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:	Dict       			=		1_6
_lowercase:	Tuple       			=		3_2






def _lowerCamelCase  (		snake_case     , snake_case = 16  ):
							_lowerCAmelCase							=							AutoTokenizer.from_pretrained('bert-base-cased'  )
							_lowerCAmelCase							=							load_dataset('glue'     , 'mrpc'  )

							def tokenize_function(snake_case  ):
														# max_length=None => use the model max length (it's actually the default)
														_lowerCAmelCase							=							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							=							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							=							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							=							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(
														    snake_case     , padding='longest'     , max_length=snake_case     , pad_to_multiple_of=snake_case     , return_tensors='pt'     , )

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

							return train_dataloader, eval_dataloader






def _lowerCamelCase  (		snake_case     , snake_case  ):
							# 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(snake_case  )
							_lowerCAmelCase			,		_lowerCAmelCase							=							get_dataloaders(snake_case     , snake_case  )
							# 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=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							=							model.to(accelerator.device  )
							# Instantiate optimizer
							_lowerCAmelCase							=							AdamW(params=model.parameters()     , lr=snake_case  )

							# Instantiate scheduler
							_lowerCAmelCase							=							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							=							accelerator.prepare(
							    snake_case     , snake_case     , snake_case     , snake_case     , snake_case  )

							# Now we train the model
							for epoch in range(snake_case  ):
														model.train()
														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							=							model(**snake_case  )
																					_lowerCAmelCase							=							outputs.loss
																					_lowerCAmelCase							=							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`.
																					batch.to(accelerator.device  )
																					with torch.no_grad():
																												_lowerCAmelCase							=							model(**snake_case  )
																					_lowerCAmelCase							=							outputs.logits.argmax(dim=-1  )
																					_lowerCAmelCase			,		_lowerCAmelCase							=							accelerator.gather_for_metrics((predictions, batch['labels'])  )
																					metric.add_batch(
																					    predictions=snake_case     , references=snake_case     , )

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






def _lowerCamelCase  (		):
							_lowerCAmelCase							=							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.'  )
							_lowerCAmelCase							=							parser.parse_args()
							_lowerCAmelCase							=							{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(snake_case     , snake_case  )


if __name__ == "__main__":
				main()







from __future__ import annotations

from random import choice






def _lowerCamelCase  (		snake_case  ):
							return choice(snake_case  )






def _lowerCamelCase  (		snake_case     , snake_case  ):
							_lowerCAmelCase							=							random_pivot(snake_case  )

							# partition based on pivot
							# linear time
							_lowerCAmelCase							=							[e for e in lst if e < pivot]
							_lowerCAmelCase							=							[e for e in lst if e > pivot]

							# if we get lucky, pivot might be the element we want.
							# we can easily see this:
							# small (elements smaller than k)
							# + pivot (kth element)
							# + big (elements larger than k)
							if len(snake_case  ) == k - 1:
														return pivot
							# pivot is in elements bigger than k
							elif len(snake_case  ) < k - 1:
														return kth_number(snake_case     , k - len(snake_case  ) - 1  )
							# pivot is in elements smaller than k
							else:
														return kth_number(snake_case     , snake_case  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()






