





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_    =				1_6
lowerCAmelCase_    =				3_2






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase = 16 )		->						List[str]:






				"""simple docstring"""


				snake_case_						:    Any				=  AutoTokenizer.from_pretrained('''bert-base-cased''' )
				snake_case_						:    List[Any]				=  load_dataset('''glue''' ,				'''mrpc''' )

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

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

				# Instantiate dataloaders.
				snake_case_						:    int				=  DataLoader(
				    tokenized_datasets['''train'''] ,				shuffle=_UpperCamelCase ,				collate_fn=_UpperCamelCase ,				batch_size=_UpperCamelCase )
				snake_case_						:    str				=  DataLoader(
				    tokenized_datasets['''validation'''] ,				shuffle=_UpperCamelCase ,				collate_fn=_UpperCamelCase ,				batch_size=_UpperCamelCase )

				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_    =				mocked_dataloaders  # noqa: F811






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						Union[str, Any]:






				"""simple docstring"""


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

				snake_case_						:    Optional[int]				=  evaluate.load('''glue''' ,				'''mrpc''' )

				set_seed(_UpperCamelCase )
				snake_case_			,							snake_case_						:    List[Any]				=  get_dataloaders(_UpperCamelCase ,				_UpperCamelCase )
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				snake_case_						:    Tuple				=  AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,				return_dict=_UpperCamelCase )

				# 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).
				snake_case_						:    Optional[int]				=  model.to(accelerator.device )

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

				# Instantiate scheduler
				snake_case_						:    Union[str, Any]				=  get_linear_schedule_with_warmup(
				    optimizer=_UpperCamelCase ,				num_warmup_steps=100 ,				num_training_steps=(len(_UpperCamelCase ) * 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.
				snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_						:    int				=  accelerator.prepare(
				    _UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )

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

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

								snake_case_						:    Optional[int]				=  metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'''epoch {epoch}:''' ,				_UpperCamelCase )






def      lowerCamelCase_						(				)		->						int:






				"""simple docstring"""


				snake_case_						:    Optional[int]				=  argparse.ArgumentParser(description='''Simple example of training script.''' )
				parser.add_argument(
				    '''--mixed_precision''' ,				type=_UpperCamelCase ,				default=_UpperCamelCase ,				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=_UpperCamelCase ,				default=1 ,				help='''The number of minibatches to be ran before gradients are accumulated.''' ,				)
				parser.add_argument(
				    '''--local_sgd_steps''' ,				type=_UpperCamelCase ,				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.''' )
				snake_case_						:    Dict				=  parser.parse_args()
				snake_case_						:    Any				=  {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(_UpperCamelCase ,				_UpperCamelCase )


if __name__ == "__main__":
	main()







from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
	if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
	from .pipeline_unclip import UnCLIPPipeline
	from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
	from .text_proj import UnCLIPTextProjModel
