
"""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


_lowercase											=			getLogger(__name__)


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






def  _snake_case			(						snake_case__				:       List[str]		, snake_case__				:       str		, snake_case__				:       str		, snake_case__				:       int = 8		, snake_case__				:       str = DEFAULT_DEVICE		, snake_case__				:       List[Any]=False		, snake_case__				:       Union[str, Any]="summarization"		, snake_case__				:       Optional[Any]=None		, **snake_case__				:       str		, ):
	A  				=				Path(snake_case__       ).open('w'		, encoding='utf-8'       )
	A  				=				str(snake_case__       )
	A  				=				AutoModelForSeqaSeqLM.from_pretrained(snake_case__       ).to(snake_case__       )
	if fpaa:
		A  				=				model.half()

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

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






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






def  _snake_case			(						snake_case__				:       Dict=True       ):
	A  				=				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  				=				parser.parse_known_args()
	A  				=				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  				=				[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
	if args.n_obs > 0:
		A  				=				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  				=				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  				=				calculate_bleu if 'translation' in args.task else calculate_rouge
	A  				=				[x.rstrip() for x in open(args.save_path       ).readlines()]
	A  				=				[x.rstrip() for x in open(args.reference_path       ).readlines()][: len(snake_case__       )]
	A  				=				score_fn(snake_case__		, snake_case__       )
	scores.update(snake_case__       )

	if args.dump_args:
		scores.update(snake_case__       )
	if args.info:
		A  				=				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"""


from __future__ import annotations






def  _snake_case			(						snake_case__				:       list[list[int]]		, snake_case__				:       list[list[int]]       ):
	assert len(snake_case__       ) == len(snake_case__       )

	A  				=				len(snake_case__       )
	A  				=				list(range(snake_case__       )       )
	A  				=				[-1] * n  # who the donor has donated to
	A  				=				[-1] * n  # who the recipient has received from
	A  				=				[0] * n

	while unmatched_donors:
		A  				=				unmatched_donors[0]
		A  				=				donor_pref[donor]
		A  				=				donor_preference[num_donations[donor]]
		num_donations[donor] += 1
		A  				=				recipient_pref[recipient]
		A  				=				rec_record[recipient]

		if prev_donor != -1:
			if rec_preference.index(snake_case__       ) > rec_preference.index(snake_case__       ):
				A  				=				donor
				A  				=				recipient
				unmatched_donors.append(snake_case__       )
				unmatched_donors.remove(snake_case__       )
		else:
			A  				=				donor
			A  				=				recipient
			unmatched_donors.remove(snake_case__       )
	return donor_record