
"""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			:       List[Any]   		=       getLogger(__name__)


def   __magic_name__	(      __snake_case				:       List[str]     , __snake_case				:       str     , __snake_case				:       str     , __snake_case				:       int = 8     , __snake_case				:       int = 1024     , __snake_case				:       str="val"     , __snake_case				:       List[str]=None     , __snake_case				:       Optional[Any]=False     , __snake_case				:       List[str]="summarization"     , __snake_case				:       Union[str, Any]=None     , __snake_case				:       Optional[int]=1     , __snake_case				:       Dict = None     , __snake_case				:       str=""     , **__snake_case				:       Optional[int]     , ) ->				List[str]:
       lowercase    :				Optional[Any]       		=							str(__UpperCamelCase  )
       assert local_rank is not None
       torch.distributed.init_process_group(backend="nccl"     , rank=__UpperCamelCase  )

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

       lowercase    :				List[str]       		=							AutoTokenizer.from_pretrained(__UpperCamelCase  )
       logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""  )  # if this is wrong, check config.model_type.

       if max_source_length is None:
              lowercase    :				str       		=							tokenizer.model_max_length
       if prefix is None:
              lowercase    :				str       		=							prefix or getattr(model.config     , "prefix"     , ""  ) or ''''''
       lowercase    :				Union[str, Any]       		=							SeqaSeqDataset(
           __UpperCamelCase     , __UpperCamelCase     , __UpperCamelCase     , max_target_length=1024     , type_path=__UpperCamelCase     , n_obs=__UpperCamelCase     , prefix=__UpperCamelCase     , **__UpperCamelCase     , )
       # 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    :				Optional[int]       		=							ds.make_sortish_sampler(__UpperCamelCase     , distributed=__UpperCamelCase     , add_extra_examples=__UpperCamelCase     , shuffle=__UpperCamelCase  )
       lowercase    :				List[Any]       		=							DataLoader(__UpperCamelCase     , sampler=__UpperCamelCase     , batch_size=__UpperCamelCase     , collate_fn=ds.collate_fn  )
       lowercase    :				List[str]       		=							[]
       for batch in tqdm(__UpperCamelCase  ):
              lowercase    :				List[str]       		=							model.generate(
                  input_ids=batch["input_ids"].to(model.device  )     , attention_mask=batch["attention_mask"].to(model.device  )     , num_return_sequences=__UpperCamelCase     , num_beams=__UpperCamelCase     , **__UpperCamelCase     , )
              lowercase    :				Tuple       		=							tokenizer.batch_decode(__UpperCamelCase     , skip_special_tokens=__UpperCamelCase     , clean_up_tokenization_spaces=__UpperCamelCase  )
              lowercase    :				List[str]       		=							batch['''ids''']
              if num_return_sequences > 1:
                     lowercase    :				List[Any]       		=							chunks(__UpperCamelCase     , __UpperCamelCase  )  # batch size chunks, each of size num_return_seq
              for i, pred in enumerate(__UpperCamelCase  ):
                     results.append({"pred": pred, "id": ids[i].item()}  )
       save_json(__UpperCamelCase     , __UpperCamelCase  )
       return results, sampler.num_replicas


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

       parser.add_argument(
           "--n_obs"     , type=__UpperCamelCase     , default=__UpperCamelCase     , required=__UpperCamelCase     , help="How many observations. Defaults to all."  )
       parser.add_argument(
           "--num_return_sequences"     , type=__UpperCamelCase     , default=1     , required=__UpperCamelCase     , help="How many sequences to return"  )
       parser.add_argument(
           "--sync_timeout"     , type=__UpperCamelCase     , default=600     , required=__UpperCamelCase     , help="How long should master process wait for other processes to finish."     , )
       parser.add_argument("--src_lang"     , type=__UpperCamelCase     , default=__UpperCamelCase     , required=__UpperCamelCase  )
       parser.add_argument("--tgt_lang"     , type=__UpperCamelCase     , default=__UpperCamelCase     , required=__UpperCamelCase  )
       parser.add_argument(
           "--prefix"     , type=__UpperCamelCase     , required=__UpperCamelCase     , default=__UpperCamelCase     , 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    :				Tuple       		=							time.time()
       lowercase    :				str       		=							parser.parse_known_args()
       lowercase    :				Dict       		=							parse_numeric_n_bool_cl_kwargs(__UpperCamelCase  )
       if generate_kwargs and args.local_rank <= 0:
              print(f"""parsed the following generate kwargs: {generate_kwargs}"""  )
       lowercase    :				Tuple       		=							Path(args.save_dir + "_tmp"  )
       Path(__UpperCamelCase  ).mkdir(exist_ok=__UpperCamelCase  )  # this handles locking.
       lowercase    :				Optional[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.
       lowercase    :				Optional[Any]       		=							{}
       if args.src_lang is not None:
              lowercase    :				Dict       		=							args.src_lang
       if args.tgt_lang is not None:
              lowercase    :				Optional[int]       		=							args.tgt_lang

       Path(args.save_dir  ).mkdir(exist_ok=__UpperCamelCase  )
       lowercase    :				Any       		=							eval_data_dir(
           args.data_dir     , __UpperCamelCase     , 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=__UpperCamelCase     , **__UpperCamelCase     , )

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

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


def   __magic_name__	(      __snake_case				:       str  ) ->				int:
       lowercase    :				Optional[Any]       		=							[]
       for partial_result in partial_results:
              records.extend(__UpperCamelCase  )
       lowercase    :				Optional[Any]       		=							sorted(__UpperCamelCase     , key=lambda __snake_case  : x["id"]  )
       lowercase    :				Dict       		=							[x['''pred'''] for x in records]
       return preds


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







A__  :  Any								=					'''Tobias Carryer'''

from time import time



class 				__snake_case						:




							def __init__(       self     :       Any				,				A_     :       Tuple				,				A_     :       Dict				,				A_     :       Tuple				,				A_     :       str=int(time())):  # noqa: B008
									lowerCAmelCase_			:     int									= multiplier
									lowerCAmelCase_			:     int									= increment
									lowerCAmelCase_			:     str									= modulo
									lowerCAmelCase_			:     str									= seed







							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     Optional[int]									= (self.multiplier * self.seed + self.increment) % self.modulo
									return self.seed


if __name__ == "__main__":
						# Show the LCG in action.
						A__  :  Union[str, Any]								=					LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
						while True:
												print(lcg.next_number())





