'''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,
)


__snake_case						  =		getLogger(__name__)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ = 8 ,	SCREAMING_SNAKE_CASE_ = 10_24 ,	SCREAMING_SNAKE_CASE_="val" ,	SCREAMING_SNAKE_CASE_=None ,	SCREAMING_SNAKE_CASE_=False ,	SCREAMING_SNAKE_CASE_="summarization" ,	SCREAMING_SNAKE_CASE_=None ,	SCREAMING_SNAKE_CASE_=1 ,	SCREAMING_SNAKE_CASE_ = None ,	SCREAMING_SNAKE_CASE_="" ,	**SCREAMING_SNAKE_CASE_ ,	)			->Dict:
  lowercase_     =       str(SCREAMING_SNAKE_CASE_						)
  assert local_rank is not None
  torch.distributed.init_process_group(backend="""nccl""" ,	rank=SCREAMING_SNAKE_CASE_						)

  lowercase_     =       Path(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       save_dir.joinpath(f"""rank_{local_rank}_output.json"""						)
  torch.cuda.set_device(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_						).cuda()
  if fpaa:
    lowercase_     =       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
  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(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:
    lowercase_     =       tokenizer.model_max_length
  if prefix is None:
    lowercase_     =       prefix or getattr(model.config ,	"""prefix""" ,	""""""						) or """"""
  lowercase_     =       SeqaSeqDataset(
      SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	max_target_length=10_24 ,	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.
  lowercase_     =       ds.make_sortish_sampler(SCREAMING_SNAKE_CASE_ ,	distributed=SCREAMING_SNAKE_CASE_ ,	add_extra_examples=SCREAMING_SNAKE_CASE_ ,	shuffle=SCREAMING_SNAKE_CASE_						)
  lowercase_     =       DataLoader(SCREAMING_SNAKE_CASE_ ,	sampler=SCREAMING_SNAKE_CASE_ ,	batch_size=SCREAMING_SNAKE_CASE_ ,	collate_fn=ds.collate_fn						)
  lowercase_     =       []
  for batch in tqdm(SCREAMING_SNAKE_CASE_						):
    lowercase_     =       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_ ,	)
    lowercase_     =       tokenizer.batch_decode(SCREAMING_SNAKE_CASE_ ,	skip_special_tokens=SCREAMING_SNAKE_CASE_ ,	clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_						)
    lowercase_     =       batch["""ids"""]
    if num_return_sequences > 1:
      lowercase_     =       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_      (       )			->Dict:
  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=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=6_00 ,	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"""						)
  lowercase_     =       time.time()
  lowercase_     ,	lowercase_     =       parser.parse_known_args()
  lowercase_     =       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}"""						)
  lowercase_     =       Path(args.save_dir + """_tmp"""						)
  Path(SCREAMING_SNAKE_CASE_						).mkdir(exist_ok=SCREAMING_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=SCREAMING_SNAKE_CASE_						)
  lowercase_     ,	lowercase_     =       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:
    lowercase_     =       Path(args.save_dir						)
    save_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_						)
    lowercase_     =       gather_results_from_each_node(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	args.sync_timeout						)
    lowercase_     =       combine_partial_results(SCREAMING_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(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
      return
    lowercase_     =       Path(args.data_dir						).joinpath(args.type_path + """.target"""						)
    with open(SCREAMING_SNAKE_CASE_						) as f:
      lowercase_     =       [x.rstrip() for x in f.readlines()][: len(SCREAMING_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(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    lowercase_     =       len(SCREAMING_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(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_      (       SCREAMING_SNAKE_CASE_						)			->List:
  lowercase_     =       []
  for partial_result in partial_results:
    records.extend(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       sorted(SCREAMING_SNAKE_CASE_ ,	key=lambda SCREAMING_SNAKE_CASE_						: x["id"]						)
  lowercase_     =       [x["""pred"""] for x in records]
  return preds

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->List[Dict[str, List]]:
  # WAIT FOR lots of .json files
  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(SCREAMING_SNAKE_CASE_						) < num_replicas:
      continue
    try:
      # make sure all json files are fully saved
      lowercase_     =       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'''


import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


__snake_case						  =		logging.get_logger(__name__)





class       _a    (		__a      ):


  """simple docstring"""

  def __init__(		self      :       int   ,				*lowercase_      :       Tuple   ,				**lowercase_      :       List[Any]      ):







    '''simple docstring'''


    warnings.warn(
        """The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
        """ Please use PoolFormerImageProcessor instead."""   ,				lowercase_   ,				)
    super().__init__(*lowercase_   ,				**lowercase_      )

