






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,
)


__magic_name__   :  int        =							getLogger(__name__)
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase = 8       ,							_UpperCamelCase = 10_24       ,							_UpperCamelCase="val"       ,							_UpperCamelCase=None       ,							_UpperCamelCase=False       ,							_UpperCamelCase="summarization"       ,							_UpperCamelCase=None       ,							_UpperCamelCase=1       ,							_UpperCamelCase = None       ,							_UpperCamelCase=""       ,							**_UpperCamelCase       ,							) ->			Dict:
      """simple docstring"""

      UpperCamelCase					       =  str(_UpperCamelCase)
      assert local_rank is not None
      torch.distributed.init_process_group(backend='nccl'       ,							rank=_UpperCamelCase)

      UpperCamelCase					       =  Path(_UpperCamelCase)
      UpperCamelCase					       =  save_dir.joinpath(F'rank_{local_rank}_output.json')
      torch.cuda.set_device(_UpperCamelCase)
      UpperCamelCase					       =  AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase).cuda()
      if fpaa:
            UpperCamelCase					       =  model.half()
      # determine if we need to increase num_beams
      use_task_specific_params(_UpperCamelCase       ,							_UpperCamelCase)  # update config with task specific params
      UpperCamelCase					       =  generate_kwargs.pop('num_beams'       ,							model.config.num_beams)  # AttributeError risk?
      if num_return_sequences > num_beams:
            UpperCamelCase					       =  num_return_sequences

      UpperCamelCase					       =  AutoTokenizer.from_pretrained(_UpperCamelCase)
      logger.info(F'Inferred tokenizer type: {tokenizer.__class__}')  # if this is wrong, check config.model_type.

      if max_source_length is None:
            UpperCamelCase					       =  tokenizer.model_max_length
      if prefix is None:
            UpperCamelCase					       =  prefix or getattr(model.config       ,							'prefix'       ,							'') or ''
      UpperCamelCase					       =  SeqaSeqDataset(
          _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							max_target_length=10_24       ,							type_path=_UpperCamelCase       ,							n_obs=_UpperCamelCase       ,							prefix=_UpperCamelCase       ,							**_UpperCamelCase       ,							)
      # 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					       =  ds.make_sortish_sampler(_UpperCamelCase       ,							distributed=_UpperCamelCase       ,							add_extra_examples=_UpperCamelCase       ,							shuffle=_UpperCamelCase)
      UpperCamelCase					       =  DataLoader(_UpperCamelCase       ,							sampler=_UpperCamelCase       ,							batch_size=_UpperCamelCase       ,							collate_fn=ds.collate_fn)
      UpperCamelCase					       =  []
      for batch in tqdm(_UpperCamelCase):
            UpperCamelCase					       =  model.generate(
                input_ids=batch['input_ids'].to(model.device)       ,							attention_mask=batch['attention_mask'].to(model.device)       ,							num_return_sequences=_UpperCamelCase       ,							num_beams=_UpperCamelCase       ,							**_UpperCamelCase       ,							)
            UpperCamelCase					       =  tokenizer.batch_decode(_UpperCamelCase       ,							skip_special_tokens=_UpperCamelCase       ,							clean_up_tokenization_spaces=_UpperCamelCase)
            UpperCamelCase					       =  batch['ids']
            if num_return_sequences > 1:
                  UpperCamelCase					       =  chunks(_UpperCamelCase       ,							_UpperCamelCase)  # batch size chunks, each of size num_return_seq
            for i, pred in enumerate(_UpperCamelCase):
                  results.append({'pred': pred, 'id': ids[i].item()})
      save_json(_UpperCamelCase       ,							_UpperCamelCase)
      return results, sampler.num_replicas
def 		lowercase__  (				) ->			Optional[Any]:
      """simple docstring"""

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

      parser.add_argument(
          '--n_obs'       ,							type=_UpperCamelCase       ,							default=_UpperCamelCase       ,							required=_UpperCamelCase       ,							help='How many observations. Defaults to all.')
      parser.add_argument(
          '--num_return_sequences'       ,							type=_UpperCamelCase       ,							default=1       ,							required=_UpperCamelCase       ,							help='How many sequences to return')
      parser.add_argument(
          '--sync_timeout'       ,							type=_UpperCamelCase       ,							default=6_00       ,							required=_UpperCamelCase       ,							help='How long should master process wait for other processes to finish.'       ,							)
      parser.add_argument('--src_lang'       ,							type=_UpperCamelCase       ,							default=_UpperCamelCase       ,							required=_UpperCamelCase)
      parser.add_argument('--tgt_lang'       ,							type=_UpperCamelCase       ,							default=_UpperCamelCase       ,							required=_UpperCamelCase)
      parser.add_argument(
          '--prefix'       ,							type=_UpperCamelCase       ,							required=_UpperCamelCase       ,							default=_UpperCamelCase       ,							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					       =  time.time()
      UpperCamelCase       ,  UpperCamelCase					       =  parser.parse_known_args()
      UpperCamelCase					       =  parse_numeric_n_bool_cl_kwargs(_UpperCamelCase)
      if generate_kwargs and args.local_rank <= 0:
            print(F'parsed the following generate kwargs: {generate_kwargs}')
      UpperCamelCase					       =  Path(args.save_dir + '_tmp')
      Path(_UpperCamelCase).mkdir(exist_ok=_UpperCamelCase)  # this handles locking.
      UpperCamelCase					       =  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					       =  {}
      if args.src_lang is not None:
            UpperCamelCase					       =  args.src_lang
      if args.tgt_lang is not None:
            UpperCamelCase					       =  args.tgt_lang

