code: 
  
  
  import inspect
  import unittest
  
  from transformers import ResNetConfig
  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_backbone_common import BackboneTesterMixin
  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 torch import nn
  
  		from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
  		from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  if is_vision_available():
  		from PIL import Image
  
  		from transformers import AutoImageProcessor
  
  
  
  
  
  
  
  class 					__lowercase					:
  
  
  
  		"""simple docstring"""
  
  		def __init__(   self		,			A		,			A=3		,			A=32		,			A=3		,			A=10		,			A=[10, 20, 30, 40]		,			A=[1, 1, 2, 1]		,			A=True		,			A=True		,			A="relu"		,			A=3		,			A=None		,			A=["stage2", "stage3", "stage4"]		,			A=[2, 3, 4]		,			)     ->   Dict:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     parent
  					lowerCamelCase								=     batch_size
  					lowerCamelCase								=     image_size
  					lowerCamelCase								=     num_channels
  					lowerCamelCase								=     embeddings_size
  					lowerCamelCase								=     hidden_sizes
  					lowerCamelCase								=     depths
  					lowerCamelCase								=     is_training
  					lowerCamelCase								=     use_labels
  					lowerCamelCase								=     hidden_act
  					lowerCamelCase								=     num_labels
  					lowerCamelCase								=     scope
  					lowerCamelCase								=     len(A  )
  					lowerCamelCase								=     out_features
  					lowerCamelCase								=     out_indices
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Optional[Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )
  
  					lowerCamelCase								=     None
  					if self.use_labels:
  								lowerCamelCase								=     ids_tensor([self.batch_size]		,			self.num_labels  )
  
  					lowerCamelCase								=     self.get_config()
  
  					return config, pixel_values, labels
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Optional[int]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					return ResNetConfig(
  					    num_channels=self.num_channels		,			embeddings_size=self.embeddings_size		,			hidden_sizes=self.hidden_sizes		,			depths=self.depths		,			hidden_act=self.hidden_act		,			num_labels=self.num_labels		,			out_features=self.out_features		,			out_indices=self.out_indices		,			)
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A		,			A  )     ->   Union[str, Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     ResNetModel(config=A  )
  					model.to(A  )
  					model.eval()
  					lowerCamelCase								=     model(A  )
  					# expected last hidden states: B, C, H // 32, W // 32
  					self.parent.assertEqual(
  					    result.last_hidden_state.shape		,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,			)
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A		,			A  )     ->   Dict:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self.num_labels
  					lowerCamelCase								=     ResNetForImageClassification(A  )
  					model.to(A  )
  					model.eval()
  					lowerCamelCase								=     model(A		,			labels=A  )
  					self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_labels)  )
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A		,			A  )     ->   List[str]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     ResNetBackbone(config=A  )
  					model.to(A  )
  					model.eval()
  					lowerCamelCase								=     model(A  )
  
  					# verify feature maps
  					self.parent.assertEqual(len(result.feature_maps  )		,			len(config.out_features  )  )
  					self.parent.assertListEqual(list(result.feature_maps[0].shape  )		,			[self.batch_size, self.hidden_sizes[1], 4, 4]  )
  
  					# verify channels
  					self.parent.assertEqual(len(model.channels  )		,			len(config.out_features  )  )
  					self.parent.assertListEqual(model.channels		,			config.hidden_sizes[1:]  )
  
  					# verify backbone works with out_features=None
  					lowerCamelCase								=     None
  					lowerCamelCase								=     ResNetBackbone(config=A  )
  					model.to(A  )
  					model.eval()
  					lowerCamelCase								=     model(A  )
  
  					# verify feature maps
  					self.parent.assertEqual(len(result.feature_maps  )		,			1  )
  					self.parent.assertListEqual(list(result.feature_maps[0].shape  )		,			[self.batch_size, self.hidden_sizes[-1], 1, 1]  )
  
  					# verify channels
  					self.parent.assertEqual(len(model.channels  )		,			1  )
  					self.parent.assertListEqual(model.channels		,			[config.hidden_sizes[-1]]  )
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   int:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self.prepare_config_and_inputs()
  					lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase								=     config_and_inputs
  					lowerCamelCase								=     {"""pixel_values""": pixel_values}
  					return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class 					__lowercase					(       a_ ,       a_ ,       unittest.TestCase     ):
  
