'''simple docstring'''





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput







def 			A__      (	snake_case_ :		List[Any]       ,				snake_case_ :		str=0.9_99       ,				snake_case_ :		Dict="cosine"       ,				):

 if alpha_transform_type == "cosine":

  def alpha_bar_fn(snake_case_ :		Optional[Any]      ):
   return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2      ) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(snake_case_ :		Optional[int]      ):
   return math.exp(t * -12.0      )

 else:
  raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'      )

 SCREAMING_SNAKE_CASE__:   List[Any]=      []
 for i in range(snake_case__      ):
  SCREAMING_SNAKE_CASE__:   Tuple=      i / num_diffusion_timesteps
  SCREAMING_SNAKE_CASE__:   Optional[int]=      (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(snake_case__      ) / alpha_bar_fn(snake_case__      )       ,				snake_case__      )      )
 return torch.tensor(snake_case__       ,				dtype=torch.floataa      )






class 		_lowerCamelCase		(   __a    ,     __a    ):
    __a  =      [e.name for e in KarrasDiffusionSchedulers]
    __a  =      2
    @register_to_config
    def __init__(  self , lowerCAmelCase = 1000 , lowerCAmelCase = 0.00085 , lowerCAmelCase = 0.012 , lowerCAmelCase = "linear" , lowerCAmelCase = None , lowerCAmelCase = "epsilon" , lowerCAmelCase = "linspace" , lowerCAmelCase = 0 , )       ->					Union[str, Any]:



     if trained_betas is not None:
      SCREAMING_SNAKE_CASE__:   Tuple=      torch.tensor(a_ , dtype=torch.floataa   )
     elif beta_schedule == "linear":
      SCREAMING_SNAKE_CASE__:   Dict=      torch.linspace(a_ , a_ , a_ , dtype=torch.floataa   )
     elif beta_schedule == "scaled_linear":
      # this schedule is very specific to the latent diffusion model.
      SCREAMING_SNAKE_CASE__:   List[str]=      (
          torch.linspace(beta_start**0.5 , beta_end**0.5 , a_ , dtype=torch.floataa   ) ** 2
      )
     elif beta_schedule == "squaredcos_cap_v2":
      # Glide cosine schedule
      SCREAMING_SNAKE_CASE__:   str=      betas_for_alpha_bar(a_   )
     else:
      raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'   )

     SCREAMING_SNAKE_CASE__:   int=      1.0 - self.betas
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.cumprod(self.alphas , dim=0   )

     #  set all values
     self.set_timesteps(a_ , a_ , a_   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase=None   )       ->					List[Any]:



     if schedule_timesteps is None:
      SCREAMING_SNAKE_CASE__:   List[Any]=      self.timesteps

     SCREAMING_SNAKE_CASE__:   Any=      (schedule_timesteps == timestep).nonzero()

     # The sigma index that is taken for the **very** first `step`
     # is always the second index (or the last index if there is only 1)
     # This way we can ensure we don't accidentally skip a sigma in
     # case we start in the middle of the denoising schedule (e.g. for image-to-image)
     if len(self._index_counter   ) == 0:
      SCREAMING_SNAKE_CASE__:   List[Any]=      1 if len(a_   ) > 1 else 0
     else:
      SCREAMING_SNAKE_CASE__:   int=      timestep.cpu().item() if torch.is_tensor(a_   ) else timestep
      SCREAMING_SNAKE_CASE__:   Tuple=      self._index_counter[timestep_int]

     return indices[pos].item()
    @property
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:



     if self.config.timestep_spacing in ["linspace", "trailing"]:
      return self.sigmas.max()

     return (self.sigmas.max() ** 2 + 1) ** 0.5
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , )       ->					int:
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.index_for_timestep(a_   )

     if self.state_in_first_order:
      SCREAMING_SNAKE_CASE__:   List[str]=      self.sigmas[step_index]
     else:
      SCREAMING_SNAKE_CASE__:   Any=      self.sigmas_interpol[step_index]

     SCREAMING_SNAKE_CASE__:   List[str]=      sample / ((sigma**2 + 1) ** 0.5)
     return sample
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase = None , )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      num_inference_steps

     SCREAMING_SNAKE_CASE__:   Optional[int]=      num_train_timesteps or self.config.num_train_timesteps

     # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
     if self.config.timestep_spacing == "linspace":
      SCREAMING_SNAKE_CASE__:   Optional[int]=      np.linspace(0 , num_train_timesteps - 1 , a_ , dtype=a_   )[::-1].copy()
     elif self.config.timestep_spacing == "leading":
      SCREAMING_SNAKE_CASE__:   int=      num_train_timesteps // self.num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # casting to int to avoid issues when num_inference_step is power of 3
      SCREAMING_SNAKE_CASE__:   int=      (np.arange(0 , a_   ) * step_ratio).round()[::-1].copy().astype(a_   )
      timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
      SCREAMING_SNAKE_CASE__:   Optional[int]=      num_train_timesteps / self.num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # casting to int to avoid issues when num_inference_step is power of 3
      SCREAMING_SNAKE_CASE__:   Tuple=      (np.arange(a_ , 0 , -step_ratio   )).round().copy().astype(a_   )
      timesteps -= 1
     else:
      raise ValueError(
          f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'   )

