'''simple docstring'''







import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput


class 					A__      (      _snake_case			):



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Tuple:





           '''simple docstring'''
           A_						=      dataset
           A_						=      process
           A_						=      params



    def __len__(					self       )						->      Any:





           '''simple docstring'''
           return len(self.dataset       )







    def __getitem__(					self		,				UpperCamelCase__       )						->      Dict:





           '''simple docstring'''
           A_						=      self.dataset[i]
           A_						=      self.process(UpperCamelCase_		,				**self.params       )
           return processed


class 					A__      (      _snake_case			):



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__=None       )						->      List[str]:





           '''simple docstring'''
           A_						=      loader
           A_						=      infer
           A_						=      params
           if loader_batch_size == 1:
                  # Let's spare some time by deactivating altogether
                  A_						=      None
           A_						=      loader_batch_size

           # Internal bookkeeping
           A_						=      None
           A_						=      None



    def __len__(					self       )						->      List[Any]:





           '''simple docstring'''
           return len(self.loader       )



    def __iter__(					self       )						->      str:





           '''simple docstring'''
           A_						=      iter(self.loader       )
           return self



    def 					snake_case_			(					self       )						->      Optional[int]:





           '''simple docstring'''
           if isinstance(self._loader_batch_data		,				torch.Tensor       ):
                  # Batch data is simple tensor, just fetch the slice
                  A_						=      self._loader_batch_data[self._loader_batch_index]
           else:
                  # Batch data is assumed to be BaseModelOutput (or dict)
                  A_						=      {}
                  for k, element in self._loader_batch_data.items():
                         if isinstance(UpperCamelCase_		,				UpperCamelCase_       ):
                                # Convert ModelOutput to tuple first
                                A_						=      element.to_tuple()
                                if isinstance(element[0]		,				torch.Tensor       ):
                                       A_						=      tuple(el[self._loader_batch_index].unsqueeze(0       ) for el in element       )
                                elif isinstance(element[0]		,				np.ndarray       ):
                                       A_						=      tuple(np.expand_dims(el[self._loader_batch_index]		,				0       ) for el in element       )
                                continue
                         if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(UpperCamelCase_		,				UpperCamelCase_       ):
                                # Those are stored as lists of tensors so need specific unbatching.
                                if isinstance(element[0]		,				torch.Tensor       ):
                                       A_						=      tuple(el[self._loader_batch_index].unsqueeze(0       ) for el in element       )
                                elif isinstance(element[0]		,				np.ndarray       ):
                                       A_						=      tuple(np.expand_dims(el[self._loader_batch_index]		,				0       ) for el in element       )
                                continue
                         if element is None:
                                # This can happen for optional data that get passed around
                                A_						=      None
                         elif isinstance(element[self._loader_batch_index]		,				torch.Tensor       ):
                                # Take correct batch data, but make it looked like batch_size=1
                                # For compatibility with other methods within transformers

                                A_						=      element[self._loader_batch_index].unsqueeze(0       )
                         elif isinstance(element[self._loader_batch_index]		,				np.ndarray       ):
                                # Take correct batch data, but make it looked like batch_size=1
                                # For compatibility with other methods within transformers
                                A_						=      np.expand_dims(element[self._loader_batch_index]		,				0       )
                         else:
                                # This is typically a list, so no need to `unsqueeze`.
                                A_						=      element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
                  A_						=      self._loader_batch_data.__class__(UpperCamelCase_       )
           self._loader_batch_index += 1
           return result







    def 					snake_case_			(					self       )						->      List[str]:





           '''simple docstring'''
           if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
                  # We are currently unrolling a batch so we just need to return
                  # the current item within a batch
                  return self.loader_batch_item()

           # We're out of items within a batch
           A_						=      next(self.iterator       )
           A_						=      self.infer(UpperCamelCase_		,				**self.params       )
           # We now have a batch of "inferred things".
           if self.loader_batch_size is not None:
                  # Try to infer the size of the batch
                  if isinstance(UpperCamelCase_		,				torch.Tensor       ):
                         A_						=      processed
                  else:
                         A_						=      list(processed.keys()       )[0]
                         A_						=      processed[key]
                  if isinstance(UpperCamelCase_		,				UpperCamelCase_       ):
                         A_						=      len(UpperCamelCase_       )
                  else:
                         A_						=      first_tensor.shape[0]
                  if 0 < observed_batch_size < self.loader_batch_size:
                         # could be last batch so we can't unroll as many
                         # elements.
                         A_						=      observed_batch_size
                  # Setting internal index to unwrap the batch
                  A_						=      processed
                  A_						=      0
                  return self.loader_batch_item()
           else:
                  # We're not unrolling batches
                  return processed


class 					A__      (      _snake_case			):



