



"""simple docstring"""






import argparse
import os

# New Code #
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.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


__snake_case							:     Optional[Any]   =					16
__snake_case							:     Optional[Any]   =					32


def     _lowercase       (						__snake_case      ,__snake_case = 16			)		->     Any:
			__lowerCAmelCase   :						str				= AutoTokenizer.from_pretrained("bert-base-cased"			)
			__lowerCAmelCase   :						str				= load_dataset("glue"      ,"mrpc"			)

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

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

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

			# Instantiate dataloaders.
			__lowerCAmelCase   :						Any				= DataLoader(
			    tokenized_datasets["train"]      ,shuffle=__snake_case      ,collate_fn=__snake_case      ,batch_size=__snake_case			)
			__lowerCAmelCase   :						Optional[Any]				= DataLoader(
			    tokenized_datasets["validation"]      ,shuffle=__snake_case      ,collate_fn=__snake_case      ,batch_size=__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

	__snake_case							:     List[str]   =					mocked_dataloaders  # noqa: F811


def     _lowercase       (						__snake_case      ,__snake_case			)		->     List[Any]:
			# For testing only
			if os.environ.get("TESTING_MOCKED_DATALOADERS"      ,__snake_case			) == "1":
						__lowerCAmelCase   :						Optional[int]				= 2
			# Initialize accelerator
			__lowerCAmelCase   :						Tuple				= Accelerator(cpu=args.cpu      ,mixed_precision=args.mixed_precision			)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			__lowerCAmelCase   :						List[Any]				= config["lr"]
			__lowerCAmelCase   :						str				= int(config["num_epochs"]			)
			__lowerCAmelCase   :						Union[str, Any]				= int(config["seed"]			)
			__lowerCAmelCase   :						Tuple				= int(config["batch_size"]			)

			__lowerCAmelCase   :						Any				= evaluate.load("glue"      ,"mrpc"			)

			# New Code #
			# We now can define an inner training loop function. It should take a batch size as the only parameter,
			# and build the dataloaders in there.
			# It also gets our decorator
			@find_executable_batch_size(starting_batch_size=__snake_case			)
			def inner_training_loop(__snake_case			):
						# And now just move everything below under this function
						# We need to bring in the Accelerator object from earlier
						nonlocal accelerator
						# And reset all of its attributes that could hold onto any memory:
						accelerator.free_memory()

						# Then we can declare the model, optimizer, and everything else:
						set_seed(__snake_case			)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__lowerCAmelCase   :						Optional[int]				= AutoModelForSequenceClassification.from_pretrained("bert-base-cased"      ,return_dict=__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).
						__lowerCAmelCase   :						List[Any]				= model.to(accelerator.device			)

						# Instantiate optimizer
						__lowerCAmelCase   :						Optional[int]				= AdamW(params=model.parameters()      ,lr=__snake_case			)
						__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= get_dataloaders(__snake_case      ,__snake_case			)

						# Instantiate scheduler
						__lowerCAmelCase   :						List[Any]				= get_linear_schedule_with_warmup(
						    optimizer=__snake_case      ,num_warmup_steps=100      ,num_training_steps=(len(__snake_case			) * 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.
						__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase   :						Any				= accelerator.prepare(
						    __snake_case      ,__snake_case      ,__snake_case      ,__snake_case      ,__snake_case			)

						# Now we train the model
						for epoch in range(__snake_case			):
									model.train()
									for step, batch in enumerate(__snake_case			):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device			)
												__lowerCAmelCase   :						int				= model(**__snake_case			)
												__lowerCAmelCase   :						Optional[int]				= outputs.loss
												accelerator.backward(__snake_case			)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

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

									__lowerCAmelCase   :						Union[str, Any]				= metric.compute()
									# Use accelerator.print to print only on the main process.
									accelerator.print(F"""epoch {epoch}:"""      ,__snake_case			)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
			inner_training_loop()


def     _lowercase       (						)		->     Any:
			__lowerCAmelCase   :						Optional[int]				= argparse.ArgumentParser(description="Simple example of training script."			)
			parser.add_argument(
			    "--mixed_precision"      ,type=__snake_case      ,default=__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."			)
			__lowerCAmelCase   :						Dict				= parser.parse_args()
			__lowerCAmelCase   :						Optional[Any]				= {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(__snake_case      ,__snake_case			)


if __name__ == "__main__":
	main()




"""simple docstring"""






import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


__snake_case							:     str   =					pytest.mark.integration


@pytest.mark.parametrize("path"      ,["paws", "csv"]			)
def     _lowercase       (						__snake_case      ,__snake_case			)		->     Any:
			inspect_dataset(__snake_case      ,__snake_case			)
			__lowerCAmelCase   :						str				= path + ".py"
			assert script_name in os.listdir(__snake_case			)
			assert "__pycache__" not in os.listdir(__snake_case			)


@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning"			)
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"			)
@pytest.mark.parametrize("path"      ,["accuracy"]			)
def     _lowercase       (						__snake_case      ,__snake_case			)		->     int:
			inspect_metric(__snake_case      ,__snake_case			)
			__lowerCAmelCase   :						str				= path + ".py"
			assert script_name in os.listdir(__snake_case			)
			assert "__pycache__" not in os.listdir(__snake_case			)


@pytest.mark.parametrize(
    "path, config_name, expected_splits"      ,[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Optional[int]:
			__lowerCAmelCase   :						str				= get_dataset_config_info(__snake_case      ,config_name=__snake_case			)
			assert info.config_name == config_name
			assert list(info.splits.keys()			) == expected_splits


@pytest.mark.parametrize(
    "path, config_name, expected_exception"      ,[
        ("paws", None, ValueError),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     List[str]:
			with pytest.raises(__snake_case			):
						get_dataset_config_info(__snake_case      ,config_name=__snake_case			)


@pytest.mark.parametrize(
    "path, expected"      ,[
        ("squad", "plain_text"),
        ("acronym_identification", "default"),
        ("lhoestq/squad", "plain_text"),
        ("lhoestq/test", "default"),
        ("lhoestq/demo1", "lhoestq--demo1"),
        ("dalle-mini/wit", "dalle-mini--wit"),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case			)		->     int:
			__lowerCAmelCase   :						Dict				= get_dataset_config_names(__snake_case			)
			assert expected in config_names


@pytest.mark.parametrize(
    "path, expected_configs, expected_splits_in_first_config"      ,[
        ("squad", ["plain_text"], ["train", "validation"]),
        ("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
        ("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Optional[Any]:
			__lowerCAmelCase   :						List[Any]				= get_dataset_infos(__snake_case			)
			assert list(infos.keys()			) == expected_configs
			__lowerCAmelCase   :						List[Any]				= expected_configs[0]
			assert expected_config in infos
			__lowerCAmelCase   :						Tuple				= infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()			) == expected_splits_in_first_config


@pytest.mark.parametrize(
    "path, expected_config, expected_splits"      ,[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Union[str, Any]:
			__lowerCAmelCase   :						Tuple				= get_dataset_infos(__snake_case			)
			assert expected_config in infos
			__lowerCAmelCase   :						int				= infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()			) == expected_splits





@pytest.mark.parametrize(
    "path, config_name, expected_exception"      ,[
        ("paws", None, ValueError),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Any:
			with pytest.raises(__snake_case			):
						get_dataset_split_names(__snake_case      ,config_name=__snake_case			)