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


_lowercase    =		16
_lowercase    =		32


def A				(__lowerCamelCase				:Accelerator			,     __lowerCamelCase				:int = 16		):
						_lowerCAmelCase  						=			AutoTokenizer.from_pretrained("""bert-base-cased"""		)
						_lowerCAmelCase  						=			load_dataset("""glue"""			,     """mrpc"""		)

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

						def collate_fn(__lowerCamelCase				:Union[str, Any]		):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												_lowerCAmelCase  						=			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  						=			16
												elif accelerator.mixed_precision != "no":
																		_lowerCAmelCase  						=			8
												else:
																		_lowerCAmelCase  						=			None

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

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

						_lowercase    =		mocked_dataloaders  # noqa: F811


def A				(__lowerCamelCase				:Any			,     __lowerCamelCase				:int		):
						# For testing only
						if os.environ.get("""TESTING_MOCKED_DATALOADERS"""			,     __lowerCamelCase		) == "1":
												_lowerCAmelCase  						=			2
						# New Code #
						_lowerCAmelCase  						=			int(args.gradient_accumulation_steps		)
						# Initialize accelerator
						_lowerCAmelCase  						=			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
						_lowerCAmelCase  						=			config["""lr"""]
						_lowerCAmelCase  						=			int(config["""num_epochs"""]		)
						_lowerCAmelCase  						=			int(config["""seed"""]		)
						_lowerCAmelCase  						=			int(config["""batch_size"""]		)

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

						set_seed(__lowerCamelCase		)
						_lowerCAmelCase						,   _lowerCAmelCase  						=			get_dataloaders(__lowerCamelCase			,     __lowerCamelCase		)
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						_lowerCAmelCase  						=			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).
						_lowerCAmelCase  						=			model.to(accelerator.device		)

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

						# Instantiate scheduler
						_lowerCAmelCase  						=			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.
						_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			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		):
																								_lowerCAmelCase  						=			model(**__lowerCamelCase		)
																								_lowerCAmelCase  						=			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():
																								_lowerCAmelCase  						=			model(**__lowerCamelCase		)
																		_lowerCAmelCase  						=			outputs.logits.argmax(dim=-1		)
																		_lowerCAmelCase						,   _lowerCAmelCase  						=			accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
																		metric.add_batch(
																		    predictions=__lowerCamelCase			,     references=__lowerCamelCase			,     )

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


def A				():
						_lowerCAmelCase  						=			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."""		)
						_lowerCAmelCase  						=			parser.parse_args()
						_lowerCAmelCase  						=			{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
						training_function(__lowerCamelCase			,     __lowerCamelCase		)


if __name__ == "__main__":
						main()


'''simple docstring'''



from ...configuration_utils import PretrainedConfig




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				Tuple									=       '''bert-generation'''




						def __init__( self   ,       _lowercase=50_358   ,       _lowercase=1_024   ,       _lowercase=24   ,       _lowercase=16   ,       _lowercase=4_096   ,       _lowercase="gelu"   ,       _lowercase=0.1   ,       _lowercase=0.1   ,       _lowercase=512   ,       _lowercase=0.02   ,       _lowercase=1e-12   ,       _lowercase=0   ,       _lowercase=2   ,       _lowercase=1   ,       _lowercase="absolute"   ,       _lowercase=True   ,       **_lowercase   ,       ):

												"""simple docstring"""



												super().__init__(pad_token_id=_lowercase   ,       bos_token_id=_lowercase   ,       eos_token_id=_lowercase   ,       **_lowercase				)

												_lowerCAmelCase  						=			vocab_size
												_lowerCAmelCase  						=			hidden_size
												_lowerCAmelCase  						=			num_hidden_layers
												_lowerCAmelCase  						=			num_attention_heads
												_lowerCAmelCase  						=			hidden_act
												_lowerCAmelCase  						=			intermediate_size
												_lowerCAmelCase  						=			hidden_dropout_prob
												_lowerCAmelCase  						=			attention_probs_dropout_prob
												_lowerCAmelCase  						=			max_position_embeddings
												_lowerCAmelCase  						=			initializer_range
												_lowerCAmelCase  						=			layer_norm_eps
												_lowerCAmelCase  						=			position_embedding_type
												_lowerCAmelCase  						=			use_cache