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__=None       )						->      Dict:





           '''simple docstring'''
           super().__init__(UpperCamelCase_		,				UpperCamelCase_		,				UpperCamelCase_       )



    def __iter__(					self       )						->      int:





           '''simple docstring'''
           A_						=      iter(self.loader       )
           A_						=      None
           return self







    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           if self.subiterator is None:
                  A_						=      self.infer(next(self.iterator       )		,				**self.params       )
           try:
                  # Try to return next item
                  A_						=      next(self.subiterator       )
           except StopIteration:
                  # When a preprocess iterator ends, we can start lookig at the next item
                  # ChunkIterator will keep feeding until ALL elements of iterator
                  # all have created their subiterator and have been iterating against.
                  #
                  # Another way to look at it, is we're basically flattening lists of lists
                  # into a single list, but with generators
                  A_						=      self.infer(next(self.iterator       )		,				**self.params       )
                  A_						=      next(self.subiterator       )
           return processed


class 					A__      (      _snake_case			):



    def __iter__(					self       )						->      Dict:





           '''simple docstring'''
           A_						=      iter(self.loader       )
           return self







    def 					snake_case_			(					self       )						->      Optional[int]:





           '''simple docstring'''
           # Extremely similar to PipelineIterator in its unpacking mechanism
           # BUT, we have an extra required item which is the presence of `is_last`
           # That is because everything is flattened by `PipelineChunkIterator` we
           # need to keep track of how to regroup here in the original `process`
           # boundaries so that `process` and `postprocess` see the same data.

           # This iterator accumulates items (possibly while unbatching) until it
           # its a `is_last` and then just passes it on to the caller.
           A_						=      False
           A_						=      []
           if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
                  while self._loader_batch_index < self.loader_batch_size:
                         A_						=      self.loader_batch_item()
                         A_						=      item.pop("""is_last"""       )
                         accumulator.append(UpperCamelCase_       )
                         if is_last:
                                return accumulator

           while not is_last:
                  A_						=      self.infer(next(self.iterator       )		,				**self.params       )
                  if self.loader_batch_size is not None:
                         if isinstance(UpperCamelCase_		,				torch.Tensor       ):
                                A_						=      processed
                         else:
                                A_						=      list(processed.keys()       )[0]
                                A_						=      processed[key]
                         if isinstance(UpperCamelCase_		,				UpperCamelCase_       ):
                                A_						=      len(UpperCamelCase_       )
                         else:
                                A_						=      first_tensor.shape[0]
                         if 0 < observed_batch_size < self.loader_batch_size:
                                # could be last batch so we can't unroll as many
                                # elements.
                                A_						=      observed_batch_size
                         A_						=      processed
                         A_						=      0
                         while self._loader_batch_index < self.loader_batch_size:
                                A_						=      self.loader_batch_item()
                                A_						=      item.pop("""is_last"""       )
                                accumulator.append(UpperCamelCase_       )
                                if is_last:
                                       return accumulator
                  else:
                         A_						=      processed
                         A_						=      item.pop("""is_last"""       )
                         accumulator.append(UpperCamelCase_       )
           return accumulator


class 					A__      (      _snake_case			):



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Tuple:





           '''simple docstring'''
           A_						=      dataset
           A_						=      key



    def __len__(					self       )						->      Optional[int]:





           '''simple docstring'''
           return len(self.dataset       )







    def __getitem__(					self		,				UpperCamelCase__       )						->      int:





           '''simple docstring'''
           return self.dataset[i][self.key]


class 					A__      (      _snake_case			):



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      List[str]:





           '''simple docstring'''
           A_						=      dataset
           A_						=      keya
           A_						=      keya



    def __len__(					self       )						->      Optional[Any]:





           '''simple docstring'''
           return len(self.dataset       )







    def __getitem__(					self		,				UpperCamelCase__       )						->      Any:





           '''simple docstring'''
           return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}



'''simple docstring'''







import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Optional[int]:
       # This defines a "chinese character" as anything in the CJK Unicode block:
       #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
       #
       # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
       # despite its name. The modern Korean Hangul alphabet is a different block,
       # as is Japanese Hiragana and Katakana. Those alphabets are used to write
       # space-separated words, so they are not treated specially and handled
       # like the all of the other languages.
       if (
           (cp >= 0X4E_00 and cp <= 0X9F_FF)
           or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
           or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
           or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
           or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
           or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
           or (cp >= 0XF9_00 and cp <= 0XFA_FF)
           or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
       ):  #
              return True

       return False
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			List[Any]:
       # word like '180' or '身高' or '神'
       for char in word:
              A_						=      ord(UpperCAmelCase__				)
              if not _is_chinese_char(UpperCAmelCase__				):
                     return 0
       return 1
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       A_						=      set()

       for token in tokens:
              A_						=      len(UpperCAmelCase__				) > 1 and is_chinese(UpperCAmelCase__				)
              if chinese_word:
                     word_set.add(UpperCAmelCase__				)
       A_						=      list(UpperCAmelCase__				)
       return word_list
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			Dict:
       if not chinese_word_set:
              return bert_tokens
       A_						=      max([len(UpperCAmelCase__				) for w in chinese_word_set]				)

