

'''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


_snake_case     						=						getLogger(__name__)


_snake_case     						=						'cuda' if torch.cuda.is_available() else 'cpu'







def       _A	(			snake_case	,							snake_case	,							snake_case	,							snake_case = 8	,							snake_case = DEFAULT_DEVICE	,							snake_case=False	,							snake_case="summarization"	,							snake_case=None	,							**snake_case	,							)    ->			Dict:
   _lowercase    :   int      =     Path(snake_case  ).open("w"	,							encoding="utf-8"  )
   _lowercase    :   Any      =     str(snake_case  )
   _lowercase    :   Union[str, Any]      =     AutoModelForSeqaSeqLM.from_pretrained(snake_case  ).to(snake_case  )
   if fpaa:
      _lowercase    :   Any      =     model.half()

   _lowercase    :   Tuple      =     AutoTokenizer.from_pretrained(snake_case  )
   logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''  )  # if this is wrong, check config.model_type.

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







def       _A	(			)    ->			int:
   return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"  )







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

   if args.reference_path is None:
      return {}

   # Compute scores
   _lowercase    :   Optional[Any]      =     calculate_bleu if "translation" in args.task else calculate_rouge
   _lowercase    :   Any      =     [x.rstrip() for x in open(args.save_path  ).readlines()]
   _lowercase    :   Dict      =     [x.rstrip() for x in open(args.reference_path  ).readlines()][: len(snake_case  )]
   _lowercase    :   dict      =     score_fn(snake_case	,							snake_case  )
   scores.update(snake_case  )

   if args.dump_args:
      scores.update(snake_case  )
   if args.info:
      _lowercase    :   Union[str, Any]      =     args.info

   if verbose:
      print(snake_case  )

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









'''simple docstring'''
import requests

try:
    from django.contrib.auth import authenticate, login
    from django.shortcuts import redirect, render
except ImportError:
    _snake_case     						=						_snake_case     						=						_snake_case     						=						_snake_case     						=						print







def       _A	(			snake_case  )    ->			Any:
   # Enter your recaptcha secret key here
   _lowercase    :   Optional[Any]      =     "secretKey"
   _lowercase    :   Union[str, Any]      =     "https://www.google.com/recaptcha/api/siteverify"

   # when method is not POST, direct user to login page
   if request.method != "POST":
      return render(snake_case	,							"login.html"  )

   # from the frontend, get username, password, and client_key
   _lowercase    :   Optional[int]      =     request.POST.get("username"  )
   _lowercase    :   Tuple      =     request.POST.get("password"  )
   _lowercase    :   Tuple      =     request.POST.get("g-recaptcha-response"  )

   # post recaptcha response to Google's recaptcha api
   _lowercase    :   str      =     requests.post(snake_case	,							data={"secret": secret_key, "response": client_key}  )
   # if the recaptcha api verified our keys
   if response.json().get("success"	,							snake_case  ):
      # authenticate the user
      _lowercase    :   Tuple      =     authenticate(snake_case	,							username=snake_case	,							password=snake_case  )
      if user_in_database:
         login(snake_case	,							snake_case  )
         return redirect("/your-webpage"  )
   return render(snake_case	,							"login.html"  )






