rewrite the below Python code into a clean and concise Summarizer class, that has a self.model and self.tokenizer etc and functions to summarize a file, etc

"""
summarize.py - a module that contains functions for summarizing text
"""
import json
import logging
from pathlib import Path

import torch
from tqdm.auto import tqdm
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

from textsum.utils import get\_timestamp
from textsum.utils import get\_mem\_footprint, postprocess\_booksummary, setup\_logging
def load\_model\_and\_tokenizer(model\_name: str, use\_cuda: bool = True):
 logger = logging.getLogger(\_\_name\_\_)
 device = "cuda" if torch.cuda.is\_available() and use\_cuda else "cpu"
 logger.debug(f"loading model {model\_name} to {device}")
 model = AutoModelForSeq2SeqLM.from\_pretrained(
 model\_name,
 ).to(device)
 tokenizer = AutoTokenizer.from\_pretrained(model\_name)

 logger.info(f"Loaded model {model\_name} to {device}")
 return model, tokenizer
def summarize\_and\_score(
 ids, mask, model, tokenizer, is\_general\_attention\_model=True, \*\*kwargs
):
 """
 summarize\_and\_score - given a batch of ids and a mask, return a summary and a score for the summary

 Args:
 ids (): the batch of ids
 mask (): the attention mask for the batch
 model (): the model to use for summarization
 tokenizer (): the tokenizer to use for summarization
 is\_general\_attention\_model (bool, optional): whether the model is a general attention model. Defaults to True.
 """

 ids = ids[None, :]
 mask = mask[None, :]

 input\_ids = ids.to("cuda") if torch.cuda.is\_available() else ids
 attention\_mask = mask.to("cuda") if torch.cuda.is\_available() else mask

 global\_attention\_mask = torch.zeros\_like(attention\_mask)
 # put global attention on  ~~token
 global\_attention\_mask[:, 0] = 1

 if is\_general\_attention\_model:
 summary\_pred\_ids = model.generate(
 input\_ids,
 attention\_mask=attention\_mask,
 output\_scores=True,
 return\_dict\_in\_generate=True,
 \*\*kwargs,
 )
 else:
 # this is for LED etc.
 summary\_pred\_ids = model.generate(
 input\_ids,
 attention\_mask=attention\_mask,
 global\_attention\_mask=global\_attention\_mask,
 output\_scores=True,
 return\_dict\_in\_generate=True,
 \*\*kwargs,
 )
 summary = tokenizer.batch\_decode(
 summary\_pred\_ids.sequences,
 skip\_special\_tokens=True,
 remove\_invalid\_values=True,
 )
 score = round(summary\_pred\_ids.sequences\_scores.cpu().numpy()[0], 4)

 return summary, score
def summarize\_via\_tokenbatches(
 input\_text: str,
 model,
 tokenizer,
 batch\_length=4096,
 batch\_stride=16,
 \*\*kwargs,
):
 """
 summarize\_via\_tokenbatches - a function that takes a string and returns a summary

 Args:
 input\_text (str): the text to summarize
 model (): the model to use for summarizationz
 tokenizer (): the tokenizer to use for summarization
 batch\_length (int, optional): the length of each batch. Defaults to 4096.
 batch\_stride (int, optional): the stride of each batch. Defaults to 16. The stride is the number of tokens that overlap between batches.
 """

 logger = logging.getLogger(\_\_name\_\_)
 # log all input parameters
 if batch\_length < 512:
 batch\_length = 512
 logger.warning("WARNING: batch\_length was set to 512")
 logger.debug(
 f"batch\_length: {batch\_length} batch\_stride: {batch\_stride}, kwargs: {kwargs}"
 )
 encoded\_input = tokenizer(
 input\_text,
 padding="max\_length",
 truncation=True,
 max\_length=batch\_length,
 stride=batch\_stride,
 return\_overflowing\_tokens=True,
 add\_special\_tokens=False,
 return\_tensors="pt",
 )

 in\_id\_arr, att\_arr = encoded\_input.input\_ids, encoded\_input.attention\_mask
 gen\_summaries = []

 pbar = tqdm(total=len(in\_id\_arr))

 for \_id, \_mask in zip(in\_id\_arr, att\_arr):

 result, score = summarize\_and\_score(
 ids=\_id,
 mask=\_mask,
 model=model,
 tokenizer=tokenizer,
 \*\*kwargs,
 )
 score = round(float(score), 4)
 \_sum = {
 "input\_tokens": \_id,
 "summary": result,
 "summary\_score": score,
 }
 gen\_summaries.append(\_sum)
 logger.debug(f"\n\t{result[0]}\nScore:\t{score}")
 pbar.update()

 pbar.close()

 return gen\_summaries
def save\_params(
 params: dict,
 output\_dir: str or Path,
 hf\_tag: str = None,
 verbose: bool = False,
) -> None:

 output\_dir = Path(output\_dir) if output\_dir is not None else Path.cwd()
 session\_settings = params
 session\_settings["huggingface-model-tag"] = "" if hf\_tag is None else hf\_tag
 session\_settings["date-run"] = get\_timestamp()

