'''simple docstring'''





import os

try:
      from .build_directory_md import good_file_paths
except ImportError:
      from build_directory_md import good_file_paths  # type: ignore

lowercase_     :   Optional[Any] 				=				list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

lowercase_     :   Optional[Any] 				=				[file for file in filepaths if file != file.lower()]
if upper_files:
      print(f'''{len(upper_files)} files contain uppercase characters:''')
      print('\n'.join(upper_files) + '\n')

lowercase_     :   int 				=				[file for file in filepaths if ' ' in file]
if space_files:
      print(f'''{len(space_files)} files contain space characters:''')
      print('\n'.join(space_files) + '\n')

lowercase_     :   Any 				=				[file for file in filepaths if '-' in file]
if hyphen_files:
      print(f'''{len(hyphen_files)} files contain hyphen characters:''')
      print('\n'.join(hyphen_files) + '\n')

lowercase_     :   Optional[int] 				=				[file for file in filepaths if os.sep not in file]
if nodir_files:
      print(f'''{len(nodir_files)} files are not in a directory:''')
      print('\n'.join(nodir_files) + '\n')

lowercase_     :   List[str] 				=				len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
      import sys

      sys.exit(bad_files)


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()

