



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 = 16	)  ->						Optional[int]:
					__lowerCAmelCase	   =AutoTokenizer.from_pretrained("""bert-base-cased"""	)
					__lowerCAmelCase	   =load_dataset("""glue"""				,   """mrpc"""	)

					def tokenize_function(__lowerCamelCase	:    List[str]	):
										# max_length=None => use the model max length (it's actually the default)
										__lowerCAmelCase	   =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():
										__lowerCAmelCase	   =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
					__lowerCAmelCase	   =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.
										__lowerCAmelCase	   =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	   =16
										elif accelerator.mixed_precision != "no":
															__lowerCAmelCase	   =8
										else:
															__lowerCAmelCase	   =None

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

					# Instantiate dataloaders.
					__lowerCAmelCase	   =DataLoader(
					    tokenized_datasets["""train"""]				,   shuffle=__lowerCamelCase				,   collate_fn=__lowerCamelCase				,   batch_size=__lowerCamelCase				,   drop_last=__lowerCamelCase	)
					__lowerCAmelCase	   =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	:    Dict				,   __lowerCamelCase	:    List[str]	)  ->						Dict:
					# Initialize accelerator
					__lowerCAmelCase	   =Accelerator(cpu=args.cpu				,   mixed_precision=args.mixed_precision	)
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__lowerCAmelCase	   =config["""lr"""]
					__lowerCAmelCase	   =int(config["""num_epochs"""]	)
					__lowerCAmelCase	   =int(config["""seed"""]	)
					__lowerCAmelCase	   =int(config["""batch_size"""]	)

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

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

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

					# Instantiate scheduler
					__lowerCAmelCase	   =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.

					__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase	   =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	)
															__lowerCAmelCase	   =model(**__lowerCamelCase	)
															__lowerCAmelCase	   =outputs.loss
															__lowerCAmelCase	   =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():
																				__lowerCAmelCase	   =model(**__lowerCamelCase	)
															__lowerCAmelCase	   =outputs.logits.argmax(dim=-1	)
															__lowerCAmelCase   ,	__lowerCAmelCase	   =accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
															metric.add_batch(
															    predictions=__lowerCamelCase				,   references=__lowerCamelCase				,   )

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






def 					__lowerCAmelCase		(  )  ->						Optional[Any]:
					__lowerCAmelCase	   =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."""	)
					__lowerCAmelCase	   =parser.parse_args()
					__lowerCAmelCase	   ={"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
					training_function(__lowerCamelCase				,   __lowerCamelCase	)


if __name__ == "__main__":
				main()









import os
from typing import Dict, List, Tuple, TypeVar, Union


lowercase_	       =			TypeVar('''T''')

lowercase_	       =			Union[List[T], Tuple[T, ...]]
lowercase_	       =			Union[T, List[T], Dict[str, T]]
lowercase_	       =			Union[str, bytes, os.PathLike]




