

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 the experiment tracking capability,
# 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
#
########################################################################

lowerCAmelCase__		      =       1_6
lowerCAmelCase__		      =       3_2



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Accelerator		,				SCREAMING_SNAKE_CASE_:							int = 1_6					)	->					Tuple:




				'''simple docstring'''





				A__						=	AutoTokenizer.from_pretrained("bert-base-cased"					)
				A__						=	load_dataset("glue"		,				"mrpc"					)

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

				def collate_fn(SCREAMING_SNAKE_CASE_:							Optional[Any]					):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								A__						=	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":
												A__						=	1_6
								elif accelerator.mixed_precision != "no":
												A__						=	8
								else:
												A__						=	None

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

				# Instantiate dataloaders.
				A__						=	DataLoader(
				    tokenized_datasets["train"]		,				shuffle=SCREAMING_SNAKE_CASE_		,				collate_fn=SCREAMING_SNAKE_CASE_		,				batch_size=SCREAMING_SNAKE_CASE_					)
				A__						=	DataLoader(
				    tokenized_datasets["validation"]		,				shuffle=SCREAMING_SNAKE_CASE_		,				collate_fn=SCREAMING_SNAKE_CASE_		,				batch_size=SCREAMING_SNAKE_CASE_					)

				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

	lowerCAmelCase__		      =       mocked_dataloaders  # noqa: F811



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Dict		,				SCREAMING_SNAKE_CASE_:							Tuple					)	->					str:




				'''simple docstring'''





				if os.environ.get("TESTING_MOCKED_DATALOADERS"		,				SCREAMING_SNAKE_CASE_					) == "1":
								A__						=	2
				# Initialize Accelerator

				# New Code #
				# We pass in "all" to `log_with` to grab all available trackers in the environment
				# Note: If using a custom `Tracker` class, should be passed in here such as:
				# >>> log_with = ["all", MyCustomTrackerClassInstance()]
				if args.with_tracking:
								A__						=	Accelerator(
								    cpu=args.cpu		,				mixed_precision=args.mixed_precision		,				log_with="all"		,				project_dir=args.project_dir					)
				else:
								A__						=	Accelerator(cpu=args.cpu		,				mixed_precision=args.mixed_precision					)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				A__						=	config["lr"]
				A__						=	int(config["num_epochs"]					)
				A__						=	int(config["seed"]					)
				A__						=	int(config["batch_size"]					)
				set_seed(SCREAMING_SNAKE_CASE_					)

				A__			, A__						=	get_dataloaders(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					)
				A__						=	evaluate.load("glue"		,				"mrpc"					)

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

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				A__						=	AutoModelForSequenceClassification.from_pretrained("bert-base-cased"		,				return_dict=SCREAMING_SNAKE_CASE_					)

				# 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).
				A__						=	model.to(accelerator.device					)

				# Instantiate optimizer
				A__						=	AdamW(params=model.parameters()		,				lr=SCREAMING_SNAKE_CASE_					)

				# Instantiate scheduler
				A__						=	get_linear_schedule_with_warmup(
				    optimizer=SCREAMING_SNAKE_CASE_		,				num_warmup_steps=1_0_0		,				num_training_steps=(len(SCREAMING_SNAKE_CASE_					) * 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.
				A__			, A__			, A__			, A__			, A__						=	accelerator.prepare(
				    SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					)

				# New Code #
				# We need to initialize the trackers we use. Overall configurations can also be stored
				if args.with_tracking:
								A__						=	os.path.split(SCREAMING_SNAKE_CASE_					)[-1].split("."					)[0]
								accelerator.init_trackers(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					)

				# Now we train the model
				for epoch in range(SCREAMING_SNAKE_CASE_					):
								model.train()
								# New Code #
								# For our tracking example, we will log the total loss of each epoch
								if args.with_tracking:
												A__						=	0
								for step, batch in enumerate(SCREAMING_SNAKE_CASE_					):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device					)
												A__						=	model(**SCREAMING_SNAKE_CASE_					)
												A__						=	outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												A__						=	loss / gradient_accumulation_steps
												accelerator.backward(SCREAMING_SNAKE_CASE_					)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

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

								# New Code #
								# To actually log, we call `Accelerator.log`
								# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
								if args.with_tracking:
												accelerator.log(
												    {
												        "accuracy": eval_metric["accuracy"],
												        "f1": eval_metric["f1"],
												        "train_loss": total_loss.item() / len(SCREAMING_SNAKE_CASE_					),
												        "epoch": epoch,
												    }		,				step=SCREAMING_SNAKE_CASE_		,				)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
				if args.with_tracking:
								accelerator.end_training()



def     lowerCAmelCase__							(		)	->					Optional[Any]:




				'''simple docstring'''





