'''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  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				str   ,    _lowercase    :				str   ,    _lowercase    :				int = 8   ,    _lowercase    :				int = 1_0_2_4   ,    _lowercase    :				Tuple="val"   ,    _lowercase    :				List[str]=None   ,    _lowercase    :				Dict=False   ,    _lowercase    :				str="summarization"   ,    _lowercase    :				Union[str, Any]=None   ,    _lowercase    :				List[str]=1   ,    _lowercase    :				Dict = None   ,    _lowercase    :				Optional[int]=""   ,    **_lowercase    :				Tuple   ,    )							->	Dict:
  __UpperCAmelCase:					Union[str, Any]		= str(_lowercase     )
  assert local_rank is not None
  torch.distributed.init_process_group(backend="""nccl"""   ,    rank=_lowercase     )

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

  __UpperCAmelCase:					Optional[Any]		= AutoTokenizer.from_pretrained(_lowercase     )
  logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''     )  # if this is wrong, check config.model_type.

  if max_source_length is None:
    __UpperCAmelCase:					Optional[int]		= tokenizer.model_max_length
  if prefix is None:
    __UpperCAmelCase:					Tuple		= prefix or getattr(model.config   ,    """prefix"""   ,    """"""     ) or """"""
  __UpperCAmelCase:					Optional[int]		= SeqaSeqDataset(
      _lowercase   ,    _lowercase   ,    _lowercase   ,    max_target_length=1_0_2_4   ,    type_path=_lowercase   ,    n_obs=_lowercase   ,    prefix=_lowercase   ,    **_lowercase   ,    )
  # 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:					int		= ds.make_sortish_sampler(_lowercase   ,    distributed=_lowercase   ,    add_extra_examples=_lowercase   ,    shuffle=_lowercase     )
  __UpperCAmelCase:					Tuple		= DataLoader(_lowercase   ,    sampler=_lowercase   ,    batch_size=_lowercase   ,    collate_fn=ds.collate_fn     )
  __UpperCAmelCase:					List[Any]		= []
  for batch in tqdm(_lowercase     ):
    __UpperCAmelCase:					List[Any]		= model.generate(
        input_ids=batch["""input_ids"""].to(model.device     )   ,    attention_mask=batch["""attention_mask"""].to(model.device     )   ,    num_return_sequences=_lowercase   ,    num_beams=_lowercase   ,    **_lowercase   ,    )
    __UpperCAmelCase:					List[str]		= tokenizer.batch_decode(_lowercase   ,    skip_special_tokens=_lowercase   ,    clean_up_tokenization_spaces=_lowercase     )
    __UpperCAmelCase:					Tuple		= batch["""ids"""]
    if num_return_sequences > 1:
      __UpperCAmelCase:					Dict		= chunks(_lowercase   ,    _lowercase     )  # batch size chunks, each of size num_return_seq
    for i, pred in enumerate(_lowercase     ):
      results.append({"""pred""": pred, """id""": ids[i].item()}     )
  save_json(_lowercase   ,    _lowercase     )
  return results, sampler.num_replicas






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

  parser.add_argument(
      """--n_obs"""   ,    type=_lowercase   ,    default=_lowercase   ,    required=_lowercase   ,    help="""How many observations. Defaults to all."""     )
  parser.add_argument(
      """--num_return_sequences"""   ,    type=_lowercase   ,    default=1   ,    required=_lowercase   ,    help="""How many sequences to return"""     )
  parser.add_argument(
      """--sync_timeout"""   ,    type=_lowercase   ,    default=6_0_0   ,    required=_lowercase   ,    help="""How long should master process wait for other processes to finish."""   ,    )
  parser.add_argument("""--src_lang"""   ,    type=_lowercase   ,    default=_lowercase   ,    required=_lowercase     )
  parser.add_argument("""--tgt_lang"""   ,    type=_lowercase   ,    default=_lowercase   ,    required=_lowercase     )
  parser.add_argument(
      """--prefix"""   ,    type=_lowercase   ,    required=_lowercase   ,    default=_lowercase   ,    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:					Tuple		= time.time()
  __UpperCAmelCase:					Optional[int]		= parser.parse_known_args()
  __UpperCAmelCase:					List[str]		= parse_numeric_n_bool_cl_kwargs(_lowercase     )
  if generate_kwargs and args.local_rank <= 0:
    print(F'''parsed the following generate kwargs: {generate_kwargs}'''     )
  __UpperCAmelCase:					List[str]		= Path(args.save_dir + """_tmp"""     )
  Path(_lowercase     ).mkdir(exist_ok=_lowercase     )  # this handles locking.
  __UpperCAmelCase:					Optional[int]		= 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:					Optional[Any]		= {}
  if args.src_lang is not None:
    __UpperCAmelCase:					Any		= args.src_lang
  if args.tgt_lang is not None:
    __UpperCAmelCase:					int		= args.tgt_lang

