
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 :str     =    16
__lowerCAmelCase :int     =    32







def    A				(							UpperCAmelCase  ,  UpperCAmelCase = 16				):
					_snake_case       :					Optional[int]				       =   AutoTokenizer.from_pretrained("bert-base-cased"				)
					_snake_case       :					str				       =   load_dataset("glue"  ,  "mrpc"				)

					def tokenize_function(UpperCAmelCase				):
										# max_length=None => use the model max length (it's actually the default)
										_snake_case       :					Optional[int]				       =   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       :					Dict				       =   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       :					Optional[int]				       =   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       :					str				       =   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       :					Any				       =   16
										elif accelerator.mixed_precision != "no":
															_snake_case       :					Tuple				       =   8
										else:
															_snake_case       :					int				       =   None

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

					# Instantiate dataloaders.
					_snake_case       :					Tuple				       =   DataLoader(
					    tokenized_datasets["train"]  ,  shuffle=UpperCAmelCase  ,  collate_fn=UpperCAmelCase  ,  batch_size=UpperCAmelCase				)
					_snake_case       :					List[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 :Dict     =    mocked_dataloaders  # noqa: F811







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					# For testing only
					if os.environ.get("TESTING_MOCKED_DATALOADERS"  ,  UpperCAmelCase				) == "1":
										_snake_case       :					str				       =   2
					# New Code #
					_snake_case       :					Union[str, 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       :					Union[str, Any]				       =   config["lr"]
					_snake_case       :					Union[str, Any]				       =   int(config["num_epochs"]				)
					_snake_case       :					str				       =   int(config["seed"]				)
					_snake_case       :					List[str]				       =   int(config["batch_size"]				)

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

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

					# Instantiate optimizer
					_snake_case       :					int				       =   AdamW(params=model.parameters()  ,  lr=UpperCAmelCase				)

					# Instantiate scheduler
					_snake_case       :					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       :					str				       =   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       :					Optional[Any]				       =   model(**UpperCAmelCase				)
																									_snake_case       :					Optional[Any]				       =   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       :					Optional[Any]				       =   model(**UpperCAmelCase				)
															_snake_case       :					List[Any]				       =   outputs.logits.argmax(dim=-1				)
															_snake_case       :					Any				       =   accelerator.gather_for_metrics((predictions, batch["labels"])				)
															metric.add_batch(
															    predictions=UpperCAmelCase  ,  references=UpperCAmelCase  ,  )

										_snake_case       :					List[str]				       =   metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"""epoch {epoch}:"""  ,  UpperCAmelCase				)







def    A				(							):
					_snake_case       :					List[Any]				       =   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       :					List[str]				       =   parser.parse_args()
					_snake_case       :					str				       =   {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(UpperCAmelCase  ,  UpperCAmelCase				)


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

from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file  # noqa: F401
from .default import default_command_parser
from .update import update_command_parser







def    A				(							UpperCAmelCase=None				):
					_snake_case       :					Union[str, Any]				       =   argparse.ArgumentParser(add_help=UpperCAmelCase  ,  allow_abbrev=UpperCAmelCase				)
					# The main config parser
					_snake_case       :					Tuple				       =   config_command_parser(UpperCAmelCase				)
					# The subparser to add commands to
					_snake_case       :					Any				       =   config_parser.add_subparsers(title="subcommands"  ,  dest="subcommand"				)

					# Then add other parsers with the parent parser
					default_command_parser(UpperCAmelCase  ,  parents=[parent_parser]				)
					update_command_parser(UpperCAmelCase  ,  parents=[parent_parser]				)

					return config_parser







def    A				(							):
					_snake_case       :					str				       =   get_config_parser()
					_snake_case       :					Union[str, Any]				       =   config_parser.parse_args()

					if not hasattr(UpperCAmelCase  ,  "func"				):
										config_parser.print_help()
										exit(1				)

					# Run
					args.func(UpperCAmelCase				)


if __name__ == "__main__":
							main()