


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)







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}")



