






import argparse
import os

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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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_		:		Dict			= 16
A_		:		str			= 32

def   snake_case					(UpperCAmelCase__	,    UpperCAmelCase__  )       ->				Dict:







  if args.with_tracking:
    UpperCamelCase_:					Any          =				Accelerator(
        cpu=args.cpu	,    mixed_precision=args.mixed_precision	,    log_with='all'	,    project_dir=args.project_dir  )
  else:
    UpperCamelCase_:					Tuple          =				Accelerator(cpu=args.cpu	,    mixed_precision=args.mixed_precision  )

  if hasattr(args.checkpointing_steps	,    'isdigit'  ):
    if args.checkpointing_steps == "epoch":
      UpperCamelCase_:					Union[str, Any]          =				args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      UpperCamelCase_:					List[str]          =				int(args.checkpointing_steps  )
    else:
      raise ValueError(
          F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''  )
  else:
    UpperCamelCase_:					Optional[int]          =				None
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  UpperCamelCase_:					Any          =				config['lr']
  UpperCamelCase_:					List[Any]          =				int(config['num_epochs']  )
  UpperCamelCase_:					Union[str, Any]          =				int(config['seed']  )
  UpperCamelCase_:					Any          =				int(config['batch_size']  )

  # We need to initialize the trackers we use, and also store our configuration
  if args.with_tracking:
    UpperCamelCase_:					int          =				os.path.split(_lowercase  )[-1].split('.'  )[0]
    accelerator.init_trackers(_lowercase	,    _lowercase  )

  UpperCamelCase_:					str          =				AutoTokenizer.from_pretrained('bert-base-cased'  )
  UpperCamelCase_:					Tuple          =				load_dataset('glue'	,    'mrpc'  )
  UpperCamelCase_:					List[str]          =				evaluate.load('glue'	,    'mrpc'  )

  def tokenize_function(UpperCAmelCase__  ):
    # max_length=None => use the model max length (it's actually the default)
    UpperCamelCase_:					List[Any]          =				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_:					int          =				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_:					Tuple          =				tokenized_datasets.rename_column('label'	,    'labels'  )

  # If the batch size is too big we use gradient accumulation
  UpperCamelCase_:					Optional[Any]          =				1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    UpperCamelCase_:					Tuple          =				batch_size // MAX_GPU_BATCH_SIZE
    UpperCamelCase_:					List[Any]          =				MAX_GPU_BATCH_SIZE

  def collate_fn(UpperCAmelCase__  ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    UpperCamelCase_:					int          =				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":
      UpperCamelCase_:					Dict          =				1_6
    elif accelerator.mixed_precision != "no":
      UpperCamelCase_:					int          =				8
    else:
      UpperCamelCase_:					List[Any]          =				None

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

  # Instantiate dataloaders.
  UpperCamelCase_:					Dict          =				DataLoader(
      tokenized_datasets['train']	,    shuffle=_lowercase	,    collate_fn=_lowercase	,    batch_size=_lowercase  )
  UpperCamelCase_:					List[Any]          =				DataLoader(
      tokenized_datasets['validation']	,    shuffle=_lowercase	,    collate_fn=_lowercase	,    batch_size=_lowercase  )

  set_seed(_lowercase  )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  UpperCamelCase_:					Dict          =				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_:					Any          =				model.to(accelerator.device  )

  # Instantiate optimizer
  UpperCamelCase_:					Optional[Any]          =				AdamW(params=model.parameters()	,    lr=_lowercase  )

  # Instantiate scheduler
  UpperCamelCase_:					Union[str, Any]          =				get_linear_schedule_with_warmup(
      optimizer=_lowercase	,    num_warmup_steps=1_0_0	,    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_:					int          =				accelerator.prepare(
      _lowercase	,    _lowercase	,    _lowercase	,    _lowercase	,    _lowercase  )

  # We need to keep track of how many total steps we have iterated over
  UpperCamelCase_:					List[Any]          =				0
  # We also need to keep track of the stating epoch so files are named properly
  UpperCamelCase_:					Optional[int]          =				0

  # Potentially load in the weights and states from a previous save
  if args.resume_from_checkpoint:
    if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
      accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''  )
      accelerator.load_state(args.resume_from_checkpoint  )
      UpperCamelCase_:					Tuple          =				os.path.basename(args.resume_from_checkpoint  )
    else:
      # Get the most recent checkpoint
      UpperCamelCase_:					Optional[Any]          =				[f.name for f in os.scandir(os.getcwd()  ) if f.is_dir()]
      dirs.sort(key=os.path.getctime  )
      UpperCamelCase_:					Dict          =				dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    UpperCamelCase_:					Union[str, Any]          =				os.path.splitext(_lowercase  )[0]

    if "epoch" in training_difference:
      UpperCamelCase_:					List[str]          =				int(training_difference.replace('epoch_'	,    ''  )  ) + 1
      UpperCamelCase_:					int          =				None
    else:
      UpperCamelCase_:					List[str]          =				int(training_difference.replace('step_'	,    ''  )  )
      UpperCamelCase_:					Dict          =				resume_step // len(_lowercase  )
      resume_step -= starting_epoch * len(_lowercase  )

