


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


_A			=						getLogger(__name__)


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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : int = 8      ,					SCREAMING_SNAKE_CASE__      : str = DEFAULT_DEVICE      ,					SCREAMING_SNAKE_CASE__      : Dict=False      ,					SCREAMING_SNAKE_CASE__      : Any="summarization"      ,					SCREAMING_SNAKE_CASE__      : Any=None      ,					**SCREAMING_SNAKE_CASE__      : int      ,					):
       __UpperCamelCase   							=Path(SCREAMING_SNAKE_CASE__    ).open('w'      ,					encoding='utf-8'    )
       __UpperCamelCase   							=str(SCREAMING_SNAKE_CASE__    )
       __UpperCamelCase   							=AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__    ).to(SCREAMING_SNAKE_CASE__    )
       if fpaa:
              __UpperCamelCase   							=model.half()

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

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

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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int=True    ):
       __UpperCamelCase   							=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
       __UpperCamelCase							,				__UpperCamelCase   							=parser.parse_known_args()
       __UpperCamelCase   							=parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE__    )
       if parsed_args and verbose:
              print(F'parsed the following generate kwargs: {parsed_args}'    )
       __UpperCamelCase   							=[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path    ).readlines()]
       if args.n_obs > 0:
              __UpperCamelCase   							=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'    )

       __UpperCamelCase   							=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
       __UpperCamelCase   							=calculate_bleu if 'translation' in args.task else calculate_rouge
       __UpperCamelCase   							=[x.rstrip() for x in open(args.save_path    ).readlines()]
       __UpperCamelCase   							=[x.rstrip() for x in open(args.reference_path    ).readlines()][: len(SCREAMING_SNAKE_CASE__    )]
       __UpperCamelCase   							=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:
              __UpperCamelCase   							=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 os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812

def 						_UpperCAmelCase    (      ):
     print('Making key files...'    )
     make_key_files('rsa'      ,					10_24    )
     print('Key files generation successful.'    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int    ):
     print('Generating prime p...'    )
     __UpperCamelCase   							=rabinMiller.generate_large_prime(SCREAMING_SNAKE_CASE__    )
     print('Generating prime q...'    )
     __UpperCamelCase   							=rabinMiller.generate_large_prime(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=p * q

     print('Generating e that is relatively prime to (p - 1) * (q - 1)...'    )
     while True:
          __UpperCamelCase   							=random.randrange(2 ** (key_size - 1)      ,					2 ** (key_size)    )
          if cryptoMath.gcd(SCREAMING_SNAKE_CASE__      ,					(p - 1) * (q - 1)    ) == 1:
               break

     print('Calculating d that is mod inverse of e...'    )
     __UpperCamelCase   							=cryptoMath.find_mod_inverse(SCREAMING_SNAKE_CASE__      ,					(p - 1) * (q - 1)    )

     __UpperCamelCase   							=(n, e)
     __UpperCamelCase   							=(n, d)
     return (public_key, private_key)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : int    ):
     if os.path.exists(F'{name}_pubkey.txt'    ) or os.path.exists(F'{name}_privkey.txt'    ):
          print('\nWARNING:'    )
          print(
              F'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
              'Use a different name or delete these files and re-run this program.'    )
          sys.exit()

     __UpperCamelCase							,				__UpperCamelCase   							=generate_key(SCREAMING_SNAKE_CASE__    )
     print(F'\nWriting public key to file {name}_pubkey.txt...'    )
     with open(F'{name}_pubkey.txt'      ,					'w'    ) as out_file:
          out_file.write(F'{key_size},{public_key[0]},{public_key[1]}'    )

     print(F'Writing private key to file {name}_privkey.txt...'    )
     with open(F'{name}_privkey.txt'      ,					'w'    ) as out_file:
          out_file.write(F'{key_size},{private_key[0]},{private_key[1]}'    )


if __name__ == "__main__":
   main()





