





'''simple docstring'''

import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
   import onnxruntime as ort
class 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	Dict									=       'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'





       def snake_case__		(				self  :						Optional[int]			,					lowerCAmelCase__  :						int=0	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			np.random.RandomState(lowerCAmelCase__	)
            _UpperCamelCase							       =			{
                '''prompt''': '''A painting of a squirrel eating a burger''',
                '''generator''': generator,
                '''num_inference_steps''': 2,
                '''guidance_scale''': 7.5,
                '''output_type''': '''numpy''',
            }
            return inputs





       def snake_case__		(				self  :						Any	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			self.get_dummy_inputs()
            _UpperCamelCase							       =			pipe(**lowerCAmelCase__	).images
            _UpperCamelCase							       =			image[0, -3:, -3:, -1]

            assert image.shape == (1, 128, 128, 3)
            _UpperCamelCase							       =			np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2





       def snake_case__		(				self  :						List[Any]	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            _UpperCamelCase							       =			PNDMScheduler.from_config(pipe.scheduler.config			,					skip_prk_steps=lowerCAmelCase__	)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			self.get_dummy_inputs()
            _UpperCamelCase							       =			pipe(**lowerCAmelCase__	).images
            _UpperCamelCase							       =			image[0, -3:, -3:, -1]

            assert image.shape == (1, 128, 128, 3)
            _UpperCamelCase							       =			np.array([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2





       def snake_case__		(				self  :						List[str]	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            _UpperCamelCase							       =			LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			self.get_dummy_inputs()
            _UpperCamelCase							       =			pipe(**lowerCAmelCase__	).images
            _UpperCamelCase							       =			image[0, -3:, -3:, -1]

            assert image.shape == (1, 128, 128, 3)
            _UpperCamelCase							       =			np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2





       def snake_case__		(				self  :						Tuple	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            _UpperCamelCase							       =			EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			self.get_dummy_inputs()
            _UpperCamelCase							       =			pipe(**lowerCAmelCase__	).images
            _UpperCamelCase							       =			image[0, -3:, -3:, -1]

            assert image.shape == (1, 128, 128, 3)
            _UpperCamelCase							       =			np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2





       def snake_case__		(				self  :						Dict	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            _UpperCamelCase							       =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			self.get_dummy_inputs()
            _UpperCamelCase							       =			pipe(**lowerCAmelCase__	).images
            _UpperCamelCase							       =			image[0, -3:, -3:, -1]

            assert image.shape == (1, 128, 128, 3)
            _UpperCamelCase							       =			np.array([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2





       def snake_case__		(				self  :						List[str]	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            _UpperCamelCase							       =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			self.get_dummy_inputs()
            _UpperCamelCase							       =			pipe(**lowerCAmelCase__	).images
            _UpperCamelCase							       =			image[0, -3:, -3:, -1]

            assert image.shape == (1, 128, 128, 3)
            _UpperCamelCase							       =			np.array([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2





       def snake_case__		(				self  :						Optional[int]	)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			self.get_dummy_inputs()
            _UpperCamelCase							       =			3 * [inputs['''prompt''']]

            # forward
            _UpperCamelCase							       =			pipe(**lowerCAmelCase__	)
            _UpperCamelCase							       =			output.images[0, -3:, -3:, -1]

            _UpperCamelCase							       =			self.get_dummy_inputs()
            _UpperCamelCase							       =			3 * [inputs.pop('''prompt'''	)]

            _UpperCamelCase							       =			pipe.tokenizer(
                lowerCAmelCase__			,					padding='''max_length'''			,					max_length=pipe.tokenizer.model_max_length			,					truncation=lowerCAmelCase__			,					return_tensors='''np'''			,					)
            _UpperCamelCase							       =			text_inputs['''input_ids''']

            _UpperCamelCase							       =			pipe.text_encoder(input_ids=text_inputs.astype(np.intaa	)	)[0]

            _UpperCamelCase							       =			prompt_embeds

            # forward
            _UpperCamelCase							       =			pipe(**lowerCAmelCase__	)
            _UpperCamelCase							       =			output.images[0, -3:, -3:, -1]

            assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()	).max() < 1e-4







       def snake_case__		(				self  :						Tuple	)       ->	int:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			self.get_dummy_inputs()
            _UpperCamelCase							       =			3 * ['''this is a negative prompt''']
            _UpperCamelCase							       =			negative_prompt
            _UpperCamelCase							       =			3 * [inputs['''prompt''']]

            # forward
            _UpperCamelCase							       =			pipe(**lowerCAmelCase__	)
            _UpperCamelCase							       =			output.images[0, -3:, -3:, -1]

            _UpperCamelCase							       =			self.get_dummy_inputs()
            _UpperCamelCase							       =			3 * [inputs.pop('''prompt'''	)]

            _UpperCamelCase							       =			[]
            for p in [prompt, negative_prompt]:
                 _UpperCamelCase							       =			pipe.tokenizer(
                     lowerCAmelCase__			,					padding='''max_length'''			,					max_length=pipe.tokenizer.model_max_length			,					truncation=lowerCAmelCase__			,					return_tensors='''np'''			,					)
                 _UpperCamelCase							       =			text_inputs['''input_ids''']

                 embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa	)	)[0]	)

