


'''simple docstring'''





def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      str						=   n ** (1 / 3)
   return (val * val * val) == n


if __name__ == "__main__":
       print(perfect_cube(27))
       print(perfect_cube(4))



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


__UpperCamelCase				:  str      					=							'true'
def      snake_case_						( __lowercase			,       __lowercase=8_2			,       __lowercase=1_6       ):
   set_seed(4_2       )
   UpperCAmelCase_							:      Optional[int]						=   RegressionModel()
   UpperCAmelCase_							:      Optional[int]						=   deepcopy(__lowercase       )
   UpperCAmelCase_							:      Union[str, Any]						=   RegressionDataset(length=__lowercase       )
   UpperCAmelCase_							:      Any						=   DataLoader(__lowercase			,       batch_size=__lowercase       )
   model.to(accelerator.device       )
   UpperCAmelCase_		,      UpperCAmelCase_							:      Dict						=   accelerator.prepare(__lowercase			,       __lowercase       )
   return model, ddp_model, dataloader
def      snake_case_						( __lowercase			,       __lowercase=False       ):
   UpperCAmelCase_							:      Optional[int]						=   AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''       )
   UpperCAmelCase_							:      List[Any]						=   load_dataset('''glue'''			,       '''mrpc'''			,       split='''validation'''       )

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

   with accelerator.main_process_first():
      UpperCAmelCase_							:      List[str]						=   dataset.map(
          __lowercase			,       batched=__lowercase			,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,       )

   UpperCAmelCase_							:      Any						=   tokenized_datasets.rename_column('''label'''			,       '''labels'''       )

   def collate_fn(__lowercase       ):
      if use_longest:
         return tokenizer.pad(__lowercase			,       padding='''longest'''			,       return_tensors='''pt'''       )
      return tokenizer.pad(__lowercase			,       padding='''max_length'''			,       max_length=1_2_8			,       return_tensors='''pt'''       )

   return DataLoader(__lowercase			,       shuffle=__lowercase			,       collate_fn=__lowercase			,       batch_size=1_6       )
def      snake_case_						( __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      Optional[int]						=   Accelerator(dispatch_batches=__lowercase			,       split_batches=__lowercase       )
   UpperCAmelCase_							:      int						=   get_dataloader(__lowercase			,       not dispatch_batches       )
   UpperCAmelCase_							:      Optional[int]						=   AutoModelForSequenceClassification.from_pretrained(
       '''hf-internal-testing/mrpc-bert-base-cased'''			,       return_dict=__lowercase       )
   UpperCAmelCase_		,      UpperCAmelCase_							:      Any						=   accelerator.prepare(__lowercase			,       __lowercase       )
   return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      Dict						=   []
   for batch in dataloader:
      UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[int]						=   batch.values()
      with torch.no_grad():
         UpperCAmelCase_							:      List[Any]						=   model(__lowercase       )
         UpperCAmelCase_		,      UpperCAmelCase_							:      Dict						=   accelerator.gather_for_metrics((logit, target)       )
         logits_and_targets.append((logit, target)       )
   UpperCAmelCase_		,      UpperCAmelCase_							:      Any						=   [], []
   for logit, targ in logits_and_targets:
      logits.append(__lowercase       )
      targs.append(__lowercase       )
   UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[int]						=   torch.cat(__lowercase       ), torch.cat(__lowercase       )
   return logits, targs
def      snake_case_						( __lowercase			,       __lowercase=8_2			,       __lowercase=False			,       __lowercase=False			,       __lowercase=1_6       ):
   UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      Any						=   get_basic_setup(__lowercase			,       __lowercase			,       __lowercase       )
   UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[int]						=   generate_predictions(__lowercase			,       __lowercase			,       __lowercase       )
   assert (
       len(__lowercase       ) == num_samples
   ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__lowercase       )}'''
def      snake_case_						( __lowercase = False			,       __lowercase = False       ):
   UpperCAmelCase_							:      Optional[Any]						=   evaluate.load('''glue'''			,       '''mrpc'''       )
   UpperCAmelCase_		,      UpperCAmelCase_							:      Tuple						=   get_mrpc_setup(__lowercase			,       __lowercase       )
   # First do baseline
   UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      Union[str, Any]						=   setup['''no''']
   model.to(__lowercase       )
   model.eval()
   for batch in dataloader:
      batch.to(__lowercase       )
      with torch.inference_mode():
         UpperCAmelCase_							:      str						=   model(**__lowercase       )
      UpperCAmelCase_							:      Dict						=   outputs.logits.argmax(dim=-1       )
      metric.add_batch(predictions=__lowercase			,       references=batch['''labels''']       )
   UpperCAmelCase_							:      Optional[int]						=   metric.compute()

   # Then do distributed
   UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[Any]						=   setup['''ddp''']
   model.eval()
   for batch in dataloader:
      with torch.inference_mode():
         UpperCAmelCase_							:      Optional[int]						=   model(**__lowercase       )
      UpperCAmelCase_							:      int						=   outputs.logits.argmax(dim=-1       )
      UpperCAmelCase_							:      Optional[int]						=   batch['''labels''']
      UpperCAmelCase_		,      UpperCAmelCase_							:      Tuple						=   accelerator.gather_for_metrics((preds, references)       )
      metric.add_batch(predictions=__lowercase			,       references=__lowercase       )
   UpperCAmelCase_							:      Dict						=   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_						( ):
   UpperCAmelCase_							:      str						=   Accelerator(split_batches=__lowercase			,       dispatch_batches=__lowercase       )
   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(__lowercase			,       __lowercase       )
            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]:
         UpperCAmelCase_							:      Optional[Any]						=   Accelerator(split_batches=__lowercase			,       dispatch_batches=__lowercase       )
         if accelerator.is_local_main_process:
            print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''       )
         test_torch_metrics(__lowercase			,       9_9       )
         accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print('''**Test last batch is not dropped when perfectly divisible**'''       )
   UpperCAmelCase_							:      List[Any]						=   Accelerator()
   test_torch_metrics(__lowercase			,       5_1_2       )
   accelerator.state._reset_state()
def      snake_case_						( __lowercase       ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
       main()