





"""simple docstring"""




import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


a		:				Any  			=    getLogger(__name__)






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Union[str, Any]					, _lowercase		:					str					, _lowercase		:					str					, _lowercase		:					int = 8					, _lowercase		:					int = 1024					, _lowercase		:					List[str]="val"					, _lowercase		:					Union[str, Any]=None					, _lowercase		:					str=False					, _lowercase		:					Optional[Any]="summarization"					, _lowercase		:					int=None					, _lowercase		:					Any=1					, _lowercase		:					Dict = None					, _lowercase		:					int=""					, **_lowercase		:					str					, )				->Dict:






	'''simple docstring'''






	a   :	Optional[Any]			=       str(_lowercase      )
	assert local_rank is not None
	torch.distributed.init_process_group(backend="nccl"					, rank=_lowercase      )

	a   :	str			=       Path(_lowercase      )
	a   :	int			=       save_dir.joinpath(F"""rank_{local_rank}_output.json"""      )
	torch.cuda.set_device(_lowercase      )
	a   :	Optional[Any]			=       AutoModelForSeqaSeqLM.from_pretrained(_lowercase      ).cuda()
	if fpaa:
		a   :	Any			=       model.half()
	# determine if we need to increase num_beams
	use_task_specific_params(_lowercase					, _lowercase      )  # update config with task specific params
	a   :	Optional[int]			=       generate_kwargs.pop("num_beams"					, model.config.num_beams      )  # AttributeError risk?
	if num_return_sequences > num_beams:
		a   :	Union[str, Any]			=       num_return_sequences

	a   :	List[Any]			=       AutoTokenizer.from_pretrained(_lowercase      )
	logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}"""      )  # if this is wrong, check config.model_type.

	if max_source_length is None:
		a   :	Union[str, Any]			=       tokenizer.model_max_length
	if prefix is None:
		a   :	Optional[Any]			=       prefix or getattr(model.config					, "prefix"					, ""      ) or ""
	a   :	List[str]			=       SeqaSeqDataset(
	    _lowercase					, _lowercase					, _lowercase					, max_target_length=1024					, type_path=_lowercase					, n_obs=_lowercase					, prefix=_lowercase					, **_lowercase					, )
	# I set shuffle=True for a more accurate progress bar.
	# If all the longest samples are first, the prog bar estimate is too high at the beginning.
	a   :	Dict			=       ds.make_sortish_sampler(_lowercase					, distributed=_lowercase					, add_extra_examples=_lowercase					, shuffle=_lowercase      )
	a   :	Optional[int]			=       DataLoader(_lowercase					, sampler=_lowercase					, batch_size=_lowercase					, collate_fn=ds.collate_fn      )
	a   :	List[Any]			=       []
	for batch in tqdm(_lowercase      ):
		a   :	List[str]			=       model.generate(
		    input_ids=batch["input_ids"].to(model.device      )					, attention_mask=batch["attention_mask"].to(model.device      )					, num_return_sequences=_lowercase					, num_beams=_lowercase					, **_lowercase					, )
		a   :	Optional[Any]			=       tokenizer.batch_decode(_lowercase					, skip_special_tokens=_lowercase					, clean_up_tokenization_spaces=_lowercase      )
		a   :	Tuple			=       batch["ids"]
		if num_return_sequences > 1:
			a   :	Union[str, Any]			=       chunks(_lowercase					, _lowercase      )  # batch size chunks, each of size num_return_seq
		for i, pred in enumerate(_lowercase      ):
			results.append({"pred": pred, "id": ids[i].item()}      )
	save_json(_lowercase					, _lowercase      )
	return results, sampler.num_replicas






def    _SCREAMING_SNAKE_CASE	(			)				->Tuple:






	'''simple docstring'''






	a   :	List[str]			=       argparse.ArgumentParser(
	    epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate"      )
	parser.add_argument("--data_dir"					, type=_lowercase					, help="like cnn_dm/test.source"      )
	parser.add_argument(
	    "--model_name"					, type=_lowercase					, help="like facebook/bart-large-cnn,t5-base, etc."					, default="sshleifer/distilbart-xsum-12-3"					, )
	parser.add_argument("--save_dir"					, type=_lowercase					, help="where to save"					, default="tmp_gen"      )
	parser.add_argument("--max_source_length"					, type=_lowercase					, default=_lowercase      )
	parser.add_argument(
	    "--type_path"					, type=_lowercase					, default="test"					, help="which subset to evaluate typically train/val/test"      )
	parser.add_argument("--task"					, type=_lowercase					, default="summarization"					, help="used for task_specific_params + metrics"      )
	parser.add_argument("--bs"					, type=_lowercase					, default=8					, required=_lowercase					, help="batch size"      )
	parser.add_argument(
	    "--local_rank"					, type=_lowercase					, default=-1					, required=_lowercase					, help="should be passed by distributed.launch"      )

