


"""simple docstring"""


import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


_snake_case          =							"true"



def   snake_case						(				_a:     List[Any]		,   _a:     Tuple=82		,   _a:     int=16							)->		Optional[Any]:
      '''simple docstring'''


      set_seed(42							)
      lowerCamelCase__           =		RegressionModel()
      lowerCamelCase__           =		deepcopy(_a							)
      lowerCamelCase__           =		RegressionDataset(length=_a							)
      lowerCamelCase__           =		DataLoader(_a		,   batch_size=_a							)
      model.to(accelerator.device							)
      lowerCamelCase__    ,  lowerCamelCase__           =		accelerator.prepare(_a		,   _a							)
      return model, ddp_model, dataloader



def   snake_case						(				_a:     Accelerator		,   _a:     int=False							)->		Optional[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'							)
      lowerCamelCase__           =		load_dataset('glue'		,   'mrpc'		,   split='validation'							)

      def tokenize_function(_a:     List[Any]							):
            lowerCamelCase__           =		tokenizer(examples['sentence1']		,   examples['sentence2']		,   truncation=_a		,   max_length=_a							)
            return outputs

      with accelerator.main_process_first():
            lowerCamelCase__           =		dataset.map(
                _a		,   batched=_a		,   remove_columns=['idx', 'sentence1', 'sentence2']		,   )

      lowerCamelCase__           =		tokenized_datasets.rename_column('label'		,   'labels'							)

      def collate_fn(_a:     List[Any]							):
            if use_longest:
                  return tokenizer.pad(_a		,   padding='longest'		,   return_tensors='pt'							)
            return tokenizer.pad(_a		,   padding='max_length'		,   max_length=128		,   return_tensors='pt'							)

      return DataLoader(_a		,   shuffle=_a		,   collate_fn=_a		,   batch_size=16							)



def   snake_case						(				_a:     Tuple		,   _a:     List[Any]							)->		Tuple:
      '''simple docstring'''


      lowerCamelCase__           =		Accelerator(dispatch_batches=_a		,   split_batches=_a							)
      lowerCamelCase__           =		get_dataloader(_a		,   not dispatch_batches							)
      lowerCamelCase__           =		AutoModelForSequenceClassification.from_pretrained(
          'hf-internal-testing/mrpc-bert-base-cased'		,   return_dict=_a							)
      lowerCamelCase__    ,  lowerCamelCase__           =		accelerator.prepare(_a		,   _a							)
      return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator



def   snake_case						(				_a:     List[Any]		,   _a:     Any		,   _a:     Optional[Any]							)->		Any:
      '''simple docstring'''


      lowerCamelCase__           =		[]
      for batch in dataloader:
            lowerCamelCase__    ,  lowerCamelCase__           =		batch.values()
            with torch.no_grad():
                  lowerCamelCase__           =		model(_a							)
                  lowerCamelCase__    ,  lowerCamelCase__           =		accelerator.gather_for_metrics((logit, target)							)
                  logits_and_targets.append((logit, target)							)
      lowerCamelCase__    ,  lowerCamelCase__           =		[], []
      for logit, targ in logits_and_targets:
            logits.append(_a							)
            targs.append(_a							)
      lowerCamelCase__    ,  lowerCamelCase__           =		torch.cat(_a							), torch.cat(_a							)
      return logits, targs



def   snake_case						(				_a:     Accelerator		,   _a:     Tuple=82		,   _a:     Dict=False		,   _a:     Dict=False		,   _a:     Any=16							)->		Any:
      '''simple docstring'''


      lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		get_basic_setup(_a		,   _a		,   _a							)
      lowerCamelCase__    ,  lowerCamelCase__           =		generate_predictions(_a		,   _a		,   _a							)
      assert (
          len(_a							) == num_samples
      ), F'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_a							)}'



def   snake_case						(				_a:     bool = False		,   _a:     bool = False							)->		List[str]:
      '''simple docstring'''


      lowerCamelCase__           =		evaluate.load('glue'		,   'mrpc'							)
      lowerCamelCase__    ,  lowerCamelCase__           =		get_mrpc_setup(_a		,   _a							)
      # First do baseline
      lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		setup['no']
      model.to(_a							)
      model.eval()
      for batch in dataloader:
            batch.to(_a							)
            with torch.inference_mode():
                  lowerCamelCase__           =		model(**_a							)
            lowerCamelCase__           =		outputs.logits.argmax(dim=-1							)
            metric.add_batch(predictions=_a		,   references=batch['labels']							)
      lowerCamelCase__           =		metric.compute()

