


'''simple docstring'''



import inspect
import unittest

from transformers import MobileViTConfig
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 MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
	from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import MobileViTImageProcessor






class  UpperCamelCase__(     lowerCAmelCase ):




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

										"""simple docstring"""




										UpperCAmelCase         =			self.config_class(**self.inputs_dict   )
										self.parent.assertTrue(hasattr(lowerCAmelCase   ,					'''hidden_sizes'''   )   )
										self.parent.assertTrue(hasattr(lowerCAmelCase   ,					'''neck_hidden_sizes'''   )   )
										self.parent.assertTrue(hasattr(lowerCAmelCase   ,					'''num_attention_heads'''   )   )







class  UpperCamelCase__:




				def __init__(	self    :				str   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				List[Any]=13   ,					lowerCAmelCase    :				List[Any]=32   ,					lowerCAmelCase    :				Optional[int]=2   ,					lowerCAmelCase    :				Union[str, Any]=3   ,					lowerCAmelCase    :				str=640   ,					lowerCAmelCase    :				Optional[Any]=4   ,					lowerCAmelCase    :				Optional[int]="silu"   ,					lowerCAmelCase    :				Optional[int]=3   ,					lowerCAmelCase    :				Any=32   ,					lowerCAmelCase    :				Optional[Any]=0.1   ,					lowerCAmelCase    :				Tuple=0.1   ,					lowerCAmelCase    :				str=0.1   ,					lowerCAmelCase    :				Union[str, Any]=0.02   ,					lowerCAmelCase    :				Any=True   ,					lowerCAmelCase    :				List[Any]=True   ,					lowerCAmelCase    :				Dict=10   ,					lowerCAmelCase    :				List[str]=None   ,					)->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			parent
										UpperCAmelCase         =			batch_size
										UpperCAmelCase         =			image_size
										UpperCAmelCase         =			patch_size
										UpperCAmelCase         =			num_channels
										UpperCAmelCase         =			last_hidden_size
										UpperCAmelCase         =			num_attention_heads
										UpperCAmelCase         =			hidden_act
										UpperCAmelCase         =			conv_kernel_size
										UpperCAmelCase         =			output_stride
										UpperCAmelCase         =			hidden_dropout_prob
										UpperCAmelCase         =			attention_probs_dropout_prob
										UpperCAmelCase         =			classifier_dropout_prob
										UpperCAmelCase         =			use_labels
										UpperCAmelCase         =			is_training
										UpperCAmelCase         =			num_labels
										UpperCAmelCase         =			initializer_range
										UpperCAmelCase         =			scope




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

										"""simple docstring"""




										UpperCAmelCase         =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

										UpperCAmelCase         =			None
										UpperCAmelCase         =			None
										if self.use_labels:
																UpperCAmelCase         =			ids_tensor([self.batch_size]   ,					self.num_labels   )
																UpperCAmelCase         =			ids_tensor([self.batch_size, self.image_size, self.image_size]   ,					self.num_labels   )

										UpperCAmelCase         =			self.get_config()

										return config, pixel_values, labels, pixel_labels




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

										"""simple docstring"""




										return MobileViTConfig(
										    image_size=self.image_size   ,					patch_size=self.patch_size   ,					num_channels=self.num_channels   ,					num_attention_heads=self.num_attention_heads   ,					hidden_act=self.hidden_act   ,					conv_kernel_size=self.conv_kernel_size   ,					output_stride=self.output_stride   ,					hidden_dropout_prob=self.hidden_dropout_prob   ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,					classifier_dropout_prob=self.classifier_dropout_prob   ,					initializer_range=self.initializer_range   ,					)




				def   a__(	self    :				Tuple   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				int   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				Union[str, Any]   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			MobileViTModel(config=lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			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,
										    )   ,					)




				def   a__(	self    :				str   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				int   ,					lowerCAmelCase    :				Any   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.num_labels
										UpperCAmelCase         =			MobileViTForImageClassification(lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   ,					labels=lowerCAmelCase   )
										self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.num_labels)   )




				def   a__(	self    :				Optional[int]   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				Dict   ,					lowerCAmelCase    :				int   ,					lowerCAmelCase    :				Any   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			self.num_labels
										UpperCAmelCase         =			MobileViTForSemanticSegmentation(lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			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,
										    )   ,					)
										UpperCAmelCase         =			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    :				str   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			self.prepare_config_and_inputs()
										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			config_and_inputs
										UpperCAmelCase         =			{'''pixel_values''': pixel_values}
										return config, inputs_dict