            _UpperCamelCase       ,							_UpperCamelCase							       =			embeds

            # forward
            _UpperCamelCase							       =			pipe(**lowerCAmelCase__	)
            _UpperCamelCase							       =			output.images[0, -3:, -3:, -1]

            assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()	).max() < 1e-4

@nightly
@require_onnxruntime
@require_torch_gpu
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





       @property
       def snake_case__		(				self  :						Any	)       ->	List[str]:

            '''simple docstring'''




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





       @property
       def snake_case__		(				self  :						Optional[Any]	)       ->	Union[str, Any]:

            '''simple docstring'''




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





       def snake_case__		(				self  :						Any	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.from_pretrained(
                '''CompVis/stable-diffusion-v1-4'''			,					revision='''onnx'''			,					safety_checker=lowerCAmelCase__			,					feature_extractor=lowerCAmelCase__			,					provider=self.gpu_provider			,					sess_options=self.gpu_options			,					)
            sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			'''A painting of a squirrel eating a burger'''
            np.random.seed(0	)
            _UpperCamelCase							       =			sd_pipe([prompt]			,					guidance_scale=6.0			,					num_inference_steps=10			,					output_type='''np'''	)
            _UpperCamelCase							       =			output.images

            _UpperCamelCase							       =			image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            _UpperCamelCase							       =			np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3





       def snake_case__		(				self  :						str	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			DDIMScheduler.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''			,					subfolder='''scheduler'''			,					revision='''onnx'''	)
            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''			,					revision='''onnx'''			,					scheduler=lowerCAmelCase__			,					safety_checker=lowerCAmelCase__			,					feature_extractor=lowerCAmelCase__			,					provider=self.gpu_provider			,					sess_options=self.gpu_options			,					)
            sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			'''open neural network exchange'''
            _UpperCamelCase							       =			np.random.RandomState(0	)
            _UpperCamelCase							       =			sd_pipe([prompt]			,					guidance_scale=7.5			,					num_inference_steps=10			,					generator=lowerCAmelCase__			,					output_type='''np'''	)
            _UpperCamelCase							       =			output.images
            _UpperCamelCase							       =			image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            _UpperCamelCase							       =			np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3





       def snake_case__		(				self  :						List[str]	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			LMSDiscreteScheduler.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''			,					subfolder='''scheduler'''			,					revision='''onnx'''	)
            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''			,					revision='''onnx'''			,					scheduler=lowerCAmelCase__			,					safety_checker=lowerCAmelCase__			,					feature_extractor=lowerCAmelCase__			,					provider=self.gpu_provider			,					sess_options=self.gpu_options			,					)
            sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			'''open neural network exchange'''
            _UpperCamelCase							       =			np.random.RandomState(0	)
            _UpperCamelCase							       =			sd_pipe([prompt]			,					guidance_scale=7.5			,					num_inference_steps=10			,					generator=lowerCAmelCase__			,					output_type='''np'''	)
            _UpperCamelCase							       =			output.images
            _UpperCamelCase							       =			image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            _UpperCamelCase							       =			np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3





       def snake_case__		(				self  :						List[Any]	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			0

            def test_callback_fn(lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						np.ndarray	) -> None:
                 _UpperCamelCase							       =			True
                 nonlocal number_of_steps
                 number_of_steps += 1
                 if step == 0:
                      assert latents.shape == (1, 4, 64, 64)
                      _UpperCamelCase							       =			latents[0, -3:, -3:, -1]
                      _UpperCamelCase							       =			np.array(
                          [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]	)

                      assert np.abs(latents_slice.flatten() - expected_slice	).max() < 1e-3
                 elif step == 5:
                      assert latents.shape == (1, 4, 64, 64)
                      _UpperCamelCase							       =			latents[0, -3:, -3:, -1]
                      _UpperCamelCase							       =			np.array(
                          [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]	)

                      assert np.abs(latents_slice.flatten() - expected_slice	).max() < 1e-3

            _UpperCamelCase							       =			False

            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''			,					revision='''onnx'''			,					safety_checker=lowerCAmelCase__			,					feature_extractor=lowerCAmelCase__			,					provider=self.gpu_provider			,					sess_options=self.gpu_options			,					)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			'''Andromeda galaxy in a bottle'''

            _UpperCamelCase							       =			np.random.RandomState(0	)
            pipe(
                prompt=lowerCAmelCase__			,					num_inference_steps=5			,					guidance_scale=7.5			,					generator=lowerCAmelCase__			,					callback=lowerCAmelCase__			,					callback_steps=1			,					)
            assert test_callback_fn.has_been_called
            assert number_of_steps == 6







       def snake_case__		(				self  :						Union[str, Any]	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''			,					revision='''onnx'''			,					safety_checker=lowerCAmelCase__			,					feature_extractor=lowerCAmelCase__			,					provider=self.gpu_provider			,					sess_options=self.gpu_options			,					)
            assert isinstance(lowerCAmelCase__			,					lowerCAmelCase__	)
            assert pipe.safety_checker is None

