


'''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   :    Any											= getLogger(__name__)







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  str				,       A__   :  str				,       A__   :  int = 8				,       A__   :  int = 10_24				,       A__   :  Optional[Any]="val"				,       A__   :  Any=None				,       A__   :  Optional[int]=False				,       A__   :  Optional[Any]="summarization"				,       A__   :  str=None				,       A__   :  Tuple=1				,       A__   :  Dict = None				,       A__   :  Any=""				,       **A__   :  Optional[Any]				,       ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  str(A__   )
  assert local_rank is not None
  torch.distributed.init_process_group(backend="""nccl"""				,       rank=A__   )

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

  lowerCAmelCase_     : 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:
    lowerCAmelCase_     : str     =  tokenizer.model_max_length
  if prefix is None:
    lowerCAmelCase_     : Union[str, Any]     =  prefix or getattr(model.config				,       """prefix"""				,       """"""   ) or """"""
  lowerCAmelCase_     : str     =  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.
  lowerCAmelCase_     : List[Any]     =  ds.make_sortish_sampler(A__				,       distributed=A__				,       add_extra_examples=A__				,       shuffle=A__   )
  lowerCAmelCase_     : List[Any]     =  DataLoader(A__				,       sampler=A__				,       batch_size=A__				,       collate_fn=ds.collate_fn   )
  lowerCAmelCase_     : Optional[Any]     =  []
  for batch in tqdm(A__   ):
    lowerCAmelCase_     : List[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__				,       )
    lowerCAmelCase_     : List[str]     =  tokenizer.batch_decode(A__				,       skip_special_tokens=A__				,       clean_up_tokenization_spaces=A__   )
    lowerCAmelCase_     : Dict     =  batch["""ids"""]
    if num_return_sequences > 1:
      lowerCAmelCase_     : Optional[Any]     =  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_     (       ):
  '''simple docstring'''





  lowerCAmelCase_     : 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=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"""   )
  lowerCAmelCase_     : Any     =  time.time()
  lowerCAmelCase_,				lowerCAmelCase_     : List[str]     =  parser.parse_known_args()
  lowerCAmelCase_     : Union[str, 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}'   )
  lowerCAmelCase_     : List[Any]     =  Path(args.save_dir + """_tmp"""   )
  Path(A__   ).mkdir(exist_ok=A__   )  # this handles locking.
  lowerCAmelCase_     : 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.
  lowerCAmelCase_     : Dict     =  {}
  if args.src_lang is not None:
    lowerCAmelCase_     : Optional[Any]     =  args.src_lang
  if args.tgt_lang is not None:
    lowerCAmelCase_     : str     =  args.tgt_lang

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

    # Calculate metrics, save metrics,  and save _generations.txt
    lowerCAmelCase_     : int     =  """translation""" in args.task
    lowerCAmelCase_     : int     =  calculate_bleu if calc_bleu else calculate_rouge
    lowerCAmelCase_     : Any     =  """bleu""" if calc_bleu else """rouge"""
    lowerCAmelCase_     : Dict     =  score_fn(A__				,       A__   )
    lowerCAmelCase_     : int     =  len(A__   )
    lowerCAmelCase_     : List[Any]     =  time.time() - start_time
    lowerCAmelCase_     : List[Any]     =  round(runtime / metrics["""n_obs"""]				,       4   )
    lowerCAmelCase_     : str     =  num_replicas
    # TODO(@stas00): add whatever metadata to metrics
    lowerCAmelCase_     : str     =  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__   :  str   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  []
  for partial_result in partial_results:
    records.extend(A__   )
  lowerCAmelCase_     : int     =  sorted(A__				,       key=lambda A__   : x["id"]   )
  lowerCAmelCase_     : Any     =  [x["""pred"""] for x in records]
  return preds







def 							UpperCamelCase_     (       A__   :  Optional[Any]				,       A__   :  List[Any]				,       A__   :  Union[str, Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : int     =  time.time()
  logger.info("""waiting for all nodes to finish"""   )
  lowerCAmelCase_     : Any     =  None
  while (time.time() - start_wait) < timeout:
    lowerCAmelCase_     : List[Any]     =  list(save_dir.glob("""rank_*.json"""   )   )
    if len(A__   ) < num_replicas:
      continue
    try:
      # make sure all json files are fully saved
      lowerCAmelCase_     : Dict     =  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 os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