@require_torch
class  UpperCamelCase__(     lowerCAmelCase		,       lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   List[Any]							= (
				    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
				    if is_torch_available()
				    else ()
				)
				__magic_name__       :   List[str]							= (
				    {
				        "feature-extraction": MobileViTModel,
				        "image-classification": MobileViTForImageClassification,
				        "image-segmentation": MobileViTForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				__magic_name__       :   str							= False
				__magic_name__       :   int							= False
				__magic_name__       :   Any							= False
				__magic_name__       :   Optional[int]							= False




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

										"""simple docstring"""




										UpperCAmelCase         =			MobileViTModelTester(self   )
										UpperCAmelCase         =			MobileViTConfigTester(self   ,					config_class=lowerCAmelCase   ,					has_text_modality=lowerCAmelCase   )




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

										"""simple docstring"""




										self.config_tester.run_common_tests()




				@unittest.skip(reason='''MobileViT does not use inputs_embeds'''   )
				def   a__(	self    :				List[str]   )->  Tuple:

										"""simple docstring"""




										pass




				@unittest.skip(reason='''MobileViT does not support input and output embeddings'''   )
				def   a__(	self    :				Dict   )->  Dict:

										"""simple docstring"""




										pass




				@unittest.skip(reason='''MobileViT does not output attentions'''   )
				def   a__(	self    :				List[Any]   )->  Union[str, Any]:

										"""simple docstring"""




										pass




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

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																UpperCAmelCase         =			model_class(lowerCAmelCase   )
																UpperCAmelCase         =			inspect.signature(model.forward   )
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																UpperCAmelCase         =			[*signature.parameters.keys()]

																UpperCAmelCase         =			['''pixel_values''']
																self.assertListEqual(arg_names[:1]   ,					lowerCAmelCase   )




				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''   )
				def   a__(	self    :				List[str]   )->  Tuple:

										"""simple docstring"""




										pass




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

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase   )




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

										"""simple docstring"""




										def check_hidden_states_output(lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				Union[str, Any]   ):
																UpperCAmelCase         =			model_class(lowerCAmelCase   )
																model.to(lowerCAmelCase   )
																model.eval()

																with torch.no_grad():
																						UpperCAmelCase         =			model(**self._prepare_for_class(lowerCAmelCase   ,					lowerCAmelCase   )   )

																UpperCAmelCase         =			outputs.hidden_states

																UpperCAmelCase         =			5
																self.assertEqual(len(lowerCAmelCase   )   ,					lowerCAmelCase   )

																# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
																# with the width and height being successively divided by 2.
																UpperCAmelCase         =			2
																for i in range(len(lowerCAmelCase   )   ):
																						self.assertListEqual(
																						    list(hidden_states[i].shape[-2:]   )   ,					[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]   ,					)
																						divisor *= 2

																self.assertEqual(self.model_tester.output_stride   ,					divisor // 2   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																UpperCAmelCase         =			True
																check_hidden_states_output(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																UpperCAmelCase         =			True

																check_hidden_states_output(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )




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

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase   )




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

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase   )





				@slow
				def   a__(	self    :				Any   )->  Tuple:

										"""simple docstring"""




										for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																UpperCAmelCase         =			MobileViTModel.from_pretrained(lowerCAmelCase   )
																self.assertIsNotNone(lowerCAmelCase   )







def    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
						return image






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




				@cached_property
				def   a__(	self    :				str   )->  int:

