"""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      : List[Any]       =getLogger(__name__)


lowerCamelCase      : Union[str, Any]       ='cuda' if torch.cuda.is_available() else 'cpu'




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				List[str]  ,       _SCREAMING_SNAKE_CASE				:				str  ,       _SCREAMING_SNAKE_CASE				:				List[Any]  ,       _SCREAMING_SNAKE_CASE				:				Tuple = 8  ,       _SCREAMING_SNAKE_CASE				:				List[Any] = DEFAULT_DEVICE  ,       _SCREAMING_SNAKE_CASE				:				str=False  ,       _SCREAMING_SNAKE_CASE				:				Optional[int]="summarization"  ,       _SCREAMING_SNAKE_CASE				:				Any=None  ,       **_SCREAMING_SNAKE_CASE				:				Optional[Any]  ,       )    ->     Any:







     '''simple docstring'''


     __A					:					Dict								=		Path(lowerCamelCase_						).open('w'  ,       encoding='utf-8'						)
     __A					:					Tuple								=		str(lowerCamelCase_						)
     __A					:					str								=		AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_						).to(lowerCamelCase_						)
     if fpaa:
          __A					:					List[str]								=		model.half()

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

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




def 						_lowercase				(  )    ->     Optional[Any]:







     '''simple docstring'''


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




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Dict=True						)    ->     int:







     '''simple docstring'''


     __A					:					Optional[Any]								=		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
     __A						,		__A					:					Tuple								=		parser.parse_known_args()
     __A					:					Union[str, Any]								=		parse_numeric_n_bool_cl_kwargs(lowerCamelCase_						)
     if parsed_args and verbose:
          print(F'parsed the following generate kwargs: {parsed_args}'						)
     with open(args.input_path						) as f:
          __A					:					str								=		[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
     if args.n_obs > 0:
          __A					:					List[Any]								=		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.'						)
     __A					:					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
     __A					:					List[str]								=		calculate_bleu if 'translation' in args.task else calculate_rouge
     __A					:					Dict								=		[x.rstrip() for x in open(args.save_path						).readlines()]
     __A					:					str								=		[x.rstrip() for x in open(args.reference_path						).readlines()][: len(lowerCamelCase_						)]
     __A					:					Tuple								=		score_fn(lowerCamelCase_  ,       lowerCamelCase_						)
     scores.update(lowerCamelCase_						)

     if args.dump_args:
          scores.update(lowerCamelCase_						)
     if args.info:
          __A					:					int								=		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"""







from __future__ import annotations





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_):

					# Base Case
					if counter == 0 or capacity == 0:
										return 0

					# If weight of the nth item is more than Knapsack of capacity,
					#   then this item cannot be included in the optimal solution,
					# else return the maximum of two cases:
					#   (1) nth item included
					#   (2) not included
					if weights[counter - 1] > capacity:
										return knapsack(lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
					else:
										a__ 							=	capacity - weights[counter - 1]
										a__ 							=	values[counter - 1] + knapsack(
										    lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
										a__ 							=	knapsack(lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
										return max(lowerCamelCase_			,  lowerCamelCase_)


if __name__ == "__main__":
	import doctest

	doctest.testmod()