     SCREAMING_SNAKE_CASE__:   Dict=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
     SCREAMING_SNAKE_CASE__:   Dict=      torch.from_numpy(np.log(a_   )   ).to(a_   )

     SCREAMING_SNAKE_CASE__:   int=      np.interp(a_ , np.arange(0 , len(a_   )   ) , a_   )
     SCREAMING_SNAKE_CASE__:   Tuple=      np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
     SCREAMING_SNAKE_CASE__:   str=      torch.from_numpy(a_   ).to(device=a_   )

     # interpolate sigmas
     SCREAMING_SNAKE_CASE__:   int=      sigmas.log().lerp(sigmas.roll(1   ).log() , 0.5   ).exp()

     SCREAMING_SNAKE_CASE__:   Optional[int]=      torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2   ), sigmas[-1:]]   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.cat(
         [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2   ), sigmas_interpol[-1:]]   )

     if str(a_   ).startswith('''mps'''   ):
      # mps does not support float64
      SCREAMING_SNAKE_CASE__:   Dict=      torch.from_numpy(a_   ).to(a_ , dtype=torch.floataa   )
     else:
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.from_numpy(a_   ).to(a_   )

     # interpolate timesteps
     SCREAMING_SNAKE_CASE__:   Tuple=      self.sigma_to_t(a_   ).to(a_ , dtype=timesteps.dtype   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) , dim=-1   ).flatten()

     SCREAMING_SNAKE_CASE__:   List[str]=      torch.cat([timesteps[:1], interleaved_timesteps]   )

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      None

     # for exp beta schedules, such as the one for `pipeline_shap_e.py`
     # we need an index counter
     SCREAMING_SNAKE_CASE__:   List[str]=      defaultdict(a_   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Tuple=      sigma.log()

     # get distribution
     SCREAMING_SNAKE_CASE__:   Any=      log_sigma - self.log_sigmas[:, None]

     # get sigmas range
     SCREAMING_SNAKE_CASE__:   int=      dists.ge(0   ).cumsum(dim=0   ).argmax(dim=0   ).clamp(max=self.log_sigmas.shape[0] - 2   )
     SCREAMING_SNAKE_CASE__:   Dict=      low_idx + 1

     SCREAMING_SNAKE_CASE__:   List[str]=      self.log_sigmas[low_idx]
     SCREAMING_SNAKE_CASE__:   int=      self.log_sigmas[high_idx]

     # interpolate sigmas
     SCREAMING_SNAKE_CASE__:   Any=      (low - log_sigma) / (low - high)
     SCREAMING_SNAKE_CASE__:   Dict=      w.clamp(0 , 1   )

     # transform interpolation to time range
     SCREAMING_SNAKE_CASE__:   Tuple=      (1 - w) * low_idx + w * high_idx
     SCREAMING_SNAKE_CASE__:   List[Any]=      t.view(sigma.shape   )
     return t
    @property
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     return self.sample is None
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = True , )       ->					Optional[Any]:
     SCREAMING_SNAKE_CASE__:   Any=      self.index_for_timestep(a_   )

     # advance index counter by 1
     SCREAMING_SNAKE_CASE__:   List[str]=      timestep.cpu().item() if torch.is_tensor(a_   ) else timestep
     self._index_counter[timestep_int] += 1

     if self.state_in_first_order:
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.sigmas[step_index]
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.sigmas_interpol[step_index + 1]
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.sigmas[step_index + 1]
     else:
      # 2nd order / KDPM2's method
      SCREAMING_SNAKE_CASE__:   int=      self.sigmas[step_index - 1]
      SCREAMING_SNAKE_CASE__:   Any=      self.sigmas_interpol[step_index]
      SCREAMING_SNAKE_CASE__:   Tuple=      self.sigmas[step_index]

