


'''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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################


lowerCAmelCase   : Any             =				1_6
lowerCAmelCase   : Any             =				3_2




def 	_A  (       A							,A = 1_6							)  ->				Tuple:
	lowercase						:		List[str]   					=		AutoTokenizer.from_pretrained("bert-base-cased"							)
	lowercase						:		Dict   					=		load_dataset("glue"							,"mrpc"							)

	def tokenize_function(A							):
		# max_length=None => use the model max length (it's actually the default)
		lowercase						:		Optional[Any]   					=		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():
		lowercase						:		List[str]   					=		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
	lowercase						:		int   					=		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.
		lowercase						:		List[str]   					=		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":
			lowercase						:		Optional[int]   					=		1_6
		elif accelerator.mixed_precision != "no":
			lowercase						:		Optional[Any]   					=		8
		else:
			lowercase						:		Optional[Any]   					=		None

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

	# Instantiate dataloaders.
	lowercase						:		Union[str, Any]   					=		DataLoader(
	    tokenized_datasets["train"]							,shuffle=A							,collate_fn=A							,batch_size=A							)
	lowercase						:		str   					=		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

					lowerCAmelCase   : List[Any]             =				mocked_dataloaders  # noqa: F811




def 	_A  (       A							,A							)  ->				int:



	if os.environ.get("TESTING_MOCKED_DATALOADERS"							,A							) == "1":
		lowercase						:		str   					=		2
	# New Code #
	lowercase						:		Tuple   					=		int(args.gradient_accumulation_steps							)
	lowercase						:		Dict   					=		int(args.local_sgd_steps							)
	# Initialize accelerator
	lowercase						:		Optional[Any]   					=		Accelerator(
	    cpu=args.cpu							,mixed_precision=args.mixed_precision							,gradient_accumulation_steps=A							)
	if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
		raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"							)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	lowercase						:		List[Any]   					=		config["lr"]
	lowercase						:		Dict   					=		int(config["num_epochs"]							)
	lowercase						:		Any   					=		int(config["seed"]							)
	lowercase						:		Dict   					=		int(config["batch_size"]							)

	lowercase						:		Union[str, Any]   					=		evaluate.load("glue"							,"mrpc"							)

	set_seed(A							)
	lowercase						:		int   					=		get_dataloaders(A							,A							)
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	lowercase						:		Union[str, Any]   					=		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).
	lowercase						:		Optional[Any]   					=		model.to(accelerator.device							)

	# Instantiate optimizer
	lowercase						:		Optional[int]   					=		AdamW(params=model.parameters()							,lr=A							)

	# Instantiate scheduler
	lowercase						:		Optional[Any]   					=		get_linear_schedule_with_warmup(
	    optimizer=A							,num_warmup_steps=1_0_0							,num_training_steps=(len(A							) * 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.
	lowercase						:		Dict   					=		accelerator.prepare(
	    A							,A							,A							,A							,A							)

	# Now we train the model
	for epoch in range(A							):
		model.train()
		with LocalSGD(
		    accelerator=A							,model=A							,local_sgd_steps=A							,enabled=local_sgd_steps is not None							) as local_sgd:
			for step, batch in enumerate(A							):
				# 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(A							):
					lowercase						:		int   					=		model(**A							)
					lowercase						:		Tuple   					=		output.loss
					accelerator.backward(A							)
					optimizer.step()
					lr_scheduler.step()
					optimizer.zero_grad()
					# LocalSGD-specific line
					local_sgd.step()

		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():
				lowercase						:		Optional[Any]   					=		model(**A							)
			lowercase						:		List[str]   					=		outputs.logits.argmax(dim=-1							)
			lowercase						:		int   					=		accelerator.gather_for_metrics((predictions, batch["labels"])							)
			metric.add_batch(
			    predictions=A							,references=A							,)

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




def 	_A  (       )  ->				Optional[int]:
	lowercase						:		List[Any]   					=		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."							,)
	# New Code #
	parser.add_argument(
	    "--gradient_accumulation_steps"							,type=A							,default=1							,help="The number of minibatches to be ran before gradients are accumulated."							,)
	parser.add_argument(
	    "--local_sgd_steps"							,type=A							,default=8							,help="Number of local SGD steps or None to disable local SGD"							)
	parser.add_argument("--cpu"							,action="store_true"							,help="If passed, will train on the CPU."							)
	lowercase						:		Tuple   					=		parser.parse_args()
	lowercase						:		List[Any]   					=		{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
	training_function(A							,A							)


if __name__ == "__main__":
					main()










"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase	:	Optional[int]				=      logging.get_logger(__name__)

_lowerCAmelCase	:	Tuple				=      {
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    ='''mra'''
   def __init__(		self      :	Any							,			snake_case__      :	List[str]=5_0_2_6_5							,			snake_case__      :	Any=7_6_8							,			snake_case__      :	Union[str, Any]=1_2							,			snake_case__      :	Optional[Any]=1_2							,			snake_case__      :	Tuple=3_0_7_2							,			snake_case__      :	str="gelu"							,			snake_case__      :	Any=0.1							,			snake_case__      :	Tuple=0.1							,			snake_case__      :	Tuple=5_1_2							,			snake_case__      :	Union[str, Any]=1							,			snake_case__      :	List[Any]=0.02							,			snake_case__      :	str=1e-5							,			snake_case__      :	List[Any]="absolute"							,			snake_case__      :	str=4							,			snake_case__      :	List[str]="full"							,			snake_case__      :	Tuple=0							,			snake_case__      :	Any=0							,			snake_case__      :	Union[str, Any]=1							,			snake_case__      :	int=0							,			snake_case__      :	int=2							,			**snake_case__      :	List[Any]							,			):


        '''simple docstring'''
        super().__init__(pad_token_id=snake_case__							,			bos_token_id=snake_case__							,			eos_token_id=snake_case__							,			**snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							vocab_size
        UpperCAmelCase__						:    str							=							max_position_embeddings
        UpperCAmelCase__						:    Any							=							hidden_size
        UpperCAmelCase__						:    Union[str, Any]							=							num_hidden_layers
        UpperCAmelCase__						:    str							=							num_attention_heads
        UpperCAmelCase__						:    int							=							intermediate_size
        UpperCAmelCase__						:    int							=							hidden_act
        UpperCAmelCase__						:    List[str]							=							hidden_dropout_prob
        UpperCAmelCase__						:    List[str]							=							attention_probs_dropout_prob
        UpperCAmelCase__						:    Any							=							initializer_range
        UpperCAmelCase__						:    Any							=							type_vocab_size
        UpperCAmelCase__						:    Dict							=							layer_norm_eps
        UpperCAmelCase__						:    Tuple							=							position_embedding_type
        UpperCAmelCase__						:    List[str]							=							block_per_row
        UpperCAmelCase__						:    Optional[Any]							=							approx_mode
        UpperCAmelCase__						:    Any							=							initial_prior_first_n_blocks
        UpperCAmelCase__						:    List[Any]							=							initial_prior_diagonal_n_blocks


