"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 				a (						lowerCAmelCase_      ):
    _snake_case   :     UNetaDModel
    _snake_case   :     KarrasVeScheduler


    def __init__(    self		:     Tuple      , __lowerCAmelCase		:     UNetaDModel      , __lowerCAmelCase		:     KarrasVeScheduler    ):
      super().__init__()
      self.register_modules(unet=__lowerCAmelCase      , scheduler=__lowerCAmelCase    )


    @torch.no_grad()
    def __call__(    self		:     Optional[Any]      , __lowerCAmelCase		:     int = 1      , __lowerCAmelCase		:     int = 50      , __lowerCAmelCase		:     Optional[Union[torch.Generator, List[torch.Generator]]] = None      , __lowerCAmelCase		:     Optional[str] = "pil"      , __lowerCAmelCase		:     bool = True      , **__lowerCAmelCase		:     Optional[int]      , ):
      _UpperCAmelCase         =					self.unet.config.sample_size
      _UpperCAmelCase         =					(batch_size, 3, img_size, img_size)

      _UpperCAmelCase         =					self.unet

      # sample x_0 ~ N(0, sigma_0^2 * I)
      _UpperCAmelCase         =					randn_tensor(__lowerCAmelCase      , generator=__lowerCAmelCase      , device=self.device    ) * self.scheduler.init_noise_sigma

      self.scheduler.set_timesteps(__lowerCAmelCase    )

      for t in self.progress_bar(self.scheduler.timesteps    ):
        # here sigma_t == t_i from the paper
        _UpperCAmelCase         =					self.scheduler.schedule[t]
        _UpperCAmelCase         =					self.scheduler.schedule[t - 1] if t > 0 else 0

        # 1. Select temporarily increased noise level sigma_hat
        # 2. Add new noise to move from sample_i to sample_hat
        _UpperCAmelCase       , _UpperCAmelCase         =					self.scheduler.add_noise_to_input(__lowerCAmelCase      , __lowerCAmelCase      , generator=__lowerCAmelCase    )

        # 3. Predict the noise residual given the noise magnitude `sigma_hat`
        # The model inputs and output are adjusted by following eq. (213) in [1].
        _UpperCAmelCase         =					(sigma_hat / 2) * model((sample_hat + 1) / 2      , sigma_hat / 2    ).sample

        # 4. Evaluate dx/dt at sigma_hat
        # 5. Take Euler step from sigma to sigma_prev
        _UpperCAmelCase         =					self.scheduler.step(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase    )

        if sigma_prev != 0:
          # 6. Apply 2nd order correction
          # The model inputs and output are adjusted by following eq. (213) in [1].
          _UpperCAmelCase         =					(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2      , sigma_prev / 2    ).sample
          _UpperCAmelCase         =					self.scheduler.step_correct(
              __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , step_output.prev_sample      , step_output["""derivative"""]      , )
        _UpperCAmelCase         =					step_output.prev_sample

      _UpperCAmelCase         =					(sample / 2 + 0.5).clamp(0      , 1    )
      _UpperCAmelCase         =					sample.cpu().permute(0      , 2      , 3      , 1    ).numpy()
      if output_type == "pil":
        _UpperCAmelCase         =					self.numpy_to_pil(__lowerCAmelCase    )

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=__lowerCAmelCase    )

"""simple docstring"""


import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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__     						=  1_6
UpperCAmelCase__     						=  3_2






def 			__UpperCAmelCase    (	lowercase      ,lowercase = 16						):







  """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         =					1_28 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						)
  _UpperCAmelCase         =					DataLoader(
      tokenized_datasets["""validation"""]      ,shuffle=lowercase      ,collate_fn=lowercase      ,batch_size=lowercase						)

  return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    UpperCAmelCase__     						=  mocked_dataloaders  # noqa: F811






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  # For testing only
  if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,lowercase						) == "1":
    _UpperCAmelCase         =					2
  # Initialize accelerator
  _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"""						)

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=lowercase						)
  def inner_training_loop(lowercase						):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(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						)
    _UpperCAmelCase       , _UpperCAmelCase         =					get_dataloaders(lowercase      ,lowercase						)

    # Instantiate scheduler
    _UpperCAmelCase         =					get_linear_schedule_with_warmup(
        optimizer=lowercase      ,num_warmup_steps=1_00      ,num_training_steps=(len(lowercase						) * num_epochs)      ,)

    # 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
        accelerator.backward(lowercase						)
        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						)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
  inner_training_loop()






def 			__UpperCAmelCase    (	):







  """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()
