



'''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  snake_case__						(    _lowerCamelCase  ,     unittest.TestCase				):
 A__				= '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
 def A_   (   self						:							str      ,     __a						:							Dict=0  )      ->   str:







       '''simple docstring'''
       __snake_case :   List[str]													=    np.random.RandomState(lowercase_  )
       __snake_case :   List[Any]													=    {
           '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 A_   (   self						:							Optional[Any]  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   Tuple													=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,     provider='CPUExecutionProvider'  )
       pipe.set_progress_bar_config(disable=lowercase_  )

       __snake_case :   Dict													=    self.get_dummy_inputs()
       __snake_case :   int													=    pipe(**lowercase_  ).images
       __snake_case :   Optional[int]													=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 128, 128, 3)
       __snake_case :   Optional[Any]													=    np.array([0.6_5_0_7_2, 0.5_8_4_9_2, 0.4_8_2_1_9, 0.5_5_5_2_1, 0.5_3_1_8_0, 0.5_5_9_3_9, 0.5_0_6_9_7, 0.3_9_8_0_0, 0.4_6_4_5_5]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
 def A_   (   self						:							int  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,     provider='CPUExecutionProvider'  )
       __snake_case :   Union[str, Any]													=    PNDMScheduler.from_config(pipe.scheduler.config      ,     skip_prk_steps=lowercase_  )
       pipe.set_progress_bar_config(disable=lowercase_  )

       __snake_case :   Tuple													=    self.get_dummy_inputs()
       __snake_case :   List[Any]													=    pipe(**lowercase_  ).images
       __snake_case :   Dict													=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 128, 128, 3)
       __snake_case :   Any													=    np.array([0.6_5_8_6_3, 0.5_9_4_2_5, 0.4_9_3_2_6, 0.5_6_3_1_3, 0.5_3_8_7_5, 0.5_6_6_2_7, 0.5_1_0_6_5, 0.3_9_7_7_7, 0.4_6_3_3_0]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
 def A_   (   self						:							Optional[Any]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   str													=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,     provider='CPUExecutionProvider'  )
       __snake_case :   Optional[int]													=    LMSDiscreteScheduler.from_config(pipe.scheduler.config  )
       pipe.set_progress_bar_config(disable=lowercase_  )

       __snake_case :   List[str]													=    self.get_dummy_inputs()
       __snake_case :   List[Any]													=    pipe(**lowercase_  ).images
       __snake_case :   Union[str, Any]													=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 128, 128, 3)
       __snake_case :   List[str]													=    np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
 def A_   (   self						:							List[Any]  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   int													=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,     provider='CPUExecutionProvider'  )
       __snake_case :   Union[str, Any]													=    EulerDiscreteScheduler.from_config(pipe.scheduler.config  )
       pipe.set_progress_bar_config(disable=lowercase_  )

       __snake_case :   Union[str, Any]													=    self.get_dummy_inputs()
       __snake_case :   str													=    pipe(**lowercase_  ).images
       __snake_case :   Any													=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 128, 128, 3)
       __snake_case :   List[str]													=    np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
 def A_   (   self						:							Optional[Any]  )      ->   Any:







       '''simple docstring'''
       __snake_case :   Optional[int]													=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,     provider='CPUExecutionProvider'  )
       __snake_case :   Union[str, Any]													=    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config  )
       pipe.set_progress_bar_config(disable=lowercase_  )

       __snake_case :   List[str]													=    self.get_dummy_inputs()
       __snake_case :   Dict													=    pipe(**lowercase_  ).images
       __snake_case :   str													=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 128, 128, 3)
       __snake_case :   Dict													=    np.array([0.5_3_8_1_7, 0.6_0_8_1_2, 0.4_7_3_8_4, 0.4_9_5_3_0, 0.5_1_8_9_4, 0.4_9_8_1_4, 0.4_7_9_8_4, 0.3_8_9_5_8, 0.4_4_2_7_1]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
 def A_   (   self						:							Dict  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,     provider='CPUExecutionProvider'  )
       __snake_case :   Optional[int]													=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
       pipe.set_progress_bar_config(disable=lowercase_  )

