






"""simple docstring"""
import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
			from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import MobileNetVaImageProcessor


class _lowerCAmelCase						(    __UpperCAmelCase       ):







						def 			A      (    self							)		->							Union[str, Any]:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    self.config_class(**self.inputs_dict							)
										self.parent.assertTrue(hasattr(lowerCAmelCase_   ,				'tf_padding'							)							)
										self.parent.assertTrue(hasattr(lowerCAmelCase_   ,				'depth_multiplier'							)							)







class _lowerCAmelCase						:







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_=1_3   ,				lowerCAmelCase_=3   ,				lowerCAmelCase_=3_2   ,				lowerCAmelCase_=0.25   ,				lowerCAmelCase_=8   ,				lowerCAmelCase_=8   ,				lowerCAmelCase_=6   ,				lowerCAmelCase_=3_2   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_="relu6"   ,				lowerCAmelCase_=1_2_8_0   ,				lowerCAmelCase_=0.1   ,				lowerCAmelCase_=0.02   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_=1_0   ,				lowerCAmelCase_=None   ,				)		->							Any:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    parent
										_SCREAMING_SNAKE_CASE   :		int       		=    batch_size
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    num_channels
										_SCREAMING_SNAKE_CASE   :		Any       		=    image_size
										_SCREAMING_SNAKE_CASE   :		int       		=    depth_multiplier
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    depth_divisible_by
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    min_depth
										_SCREAMING_SNAKE_CASE   :		Any       		=    expand_ratio
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    tf_padding
										_SCREAMING_SNAKE_CASE   :		Dict       		=    output_stride
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    first_layer_is_expansion
										_SCREAMING_SNAKE_CASE   :		Any       		=    finegrained_output
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    hidden_act
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    classifier_dropout_prob
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    use_labels
										_SCREAMING_SNAKE_CASE   :		int       		=    is_training
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    num_labels
										_SCREAMING_SNAKE_CASE   :		str       		=    initializer_range
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    scope







						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    None
										_SCREAMING_SNAKE_CASE   :		Any       		=    None
										if self.use_labels:
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    ids_tensor([self.batch_size]   ,				self.num_labels							)
														_SCREAMING_SNAKE_CASE   :		Any       		=    ids_tensor([self.batch_size, self.image_size, self.image_size]   ,				self.num_labels							)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.get_config()

										return config, pixel_values, labels, pixel_labels







						def 			A      (    self							)		->							Union[str, Any]:
										return MobileNetVaConfig(
										    num_channels=self.num_channels   ,				image_size=self.image_size   ,				depth_multiplier=self.depth_multiplier   ,				depth_divisible_by=self.depth_divisible_by   ,				min_depth=self.min_depth   ,				expand_ratio=self.expand_ratio   ,				output_stride=self.output_stride   ,				first_layer_is_expansion=self.first_layer_is_expansion   ,				finegrained_output=self.finegrained_output   ,				hidden_act=self.hidden_act   ,				tf_padding=self.tf_padding   ,				classifier_dropout_prob=self.classifier_dropout_prob   ,				initializer_range=self.initializer_range   ,				)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							int:
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    MobileNetVaModel(config=lowerCAmelCase_							)
										model.to(lowerCAmelCase_							)
										model.eval()
										_SCREAMING_SNAKE_CASE   :		int       		=    model(lowerCAmelCase_							)
										self.parent.assertEqual(
										    result.last_hidden_state.shape   ,				(
										        self.batch_size,
										        self.last_hidden_size,
										        self.image_size // self.output_stride,
										        self.image_size // self.output_stride,
										    )   ,				)
										self.parent.assertEqual(
										    result.pooler_output.shape   ,				(self.batch_size, self.last_hidden_size)   ,				)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							Optional[Any]:
										_SCREAMING_SNAKE_CASE   :		Any       		=    self.num_labels
										_SCREAMING_SNAKE_CASE   :		str       		=    MobileNetVaForImageClassification(lowerCAmelCase_							)
										model.to(lowerCAmelCase_							)
										model.eval()
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    model(lowerCAmelCase_   ,				labels=lowerCAmelCase_							)
										self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.num_labels
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    MobileNetVaForSemanticSegmentation(lowerCAmelCase_							)
										model.to(lowerCAmelCase_							)
										model.eval()
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    model(lowerCAmelCase_							)
										self.parent.assertEqual(
										    result.logits.shape   ,				(
										        self.batch_size,
										        self.num_labels,
										        self.image_size // self.output_stride,
										        self.image_size // self.output_stride,
										    )   ,				)
										_SCREAMING_SNAKE_CASE   :		Any       		=    model(lowerCAmelCase_   ,				labels=lowerCAmelCase_							)
										self.parent.assertEqual(
										    result.logits.shape   ,				(
										        self.batch_size,
										        self.num_labels,
										        self.image_size // self.output_stride,
										        self.image_size // self.output_stride,
										    )   ,				)







