


import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


UpperCAmelCase      =   getLogger(__name__)


UpperCAmelCase      =   """cuda""" if torch.cuda.is_available() else """cpu"""






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 8 , SCREAMING_SNAKE_CASE = DEFAULT_DEVICE , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE="summarization" , SCREAMING_SNAKE_CASE=None , **SCREAMING_SNAKE_CASE , )->  Dict:




      """simple docstring"""

      snake_case_	=				Path(SCREAMING_SNAKE_CASE ).open('''w''' , encoding='''utf-8''' )
      snake_case_	=				str(SCREAMING_SNAKE_CASE )
      snake_case_	=				AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE ).to(SCREAMING_SNAKE_CASE )
      if fpaa:
            snake_case_	=				model.half()

      snake_case_	=				AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE )
      logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}''' )  # if this is wrong, check config.model_type.

      snake_case_	=				time.time()
      # update config with task specific params
      use_task_specific_params(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
      if prefix is None:
            snake_case_	=				prefix or getattr(model.config , '''prefix''' , '''''' ) or ''''''
      for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) ) ):
            snake_case_	=				[prefix + text for text in examples_chunk]
            snake_case_	=				tokenizer(SCREAMING_SNAKE_CASE , return_tensors='''pt''' , truncation=SCREAMING_SNAKE_CASE , padding='''longest''' ).to(SCREAMING_SNAKE_CASE )
            snake_case_	=				model.generate(
                input_ids=batch.input_ids , attention_mask=batch.attention_mask , **SCREAMING_SNAKE_CASE , )
            snake_case_	=				tokenizer.batch_decode(SCREAMING_SNAKE_CASE , skip_special_tokens=SCREAMING_SNAKE_CASE , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE )
            for hypothesis in dec:
                  fout.write(hypothesis + '''\n''' )
                  fout.flush()
      fout.close()
      snake_case_	=				int(time.time() - start_time )  # seconds
      snake_case_	=				len(SCREAMING_SNAKE_CASE )
      return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs , 4 )}






def 	__lowerCAmelCase   ()->  Optional[int]:




      """simple docstring"""

      return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S''' )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE=True )->  List[Any]:




      """simple docstring"""

      snake_case_	=				argparse.ArgumentParser()
      parser.add_argument('''model_name''' , type=SCREAMING_SNAKE_CASE , help='''like facebook/bart-large-cnn,t5-base, etc.''' )
      parser.add_argument('''input_path''' , type=SCREAMING_SNAKE_CASE , help='''like cnn_dm/test.source''' )
      parser.add_argument('''save_path''' , type=SCREAMING_SNAKE_CASE , help='''where to save summaries''' )
      parser.add_argument('''--reference_path''' , type=SCREAMING_SNAKE_CASE , required=SCREAMING_SNAKE_CASE , help='''like cnn_dm/test.target''' )
      parser.add_argument('''--score_path''' , type=SCREAMING_SNAKE_CASE , required=SCREAMING_SNAKE_CASE , default='''metrics.json''' , help='''where to save metrics''' )
      parser.add_argument('''--device''' , type=SCREAMING_SNAKE_CASE , required=SCREAMING_SNAKE_CASE , default=SCREAMING_SNAKE_CASE , help='''cuda, cuda:1, cpu etc.''' )
      parser.add_argument(
          '''--prefix''' , type=SCREAMING_SNAKE_CASE , required=SCREAMING_SNAKE_CASE , default=SCREAMING_SNAKE_CASE , help='''will be added to the begininng of src examples''' )
      parser.add_argument('''--task''' , type=SCREAMING_SNAKE_CASE , default='''summarization''' , help='''used for task_specific_params + metrics''' )
      parser.add_argument('''--bs''' , type=SCREAMING_SNAKE_CASE , default=8 , required=SCREAMING_SNAKE_CASE , help='''batch size''' )
      parser.add_argument(
          '''--n_obs''' , type=SCREAMING_SNAKE_CASE , default=-1 , required=SCREAMING_SNAKE_CASE , help='''How many observations. Defaults to all.''' )
      parser.add_argument('''--fp16''' , action='''store_true''' )
      parser.add_argument('''--dump-args''' , action='''store_true''' , help='''print the custom hparams with the results''' )
      parser.add_argument(
          '''--info''' , nargs='''?''' , type=SCREAMING_SNAKE_CASE , const=datetime_now() , help=(
              '''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
              ''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
          ) , )
      # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
      snake_case_	,   snake_case_	=				parser.parse_known_args()
      snake_case_	=				parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE )
      if parsed_args and verbose:
            print(f'''parsed the following generate kwargs: {parsed_args}''' )
      snake_case_	=				[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
      if args.n_obs > 0:
            snake_case_	=				examples[: args.n_obs]
      Path(args.save_path ).parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE )

      if args.reference_path is None and Path(args.score_path ).exists():
            warnings.warn(f'''score_path {args.score_path} will be overwritten unless you type ctrl-c.''' )

      if args.device == "cpu" and args.fpaa:
            # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
            raise ValueError('''Can\'t mix --fp16 and --device cpu''' )

      snake_case_	=				generate_summaries_or_translations(
          SCREAMING_SNAKE_CASE , args.save_path , args.model_name , batch_size=args.bs , device=args.device , fpaa=args.fpaa , task=args.task , prefix=args.prefix , **SCREAMING_SNAKE_CASE , )

      if args.reference_path is None:
            return {}

      # Compute scores
      snake_case_	=				calculate_bleu if '''translation''' in args.task else calculate_rouge
      snake_case_	=				[x.rstrip() for x in open(args.save_path ).readlines()]
      snake_case_	=				[x.rstrip() for x in open(args.reference_path ).readlines()][: len(SCREAMING_SNAKE_CASE )]
      snake_case_	=				score_fn(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
      scores.update(SCREAMING_SNAKE_CASE )

      if args.dump_args:
            scores.update(SCREAMING_SNAKE_CASE )
      if args.info:
            snake_case_	=				args.info

      if verbose:
            print(SCREAMING_SNAKE_CASE )

      if args.score_path is not None:
            json.dump(SCREAMING_SNAKE_CASE , open(args.score_path , '''w''' ) )

      return scores


if __name__ == "__main__":
   # Usage for MT:
   # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
   run_generate(verbose=True)



import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Tuple:




      """simple docstring"""

      if (
          (cp >= 0X4E00 and cp <= 0X9FFF)
          or (cp >= 0X3400 and cp <= 0X4DBF)  #
          or (cp >= 0X2_0000 and cp <= 0X2_A6DF)  #
          or (cp >= 0X2_A700 and cp <= 0X2_B73F)  #
          or (cp >= 0X2_B740 and cp <= 0X2_B81F)  #
          or (cp >= 0X2_B820 and cp <= 0X2_CEAF)  #
          or (cp >= 0XF900 and cp <= 0XFAFF)
          or (cp >= 0X2_F800 and cp <= 0X2_FA1F)  #
      ):  #
            return True

      return False






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  List[Any]:




      """simple docstring"""

      for char in word:
            snake_case_	=				ord(SCREAMING_SNAKE_CASE )
            if not _is_chinese_char(SCREAMING_SNAKE_CASE ):
                  return 0
      return 1






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  str:




      """simple docstring"""

      snake_case_	=				set()

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

      if not chinese_word_set:
            return bert_tokens
      snake_case_	=				max([len(SCREAMING_SNAKE_CASE ) for w in chinese_word_set] )

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      snake_case_	=				[]

      for i in range(0 , len(SCREAMING_SNAKE_CASE ) , 100 ):
            snake_case_	=				ltp_tokenizer.pipeline(lines[i : i + 100] , tasks=['''cws'''] ).cws
            snake_case_	=				[get_chinese_word(SCREAMING_SNAKE_CASE ) for r in res]
            ltp_res.extend(SCREAMING_SNAKE_CASE )
      assert len(SCREAMING_SNAKE_CASE ) == len(SCREAMING_SNAKE_CASE )

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

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

      assert len(SCREAMING_SNAKE_CASE ) == len(SCREAMING_SNAKE_CASE )

      return ref_ids






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Tuple:




      """simple docstring"""

      with open(args.file_name , '''r''' , encoding='''utf-8''' ) as f:
            snake_case_	=				f.readlines()
      snake_case_	=				[line.strip() for line in data if len(SCREAMING_SNAKE_CASE ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
      snake_case_	=				LTP(args.ltp )  # faster in GPU device
      snake_case_	=				BertTokenizer.from_pretrained(args.bert )

      snake_case_	=				prepare_ref(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )

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


if __name__ == "__main__":
   UpperCAmelCase      =   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""",
   )

   UpperCAmelCase      =   parser.parse_args()
   main(args)