       __snake_case :   List[Any]													=    self.get_dummy_inputs()
       __snake_case :   Optional[int]													=    pipe(**lowercase_  ).images
       __snake_case :   Union[str, Any]													=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 128, 128, 3)
       __snake_case :   Dict													=    np.array([0.5_3_8_9_5, 0.6_0_8_0_8, 0.4_7_9_3_3, 0.4_9_6_0_8, 0.5_1_8_8_6, 0.4_9_9_5_0, 0.4_8_0_5_3, 0.3_8_9_5_7, 0.4_4_2_0_0]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
 def A_   (   self						:							List[Any]  )      ->   int:







       '''simple docstring'''
       __snake_case :   Tuple													=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,     provider='CPUExecutionProvider'  )
       pipe.set_progress_bar_config(disable=lowercase_  )

       __snake_case :   Union[str, Any]													=    self.get_dummy_inputs()
       __snake_case :   Union[str, Any]													=    3 * [inputs['prompt']]

       # forward
       __snake_case :   Tuple													=    pipe(**lowercase_  )
       __snake_case :   Optional[Any]													=    output.images[0, -3:, -3:, -1]

       __snake_case :   Optional[Any]													=    self.get_dummy_inputs()
       __snake_case :   Optional[Any]													=    3 * [inputs.pop('prompt'  )]

       __snake_case :   int													=    pipe.tokenizer(
           lowercase_      ,     padding='max_length'      ,     max_length=pipe.tokenizer.model_max_length      ,     truncation=lowercase_      ,     return_tensors='np'      ,     )
       __snake_case :   Optional[int]													=    text_inputs['input_ids']

       __snake_case :   str													=    pipe.text_encoder(input_ids=text_inputs.astype(np.intaa  )  )[0]

       __snake_case :   List[Any]													=    prompt_embeds

       # forward
       __snake_case :   Any													=    pipe(**lowercase_  )
       __snake_case :   Union[str, Any]													=    output.images[0, -3:, -3:, -1]

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





 def A_   (   self						:							Optional[int]  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   str													=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,     provider='CPUExecutionProvider'  )
       pipe.set_progress_bar_config(disable=lowercase_  )

       __snake_case :   Tuple													=    self.get_dummy_inputs()
       __snake_case :   Any													=    3 * ['this is a negative prompt']
       __snake_case :   Any													=    negative_prompt
       __snake_case :   str													=    3 * [inputs['prompt']]

       # forward
       __snake_case :   Tuple													=    pipe(**lowercase_  )
       __snake_case :   Any													=    output.images[0, -3:, -3:, -1]

       __snake_case :   List[Any]													=    self.get_dummy_inputs()
       __snake_case :   List[Any]													=    3 * [inputs.pop('prompt'  )]

       __snake_case :   Any													=    []
       for p in [prompt, negative_prompt]:
             __snake_case :   Tuple													=    pipe.tokenizer(
                 lowercase_      ,     padding='max_length'      ,     max_length=pipe.tokenizer.model_max_length      ,     truncation=lowercase_      ,     return_tensors='np'      ,     )
             __snake_case :   int													=    text_inputs['input_ids']

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

       __snake_case							,     __snake_case :   str													=    embeds

       # forward
       __snake_case :   List[str]													=    pipe(**lowercase_  )
       __snake_case :   List[Any]													=    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  snake_case__						(    unittest.TestCase				):
 @property
 def A_   (   self						:							List[Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )
 @property
 def A_   (   self						:							Tuple  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    ort.SessionOptions()
       __snake_case :   int													=    False
       return options
 def A_   (   self						:							Dict  )      ->   List[Any]:







       '''simple docstring'''
       # using the PNDM scheduler by default
       __snake_case :   Dict													=    OnnxStableDiffusionPipeline.from_pretrained(
           'CompVis/stable-diffusion-v1-4'      ,     revision='onnx'      ,     safety_checker=lowercase_      ,     feature_extractor=lowercase_      ,     provider=self.gpu_provider      ,     sess_options=self.gpu_options      ,     )
       sd_pipe.set_progress_bar_config(disable=lowercase_  )

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

       __snake_case :   List[str]													=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       __snake_case :   Optional[Any]													=    np.array([0.0_4_5_2, 0.0_3_9_0, 0.0_0_8_7, 0.0_3_5_0, 0.0_6_1_7, 0.0_3_6_4, 0.0_5_4_4, 0.0_5_2_3, 0.0_7_2_0]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-3
 def A_   (   self						:							str  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    DDIMScheduler.from_pretrained(
           'runwayml/stable-diffusion-v1-5'      ,     subfolder='scheduler'      ,     revision='onnx'  )
       __snake_case :   List[str]													=    OnnxStableDiffusionPipeline.from_pretrained(
           'runwayml/stable-diffusion-v1-5'      ,     revision='onnx'      ,     scheduler=lowercase_      ,     safety_checker=lowercase_      ,     feature_extractor=lowercase_      ,     provider=self.gpu_provider      ,     sess_options=self.gpu_options      ,     )
       sd_pipe.set_progress_bar_config(disable=lowercase_  )