 metadata\_path = output\_dir / "summarization-parameters.json"
 logging.info(f"Saving parameters to {metadata\_path}")
 with open(metadata\_path, "w") as write\_file:
 json.dump(session\_settings, write\_file)

 logging.debug(f"Saved parameters to {metadata\_path}")
 if verbose:
 # log the parameters
 logging.info(f"parameters: {session\_settings}")
def summarize\_text\_file(
 file\_path: str or Path,
 model,
 tokenizer,
 batch\_length: int = 4096,
 batch\_stride: int = 16,
 lowercase: bool = False,
 \*\*kwargs,
) -> dict:
 """
 summarize\_text\_file - given a file path, summarize the text in the file
 :param str or Path file\_path: the path to the file to summarize
 :param model: the model to use for summarization
 :param tokenizer: the tokenizer to use for summarization
 :param int batch\_length: length of each batch in tokens to summarize, defaults to 4096
 :param int batch\_stride: stride between batches in tokens, defaults to 16
 :param bool lowercase: whether to lowercase the text before summarizing, defaults to False
 :return: a dictionary containing the summary and other information
 """
 file\_path = Path(file\_path)
 ALLOWED\_EXTENSIONS = [".txt", ".md", ".rst", ".py", ".ipynb"]
 assert (
 file\_path.exists() and file\_path.suffix in ALLOWED\_EXTENSIONS
 ), f"File {file\_path} does not exist or is not a text file"

 logging.info(f"Summarizing {file\_path}")
 with open(file\_path, "r", encoding="utf-8", errors="ignore") as f:
 text = clean(f.read(), lower=lowercase, no\_line\_breaks=True)
 logging.debug(
 f"Text length: {len(text)}. batch length: {batch\_length} batch stride: {batch\_stride}"
 )
 summary\_data = summarize\_via\_tokenbatches(
 input\_text=text,
 model=model,
 tokenizer=tokenizer,
 batch\_length=batch\_length,
 batch\_stride=batch\_stride,
 \*\*kwargs,
 )
 logging.info(f"Finished summarizing {file\_path}")
 return summary\_data
def process\_summarization(
 summary\_data: dict,
 target\_file: str or Path,
 custom\_phrases: list = None,
 save\_scores: bool = True,
) -> None:

 target\_file = Path(target\_file).resolve()
 if target\_file.exists():
 warnings.warn(f"File {target\_file} exists, overwriting")

 sum\_text = [
 postprocess\_booksummary(
 s["summary"][0],
 custom\_phrases=custom\_phrases,
 )
 for s in summary\_data
 ]
 sum\_scores = [f"\n - {round(s['summary\_score'],4)}" for s in summary\_data]
 scores\_text = "\n".join(sum\_scores)
 full\_summary = "\n\t".join(sum\_text)

 with open(
 target\_file,
 "w",
 ) as fo:

 fo.writelines(full\_summary)

 if save\_scores:
 with open(
 target\_file,
 "a",
 ) as fo:

 fo.write("\n" \* 3)
 fo.write(f"\n\nSection Scores for {target\_file.stem}:\n")
 fo.writelines(scores\_text)
 fo.write("\n\n---\n")

 logging.info(f"Saved summary to {target\_file.resolve()}")~~
아래 Python 코드를 깔끔하고 간결한 Summarizer 클래스로 작성하고, 이 클래스에는 self.model 및 self.tokenizer 등과 파일을 요약하는 함수 등이 있습니다.

"""
summarize.py - 텍스트를 요약하는 함수를 포함하는 모듈
"""
import json
import logging
pathlib에서 경로를 가져옵니다.

import torch
tqdm.auto에서 tqdm을 임포트한다.
transformers에서 AutoModelForSeq2SeqLM, AutoTokenizer를 임포트합니다.

textsum.utils에서 get\_timestamp를 가져옵니다.
textsum.utils에서 get\_mem\_footprint, postprocess\_booksummary, setup\_logging을 임포트합니다.
def load\_model\_and\_tokenizer(model\_name: str, use\_cuda: bool = True):
 logger = logging.getLogger(\_\_name\_\_)
 device = "cuda" if torch.cuda.is\_available() and use\_cuda else "cpu"
 logger.debug(f"{모델\_이름} 모델을 {장치}로 로드 중")
 model = AutoModelForSeq2SeqLM.from\_pretrained(
 모델\_이름,
 ).to(device)
 토큰화기 = AutoTokenizer.from\_pretrained(모델\_이름)

 logger.info(f"로드된 모델 {모델\_이름}을 {장치}로")
 반환 모델, 토큰화기
def summarize\_and\_score(
 아이디, 마스크, 모델, 토큰라이저, is\_일반\_주의\_model=True, \*\*kwargs
):
 """
 summarize\_and\_score - id와 마스크 배치가 주어지면 요약과 요약에 대한 점수를 반환합니다.

