




import argparse

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
#
# 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						:							Union[str, Any]							=  1_6
__A						:							Optional[int]							=  3_2
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE = 16   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained("""bert-base-cased"""   )
			SCREAMING_SNAKE_CASE           =					load_dataset("""glue"""					,	"""mrpc"""   )

			def tokenize_function(SCREAMING_SNAKE_CASE   ):
						# max_length=None => use the model max length (it's actually the default)
						SCREAMING_SNAKE_CASE           =					tokenizer(examples["""sentence1"""]					,	examples["""sentence2"""]					,	truncation=SCREAMING_SNAKE_CASE					,	max_length=SCREAMING_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():
						SCREAMING_SNAKE_CASE           =					datasets.map(
						    SCREAMING_SNAKE_CASE					,	batched=SCREAMING_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
			SCREAMING_SNAKE_CASE           =					tokenized_datasets.rename_column("""label"""					,	"""labels"""   )

			def collate_fn(SCREAMING_SNAKE_CASE   ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						SCREAMING_SNAKE_CASE           =					1_28 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									SCREAMING_SNAKE_CASE           =					16
						elif accelerator.mixed_precision != "no":
									SCREAMING_SNAKE_CASE           =					8
						else:
									SCREAMING_SNAKE_CASE           =					None

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

			# Instantiate dataloaders.
			SCREAMING_SNAKE_CASE           =					DataLoader(
			    tokenized_datasets["""train"""]					,	shuffle=SCREAMING_SNAKE_CASE					,	collate_fn=SCREAMING_SNAKE_CASE					,	batch_size=SCREAMING_SNAKE_CASE					,	drop_last=SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					DataLoader(
			    tokenized_datasets["""validation"""]					,	shuffle=SCREAMING_SNAKE_CASE					,	collate_fn=SCREAMING_SNAKE_CASE					,	batch_size=SCREAMING_SNAKE_CASE					,	drop_last=(accelerator.mixed_precision == """fp8""")					,	)

			return train_dataloader, eval_dataloader
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			# Initialize accelerator
			SCREAMING_SNAKE_CASE           =					Accelerator(cpu=args.cpu					,	mixed_precision=args.mixed_precision   )
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			SCREAMING_SNAKE_CASE           =					config["""lr"""]
			SCREAMING_SNAKE_CASE           =					int(config["""num_epochs"""]   )
			SCREAMING_SNAKE_CASE           =					int(config["""seed"""]   )
			SCREAMING_SNAKE_CASE           =					int(config["""batch_size"""]   )

			SCREAMING_SNAKE_CASE           =					evaluate.load("""glue"""					,	"""mrpc"""   )

			# If the batch size is too big we use gradient accumulation
			SCREAMING_SNAKE_CASE           =					1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						SCREAMING_SNAKE_CASE           =					batch_size // MAX_GPU_BATCH_SIZE
						SCREAMING_SNAKE_CASE           =					MAX_GPU_BATCH_SIZE

			set_seed(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					get_dataloaders(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			SCREAMING_SNAKE_CASE           =					AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""					,	return_dict=SCREAMING_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).
			SCREAMING_SNAKE_CASE           =					model.to(accelerator.device   )
			# Instantiate optimizer
			SCREAMING_SNAKE_CASE           =					AdamW(params=model.parameters()					,	lr=SCREAMING_SNAKE_CASE   )

			# Instantiate scheduler
			SCREAMING_SNAKE_CASE           =					get_linear_schedule_with_warmup(
			    optimizer=SCREAMING_SNAKE_CASE					,	num_warmup_steps=1_00					,	num_training_steps=(len(SCREAMING_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.

			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					accelerator.prepare(
			    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

			# Now we train the model
			for epoch in range(SCREAMING_SNAKE_CASE   ):
						model.train()
						for step, batch in enumerate(SCREAMING_SNAKE_CASE   ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device   )
									SCREAMING_SNAKE_CASE           =					model(**SCREAMING_SNAKE_CASE   )
									SCREAMING_SNAKE_CASE           =					outputs.loss
									SCREAMING_SNAKE_CASE           =					loss / gradient_accumulation_steps
									accelerator.backward(SCREAMING_SNAKE_CASE   )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

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

						SCREAMING_SNAKE_CASE           =					metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f"""epoch {epoch}:"""					,	SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					argparse.ArgumentParser(description="""Simple example of training script."""   )
			parser.add_argument(
			    """--mixed_precision"""					,	type=SCREAMING_SNAKE_CASE					,	default=SCREAMING_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."""   )
			SCREAMING_SNAKE_CASE           =					parser.parse_args()
			SCREAMING_SNAKE_CASE           =					{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
			training_function(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
		main()












def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			if not all(char in """01""" for char in bin_string   ):
						raise ValueError("""Non-binary value was passed to the function"""   )
			if not bin_string:
						raise ValueError("""Empty string was passed to the function"""   )
			SCREAMING_SNAKE_CASE           =					""""""
			while len(SCREAMING_SNAKE_CASE   ) % 3 != 0:
						SCREAMING_SNAKE_CASE           =					"""0""" + bin_string
			SCREAMING_SNAKE_CASE           =					[
			    bin_string[index : index + 3]
			    for index in range(len(SCREAMING_SNAKE_CASE   )   )
			    if index % 3 == 0
			]
			for bin_group in bin_string_in_3_list:
						SCREAMING_SNAKE_CASE           =					0
						for index, val in enumerate(SCREAMING_SNAKE_CASE   ):
									oct_val += int(2 ** (2 - index) * int(SCREAMING_SNAKE_CASE   )   )
						oct_string += str(SCREAMING_SNAKE_CASE   )
			return oct_string


if __name__ == "__main__":
		from doctest import testmod

		testmod()






