorflow-cpu>=2.6,<2.14''',
    '''tensorflow''': '''tensorflow>=2.6,<2.14''',
    '''tensorflow-text''': '''tensorflow-text<2.14''',
    '''tf2onnx''': '''tf2onnx''',
    '''timeout-decorator''': '''timeout-decorator''',
    '''timm''': '''timm''',
    '''tokenizers''': '''tokenizers>=0.11.1,!=0.11.3,<0.14''',
    '''torch''': '''torch>=1.9,!=1.12.0''',
    '''torchaudio''': '''torchaudio''',
    '''torchvision''': '''torchvision''',
    '''pyctcdecode''': '''pyctcdecode>=0.4.0''',
    '''tqdm''': '''tqdm>=4.27''',
    '''unidic''': '''unidic>=1.0.2''',
    '''unidic_lite''': '''unidic_lite>=1.0.7''',
    '''urllib3''': '''urllib3<2.0.0''',
    '''uvicorn''': '''uvicorn''',
}