


'''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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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 :  Optional[int]    							=				16
__A :  Dict    							=				32







def     UpperCamelCase_      (   A__ :       Accelerator							,   A__ :       int = 16 ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       AutoTokenizer.from_pretrained("""bert-base-cased""" )
  lowerCAmelCase_      :				Any      =       load_dataset("""glue"""							,   """mrpc""" )

  def tokenize_function(A__ :       Optional[Any] ):
    # max_length=None => use the model max length (it's actually the default)
    lowerCAmelCase_      :				Tuple      =       tokenizer(examples["""sentence1"""]							,   examples["""sentence2"""]							,   truncation=A__							,   max_length=A__ )
    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():
    lowerCAmelCase_      :				List[str]      =       datasets.map(
        A__							,   batched=A__							,   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
  lowerCAmelCase_      :				int      =       tokenized_datasets.rename_column("""label"""							,   """labels""" )

  def collate_fn(A__ :       List[Any] ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    lowerCAmelCase_      :				Any      =       1_28 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      lowerCAmelCase_      :				List[str]      =       16
    elif accelerator.mixed_precision != "no":
      lowerCAmelCase_      :				int      =       8
    else:
      lowerCAmelCase_      :				Dict      =       None

    return tokenizer.pad(
        A__							,   padding="""longest"""							,   max_length=A__							,   pad_to_multiple_of=A__							,   return_tensors="""pt"""							,   )

  # Instantiate dataloaders.
  lowerCAmelCase_      :				Union[str, Any]      =       DataLoader(
      tokenized_datasets["""train"""]							,   shuffle=A__							,   collate_fn=A__							,   batch_size=A__ )
  lowerCAmelCase_      :				Dict      =       DataLoader(
      tokenized_datasets["""validation"""]							,   shuffle=A__							,   collate_fn=A__							,   batch_size=A__ )

  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 :  Tuple    							=				mocked_dataloaders  # noqa: F811







def     UpperCamelCase_      (   A__ :       List[str]							,   A__ :       Dict ):
  '''simple docstring'''





  if os.environ.get("""TESTING_MOCKED_DATALOADERS"""							,   A__ ) == "1":
    lowerCAmelCase_      :				str      =       2
  # Initialize accelerator
  lowerCAmelCase_      :				int      =       Accelerator(cpu=args.cpu							,   mixed_precision=args.mixed_precision )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowerCAmelCase_      :				Optional[int]      =       config["""lr"""]
  lowerCAmelCase_      :				Any      =       int(config["""num_epochs"""] )
  lowerCAmelCase_      :				Dict      =       int(config["""seed"""] )
  lowerCAmelCase_      :				Optional[int]      =       int(config["""batch_size"""] )

  lowerCAmelCase_      :				List[str]      =       evaluate.load("""glue"""							,   """mrpc""" )

  # If the batch size is too big we use gradient accumulation
  lowerCAmelCase_      :				str      =       1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    lowerCAmelCase_      :				Optional[int]      =       batch_size // MAX_GPU_BATCH_SIZE
    lowerCAmelCase_      :				List[str]      =       MAX_GPU_BATCH_SIZE

  set_seed(A__ )
  lowerCAmelCase_,							lowerCAmelCase_      :				Optional[Any]      =       get_dataloaders(A__							,   A__ )
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowerCAmelCase_      :				Dict      =       AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""							,   return_dict=A__ )

  # 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).
  lowerCAmelCase_      :				int      =       model.to(accelerator.device )

  # Instantiate optimizer
  lowerCAmelCase_      :				Dict      =       AdamW(params=model.parameters()							,   lr=A__ )

  # Instantiate scheduler
  lowerCAmelCase_      :				Union[str, Any]      =       get_linear_schedule_with_warmup(
      optimizer=A__							,   num_warmup_steps=1_00							,   num_training_steps=(len(A__ ) * num_epochs) // gradient_accumulation_steps							,   )

  # 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.
  lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_      :				List[str]      =       accelerator.prepare(
      A__							,   A__							,   A__							,   A__							,   A__ )

  # Now we train the model
  for epoch in range(A__ ):
    model.train()
    for step, batch in enumerate(A__ ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device )
      lowerCAmelCase_      :				List[str]      =       model(**A__ )
      lowerCAmelCase_      :				int      =       outputs.loss
      lowerCAmelCase_      :				Union[str, Any]      =       loss / gradient_accumulation_steps
      accelerator.backward(A__ )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    lowerCAmelCase_      :				str      =       0
    for step, batch in enumerate(A__ ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device )
      with torch.no_grad():
        lowerCAmelCase_      :				Any      =       model(**A__ )
      lowerCAmelCase_      :				Union[str, Any]      =       outputs.logits.argmax(dim=-1 )
      lowerCAmelCase_,							lowerCAmelCase_      :				List[Any]      =       accelerator.gather((predictions, batch["""labels"""]) )
      # New Code #
      # First we check if it's a distributed system
      if accelerator.use_distributed:
        # Then see if we're on the last batch of our eval dataloader
        if step == len(A__ ) - 1:
          # Last batch needs to be truncated on distributed systems as it contains additional samples
          lowerCAmelCase_      :				List[str]      =       predictions[: len(eval_dataloader.dataset ) - samples_seen]
          lowerCAmelCase_      :				Dict      =       references[: len(eval_dataloader.dataset ) - samples_seen]
        else:
          # Otherwise we add the number of samples seen
          samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
      metric.add_batch(
          predictions=A__							,   references=A__							,   )

    lowerCAmelCase_      :				Any      =       metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'epoch {epoch}:'							,   A__ )







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				Dict      =       argparse.ArgumentParser(description="""Simple example of training script.""" )
  parser.add_argument(
      """--mixed_precision"""							,   type=A__							,   default=A__							,   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."""							,   )
  parser.add_argument("""--cpu"""							,   action="""store_true"""							,   help="""If passed, will train on the CPU.""" )
  lowerCAmelCase_      :				Optional[Any]      =       parser.parse_args()
  lowerCAmelCase_      :				int      =       {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(A__							,   A__ )


if __name__ == "__main__":
  main()







'''simple docstring'''






from __future__ import annotations







def     UpperCamelCase_      (   A__ :       int | float | str							,   A__ :       int | float | str ):
  '''simple docstring'''





  if nth_term == "":
    return [""]
  lowerCAmelCase_      :				str      =       int(A__ )
  lowerCAmelCase_      :				Tuple      =       int(A__ )
  lowerCAmelCase_      :				list[str]      =       []
  for temp in range(int(A__ ) ):
    series.append(f'1 / {pow(temp + 1							,   int(A__ ) )}' if series else """1""" )
  return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __A :  str    							=				int(input("Enter the last number (nth term) of the P-Series"))
  __A :  Tuple    							=				int(input("Enter the power for  P-Series"))
  print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
  print(p_series(nth_term, power))



