






'''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,
)


__lowerCAmelCase									=   getLogger(__name__)





def 		_UpperCAmelCase (				__A				: Union[str, Any]    ,		__A				: str    ,		__A				: str    ,		__A				: int = 8    ,		__A				: int = 10_24    ,		__A				: Tuple="val"    ,		__A				: int=None    ,		__A				: Optional[Any]=False    ,		__A				: str="summarization"    ,		__A				: Optional[int]=None    ,		__A				: Optional[Any]=1    ,		__A				: Dict = None    ,		__A				: Optional[int]=""    ,		**__A				: List[str]    ,		):
				a_		:	Optional[int]									=   str(__A   )
				assert local_rank is not None
				torch.distributed.init_process_group(backend='''nccl'''    ,		rank=__A   )

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

				a_		:	List[Any]									=   AutoTokenizer.from_pretrained(__A   )
				logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'   )  # if this is wrong, check config.model_type.

				if max_source_length is None:
								a_		:	str									=   tokenizer.model_max_length
				if prefix is None:
								a_		:	List[Any]									=   prefix or getattr(model.config    ,		'''prefix'''    ,		''''''   ) or ''''''
				a_		:	Union[str, Any]									=   SeqaSeqDataset(
				    __A    ,		__A    ,		__A    ,		max_target_length=10_24    ,		type_path=__A    ,		n_obs=__A    ,		prefix=__A    ,		**__A    ,		)
				# 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_		:	Tuple									=   ds.make_sortish_sampler(__A    ,		distributed=__A    ,		add_extra_examples=__A    ,		shuffle=__A   )
				a_		:	str									=   DataLoader(__A    ,		sampler=__A    ,		batch_size=__A    ,		collate_fn=ds.collate_fn   )
				a_		:	Optional[int]									=   []
				for batch in tqdm(__A   ):
								a_		:	Any									=   model.generate(
								    input_ids=batch['''input_ids'''].to(model.device   )    ,		attention_mask=batch['''attention_mask'''].to(model.device   )    ,		num_return_sequences=__A    ,		num_beams=__A    ,		**__A    ,		)
								a_		:	Optional[int]									=   tokenizer.batch_decode(__A    ,		skip_special_tokens=__A    ,		clean_up_tokenization_spaces=__A   )
								a_		:	Union[str, Any]									=   batch['''ids''']
								if num_return_sequences > 1:
												a_		:	int									=   chunks(__A    ,		__A   )  # batch size chunks, each of size num_return_seq
								for i, pred in enumerate(__A   ):
												results.append({'''pred''': pred, '''id''': ids[i].item()}   )
				save_json(__A    ,		__A   )
				return results, sampler.num_replicas





def 		_UpperCAmelCase (				):
				a_		:	Any									=   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=__A    ,		help='''like cnn_dm/test.source'''   )
				parser.add_argument(
				    '''--model_name'''    ,		type=__A    ,		help='''like facebook/bart-large-cnn,t5-base, etc.'''    ,		default='''sshleifer/distilbart-xsum-12-3'''    ,		)
				parser.add_argument('''--save_dir'''    ,		type=__A    ,		help='''where to save'''    ,		default='''tmp_gen'''   )
				parser.add_argument('''--max_source_length'''    ,		type=__A    ,		default=__A   )
				parser.add_argument(
				    '''--type_path'''    ,		type=__A    ,		default='''test'''    ,		help='''which subset to evaluate typically train/val/test'''   )
				parser.add_argument('''--task'''    ,		type=__A    ,		default='''summarization'''    ,		help='''used for task_specific_params + metrics'''   )
				parser.add_argument('''--bs'''    ,		type=__A    ,		default=8    ,		required=__A    ,		help='''batch size'''   )
				parser.add_argument(
				    '''--local_rank'''    ,		type=__A    ,		default=-1    ,		required=__A    ,		help='''should be passed by distributed.launch'''   )

				parser.add_argument(
				    '''--n_obs'''    ,		type=__A    ,		default=__A    ,		required=__A    ,		help='''How many observations. Defaults to all.'''   )
				parser.add_argument(
				    '''--num_return_sequences'''    ,		type=__A    ,		default=1    ,		required=__A    ,		help='''How many sequences to return'''   )
				parser.add_argument(
				    '''--sync_timeout'''    ,		type=__A    ,		default=6_00    ,		required=__A    ,		help='''How long should master process wait for other processes to finish.'''    ,		)
				parser.add_argument('''--src_lang'''    ,		type=__A    ,		default=__A    ,		required=__A   )
				parser.add_argument('''--tgt_lang'''    ,		type=__A    ,		default=__A    ,		required=__A   )
				parser.add_argument(
				    '''--prefix'''    ,		type=__A    ,		required=__A    ,		default=__A    ,		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_		:	Union[str, Any]									=   time.time()
				a_					,      a_		:	Dict									=   parser.parse_known_args()
				a_		:	Optional[Any]									=   parse_numeric_n_bool_cl_kwargs(__A   )
				if generate_kwargs and args.local_rank <= 0:
								print(f'parsed the following generate kwargs: {generate_kwargs}'   )
				a_		:	Any									=   Path(args.save_dir + '''_tmp'''   )
				Path(__A   ).mkdir(exist_ok=__A   )  # this handles locking.
				a_		:	List[str]									=   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_		:	Tuple									=   {}
				if args.src_lang is not None:
								a_		:	Optional[Any]									=   args.src_lang
				if args.tgt_lang is not None:
								a_		:	List[str]									=   args.tgt_lang

