

"""simple docstring"""






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


_lowerCAmelCase						= getLogger(__name__)


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

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase = 8 ,   _lowerCamelCase = DEFAULT_DEVICE ,   _lowerCamelCase=False ,   _lowerCamelCase="summarization" ,   _lowerCamelCase=None ,   **_lowerCamelCase ,   ):

    '''simple docstring'''



    _lowerCAmelCase       :   str  =    Path(_lowerCamelCase   ).open('w' ,   encoding='utf-8'   )
    _lowerCAmelCase       :   int  =    str(_lowerCamelCase   )
    _lowerCAmelCase       :   Dict  =    AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase   ).to(_lowerCamelCase   )
    if fpaa:
        _lowerCAmelCase       :   List[Any]  =    model.half()

    _lowerCAmelCase       :   Tuple  =    AutoTokenizer.from_pretrained(_lowerCamelCase   )
    logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""   )  # if this is wrong, check config.model_type.

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

def 		lowerCamelCase__		(					):

    '''simple docstring'''



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

def 		lowerCamelCase__		(					_lowerCamelCase=True   ):

    '''simple docstring'''



    _lowerCAmelCase       :   int  =    argparse.ArgumentParser()
    parser.add_argument('model_name' ,   type=_lowerCamelCase ,   help='like facebook/bart-large-cnn,t5-base, etc.'   )
    parser.add_argument('input_path' ,   type=_lowerCamelCase ,   help='like cnn_dm/test.source'   )
    parser.add_argument('save_path' ,   type=_lowerCamelCase ,   help='where to save summaries'   )
    parser.add_argument('--reference_path' ,   type=_lowerCamelCase ,   required=_lowerCamelCase ,   help='like cnn_dm/test.target'   )
    parser.add_argument('--score_path' ,   type=_lowerCamelCase ,   required=_lowerCamelCase ,   default='metrics.json' ,   help='where to save metrics'   )
    parser.add_argument('--device' ,   type=_lowerCamelCase ,   required=_lowerCamelCase ,   default=_lowerCamelCase ,   help='cuda, cuda:1, cpu etc.'   )
    parser.add_argument(
        '--prefix' ,   type=_lowerCamelCase ,   required=_lowerCamelCase ,   default=_lowerCamelCase ,   help='will be added to the begininng of src examples'   )
    parser.add_argument('--task' ,   type=_lowerCamelCase ,   default='summarization' ,   help='used for task_specific_params + metrics'   )
    parser.add_argument('--bs' ,   type=_lowerCamelCase ,   default=8 ,   required=_lowerCamelCase ,   help='batch size'   )
    parser.add_argument(
        '--n_obs' ,   type=_lowerCamelCase ,   default=-1 ,   required=_lowerCamelCase ,   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=_lowerCamelCase ,   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
    _lowerCAmelCase       :   Any  =    parser.parse_known_args()
    _lowerCAmelCase       :   Optional[Any]  =    parse_numeric_n_bool_cl_kwargs(_lowerCamelCase   )
    if parsed_args and verbose:
        print(f"""parsed the following generate kwargs: {parsed_args}"""   )
    _lowerCAmelCase       :   List[Any]  =    [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path   ).readlines()]
    if args.n_obs > 0:
        _lowerCAmelCase       :   Dict  =    examples[: args.n_obs]
    Path(args.save_path   ).parent.mkdir(exist_ok=_lowerCamelCase   )

    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'   )

    _lowerCAmelCase       :   str  =    generate_summaries_or_translations(
        _lowerCamelCase ,   args.save_path ,   args.model_name ,   batch_size=args.bs ,   device=args.device ,   fpaa=args.fpaa ,   task=args.task ,   prefix=args.prefix ,   **_lowerCamelCase ,   )

    if args.reference_path is None:
        return {}

    # Compute scores
    _lowerCAmelCase       :   Dict  =    calculate_bleu if 'translation' in args.task else calculate_rouge
    _lowerCAmelCase       :   Dict  =    [x.rstrip() for x in open(args.save_path   ).readlines()]
    _lowerCAmelCase       :   List[str]  =    [x.rstrip() for x in open(args.reference_path   ).readlines()][: len(_lowerCamelCase   )]
    _lowerCAmelCase       :   dict  =    score_fn(_lowerCamelCase ,   _lowerCamelCase   )
    scores.update(_lowerCamelCase   )

    if args.dump_args:
        scores.update(_lowerCamelCase   )
    if args.info:
        _lowerCAmelCase       :   Any  =    args.info

    if verbose:
        print(_lowerCamelCase   )

    if args.score_path is not None:
        json.dump(_lowerCamelCase ,   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)








"""simple docstring"""






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    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 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    for char in word:
        _lowerCAmelCase       :   Dict  =    ord(_lowerCamelCase   )
        if not _is_chinese_char(_lowerCamelCase   ):
            return 0
    return 1

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Tuple  =    set()

    for token in tokens:
        _lowerCAmelCase       :   Optional[int]  =    len(_lowerCamelCase   ) > 1 and is_chinese(_lowerCamelCase   )
        if chinese_word:
            word_set.add(_lowerCamelCase   )
    _lowerCAmelCase       :   Union[str, Any]  =    list(_lowerCamelCase   )
    return word_list

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    if not chinese_word_set:
        return bert_tokens
    _lowerCAmelCase       :   Optional[Any]  =    max([len(_lowerCamelCase   ) for w in chinese_word_set]   )

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

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    []

    for i in range(0 ,   len(_lowerCamelCase   ) ,   100   ):
        _lowerCAmelCase       :   Tuple  =    ltp_tokenizer.seg(lines[i : i + 100]   )[0]
        _lowerCAmelCase       :   List[Any]  =    [get_chinese_word(_lowerCamelCase   ) for r in res]
        ltp_res.extend(_lowerCamelCase   )
    assert len(_lowerCamelCase   ) == len(_lowerCamelCase   )

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

    _lowerCAmelCase       :   Union[str, Any]  =    []
    for input_ids, chinese_word in zip(_lowerCamelCase ,   _lowerCamelCase   ):
        _lowerCAmelCase       :   Optional[int]  =    []
        for id in input_ids:
            _lowerCAmelCase       :   List[Any]  =    bert_tokenizer._convert_id_to_token(_lowerCamelCase   )
            input_tokens.append(_lowerCamelCase   )
        _lowerCAmelCase       :   Any  =    add_sub_symbol(_lowerCamelCase ,   _lowerCamelCase   )
        _lowerCAmelCase       :   List[str]  =    []
        # 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] == "##":
                _lowerCAmelCase       :   List[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 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



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

    _lowerCAmelCase       :   Optional[Any]  =    prepare_ref(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )

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


if __name__ == "__main__":
 _lowerCAmelCase						=		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""")

 _lowerCAmelCase						=		parser.parse_args()
 main(args)





