
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

lowercase  :					Optional[int]													=       16
lowercase  :					Optional[int]													=       32




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Accelerator						,  _lowerCamelCase :			int = 16)					->    List[Any]:






							'''simple docstring'''





							__UpperCamelCase	:   Any			   =					AutoTokenizer.from_pretrained("bert-base-cased")
							__UpperCamelCase	:   Tuple			   =					load_dataset("glue"						,  "mrpc")

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

							def collate_fn(_lowerCamelCase :			Dict):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														__UpperCamelCase	:   int			   =					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":
																					__UpperCamelCase	:   str			   =					16
														elif accelerator.mixed_precision != "no":
																					__UpperCamelCase	:   List[Any]			   =					8
														else:
																					__UpperCamelCase	:   Dict			   =					None

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

							# Instantiate dataloaders.
							__UpperCamelCase	:   Any			   =					DataLoader(
							    tokenized_datasets["train"]						,  shuffle=_lowerCamelCase						,  collate_fn=_lowerCamelCase						,  batch_size=_lowerCamelCase)
							__UpperCamelCase	:   Optional[int]			   =					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

		lowercase  :					Any													=       mocked_dataloaders  # noqa: F811




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int						,  _lowerCamelCase :			Optional[int])					->    Optional[Any]:






							'''simple docstring'''





							if os.environ.get("TESTING_MOCKED_DATALOADERS"						,  _lowerCamelCase) == "1":
														__UpperCamelCase	:   List[str]			   =					2
							# Initialize Accelerator

							# New Code #
							# We pass in "all" to `log_with` to grab all available trackers in the environment
							# Note: If using a custom `Tracker` class, should be passed in here such as:
							# >>> log_with = ["all", MyCustomTrackerClassInstance()]
							if args.with_tracking:
														__UpperCamelCase	:   Any			   =					Accelerator(
														    cpu=args.cpu						,  mixed_precision=args.mixed_precision						,  log_with="all"						,  project_dir=args.project_dir)
							else:
														__UpperCamelCase	:   List[Any]			   =					Accelerator(cpu=args.cpu						,  mixed_precision=args.mixed_precision)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							__UpperCamelCase	:   Optional[Any]			   =					config["lr"]
							__UpperCamelCase	:   Optional[int]			   =					int(config["num_epochs"])
							__UpperCamelCase	:   List[Any]			   =					int(config["seed"])
							__UpperCamelCase	:   Any			   =					int(config["batch_size"])
							set_seed(_lowerCamelCase)

							__UpperCamelCase   ,						__UpperCamelCase	:   int			   =					get_dataloaders(_lowerCamelCase						,  _lowerCamelCase)
							__UpperCamelCase	:   Union[str, Any]			   =					evaluate.load("glue"						,  "mrpc")

							# If the batch size is too big we use gradient accumulation
							__UpperCamelCase	:   int			   =					1
							if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
														__UpperCamelCase	:   Optional[Any]			   =					batch_size // MAX_GPU_BATCH_SIZE
														__UpperCamelCase	:   Union[str, Any]			   =					MAX_GPU_BATCH_SIZE

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							__UpperCamelCase	:   List[Any]			   =					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).
							__UpperCamelCase	:   Tuple			   =					model.to(accelerator.device)

							# Instantiate optimizer
							__UpperCamelCase	:   Dict			   =					AdamW(params=model.parameters()						,  lr=_lowerCamelCase)

							# Instantiate scheduler
							__UpperCamelCase	:   Optional[int]			   =					get_linear_schedule_with_warmup(
							    optimizer=_lowerCamelCase						,  num_warmup_steps=100						,  num_training_steps=(len(_lowerCamelCase) * num_epochs) // gradient_accumulation_steps						,  )

							# 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.
							__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase	:   List[str]			   =					accelerator.prepare(
							    _lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase)

							# New Code #
							# We need to initialize the trackers we use. Overall configurations can also be stored
							if args.with_tracking:
														__UpperCamelCase	:   List[Any]			   =					os.path.split(_lowerCamelCase)[-1].split(".")[0]
														accelerator.init_trackers(_lowerCamelCase						,  _lowerCamelCase)

							# Now we train the model
							for epoch in range(_lowerCamelCase):
														model.train()
														# New Code #
														# For our tracking example, we will log the total loss of each epoch
														if args.with_tracking:
																					__UpperCamelCase	:   Tuple			   =					0
														for step, batch in enumerate(_lowerCamelCase):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device)
																					__UpperCamelCase	:   List[Any]			   =					model(**_lowerCamelCase)
																					__UpperCamelCase	:   Optional[int]			   =					outputs.loss
																					# New Code #
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					__UpperCamelCase	:   int			   =					loss / gradient_accumulation_steps
																					accelerator.backward(_lowerCamelCase)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

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

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

														# New Code #
														# To actually log, we call `Accelerator.log`
														# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
														if args.with_tracking:
																					accelerator.log(
																					    {
																					        "accuracy": eval_metric["accuracy"],
																					        "f1": eval_metric["f1"],
																					        "train_loss": total_loss.item() / len(_lowerCamelCase),
																					        "epoch": epoch,
																					    }						,  step=_lowerCamelCase						,  )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
							if args.with_tracking:
														accelerator.end_training()




def 	_SCREAMING_SNAKE_CASE					(		)					->    Optional[int]:






							'''simple docstring'''





							__UpperCamelCase	:   List[str]			   =					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."						,  )
							parser.add_argument("--cpu"						,  action="store_true"						,  help="If passed, will train on the CPU.")
							parser.add_argument(
							    "--with_tracking"						,  action="store_true"						,  help="Whether to load in all available experiment trackers from the environment and use them for logging."						,  )
							parser.add_argument(
							    "--project_dir"						,  type=_lowerCamelCase						,  default="logs"						,  help="Location on where to store experiment tracking logs` and relevent project information"						,  )
							__UpperCamelCase	:   Any			   =					parser.parse_args()
							__UpperCamelCase	:   Union[str, Any]			   =					{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
							training_function(_lowerCamelCase						,  _lowerCamelCase)


if __name__ == "__main__":
		main()

import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     ComputeEnvironment.AMAZON_SAGEMAKER
	_A								=     True
	_A								=     'ml.p3.2xlarge'
	_A								=     'accelerate_sagemaker_execution_role'
	_A								=     'hf-sm'
	_A								=     'us-east-1'
	_A								=     1
	_A								=     'accelerate-sagemaker-1'
	_A								=     '1.6'
	_A								=     '4.4'
	_A								=     'train.py'
	_A								=     [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    'False',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]
	_A								=     [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    '--do_test',
	    'False',
	    '--do_predict',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]




class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:Any						)		-> Optional[int]:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								__UpperCamelCase	:   Optional[int]			   =					_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args						)
								assert isinstance(converted_args["model_name_or_path"]     ,   a						)
								assert isinstance(converted_args["do_train"]     ,   a						)
								assert isinstance(converted_args["epochs"]     ,   a						)
								assert isinstance(converted_args["learning_rate"]     ,   a						)
								assert isinstance(converted_args["max_steps"]     ,   a						)

								with pytest.raises(a						):
															_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args						)