






"""simple docstring"""

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


lowercase    			=       16
lowercase    			=       32






def _lowerCAmelCase	(  __lowerCamelCase:Accelerator ,						__lowerCamelCase:int = 1_6   ):

						'''simple docstring'''






						__magic_name__            =     AutoTokenizer.from_pretrained("bert-base-cased"   )
						__magic_name__            =     load_dataset("glue" ,						"mrpc"   )

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

						def collate_fn(__lowerCamelCase:List[Any]   ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												__magic_name__            =     1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		__magic_name__            =     1_6
												elif accelerator.mixed_precision != "no":
																		__magic_name__            =     8
												else:
																		__magic_name__            =     None

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

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

						return train_dataloader, eval_dataloader






def _lowerCAmelCase	(  __lowerCamelCase:List[str] ,						__lowerCamelCase:List[str]   ):

						'''simple docstring'''






						__magic_name__            =     Accelerator(cpu=args.cpu ,						mixed_precision=args.mixed_precision   )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__magic_name__            =     config["lr"]
						__magic_name__            =     int(config["num_epochs"]   )
						__magic_name__            =     int(config["seed"]   )
						__magic_name__            =     int(config["batch_size"]   )

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

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

						set_seed(__lowerCamelCase   )
						__magic_name__      ,    __magic_name__            =     get_dataloaders(__lowerCamelCase ,						__lowerCamelCase   )
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__magic_name__            =     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).
						__magic_name__            =     model.to(accelerator.device   )
						# Instantiate optimizer
						__magic_name__            =     AdamW(params=model.parameters() ,						lr=__lowerCamelCase   )

						# Instantiate scheduler
						__magic_name__            =     get_linear_schedule_with_warmup(
						    optimizer=__lowerCamelCase ,						num_warmup_steps=1_0_0 ,						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.

						__magic_name__      ,    __magic_name__      ,    __magic_name__      ,    __magic_name__      ,    __magic_name__            =     accelerator.prepare(
						    __lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )

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

												__magic_name__            =     metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'''epoch {epoch}:''' ,						__lowerCamelCase   )






def _lowerCAmelCase	(  ):

						'''simple docstring'''






						__magic_name__            =     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."   )
						__magic_name__            =     parser.parse_args()
						__magic_name__            =     {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
						training_function(__lowerCamelCase ,						__lowerCamelCase   )


if __name__ == "__main__":
			main()











"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:list ,						__lowerCamelCase:list   ):

						'''simple docstring'''






						_validate_point(__lowerCamelCase   )
						_validate_point(__lowerCamelCase   )
						if len(__lowerCamelCase   ) != len(__lowerCamelCase   ):
												raise ValueError("Both points must be in the same n-dimensional space"   )

						return float(sum(abs(a - b   ) for a, b in zip(__lowerCamelCase ,						__lowerCamelCase   )   )   )






def _lowerCAmelCase	(  __lowerCamelCase:list[float]   ):

						'''simple docstring'''






						if point:
												if isinstance(__lowerCamelCase ,						__lowerCamelCase   ):
																		for item in point:
																								if not isinstance(__lowerCamelCase ,						(int, float)   ):
																														__magic_name__            =     (
																														    "Expected a list of numbers as input, found "
																														    f'''{type(__lowerCamelCase   ).__name__}'''
																														)
																														raise TypeError(__lowerCamelCase   )
												else:
																		__magic_name__            =     f'''Expected a list of numbers as input, found {type(__lowerCamelCase   ).__name__}'''
																		raise TypeError(__lowerCamelCase   )
						else:
												raise ValueError("Missing an input"   )






def _lowerCAmelCase	(  __lowerCamelCase:list ,						__lowerCamelCase:list   ):

						'''simple docstring'''






						_validate_point(__lowerCamelCase   )
						_validate_point(__lowerCamelCase   )
						if len(__lowerCamelCase   ) != len(__lowerCamelCase   ):
												raise ValueError("Both points must be in the same n-dimensional space"   )

						return float(sum(abs(x - y   ) for x, y in zip(__lowerCamelCase ,						__lowerCamelCase   )   )   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



