






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
# and perform 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
#
########################################################################


a_						:  Any									=			16
a_						:  Union[str, Any]									=			32






def  lowerCamelCase__ (_UpperCAmelCase							,				_UpperCAmelCase = 16):
			SCREAMING_SNAKE_CASE						 =       AutoTokenizer.from_pretrained('bert-base-cased')
			SCREAMING_SNAKE_CASE						 =       load_dataset('glue'							,				'mrpc')

			def tokenize_function(_UpperCAmelCase):
						# max_length=None => use the model max length (it's actually the default)
						SCREAMING_SNAKE_CASE						 =       tokenizer(examples['sentence1']							,				examples['sentence2']							,				truncation=__lowerCamelCase							,				max_length=__lowerCamelCase)
						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():
						SCREAMING_SNAKE_CASE						 =       datasets.map(
						    __lowerCamelCase							,				batched=__lowerCamelCase							,				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
			SCREAMING_SNAKE_CASE						 =       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.
						SCREAMING_SNAKE_CASE						 =       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":
									SCREAMING_SNAKE_CASE						 =       16
						elif accelerator.mixed_precision != "no":
									SCREAMING_SNAKE_CASE						 =       8
						else:
									SCREAMING_SNAKE_CASE						 =       None

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

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

			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

				a_						:  Any									=			mocked_dataloaders  # noqa: F811






def  lowerCamelCase__ (_UpperCAmelCase							,				_UpperCAmelCase):
			# For testing only
			if os.environ.get('TESTING_MOCKED_DATALOADERS'							,				__lowerCamelCase) == "1":
						SCREAMING_SNAKE_CASE						 =       2
			# New Code #
			SCREAMING_SNAKE_CASE						 =       int(args.gradient_accumulation_steps)
			# Initialize accelerator
			SCREAMING_SNAKE_CASE						 =       Accelerator(
			    cpu=args.cpu							,				mixed_precision=args.mixed_precision							,				gradient_accumulation_steps=__lowerCamelCase)
			if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
						raise NotImplementedError(
						    'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`')
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			SCREAMING_SNAKE_CASE						 =       config['lr']
			SCREAMING_SNAKE_CASE						 =       int(config['num_epochs'])
			SCREAMING_SNAKE_CASE						 =       int(config['seed'])
			SCREAMING_SNAKE_CASE						 =       int(config['batch_size'])

			SCREAMING_SNAKE_CASE						 =       evaluate.load('glue'							,				'mrpc')

			set_seed(__lowerCamelCase)
			SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       get_dataloaders(__lowerCamelCase							,				__lowerCamelCase)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			SCREAMING_SNAKE_CASE						 =       AutoModelForSequenceClassification.from_pretrained('bert-base-cased'							,				return_dict=__lowerCamelCase)

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

			# Instantiate optimizer
			SCREAMING_SNAKE_CASE						 =       AdamW(params=model.parameters()							,				lr=__lowerCamelCase)

			# Instantiate scheduler
			SCREAMING_SNAKE_CASE						 =       get_linear_schedule_with_warmup(
			    optimizer=__lowerCamelCase							,				num_warmup_steps=100							,				num_training_steps=(len(__lowerCamelCase) * 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.
			SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       accelerator.prepare(
			    __lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase)

			# Now we train the model
			for epoch in range(__lowerCamelCase):
						model.train()
						for step, batch in enumerate(__lowerCamelCase):
									# 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(__lowerCamelCase):
												SCREAMING_SNAKE_CASE						 =       model(**__lowerCamelCase)
												SCREAMING_SNAKE_CASE						 =       output.loss
												accelerator.backward(__lowerCamelCase)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

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

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






def  lowerCamelCase__ ():
			SCREAMING_SNAKE_CASE						 =       argparse.ArgumentParser(description='Simple example of training script.')
			parser.add_argument(
			    '--mixed_precision'							,				type=__lowerCamelCase							,				default=__lowerCamelCase							,				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=__lowerCamelCase							,				default=1							,				help='The number of minibatches to be ran before gradients are accumulated.'							,				)
			parser.add_argument('--cpu'							,				action='store_true'							,				help='If passed, will train on the CPU.')
			SCREAMING_SNAKE_CASE						 =       parser.parse_args()
			SCREAMING_SNAKE_CASE						 =       {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
			training_function(__lowerCamelCase							,				__lowerCamelCase)


if __name__ == "__main__":
				main()










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase					:       Optional[Any]    					=      {
    "configuration_jukebox": [
        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "JukeboxConfig",
        "JukeboxPriorConfig",
        "JukeboxVQVAEConfig",
    ],
    "tokenization_jukebox": ["JukeboxTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase					:       Tuple    					=      [
		    "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "JukeboxModel",
		    "JukeboxPreTrainedModel",
		    "JukeboxVQVAE",
		    "JukeboxPrior",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		__UpperCamelCase					:       List[Any]    					=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