      Path(args.save_dir).mkdir(exist_ok=_UpperCamelCase)
      UpperCamelCase       ,  UpperCamelCase					       =  eval_data_dir(
          args.data_dir       ,							_UpperCamelCase       ,							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=_UpperCamelCase       ,							**_UpperCamelCase       ,							)

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

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

      UpperCamelCase					       =  []
      for partial_result in partial_results:
            records.extend(_UpperCamelCase)
      UpperCamelCase					       =  sorted(_UpperCamelCase       ,							key=lambda _UpperCamelCase: x["id"])
      UpperCamelCase					       =  [x['pred'] for x in records]
      return preds
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			List[Dict[str, List]]:
      """simple docstring"""

      UpperCamelCase					       =  time.time()
      logger.info('waiting for all nodes to finish')
      UpperCamelCase					       =  None
      while (time.time() - start_wait) < timeout:
            UpperCamelCase					       =  list(save_dir.glob('rank_*.json'))
            if len(_UpperCamelCase) < num_replicas:
                  continue
            try:
                  # make sure all json files are fully saved
                  UpperCamelCase					       =  lmap(_UpperCamelCase       ,							_UpperCamelCase)
                  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()











from __future__ import annotations

from collections.abc import MutableSequence





class 					A__     :

     '''simple docstring'''






     def __init__( self      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    int   ,						_SCREAMING_SNAKE_CASE      :    MutableSequence[float] ):






           """simple docstring"""




           if len(_SCREAMING_SNAKE_CASE ) != degree + 1:
                 raise ValueError(
                     'The number of coefficients should be equal to the degree + 1.' )

           UpperCamelCase					       =  list(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  degree







     def __add__( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    Polynomial ):






           """simple docstring"""




           if self.degree > polynomial_a.degree:
                 UpperCamelCase					       =  self.coefficients[:]
                 for i in range(polynomial_a.degree + 1 ):
                       coefficients[i] += polynomial_a.coefficients[i]
                 return Polynomial(self.degree   ,						_SCREAMING_SNAKE_CASE )
           else:
                 UpperCamelCase					       =  polynomial_a.coefficients[:]
                 for i in range(self.degree + 1 ):
                       coefficients[i] += self.coefficients[i]
                 return Polynomial(polynomial_a.degree   ,						_SCREAMING_SNAKE_CASE )







     def __sub__( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    Polynomial ):






           """simple docstring"""




           return self + polynomial_a * Polynomial(0   ,						[-1] )







     def __neg__( self      :    Any ):






           """simple docstring"""




           return Polynomial(self.degree   ,						[-c for c in self.coefficients] )







     def __mul__( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    Polynomial ):






           """simple docstring"""




           UpperCamelCase					       =  [0] * (self.degree + polynomial_a.degree + 1)
           for i in range(self.degree + 1 ):
                 for j in range(polynomial_a.degree + 1 ):
                       coefficients[i + j] += (
                           self.coefficients[i] * polynomial_a.coefficients[j]
                       )

           return Polynomial(self.degree + polynomial_a.degree   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    int | float ):






           """simple docstring"""




           UpperCamelCase					       =  0
           for i in range(self.degree + 1 ):
                 result += self.coefficients[i] * (substitution**i)
           return result







     def __str__( self      :    Union[str, Any] ):






           """simple docstring"""




           UpperCamelCase					       =  ''
           for i in range(self.degree   ,						-1   ,						-1 ):
                 if self.coefficients[i] == 0:
                       continue
                 elif self.coefficients[i] > 0:
                       if polynomial:
                             polynomial += " + "
                 else:
                       polynomial += " - "

                 if i == 0:
                       polynomial += str(abs(self.coefficients[i] ) )
                 elif i == 1:
                       polynomial += str(abs(self.coefficients[i] ) ) + "x"
                 else:
                       polynomial += str(abs(self.coefficients[i] ) ) + "x^" + str(_SCREAMING_SNAKE_CASE )

           return polynomial







     def __repr__( self      :    str ):






           """simple docstring"""




           return self.__str__()







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  [0] * self.degree
           for i in range(self.degree ):
                 UpperCamelCase					       =  self.coefficients[i + 1] * (i + 1)
           return Polynomial(self.degree - 1   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    int | float = 0 ):






           """simple docstring"""




           UpperCamelCase					       =  [0] * (self.degree + 2)
           UpperCamelCase					       =  constant
           for i in range(self.degree + 1 ):
                 UpperCamelCase					       =  self.coefficients[i] / (i + 1)
           return Polynomial(self.degree + 1   ,						_SCREAMING_SNAKE_CASE )







     def __eq__( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    object ):






           """simple docstring"""




           if not isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ):
                 return False

           if self.degree != polynomial_a.degree:
                 return False

           for i in range(self.degree + 1 ):
                 if self.coefficients[i] != polynomial_a.coefficients[i]:
                       return False

           return True







     def __ne__( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    object ):






           """simple docstring"""




           return not self.__eq__(_SCREAMING_SNAKE_CASE )



