
import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[Any]  ):
 # 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 >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
     or (cp >= 0x3_4_0_0 and cp <= 0x4_D_B_F)  #
     or (cp >= 0x2_0_0_0_0 and cp <= 0x2_A_6_D_F)  #
     or (cp >= 0x2_A_7_0_0 and cp <= 0x2_B_7_3_F)  #
     or (cp >= 0x2_B_7_4_0 and cp <= 0x2_B_8_1_F)  #
     or (cp >= 0x2_B_8_2_0 and cp <= 0x2_C_E_A_F)  #
     or (cp >= 0xF_9_0_0 and cp <= 0xF_A_F_F)
     or (cp >= 0x2_F_8_0_0 and cp <= 0x2_F_A_1_F)  #
 ):  #
  return True

 return False




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




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[str]  ):
 __a			:  int		    =  set()

 for token in tokens:
  __a			:  List[Any]		    =  len(lowerCAmelCase__  ) > 1 and is_chinese(lowerCAmelCase__  )
  if chinese_word:
   word_set.add(lowerCAmelCase__  )
 __a			:  Tuple		    =  list(lowerCAmelCase__  )
 return word_list




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

 __a			:  Optional[int]		    =  bert_tokens
 __a    ,	__a			:  Optional[int]		    =  0, len(lowerCAmelCase__  )
 while start < end:
  __a			:  Tuple		    =  True
  if is_chinese(bert_word[start]  ):
   __a			:  int		    =  min(end - start  ,       lowerCAmelCase__  )
   for i in range(lowerCAmelCase__  ,       1  ,       -1  ):
    __a			:  Optional[int]		    =  ''''''.join(bert_word[start : start + i]  )
    if whole_word in chinese_word_set:
     for j in range(start + 1  ,       start + i  ):
      __a			:  List[str]		    =  '''##''' + bert_word[j]
     __a			:  Dict		    =  start + i
     __a			:  int		    =  False
     break
  if single_word:
   start += 1
 return bert_word




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    LTP  ,       lowerCAmelCase__							:    BertTokenizer  ):
 __a			:  str		    =  []

 for i in range(0  ,       len(lowerCAmelCase__  )  ,       1_0_0  ):
  __a			:  Dict		    =  ltp_tokenizer.seg(lines[i : i + 1_0_0]  )[0]
  __a			:  str		    =  [get_chinese_word(lowerCAmelCase__  ) for r in res]
  ltp_res.extend(lowerCAmelCase__  )
 assert len(lowerCAmelCase__  ) == len(lowerCAmelCase__  )

 __a			:  List[Any]		    =  []
 for i in range(0  ,       len(lowerCAmelCase__  )  ,       1_0_0  ):
  __a			:  Dict		    =  bert_tokenizer(lines[i : i + 1_0_0]  ,       add_special_tokens=lowerCAmelCase__  ,       truncation=lowerCAmelCase__  ,       max_length=5_1_2  )
  bert_res.extend(res['''input_ids''']  )
 assert len(lowerCAmelCase__  ) == len(lowerCAmelCase__  )

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

 assert len(lowerCAmelCase__  ) == len(lowerCAmelCase__  )

 return ref_ids




def        __UpperCamelCase					(    lowerCAmelCase__							:    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			:  Optional[Any]		    =  f.readlines()
 __a			:  Tuple		    =  [line.strip() for line in data if len(lowerCAmelCase__  ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
 __a			:  str		    =  LTP(args.ltp  )  # faster in GPU device
 __a			:  Optional[int]		    =  BertTokenizer.from_pretrained(args.bert  )

 __a			:  Union[str, Any]		    =  prepare_ref(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )

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


if __name__ == "__main__":
      lowercase__          =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')

      lowercase__          =parser.parse_args()
      main(args)


import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


lowercase__          ='bert-base-cased'
lowercase__          ='google/pegasus-xsum'
lowercase__          =[' Sam ate lunch today.', 'Sams lunch ingredients.']
lowercase__          =['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
lowercase__          ='patrickvonplaten/t5-tiny-random'
lowercase__          ='sshleifer/bart-tiny-random'
lowercase__          ='sshleifer/tiny-mbart'
lowercase__          ='sshleifer/tiny-marian-en-de'




def        __UpperCamelCase					(    lowerCAmelCase__							:    Path  ,       lowerCAmelCase__							:    list  ):
 __a			:  List[Any]		    =  '''\n'''.join(lowerCAmelCase__  )
 Path(lowerCAmelCase__  ).open('''w'''  ).writelines(lowerCAmelCase__  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ):
 for split in ["train", "val", "test"]:
  _dump_articles(os.path.join(lowerCAmelCase__  ,       f"{split}.source"  )  ,       lowerCAmelCase__  )
  _dump_articles(os.path.join(lowerCAmelCase__  ,       f"{split}.target"  )  ,       lowerCAmelCase__  )
 return tmp_dir



