


'''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_			:  int										=   getLogger(__name__)


def       _lowercase							(					UpperCamelCase__							:       List[Any],					UpperCamelCase__							:       str,					UpperCamelCase__							:       str,					UpperCamelCase__							:       int = 8,					UpperCamelCase__							:       int = 1024,					UpperCamelCase__							:       List[Any]="val",					UpperCamelCase__							:       str=None,					UpperCamelCase__							:       Optional[int]=False,					UpperCamelCase__							:       List[Any]="summarization",					UpperCamelCase__							:       List[str]=None,					UpperCamelCase__							:       Tuple=1,					UpperCamelCase__							:       Dict = None,					UpperCamelCase__							:       Optional[Any]="",					**UpperCamelCase__							:       Optional[Any],					):
 __A	:		Union[str, Any]	  =      str(UpperCamelCase__      )
 assert local_rank is not None
 torch.distributed.init_process_group(backend='nccl',					rank=UpperCamelCase__      )

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

 __A	:		Tuple	  =      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:
  __A	:		Dict	  =      tokenizer.model_max_length
 if prefix is None:
  __A	:		List[Any]	  =      prefix or getattr(model.config,					'prefix',					''      ) or ''
 __A	:		Optional[Any]	  =      SeqaSeqDataset(
     UpperCamelCase__,					UpperCamelCase__,					UpperCamelCase__,					max_target_length=1024,					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.
 __A	:		Union[str, Any]	  =      ds.make_sortish_sampler(UpperCamelCase__,					distributed=UpperCamelCase__,					add_extra_examples=UpperCamelCase__,					shuffle=UpperCamelCase__      )
 __A	:		Tuple	  =      DataLoader(UpperCamelCase__,					sampler=UpperCamelCase__,					batch_size=UpperCamelCase__,					collate_fn=ds.collate_fn      )
 __A	:		List[str]	  =      []
 for batch in tqdm(UpperCamelCase__      ):
  __A	:		Union[str, Any]	  =      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__,					)
  __A	:		List[Any]	  =      tokenizer.batch_decode(UpperCamelCase__,					skip_special_tokens=UpperCamelCase__,					clean_up_tokenization_spaces=UpperCamelCase__      )
  __A	:		List[Any]	  =      batch['ids']
  if num_return_sequences > 1:
   __A	:		str	  =      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							(					):
 __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=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=600,					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'      )
 __A	:		Optional[Any]	  =      time.time()
 __A  ,__A	:		Optional[Any]	  =      parser.parse_known_args()
 __A	:		Dict	  =      parse_numeric_n_bool_cl_kwargs(UpperCamelCase__      )
 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(UpperCamelCase__      ).mkdir(exist_ok=UpperCamelCase__      )  # this handles locking.
 __A	:		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.
 __A	:		Dict	  =      {}
 if args.src_lang is not None:
  __A	:		Any	  =      args.src_lang
 if args.tgt_lang is not None:
  __A	:		Optional[int]	  =      args.tgt_lang

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

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


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










'''simple docstring'''
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase = True   ,   __lowercase = False    ):

  """simple docstring"""







  __A	:		List[str]	  =      scheduler
  __A	:		Dict	  =      optimizers if isinstance(__lowercase   ,   (list, tuple)    ) else [optimizers]
  __A	:		List[Any]	  =      split_batches
  __A	:		Any	  =      step_with_optimizer
  __A	:		List[Any]	  =      GradientState()


 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  if not self.step_with_optimizer:
   # No link between scheduler and optimizer -> just step
   self.scheduler.step(*__lowercase   ,   **__lowercase    )
   return

  # Otherwise, first make sure the optimizer was stepped.
  if not self.gradient_state.sync_gradients:
   if self.gradient_state.adjust_scheduler:
    self.scheduler._step_count += 1
   return

  for opt in self.optimizers:
   if opt.step_was_skipped:
    return
  if self.split_batches:
   # Split batches -> the training dataloader batch size is not changed so one step per training step
   self.scheduler.step(*__lowercase   ,   **__lowercase    )
  else:
   # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
   # num_processes steps per training step
   __A	:		Tuple	  =      AcceleratorState().num_processes
   for _ in range(__lowercase    ):
    # Special case when using OneCycle and `drop_last` was not used
    if hasattr(self.scheduler   ,   'total_steps'    ):
     if self.scheduler._step_count <= self.scheduler.total_steps:
      self.scheduler.step(*__lowercase   ,   **__lowercase    )
    else:
     self.scheduler.step(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.get_last_lr()


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.state_dict()


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  self.scheduler.load_state_dict(__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.get_lr()







 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  return self.scheduler.print_lr(*__lowercase   ,   **__lowercase    )