  
  
  		"""simple docstring"""
  
  		UpperCamelCase    :				Any 					=		(
  		    (
  		        ResNetModel,
  		        ResNetForImageClassification,
  		        ResNetBackbone,
  		    )
  		    if is_torch_available()
  		    else ()
  		)
  		UpperCamelCase    :				Optional[Any] 					=		(
  		    {"feature-extraction": ResNetModel, "image-classification": ResNetForImageClassification}
  		    if is_torch_available()
  		    else {}
  		)
  
  		UpperCamelCase    :				Any 					=		True
  		UpperCamelCase    :				List[str] 					=		False
  		UpperCamelCase    :				List[Any] 					=		False
  		UpperCamelCase    :				Union[str, Any] 					=		False
  		UpperCamelCase    :				Optional[int] 					=		False
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   int:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     ResNetModelTester(self  )
  					lowerCamelCase								=     ConfigTester(self		,			config_class=A		,			has_text_modality=A  )
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Optional[Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					self.create_and_test_config_common_properties()
  					self.config_tester.create_and_test_config_to_json_string()
  					self.config_tester.create_and_test_config_to_json_file()
  					self.config_tester.create_and_test_config_from_and_save_pretrained()
  					self.config_tester.create_and_test_config_with_num_labels()
  					self.config_tester.check_config_can_be_init_without_params()
  					self.config_tester.check_config_arguments_init()
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   List[str]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					return
  
  
  
  
  
  
  
  		@unittest.skip(reason="""ResNet does not use inputs_embeds"""  )
  		def 				__A      (   self  )     ->   Optional[int]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					pass
  
  
  
  
  
  
  
  		@unittest.skip(reason="""ResNet does not support input and output embeddings"""  )
  		def 				__A      (   self  )     ->   Dict:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					pass
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Optional[int]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase		,		lowerCamelCase								=     self.model_tester.prepare_config_and_inputs_for_common()
  
  					for model_class in self.all_model_classes:
  								lowerCamelCase								=     model_class(A  )
  								lowerCamelCase								=     inspect.signature(model.forward  )
  								# signature.parameters is an OrderedDict => so arg_names order is deterministic
  								lowerCamelCase								=     [*signature.parameters.keys()]
  
  								lowerCamelCase								=     ["""pixel_values"""]
  								self.assertListEqual(arg_names[:1]		,			A  )
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self.model_tester.prepare_config_and_inputs()
  					self.model_tester.create_and_check_model(*A  )
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   List[str]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self.model_tester.prepare_config_and_inputs()
  					self.model_tester.create_and_check_backbone(*A  )
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Optional[Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase		,		lowerCamelCase								=     self.model_tester.prepare_config_and_inputs_for_common()
  
  					for model_class in self.all_model_classes:
  								lowerCamelCase								=     model_class(config=A  )
  								for name, module in model.named_modules():
  											if isinstance(A		,			(nn.BatchNormad, nn.GroupNorm)  ):
  														self.assertTrue(
  														    torch.all(module.weight == 1  )		,			msg=F'Parameter {name} of model {model_class} seems not properly initialized'		,			)
  														self.assertTrue(
  														    torch.all(module.bias == 0  )		,			msg=F'Parameter {name} of model {model_class} seems not properly initialized'		,			)
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   List[str]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					def check_hidden_states_output(A		,			A		,			A  ):
  								lowerCamelCase								=     model_class(A  )
  								model.to(A  )
  								model.eval()
  
  								with torch.no_grad():
  											lowerCamelCase								=     model(**self._prepare_for_class(A		,			A  )  )
  
  								lowerCamelCase								=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
  								lowerCamelCase								=     self.model_tester.num_stages
  								self.assertEqual(len(A  )		,			expected_num_stages + 1  )
  
  								# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
  								self.assertListEqual(
  								    list(hidden_states[0].shape[-2:]  )		,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]		,			)
  
  					lowerCamelCase		,		lowerCamelCase								=     self.model_tester.prepare_config_and_inputs_for_common()
  					lowerCamelCase								=     ["""basic""", """bottleneck"""]
  					for model_class in self.all_model_classes:
  								for layer_type in layers_type:
  											lowerCamelCase								=     layer_type
  											lowerCamelCase								=     True
  											check_hidden_states_output(A		,			A		,			A  )
  