__A   :    int											= pytest.mark.integration







@pytest.mark.parametrize("""path"""				,       ["""paws""", """csv"""]   )
def 							UpperCamelCase_     (       A__   :  Union[str, Any]				,       A__   :  Tuple   ):
  '''simple docstring'''





  inspect_dataset(A__				,       A__   )
  lowerCAmelCase_     : Any     =  path + """.py"""
  assert script_name in os.listdir(A__   )
  assert "__pycache__" not in os.listdir(A__   )







@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning"""   )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""   )
@pytest.mark.parametrize("""path"""				,       ["""accuracy"""]   )
def 							UpperCamelCase_     (       A__   :  Tuple				,       A__   :  str   ):
  '''simple docstring'''





  inspect_metric(A__				,       A__   )
  lowerCAmelCase_     : Any     =  path + """.py"""
  assert script_name in os.listdir(A__   )
  assert "__pycache__" not in os.listdir(A__   )







@pytest.mark.parametrize(
    """path, config_name, expected_splits"""				,       [
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ]				,       )
def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  List[str]				,       A__   :  Optional[Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : List[Any]     =  get_dataset_config_info(A__				,       config_name=A__   )
  assert info.config_name == config_name
  assert list(info.splits.keys()   ) == expected_splits







@pytest.mark.parametrize(
    """path, config_name, expected_exception"""				,       [
        ("""paws""", None, ValueError),
    ]				,       )
def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  List[str]				,       A__   :  int   ):
  '''simple docstring'''





  with pytest.raises(A__   ):
    get_dataset_config_info(A__				,       config_name=A__   )







@pytest.mark.parametrize(
    """path, expected"""				,       [
        ("""squad""", """plain_text"""),
        ("""acronym_identification""", """default"""),
        ("""lhoestq/squad""", """plain_text"""),
        ("""lhoestq/test""", """default"""),
        ("""lhoestq/demo1""", """lhoestq--demo1"""),
        ("""dalle-mini/wit""", """dalle-mini--wit"""),
    ]				,       )
def 							UpperCamelCase_     (       A__   :  str				,       A__   :  List[str]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  get_dataset_config_names(A__   )
  assert expected in config_names







@pytest.mark.parametrize(
    """path, expected_configs, expected_splits_in_first_config"""				,       [
        ("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
        ("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
        ("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
    ]				,       )
def 							UpperCamelCase_     (       A__   :  List[Any]				,       A__   :  Dict				,       A__   :  Optional[int]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Tuple     =  get_dataset_infos(A__   )
  assert list(infos.keys()   ) == expected_configs
  lowerCAmelCase_     : Optional[int]     =  expected_configs[0]
  assert expected_config in infos
  lowerCAmelCase_     : int     =  infos[expected_config]
  assert info.config_name == expected_config
  assert list(info.splits.keys()   ) == expected_splits_in_first_config







@pytest.mark.parametrize(
    """path, expected_config, expected_splits"""				,       [
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ]				,       )
def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  Optional[Any]				,       A__   :  List[Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  get_dataset_infos(A__   )
  assert expected_config in infos
  lowerCAmelCase_     : List[Any]     =  infos[expected_config]
  assert info.config_name == expected_config
  assert list(info.splits.keys()   ) == expected_splits







@pytest.mark.parametrize(
    """path, config_name, expected_exception"""				,       [
        ("""paws""", None, ValueError),
    ]				,       )
def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  Any				,       A__   :  Dict   ):
  '''simple docstring'''





  with pytest.raises(A__   ):
    get_dataset_split_names(A__				,       config_name=A__   )