  Path(args.save_dir     ).mkdir(exist_ok=_lowercase     )
  __UpperCAmelCase:					Any		= eval_data_dir(
      args.data_dir   ,    _lowercase   ,    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=_lowercase   ,    **_lowercase   ,    )

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

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






def  UpperCamelCase__					(					_lowercase    :				str     )							->	List:
  __UpperCAmelCase:					List[str]		= []
  for partial_result in partial_results:
    records.extend(_lowercase     )
  __UpperCAmelCase:					Optional[Any]		= sorted(_lowercase   ,    key=lambda _lowercase     : x["id"]     )
  __UpperCAmelCase:					List[Any]		= [x["""pred"""] for x in records]
  return preds






def  UpperCamelCase__					(					_lowercase    :				int   ,    _lowercase    :				int   ,    _lowercase    :				Union[str, Any]     )							->	List[Dict[str, List]]:
  # WAIT FOR lots of .json files
  __UpperCAmelCase:					int		= time.time()
  logger.info("""waiting for all nodes to finish"""     )
  __UpperCAmelCase:					Optional[int]		= None
  while (time.time() - start_wait) < timeout:
    __UpperCAmelCase:					Optional[Any]		= list(save_dir.glob("""rank_*.json"""     )     )
    if len(_lowercase     ) < num_replicas:
      continue
    try:
      # make sure all json files are fully saved
      __UpperCAmelCase:					Optional[int]		= lmap(_lowercase   ,    _lowercase     )
      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 ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	   =			{
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}




class    a (       __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					"""timesformer"""







  def __init__(							self   ,    snake_case_=224   ,    snake_case_=16   ,    snake_case_=3   ,    snake_case_=8   ,    snake_case_=768   ,    snake_case_=12   ,    snake_case_=12   ,    snake_case_=3072   ,    snake_case_="gelu"   ,    snake_case_=0.0   ,    snake_case_=0.0   ,    snake_case_=0.0_2   ,    snake_case_=1e-6   ,    snake_case_=True   ,    snake_case_="divided_space_time"   ,    snake_case_=0   ,    **snake_case_   ,    ):

    '''simple docstring'''







    super().__init__(**snake_case_					)

    __UpperCAmelCase:					Tuple		= image_size
    __UpperCAmelCase:					List[Any]		= patch_size
    __UpperCAmelCase:					Optional[Any]		= num_channels
    __UpperCAmelCase:					int		= num_frames

    __UpperCAmelCase:					List[str]		= hidden_size
    __UpperCAmelCase:					List[str]		= num_hidden_layers
    __UpperCAmelCase:					Dict		= num_attention_heads
    __UpperCAmelCase:					List[str]		= intermediate_size
    __UpperCAmelCase:					Union[str, Any]		= hidden_act
    __UpperCAmelCase:					Dict		= hidden_dropout_prob
    __UpperCAmelCase:					Optional[Any]		= attention_probs_dropout_prob
    __UpperCAmelCase:					Union[str, Any]		= initializer_range
    __UpperCAmelCase:					List[Any]		= layer_norm_eps
    __UpperCAmelCase:					int		= qkv_bias

    __UpperCAmelCase:					str		= attention_type
    __UpperCAmelCase:					Tuple		= drop_path_rate