code: 
  
  
  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       :       int				   =			getLogger(__name__)
  
  
  __lowerCamelCase       :       str				   =			"cuda" if torch.cuda.is_available() else "cpu"
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							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:
  				A__     :							Tuple   				=Path(snake_case_	).open('''w''',	encoding='''utf-8'''	)
  				A__     :							List[str]   				=str(snake_case_	)
  				A__     :							Tuple   				=AutoModelForSeqaSeqLM.from_pretrained(snake_case_	).to(snake_case_	)
  				if fpaa:
  								A__     :							Union[str, Any]   				=model.half()
  
  				A__     :							List[str]   				=AutoTokenizer.from_pretrained(snake_case_	)
  				logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'	)  # if this is wrong, check config.model_type.
  
  				A__     :							int   				=time.time()
  				# update config with task specific params
  				use_task_specific_params(snake_case_,	snake_case_	)
  				if prefix is None:
  								A__     :							Optional[Any]   				=prefix or getattr(model.config,	'''prefix''',	''''''	) or ''''''
  				for examples_chunk in tqdm(list(chunks(snake_case_,	snake_case_	)	)	):
  								A__     :							List[Any]   				=[prefix + text for text in examples_chunk]
  								A__     :							Dict   				=tokenizer(snake_case_,	return_tensors='''pt''',	truncation=snake_case_,	padding='''longest'''	).to(snake_case_	)
  								A__     :							Dict   				=model.generate(
  								    input_ids=batch.input_ids,	attention_mask=batch.attention_mask,	**snake_case_,	)
  								A__     :							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()
  				A__     :							str   				=int(time.time() - start_time	)  # seconds
  				A__     :							Any   				=len(snake_case_	)
  				return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs,	4	)}
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							)			->							List[Any]:
  				return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_=True	)			->							int:
  				A__     :							Optional[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
  				A__				,  A__     :							Tuple   				=parser.parse_known_args()
  				A__     :							List[str]   				=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:
  								A__     :							Optional[int]   				=[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in f.readlines()]
  				if args.n_obs > 0:
  								A__     :							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.'	)
  				A__     :							List[Any]   				=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
  				A__     :							List[Any]   				=calculate_bleu if '''translation''' in args.task else calculate_rouge
  				A__     :							Optional[int]   				=[x.rstrip() for x in open(args.save_path	).readlines()]
  				A__     :							Any   				=[x.rstrip() for x in open(args.reference_path	).readlines()][: len(snake_case_	)]
  				A__     :							dict   				=score_fn(snake_case_,	snake_case_	)
  				scores.update(snake_case_	)
  
  				if args.dump_args:
  								scores.update(snake_case_	)
  				if args.info:
  								A__     :							int   				=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)
  
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							list[int]:
  				A__     :							Optional[int]   				=[0] * len(snake_case_	)
  				for i in range(1,	len(snake_case_	)	):
  								A__     :							List[Any]   				=duration_times[i - 1] + waiting_times[i - 1]
  				return waiting_times
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							list[int]:
  				return [
  				    duration_time + waiting_times[i]
  				    for i, duration_time in enumerate(snake_case_	)
  				]
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							float:
  				return sum(snake_case_	) / len(snake_case_	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							float:
  				return sum(snake_case_	) / len(snake_case_	)
  
  
  if __name__ == "__main__":
  						# process id's
  						__lowerCamelCase       :       List[str]				   =			[1, 2, 3]
  
  						# ensure that we actually have processes
  						if len(processes) == 0:
  												print("Zero amount of processes")
  												raise SystemExit(0)
  
  						# duration time of all processes
  						__lowerCamelCase       :       Dict				   =			[19, 8, 9]
  
  						# ensure we can match each id to a duration time
  						if len(duration_times) != len(processes):
  												print("Unable to match all id's with their duration time")
  												raise SystemExit(0)
  
  						# get the waiting times and the turnaround times
  						__lowerCamelCase       :       int				   =			calculate_waiting_times(duration_times)
  						__lowerCamelCase       :       Optional[int]				   =			calculate_turnaround_times(duration_times, waiting_times)
  
  						# get the average times
  						__lowerCamelCase       :       List[str]				   =			calculate_average_waiting_time(waiting_times)
  						__lowerCamelCase       :       Dict				   =			calculate_average_turnaround_time(turnaround_times)
  
  						# print all the results
  						print("Process ID\tDuration Time\tWaiting Time\tTurnaround Time")
  						for i, process in enumerate(processes):
  												print(
  												    F"{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"
  												    F"{turnaround_times[i]}"
  												)
  						print(F"Average waiting time = {average_waiting_time}")
  						print(F"Average turn around time = {average_turnaround_time}")
  
  
  
  