 Args:
 ids (): 아이디 배치
 mask (): 배치에 대한 주의 마스크
 model (): 요약에 사용할 모델
 tokenizer (): 요약에 사용할 토큰화 도구
 is\_일반\_주의\_model(부울, 선택 사항): 모델이 일반 주의 모델인지 여부. 기본값은 True입니다.
 """

 ids = ids[None, :]
 마스크 = 마스크[없음, :]

 input\_ids = ids.to("cuda") if torch.cuda.is\_available() else ids
 attention\_mask = mask.to("cuda") if torch.cuda.is\_available() else mask

 global\_attention\_mask = torch.zeros\_like(attention\_mask)
 ~~토큰에 글로벌 관심 설정
 global\_attention\_mask[:, 0] = 1

 if is\_일반\_attention\_model:
 summary\_pred\_ids = model.generate(
 input\_ids,
 attention\_mask=attention\_mask,
 output\_scores=True,
 return\_dict\_in\_generate=True,
 \*\*kwargs,
 )
 else:
 # 이것은 LED 등을 위한 것입니다.
 summary\_pred\_ids = model.generate(
 input\_ids,
 attention\_mask=attention\_mask,
 global\_attention\_mask=global\_attention\_mask,
 output\_scores=True,
 return\_dict\_in\_generate=True,
 \*\*kwargs,
 )
 summary = tokenizer.batch\_decode(
 summary\_pred\_ids.sequences,
 skip\_special\_tokens=True,
 remove\_invalid\_values=True,
 )
 score = round(summary\_pred\_ids.sequences\_scores.cpu().numpy()[0], 4)

 반환 요약, 점수
def summarize\_via\_tokenbatches(
 입력\_text: str,
 model,
 토큰화기,
 batch\_length=4096,
 batch\_stride=16,
 \*\*kwargs,
):
 """
 summarize\_via\_tokenbatches - 문자열을 받아 요약을 반환하는 함수입니다.

 Args:
 input\_text (str): 요약할 텍스트
 model (): 요약에 사용할 모델입니다.
 tokenizer (): 요약에 사용할 토큰화 도구
 배치\_길이(int, 선택 사항): 각 배치의 길이. 기본값은 4096입니다.
 batch\_stride (int, 선택 사항): 각 배치의 보폭. 기본값은 16입니다. 보폭은 배치 간에 겹치는 토큰의 수입니다.
 """

 logger = logging.getLogger(\_\_name\_\_)
 # 모든 입력 매개변수 기록
 batch\_length < 512:
 batch\_length = 512
 logger.warning("경고: 배치\_길이 512로 설정되었습니다")
 logger.debug(
 f"배치\_길이: {배치\_길이} 배치\_보폭: {batch\_stride}, kwargs: {kwargs}"
 )
 encoded\_input = 토큰화기(
 input\_text,
 padding="최대\_길이",
 잘림=True,
 최대\_길이=배치\_길이,
 보폭=배치\_보폭,
 return\_overflowing\_tokens=True,
 add\_special\_tokens=False,
 return\_tensors="pt",
 )

 in\_id\_arr, att\_arr = encoded\_input.input\_ids, encoded\_input.attention\_mask
 gen\_summaries = []

 pbar = tqdm(total=len(in\_id\_arr))

 for \_id, \_mask in zip(in\_id\_arr, att\_arr):

 결과, 점수 = summarize\_and\_score(
 ids=\_id,
 mask=\_mask,
 model=모델,
 토큰화=토큰화,
 \*\*kwargs,
 )
 score = round(float(score), 4)
 \합계 = {
 "입력\_토큰": \_id,
 "summary": 결과,
 "summary\_score": 점수,
 }
 gen\_summaries.append(\_sum)
 logger.debug(f"\n\t{result[0]}\nScore:\t{score}")
 pbar.update()

 pbar.close()

 반환 gen\_summaries
def save\_params(
 params: 딕셔너리,
 output\_dir: str 또는 Path,
 hf\_tag: str = None,
 verbose: bool = False,
) -> None:

 출력\_dir = Path(출력\_dir) 출력\_dir이 None이 아닌 경우 그렇지 않으면 Path.cwd()
 세션\_설정 = 매개변수
 세션\_settings["허깅페이스-모델-태그"] = "" hf\_tag가 None이면 hf\_tag
 세션\_settings["date-run"] = get\_timestamp()

 메타데이터\_경로 = 출력\_dir / "요약-매개변수.json"
 logging.info(f"{metadata\_path}에 매개변수 저장")
 열기(메타데이터\_경로, "w")를 쓰기\_파일로 사용합니다:
 json.dump(세션\_settings, 쓰기\_파일)

 logging.debug(f"{metadata\_path}에 저장된 매개변수")
 자세한 경우
 # 매개변수 로깅
 logging.info(f"매개변수: {세션\_settings}")
def summarize\_text\_file(
 파일\_경로: str 또는 Path,
 모델
 토큰화기,
 배치\_길이: int = 4096,
 batch\_stride: int = 16,
 소문자: 부울 = 거짓,
 \*\*kwargs,