class 		UpperCamelCase__  (	__lowercase   ):
   @parameterized.expand(
       [
           MBART_TINY,
           MARIAN_TINY,
           T5_TINY,
           BART_TINY,
           PEGASUS_XSUM,
       ] ,    )
   @slow
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: int       ):
    __a			:  Optional[Any]		    =  AutoTokenizer.from_pretrained(snake_case_       )
    __a			:  Optional[Any]		    =  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
    __a			:  Union[str, Any]		    =  max(len(tokenizer.encode(snake_case_       )       ) for a in ARTICLES       )
    __a			:  str		    =  max(len(tokenizer.encode(snake_case_       )       ) for a in SUMMARIES       )
    __a			:  str		    =  4
    __a			:  Dict		    =  8
    assert max_len_target > max_src_len  # Will be truncated
    assert max_len_source > max_src_len  # Will be truncated
    __a    ,	__a			:  Any		    =  '''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
    __a			:  List[Any]		    =  SeqaSeqDataset(
        snake_case_ ,    data_dir=snake_case_ ,    type_path='''train''' ,    max_source_length=snake_case_ ,    max_target_length=snake_case_ ,    src_lang=snake_case_ ,    tgt_lang=snake_case_ ,    )
    __a			:  Dict		    =  DataLoader(snake_case_ ,    batch_size=2 ,    collate_fn=train_dataset.collate_fn       )
    for batch in dataloader:
     assert isinstance(snake_case_ ,    snake_case_       )
     assert batch["attention_mask"].shape == batch["input_ids"].shape
     # show that articles were trimmed.
     assert batch["input_ids"].shape[1] == max_src_len
     # show that targets are the same len
     assert batch["labels"].shape[1] == max_tgt_len
     if tok_name != MBART_TINY:
      continue
     # check language codes in correct place
     __a			:  Dict		    =  shift_tokens_right(batch['''labels'''] ,    tokenizer.pad_token_id       )
     assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
     assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
     assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
     assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

     break  # No need to test every batch
   @parameterized.expand([BART_TINY, BERT_BASE_CASED]       )
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: str       ):
    __a			:  Union[str, Any]		    =  AutoTokenizer.from_pretrained(snake_case_       )
    __a			:  str		    =  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
    __a			:  Any		    =  max(len(tokenizer.encode(snake_case_       )       ) for a in ARTICLES       )
    __a			:  Any		    =  max(len(tokenizer.encode(snake_case_       )       ) for a in SUMMARIES       )
    __a			:  Dict		    =  4
    __a			:  Optional[int]		    =  LegacySeqaSeqDataset(
        snake_case_ ,    data_dir=snake_case_ ,    type_path='''train''' ,    max_source_length=2_0 ,    max_target_length=snake_case_ ,    )
    __a			:  Optional[Any]		    =  DataLoader(snake_case_ ,    batch_size=2 ,    collate_fn=train_dataset.collate_fn       )
    for batch in dataloader:
     assert batch["attention_mask"].shape == batch["input_ids"].shape
     # show that articles were trimmed.
     assert batch["input_ids"].shape[1] == max_len_source
     assert 2_0 >= batch["input_ids"].shape[1]  # trimmed significantly
     # show that targets were truncated
     assert batch["labels"].shape[1] == trunc_target  # Truncated
     assert max_len_target > trunc_target  # Truncated
     break  # No need to test every batch
   def 				lowerCAmelCase      (self			: List[str]       ):
    __a			:  int		    =  AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''       )

    __a			:  Any		    =  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
    __a			:  Optional[int]		    =  tmp_dir.joinpath('''train.source'''       ).open().readlines()
    __a			:  List[Any]		    =  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
    pack_data_dir(snake_case_ ,    snake_case_ ,    1_2_8 ,    snake_case_       )
    __a			:  Optional[Any]		    =  {x.name for x in tmp_dir.iterdir()}
    __a			:  Union[str, Any]		    =  {x.name for x in save_dir.iterdir()}
    __a			:  str		    =  save_dir.joinpath('''train.source'''       ).open().readlines()
    # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
    # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
    assert len(snake_case_       ) < len(snake_case_       )
    assert len(snake_case_       ) == 1
    assert len(packed_examples[0]       ) == sum(len(snake_case_       ) for x in orig_examples       )
    assert orig_paths == new_paths
   @pytest.mark.skipif(not FAIRSEQ_AVAILABLE ,    reason='''This test requires fairseq'''       )
   def 				lowerCAmelCase      (self			: Any       ):
    if not FAIRSEQ_AVAILABLE:
     return
    __a    ,	__a    ,	__a			:  Any		    =  self._get_dataset(max_len=6_4       )
    __a			:  int		    =  6_4
    __a			:  List[str]		    =  ds.make_dynamic_sampler(snake_case_ ,    required_batch_size_multiple=snake_case_       )
    __a			:  List[str]		    =  [len(snake_case_       ) for x in batch_sampler]
    assert len(set(snake_case_       )       ) > 1  # it's not dynamic batch size if every batch is the same length
    assert sum(snake_case_       ) == len(snake_case_       )  # no dropped or added examples
    __a			:  Union[str, Any]		    =  DataLoader(snake_case_ ,    batch_sampler=snake_case_ ,    collate_fn=ds.collate_fn ,    num_workers=2       )
    __a			:  Tuple		    =  []
    __a			:  Union[str, Any]		    =  []
    for batch in data_loader:
     __a			:  Any		    =  batch['''input_ids'''].shape
     __a			:  str		    =  src_shape[0]
     assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
     __a			:  Optional[Any]		    =  np.product(batch['''input_ids'''].shape       )
     num_src_per_batch.append(snake_case_       )
     if num_src_tokens > (max_tokens * 1.1):
      failures.append(snake_case_       )
    assert num_src_per_batch[0] == max(snake_case_       )
    if failures:
     raise AssertionError(f"too many tokens in {len(snake_case_       )} batches"       )
   def 				lowerCAmelCase      (self			: int       ):
    __a    ,	__a    ,	__a			:  Optional[int]		    =  self._get_dataset(max_len=5_1_2       )
    __a			:  Union[str, Any]		    =  2
    __a			:  str		    =  ds.make_sortish_sampler(snake_case_ ,    shuffle=snake_case_       )