     # currently only gamma=0 is supported. This usually works best anyways.
     # We can support gamma in the future but then need to scale the timestep before
     # passing it to the model which requires a change in API
     SCREAMING_SNAKE_CASE__:   Optional[int]=      0
     SCREAMING_SNAKE_CASE__:   List[str]=      sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
      SCREAMING_SNAKE_CASE__:   str=      sigma_hat if self.state_in_first_order else sigma_interpol
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      sample - sigma_input * model_output
     elif self.config.prediction_type == "v_prediction":
      SCREAMING_SNAKE_CASE__:   Dict=      sigma_hat if self.state_in_first_order else sigma_interpol
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
          sample / (sigma_input**2 + 1)
      )
     elif self.config.prediction_type == "sample":
      raise NotImplementedError('''prediction_type not implemented yet: sample'''   )
     else:
      raise ValueError(
          f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'   )

     if self.state_in_first_order:
      # 2. Convert to an ODE derivative for 1st order
      SCREAMING_SNAKE_CASE__:   int=      (sample - pred_original_sample) / sigma_hat
      # 3. delta timestep
      SCREAMING_SNAKE_CASE__:   Tuple=      sigma_interpol - sigma_hat

      # store for 2nd order step
      SCREAMING_SNAKE_CASE__:   str=      sample
     else:
      # DPM-Solver-2
      # 2. Convert to an ODE derivative for 2nd order
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      (sample - pred_original_sample) / sigma_interpol

      # 3. delta timestep
      SCREAMING_SNAKE_CASE__:   Any=      sigma_next - sigma_hat

      SCREAMING_SNAKE_CASE__:   Optional[int]=      self.sample
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      None

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      sample + derivative * dt

     if not return_dict:
      return (prev_sample,)

     return SchedulerOutput(prev_sample=a_   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , )       ->					Union[str, Any]:
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype   )
     if original_samples.device.type == "mps" and torch.is_floating_point(a_   ):
      # mps does not support float64
      SCREAMING_SNAKE_CASE__:   Dict=      self.timesteps.to(original_samples.device , dtype=torch.floataa   )
      SCREAMING_SNAKE_CASE__:   List[Any]=      timesteps.to(original_samples.device , dtype=torch.floataa   )
     else:
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.timesteps.to(original_samples.device   )
      SCREAMING_SNAKE_CASE__:   Any=      timesteps.to(original_samples.device   )

     SCREAMING_SNAKE_CASE__:   List[str]=      [self.index_for_timestep(a_ , a_   ) for t in timesteps]

     SCREAMING_SNAKE_CASE__:   Tuple=      sigmas[step_indices].flatten()
     while len(sigma.shape   ) < len(original_samples.shape   ):
      SCREAMING_SNAKE_CASE__:   List[Any]=      sigma.unsqueeze(-1   )

     SCREAMING_SNAKE_CASE__:   Any=      original_samples + noise * sigma
     return noisy_samples



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


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_     :   Tuple 				=				getLogger(__name__)







def 			A__      (	snake_case_ :		Optional[int]       ,				snake_case_ :		str       ,				snake_case_ :		str       ,				snake_case_ :		int = 8       ,				snake_case_ :		int = 1_024       ,				snake_case_ :		Union[str, Any]="val"       ,				snake_case_ :		Tuple=None       ,				snake_case_ :		int=False       ,				snake_case_ :		Optional[Any]="summarization"       ,				snake_case_ :		Optional[Any]=None       ,				snake_case_ :		List[Any]=1       ,				snake_case_ :		Dict = None       ,				snake_case_ :		int=""       ,				**snake_case_ :		Any       ,				):
 SCREAMING_SNAKE_CASE__:   List[str]=      str(snake_case_      )
 assert local_rank is not None
 torch.distributed.init_process_group(backend='''nccl'''       ,				rank=snake_case_      )

 SCREAMING_SNAKE_CASE__:   str=      Path(snake_case_      )
 SCREAMING_SNAKE_CASE__:   Tuple=      save_dir.joinpath(F'rank_{local_rank}_output.json'      )
 torch.cuda.set_device(snake_case_      )
 SCREAMING_SNAKE_CASE__:   Tuple=      AutoModelForSeqaSeqLM.from_pretrained(snake_case_      ).cuda()
 if fpaa:
  SCREAMING_SNAKE_CASE__:   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
 SCREAMING_SNAKE_CASE__:   List[Any]=      generate_kwargs.pop('''num_beams'''       ,				model.config.num_beams      )  # AttributeError risk?
 if num_return_sequences > num_beams:
  SCREAMING_SNAKE_CASE__:   str=      num_return_sequences

