




'''simple docstring'''


import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer


def 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							Any				,					a_	:							int				,					a_	:							List[str]				,					a_	:							Optional[Any]=True				,					a_	:							Optional[Any]="pt"			):
  __a			=					{'add_prefix_space': True} if isinstance(a_				,					a_			) and not line.startswith(' '			) else {}
  __a			=					padding_side
  return tokenizer(
      [line]				,					max_length=a_				,					padding='max_length' if pad_to_max_length else None				,					truncation=a_				,					return_tensors=a_				,					add_special_tokens=a_				,					**a_				,					)


def 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							Optional[int]				,					a_	:							Tuple=None				,					):
  __a			=					input_ids.ne(a_			).any(dim=0			)
  if attention_mask is None:
    return input_ids[:, keep_column_mask]
  else:
    return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])





class    __lowercase    (  __magic_name__ ):







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase="train"      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=""      ,							)				->    Union[str, Any]:
     super().__init__()
     __a			=					Path(UpperCamelCase      ).joinpath(type_path + '.source'      )
     __a			=					Path(UpperCamelCase      ).joinpath(type_path + '.target'      )
     __a			=					self.get_char_lens(self.src_file      )
     __a			=					max_source_length
     __a			=					max_target_length
     assert min(self.src_lens      ) > 0, f"found empty line in {self.src_file}"
     __a			=					tokenizer
     __a			=					prefix
     if n_obs is not None:
       __a			=					self.src_lens[:n_obs]
     __a			=					src_lang
     __a			=					tgt_lang







   def __len__(	self      )				->    str:
     return len(self.src_lens      )







   def __getitem__(	self      ,							UpperCamelCase      )				->    Dict[str, torch.Tensor]:
     __a			=					index + 1  # linecache starts at 1
     __a			=					self.prefix + linecache.getline(str(self.src_file      )      ,							UpperCamelCase      ).rstrip('\n'      )
     __a			=					linecache.getline(str(self.tgt_file      )      ,							UpperCamelCase      ).rstrip('\n'      )
     assert source_line, f"empty source line for index {index}"
     assert tgt_line, f"empty tgt line for index {index}"

     # Need to add eos token manually for T5
     if isinstance(self.tokenizer      ,							UpperCamelCase      ):
       source_line += self.tokenizer.eos_token
       tgt_line += self.tokenizer.eos_token

     # Pad source and target to the right
     __a			=					(
         self.tokenizer.question_encoder if isinstance(self.tokenizer      ,							UpperCamelCase      ) else self.tokenizer
     )
     __a			=					self.tokenizer.generator if isinstance(self.tokenizer      ,							UpperCamelCase      ) else self.tokenizer

     __a			=					encode_line(UpperCamelCase      ,							UpperCamelCase      ,							self.max_source_length      ,							'right'      )
     __a			=					encode_line(UpperCamelCase      ,							UpperCamelCase      ,							self.max_target_length      ,							'right'      )

     __a			=					source_inputs['input_ids'].squeeze()
     __a			=					target_inputs['input_ids'].squeeze()
     __a			=					source_inputs['attention_mask'].squeeze()
     return {
         "input_ids": source_ids,
         "attention_mask": src_mask,
         "decoder_input_ids": target_ids,
     }







   @staticmethod
   def 		UpperCamelCase__			(	UpperCamelCase      )				->    Optional[Any]:
     return [len(UpperCamelCase      ) for x in Path(UpperCamelCase      ).open().readlines()]







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Dict[str, torch.Tensor]:
     __a			=					torch.stack([x['input_ids'] for x in batch]      )
     __a			=					torch.stack([x['attention_mask'] for x in batch]      )
     __a			=					torch.stack([x['decoder_input_ids'] for x in batch]      )
     __a			=					(
         self.tokenizer.generator.pad_token_id
         if isinstance(self.tokenizer      ,							UpperCamelCase      )
         else self.tokenizer.pad_token_id
     )
     __a			=					(
         self.tokenizer.question_encoder.pad_token_id
         if isinstance(self.tokenizer      ,							UpperCamelCase      )
         else self.tokenizer.pad_token_id
     )
     __a			=					trim_batch(UpperCamelCase      ,							UpperCamelCase      )
     __a	,       __a			=					trim_batch(UpperCamelCase      ,							UpperCamelCase      ,							attention_mask=UpperCamelCase      )
     __a			=					{
         'input_ids': source_ids,
         'attention_mask': source_mask,
         'decoder_input_ids': y,
     }
     return batch

UpperCAmelCase_ 		=      getLogger(__name__)


