



"""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


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

__snake_case							:     List[str]   =					16
__snake_case							:     int   =					32


def     _lowercase       (						__snake_case      ,__snake_case = 16			)		->     str:
			__lowerCAmelCase   :						Any				= AutoTokenizer.from_pretrained("bert-base-cased"			)
			__lowerCAmelCase   :						List[Any]				= load_dataset("glue"      ,"mrpc"			)

			def tokenize_function(__snake_case			):
						# max_length=None => use the model max length (it's actually the default)
						__lowerCAmelCase   :						int				= 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():
						__lowerCAmelCase   :						Optional[int]				= 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
			__lowerCAmelCase   :						int				= tokenized_datasets.rename_column("label"      ,"labels"			)

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

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

			# Instantiate dataloaders.
			__lowerCAmelCase   :						Dict				= DataLoader(
			    tokenized_datasets["train"]      ,shuffle=__snake_case      ,collate_fn=__snake_case      ,batch_size=__snake_case			)
			__lowerCAmelCase   :						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

	__snake_case							:     int   =					mocked_dataloaders  # noqa: F811


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Optional[int]:
			# For testing only
			if os.environ.get("TESTING_MOCKED_DATALOADERS"      ,__snake_case			) == "1":
						__lowerCAmelCase   :						int				= 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:
						__lowerCAmelCase   :						Dict				= Accelerator(
						    cpu=args.cpu      ,mixed_precision=args.mixed_precision      ,log_with="all"      ,project_dir=args.project_dir			)
			else:
						__lowerCAmelCase   :						Optional[Any]				= Accelerator(cpu=args.cpu      ,mixed_precision=args.mixed_precision			)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			__lowerCAmelCase   :						Any				= config["lr"]
			__lowerCAmelCase   :						List[str]				= int(config["num_epochs"]			)
			__lowerCAmelCase   :						int				= int(config["seed"]			)
			__lowerCAmelCase   :						Any				= int(config["batch_size"]			)
			set_seed(__snake_case			)

			__lowerCAmelCase		,			__lowerCAmelCase   :						str				= get_dataloaders(__snake_case      ,__snake_case			)
			__lowerCAmelCase   :						Any				= evaluate.load("glue"      ,"mrpc"			)

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

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

			# Instantiate optimizer
			__lowerCAmelCase   :						Optional[int]				= AdamW(params=model.parameters()      ,lr=__snake_case			)

			# Instantiate scheduler
			__lowerCAmelCase   :						int				= get_linear_schedule_with_warmup(
			    optimizer=__snake_case      ,num_warmup_steps=100      ,num_training_steps=(len(__snake_case			) * 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.
			__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase   :						Any				= accelerator.prepare(
			    __snake_case      ,__snake_case      ,__snake_case      ,__snake_case      ,__snake_case			)

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

			# Now we train the model
			for epoch in range(__snake_case			):
						model.train()
						# New Code #
						# For our tracking example, we will log the total loss of each epoch
						if args.with_tracking:
									__lowerCAmelCase   :						str				= 0
						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			)
									__lowerCAmelCase   :						str				= model(**__snake_case			)
									__lowerCAmelCase   :						str				= outputs.loss
									# New Code #
									if args.with_tracking:
												total_loss += loss.detach().float()
									__lowerCAmelCase   :						str				= loss / gradient_accumulation_steps
									accelerator.backward(__snake_case			)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

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

						__lowerCAmelCase   :						Union[str, Any]				= metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F"""epoch {epoch}:"""      ,__snake_case			)

						# 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(__snake_case			),
									        "epoch": epoch,
									    }      ,step=__snake_case      ,)

    # 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     _lowercase       (						)		->     Optional[Any]:
			__lowerCAmelCase   :						int				= 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."      ,)
			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=__snake_case      ,default="logs"      ,help="Location on where to store experiment tracking logs` and relevent project information"      ,)
			__lowerCAmelCase   :						str				= parser.parse_args()
			__lowerCAmelCase   :						Dict				= {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(__snake_case      ,__snake_case			)


if __name__ == "__main__":
	main()




"""simple docstring"""






from abc import ABC, abstractmethod
from argparse import ArgumentParser



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	@staticmethod
	@abstractmethod
	def 							_SCREAMING_SNAKE_CASE     (  _SCREAMING_SNAKE_CASE:	ArgumentParser)       -> str:

				"""simple docstring"""

				raise NotImplementedError()







	@abstractmethod
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> str:

				"""simple docstring"""

				raise NotImplementedError()