

'''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,
)


lowercase__						:      Union[str, Any]	=    getLogger(__name__)







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





     """simple docstring"""

     _UpperCamelCase       =	str(SCREAMING_SNAKE_CASE_				)
     assert local_rank is not None
     torch.distributed.init_process_group(backend='''nccl''', rank=SCREAMING_SNAKE_CASE_				)

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

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

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







def  a__    (    )		->		List[str]:





     """simple docstring"""

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

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

     Path(args.save_dir				).mkdir(exist_ok=SCREAMING_SNAKE_CASE_				)
     _UpperCamelCase   ,    _UpperCamelCase       =	eval_data_dir(
         args.data_dir, SCREAMING_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=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_, )

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

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







def  a__    (    lowercase :					List[Any]				)		->		Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase       =	[]
     for partial_result in partial_results:
          records.extend(SCREAMING_SNAKE_CASE_				)
     _UpperCamelCase       =	sorted(SCREAMING_SNAKE_CASE_, key=lambda lowercase				: x["id"]				)
     _UpperCamelCase       =	[x['''pred'''] for x in records]
     return preds







def  a__    (    lowercase :					List[Any], lowercase :					List[str], lowercase :					str				)		->		int:





     """simple docstring"""

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

from __future__ import annotations







def  a__    (    lowercase :					list[int], lowercase :					list[int], lowercase :					int				)		->		tuple[float, list[float]]:





     """simple docstring"""

     _UpperCamelCase       =	list(range(len(lowercase				)				)				)
     _UpperCamelCase       =	[v / w for v, w in zip(lowercase, lowercase				)]
     index.sort(key=lambda lowercase				: ratio[i], reverse=lowercase				)

     _UpperCamelCase       =	0
     _UpperCamelCase       =	[0] * len(lowercase				)
     for i in index:
          if weight[i] <= capacity:
               _UpperCamelCase       =	1
               max_value += value[i]
               capacity -= weight[i]
          else:
               _UpperCamelCase       =	capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
   import doctest

   doctest.testmod()



