





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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Dict			,							snake_case__  :Any=82			,							snake_case__  :Optional[int]=16     )      ->						Union[str, Any]:
  set_seed(42     )
  _lowercase							= RegressionModel()
  _lowercase							= deepcopy(snake_case__     )
  _lowercase							= RegressionDataset(length=snake_case__     )
  _lowercase							= DataLoader(snake_case__			,							batch_size=snake_case__     )
  model.to(accelerator.device     )
  _lowercase							= accelerator.prepare(snake_case__			,							snake_case__     )
  return model, ddp_model, dataloader

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Accelerator			,							snake_case__  :Union[str, Any]=False     )      ->						Tuple:
  _lowercase							= AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'     )
  _lowercase							= load_dataset('glue'			,							'mrpc'			,							split='validation'     )

  def tokenize_function(snake_case__  :int     ):
    _lowercase							= tokenizer(examples['sentence1']			,							examples['sentence2']			,							truncation=snake_case__			,							max_length=snake_case__     )
    return outputs

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

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

  def collate_fn(snake_case__  :int     ):
    if use_longest:
      return tokenizer.pad(snake_case__			,							padding='longest'			,							return_tensors='pt'     )
    return tokenizer.pad(snake_case__			,							padding='max_length'			,							max_length=128			,							return_tensors='pt'     )

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

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Optional[int]			,							snake_case__  :List[Any]     )      ->						List[str]:
  _lowercase							= Accelerator(dispatch_batches=snake_case__			,							split_batches=snake_case__     )
  _lowercase							= get_dataloader(snake_case__			,							not dispatch_batches     )
  _lowercase							= AutoModelForSequenceClassification.from_pretrained(
      'hf-internal-testing/mrpc-bert-base-cased'			,							return_dict=snake_case__     )
  _lowercase							= accelerator.prepare(snake_case__			,							snake_case__     )
  return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :str			,							snake_case__  :Union[str, Any]			,							snake_case__  :List[str]     )      ->						List[str]:
  _lowercase							= []
  for batch in dataloader:
    _lowercase							= batch.values()
    with torch.no_grad():
      _lowercase							= model(snake_case__     )
      _lowercase							= accelerator.gather_for_metrics((logit, target)     )
      logits_and_targets.append((logit, target)     )
  _lowercase							= [], []
  for logit, targ in logits_and_targets:
    logits.append(snake_case__     )
    targs.append(snake_case__     )
  _lowercase							= torch.cat(snake_case__     ), torch.cat(snake_case__     )
  return logits, targs

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Accelerator			,							snake_case__  :Optional[int]=82			,							snake_case__  :Optional[Any]=False			,							snake_case__  :List[Any]=False			,							snake_case__  :List[Any]=16     )      ->						List[Any]:
  _lowercase							= get_basic_setup(snake_case__			,							snake_case__			,							snake_case__     )
  _lowercase							= generate_predictions(snake_case__			,							snake_case__			,							snake_case__     )
  assert (
      len(snake_case__     ) == num_samples
  ), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(snake_case__     )}"""

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :bool = False			,							snake_case__  :bool = False     )      ->						str:
  _lowercase							= evaluate.load('glue'			,							'mrpc'     )
  _lowercase							= get_mrpc_setup(snake_case__			,							snake_case__     )
  # First do baseline
  _lowercase							= setup["""no"""]
  model.to(snake_case__     )
  model.eval()
  for batch in dataloader:
    batch.to(snake_case__     )
    with torch.inference_mode():
      _lowercase							= model(**snake_case__     )
    _lowercase							= outputs.logits.argmax(dim=-1     )
    metric.add_batch(predictions=snake_case__			,							references=batch['labels']     )
  _lowercase							= metric.compute()

  # Then do distributed
  _lowercase							= setup["""ddp"""]
  model.eval()
  for batch in dataloader:
    with torch.inference_mode():
      _lowercase							= model(**snake_case__     )
    _lowercase							= outputs.logits.argmax(dim=-1     )
    _lowercase							= batch["""labels"""]
    _lowercase							= accelerator.gather_for_metrics((preds, references)     )
    metric.add_batch(predictions=snake_case__			,							references=snake_case__     )
  _lowercase							= 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 							SCREAMING_SNAKE_CASE__       (  )      ->						Union[str, Any]:
  _lowercase							= Accelerator(split_batches=snake_case__			,							dispatch_batches=snake_case__     )
  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(snake_case__			,							snake_case__     )
        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]:
      _lowercase							= Accelerator(split_batches=snake_case__			,							dispatch_batches=snake_case__     )
      if accelerator.is_local_main_process:
        print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""     )
      test_torch_metrics(snake_case__			,							99     )
      accelerator.state._reset_state()
  if accelerator.is_local_main_process:
    print('**Test last batch is not dropped when perfectly divisible**'     )
  _lowercase							= Accelerator()
  test_torch_metrics(snake_case__			,							512     )
  accelerator.state._reset_state()

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[Any]     )      ->						Optional[int]:
  main()


if __name__ == "__main__":
      main()












from __future__ import annotations

from collections.abc import Generator

def 							SCREAMING_SNAKE_CASE__       (  )      ->						Generator[int, None, None]:
  _lowercase							= {}
  _lowercase							= 2
  while True:
    _lowercase							= factor_map.pop(snake_case__			,							snake_case__     )
    if factor:
      _lowercase							= factor + prime
      while x in factor_map:
        x += factor
      _lowercase							= factor
    else:
      _lowercase							= prime
      yield prime
    prime += 1

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :float = 1E10     )      ->						int:
  _lowercase							= sieve()

  _lowercase							= 1
  while True:
    _lowercase							= next(snake_case__     )
    if (2 * prime * n) > limit:
      return n
    # Ignore the next prime as the reminder will be 2.
    next(snake_case__     )
    n += 2


if __name__ == "__main__":
      print(solution())