    # Now we train the model
  for epoch in range(_lowercase	,    _lowercase  ):
    model.train()
    if args.with_tracking:
      UpperCamelCase_:					Optional[Any]          =				0
    if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
      # We need to skip steps until we reach the resumed step
      UpperCamelCase_:					str          =				accelerator.skip_first_batches(_lowercase	,    _lowercase  )
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      UpperCamelCase_:					List[Any]          =				train_dataloader
    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_:					List[str]          =				model(**_lowercase  )
      UpperCamelCase_:					Optional[int]          =				outputs.loss
      UpperCamelCase_:					Tuple          =				loss / gradient_accumulation_steps
      # We keep track of the loss at each epoch
      if args.with_tracking:
        total_loss += loss.detach().float()
      accelerator.backward(_lowercase  )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1

      if isinstance(_lowercase	,    _lowercase  ):
        UpperCamelCase_:					Dict          =				F'''step_{overall_step}'''
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            UpperCamelCase_:					List[Any]          =				os.path.join(args.output_dir	,    _lowercase  )
          accelerator.save_state(_lowercase  )

    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_:					str          =				model(**_lowercase  )
      UpperCamelCase_:					Any          =				outputs.logits.argmax(dim=-1  )
      UpperCamelCase_      ,UpperCamelCase_:					Dict          =				accelerator.gather_for_metrics((predictions, batch['labels'])  )
      metric.add_batch(
          predictions=_lowercase	,    references=_lowercase	,    )

    UpperCamelCase_:					List[Any]          =				metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F'''epoch {epoch}:'''	,    _lowercase  )
    if args.with_tracking:
      accelerator.log(
          {
              'accuracy': eval_metric['accuracy'],
              'f1': eval_metric['f1'],
              'train_loss': total_loss.item() / len(_lowercase  ),
              'epoch': epoch,
          }	,    step=_lowercase	,    )

    if checkpointing_steps == "epoch":
      UpperCamelCase_:					Optional[Any]          =				F'''epoch_{epoch}'''
      if args.output_dir is not None:
        UpperCamelCase_:					Any          =				os.path.join(args.output_dir	,    _lowercase  )
      accelerator.save_state(_lowercase  )

  if args.with_tracking:
    accelerator.end_training()

def   snake_case					()       ->				Tuple:
  UpperCamelCase_:					str          =				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.'  )
  parser.add_argument(
      '--checkpointing_steps'	,    type=_lowercase	,    default=_lowercase	,    help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'	,    )
  parser.add_argument(
      '--resume_from_checkpoint'	,    type=_lowercase	,    default=_lowercase	,    help='If the training should continue from a checkpoint folder.'	,    )
  parser.add_argument(
      '--with_tracking'	,    action='store_true'	,    help='Whether to load in all available experiment trackers from the environment and use them for logging.'	,    )
  parser.add_argument(
      '--output_dir'	,    type=_lowercase	,    default='.'	,    help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'	,    )
  parser.add_argument(
      '--project_dir'	,    type=_lowercase	,    default='logs'	,    help='Location on where to store experiment tracking logs` and relevent project information'	,    )
  UpperCamelCase_:					str          =				parser.parse_args()
  UpperCamelCase_:					Any          =				{'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
  training_function(_lowercase	,    _lowercase  )


if __name__ == "__main__":
   main()







import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()





class   SCREAMING_SNAKE_CASE (       unittest.TestCase    ):





							"""simple docstring"""

							A_       =    "hf-internal-testing/tiny-stable-diffusion-pipe"
							A_       =    1
							A_       =    77
							A_       =    32
							A_       =    4
							A_       =    A_       =    64
							A_       =    torch.manual_seed(0    )

							def 					__A       (     self:      List[Any]						,   __A:      List[Any]=0.9_999      )			-> Dict:
												_A									=       UNetaDConditionModel.from_pretrained(self.model_id						,   subfolder='''unet'''      )
												_A									=       unet.to(__A      )
												_A									=       EMAModel(unet.parameters()						,   decay=__A						,   model_cls=__A						,   model_config=unet.config      )
												return unet, ema_unet

							def 					__A       (     self:      Tuple      )			-> Union[str, Any]:
												_A									=       torch.randn(
												    self.batch_size						,   self.num_in_channels						,   self.latent_height						,   self.latent_width						,   generator=self.generator      ).to(__A      )
												_A									=       torch.randint(0						,   10_00						,   size=(self.batch_size,)						,   generator=self.generator      ).to(__A      )
												_A									=       torch.randn(
												    self.batch_size						,   self.prompt_length						,   self.text_encoder_hidden_dim						,   generator=self.generator      ).to(__A      )
												return noisy_latents, timesteps, encoder_hidden_states

