






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
#
########################################################################


A__      :     List[Any]       =					16
A__      :     Optional[int]       =					32






def     a     (		lowerCamelCase_						,  lowerCamelCase_ = 16							):






				'''simple docstring'''
				lowercase__  					=     AutoTokenizer.from_pretrained('''bert-base-cased'''							)
				lowercase__  					=     load_dataset('''glue'''						,  '''mrpc'''							)

				def tokenize_function(lowerCamelCase_							):
								# max_length=None => use the model max length (it's actually the default)
								lowercase__  					=     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():
								lowercase__  					=     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
				lowercase__  					=     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.
								lowercase__  					=     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":
												lowercase__  					=     16
								elif accelerator.mixed_precision != "no":
												lowercase__  					=     8
								else:
												lowercase__  					=     None

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

				# Instantiate dataloaders.
				lowercase__  					=     DataLoader(
				    tokenized_datasets['''train''']						,  shuffle=lowerCamelCase_						,  collate_fn=lowerCamelCase_						,  batch_size=lowerCamelCase_							)
				lowercase__  					=     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__      :     Union[str, Any]       =					mocked_dataloaders  # noqa: F811






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				# For testing only
				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''						,  lowerCamelCase_							) == "1":
								lowercase__  					=     2
				# New Code #
				lowercase__  					=     int(args.gradient_accumulation_steps							)
				lowercase__  					=     int(args.local_sgd_steps							)
				# Initialize accelerator
				lowercase__  					=     Accelerator(
				    cpu=args.cpu						,  mixed_precision=args.mixed_precision						,  gradient_accumulation_steps=lowerCamelCase_							)
				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__  					=     config['''lr''']
				lowercase__  					=     int(config['''num_epochs''']							)
				lowercase__  					=     int(config['''seed''']							)
				lowercase__  					=     int(config['''batch_size''']							)

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

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

				# Instantiate optimizer
				lowercase__  					=     AdamW(params=model.parameters()						,  lr=lowerCamelCase_							)

				# Instantiate scheduler
				lowercase__  					=     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.
				lowercase__					,		lowercase__					,		lowercase__					,		lowercase__					,		lowercase__  					=     accelerator.prepare(
				    lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							)

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

								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():
																lowercase__  					=     model(**lowerCamelCase_							)
												lowercase__  					=     outputs.logits.argmax(dim=-1							)
												lowercase__					,		lowercase__  					=     accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
												metric.add_batch(
												    predictions=lowerCamelCase_						,  references=lowerCamelCase_						,  )

								lowercase__  					=     metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"""epoch {epoch}:"""						,  lowerCamelCase_							)






def     a     (		):






				'''simple docstring'''
				lowercase__  					=     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(
				    '''--local_sgd_steps'''						,  type=lowerCamelCase_						,  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__  					=     parser.parse_args()
				lowercase__  					=     {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowerCamelCase_						,  lowerCamelCase_							)


if __name__ == "__main__":
							main()












# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async






def     a     (		lowerCamelCase_=None							):






				'''simple docstring'''
				if subparsers is not None:
								lowercase__  					=     subparsers.add_parser('''test'''							)
				else:
								lowercase__  					=     argparse.ArgumentParser('''Accelerate test command'''							)

				parser.add_argument(
				    '''--config_file'''						,  default=lowerCamelCase_						,  help=(
				        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
				        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
				        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
				        '''with \'huggingface\'.'''
				    )						,  )

				if subparsers is not None:
								parser.set_defaults(func=lowerCamelCase_							)
				return parser






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     os.path.sep.join(__file__.split(os.path.sep							)[:-2] + ['''test_utils''', '''scripts''', '''test_script.py''']							)

				if args.config_file is None:
								lowercase__  					=     script_name
				else:
								lowercase__  					=     F"""--config_file={args.config_file} {script_name}"""

				lowercase__  					=     ['''accelerate-launch'''] + test_args.split()
				lowercase__  					=     execute_subprocess_async(lowerCamelCase_						,  env=os.environ.copy()							)
				if result.returncode == 0:
								print('''Test is a success! You are ready for your distributed training!'''							)






def     a     (		):






				'''simple docstring'''
				lowercase__  					=     test_command_parser()
				lowercase__  					=     parser.parse_args()
				test_command(lowerCamelCase_							)


if __name__ == "__main__":
							main()