      # Then do distributed
      lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		setup['ddp']
      model.eval()
      for batch in dataloader:
            with torch.inference_mode():
                  lowerCamelCase__           =		model(**_a							)
            lowerCamelCase__           =		outputs.logits.argmax(dim=-1							)
            lowerCamelCase__           =		batch['labels']
            lowerCamelCase__    ,  lowerCamelCase__           =		accelerator.gather_for_metrics((preds, references)							)
            metric.add_batch(predictions=_a		,   references=_a							)
      lowerCamelCase__           =		metric.compute()

      for key in "accuracy f1".split():
            assert math.isclose(
                baseline[key]		,   distributed[key]							), F'Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'



def   snake_case						(				)->		Dict:
      '''simple docstring'''


      lowerCamelCase__           =		Accelerator(split_batches=_a		,   dispatch_batches=_a							)
      if accelerator.is_local_main_process:
            datasets.utils.logging.set_verbosity_warning()
            transformers.utils.logging.set_verbosity_warning()
      else:
            datasets.utils.logging.set_verbosity_error()
            transformers.utils.logging.set_verbosity_error()
      # These are a bit slower so they should only be ran on the GPU or TPU
      if torch.cuda.is_available() or is_tpu_available():
            if accelerator.is_local_main_process:
                  print('**Testing gather_for_metrics**'							)
            for split_batches in [True, False]:
                  for dispatch_batches in [True, False]:
                        if accelerator.is_local_main_process:
                              print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'							)
                        test_mrpc(_a		,   _a							)
                        accelerator.state._reset_state()
      if accelerator.is_local_main_process:
            print('**Test torch metrics**'							)
      for split_batches in [True, False]:
            for dispatch_batches in [True, False]:
                  lowerCamelCase__           =		Accelerator(split_batches=_a		,   dispatch_batches=_a							)
                  if accelerator.is_local_main_process:
                        print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'							)
                  test_torch_metrics(_a		,   99							)
                  accelerator.state._reset_state()
      if accelerator.is_local_main_process:
            print('**Test last batch is not dropped when perfectly divisible**'							)
      lowerCamelCase__           =		Accelerator()
      test_torch_metrics(_a		,   512							)
      accelerator.state._reset_state()



def   snake_case						(				_a:     List[Any]							)->		int:
      '''simple docstring'''


      main()


if __name__ == "__main__":
   main()







"""simple docstring"""


from __future__ import annotations

from math import gcd



def   snake_case						(				_a:     int		,   _a:     int = 2		,   _a:     int = 1		,   _a:     int = 3		,   )->		int | None:
      '''simple docstring'''


      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(_a:     int		,   _a:     int		,   _a:     int							) -> int:
            return (pow(_a		,   2							) + step) % modulus

      for _ in range(_a							):
            # These track the position within the cycle detection logic.
            lowerCamelCase__           =		seed
            lowerCamelCase__           =		seed

            while True:
                  # At each iteration, the tortoise moves one step and the hare moves two.
                  lowerCamelCase__           =		rand_fn(_a		,   _a		,   _a							)
                  lowerCamelCase__           =		rand_fn(_a		,   _a		,   _a							)
                  lowerCamelCase__           =		rand_fn(_a		,   _a		,   _a							)

                  # 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``.
                  lowerCamelCase__           =		gcd(hare - tortoise		,   _a							)

                  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.
            lowerCamelCase__           =		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

   _snake_case          =							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",
   )
   _snake_case          =							parser.parse_args()

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



