




"""simple docstring"""





from __future__ import annotations

from math import gcd

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase = 2					,							UpperCamelCase = 1					,							UpperCamelCase = 3					,							)      ->      int | None:


			"""simple docstring"""
			# A value less than 2 can cause an infinite loop in the algorithm.
			if num < 2:
						raise ValueError("The input value cannot be less than 2"				)

			# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
			# algorithm struggles to find factors that are divisible by two.
			# As a workaround, we specifically check for two and even inputs.
			#   See: https://math.stackexchange.com/a/2856214/165820
			if num > 2 and num % 2 == 0:
						return 2

			# Pollard's Rho algorithm requires a function that returns pseudorandom
			# values between 0 <= X < ``num``.  It doesn't need to be random in the
			# sense that the output value is cryptographically secure or difficult
			# to calculate, it only needs to be random in the sense that all output
			# values should be equally likely to appear.
			# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
			# However, the success of Pollard's algorithm isn't guaranteed and is
			# determined in part by the initial seed and the chosen random function.
			# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
			# where ``C`` is a value that we can modify between each attempt.
			def rand_fn(UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				) -> int:
						return (pow(UpperCamelCase					,							2				) + step) % modulus

			for _ in range(UpperCamelCase				):
						# These track the position within the cycle detection logic.
						__UpperCAmelCase						:			int												=       seed
						__UpperCAmelCase						:			Tuple												=       seed

						while True:
									# At each iteration, the tortoise moves one step and the hare moves two.
									__UpperCAmelCase						:			Tuple												=       rand_fn(UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)
									__UpperCAmelCase						:			Tuple												=       rand_fn(UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)
									__UpperCAmelCase						:			str												=       rand_fn(UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)

									# At some point both the tortoise and the hare will enter a cycle whose
									# length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
									# the tortoise and hare will end up on the same value modulo ``p``.
									# We can detect when this happens because the position difference between
									# the tortoise and the hare will share a common divisor with ``num``.
									__UpperCAmelCase						:			Union[str, Any]												=       gcd(hare - tortoise					,							UpperCamelCase				)

									if divisor == 1:
												# No common divisor yet, just keep searching.
												continue
									else:
												# We found a common divisor!
												if divisor == num:
															# Unfortunately, the divisor is ``num`` itself and is useless.
															break
												else:
															# The divisor is a nontrivial factor of ``num``!
															return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
						__UpperCAmelCase						:			List[str]												=       hare

						# The new step value for the random function can just be incremented.
						# At first the results will be similar to what the old function would
						# have produced, but the value will quickly diverge after a bit.
						step += 1

			# We haven't found a divisor within the requested number of attempts.
			# We were unlucky or ``num`` itself is actually prime.
			return None


if __name__ == "__main__":
					import argparse

					A				=							argparse.ArgumentParser()
					parser.add_argument(
					    """num""",
					    type=int,
					    help="""The value to find a divisor of""",
					)
					parser.add_argument(
					    """--attempts""",
					    type=int,
					    default=3,
					    help="""The number of attempts before giving up""",
					)
					A				=							parser.parse_args()

					A				=							pollard_rho(args.num, attempts=args.attempts)
					if divisor is None:
										print(f'''{args.num} is probably prime''')
					else:
										A				=							args.num // divisor
										print(f'''{args.num} = {divisor} * {quotient}''')









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      _UpperCamelCase  (a__ :Union[str, Any] ,      a__ :str ,      a__ :str ,      a__ :int = 8 ,      a__ :int = 1024 ,      a__ :Dict="val" ,      a__ :Optional[int]=None ,      a__ :List[str]=False ,      a__ :Union[str, Any]="summarization" ,      a__ :str=None ,      a__ :Optional[Any]=1 ,      a__ :Dict = None ,      a__ :List[Any]="" ,      **a__ :List[str] ,      ):






							"""simple docstring"""





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

							UpperCamelCase__					  =      Path(a__							)
							UpperCamelCase__					  =      save_dir.joinpath(f"""rank_{local_rank}_output.json"""							)
							torch.cuda.set_device(a__							)
							UpperCamelCase__					  =      AutoModelForSeqaSeqLM.from_pretrained(a__							).cuda()
							if fpaa:
														UpperCamelCase__					  =      model.half()
							# determine if we need to increase num_beams
							use_task_specific_params(a__ ,      a__							)  # 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(a__							)
							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(
							    a__ ,      a__ ,      a__ ,      max_target_length=1024 ,      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.
							UpperCamelCase__					  =      ds.make_sortish_sampler(a__ ,      distributed=a__ ,      add_extra_examples=a__ ,      shuffle=a__							)
							UpperCamelCase__					  =      DataLoader(a__ ,      sampler=a__ ,      batch_size=a__ ,      collate_fn=ds.collate_fn							)
							UpperCamelCase__					  =      []
							for batch in tqdm(a__							):
														UpperCamelCase__					  =      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__ ,      )
														UpperCamelCase__					  =      tokenizer.batch_decode(a__ ,      skip_special_tokens=a__ ,      clean_up_tokenization_spaces=a__							)
														UpperCamelCase__					  =      batch["""ids"""]
														if num_return_sequences > 1:
																					UpperCamelCase__					  =      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      _UpperCamelCase  ():






							"""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=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=600 ,      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"""							)
							UpperCamelCase__					  =      time.time()
							UpperCamelCase__  ,  UpperCamelCase__					  =      parser.parse_known_args()
							UpperCamelCase__					  =      parse_numeric_n_bool_cl_kwargs(a__							)
							if generate_kwargs and args.local_rank <= 0:
														print(f"""parsed the following generate kwargs: {generate_kwargs}"""							)
							UpperCamelCase__					  =      Path(args.save_dir + """_tmp"""							)
							Path(a__							).mkdir(exist_ok=a__							)  # 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=a__							)
							UpperCamelCase__  ,  UpperCamelCase__					  =      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:
														UpperCamelCase__					  =      Path(args.save_dir							)
														save_dir.mkdir(exist_ok=a__							)
														UpperCamelCase__					  =      gather_results_from_each_node(a__ ,      a__ ,      args.sync_timeout							)
														UpperCamelCase__					  =      combine_partial_results(a__							)
														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(a__ ,      a__							)
																					return
														UpperCamelCase__					  =      Path(args.data_dir							).joinpath(args.type_path + """.target"""							)
														with open(a__							) as f:
																					UpperCamelCase__					  =      [x.rstrip() for x in f.readlines()][: len(a__							)]

														# 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(a__ ,      a__							)
														UpperCamelCase__					  =      len(a__							)
														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(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      _UpperCamelCase  (a__ :Dict							):






							"""simple docstring"""





							UpperCamelCase__					  =      []
							for partial_result in partial_results:
														records.extend(a__							)
							UpperCamelCase__					  =      sorted(a__ ,      key=lambda a__							: x["id"]							)
							UpperCamelCase__					  =      [x["""pred"""] for x in records]
							return preds





def      _UpperCamelCase  (a__ :List[str] ,      a__ :str ,      a__ :List[Any]							):






							"""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(a__							) < num_replicas:
																					continue
														try:
																					# make sure all json files are fully saved
																					UpperCamelCase__					  =      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()