				A__						=	argparse.ArgumentParser(description="Simple example of training script."					)
				parser.add_argument(
				    "--mixed_precision"		,				type=SCREAMING_SNAKE_CASE_		,				default=SCREAMING_SNAKE_CASE_		,				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(
				    "--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(
				    "--project_dir"		,				type=SCREAMING_SNAKE_CASE_		,				default="logs"		,				help="Location on where to store experiment tracking logs` and relevent project information"		,				)
				A__						=	parser.parse_args()
				A__						=	{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
				training_function(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
	main()



import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 perform Cross Validation,
# 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
#
########################################################################


__lowerCAmelCase			:     List[Any]           =   16
__lowerCAmelCase			:     Any           =   32

def a__    (       A_,     A_,     A_,     A_,     A_ = 16					):






  '''simple docstring'''





  __magic_name__				     =       AutoTokenizer.from_pretrained("""bert-base-cased"""					)
  __magic_name__				     =       DatasetDict(
      {
          """train""": dataset["""train"""].select(A_					),
          """validation""": dataset["""train"""].select(A_					),
          """test""": dataset["""validation"""],
      }					)

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

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

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

  # Instantiate dataloaders.
  __magic_name__				     =       DataLoader(
      tokenized_datasets["""train"""],     shuffle=A_,     collate_fn=A_,     batch_size=A_					)
  __magic_name__				     =       DataLoader(
      tokenized_datasets["""validation"""],     shuffle=A_,     collate_fn=A_,     batch_size=A_					)

  __magic_name__				     =       DataLoader(
      tokenized_datasets["""test"""],     shuffle=A_,     collate_fn=A_,     batch_size=A_					)

  return train_dataloader, eval_dataloader, test_dataloader

def a__    (       A_,     A_					):






  '''simple docstring'''





  __magic_name__				     =       []
  # Download the dataset
  __magic_name__				     =       load_dataset("""glue""",     """mrpc"""					)
  # Create our splits
  __magic_name__				     =       StratifiedKFold(n_splits=int(args.num_folds					)					)
  # Initialize accelerator
  __magic_name__				     =       Accelerator(cpu=args.cpu,     mixed_precision=args.mixed_precision					)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  __magic_name__				     =       config["""lr"""]
  __magic_name__				     =       int(config["""num_epochs"""]					)
  __magic_name__				     =       int(config["""seed"""]					)
  __magic_name__				     =       int(config["""batch_size"""]					)

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

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

  set_seed(A_					)

  # New Code #
  # Create our folds:
  __magic_name__				     =       kfold.split(np.zeros(datasets["""train"""].num_rows					),     datasets["""train"""]["""label"""]					)
  __magic_name__				     =       []
  # Iterate over them
  for i, (train_idxs, valid_idxs) in enumerate(A_					):
    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       get_fold_dataloaders(
        A_,     A_,     A_,     A_,     )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __magic_name__				     =       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).
    __magic_name__				     =       model.to(accelerator.device					)

    # Instantiate optimizer
    __magic_name__				     =       AdamW(params=model.parameters(),     lr=A_					)

    # Instantiate scheduler
    __magic_name__				     =       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.
    __magic_name__     ,    __magic_name__     ,    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       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					)
        __magic_name__				     =       model(**A_					)
        __magic_name__				     =       outputs.loss
        __magic_name__				     =       loss / gradient_accumulation_steps
        accelerator.backward(A_					)
        if step % gradient_accumulation_steps == 0:
          optimizer.step()
          lr_scheduler.step()
          optimizer.zero_grad()

      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():
          __magic_name__				     =       model(**A_					)
        __magic_name__				     =       outputs.logits.argmax(dim=-1					)
        __magic_name__     ,    __magic_name__				     =       accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
        metric.add_batch(
            predictions=A_,     references=A_,     )

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

    # New Code #
    # We also run predictions on the test set at the very end
    __magic_name__				     =       []
    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():
        __magic_name__				     =       model(**A_					)
      __magic_name__				     =       outputs.logits
      __magic_name__     ,    __magic_name__				     =       accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
      fold_predictions.append(predictions.cpu()					)
      if i == 0:
        # We need all of the test predictions
        test_references.append(references.cpu()					)
        # Use accelerator.print to print only on the main process.
    test_predictions.append(torch.cat(A_,     dim=0					)					)
    # We now need to release all our memory and get rid of the current model, optimizer, etc
    accelerator.free_memory()
  # New Code #
  # Finally we check the accuracy of our folded results:
  __magic_name__				     =       torch.cat(A_,     dim=0					)
  __magic_name__				     =       torch.stack(A_,     dim=0					).sum(dim=0					).div(int(args.num_folds					)					).argmax(dim=-1					)
  __magic_name__				     =       metric.compute(predictions=A_,     references=A_					)
  accelerator.print("""Average test metrics from all folds:""",     A_					)

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       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."""					)
  # New Code #
  parser.add_argument("""--num_folds""",     type=A_,     default=3,     help="""The number of splits to perform across the dataset"""					)
  __magic_name__				     =       parser.parse_args()
  __magic_name__				     =       {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(A_,     A_					)


if __name__ == "__main__":
    main()