										"""simple docstring"""




										return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small'''   ) if is_vision_available() else None




				@slow
				def   a__(	self    :				Union[str, Any]   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small'''   ).to(lowerCAmelCase   )

										UpperCAmelCase         =			self.default_image_processor
										UpperCAmelCase         =			prepare_img()
										UpperCAmelCase         =			image_processor(images=lowerCAmelCase   ,					return_tensors='''pt'''   ).to(lowerCAmelCase   )

										# forward pass
										with torch.no_grad():
																UpperCAmelCase         =			model(**lowerCAmelCase   )

										# verify the logits
										UpperCAmelCase         =			torch.Size((1, 1000)   )
										self.assertEqual(outputs.logits.shape   ,					lowerCAmelCase   )

										UpperCAmelCase         =			torch.tensor([-1.9364, -1.2327, -0.4653]   ).to(lowerCAmelCase   )

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




				@slow
				def   a__(	self    :				Optional[int]   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''   )
										UpperCAmelCase         =			model.to(lowerCAmelCase   )

										UpperCAmelCase         =			MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''   )

										UpperCAmelCase         =			prepare_img()
										UpperCAmelCase         =			image_processor(images=lowerCAmelCase   ,					return_tensors='''pt'''   ).to(lowerCAmelCase   )

										# forward pass
										with torch.no_grad():
																UpperCAmelCase         =			model(**lowerCAmelCase   )
										UpperCAmelCase         =			outputs.logits

										# verify the logits
										UpperCAmelCase         =			torch.Size((1, 21, 32, 32)   )
										self.assertEqual(logits.shape   ,					lowerCAmelCase   )

										UpperCAmelCase         =			torch.tensor(
										    [
										        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
										        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
										        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
										    ]   ,					device=lowerCAmelCase   ,					)

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





				@slow
				def   a__(	self    :				Dict   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''   )
										UpperCAmelCase         =			model.to(lowerCAmelCase   )

										UpperCAmelCase         =			MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''   )

										UpperCAmelCase         =			prepare_img()
										UpperCAmelCase         =			image_processor(images=lowerCAmelCase   ,					return_tensors='''pt'''   ).to(lowerCAmelCase   )

										# forward pass
										with torch.no_grad():
																UpperCAmelCase         =			model(**lowerCAmelCase   )

										UpperCAmelCase         =			outputs.logits.detach().cpu()

										UpperCAmelCase         =			image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase   ,					target_sizes=[(50, 60)]   )
										UpperCAmelCase         =			torch.Size((50, 60)   )
										self.assertEqual(segmentation[0].shape   ,					lowerCAmelCase   )

										UpperCAmelCase         =			image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase   )
										UpperCAmelCase         =			torch.Size((32, 32)   )
										self.assertEqual(segmentation[0].shape   ,					lowerCAmelCase   )









'''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,
# specifically showcasing the experiment tracking 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
#
########################################################################

_lowercase    :    Optional[Any]			     =  16
_lowercase    :    Dict			     =  32


def    lowerCamelCase__   ( A     :    Accelerator   ,     A     :    int = 16  ):




						'''simple docstring'''






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

						def tokenize_function(A     :    int  ):
												# max_length=None => use the model max length (it's actually the default)
												UpperCAmelCase         =			tokenizer(examples['''sentence1''']   ,     examples['''sentence2''']   ,     truncation=A   ,     max_length=A  )
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												UpperCAmelCase         =			datasets.map(
												    A   ,     batched=A   ,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']   ,     )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						UpperCAmelCase         =			tokenized_datasets.rename_column('''label'''   ,     '''labels'''  )

						def collate_fn(A     :    List[Any]  ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												UpperCAmelCase         =			1_28 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		UpperCAmelCase         =			16
												elif accelerator.mixed_precision != "no":
																		UpperCAmelCase         =			8
												else:
																		UpperCAmelCase         =			None