						def 			A      (    self							)		->							List[Any]:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    self.prepare_config_and_inputs()
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Any       		=    config_and_inputs
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    {'pixel_values': pixel_values}
										return config, inputs_dict







@require_torch
class _lowerCAmelCase						(    __UpperCAmelCase   ,  __UpperCAmelCase   ,  unittest.TestCase       ):
						SCREAMING_SNAKE_CASE_:					Tuple 					=				(
						    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						SCREAMING_SNAKE_CASE_:					Any 					=				(
						    {
						        'feature-extraction': MobileNetVaModel,
						        'image-classification': MobileNetVaForImageClassification,
						        'image-segmentation': MobileNetVaForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						SCREAMING_SNAKE_CASE_:					int 					=				False
						SCREAMING_SNAKE_CASE_:					Any 					=				False
						SCREAMING_SNAKE_CASE_:					int 					=				False
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				False







						def 			A      (    self							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    MobileNetVaModelTester(self							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    MobileNetVaConfigTester(self   ,				config_class=lowerCAmelCase_   ,				has_text_modality=lowerCAmelCase_							)







						def 			A      (    self							)		->							int:
										self.config_tester.run_common_tests()







						@unittest.skip(reason='MobileNetV2 does not use inputs_embeds'							)
						def 			A      (    self							)		->							str:
										pass







						@unittest.skip(reason='MobileNetV2 does not support input and output embeddings'							)
						def 			A      (    self							)		->							Any:
										pass







						@unittest.skip(reason='MobileNetV2 does not output attentions'							)
						def 			A      (    self							)		->							Optional[Any]:
										pass







						def 			A      (    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    model_class(lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    inspect.signature(model.forward							)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														_SCREAMING_SNAKE_CASE   :		Any       		=    [*signature.parameters.keys()]

														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    ['pixel_values']
														self.assertListEqual(arg_names[:1]   ,				lowerCAmelCase_							)







						def 			A      (    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		int       		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase_							)







						def 			A      (    self							)		->							List[Any]:
										def check_hidden_states_output(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							):
														_SCREAMING_SNAKE_CASE   :		str       		=    model_class(lowerCAmelCase_							)
														model.to(lowerCAmelCase_							)
														model.eval()

														with torch.no_grad():
																		_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    model(**self._prepare_for_class(lowerCAmelCase_   ,				lowerCAmelCase_							)							)

														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    outputs.hidden_states

														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    1_6
														self.assertEqual(len(lowerCAmelCase_							)   ,				lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Any       		=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    True
														check_hidden_states_output(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														_SCREAMING_SNAKE_CASE   :		Dict       		=    True

														check_hidden_states_output(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)







						def 			A      (    self							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_							)







						def 			A      (    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase_							)







						@slow
						def 			A      (    self							)		->							str:
										for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    MobileNetVaModel.from_pretrained(lowerCAmelCase_							)
														self.assertIsNotNone(lowerCAmelCase_							)







def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
				return image






@require_torch
@require_vision
class _lowerCAmelCase						(    unittest.TestCase       ):







						@cached_property
						def 			A      (    self							)		->							Optional[int]:
										return (
										    MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224'							) if is_vision_available() else None
										)







						@slow
						def 			A      (    self							)		->							Optional[Any]:
										_SCREAMING_SNAKE_CASE   :		str       		=    MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224'							).to(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		int       		=    self.default_image_processor
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    prepare_img()
										_SCREAMING_SNAKE_CASE   :		str       		=    image_processor(images=lowerCAmelCase_   ,				return_tensors='pt'							).to(lowerCAmelCase_							)