 SCREAMING_SNAKE_CASE__:   Optional[int]=      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:
  SCREAMING_SNAKE_CASE__:   Dict=      tokenizer.model_max_length
 if prefix is None:
  SCREAMING_SNAKE_CASE__:   List[Any]=      prefix or getattr(model.config       ,				'''prefix'''       ,				''''''      ) or ''''''
 SCREAMING_SNAKE_CASE__:   Optional[int]=      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.
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      ds.make_sortish_sampler(snake_case_       ,				distributed=snake_case_       ,				add_extra_examples=snake_case_       ,				shuffle=snake_case_      )
 SCREAMING_SNAKE_CASE__:   List[Any]=      DataLoader(snake_case_       ,				sampler=snake_case_       ,				batch_size=snake_case_       ,				collate_fn=ds.collate_fn      )
 SCREAMING_SNAKE_CASE__:   List[Any]=      []
 for batch in tqdm(snake_case_      ):
  SCREAMING_SNAKE_CASE__:   List[Any]=      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_       ,				)
  SCREAMING_SNAKE_CASE__:   int=      tokenizer.batch_decode(snake_case_       ,				skip_special_tokens=snake_case_       ,				clean_up_tokenization_spaces=snake_case_      )
  SCREAMING_SNAKE_CASE__:   int=      batch['''ids''']
  if num_return_sequences > 1:
   SCREAMING_SNAKE_CASE__:   List[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__      (	):
 SCREAMING_SNAKE_CASE__:   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=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'''      )
 SCREAMING_SNAKE_CASE__:   Dict=      time.time()
 SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Dict=      parser.parse_known_args()
 SCREAMING_SNAKE_CASE__:   Tuple=      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}'      )
 SCREAMING_SNAKE_CASE__:   List[Any]=      Path(args.save_dir + '''_tmp'''      )
 Path(snake_case_      ).mkdir(exist_ok=snake_case_      )  # this handles locking.
 SCREAMING_SNAKE_CASE__:   Dict=      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.
 SCREAMING_SNAKE_CASE__:   List[str]=      {}
 if args.src_lang is not None:
  SCREAMING_SNAKE_CASE__:   str=      args.src_lang
 if args.tgt_lang is not None:
  SCREAMING_SNAKE_CASE__:   Any=      args.tgt_lang

 Path(args.save_dir      ).mkdir(exist_ok=snake_case_      )
 SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   str=      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:
  SCREAMING_SNAKE_CASE__:   str=      Path(args.save_dir      )
  save_dir.mkdir(exist_ok=snake_case_      )
  SCREAMING_SNAKE_CASE__:   Optional[Any]=      gather_results_from_each_node(snake_case_       ,				snake_case_       ,				args.sync_timeout      )
  SCREAMING_SNAKE_CASE__:   Union[str, Any]=      combine_partial_results(snake_case_      )
  if args.num_return_sequences > 1:
   SCREAMING_SNAKE_CASE__:   Optional[Any]=      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
  SCREAMING_SNAKE_CASE__:   Union[str, Any]=      Path(args.data_dir      ).joinpath(args.type_path + '''.target'''      )
  with open(snake_case_      ) as f:
   SCREAMING_SNAKE_CASE__:   List[str]=      [x.rstrip() for x in f.readlines()][: len(snake_case_      )]

  # Calculate metrics, save metrics,  and save _generations.txt
  SCREAMING_SNAKE_CASE__:   Optional[int]=      '''translation''' in args.task
  SCREAMING_SNAKE_CASE__:   str=      calculate_bleu if calc_bleu else calculate_rouge
  SCREAMING_SNAKE_CASE__:   Dict=      '''bleu''' if calc_bleu else '''rouge'''
  SCREAMING_SNAKE_CASE__:   Dict=      score_fn(snake_case_       ,				snake_case_      )
  SCREAMING_SNAKE_CASE__:   List[str]=      len(snake_case_      )
  SCREAMING_SNAKE_CASE__:   Dict=      time.time() - start_time
  SCREAMING_SNAKE_CASE__:   List[str]=      round(runtime / metrics['''n_obs''']       ,				4      )
  SCREAMING_SNAKE_CASE__:   str=      num_replicas
  # TODO(@stas00): add whatever metadata to metrics
  SCREAMING_SNAKE_CASE__:   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__      (	snake_case_ :		Optional[int]      ):
 SCREAMING_SNAKE_CASE__:   int=      []
 for partial_result in partial_results:
  records.extend(snake_case_      )
 SCREAMING_SNAKE_CASE__:   str=      sorted(snake_case_       ,				key=lambda snake_case_      : x["id"]      )
 SCREAMING_SNAKE_CASE__:   int=      [x['''pred'''] for x in records]
 return preds







def 			A__      (	snake_case_ :		Union[str, Any]       ,				snake_case_ :		List[Any]       ,				snake_case_ :		int      ):
 # WAIT FOR lots of .json files
 SCREAMING_SNAKE_CASE__:   Optional[int]=      time.time()
 logger.info('''waiting for all nodes to finish'''      )
 SCREAMING_SNAKE_CASE__:   Optional[Any]=      None
 while (time.time() - start_wait) < timeout:
  SCREAMING_SNAKE_CASE__:   List[Any]=      list(save_dir.glob('''rank_*.json'''      )      )
  if len(snake_case_      ) < num_replicas:
   continue
  try:
   # make sure all json files are fully saved
   SCREAMING_SNAKE_CASE__:   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()

