






'''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
#
########################################################################


SCREAMING_SNAKE_CASE__											=     1_6
SCREAMING_SNAKE_CASE__											=     3_2







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase = 16					)->				Dict:
						UpperCamelCase       						=      AutoTokenizer.from_pretrained("""bert-base-cased"""					)
						UpperCamelCase       						=      load_dataset("""glue"""	,		"""mrpc"""					)

						def tokenize_function(__UpperCamelCase					):
												# max_length=None => use the model max length (it's actually the default)
												UpperCamelCase       						=      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():
												UpperCamelCase       						=      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
						UpperCamelCase       						=      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.
												UpperCamelCase       						=      128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		UpperCamelCase       						=      16
												elif accelerator.mixed_precision != "no":
																		UpperCamelCase       						=      8
												else:
																		UpperCamelCase       						=      None

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

						# Instantiate dataloaders.
						UpperCamelCase       						=      DataLoader(
						    tokenized_datasets["""train"""]	,		shuffle=__UpperCamelCase	,		collate_fn=__UpperCamelCase	,		batch_size=__UpperCamelCase					)
						UpperCamelCase       						=      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

					SCREAMING_SNAKE_CASE__											=     mocked_dataloaders  # noqa: F811







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				List[Any]:
						# For testing only
						if os.environ.get("""TESTING_MOCKED_DATALOADERS"""	,		__UpperCamelCase					) == "1":
												UpperCamelCase       						=      2
						# New Code #
						UpperCamelCase       						=      int(args.gradient_accumulation_steps					)
						UpperCamelCase       						=      int(args.local_sgd_steps					)
						# Initialize accelerator
						UpperCamelCase       						=      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
						UpperCamelCase       						=      config["""lr"""]
						UpperCamelCase       						=      int(config["""num_epochs"""]					)
						UpperCamelCase       						=      int(config["""seed"""]					)
						UpperCamelCase       						=      int(config["""batch_size"""]					)

						UpperCamelCase       						=      evaluate.load("""glue"""	,		"""mrpc"""					)

						set_seed(__UpperCamelCase					)
						UpperCamelCase						,UpperCamelCase       						=      get_dataloaders(__UpperCamelCase	,		__UpperCamelCase					)
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						UpperCamelCase       						=      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).
						UpperCamelCase       						=      model.to(accelerator.device					)

						# Instantiate optimizer
						UpperCamelCase       						=      AdamW(params=model.parameters()	,		lr=__UpperCamelCase					)

						# Instantiate scheduler
						UpperCamelCase       						=      get_linear_schedule_with_warmup(
						    optimizer=__UpperCamelCase	,		num_warmup_steps=100	,		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.
						UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase       						=      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					):
																														UpperCamelCase       						=      model(**__UpperCamelCase					)
																														UpperCamelCase       						=      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():
																								UpperCamelCase       						=      model(**__UpperCamelCase					)
																		UpperCamelCase       						=      outputs.logits.argmax(dim=-1					)
																		UpperCamelCase						,UpperCamelCase       						=      accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
																		metric.add_batch(
																		    predictions=__UpperCamelCase	,		references=__UpperCamelCase	,		)

												UpperCamelCase       						=      metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"epoch {epoch}:"	,		__UpperCamelCase					)







def 					lowercase__				(   )->				List[Any]:
						UpperCamelCase       						=      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."""					)
						UpperCamelCase       						=      parser.parse_args()
						UpperCamelCase       						=      {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
						training_function(__UpperCamelCase	,		__UpperCamelCase					)


if __name__ == "__main__":
					main()








'''simple docstring'''

from __future__ import annotations

from typing import Any






class        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE = 6   )						->   None:



        """simple docstring"""






        UpperCamelCase       						=      None
        UpperCamelCase       						=      None
        self.create_linked_list(_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   None:



        """simple docstring"""






        UpperCamelCase       						=      Node()
        UpperCamelCase       						=      current_node
        UpperCamelCase       						=      current_node
        UpperCamelCase       						=      current_node
        for _ in range(1    ,	_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      Node()
              UpperCamelCase       						=      current_node
              UpperCamelCase       						=      previous_node
              UpperCamelCase       						=      current_node
        UpperCamelCase       						=      self.front
        UpperCamelCase       						=      previous_node

  def 						A__		(   self   )						->   bool:



        """simple docstring"""






        return (
            self.front == self.rear
            and self.front is not None
            and self.front.data is None
        )

  def 						A__		(   self   )						->   Any | None:



        """simple docstring"""






        self.check_can_perform_operation()
        return self.front.data if self.front else None

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   None:



        """simple docstring"""






        if self.rear is None:
              return

        self.check_is_full()
        if not self.is_empty():
              UpperCamelCase       						=      self.rear.next
        if self.rear:
              UpperCamelCase       						=      data

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        self.check_can_perform_operation()
        if self.rear is None or self.front is None:
              return None
        if self.front == self.rear:
              UpperCamelCase       						=      self.front.data
              UpperCamelCase       						=      None
              return data

        UpperCamelCase       						=      self.front
        UpperCamelCase       						=      old_front.next
        UpperCamelCase       						=      old_front.data
        UpperCamelCase       						=      None
        return data

  def 						A__		(   self   )						->   None:



        """simple docstring"""






        if self.is_empty():
              raise Exception("""Empty Queue"""   )


  def 						A__		(   self   )						->   None:



        """simple docstring"""






        if self.rear and self.rear.next == self.front:
              raise Exception("""Full Queue"""   )






class        a_		:

  def __init__(   self   )						->   None:



        """simple docstring"""






        UpperCamelCase       						=      None
        UpperCamelCase       						=      None
        UpperCamelCase       						=      None


if __name__ == "__main__":
     import doctest

     doctest.testmod()