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

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

						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

	_lowercase    :    Union[str, Any]			     =  mocked_dataloaders  # noqa: F811


def    lowerCamelCase__   ( A     :    Optional[Any]   ,     A     :    Tuple  ):




						'''simple docstring'''






						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,     A  ) == "1":
												UpperCAmelCase         =			2
						# Initialize Accelerator

						# New Code #
						# We pass in "all" to `log_with` to grab all available trackers in the environment
						# Note: If using a custom `Tracker` class, should be passed in here such as:
						# >>> log_with = ["all", MyCustomTrackerClassInstance()]
						if args.with_tracking:
												UpperCAmelCase         =			Accelerator(
												    cpu=args.cpu   ,     mixed_precision=args.mixed_precision   ,     log_with='''all'''   ,     project_dir=args.project_dir  )
						else:
												UpperCAmelCase         =			Accelerator(cpu=args.cpu   ,     mixed_precision=args.mixed_precision  )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						UpperCAmelCase         =			config['''lr''']
						UpperCAmelCase         =			int(config['''num_epochs''']  )
						UpperCAmelCase         =			int(config['''seed''']  )
						UpperCAmelCase         =			int(config['''batch_size''']  )
						set_seed(A  )

						UpperCAmelCase       ,       UpperCAmelCase         =			get_dataloaders(A   ,     A  )
						UpperCAmelCase         =			evaluate.load('''glue'''   ,     '''mrpc'''  )

						# If the batch size is too big we use gradient accumulation
						UpperCAmelCase         =			1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												UpperCAmelCase         =			batch_size // MAX_GPU_BATCH_SIZE
												UpperCAmelCase         =			MAX_GPU_BATCH_SIZE

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

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						UpperCAmelCase         =			model.to(accelerator.device  )

						# Instantiate optimizer
						UpperCAmelCase         =			AdamW(params=model.parameters()   ,     lr=A  )

						# Instantiate scheduler
						UpperCAmelCase         =			get_linear_schedule_with_warmup(
						    optimizer=A   ,     num_warmup_steps=1_00   ,     num_training_steps=(len(A  ) * 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.
						UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			accelerator.prepare(
						    A   ,     A   ,     A   ,     A   ,     A  )

						# New Code #
						# We need to initialize the trackers we use. Overall configurations can also be stored
						if args.with_tracking:
												UpperCAmelCase         =			os.path.split(A  )[-1].split('''.'''  )[0]
												accelerator.init_trackers(A   ,     A  )

						# Now we train the model
						for epoch in range(A  ):
												model.train()
												# New Code #
												# For our tracking example, we will log the total loss of each epoch
												if args.with_tracking:
																		UpperCAmelCase         =			0
												for step, batch in enumerate(A  ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device  )
																		UpperCAmelCase         =			model(**A  )
																		UpperCAmelCase         =			outputs.loss
																		# New Code #
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		UpperCAmelCase         =			loss / gradient_accumulation_steps
																		accelerator.backward(A  )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

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

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

												# New Code #
												# To actually log, we call `Accelerator.log`
												# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        '''accuracy''': eval_metric['''accuracy'''],
																		        '''f1''': eval_metric['''f1'''],
																		        '''train_loss''': total_loss.item() / len(A  ),
																		        '''epoch''': epoch,
																		    }   ,     step=A   ,     )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
						if args.with_tracking:
												accelerator.end_training()


def    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			argparse.ArgumentParser(description='''Simple example of training script.'''  )
						parser.add_argument(
						    '''--mixed_precision'''   ,     type=A   ,     default=A   ,     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(
						    '''--with_tracking'''   ,     action='''store_true'''   ,     help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''   ,     )
						parser.add_argument(
						    '''--project_dir'''   ,     type=A   ,     default='''logs'''   ,     help='''Location on where to store experiment tracking logs` and relevent project information'''   ,     )
						UpperCAmelCase         =			parser.parse_args()
						UpperCAmelCase         =			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(A   ,     A  )


if __name__ == "__main__":
	main()