	parser.add_argument(
	    "--n_obs"					, type=_lowercase					, default=_lowercase					, required=_lowercase					, help="How many observations. Defaults to all."      )
	parser.add_argument(
	    "--num_return_sequences"					, type=_lowercase					, default=1					, required=_lowercase					, help="How many sequences to return"      )
	parser.add_argument(
	    "--sync_timeout"					, type=_lowercase					, default=600					, required=_lowercase					, help="How long should master process wait for other processes to finish."					, )
	parser.add_argument("--src_lang"					, type=_lowercase					, default=_lowercase					, required=_lowercase      )
	parser.add_argument("--tgt_lang"					, type=_lowercase					, default=_lowercase					, required=_lowercase      )
	parser.add_argument(
	    "--prefix"					, type=_lowercase					, required=_lowercase					, default=_lowercase					, help="will be added to the begininng of src examples"      )
	parser.add_argument("--fp16"					, action="store_true"      )
	parser.add_argument("--debug"					, action="store_true"      )
	a   :	str			=       time.time()
	a, a   :	Any			=       parser.parse_known_args()
	a   :	Tuple			=       parse_numeric_n_bool_cl_kwargs(_lowercase      )
	if generate_kwargs and args.local_rank <= 0:
		print(F"""parsed the following generate kwargs: {generate_kwargs}"""      )
	a   :	str			=       Path(args.save_dir + "_tmp"      )
	Path(_lowercase      ).mkdir(exist_ok=_lowercase      )  # this handles locking.
	a   :	List[Any]			=       list(json_save_dir.glob("rank_*.json"      )      )
	if intermediate_files:
		raise ValueError(F"""Found files at {json_save_dir} please move or remove them."""      )
		# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
	a   :	str			=       {}
	if args.src_lang is not None:
		a   :	List[Any]			=       args.src_lang
	if args.tgt_lang is not None:
		a   :	Dict			=       args.tgt_lang

	Path(args.save_dir      ).mkdir(exist_ok=_lowercase      )
	a, a   :	Any			=       eval_data_dir(
	    args.data_dir					, _lowercase					, args.model_name					, type_path=args.type_path					, bs=args.bs					, fpaa=args.fpaa					, task=args.task					, local_rank=args.local_rank					, n_obs=args.n_obs					, max_source_length=args.max_source_length					, num_return_sequences=args.num_return_sequences					, prefix=args.prefix					, dataset_kwargs=_lowercase					, **_lowercase					, )

	if args.local_rank <= 0:
		a   :	List[Any]			=       Path(args.save_dir      )
		save_dir.mkdir(exist_ok=_lowercase      )
		a   :	Union[str, Any]			=       gather_results_from_each_node(_lowercase					, _lowercase					, args.sync_timeout      )
		a   :	str			=       combine_partial_results(_lowercase      )
		if args.num_return_sequences > 1:
			a   :	int			=       save_dir.joinpath("pseudolabel_results.json"      )
			print(F"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"""      )
			save_json(_lowercase					, _lowercase      )
			return
		a   :	Tuple			=       Path(args.data_dir      ).joinpath(args.type_path + ".target"      )
		with open(_lowercase      ) as f:
			a   :	str			=       [x.rstrip() for x in f.readlines()][: len(_lowercase      )]

		# Calculate metrics, save metrics,  and save _generations.txt
		a   :	Any			=       "translation" in args.task
		a   :	Tuple			=       calculate_bleu if calc_bleu else calculate_rouge
		a   :	Tuple			=       "bleu" if calc_bleu else "rouge"
		a   :	Dict			=       score_fn(_lowercase					, _lowercase      )
		a   :	Dict			=       len(_lowercase      )
		a   :	Optional[int]			=       time.time() - start_time
		a   :	Tuple			=       round(runtime / metrics["n_obs"]					, 4      )
		a   :	Optional[Any]			=       num_replicas
		# TODO(@stas00): add whatever metadata to metrics
		a   :	Any			=       save_dir.joinpath(F"""{args.type_path}_{metric_name}.json"""      )
		save_json(_lowercase					, _lowercase					, indent=_lowercase      )
		print(_lowercase      )
		write_txt_file(_lowercase					, save_dir.joinpath(F"""{args.type_path}_generations.txt"""      )      )
		if args.debug:
			write_txt_file(_lowercase					, save_dir.joinpath(F"""{args.type_path}.target"""      )      )
		else:
			shutil.rmtree(_lowercase      )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str      )				->List:






	'''simple docstring'''






	a   :	List[str]			=       []
	for partial_result in partial_results:
		records.extend(_lowercase      )
	a   :	str			=       sorted(_lowercase					, key=lambda _lowercase      : x["id"]      )
	a   :	List[Any]			=       [x["pred"] for x in records]
	return preds






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					Optional[int]					, _lowercase		:					str      )				->List[Dict[str, List]]:






	'''simple docstring'''






	a   :	Optional[Any]			=       time.time()
	logger.info("waiting for all nodes to finish"      )
	a   :	str			=       None
	while (time.time() - start_wait) < timeout:
		a   :	int			=       list(save_dir.glob("rank_*.json"      )      )
		if len(_lowercase      ) < num_replicas:
			continue
		try:
			# make sure all json files are fully saved
			a   :	Tuple			=       lmap(_lowercase					, _lowercase      )
			return json_data
		except JSONDecodeError:
			continue
	else:
		raise TimeoutError("Rank 0 gave up on waiting for other processes"      )
	# Unreachable


if __name__ == "__main__":
		# Usage for MT:
		run_generate()









"""simple docstring"""




def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int = 200      )				->int:






	'''simple docstring'''






	a   :	Dict			=       [1, 2, 5, 10, 20, 50, 100, 200]
	a   :	Optional[Any]			=       [0] * (pence + 1)
	a   :	List[Any]			=       1  # base case: 1 way to make 0 pence

	for coin in coins:
		for i in range(_lowercase					, pence + 1					, 1      ):
			number_of_ways[i] += number_of_ways[i - coin]
	return number_of_ways[pence]


if __name__ == "__main__":
		assert solution(200) == 73682


