

"""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,
)


__UpperCAmelCase								=			getLogger(__name__)





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

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

   __lowerCAmelCase  :      List[str]    =						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  :      Optional[Any]    =						tokenizer.model_max_length
   if prefix is None:
      __lowerCAmelCase  :      Any    =						prefix or getattr(model.config ,       """prefix""" ,       """"""					) or """"""
   __lowerCAmelCase  :      List[Any]    =						SeqaSeqDataset(
       __A ,       __A ,       __A ,       max_target_length=1_0_2_4 ,       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  :      Any    =						ds.make_sortish_sampler(__A ,       distributed=__A ,       add_extra_examples=__A ,       shuffle=__A					)
   __lowerCAmelCase  :      int    =						DataLoader(__A ,       sampler=__A ,       batch_size=__A ,       collate_fn=ds.collate_fn					)
   __lowerCAmelCase  :      str    =						[]
   for batch in tqdm(__A					):
      __lowerCAmelCase  :      str    =						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  :      Dict    =						tokenizer.batch_decode(__A ,       skip_special_tokens=__A ,       clean_up_tokenization_spaces=__A					)
      __lowerCAmelCase  :      Any    =						batch["""ids"""]
      if num_return_sequences > 1:
         __lowerCAmelCase  :      int    =						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      snake_case_				()      ->     List[Any]:
   __lowerCAmelCase  :      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=__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_0_0 ,       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  :      int    =						time.time()
   __lowerCAmelCase  :      int    =						parser.parse_known_args()
   __lowerCAmelCase  :      List[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  :      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.
   __lowerCAmelCase  :      List[Any]    =						{}
   if args.src_lang is not None:
      __lowerCAmelCase  :      List[Any]    =						args.src_lang
   if args.tgt_lang is not None:
      __lowerCAmelCase  :      List[str]    =						args.tgt_lang

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

      # Calculate metrics, save metrics,  and save _generations.txt
      __lowerCAmelCase  :      List[str]    =						"""translation""" in args.task
      __lowerCAmelCase  :      List[str]    =						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  :      Optional[Any]    =						time.time() - start_time
      __lowerCAmelCase  :      Optional[Any]    =						round(runtime / metrics["""n_obs"""] ,       4					)
      __lowerCAmelCase  :      List[Any]    =						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      snake_case_				(__A					:						Union[str, Any]					)      ->     List:
   __lowerCAmelCase  :      Union[str, Any]    =						[]
   for partial_result in partial_results:
      records.extend(__A					)
   __lowerCAmelCase  :      Any    =						sorted(__A ,       key=lambda __A					: x["id"]					)
   __lowerCAmelCase  :      Any    =						[x["""pred"""] for x in records]
   return preds





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





import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
   import transformers





def      snake_case_				(__A					:						int					)      ->     List[Any]:
   random.seed(__A					)
   np.random.seed(__A					)
   torch.manual_seed(__A					)
   torch.cuda.manual_seed_all(__A					)
   # ^^ safe to call this function even if cuda is not available



class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   Any	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]	,							lowerCAmelCase			:   float = 0.9999	,							lowerCAmelCase			:   float = 0.0	,							lowerCAmelCase			:   int = 0	,							lowerCAmelCase			:   bool = False	,							lowerCAmelCase			:   Union[float, int] = 1.0	,							lowerCAmelCase			:   Union[float, int] = 2 / 3	,							lowerCAmelCase			:   Optional[Any] = None	,							lowerCAmelCase			:   Dict[str, Any] = None	,							**lowerCAmelCase			:   Any	,							)							->     List[str]:






     """simple docstring"""







     if isinstance(lowerCAmelCase	,							torch.nn.Module   ):
        __lowerCAmelCase  :      Optional[int]    =						(
            """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
            """Please pass the parameters of the module instead."""
        )
        deprecate(
            """passing a `torch.nn.Module` to `ExponentialMovingAverage`"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase	,							)
        __lowerCAmelCase  :      int    =						parameters.parameters()

        # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
        __lowerCAmelCase  :      Optional[Any]    =						True

     if kwargs.get("""max_value"""	,							lowerCAmelCase   ) is not None:
        __lowerCAmelCase  :      Optional[Any]    =						"""The `max_value` argument is deprecated. Please use `decay` instead."""
        deprecate("""max_value"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase   )
        __lowerCAmelCase  :      Dict    =						kwargs["""max_value"""]

     if kwargs.get("""min_value"""	,							lowerCAmelCase   ) is not None:
        __lowerCAmelCase  :      Optional[int]    =						"""The `min_value` argument is deprecated. Please use `min_decay` instead."""
        deprecate("""min_value"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase   )
        __lowerCAmelCase  :      Any    =						kwargs["""min_value"""]

     __lowerCAmelCase  :      Optional[Any]    =						list(lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						[p.clone().detach() for p in parameters]

     if kwargs.get("""device"""	,							lowerCAmelCase   ) is not None:
        __lowerCAmelCase  :      str    =						"""The `device` argument is deprecated. Please use `to` instead."""
        deprecate("""device"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase   )
        self.to(device=kwargs["""device"""]   )

     __lowerCAmelCase  :      List[Any]    =						None

     __lowerCAmelCase  :      Any    =						decay
     __lowerCAmelCase  :      Any    =						min_decay
     __lowerCAmelCase  :      List[Any]    =						update_after_step
     __lowerCAmelCase  :      Optional[Any]    =						use_ema_warmup
     __lowerCAmelCase  :      Union[str, Any]    =						inv_gamma
     __lowerCAmelCase  :      Union[str, Any]    =						power
     __lowerCAmelCase  :      Optional[Any]    =						0
     __lowerCAmelCase  :      str    =						None  # set in `step()`

     __lowerCAmelCase  :      int    =						model_cls
     __lowerCAmelCase  :      List[Any]    =						model_config







  @classmethod
  def 	SCREAMING_SNAKE_CASE   (      cls			:   Optional[int]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Dict   )							->     "EMAModel":






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      List[Any]    =						model_cls.load_config(lowerCAmelCase	,							return_unused_kwargs=lowerCAmelCase   )
     __lowerCAmelCase  :      Any    =						model_cls.from_pretrained(lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[Any]    =						cls(model.parameters()	,							model_cls=lowerCAmelCase	,							model_config=model.config   )

     ema_model.load_state_dict(lowerCAmelCase   )
     return ema_model







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]   )							->     int:






     """simple docstring"""







     if self.model_cls is None:
        raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__."""   )

     if self.model_config is None:
        raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__."""   )

     __lowerCAmelCase  :      Dict    =						self.model_cls.from_config(self.model_config   )
     __lowerCAmelCase  :      Optional[Any]    =						self.state_dict()
     state_dict.pop("""shadow_params"""	,							lowerCAmelCase   )

     model.register_to_config(**lowerCAmelCase   )
     self.copy_to(model.parameters()   )
     model.save_pretrained(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   int   )							->     float:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						max(0	,							optimization_step - self.update_after_step - 1   )

     if step <= 0:
        return 0.0

     if self.use_ema_warmup:
        __lowerCAmelCase  :      str    =						1 - (1 + step / self.inv_gamma) ** -self.power
     else:
        __lowerCAmelCase  :      Optional[int]    =						(1 + step) / (10 + step)

     __lowerCAmelCase  :      Any    =						min(lowerCAmelCase	,							self.decay   )
     # make sure decay is not smaller than min_decay
     __lowerCAmelCase  :      Union[str, Any]    =						max(lowerCAmelCase	,							self.min_decay   )
     return cur_decay_value







  @torch.no_grad()
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     Optional[int]:






     """simple docstring"""







     if isinstance(lowerCAmelCase	,							torch.nn.Module   ):
        __lowerCAmelCase  :      int    =						(
            """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
            """Please pass the parameters of the module instead."""
        )
        deprecate(
            """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"""	,							"""1.0.0"""	,							lowerCAmelCase	,							standard_warn=lowerCAmelCase	,							)
        __lowerCAmelCase  :      Any    =						parameters.parameters()

     __lowerCAmelCase  :      Any    =						list(lowerCAmelCase   )

     self.optimization_step += 1

     # Compute the decay factor for the exponential moving average.
     __lowerCAmelCase  :      Optional[Any]    =						self.get_decay(self.optimization_step   )
     __lowerCAmelCase  :      Dict    =						decay
     __lowerCAmelCase  :      Tuple    =						1 - decay