       A_						=      bert_tokens
       A_					,			A_						=      0, len(UpperCAmelCase__				)
       while start < end:
              A_						=      True
              if is_chinese(bert_word[start]				):
                     A_						=      min(end - start,       UpperCAmelCase__				)
                     for i in range(UpperCAmelCase__,       1,       -1				):
                            A_						=      """""".join(bert_word[start : start + i]				)
                            if whole_word in chinese_word_set:
                                   for j in range(start + 1,       start + i				):
                                          A_						=      """##""" + bert_word[j]
                                   A_						=      start + i
                                   A_						=      False
                                   break
              if single_word:
                     start += 1
       return bert_word
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Dict:
       A_						=      []

       for i in range(0,       len(UpperCAmelCase__				),       1_00				):
              A_						=      ltp_tokenizer.pipeline(lines[i : i + 1_00],       tasks=["""cws"""]				).cws
              A_						=      [get_chinese_word(UpperCAmelCase__				) for r in res]
              ltp_res.extend(UpperCAmelCase__				)
       assert len(UpperCAmelCase__				) == len(UpperCAmelCase__				)

       A_						=      []
       for i in range(0,       len(UpperCAmelCase__				),       1_00				):
              A_						=      bert_tokenizer(lines[i : i + 1_00],       add_special_tokens=UpperCAmelCase__,       truncation=UpperCAmelCase__,       max_length=5_12				)
              bert_res.extend(res["""input_ids"""]				)
       assert len(UpperCAmelCase__				) == len(UpperCAmelCase__				)

       A_						=      []
       for input_ids, chinese_word in zip(UpperCAmelCase__,       UpperCAmelCase__				):
              A_						=      []
              for id in input_ids:
                     A_						=      bert_tokenizer._convert_id_to_token(UpperCAmelCase__				)
                     input_tokens.append(UpperCAmelCase__				)
              A_						=      add_sub_symbol(UpperCAmelCase__,       UpperCAmelCase__				)
              A_						=      []
              # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
              for i, token in enumerate(UpperCAmelCase__				):
                     if token[:2] == "##":
                            A_						=      token[2:]
                            # save chinese tokens' pos
                            if len(UpperCAmelCase__				) == 1 and _is_chinese_char(ord(UpperCAmelCase__				)				):
                                   ref_id.append(UpperCAmelCase__				)
              ref_ids.append(UpperCAmelCase__				)

       assert len(UpperCAmelCase__				) == len(UpperCAmelCase__				)

       return ref_ids
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Optional[Any]:
       # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
       # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
       with open(args.file_name,       """r""",       encoding="""utf-8"""				) as f:
              A_						=      f.readlines()
       A_						=      [line.strip() for line in data if len(UpperCAmelCase__				) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
       A_						=      LTP(args.ltp				)  # faster in GPU device
       A_						=      BertTokenizer.from_pretrained(args.bert				)

       A_						=      prepare_ref(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       with open(args.save_path,       """w""",       encoding="""utf-8"""				) as f:
              A_						=      [json.dumps(UpperCAmelCase__				) + """\n""" for ref in ref_ids]
              f.writelines(UpperCAmelCase__				)


if __name__ == "__main__":
      __lowerCamelCase		       =    argparse.ArgumentParser(description='''prepare_chinese_ref''')
      parser.add_argument(
          '''--file_name''',
          required=False,
          type=str,
          default='''./resources/chinese-demo.txt''',
          help='''file need process, same as training data in lm''',
      )
      parser.add_argument(
          '''--ltp''',
          required=False,
          type=str,
          default='''./resources/ltp''',
          help='''resources for LTP tokenizer, usually a path''',
      )
      parser.add_argument(
          '''--bert''',
          required=False,
          type=str,
          default='''./resources/robert''',
          help='''resources for Bert tokenizer''',
      )
      parser.add_argument(
          '''--save_path''',
          required=False,
          type=str,
          default='''./resources/ref.txt''',
          help='''path to save res''',
      )

      __lowerCamelCase		       =    parser.parse_args()
      main(args)