										# forward pass
										with torch.no_grad():
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    model(**lowerCAmelCase_							)

										# verify the logits
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    torch.Size((1, 1_0_0_1)							)
										self.assertEqual(outputs.logits.shape   ,				lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    torch.tensor([0.2_445, -1.1_993, 0.1_905]							).to(lowerCAmelCase_							)

										self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,				lowerCAmelCase_   ,				atol=1e-4							)							)







						@slow
						def 			A      (    self							)		->							List[Any]:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'							)
										_SCREAMING_SNAKE_CASE   :		str       		=    model.to(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'							)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    prepare_img()
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    image_processor(images=lowerCAmelCase_   ,				return_tensors='pt'							).to(lowerCAmelCase_							)

										# forward pass
										with torch.no_grad():
														_SCREAMING_SNAKE_CASE   :		Dict       		=    model(**lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    outputs.logits

										# verify the logits
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    torch.Size((1, 2_1, 6_5, 6_5)							)
										self.assertEqual(logits.shape   ,				lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    torch.tensor(
										    [
										        [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
										        [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
										        [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
										    ]   ,				device=lowerCAmelCase_   ,				)

										self.assertTrue(torch.allclose(logits[0, :3, :3, :3]   ,				lowerCAmelCase_   ,				atol=1e-4							)							)









"""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 				lowercase__		(					lowerCamelCase,						lowerCamelCase = 16       ):
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    AutoTokenizer.from_pretrained('bert-base-cased'       )
				_SCREAMING_SNAKE_CASE   :		Any       		=    load_dataset('glue',						'mrpc'       )

				def tokenize_function(lowerCamelCase       ):
								# max_length=None => use the model max length (it's actually the default)
								_SCREAMING_SNAKE_CASE   :		int       		=    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():
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    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
				_SCREAMING_SNAKE_CASE   :		Dict       		=    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.
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    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":
												_SCREAMING_SNAKE_CASE   :		List[str]       		=    16
								elif accelerator.mixed_precision != "no":
												_SCREAMING_SNAKE_CASE   :		Dict       		=    8
								else:
												_SCREAMING_SNAKE_CASE   :		str       		=    None

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

				# Instantiate dataloaders.
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    DataLoader(
				    tokenized_datasets['train'],						shuffle=lowerCamelCase,						collate_fn=lowerCamelCase,						batch_size=lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Dict       		=    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 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				# For testing only
				if os.environ.get('TESTING_MOCKED_DATALOADERS',						lowerCamelCase       ) == "1":
								_SCREAMING_SNAKE_CASE   :		int       		=    2
				# Initialize accelerator
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    Accelerator(cpu=args.cpu,						mixed_precision=args.mixed_precision       )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				_SCREAMING_SNAKE_CASE   :		Any       		=    config['lr']
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    int(config['num_epochs']       )
				_SCREAMING_SNAKE_CASE   :		Dict       		=    int(config['seed']       )
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    int(config['batch_size']       )

				_SCREAMING_SNAKE_CASE   :		Tuple       		=    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)
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    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).
								_SCREAMING_SNAKE_CASE   :		Any       		=    model.to(accelerator.device       )

								# Instantiate optimizer
								_SCREAMING_SNAKE_CASE   :		str       		=    AdamW(params=model.parameters(),						lr=lowerCamelCase       )
								_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    get_dataloaders(lowerCamelCase,						lowerCamelCase       )

								# Instantiate scheduler
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    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.
								_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[Any]       		=    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       )
																_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    model(**lowerCamelCase       )
																_SCREAMING_SNAKE_CASE   :		List[str]       		=    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():
																				_SCREAMING_SNAKE_CASE   :		List[str]       		=    model(**lowerCamelCase       )
																_SCREAMING_SNAKE_CASE   :		List[Any]       		=    outputs.logits.argmax(dim=-1       )
																_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Dict       		=    accelerator.gather_for_metrics((predictions, batch['labels'])       )
																metric.add_batch(
																    predictions=lowerCamelCase,						references=lowerCamelCase,						)

												_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    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 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		str       		=    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.'       )
				_SCREAMING_SNAKE_CASE   :		Any       		=    parser.parse_args()
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
				training_function(lowerCamelCase,						lowerCamelCase       )


if __name__ == "__main__":
			main()

