



'''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,
)


SCREAMING_SNAKE_CASE__      					=     getLogger(__name__)




def        lowerCamelCase  (				_snake_case  :						Optional[Any]     ,_snake_case  :						str     ,_snake_case  :						str     ,_snake_case  :						int = 8     ,_snake_case  :						int = 1_024     ,_snake_case  :						Tuple="val"     ,_snake_case  :						List[Any]=None     ,_snake_case  :						List[str]=False     ,_snake_case  :						Optional[int]="summarization"     ,_snake_case  :						Optional[Any]=None     ,_snake_case  :						str=1     ,_snake_case  :						Dict = None     ,_snake_case  :						Any=""     ,**_snake_case  :						List[Any]     ,):





    '''simple docstring'''


    lowercase__		       =						str(_snake_case       )
    assert local_rank is not None
    torch.distributed.init_process_group(backend="nccl"     ,rank=_snake_case       )

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

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

    if max_source_length is None:
        lowercase__		       =						tokenizer.model_max_length
    if prefix is None:
        lowercase__		       =						prefix or getattr(model.config     ,"prefix"     ,""       ) or ""
    lowercase__		       =						SeqaSeqDataset(
        _snake_case     ,_snake_case     ,_snake_case     ,max_target_length=1_024     ,type_path=_snake_case     ,n_obs=_snake_case     ,prefix=_snake_case     ,**_snake_case     ,)
    # 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.
    lowercase__		       =						ds.make_sortish_sampler(_snake_case     ,distributed=_snake_case     ,add_extra_examples=_snake_case     ,shuffle=_snake_case       )
    lowercase__		       =						DataLoader(_snake_case     ,sampler=_snake_case     ,batch_size=_snake_case     ,collate_fn=ds.collate_fn       )
    lowercase__		       =						[]
    for batch in tqdm(_snake_case       ):
        lowercase__		       =						model.generate(
            input_ids=batch["input_ids"].to(model.device       )     ,attention_mask=batch["attention_mask"].to(model.device       )     ,num_return_sequences=_snake_case     ,num_beams=_snake_case     ,**_snake_case     ,)
        lowercase__		       =						tokenizer.batch_decode(_snake_case     ,skip_special_tokens=_snake_case     ,clean_up_tokenization_spaces=_snake_case       )
        lowercase__		       =						batch["ids"]
        if num_return_sequences > 1:
            lowercase__		       =						chunks(_snake_case     ,_snake_case       )  # batch size chunks, each of size num_return_seq
        for i, pred in enumerate(_snake_case       ):
            results.append({"pred": pred, "id": ids[i].item()}       )
    save_json(_snake_case     ,_snake_case       )
    return results, sampler.num_replicas




def        lowerCamelCase  (				):





    '''simple docstring'''


    lowercase__		       =						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=_snake_case     ,help="like cnn_dm/test.source"       )
    parser.add_argument(
        "--model_name"     ,type=_snake_case     ,help="like facebook/bart-large-cnn,t5-base, etc."     ,default="sshleifer/distilbart-xsum-12-3"     ,)
    parser.add_argument("--save_dir"     ,type=_snake_case     ,help="where to save"     ,default="tmp_gen"       )
    parser.add_argument("--max_source_length"     ,type=_snake_case     ,default=_snake_case       )
    parser.add_argument(
        "--type_path"     ,type=_snake_case     ,default="test"     ,help="which subset to evaluate typically train/val/test"       )
    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(
        "--local_rank"     ,type=_snake_case     ,default=-1     ,required=_snake_case     ,help="should be passed by distributed.launch"       )

    parser.add_argument(
        "--n_obs"     ,type=_snake_case     ,default=_snake_case     ,required=_snake_case     ,help="How many observations. Defaults to all."       )
    parser.add_argument(
        "--num_return_sequences"     ,type=_snake_case     ,default=1     ,required=_snake_case     ,help="How many sequences to return"       )
    parser.add_argument(
        "--sync_timeout"     ,type=_snake_case     ,default=600     ,required=_snake_case     ,help="How long should master process wait for other processes to finish."     ,)
    parser.add_argument("--src_lang"     ,type=_snake_case     ,default=_snake_case     ,required=_snake_case       )
    parser.add_argument("--tgt_lang"     ,type=_snake_case     ,default=_snake_case     ,required=_snake_case       )
    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("--fp16"     ,action="store_true"       )
    parser.add_argument("--debug"     ,action="store_true"       )
    lowercase__		       =						time.time()
    lowercase__       ,       lowercase__		       =						parser.parse_known_args()
    lowercase__		       =						parse_numeric_n_bool_cl_kwargs(_snake_case       )
    if generate_kwargs and args.local_rank <= 0:
        print(f'''parsed the following generate kwargs: {generate_kwargs}'''       )
    lowercase__		       =						Path(args.save_dir + "_tmp"       )
    Path(_snake_case       ).mkdir(exist_ok=_snake_case       )  # this handles locking.
    lowercase__		       =						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.
    lowercase__		       =						{}
    if args.src_lang is not None:
        lowercase__		       =						args.src_lang
    if args.tgt_lang is not None:
        lowercase__		       =						args.tgt_lang

    Path(args.save_dir       ).mkdir(exist_ok=_snake_case       )
    lowercase__       ,       lowercase__		       =						eval_data_dir(
        args.data_dir     ,_snake_case     ,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=_snake_case     ,**_snake_case     ,)

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

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




def        lowerCamelCase  (				_snake_case  :						Union[str, Any]       ):





    '''simple docstring'''


    lowercase__		       =						[]
    for partial_result in partial_results:
        records.extend(_snake_case       )
    lowercase__		       =						sorted(_snake_case     ,key=lambda _snake_case       : x["id"]       )
    lowercase__		       =						[x["pred"] for x in records]
    return preds




def        lowerCamelCase  (				_snake_case  :						Union[str, Any]     ,_snake_case  :						Dict     ,_snake_case  :						List[str]       ):





    '''simple docstring'''


    lowercase__		       =						time.time()
    logger.info("waiting for all nodes to finish"       )
    lowercase__		       =						None
    while (time.time() - start_wait) < timeout:
        lowercase__		       =						list(save_dir.glob("rank_*.json"       )       )
        if len(_snake_case       ) < num_replicas:
            continue
        try:
            # make sure all json files are fully saved
            lowercase__		       =						lmap(_snake_case     ,_snake_case       )
            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        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						float     ,_snake_case  :						float       ):





    '''simple docstring'''


    return round(float(moles / volume       ) * nfactor       )




def        lowerCamelCase  (				_snake_case  :						float     ,_snake_case  :						float     ,_snake_case  :						float       ):





    '''simple docstring'''


    return round(float((moles * 0.0_821 * temperature) / (volume)       )       )




def        lowerCamelCase  (				_snake_case  :						float     ,_snake_case  :						float     ,_snake_case  :						float       ):





    '''simple docstring'''


    return round(float((moles * 0.0_821 * temperature) / (pressure)       )       )




def        lowerCamelCase  (				_snake_case  :						float     ,_snake_case  :						float     ,_snake_case  :						float       ):





    '''simple docstring'''


    return round(float((pressure * volume) / (0.0_821 * moles)       )       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