     __lowerCAmelCase  :      str    =						contextlib.nullcontext
     if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
        import deepspeed

     for s_param, param in zip(self.shadow_params	,							lowerCAmelCase   ):
        if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
           __lowerCAmelCase  :      List[Any]    =						deepspeed.zero.GatheredParameters(lowerCAmelCase	,							modifier_rank=lowerCAmelCase   )

        with context_manager():
           if param.requires_grad:
              s_param.sub_(one_minus_decay * (s_param - param)   )
           else:
              s_param.copy_(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						list(lowerCAmelCase   )
     for s_param, param in zip(self.shadow_params	,							lowerCAmelCase   ):
        param.data.copy_(s_param.to(param.device   ).data   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   Dict=None	,							lowerCAmelCase			:   Optional[Any]=None   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						[
         p.to(device=lowerCAmelCase	,							dtype=lowerCAmelCase   ) if p.is_floating_point() else p.to(device=lowerCAmelCase   )
         for p in self.shadow_params
     ]







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     dict:






     """simple docstring"""







     return {
         "decay": self.decay,
         "min_decay": self.min_decay,
         "optimization_step": self.optimization_step,
         "update_after_step": self.update_after_step,
         "use_ema_warmup": self.use_ema_warmup,
         "inv_gamma": self.inv_gamma,
         "power": self.power,
         "shadow_params": self.shadow_params,
     }







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						[param.detach().cpu().clone() for param in parameters]







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   Iterable[torch.nn.Parameter]   )							->     None:






     """simple docstring"""







     if self.temp_stored_params is None:
        raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`"""   )
     for c_param, param in zip(self.temp_stored_params	,							lowerCAmelCase   ):
        param.data.copy_(c_param.data   )

     # Better memory-wise.
     __lowerCAmelCase  :      Union[str, Any]    =						None







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   dict   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						copy.deepcopy(lowerCAmelCase   )

     __lowerCAmelCase  :      int    =						state_dict.get("""decay"""	,							self.decay   )
     if self.decay < 0.0 or self.decay > 1.0:
        raise ValueError("""Decay must be between 0 and 1"""   )

     __lowerCAmelCase  :      Tuple    =						state_dict.get("""min_decay"""	,							self.min_decay   )
     if not isinstance(self.min_decay	,							lowerCAmelCase   ):
        raise ValueError("""Invalid min_decay"""   )

     __lowerCAmelCase  :      List[Any]    =						state_dict.get("""optimization_step"""	,							self.optimization_step   )
     if not isinstance(self.optimization_step	,							lowerCAmelCase   ):
        raise ValueError("""Invalid optimization_step"""   )

     __lowerCAmelCase  :      Union[str, Any]    =						state_dict.get("""update_after_step"""	,							self.update_after_step   )
     if not isinstance(self.update_after_step	,							lowerCAmelCase   ):
        raise ValueError("""Invalid update_after_step"""   )

     __lowerCAmelCase  :      List[str]    =						state_dict.get("""use_ema_warmup"""	,							self.use_ema_warmup   )
     if not isinstance(self.use_ema_warmup	,							lowerCAmelCase   ):
        raise ValueError("""Invalid use_ema_warmup"""   )

     __lowerCAmelCase  :      Optional[int]    =						state_dict.get("""inv_gamma"""	,							self.inv_gamma   )
     if not isinstance(self.inv_gamma	,							(float, int)   ):
        raise ValueError("""Invalid inv_gamma"""   )

     __lowerCAmelCase  :      Any    =						state_dict.get("""power"""	,							self.power   )
     if not isinstance(self.power	,							(float, int)   ):
        raise ValueError("""Invalid power"""   )

     __lowerCAmelCase  :      Optional[Any]    =						state_dict.get("""shadow_params"""	,							lowerCAmelCase   )
     if shadow_params is not None:
        __lowerCAmelCase  :      int    =						shadow_params
        if not isinstance(self.shadow_params	,							lowerCAmelCase   ):
           raise ValueError("""shadow_params must be a list"""   )
        if not all(isinstance(lowerCAmelCase	,							torch.Tensor   ) for p in self.shadow_params   ):
           raise ValueError("""shadow_params must all be Tensors"""   )


