"""simple docstring"""






import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__A			   = 1_6
__A			   = 3_2



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase = 1_6 )  ->  Optional[Any]:
   lowercase__:   Union[str, Any]					=      AutoTokenizer.from_pretrained('''bert-base-cased''' )
   lowercase__:   Optional[int]					=      load_dataset('''glue'''       ,    '''mrpc''' )

   def tokenize_function(__UpperCAmelCase ):
      # max_length=None => use the model max length (it's actually the default)
      lowercase__:   List[str]					=      tokenizer(examples['''sentence1''']       ,    examples['''sentence2''']       ,    truncation=__UpperCAmelCase       ,    max_length=__UpperCAmelCase )
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   # starting with the main process first:
   with accelerator.main_process_first():
      lowercase__:   Tuple					=      datasets.map(
          __UpperCAmelCase       ,    batched=__UpperCAmelCase       ,    remove_columns=['''idx''', '''sentence1''', '''sentence2''']       ,    )

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   lowercase__:   Any					=      tokenized_datasets.rename_column('''label'''       ,    '''labels''' )

   def collate_fn(__UpperCAmelCase ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      lowercase__:   str					=      1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         lowercase__:   Dict					=      1_6
      elif accelerator.mixed_precision != "no":
         lowercase__:   Union[str, Any]					=      8
      else:
         lowercase__:   Optional[int]					=      None

      return tokenizer.pad(
          __UpperCAmelCase       ,    padding='''longest'''       ,    max_length=__UpperCAmelCase       ,    pad_to_multiple_of=__UpperCAmelCase       ,    return_tensors='''pt'''       ,    )

   # Instantiate dataloaders.
   lowercase__:   int					=      DataLoader(
       tokenized_datasets['''train''']       ,    shuffle=__UpperCAmelCase       ,    collate_fn=__UpperCAmelCase       ,    batch_size=__UpperCAmelCase )
   lowercase__:   Tuple					=      DataLoader(
       tokenized_datasets['''validation''']       ,    shuffle=__UpperCAmelCase       ,    collate_fn=__UpperCAmelCase       ,    batch_size=__UpperCAmelCase )

   return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
       from accelerate.test_utils.training import mocked_dataloaders

       __A			   = mocked_dataloaders  # noqa: F811



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Any:
   # For testing only
   if os.environ.get('''TESTING_MOCKED_DATALOADERS'''       ,    __UpperCAmelCase ) == "1":
      lowercase__:   Any					=      2
   # New Code #
   lowercase__:   Union[str, Any]					=      int(args.gradient_accumulation_steps )
   lowercase__:   List[str]					=      int(args.local_sgd_steps )
   # Initialize accelerator
   lowercase__:   Union[str, Any]					=      Accelerator(
       cpu=args.cpu       ,    mixed_precision=args.mixed_precision       ,    gradient_accumulation_steps=__UpperCAmelCase )
   if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
      raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)''' )
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowercase__:   Tuple					=      config['''lr''']
   lowercase__:   Any					=      int(config['''num_epochs'''] )
   lowercase__:   Optional[int]					=      int(config['''seed'''] )
   lowercase__:   str					=      int(config['''batch_size'''] )

   lowercase__:   Optional[int]					=      evaluate.load('''glue'''       ,    '''mrpc''' )

   set_seed(__UpperCAmelCase )
   lowercase__, lowercase__:   str					=      get_dataloaders(__UpperCAmelCase       ,    __UpperCAmelCase )
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   lowercase__:   Dict					=      AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''       ,    return_dict=__UpperCAmelCase )

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   lowercase__:   Any					=      model.to(accelerator.device )

   # Instantiate optimizer
   lowercase__:   Optional[int]					=      AdamW(params=model.parameters()       ,    lr=__UpperCAmelCase )

   # Instantiate scheduler
   lowercase__:   int					=      get_linear_schedule_with_warmup(
       optimizer=__UpperCAmelCase       ,    num_warmup_steps=1_0_0       ,    num_training_steps=(len(__UpperCAmelCase ) * num_epochs)       ,    )

   # Prepare everything
   # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
   # prepare method.
   lowercase__, lowercase__, lowercase__, lowercase__, lowercase__:   Any					=      accelerator.prepare(
       __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )

   # Now we train the model
   for epoch in range(__UpperCAmelCase ):
      model.train()
      with LocalSGD(
          accelerator=__UpperCAmelCase       ,    model=__UpperCAmelCase       ,    local_sgd_steps=__UpperCAmelCase       ,    enabled=local_sgd_steps is not None ) as local_sgd:
         for step, batch in enumerate(__UpperCAmelCase ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device )
            # New code #
            # We use the new `accumulate` context manager to perform gradient accumulation
            # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
            with accelerator.accumulate(__UpperCAmelCase ):
               lowercase__:   Union[str, Any]					=      model(**__UpperCAmelCase )
               lowercase__:   Optional[Any]					=      output.loss
               accelerator.backward(__UpperCAmelCase )
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()
               # LocalSGD-specific line
               local_sgd.step()

      model.eval()
      for step, batch in enumerate(__UpperCAmelCase ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device )
         with torch.no_grad():
            lowercase__:   List[str]					=      model(**__UpperCAmelCase )
         lowercase__:   List[Any]					=      outputs.logits.argmax(dim=-1 )
         lowercase__, lowercase__:   Any					=      accelerator.gather_for_metrics((predictions, batch['''labels''']) )
         metric.add_batch(
             predictions=__UpperCAmelCase       ,    references=__UpperCAmelCase       ,    )

      lowercase__:   Union[str, Any]					=      metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:"""       ,    __UpperCAmelCase )



def SCREAMING_SNAKE_CASE__			(   )  ->  Optional[Any]:
   lowercase__:   Dict					=      argparse.ArgumentParser(description='''Simple example of training script.''' )
   parser.add_argument(
       '''--mixed_precision'''       ,    type=__UpperCAmelCase       ,    default=__UpperCAmelCase       ,    choices=['''no''', '''fp16''', '''bf16''', '''fp8''']       ,    help='''Whether to use mixed precision. Choose'''
       '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
       '''and an Nvidia Ampere GPU.'''       ,    )
   # New Code #
   parser.add_argument(
       '''--gradient_accumulation_steps'''       ,    type=__UpperCAmelCase       ,    default=1       ,    help='''The number of minibatches to be ran before gradients are accumulated.'''       ,    )
   parser.add_argument(
       '''--local_sgd_steps'''       ,    type=__UpperCAmelCase       ,    default=8       ,    help='''Number of local SGD steps or None to disable local SGD''' )
   parser.add_argument('''--cpu'''       ,    action='''store_true'''       ,    help='''If passed, will train on the CPU.''' )
   lowercase__:   str					=      parser.parse_args()
   lowercase__:   Optional[int]					=      {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
   training_function(__UpperCAmelCase       ,    __UpperCAmelCase )


if __name__ == "__main__":
       main()

"""simple docstring"""






from __future__ import annotations

from math import pi, sqrt



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  tuple:

   if inductance <= 0:
      raise ValueError('''Inductance cannot be 0 or negative''' )

   elif capacitance <= 0:
      raise ValueError('''Capacitance cannot be 0 or negative''' )

   else:
      return (
          "Resonant frequency",
          float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
