'''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,
)


SCREAMING_SNAKE_CASE_:     List[str]  =getLogger(__name__)





def 	lowerCAmelCase_				(					snake_case_       : Tuple					,    snake_case_       : str					,    snake_case_       : str					,    snake_case_       : int = 8					,    snake_case_       : int = 10_24					,    snake_case_       : Any="val"					,    snake_case_       : Tuple=None					,    snake_case_       : Any=False					,    snake_case_       : str="summarization"					,    snake_case_       : Dict=None					,    snake_case_       : Optional[Any]=1					,    snake_case_       : Dict = None					,    snake_case_       : List[Any]=""					,    **snake_case_       : int					,    )    ->      Dict:


     '''simple docstring'''




     UpperCAmelCase_           =   str(_snake_case			)
     assert local_rank is not None
     torch.distributed.init_process_group(backend="nccl"					,    rank=_snake_case			)

     UpperCAmelCase_           =   Path(_snake_case			)
     UpperCAmelCase_           =   save_dir.joinpath(f"""rank_{local_rank}_output.json"""			)
     torch.cuda.set_device(_snake_case			)
     UpperCAmelCase_           =   AutoModelForSeqaSeqLM.from_pretrained(_snake_case			).cuda()
     if fpaa:
          UpperCAmelCase_           =   model.half()
     # determine if we need to increase num_beams
     use_task_specific_params(_snake_case					,    _snake_case			)  # 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(_snake_case			)
     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(
         _snake_case					,    _snake_case					,    _snake_case					,    max_target_length=10_24					,    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.
     UpperCAmelCase_           =   ds.make_sortish_sampler(_snake_case					,    distributed=_snake_case					,    add_extra_examples=_snake_case					,    shuffle=_snake_case			)
     UpperCAmelCase_           =   DataLoader(_snake_case					,    sampler=_snake_case					,    batch_size=_snake_case					,    collate_fn=ds.collate_fn			)
     UpperCAmelCase_           =   []
     for batch in tqdm(_snake_case			):
          UpperCAmelCase_           =   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					,    )
          UpperCAmelCase_           =   tokenizer.batch_decode(_snake_case					,    skip_special_tokens=_snake_case					,    clean_up_tokenization_spaces=_snake_case			)
          UpperCAmelCase_           =   batch['''ids''']
          if num_return_sequences > 1:
               UpperCAmelCase_           =   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 	lowerCAmelCase_				(					)    ->      int:


     '''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=_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=6_00					,    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"			)
     UpperCAmelCase_           =   time.time()
     UpperCAmelCase_           =   parser.parse_known_args()
     UpperCAmelCase_           =   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}"""			)
     UpperCAmelCase_           =   Path(args.save_dir + "_tmp"			)
     Path(_snake_case			).mkdir(exist_ok=_snake_case			)  # 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=_snake_case			)
     UpperCAmelCase_           =   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:
          UpperCAmelCase_           =   Path(args.save_dir			)
          save_dir.mkdir(exist_ok=_snake_case			)
          UpperCAmelCase_           =   gather_results_from_each_node(_snake_case					,    _snake_case					,    args.sync_timeout			)
          UpperCAmelCase_           =   combine_partial_results(_snake_case			)
          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(_snake_case					,    _snake_case			)
               return
          UpperCAmelCase_           =   Path(args.data_dir			).joinpath(args.type_path + ".target"			)
          with open(_snake_case			) as f:
               UpperCAmelCase_           =   [x.rstrip() for x in f.readlines()][: len(_snake_case			)]

          # 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(_snake_case					,    _snake_case			)
          UpperCAmelCase_           =   len(_snake_case			)
          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(_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 	lowerCAmelCase_				(					snake_case_       : Union[str, Any]			)    ->      List:


     '''simple docstring'''




     UpperCAmelCase_           =   []
     for partial_result in partial_results:
          records.extend(_snake_case			)
     UpperCAmelCase_           =   sorted(_snake_case					,    key=lambda snake_case_			: x["id"]			)
     UpperCAmelCase_           =   [x['''pred'''] for x in records]
     return preds





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : List[str]					,    snake_case_       : List[Any]			)    ->      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(_snake_case			) < num_replicas:
               continue
          try:
               # make sure all json files are fully saved
               UpperCAmelCase_           =   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()








"""simple docstring"""
import math






def 		lowercase							(     _snake_case						:      int     )     ->bool:
	"""simple docstring"""





	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5     ,   int(math.sqrt(_snake_case     ) + 1     )     ,   6     ):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True






def 		lowercase							(     _snake_case						:      float = 0.1     )     ->int:
	"""simple docstring"""





	__snake_case	:						Tuple						=				3
	__snake_case	:						Any						=				3

	while primes / (2 * j - 1) >= ratio:
		for i in range(j * j + j + 1     ,   (j + 2) * (j + 2)     ,   j + 1     ):
			primes += is_prime(_snake_case     )
		j += 2
	return j


if __name__ == "__main__":
			import doctest

			doctest.testmod()





