





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,
)


UpperCAmelCase	:	List[str]		     =       getLogger(__name__)
def      _A       (		SCREAMING_SNAKE_CASE     :   Optional[Any]       ,       SCREAMING_SNAKE_CASE     :   Any       ,       SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   Dict = 8       ,       SCREAMING_SNAKE_CASE     :   Tuple = 1_024       ,       SCREAMING_SNAKE_CASE     :   Any="val"       ,       SCREAMING_SNAKE_CASE     :   Optional[int]=None       ,       SCREAMING_SNAKE_CASE     :   Optional[Any]=False       ,       SCREAMING_SNAKE_CASE     :   int="summarization"       ,       SCREAMING_SNAKE_CASE     :   Optional[Any]=None       ,       SCREAMING_SNAKE_CASE     :   List[Any]=1       ,       SCREAMING_SNAKE_CASE     :   Optional[int] = None       ,       SCREAMING_SNAKE_CASE     :   Optional[Any]=""       ,       **SCREAMING_SNAKE_CASE     :   int       ,       ):





  """simple docstring"""



  a__	:					List[str]										=str(__snake_case						)
  assert local_rank is not None
  torch.distributed.init_process_group(backend="nccl"       ,       rank=__snake_case						)

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

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

  if max_source_length is None:
    a__	:					str										=tokenizer.model_max_length
  if prefix is None:
    a__	:					Optional[Any]										=prefix or getattr(model.config       ,       "prefix"       ,       ""						) or ""
  a__	:					Tuple										=SeqaSeqDataset(
      __snake_case       ,       __snake_case       ,       __snake_case       ,       max_target_length=1_024       ,       type_path=__snake_case       ,       n_obs=__snake_case       ,       prefix=__snake_case       ,       **__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.
  a__	:					Tuple										=ds.make_sortish_sampler(__snake_case       ,       distributed=__snake_case       ,       add_extra_examples=__snake_case       ,       shuffle=__snake_case						)
  a__	:					Union[str, Any]										=DataLoader(__snake_case       ,       sampler=__snake_case       ,       batch_size=__snake_case       ,       collate_fn=ds.collate_fn						)
  a__	:					Optional[int]										=[]
  for batch in tqdm(__snake_case						):
    a__	:					List[str]										=model.generate(
        input_ids=batch["input_ids"].to(model.device						)       ,       attention_mask=batch["attention_mask"].to(model.device						)       ,       num_return_sequences=__snake_case       ,       num_beams=__snake_case       ,       **__snake_case       ,       )
    a__	:					Union[str, Any]										=tokenizer.batch_decode(__snake_case       ,       skip_special_tokens=__snake_case       ,       clean_up_tokenization_spaces=__snake_case						)
    a__	:					Any										=batch["ids"]
    if num_return_sequences > 1:
      a__	:					Any										=chunks(__snake_case       ,       __snake_case						)  # batch size chunks, each of size num_return_seq
    for i, pred in enumerate(__snake_case						):
      results.append({"pred": pred, "id": ids[i].item()}						)
  save_json(__snake_case       ,       __snake_case						)
  return results, sampler.num_replicas
def      _A       (		):





  """simple docstring"""



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

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

  Path(args.save_dir						).mkdir(exist_ok=__snake_case						)
  a__					,							a__	:					Tuple										=eval_data_dir(
      args.data_dir       ,       __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=__snake_case       ,       **__snake_case       ,       )

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

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





  """simple docstring"""



  a__	:					Dict										=[]
  for partial_result in partial_results:
    records.extend(__snake_case						)
  a__	:					Any										=sorted(__snake_case       ,       key=lambda SCREAMING_SNAKE_CASE						: x["id"]						)
  a__	:					Union[str, Any]										=[x["pred"] for x in records]
  return preds
def      _A       (		SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   Optional[Any]       ,       SCREAMING_SNAKE_CASE     :   Union[str, Any]						):





  """simple docstring"""



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










# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin







class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			1





     @register_to_config
     def __init__(self		,     UpperCAmelCase=2_0_0_0		,     UpperCAmelCase=0.1		,     UpperCAmelCase=2_0		,     UpperCAmelCase=1e-3       )     ->   List[str]:
      _lowercase							      =None
      _lowercase							      =None
      _lowercase							      =None





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   str:
      _lowercase							      =torch.linspace(1		,     self.config.sampling_eps		,     UpperCAmelCase		,     device=UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None       )     ->   Optional[int]:
      if self.timesteps is None:
       raise ValueError(
           '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''       )

      # TODO(Patrick) better comments + non-PyTorch
      # postprocess model score
      _lowercase							      =(
          -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
      )
      _lowercase							      =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff       )       )
      _lowercase							      =std.flatten()
      while len(std.shape       ) < len(score.shape       ):
       _lowercase							      =std.unsqueeze(-1       )
      _lowercase							      =-score / std

      # compute
      _lowercase							      =-1.0 / len(self.timesteps       )

      _lowercase							      =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
      _lowercase							      =beta_t.flatten()
      while len(beta_t.shape       ) < len(x.shape       ):
       _lowercase							      =beta_t.unsqueeze(-1       )
      _lowercase							      =-0.5 * beta_t * x

      _lowercase							      =torch.sqrt(UpperCAmelCase       )
      _lowercase							      =drift - diffusion**2 * score
      _lowercase							      =x + drift * dt

      # add noise
      _lowercase							      =randn_tensor(x.shape		,     layout=x.layout		,     generator=UpperCAmelCase		,     device=x.device		,     dtype=x.dtype       )
      _lowercase							      =x_mean + diffusion * math.sqrt(-dt       ) * noise

      return x, x_mean





     def __len__(self       )     ->   str:
      return self.config.num_train_timesteps