    __a			:  Tuple		    =  DataLoader(snake_case_ ,    batch_size=snake_case_ ,    collate_fn=ds.collate_fn ,    num_workers=2       )
    __a			:  Tuple		    =  DataLoader(snake_case_ ,    batch_size=snake_case_ ,    collate_fn=ds.collate_fn ,    num_workers=2 ,    sampler=snake_case_       )

    __a			:  Optional[int]		    =  tokenizer.pad_token_id

    def count_pad_tokens(snake_case_			: Union[str, Any] ,    snake_case_			: List[str]="input_ids"       ):
     return [batch[k].eq(snake_case_       ).sum().item() for batch in data_loader]

    assert sum(count_pad_tokens(snake_case_ ,    k='''labels'''       )       ) < sum(count_pad_tokens(snake_case_ ,    k='''labels'''       )       )
    assert sum(count_pad_tokens(snake_case_       )       ) < sum(count_pad_tokens(snake_case_       )       )
    assert len(snake_case_       ) == len(snake_case_       )
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: int=1_0_0_0 ,    snake_case_			: Optional[Any]=1_2_8       ):
    if os.getenv('''USE_REAL_DATA''' ,    snake_case_       ):
     __a			:  Optional[int]		    =  '''examples/seq2seq/wmt_en_ro'''
     __a			:  List[Any]		    =  max_len * 2 * 6_4
     if not Path(snake_case_       ).joinpath('''train.len'''       ).exists():
      save_len_file(snake_case_ ,    snake_case_       )
    else:
     __a			:  int		    =  '''examples/seq2seq/test_data/wmt_en_ro'''
     __a			:  List[str]		    =  max_len * 4
     save_len_file(snake_case_ ,    snake_case_       )

    __a			:  str		    =  AutoTokenizer.from_pretrained(snake_case_       )
    __a			:  Optional[int]		    =  SeqaSeqDataset(
        snake_case_ ,    data_dir=snake_case_ ,    type_path='''train''' ,    max_source_length=snake_case_ ,    max_target_length=snake_case_ ,    n_obs=snake_case_ ,    )
    return ds, max_tokens, tokenizer
   def 				lowerCAmelCase      (self			: List[str]       ):
    __a    ,	__a    ,	__a			:  str		    =  self._get_dataset()
    __a			:  Optional[Any]		    =  set(DistributedSortishSampler(snake_case_ ,    2_5_6 ,    num_replicas=2 ,    rank=0 ,    add_extra_examples=snake_case_       )       )
    __a			:  Tuple		    =  set(DistributedSortishSampler(snake_case_ ,    2_5_6 ,    num_replicas=2 ,    rank=1 ,    add_extra_examples=snake_case_       )       )
    assert idsa.intersection(snake_case_       ) == set()







   @parameterized.expand(
       [
           MBART_TINY,
           MARIAN_TINY,
           T5_TINY,
           BART_TINY,
           PEGASUS_XSUM,
       ] ,    )
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: Union[str, Any]       ):
    __a			:  Union[str, Any]		    =  AutoTokenizer.from_pretrained(snake_case_ ,    use_fast=snake_case_       )
    if tok_name == MBART_TINY:
     __a			:  Any		    =  SeqaSeqDataset(
         snake_case_ ,    data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       ) ,    type_path='''train''' ,    max_source_length=4 ,    max_target_length=8 ,    src_lang='''EN''' ,    tgt_lang='''FR''' ,    )
     __a			:  Tuple		    =  train_dataset.dataset_kwargs
     assert "src_lang" in kwargs and "tgt_lang" in kwargs
    else:
     __a			:  Optional[Any]		    =  SeqaSeqDataset(
         snake_case_ ,    data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       ) ,    type_path='''train''' ,    max_source_length=4 ,    max_target_length=8 ,    )
     __a			:  List[Any]		    =  train_dataset.dataset_kwargs
     assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
     assert len(snake_case_       ) == 1 if tok_name == BART_TINY else len(snake_case_       ) == 0
