


'''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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


lowerCamelCase							:       str  						=     16
lowerCamelCase							:       Optional[int]  						=     32

def _SCREAMING_SNAKE_CASE				(A  ,			A	)	->	Optional[int]:






       """simple docstring"""







       if args.with_tracking:
              lowercase__				      =			Accelerator(
                  cpu=args.cpu  ,			mixed_precision=args.mixed_precision  ,			log_with='''all'''  ,			project_dir=args.project_dir	)
       else:
              lowercase__				      =			Accelerator(cpu=args.cpu  ,			mixed_precision=args.mixed_precision	)

       if hasattr(args.checkpointing_steps  ,			'''isdigit'''	):
              if args.checkpointing_steps == "epoch":
                     lowercase__				      =			args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     lowercase__				      =			int(args.checkpointing_steps	)
              else:
                     raise ValueError(
                         f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."	)
       else:
              lowercase__				      =			None
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowercase__				      =			config['''lr''']
       lowercase__				      =			int(config['''num_epochs''']	)
       lowercase__				      =			int(config['''seed''']	)
       lowercase__				      =			int(config['''batch_size''']	)

       # We need to initialize the trackers we use, and also store our configuration
       if args.with_tracking:
              lowercase__				      =			os.path.split(A	)[-1].split('''.'''	)[0]
              accelerator.init_trackers(A  ,			A	)

       lowercase__				      =			AutoTokenizer.from_pretrained('''bert-base-cased'''	)
       lowercase__				      =			load_dataset('''glue'''  ,			'''mrpc'''	)
       lowercase__				      =			evaluate.load('''glue'''  ,			'''mrpc'''	)

       def tokenize_function(A	):
              # max_length=None => use the model max length (it's actually the default)
              lowercase__				      =			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():
              lowercase__				      =			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
       lowercase__				      =			tokenized_datasets.rename_column('''label'''  ,			'''labels'''	)

       # If the batch size is too big we use gradient accumulation
       lowercase__				      =			1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              lowercase__				      =			batch_size // MAX_GPU_BATCH_SIZE
              lowercase__				      =			MAX_GPU_BATCH_SIZE

       def collate_fn(A	):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              lowercase__				      =			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":
                     lowercase__				      =			16
              elif accelerator.mixed_precision != "no":
                     lowercase__				      =			8
              else:
                     lowercase__				      =			None

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

       # Instantiate dataloaders.
       lowercase__				      =			DataLoader(
           tokenized_datasets['''train''']  ,			shuffle=A  ,			collate_fn=A  ,			batch_size=A	)
       lowercase__				      =			DataLoader(
           tokenized_datasets['''validation''']  ,			shuffle=A  ,			collate_fn=A  ,			batch_size=A	)

       set_seed(A	)

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowercase__				      =			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).
       lowercase__				      =			model.to(accelerator.device	)

       # Instantiate optimizer
       lowercase__				      =			AdamW(params=model.parameters()  ,			lr=A	)

       # Instantiate scheduler
       lowercase__				      =			get_linear_schedule_with_warmup(
           optimizer=A  ,			num_warmup_steps=100  ,			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.
       lowercase__      ,lowercase__      ,lowercase__      ,lowercase__      ,lowercase__				      =			accelerator.prepare(
           A  ,			A  ,			A  ,			A  ,			A	)

       # We need to keep track of how many total steps we have iterated over
       lowercase__				      =			0
       # We also need to keep track of the stating epoch so files are named properly
       lowercase__				      =			0

       # Potentially load in the weights and states from a previous save
       if args.resume_from_checkpoint:
              if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
                     accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}"	)
                     accelerator.load_state(args.resume_from_checkpoint	)
                     lowercase__				      =			os.path.basename(args.resume_from_checkpoint	)
              else:
                     # Get the most recent checkpoint
                     lowercase__				      =			[f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
                     dirs.sort(key=os.path.getctime	)
                     lowercase__				      =			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              lowercase__				      =			os.path.splitext(A	)[0]

              if "epoch" in training_difference:
                     lowercase__				      =			int(training_difference.replace('''epoch_'''  ,			''''''	)	) + 1
                     lowercase__				      =			None
              else:
                     lowercase__				      =			int(training_difference.replace('''step_'''  ,			''''''	)	)
                     lowercase__				      =			resume_step // len(A	)
                     resume_step -= starting_epoch * len(A	)

    # Now we train the model
       for epoch in range(A  ,			A	):
              model.train()
              if args.with_tracking:
                     lowercase__				      =			0
              if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                     # We need to skip steps until we reach the resumed step
                     lowercase__				      =			accelerator.skip_first_batches(A  ,			A	)
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     lowercase__				      =			train_dataloader
              for step, batch in enumerate(A	):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device	)
                     lowercase__				      =			model(**A	)
                     lowercase__				      =			outputs.loss
                     lowercase__				      =			loss / gradient_accumulation_steps
                     # We keep track of the loss at each epoch
                     if args.with_tracking:
                            total_loss += loss.detach().float()
                     accelerator.backward(A	)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1

                     if isinstance(A  ,			A	):
                            lowercase__				      =			f"step_{overall_step}"
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          lowercase__				      =			os.path.join(args.output_dir  ,			A	)
                                   accelerator.save_state(A	)