  											# check that output_hidden_states also work using config
  											del inputs_dict["output_hidden_states"]
  											lowerCamelCase								=     True
  
  											check_hidden_states_output(A		,			A		,			A  )
  
  
  
  
  
  
  
  		@unittest.skip(reason="""ResNet does not use feedforward chunking"""  )
  		def 				__A      (   self  )     ->   Optional[int]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					pass
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self.model_tester.prepare_config_and_inputs()
  					self.model_tester.create_and_check_for_image_classification(*A  )
  
  
  
  
  
  
  
  		@slow
  		def 				__A      (   self  )     ->   int:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  								lowerCamelCase								=     ResNetModel.from_pretrained(A  )
  								self.assertIsNotNone(A  )
  
  
  
  
  
  
  def   __lowerCamelCase	(       ):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
  			return image
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 					__lowercase					(       unittest.TestCase     ):
  
  
  
  		"""simple docstring"""
  
  		@cached_property
  		def 				__A      (   self  )     ->   Union[str, Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					return (
  					    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
  					    if is_vision_available()
  					    else None
  					)
  
  
  
  
  
  
  
  		@slow
  		def 				__A      (   self  )     ->   str:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(A  )
  
  					lowerCamelCase								=     self.default_image_processor
  					lowerCamelCase								=     prepare_img()
  					lowerCamelCase								=     image_processor(images=A		,			return_tensors="""pt"""  ).to(A  )
  
  					# forward pass
  					with torch.no_grad():
  								lowerCamelCase								=     model(**A  )
  
  					# verify the logits
  					lowerCamelCase								=     torch.Size((1, 10_00)  )
  					self.assertEqual(outputs.logits.shape		,			A  )
  
  					lowerCamelCase								=     torch.tensor([-11.1069, -9.7877, -8.3777]  ).to(A  )
  
  					self.assertTrue(torch.allclose(outputs.logits[0, :3]		,			A		,			atol=1e-4  )  )
  
  
  
  
  
  
  
  @require_torch
  class 					__lowercase					(       a_ ,       unittest.TestCase     ):
  
  
  
  		"""simple docstring"""
  
  		UpperCamelCase    :				Tuple 					=		(ResNetBackbone,) if is_torch_available() else ()
  		UpperCamelCase    :				List[Any] 					=		False
  		UpperCamelCase    :				int 					=		ResNetConfig
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Tuple:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     ResNetModelTester(self  )
  
  
style_context: 
  
  
  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,
  # specifically showcasing the checkpointing capability,
  # 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)
  #
  # To help focus on the differences in the code, building `DataLoaders`
  # was refactored into its own function.
  # New additions from the base script can be found quickly by
  # looking for the # New Code # tags
  #
  # To run it in each of these various modes, follow the instructions
  # in the readme for examples:
  # https://github.com/huggingface/accelerate/tree/main/examples
  #
  ########################################################################
  
  UpperCAmelCase      :						str    =		16
  UpperCAmelCase      :						Union[str, Any]    =		32
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	Accelerator     ,       lowerCamelCase__		:	int = 16			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     AutoTokenizer.from_pretrained("""bert-base-cased"""			)
  			lowerCamelCase								=     load_dataset("""glue"""     ,       """mrpc"""			)
  
  			def tokenize_function(lowerCamelCase__		:	List[str]			):
  						# max_length=None => use the model max length (it's actually the default)
  						lowerCamelCase								=     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():
  						lowerCamelCase								=     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
  			lowerCamelCase								=     tokenized_datasets.rename_column("""label"""     ,       """labels"""			)
  
  			def collate_fn(lowerCamelCase__		:	Optional[Any]			):
  						# On TPU it's best to pad everything to the same length or training will be very slow.
  						lowerCamelCase								=     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":
  									lowerCamelCase								=     16
  						elif accelerator.mixed_precision != "no":
  									lowerCamelCase								=     8
  						else:
  									lowerCamelCase								=     None
  
  						return tokenizer.pad(
  						    lowerCamelCase__     ,       padding="""longest"""     ,       max_length=lowerCamelCase__     ,       pad_to_multiple_of=lowerCamelCase__     ,       return_tensors="""pt"""     ,       )
  
