




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

_SCREAMING_SNAKE_CASE							:       Tuple				=			16
_SCREAMING_SNAKE_CASE							:       Dict				=			32


def 					UpperCamelCase_(     snake_case							:	Accelerator							,    snake_case							:	int = 1_6					):

							'''simple docstring'''






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

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

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

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

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

		_SCREAMING_SNAKE_CASE							:       Optional[Any]				=			mocked_dataloaders  # noqa: F811


def 					UpperCamelCase_(     snake_case							:	List[Any]							,    snake_case							:	List[str]					):

							'''simple docstring'''






							if os.environ.get("TESTING_MOCKED_DATALOADERS"							,    __a					) == "1":
														snake_case_      =				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:
														snake_case_      =				Accelerator(
														    cpu=args.cpu							,    mixed_precision=args.mixed_precision							,    log_with="all"							,    project_dir=args.project_dir					)
							else:
														snake_case_      =				Accelerator(cpu=args.cpu							,    mixed_precision=args.mixed_precision					)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							snake_case_      =				config["lr"]
							snake_case_      =				int(config["num_epochs"]					)
							snake_case_      =				int(config["seed"]					)
							snake_case_      =				int(config["batch_size"]					)
							set_seed(__a					)

							snake_case_ ,				snake_case_      =				get_dataloaders(__a							,    __a					)
							snake_case_      =				evaluate.load("glue"							,    "mrpc"					)

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

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

							# Instantiate optimizer
							snake_case_      =				AdamW(params=model.parameters()							,    lr=__a					)

							# Instantiate scheduler
							snake_case_      =				get_linear_schedule_with_warmup(
							    optimizer=__a							,    num_warmup_steps=1_0_0							,    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.
							snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      =				accelerator.prepare(
							    __a							,    __a							,    __a							,    __a							,    __a					)

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

							# Now we train the model
							for epoch in range(__a					):
														model.train()
														# New Code #
														# For our tracking example, we will log the total loss of each epoch
														if args.with_tracking:
																					snake_case_      =				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					)
																					snake_case_      =				model(**__a					)
																					snake_case_      =				outputs.loss
																					# New Code #
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					snake_case_      =				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` (the default).
																					batch.to(accelerator.device					)
																					with torch.no_grad():
																												snake_case_      =				model(**__a					)
																					snake_case_      =				outputs.logits.argmax(dim=-1					)
																					snake_case_ ,				snake_case_      =				accelerator.gather_for_metrics((predictions, batch["labels"])					)
																					metric.add_batch(
																					    predictions=__a							,    references=__a							,    )

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

														# 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(__a					),
																					        "epoch": epoch,
																					    }							,    step=__a							,    )

    # 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 					UpperCamelCase_(     ):

							'''simple docstring'''






							snake_case_      =				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(
							    "--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=__a							,    default="logs"							,    help="Location on where to store experiment tracking logs` and relevent project information"							,    )
							snake_case_      =				parser.parse_args()
							snake_case_      =				{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
							training_function(__a							,    __a					)


if __name__ == "__main__":
		main()










'''simple docstring'''





def 					UpperCamelCase_(     snake_case							:	int = 1_0_0_0_0_0_0					):

							'''simple docstring'''






							snake_case_      =				set(range(3							,    snake_case							,    2					)					)
							primes.add(2					)
							for p in range(3							,    snake_case							,    2					):
														if p not in primes:
																					continue
														primes.difference_update(set(range(p * p							,    snake_case							,    snake_case					)					)					)

							snake_case_      =				[float(snake_case					) for n in range(limit + 1					)]

							for p in primes:
														for n in range(snake_case							,    limit + 1							,    snake_case					):
																					phi[n] *= 1 - 1 / p

							return int(sum(phi[2:]					)					)


if __name__ == "__main__":
		print(F"{solution() = }")