       __snake_case :   Optional[Any]													=    'open neural network exchange'
       __snake_case :   List[str]													=    np.random.RandomState(0  )
       __snake_case :   Dict													=    sd_pipe([prompt]      ,     guidance_scale=7.5      ,     num_inference_steps=10      ,     generator=lowercase_      ,     output_type='np'  )
       __snake_case :   Any													=    output.images
       __snake_case :   str													=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       __snake_case :   str													=    np.array([0.2_8_6_7, 0.1_9_7_4, 0.1_4_8_1, 0.7_2_9_4, 0.7_2_5_1, 0.6_6_6_7, 0.4_1_9_4, 0.5_6_4_2, 0.6_4_8_6]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-3
 def A_   (   self						:							Dict  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Dict													=    LMSDiscreteScheduler.from_pretrained(
           'runwayml/stable-diffusion-v1-5'      ,     subfolder='scheduler'      ,     revision='onnx'  )
       __snake_case :   Optional[int]													=    OnnxStableDiffusionPipeline.from_pretrained(
           'runwayml/stable-diffusion-v1-5'      ,     revision='onnx'      ,     scheduler=lowercase_      ,     safety_checker=lowercase_      ,     feature_extractor=lowercase_      ,     provider=self.gpu_provider      ,     sess_options=self.gpu_options      ,     )
       sd_pipe.set_progress_bar_config(disable=lowercase_  )

       __snake_case :   Tuple													=    'open neural network exchange'
       __snake_case :   str													=    np.random.RandomState(0  )
       __snake_case :   str													=    sd_pipe([prompt]      ,     guidance_scale=7.5      ,     num_inference_steps=10      ,     generator=lowercase_      ,     output_type='np'  )
       __snake_case :   str													=    output.images
       __snake_case :   List[Any]													=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       __snake_case :   str													=    np.array([0.2_3_0_6, 0.1_9_5_9, 0.1_5_9_3, 0.6_5_4_9, 0.6_3_9_4, 0.5_4_0_8, 0.5_0_6_5, 0.6_0_1_0, 0.6_1_6_1]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-3
 def A_   (   self						:							Optional[Any]  )      ->   str:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    0

       def test_callback_fn(__a						:							int      ,     __a						:							int      ,     __a						:							np.ndarray  ) -> None:
             __snake_case :   Union[str, Any]													=    True
             nonlocal number_of_steps
             number_of_steps += 1
             if step == 0:
                   assert latents.shape == (1, 4, 64, 64)
                   __snake_case :   Union[str, Any]													=    latents[0, -3:, -3:, -1]
                   __snake_case :   Any													=    np.array(
                       [-0.6_7_7_2, -0.3_8_3_5, -1.2_4_5_6, 0.1_9_0_5, -1.0_9_7_4, 0.6_9_6_7, -1.9_3_5_3, 0.0_1_7_8, 1.0_1_6_7]  )

                   assert np.abs(latents_slice.flatten() - expected_slice  ).max() < 1e-3
             elif step == 5:
                   assert latents.shape == (1, 4, 64, 64)
                   __snake_case :   List[Any]													=    latents[0, -3:, -3:, -1]
                   __snake_case :   int													=    np.array(
                       [-0.3_3_5_1, 0.2_2_4_1, -0.1_8_3_7, -0.2_3_2_5, -0.6_5_7_7, 0.3_3_9_3, -0.0_2_4_1, 0.5_8_9_9, 1.3_8_7_5]  )

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

       __snake_case :   str													=    False

       __snake_case :   List[str]													=    OnnxStableDiffusionPipeline.from_pretrained(
           'runwayml/stable-diffusion-v1-5'      ,     revision='onnx'      ,     safety_checker=lowercase_      ,     feature_extractor=lowercase_      ,     provider=self.gpu_provider      ,     sess_options=self.gpu_options      ,     )
       pipe.set_progress_bar_config(disable=lowercase_  )