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[List]			):
  return list(itertools.chain.from_iterable(a_			)			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  __a			=					get_git_info()
  save_json(a_				,					os.path.join(a_				,					'git_log.json'			)			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple				,					a_	:							Union[str, Any]				,					a_	:							Tuple=4				,					**a_	:							Tuple			):
  with open(a_				,					'w'			) as f:
    json.dump(a_				,					a_				,					indent=a_				,					**a_			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple			):
  with open(a_			) as f:
    return json.load(a_			)


def 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					git.Repo(search_parent_directories=a_			)
  __a			=					{
      'repo_id': str(a_			),
      'repo_sha': str(repo.head.object.hexsha			),
      'repo_branch': str(repo.active_branch			),
      'hostname': str(socket.gethostname()			),
  }
  return repo_infos


def 						SCREAMING_SNAKE_CASE       (      a_	:							Callable				,					a_	:							Iterable			):
  return list(map(a_				,					a_			)			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							Optional[Any]			):
  with open(a_				,					'wb'			) as f:
    return pickle.dump(a_				,					a_			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[Any]			):

  def remove_articles(a_	:							Union[str, Any]			):
    return re.sub(R'\b(a|an|the)\b'				,					' '				,					a_			)

  def white_space_fix(a_	:							Any			):
    return " ".join(text.split()			)

  def remove_punc(a_	:							str			):
    __a			=					set(string.punctuation			)
    return "".join(ch for ch in text if ch not in exclude			)

  def lower(a_	:							Optional[int]			):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(a_			)			)			)			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple				,					a_	:							List[Any]			):
  __a			=					normalize_answer(a_			).split()
  __a			=					normalize_answer(a_			).split()
  __a			=					Counter(a_			) & Counter(a_			)
  __a			=					sum(common.values()			)
  if num_same == 0:
    return 0
  __a			=					1.0 * num_same / len(a_			)
  __a			=					1.0 * num_same / len(a_			)
  __a			=					(2 * precision * recall) / (precision + recall)
  return fa


def 						SCREAMING_SNAKE_CASE       (      a_	:							Dict				,					a_	:							Optional[Any]			):
  return normalize_answer(a_			) == normalize_answer(a_			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]				,					a_	:							List[str]			):
  assert len(a_			) == len(a_			)
  __a			=					0
  for hypo, pred in zip(a_				,					a_			):
    em += exact_match_score(a_				,					a_			)
  if len(a_			) > 0:
    em /= len(a_			)
  return {"em": em}


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  return model_prefix.startswith('rag'			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Dict				,					a_	:							Optional[int]				,					a_	:							Any			):
  __a			=					{p: p for p in extra_params}
  # T5 models don't have `dropout` param, they have `dropout_rate` instead
  __a			=					'dropout_rate'
  for p in extra_params:
    if getattr(a_				,					a_				,					a_			):
      if not hasattr(a_				,					a_			) and not hasattr(a_				,					equivalent_param[p]			):
        logger.info('config doesn\'t have a `{}` attribute'.format(a_			)			)
        delattr(a_				,					a_			)
        continue
      __a			=					p if hasattr(a_				,					a_			) else equivalent_param[p]
      setattr(a_				,					a_				,					getattr(a_				,					a_			)			)
      delattr(a_				,					a_			)
  return hparams, config









'''simple docstring'''


import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple			):
  # 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 >= 0X4e00 and cp <= 0X9fff)
      or (cp >= 0X3400 and cp <= 0X4dbf)  #
      or (cp >= 0X20000 and cp <= 0X2a6df)  #
      or (cp >= 0X2a700 and cp <= 0X2b73f)  #
      or (cp >= 0X2b740 and cp <= 0X2b81f)  #
      or (cp >= 0X2b820 and cp <= 0X2ceaf)  #
      or (cp >= 0Xf900 and cp <= 0Xfaff)
      or (cp >= 0X2f800 and cp <= 0X2fa1f)  #
  ):  #
    return True

  return False


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  # word like '180' or '身高' or '神'
  for char in word:
    __a			=					ord(a_			)
    if not _is_chinese_char(a_			):
      return 0
  return 1


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]			):
  __a			=					set()

  for token in tokens:
    __a			=					len(a_			) > 1 and is_chinese(a_			)
    if chinese_word:
      word_set.add(a_			)
  __a			=					list(a_			)
  return word_list


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]				,					a_	:							set()			):
  if not chinese_word_set:
    return bert_tokens
  __a			=					max([len(a_			) for w in chinese_word_set]			)

  __a			=					bert_tokens
  __a	,       __a			=					0, len(a_			)
  while start < end:
    __a			=					True
    if is_chinese(bert_word[start]			):
      __a			=					min(end - start				,					a_			)
      for i in range(a_				,					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 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]				,					a_	:							LTP				,					a_	:							BertTokenizer			):
  __a			=					[]

  for i in range(0				,					len(a_			)				,					100			):
    __a			=					ltp_tokenizer.seg(lines[i : i + 100]			)[0]
    __a			=					[get_chinese_word(a_			) for r in res]
    ltp_res.extend(a_			)
  assert len(a_			) == len(a_			)

  __a			=					[]
  for i in range(0				,					len(a_			)				,					100			):
    __a			=					bert_tokenizer(lines[i : i + 100]				,					add_special_tokens=a_				,					truncation=a_				,					max_length=512			)
    bert_res.extend(res['input_ids']			)
  assert len(a_			) == len(a_			)

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

  assert len(a_			) == len(a_			)

  return ref_ids


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  # 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(a_			) > 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(a_				,					a_				,					a_			)

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


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

  UpperCAmelCase_ 		=      parser.parse_args()
  main(args)