  			# Instantiate dataloaders.
  			lowerCamelCase								=     DataLoader(
  			    tokenized_datasets["""train"""]     ,       shuffle=lowerCamelCase__     ,       collate_fn=lowerCamelCase__     ,       batch_size=lowerCamelCase__			)
  			lowerCamelCase								=     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
  
  		UpperCAmelCase      :						Tuple    =		mocked_dataloaders  # noqa: F811
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	List[str]     ,       lowerCamelCase__		:	Optional[int]			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			if os.environ.get("""TESTING_MOCKED_DATALOADERS"""     ,       lowerCamelCase__			) == "1":
  						lowerCamelCase								=     2
  			# Initialize accelerator
  			lowerCamelCase								=     Accelerator(cpu=args.cpu     ,       mixed_precision=args.mixed_precision			)
  			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  			lowerCamelCase								=     config["""lr"""]
  			lowerCamelCase								=     int(config["""num_epochs"""]			)
  			lowerCamelCase								=     int(config["""seed"""]			)
  			lowerCamelCase								=     int(config["""batch_size"""]			)
  
  			# New Code #
  			# Parse out whether we are saving every epoch or after a certain number of batches
  			if hasattr(args.checkpointing_steps     ,       """isdigit"""			):
  						if args.checkpointing_steps == "epoch":
  									lowerCamelCase								=     args.checkpointing_steps
  						elif args.checkpointing_steps.isdigit():
  									lowerCamelCase								=     int(args.checkpointing_steps			)
  						else:
  									raise ValueError(
  									    f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'			)
  			else:
  						lowerCamelCase								=     None
  
  			set_seed(lowerCamelCase__			)
  
  			lowerCamelCase		,		lowerCamelCase								=     get_dataloaders(lowerCamelCase__     ,       lowerCamelCase__			)
  			lowerCamelCase								=     evaluate.load("""glue"""     ,       """mrpc"""			)
  
  			# If the batch size is too big we use gradient accumulation
  			lowerCamelCase								=     1
  			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  						lowerCamelCase								=     batch_size // MAX_GPU_BATCH_SIZE
  						lowerCamelCase								=     MAX_GPU_BATCH_SIZE
  
  			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  			lowerCamelCase								=     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).
  			lowerCamelCase								=     model.to(accelerator.device			)
  
  			# Instantiate optimizer
  			lowerCamelCase								=     AdamW(params=model.parameters()     ,       lr=lowerCamelCase__			)
  
  			# Instantiate scheduler
  			lowerCamelCase								=     get_linear_schedule_with_warmup(
  			    optimizer=lowerCamelCase__     ,       num_warmup_steps=100     ,       num_training_steps=(len(lowerCamelCase__			) * num_epochs) // gradient_accumulation_steps     ,       )
  
  			# 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.
  			lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase								=     accelerator.prepare(
  			    lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__			)
  
  			# New Code #
  			# We need to keep track of how many total steps we have iterated over
  			lowerCamelCase								=     0
  			# We also need to keep track of the stating epoch so files are named properly
  			lowerCamelCase								=     0
  
  			# We need to load the checkpoint back in before training here with `load_state`
  			# The total number of epochs is adjusted based on where the state is being loaded from,
  			# as we assume continuation of the same training script
  			if args.resume_from_checkpoint:
  						if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
  									accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}'			)
  									accelerator.load_state(args.resume_from_checkpoint			)
  									lowerCamelCase								=     os.path.basename(args.resume_from_checkpoint			)
  						else:
  									# Get the most recent checkpoint
  									lowerCamelCase								=     [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
  									dirs.sort(key=os.path.getctime			)
  									lowerCamelCase								=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  						# Extract `epoch_{i}` or `step_{i}`
  						lowerCamelCase								=     os.path.splitext(lowerCamelCase__			)[0]
  
  						if "epoch" in training_difference:
  									lowerCamelCase								=     int(training_difference.replace("""epoch_"""     ,       """"""			)			) + 1
  									lowerCamelCase								=     None
  						else:
  									lowerCamelCase								=     int(training_difference.replace("""step_"""     ,       """"""			)			)
  									lowerCamelCase								=     resume_step // len(lowerCamelCase__			)
  									resume_step -= starting_epoch * len(lowerCamelCase__			)
  