							def 					__A       (     self:      Tuple						,   __A:      Optional[int]      )			-> Optional[Any]:
												_A									=       {}
												for k, param in unet.state_dict().items():
																	_A									=       torch.randn_like(__A      ) + (param * torch.randn_like(__A      ))
																	updated_state_dict.update({k: updated_param}      )
												unet.load_state_dict(__A      )
												return unet

							def 					__A       (     self:      Any      )			-> Any:
												_A							,_A									=       self.get_models()
												# Take the first (hypothetical) EMA step.
												ema_unet.step(unet.parameters()      )
												assert ema_unet.optimization_step == 1

												# Take two more.
												for _ in range(2      ):
																	ema_unet.step(unet.parameters()      )
												assert ema_unet.optimization_step == 3

							def 					__A       (     self:      Union[str, Any]      )			-> str:
												_A							,_A									=       self.get_models()
												# Since the `unet` is not being updated (i.e., backprop'd)
												# there won't be any difference between the `params` of `unet`
												# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
												ema_unet.step(unet.parameters()      )
												_A									=       list(unet.parameters()      )
												for s_param, param in zip(ema_unet.shadow_params						,   __A      ):
																	assert torch.allclose(__A						,   __A      )

												# The above holds true even if we call `ema.step()` multiple times since
												# `unet` params are still not being updated.
												for _ in range(4      ):
																	ema_unet.step(unet.parameters()      )
												for s_param, param in zip(ema_unet.shadow_params						,   __A      ):
																	assert torch.allclose(__A						,   __A      )

							def 					__A       (     self:      Tuple      )			-> List[str]:
												_A							,_A									=       self.get_models()
												# Here we simulate the parameter updates for `unet`. Since there might
												# be some parameters which are initialized to zero we take extra care to
												# initialize their values to something non-zero before the multiplication.
												_A									=       self.simulate_backprop(__A      )

												# Take the EMA step.
												ema_unet.step(unet_pseudo_updated_step_one.parameters()      )

												# Now the EMA'd parameters won't be equal to the original model parameters.
												_A									=       list(unet_pseudo_updated_step_one.parameters()      )
												for s_param, param in zip(ema_unet.shadow_params						,   __A      ):
																	assert ~torch.allclose(__A						,   __A      )

												# Ensure this is the case when we take multiple EMA steps.
												for _ in range(4      ):
																	ema_unet.step(unet.parameters()      )
												for s_param, param in zip(ema_unet.shadow_params						,   __A      ):
																	assert ~torch.allclose(__A						,   __A      )

							def 					__A       (     self:      Optional[int]      )			-> Optional[int]:
												# If we call EMA step after a backpropagation consecutively for two times,
												# the shadow params from those two steps should be different.
												_A							,_A									=       self.get_models()

												# First backprop + EMA
												_A									=       self.simulate_backprop(__A      )
												ema_unet.step(unet_step_one.parameters()      )
												_A									=       ema_unet.shadow_params

												# Second backprop + EMA
												_A									=       self.simulate_backprop(__A      )
												ema_unet.step(unet_step_two.parameters()      )
												_A									=       ema_unet.shadow_params

												for step_one, step_two in zip(__A						,   __A      ):
																	assert ~torch.allclose(__A						,   __A      )

							def 					__A       (     self:      int      )			-> Any:
												# If there's no decay even if there are backprops, EMA steps
												# won't take any effect i.e., the shadow params would remain the
												# same.
												_A							,_A									=       self.get_models(decay=0.0      )
												_A									=       self.simulate_backprop(__A      )
												ema_unet.step(unet_step_one.parameters()      )
												_A									=       ema_unet.shadow_params

												_A									=       self.simulate_backprop(__A      )
												ema_unet.step(unet_step_two.parameters()      )
												_A									=       ema_unet.shadow_params

												for step_one, step_two in zip(__A						,   __A      ):
																	assert torch.allclose(__A						,   __A      )



							@skip_mps
							def 					__A       (     self:      Optional[Any]      )			-> List[str]:
												_A							,_A									=       self.get_models()
												_A							,_A							,_A									=       self.get_dummy_inputs()

												with tempfile.TemporaryDirectory() as tmpdir:
																	ema_unet.save_pretrained(__A      )
																	_A									=       UNetaDConditionModel.from_pretrained(__A						,   model_cls=__A      )
																	_A									=       loaded_unet.to(unet.device      )

												# Since no EMA step has been performed the outputs should match.
												_A									=       unet(__A						,   __A						,   __A      ).sample
												_A									=       loaded_unet(__A						,   __A						,   __A      ).sample

												assert torch.allclose(__A						,   __A						,   atol=1e-4      )