       __snake_case :   Dict													=    'Andromeda galaxy in a bottle'

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





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







       '''simple docstring'''
       __snake_case :   Any													=    OnnxStableDiffusionPipeline.from_pretrained(
           'runwayml/stable-diffusion-v1-5'      ,     revision='onnx'      ,     safety_checker=lowercase_      ,     feature_extractor=lowercase_      ,     provider=self.gpu_provider      ,     sess_options=self.gpu_options      ,     )
       assert isinstance(lowercase_      ,     lowercase_  )
       assert pipe.safety_checker is None

       __snake_case :   List[Any]													=    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(lowercase_  )
             __snake_case :   Dict													=    OnnxStableDiffusionPipeline.from_pretrained(lowercase_  )

       # sanity check that the pipeline still works
       assert pipe.safety_checker is None
       __snake_case :   Any													=    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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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    :							str										=    16
a    :							Union[str, Any]										=    32


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase = 16      )						->   Union[str, Any]:

					'''simple docstring'''

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

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

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

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

					# Instantiate dataloaders.
					snake_case_		     =							DataLoader(
					    tokenized_datasets['''train'''], shuffle=__UpperCAmelCase, collate_fn=__UpperCAmelCase, batch_size=__UpperCAmelCase      )
					snake_case_		     =							DataLoader(
					    tokenized_datasets['''validation'''], shuffle=__UpperCAmelCase, collate_fn=__UpperCAmelCase, batch_size=__UpperCAmelCase      )

					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    :							Optional[Any]										=    mocked_dataloaders  # noqa: F811


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[Any]:

					'''simple docstring'''

					if os.environ.get('''TESTING_MOCKED_DATALOADERS''', __UpperCAmelCase      ) == "1":
										snake_case_		     =							2
					# New Code #
					snake_case_		     =							int(args.gradient_accumulation_steps      )
					snake_case_		     =							int(args.local_sgd_steps      )
					# Initialize accelerator
					snake_case_		     =							Accelerator(
					    cpu=args.cpu, mixed_precision=args.mixed_precision, gradient_accumulation_steps=__UpperCAmelCase      )
					if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
										raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''      )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					snake_case_		     =							config['''lr''']
					snake_case_		     =							int(config['''num_epochs''']      )
					snake_case_		     =							int(config['''seed''']      )
					snake_case_		     =							int(config['''batch_size''']      )

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

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

					# 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_		     =							model.to(accelerator.device      )

					# Instantiate optimizer
					snake_case_		     =							AdamW(params=model.parameters(), lr=__UpperCAmelCase      )

					# Instantiate scheduler
					snake_case_		     =							get_linear_schedule_with_warmup(
					    optimizer=__UpperCAmelCase, num_warmup_steps=100, num_training_steps=(len(__UpperCAmelCase      ) * 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_		     =							accelerator.prepare(
					    __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )

					# Now we train the model
					for epoch in range(__UpperCAmelCase      ):
										model.train()
										with LocalSGD(
										    accelerator=__UpperCAmelCase, model=__UpperCAmelCase, local_sgd_steps=__UpperCAmelCase, enabled=local_sgd_steps is not None      ) as local_sgd:
															for step, batch in enumerate(__UpperCAmelCase      ):
																				# 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(__UpperCAmelCase      ):
																									snake_case_		     =							model(**__UpperCAmelCase      )
																									snake_case_		     =							output.loss
																									accelerator.backward(__UpperCAmelCase      )
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()
																									# LocalSGD-specific line
																									local_sgd.step()

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

										snake_case_		     =							metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"epoch {epoch}:", __UpperCAmelCase      )


def 						__magic_name__       (      )						->   str:

					'''simple docstring'''

					snake_case_		     =							argparse.ArgumentParser(description='''Simple example of training script.'''      )
					parser.add_argument(
					    '''--mixed_precision''', type=__UpperCAmelCase, default=__UpperCAmelCase, 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=__UpperCAmelCase, default=1, help='''The number of minibatches to be ran before gradients are accumulated.''', )
					parser.add_argument(
					    '''--local_sgd_steps''', type=__UpperCAmelCase, default=8, help='''Number of local SGD steps or None to disable local SGD'''      )
					parser.add_argument('''--cpu''', action='''store_true''', help='''If passed, will train on the CPU.'''      )
					snake_case_		     =							parser.parse_args()
					snake_case_		     =							{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(__UpperCAmelCase, __UpperCAmelCase      )


if __name__ == "__main__":
							main()

