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


_UpperCAmelCase :			Union[str, Any]              =	16
_UpperCAmelCase :			Dict              =	32


def 				A	( lowercase   ,	lowercase = 16			)       ->  str:
  '''simple docstring'''

  UpperCamelCase          =					AutoTokenizer.from_pretrained('bert-base-cased'			)
  UpperCamelCase          =					load_dataset('glue'   ,	'mrpc'			)

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

  def collate_fn(lowercase			):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    UpperCamelCase          =					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":
      UpperCamelCase          =					16
    elif accelerator.mixed_precision != "no":
      UpperCamelCase          =					8
    else:
      UpperCamelCase          =					None

    return tokenizer.pad(
        lowercase   ,	padding='longest'   ,	max_length=lowercase   ,	pad_to_multiple_of=lowercase   ,	return_tensors='pt'   ,	)

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

  return train_dataloader, eval_dataloader


def 				A	( lowercase   ,	lowercase			)       ->  Optional[Any]:
  '''simple docstring'''

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

  UpperCamelCase          =					evaluate.load('glue'   ,	'mrpc'			)

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

  set_seed(lowercase			)
  UpperCamelCase     ,  UpperCamelCase          =					get_dataloaders(lowercase   ,	lowercase			)
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  UpperCamelCase          =					AutoModelForSequenceClassification.from_pretrained('bert-base-cased'   ,	return_dict=lowercase			)

  # 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).
  UpperCamelCase          =					model.to(accelerator.device			)
  # Instantiate optimizer
  UpperCamelCase          =					AdamW(params=model.parameters()   ,	lr=lowercase			)

  # Instantiate scheduler
  UpperCamelCase          =					get_linear_schedule_with_warmup(
      optimizer=lowercase   ,	num_warmup_steps=100   ,	num_training_steps=(len(lowercase			) * 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.

  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					accelerator.prepare(
      lowercase   ,	lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)

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

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

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


def 				A	( )       ->  Union[str, Any]:
  '''simple docstring'''

  UpperCamelCase          =					argparse.ArgumentParser(description='Simple example of training script.'			)
  parser.add_argument(
      '--mixed_precision'   ,	type=lowercase   ,	default=lowercase   ,	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.'			)
  UpperCamelCase          =					parser.parse_args()
  UpperCamelCase          =					{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(lowercase   ,	lowercase			)


if __name__ == "__main__":
     main()


import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation


class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('swish'	)

				self.assertIsInstance(A_    ,				nn.SiLU	)

				self.assertEqual(act(torch.tensor(-100    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('silu'	)

				self.assertIsInstance(A_    ,				nn.SiLU	)

				self.assertEqual(act(torch.tensor(-100    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('mish'	)

				self.assertIsInstance(A_    ,				nn.Mish	)

				self.assertEqual(act(torch.tensor(-200    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('gelu'	)

				self.assertIsInstance(A_    ,				nn.GELU	)

				self.assertEqual(act(torch.tensor(-100    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)