              model.eval()
              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():
                            lowercase__				      =			model(**A	)
                     lowercase__				      =			outputs.logits.argmax(dim=-1	)
                     lowercase__      ,lowercase__				      =			accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
                     metric.add_batch(
                         predictions=A  ,			references=A  ,			)

              lowercase__				      =			metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"epoch {epoch}:"  ,			A	)
              if args.with_tracking:
                     accelerator.log(
                         {
                             '''accuracy''': eval_metric['''accuracy'''],
                             '''f1''': eval_metric['''f1'''],
                             '''train_loss''': total_loss.item() / len(A	),
                             '''epoch''': epoch,
                         }  ,			step=A  ,			)

              if checkpointing_steps == "epoch":
                     lowercase__				      =			f"epoch_{epoch}"
                     if args.output_dir is not None:
                            lowercase__				      =			os.path.join(args.output_dir  ,			A	)
                     accelerator.save_state(A	)

       if args.with_tracking:
              accelerator.end_training()

def _SCREAMING_SNAKE_CASE				()	->	Union[str, Any]:






       """simple docstring"""







       lowercase__				      =			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.'''	)
       parser.add_argument(
           '''--checkpointing_steps'''  ,			type=A  ,			default=A  ,			help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''  ,			)
       parser.add_argument(
           '''--resume_from_checkpoint'''  ,			type=A  ,			default=A  ,			help='''If the training should continue from a checkpoint folder.'''  ,			)
       parser.add_argument(
           '''--with_tracking'''  ,			action='''store_true'''  ,			help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''  ,			)
       parser.add_argument(
           '''--output_dir'''  ,			type=A  ,			default='''.'''  ,			help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''  ,			)
       parser.add_argument(
           '''--project_dir'''  ,			type=A  ,			default='''logs'''  ,			help='''Location on where to store experiment tracking logs` and relevent project information'''  ,			)
       lowercase__				      =			parser.parse_args()
       lowercase__				      =			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(A  ,			A	)


if __name__ == "__main__":
 main()








'''simple docstring'''

import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm

def _SCREAMING_SNAKE_CASE				(A  ,			A = False	)	->	Tuple:






       """simple docstring"""







       if device_specific:
              seed += AcceleratorState().process_index
       random.seed(A	)
       np.random.seed(A	)
       torch.manual_seed(A	)
       if is_xpu_available():
              torch.xpu.manual_seed_all(A	)
       elif is_npu_available():
              torch.npu.manual_seed_all(A	)
       else:
              torch.cuda.manual_seed_all(A	)
       # ^^ safe to call this function even if cuda is not available
       if is_tpu_available():
              xm.set_rng_state(A	)

def _SCREAMING_SNAKE_CASE				(A = None  ,			A = None	)	->	Tuple:






       """simple docstring"""







       if rng_type == RNGType.TORCH:
              lowercase__				      =			torch.get_rng_state()
       elif rng_type == RNGType.CUDA:
              lowercase__				      =			torch.cuda.get_rng_state()
       elif rng_type == RNGType.XLA:
              assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
              lowercase__				      =			torch.tensor(xm.get_rng_state()	)
       elif rng_type == RNGType.NPU:
              assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
              lowercase__				      =			torch.npu.get_rng_state()
       elif rng_type == RNGType.XPU:
              assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
              lowercase__				      =			torch.xpu.get_rng_state()
       elif rng_type == RNGType.GENERATOR:
              assert generator is not None, "Need a generator to synchronize its seed."
              lowercase__				      =			generator.get_state()

       # Broadcast the rng state from device 0 to other devices
       lowercase__				      =			AcceleratorState()
       if state.distributed_type == DistributedType.TPU:
              lowercase__				      =			rng_state.to(xm.xla_device()	)
              xm.collective_broadcast([rng_state]	)
              xm.mark_step()
              lowercase__				      =			rng_state.cpu()
       elif (
           state.distributed_type in CUDA_DISTRIBUTED_TYPES
           or state.distributed_type == DistributedType.MULTI_NPU
           or state.distributed_type == DistributedType.MULTI_XPU
       ):
              lowercase__				      =			rng_state.to(state.device	)
              torch.distributed.broadcast(A  ,			0	)
              lowercase__				      =			rng_state.cpu()
       elif state.distributed_type == DistributedType.MULTI_CPU:
              torch.distributed.broadcast(A  ,			0	)

       # Set the broadcast rng state
       if rng_type == RNGType.TORCH:
              torch.set_rng_state(A	)
       elif rng_type == RNGType.CUDA:
              torch.cuda.set_rng_state(A	)
       elif rng_type == RNGType.NPU:
              torch.npu.set_rng_state(A	)
       elif rng_type == RNGType.XPU:
              torch.xpu.set_rng_state(A	)
       elif rng_type == RNGType.XLA:
              xm.set_rng_state(rng_state.item()	)
       elif rng_type == RNGType.GENERATOR:
              generator.set_state(A	)

def _SCREAMING_SNAKE_CASE				(A  ,			A = None	)	->	int:






       """simple docstring"""







       for rng_type in rng_types:
              synchronize_rng_state(RNGType(A	)  ,			generator=A	)