            _UpperCamelCase							       =			pipe('''example prompt'''			,					num_inference_steps=2	).images[0]
            assert image is not None

            # check that there's no error when saving a pipeline with one of the models being None
            with tempfile.TemporaryDirectory() as tmpdirname:
                 pipe.save_pretrained(lowerCAmelCase__	)
                 _UpperCamelCase							       =			OnnxStableDiffusionPipeline.from_pretrained(lowerCAmelCase__	)

            # sanity check that the pipeline still works
            assert pipe.safety_checker is None
            _UpperCamelCase							       =			pipe('''example prompt'''			,					num_inference_steps=2	).images[0]
            assert image is not None









'''simple docstring'''






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
# and perform gradient accumulation
#
# 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
#
########################################################################


A_			      =		16
A_			      =		32






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE = 16		)    ->   int:
      snake_case__       :   Tuple	      =    AutoTokenizer.from_pretrained('bert-base-cased'		)
      snake_case__       :   Tuple	      =    load_dataset('glue'				,      'mrpc'		)

      def tokenize_function(__SCREAMING_SNAKE_CASE		):
            # max_length=None => use the model max length (it's actually the default)
            snake_case__       :   int	      =    tokenizer(examples['sentence1']				,      examples['sentence2']				,      truncation=__SCREAMING_SNAKE_CASE				,      max_length=__SCREAMING_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():
            snake_case__       :   Union[str, Any]	      =    datasets.map(
                __SCREAMING_SNAKE_CASE				,      batched=__SCREAMING_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
      snake_case__       :   Optional[int]	      =    tokenized_datasets.rename_column('label'				,      'labels'		)

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

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

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

      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

      A_			      =		mocked_dataloaders  # noqa: F811






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Optional[Any]:
      # For testing only
      if os.environ.get('TESTING_MOCKED_DATALOADERS'				,      __SCREAMING_SNAKE_CASE		) == "1":
            snake_case__       :   Optional[int]	      =    2
      # New Code #
      snake_case__       :   List[str]	      =    int(args.gradient_accumulation_steps		)
      # Initialize accelerator
      snake_case__       :   Optional[Any]	      =    Accelerator(
          cpu=args.cpu				,      mixed_precision=args.mixed_precision				,      gradient_accumulation_steps=__SCREAMING_SNAKE_CASE		)
      if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
            raise NotImplementedError(
                'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'		)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      snake_case__       :   Optional[Any]	      =    config['lr']
      snake_case__       :   Dict	      =    int(config['num_epochs']		)
      snake_case__       :   Any	      =    int(config['seed']		)
      snake_case__       :   List[str]	      =    int(config['batch_size']		)

      snake_case__       :   Tuple	      =    evaluate.load('glue'				,      'mrpc'		)

      set_seed(__SCREAMING_SNAKE_CASE		)
      snake_case__      ,							snake_case__       :   Any	      =    get_dataloaders(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      snake_case__       :   Dict	      =    AutoModelForSequenceClassification.from_pretrained('bert-base-cased'				,      return_dict=__SCREAMING_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).
      snake_case__       :   Dict	      =    model.to(accelerator.device		)

      # Instantiate optimizer
      snake_case__       :   int	      =    AdamW(params=model.parameters()				,      lr=__SCREAMING_SNAKE_CASE		)

      # Instantiate scheduler
      snake_case__       :   str	      =    get_linear_schedule_with_warmup(
          optimizer=__SCREAMING_SNAKE_CASE				,      num_warmup_steps=100				,      num_training_steps=(len(__SCREAMING_SNAKE_CASE		) * 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.
      snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__       :   str	      =    accelerator.prepare(
          __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)

      # Now we train the model
      for epoch in range(__SCREAMING_SNAKE_CASE		):
            model.train()
            for step, batch in enumerate(__SCREAMING_SNAKE_CASE		):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device		)
                  # New code #
                  # We use the new `accumulate` context manager to perform gradient accumulation
                  # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                  with accelerator.accumulate(__SCREAMING_SNAKE_CASE		):
                        snake_case__       :   Any	      =    model(**__SCREAMING_SNAKE_CASE		)
                        snake_case__       :   Dict	      =    output.loss
                        accelerator.backward(__SCREAMING_SNAKE_CASE		)
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

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

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






def 		UpperCamelCase__      (		)    ->   Optional[int]:
      snake_case__       :   Tuple	      =    argparse.ArgumentParser(description='Simple example of training script.'		)
      parser.add_argument(
          '--mixed_precision'				,      type=__SCREAMING_SNAKE_CASE				,      default=__SCREAMING_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.'				,      )
      # New Code #
      parser.add_argument(
          '--gradient_accumulation_steps'				,      type=__SCREAMING_SNAKE_CASE				,      default=1				,      help='The number of minibatches to be ran before gradients are accumulated.'				,      )
      parser.add_argument('--cpu'				,      action='store_true'				,      help='If passed, will train on the CPU.'		)
      snake_case__       :   Any	      =    parser.parse_args()
      snake_case__       :   Dict	      =    {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
      training_function(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
      main()
