



"""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
#
########################################################################


lowerCamelCase       	=						16
lowerCamelCase       	=						32
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__ = 16				):
      UpperCAmelCase_      			=							AutoTokenizer.from_pretrained("bert-base-cased"				)
      UpperCAmelCase_      			=							load_dataset("glue"     ,      "mrpc"				)

      def tokenize_function(lowerCAmelCase__				):
            # max_length=None => use the model max length (it's actually the default)
            UpperCAmelCase_      			=							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():
            UpperCAmelCase_      			=							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
      UpperCAmelCase_      			=							tokenized_datasets.rename_column("label"     ,      "labels"				)

      def collate_fn(lowerCAmelCase__				):
            # 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(
                lowerCAmelCase__     ,      padding="longest"     ,      max_length=lowerCAmelCase__     ,      pad_to_multiple_of=lowerCAmelCase__     ,      return_tensors="pt"     ,      )

      # Instantiate dataloaders.
      UpperCAmelCase_      			=							DataLoader(
          tokenized_datasets["train"]     ,      shuffle=lowerCAmelCase__     ,      collate_fn=lowerCAmelCase__     ,      batch_size=lowerCAmelCase__				)
      UpperCAmelCase_      			=							DataLoader(
          tokenized_datasets["validation"]     ,      shuffle=lowerCAmelCase__     ,      collate_fn=lowerCAmelCase__     ,      batch_size=lowerCAmelCase__				)

      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

  lowerCamelCase       	=						mocked_dataloaders  # noqa: F811
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):



      if os.environ.get("TESTING_MOCKED_DATALOADERS"     ,      lowerCAmelCase__				) == "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=lowerCAmelCase__				)
      def inner_training_loop(lowerCAmelCase__				):
            # 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(lowerCAmelCase__				)

            # 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=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).
            UpperCAmelCase_      			=							model.to(accelerator.device				)

            # Instantiate optimizer
            UpperCAmelCase_      			=							AdamW(params=model.parameters()     ,      lr=lowerCAmelCase__				)
            UpperCAmelCase_      			=							get_dataloaders(lowerCAmelCase__     ,      lowerCAmelCase__				)

            # Instantiate scheduler
            UpperCAmelCase_      			=							get_linear_schedule_with_warmup(
                optimizer=lowerCAmelCase__     ,      num_warmup_steps=100     ,      num_training_steps=(len(lowerCAmelCase__				) * 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_      			=							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				)
                        UpperCAmelCase_      			=							model(**lowerCAmelCase__				)
                        UpperCAmelCase_      			=							outputs.loss
                        accelerator.backward(lowerCAmelCase__				)
                        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():
                              UpperCAmelCase_      			=							model(**lowerCAmelCase__				)
                        UpperCAmelCase_      			=							outputs.logits.argmax(dim=-1				)
                        UpperCAmelCase_      			=							accelerator.gather_for_metrics((predictions, batch["labels"])				)
                        metric.add_batch(
                            predictions=lowerCAmelCase__     ,      references=lowerCAmelCase__     ,      )

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

                   # 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        a__     (				):
      UpperCAmelCase_      			=							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."				)
      UpperCAmelCase_      			=							parser.parse_args()
      UpperCAmelCase_      			=							{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      training_function(lowerCAmelCase__     ,      lowerCAmelCase__				)


if __name__ == "__main__":
  main()









"""simple docstring"""





import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
  import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


       @property
       def 					lowercase__    (							self      :  Tuple   )			->						Any:


             '''simple docstring'''




             return (
                 "CUDAExecutionProvider",
                 {
                     "gpu_mem_limit": "15000000000",  # 15GB
                     "arena_extend_strategy": "kSameAsRequested",
                 },
             )



       @property
       def 					lowercase__    (							self      :  List[str]   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							ort.SessionOptions()
             UpperCAmelCase_      			=							False
             return options







       def 					lowercase__    (							self      :  Dict   )			->						Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/in_paint/overture-creations-5sI6fQgYIuo.png"   )
             UpperCAmelCase_      			=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"   )
             UpperCAmelCase_      			=							load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"   )

             # using the PNDM scheduler by default
             UpperCAmelCase_      			=							OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
                 "CompVis/stable-diffusion-v1-4"		,    revision="onnx"		,    safety_checker=_UpperCAmelCase		,    feature_extractor=_UpperCAmelCase		,    provider=self.gpu_provider		,    sess_options=self.gpu_options		,    )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

             UpperCAmelCase_      			=							"A red cat sitting on a park bench"

             UpperCAmelCase_      			=							np.random.RandomState(0   )
             UpperCAmelCase_      			=							pipe(
                 prompt=_UpperCAmelCase		,    image=_UpperCAmelCase		,    mask_image=_UpperCAmelCase		,    strength=0.75		,    guidance_scale=7.5		,    num_inference_steps=15		,    generator=_UpperCAmelCase		,    output_type="np"		,    )

             UpperCAmelCase_      			=							output.images[0]

             assert image.shape == (512, 512, 3)
             assert np.abs(expected_image - image   ).max() < 1e-2




