




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


_lowerCAmelCase						:   Tuple		=   logging.get_logger(__name__)

_lowerCAmelCase						:   Optional[int]		=   {"vocab_file": "vocab.txt"}

_lowerCAmelCase						:   Optional[Any]		=   {
    "vocab_file": {
        "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt",
    },
}

_lowerCAmelCase						:   Optional[int]		=   {
    "openbmb/cpm-ant-10b": 1_0_2_4,
}



def 						lowerCAmelCase		(		_lowerCAmelCase							:   int	)     ->   Optional[Any]:




 """simple docstring"""







 UpperCAmelCase__							 =						collections.OrderedDict()
 with open(_lowerCAmelCase						,							"r"						,							encoding="utf-8"	) as reader:
  UpperCAmelCase__							 =						reader.readlines()
 for index, token in enumerate(_lowerCAmelCase	):
  UpperCAmelCase__							 =						token.rstrip("\n"	)
  UpperCAmelCase__							 =						index
 return vocab
class 			_UpperCamelCase      (				lowerCAmelCase		):
      def __init__(			self   :Any ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :Tuple="<unk>" ,      lowerCamelCase   :List[str]=200      )     ->			List[Any]:
       UpperCAmelCase__							 =						vocab
       UpperCAmelCase__							 =						unk_token
       UpperCAmelCase__							 =						max_input_chars_per_word







      def 	UpperCAmelCase_						(			self   :Optional[int] ,      lowerCamelCase   :Any      )     ->			Any:
       UpperCAmelCase__							 =						list(lowerCamelCase      )
       if len(lowerCamelCase      ) > self.max_input_chars_per_word:
        return [self.unk_token]

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

       return sub_tokens



class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		VOCAB_FILES_NAMES
      UpperCAmelCase_        =		PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_        =		["""input_ids""", """attention_mask"""]
      UpperCAmelCase_        =		False
      def __init__(			self   :Tuple ,      lowerCamelCase   :Optional[Any] ,      lowerCamelCase   :Optional[int]="<d>" ,      lowerCamelCase   :Optional[int]="</d>" ,      lowerCamelCase   :Optional[int]="<s>" ,      lowerCamelCase   :List[str]="</s>" ,      lowerCamelCase   :Optional[Any]="<pad>" ,      lowerCamelCase   :Optional[Any]="<unk>" ,      lowerCamelCase   :List[Any]="</n>" ,      lowerCamelCase   :Optional[Any]="</_>" ,      lowerCamelCase   :Union[str, Any]="left" ,      **lowerCamelCase   :Dict ,      )     ->			Any:
       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 ,      )
       UpperCAmelCase__							 =						bod_token
       UpperCAmelCase__							 =						eod_token
       UpperCAmelCase__							 =						load_vocab(lowerCamelCase      )
       UpperCAmelCase__							 =						self.encoder[space_token]
       UpperCAmelCase__							 =						self.encoder[line_token]

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

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

       UpperCAmelCase__							 =						WordpieceTokenizer(vocab=self.encoder ,      unk_token=self.unk_token      )
      @property
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Any:
       return self.encoder[self.bod_token]
      @property
      def 	UpperCAmelCase_						(			self   :str      )     ->			List[Any]:
       return self.encoder[self.eod_token]
      @property
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			List[Any]:
       return self.encoder["\n"]
      @property
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			int:
       return len(self.encoder      )
      def 	UpperCAmelCase_						(			self   :int      )     ->			Any:
       return dict(self.encoder ,      **self.added_tokens_encoder      )
      def 	UpperCAmelCase_						(			self   :str ,      lowerCamelCase   :int      )     ->			int:
       UpperCAmelCase__							 =						[]
       for x in jieba.cut(lowerCamelCase ,      cut_all=lowerCamelCase      ):
        output_tokens.extend(self.wordpiece_tokenizer.tokenize(lowerCamelCase      )      )
       return output_tokens
      def 	UpperCAmelCase_						(			self   :Dict ,      lowerCamelCase   :Any ,      **lowerCamelCase   :Optional[Any]      )     ->			Optional[int]:
       UpperCAmelCase__							 =						[i for i in token_ids if i >= 0]
       UpperCAmelCase__							 =						[
           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 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :List[str]      )     ->			Optional[int]:
       return token in self.encoder
      def 	UpperCAmelCase_						(			self   :Optional[int] ,      lowerCamelCase   :List[str]      )     ->			str:
       return "".join(lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :List[Any] ,      lowerCamelCase   :List[Any]      )     ->			Any:
       return self.encoder.get(lowerCamelCase ,      self.encoder.get(self.unk_token      )      )
      def 	UpperCAmelCase_						(			self   :str ,      lowerCamelCase   :Tuple      )     ->			Dict:
       return self.decoder.get(lowerCamelCase ,      self.unk_token      )
      def 	UpperCAmelCase_						(			self   :Dict ,      lowerCamelCase   :str ,      lowerCamelCase   :Optional[str] = None      )     ->			Tuple[str]:
       if os.path.isdir(lowerCamelCase      ):
        UpperCAmelCase__							 =						os.path.join(
            lowerCamelCase ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )
       else:
        UpperCAmelCase__							 =						(filename_prefix + "-" if filename_prefix else "") + save_directory
       UpperCAmelCase__							 =						0
       if " " in self.encoder:
        UpperCAmelCase__							 =						self.encoder[" "]
        del self.encoder[" "]
       if "\n" in self.encoder:
        UpperCAmelCase__							 =						self.encoder["\n"]
        del self.encoder["\n"]
       UpperCAmelCase__							 =						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!"      )
          UpperCAmelCase__							 =						token_index
         writer.write(token + "\n"      )
         index += 1
       return (vocab_file,)
      def 	UpperCAmelCase_						(			self   :Dict ,      lowerCamelCase   :List[int] ,      lowerCamelCase   :List[int] = None      )     ->			List[int]:
       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   :int ,      lowerCamelCase   :List[int] ,      lowerCamelCase   :Optional[List[int]] = None ,      lowerCamelCase   :bool = False      )     ->			List[int]:

       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      ))











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   List[str]		=   [
      "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
      "GPTNeoForCausalLM",
      "GPTNeoForQuestionAnswering",
      "GPTNeoForSequenceClassification",
      "GPTNeoForTokenClassification",
      "GPTNeoModel",
      "GPTNeoPreTrainedModel",
      "load_tf_weights_in_gpt_neo",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Tuple		=   [
      "FlaxGPTNeoForCausalLM",
      "FlaxGPTNeoModel",
      "FlaxGPTNeoPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neo import (
        GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoForCausalLM,
        GPTNeoForQuestionAnswering,
        GPTNeoForSequenceClassification,
        GPTNeoForTokenClassification,
        GPTNeoModel,
        GPTNeoPreTrainedModel,
        load_tf_weights_in_gpt_neo,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
  import sys

  _lowerCAmelCase						:   Union[str, Any]		=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