      # Now we train the model
  			for epoch in range(lowerCamelCase__     ,       lowerCamelCase__			):
  						model.train()
  						# New Code #
  						if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
  									# We need to skip steps until we reach the resumed step
  									lowerCamelCase								=     accelerator.skip_first_batches(lowerCamelCase__     ,       lowerCamelCase__			)
  									overall_step += resume_step
  						else:
  									# After the first iteration though, we need to go back to the original dataloader
  									lowerCamelCase								=     train_dataloader
  						for step, batch in enumerate(lowerCamelCase__			):
  									# We could avoid this line since we set the accelerator with `device_placement=True`.
  									batch.to(accelerator.device			)
  									lowerCamelCase								=     model(**lowerCamelCase__			)
  									lowerCamelCase								=     outputs.loss
  									lowerCamelCase								=     loss / gradient_accumulation_steps
  									accelerator.backward(lowerCamelCase__			)
  									if step % gradient_accumulation_steps == 0:
  												optimizer.step()
  												lr_scheduler.step()
  												optimizer.zero_grad()
  									# New Code #
  									overall_step += 1
  
  									# New Code #
  									# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
  									# These are saved to folders named `step_{overall_step}`
  									# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
  									# If mixed precision was used, will also save a "scalar.bin" file
  									if isinstance(lowerCamelCase__     ,       lowerCamelCase__			):
  												lowerCamelCase								=     f'step_{overall_step}'
  												if overall_step % checkpointing_steps == 0:
  															if args.output_dir is not None:
  																		lowerCamelCase								=     os.path.join(args.output_dir     ,       lowerCamelCase__			)
  															accelerator.save_state(lowerCamelCase__			)
  
  						model.eval()
  						for step, batch in enumerate(lowerCamelCase__			):
  									# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
  									batch.to(accelerator.device			)
  									with torch.no_grad():
  												lowerCamelCase								=     model(**lowerCamelCase__			)
  									lowerCamelCase								=     outputs.logits.argmax(dim=-1			)
  									lowerCamelCase		,		lowerCamelCase								=     accelerator.gather_for_metrics((predictions, batch["""labels"""])			)
  									metric.add_batch(
  									    predictions=lowerCamelCase__     ,       references=lowerCamelCase__     ,       )
  
  						lowerCamelCase								=     metric.compute()
  						# Use accelerator.print to print only on the main process.
  						accelerator.print(f'epoch {epoch}:'     ,       lowerCamelCase__			)
  
  						# New Code #
  						# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
  						# These are saved to folders named `epoch_{epoch}`
  						# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
  						# If mixed precision was used, will also save a "scalar.bin" file
  						if checkpointing_steps == "epoch":
  									lowerCamelCase								=     f'epoch_{epoch}'
  									if args.output_dir is not None:
  												lowerCamelCase								=     os.path.join(args.output_dir     ,       lowerCamelCase__			)
  									accelerator.save_state(lowerCamelCase__			)
  
  
  
  
  
  
  def   __lowerCamelCase	(       ):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     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."""			)
  			parser.add_argument(
  			    """--checkpointing_steps"""     ,       type=lowerCamelCase__     ,       default=lowerCamelCase__     ,       help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""     ,       )
  			parser.add_argument(
  			    """--output_dir"""     ,       type=lowerCamelCase__     ,       default="""."""     ,       help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""     ,       )
  			parser.add_argument(
  			    """--resume_from_checkpoint"""     ,       type=lowerCamelCase__     ,       default=lowerCamelCase__     ,       help="""If the training should continue from a checkpoint folder."""     ,       )
  			lowerCamelCase								=     parser.parse_args()
  			lowerCamelCase								=     {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  			training_function(lowerCamelCase__     ,       lowerCamelCase__			)
  
  
  if __name__ == "__main__":
  		main()
  
  