



'''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_							:List[str]       = 1_6
lowerCamelCase_							:Tuple       = 3_2



def      a      (			lowerCamelCase__						,				lowerCamelCase__ = 16   ):
			'''simple docstring'''





			A_     :						str     =					AutoTokenizer.from_pretrained("""bert-base-cased"""   )
			A_     :						str     =					load_dataset("""glue"""						,				"""mrpc"""   )

			def tokenize_function(lowerCamelCase__   ):
						# max_length=None => use the model max length (it's actually the default)
						A_     :						Optional[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():
						A_     :						List[str]     =					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
			A_     :						Dict     =					tokenized_datasets.rename_column("""label"""						,				"""labels"""   )

			def collate_fn(lowerCamelCase__   ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						A_     :						List[str]     =					1_28 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									A_     :						str     =					16
						elif accelerator.mixed_precision != "no":
									A_     :						Dict     =					8
						else:
									A_     :						Union[str, Any]     =					None

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

			# Instantiate dataloaders.
			A_     :						Optional[int]     =					DataLoader(
			    tokenized_datasets["""train"""]						,				shuffle=snake_case__						,				collate_fn=snake_case__						,				batch_size=snake_case__   )
			A_     :						List[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

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



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			if os.environ.get("""TESTING_MOCKED_DATALOADERS"""						,				snake_case__   ) == "1":
						A_     :						List[str]     =					2
			# New Code #
			A_     :						Optional[int]     =					int(args.gradient_accumulation_steps   )
			A_     :						List[str]     =					int(args.local_sgd_steps   )
			# Initialize accelerator
			A_     :						Optional[Any]     =					Accelerator(
			    cpu=args.cpu						,				mixed_precision=args.mixed_precision						,				gradient_accumulation_steps=snake_case__   )
			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
			A_     :						Union[str, Any]     =					config['lr']
			A_     :						Optional[int]     =					int(config["""num_epochs"""]   )
			A_     :						List[Any]     =					int(config["""seed"""]   )
			A_     :						List[str]     =					int(config["""batch_size"""]   )

			A_     :						Optional[Any]     =					evaluate.load("""glue"""						,				"""mrpc"""   )

			set_seed(snake_case__   )
			A_     :						int     =					get_dataloaders(snake_case__						,				snake_case__   )
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			A_     :						Tuple     =					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).
			A_     :						Dict     =					model.to(accelerator.device   )

			# Instantiate optimizer
			A_     :						List[str]     =					AdamW(params=model.parameters()						,				lr=snake_case__   )

			# Instantiate scheduler
			A_     :						Any     =					get_linear_schedule_with_warmup(
			    optimizer=snake_case__						,				num_warmup_steps=1_00						,				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.
			A_     :						str     =					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()
						with LocalSGD(
						    accelerator=snake_case__						,				model=snake_case__						,				local_sgd_steps=snake_case__						,				enabled=local_sgd_steps is not None   ) as local_sgd:
									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   )
												# 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(snake_case__   ):
															A_     :						int     =					model(**snake_case__   )
															A_     :						Union[str, Any]     =					output.loss
															accelerator.backward(snake_case__   )
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()
															# LocalSGD-specific line
															local_sgd.step()

						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():
												A_     :						Dict     =					model(**snake_case__   )
									A_     :						Any     =					outputs.logits.argmax(dim=-1   )
									A_     :						Any     =					accelerator.gather_for_metrics((predictions, batch["""labels"""])   )
									metric.add_batch(
									    predictions=snake_case__						,				references=snake_case__						,				)

						A_     :						List[Any]     =					metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'epoch {epoch}:'						,				snake_case__   )



def      a      (			):
			'''simple docstring'''





			A_     :						Any     =					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."""						,				)
			# New Code #
			parser.add_argument(
			    """--gradient_accumulation_steps"""						,				type=snake_case__						,				default=1						,				help="""The number of minibatches to be ran before gradients are accumulated."""						,				)
			parser.add_argument(
			    """--local_sgd_steps"""						,				type=snake_case__						,				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."""   )
			A_     :						str     =					parser.parse_args()
			A_     :						Tuple     =					{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
			training_function(snake_case__						,				snake_case__   )


if __name__ == "__main__":
						main()




'''simple docstring'''




from typing import Dict

from .base import GenericTensor, Pipeline







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def    _a     (self	,				lowercase=None	,				lowercase=None	,				lowercase=None	,				**lowercase							):
				if tokenize_kwargs is None:
							A_     :						Optional[Any]     =					{}

				if truncation is not None:
							if "truncation" in tokenize_kwargs:
										raise ValueError(
										    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""							)
							A_     :						str     =					truncation

				A_     :						List[str]     =					tokenize_kwargs

				A_     :						Dict     =					{}
				if return_tensors is not None:
							A_     :						List[Any]     =					return_tensors

				return preprocess_params, {}, postprocess_params


	def    _a     (self	,				lowercase	,				**lowercase							):
				A_     :						Optional[int]     =					self.framework
				A_     :						str     =					self.tokenizer(lowercase	,				return_tensors=lowercase	,				**lowercase							)
				return model_inputs


	def    _a     (self	,				lowercase							):
				A_     :						str     =					self.model(**lowercase							)
				return model_outputs


	def    _a     (self	,				lowercase	,				lowercase=False							):
				# [0] is the first available tensor, logits or last_hidden_state.
				if return_tensors:
							return model_outputs[0]
				if self.framework == "pt":
							return model_outputs[0].tolist()
				elif self.framework == "tf":
							return model_outputs[0].numpy().tolist()


	def __call__(self	,				*lowercase	,				**lowercase							):
				return super().__call__(*lowercase	,				**lowercase							)