				Path(args.save_dir   ).mkdir(exist_ok=__A   )
				a_					,      a_		:	Union[str, Any]									=   eval_data_dir(
				    args.data_dir    ,		__A    ,		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=__A    ,		**__A    ,		)

				if args.local_rank <= 0:
								a_		:	Union[str, Any]									=   Path(args.save_dir   )
								save_dir.mkdir(exist_ok=__A   )
								a_		:	Tuple									=   gather_results_from_each_node(__A    ,		__A    ,		args.sync_timeout   )
								a_		:	int									=   combine_partial_results(__A   )
								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(__A    ,		__A   )
												return
								a_		:	Dict									=   Path(args.data_dir   ).joinpath(args.type_path + '''.target'''   )
								with open(__A   ) as f:
												a_		:	Union[str, Any]									=   [x.rstrip() for x in f.readlines()][: len(__A   )]

								# Calculate metrics, save metrics,  and save _generations.txt
								a_		:	Dict									=   '''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(__A    ,		__A   )
								a_		:	List[Any]									=   len(__A   )
								a_		:	Optional[Any]									=   time.time() - start_time
								a_		:	Any									=   round(runtime / metrics['''n_obs''']    ,		4   )
								a_		:	Tuple									=   num_replicas
								# TODO(@stas00): add whatever metadata to metrics
								a_		:	Any									=   save_dir.joinpath(f'{args.type_path}_{metric_name}.json'   )
								save_json(__A    ,		__A    ,		indent=__A   )
								print(__A   )
								write_txt_file(__A    ,		save_dir.joinpath(f'{args.type_path}_generations.txt'   )   )
								if args.debug:
												write_txt_file(__A    ,		save_dir.joinpath(f'{args.type_path}.target'   )   )
								else:
												shutil.rmtree(__A   )





def 		_UpperCAmelCase (				__A				: Any   ):
				a_		:	Dict									=   []
				for partial_result in partial_results:
								records.extend(__A   )
				a_		:	List[Any]									=   sorted(__A    ,		key=lambda __A   : x["id"]   )
				a_		:	Tuple									=   [x['''pred'''] for x in records]
				return preds





def 		_UpperCAmelCase (				__A				: Tuple    ,		__A				: Any    ,		__A				: Union[str, Any]   ):
				# WAIT FOR lots of .json files
				a_		:	List[str]									=   time.time()
				logger.info('''waiting for all nodes to finish'''   )
				a_		:	Dict									=   None
				while (time.time() - start_wait) < timeout:
								a_		:	str									=   list(save_dir.glob('''rank_*.json'''   )   )
								if len(__A   ) < num_replicas:
												continue
								try:
												# make sure all json files are fully saved
												a_		:	Tuple									=   lmap(__A    ,		__A   )
												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'''







import functools





def 		_UpperCAmelCase (				__A				: list[int]    ,		__A				: list[int]   ):

				# Validation
				if not isinstance(__A    ,		__A   ) or not all(isinstance(__A    ,		__A   ) for day in days   ):
								raise ValueError('''The parameter days should be a list of integers'''   )

				if len(__A   ) != 3 or not all(isinstance(__A    ,		__A   ) for cost in costs   ):
								raise ValueError('''The parameter costs should be a list of three integers'''   )

				if len(__A   ) == 0:
								return 0

				if min(__A   ) <= 0:
								raise ValueError('''All days elements should be greater than 0'''   )

				if max(__A   ) >= 3_66:
								raise ValueError('''All days elements should be less than 366'''   )

				a_		:	List[Any]									=   set(__A   )

				@functools.cache
				def dynamic_programming(__A				: int   ) -> int:
								if index > 3_65:
												return 0

								if index not in days_set:
												return dynamic_programming(index + 1   )

								return min(
								    costs[0] + dynamic_programming(index + 1   )    ,		costs[1] + dynamic_programming(index + 7   )    ,		costs[2] + dynamic_programming(index + 30   )    ,		)

				return dynamic_programming(1   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




