


'''simple docstring'''



import inspect
import unittest

from transformers import BitConfig
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 BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
	from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image






class  UpperCamelCase__:




				def __init__(	self    :				Dict   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				str=3   ,					lowerCAmelCase    :				List[str]=32   ,					lowerCAmelCase    :				List[str]=3   ,					lowerCAmelCase    :				Union[str, Any]=10   ,					lowerCAmelCase    :				Optional[int]=[8, 16, 32, 64]   ,					lowerCAmelCase    :				Tuple=[1, 1, 2, 1]   ,					lowerCAmelCase    :				str=True   ,					lowerCAmelCase    :				List[str]=True   ,					lowerCAmelCase    :				Optional[int]="relu"   ,					lowerCAmelCase    :				Optional[int]=3   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Dict=["stage2", "stage3", "stage4"]   ,					lowerCAmelCase    :				List[Any]=[2, 3, 4]   ,					lowerCAmelCase    :				int=1   ,					)->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			parent
										UpperCAmelCase         =			batch_size
										UpperCAmelCase         =			image_size
										UpperCAmelCase         =			num_channels
										UpperCAmelCase         =			embeddings_size
										UpperCAmelCase         =			hidden_sizes
										UpperCAmelCase         =			depths
										UpperCAmelCase         =			is_training
										UpperCAmelCase         =			use_labels
										UpperCAmelCase         =			hidden_act
										UpperCAmelCase         =			num_labels
										UpperCAmelCase         =			scope
										UpperCAmelCase         =			len(lowerCAmelCase   )
										UpperCAmelCase         =			out_features
										UpperCAmelCase         =			out_indices
										UpperCAmelCase         =			num_groups




				def   a__(	self    :				str   )->  Tuple:

										"""simple docstring"""




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

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

										UpperCAmelCase         =			self.get_config()

										return config, pixel_values, labels




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

										"""simple docstring"""




										return BitConfig(
										    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   ,					num_groups=self.num_groups   ,					)




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

										"""simple docstring"""




										UpperCAmelCase         =			BitModel(config=lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   )
										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    :				List[str]   ,					lowerCAmelCase    :				Dict   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				Tuple   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			self.num_labels
										UpperCAmelCase         =			BitForImageClassification(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    :				int   ,					lowerCAmelCase    :				int   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				Any   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			BitBackbone(config=lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   )

										# 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
										UpperCAmelCase         =			None
										UpperCAmelCase         =			BitBackbone(config=lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   )

										# 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    :				Any   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			self.prepare_config_and_inputs()
										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__       :   Any							= (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
				__magic_name__       :   Tuple							= (
				    {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
				    if is_torch_available()
				    else {}
				)

				__magic_name__       :   Union[str, Any]							= False
				__magic_name__       :   Any							= False
				__magic_name__       :   Any							= False
				__magic_name__       :   int							= False
				__magic_name__       :   List[str]							= False




				def   a__(	self    :				Tuple   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			BitModelTester(self   )
										UpperCAmelCase         =			ConfigTester(self   ,					config_class=lowerCAmelCase   ,					has_text_modality=lowerCAmelCase   )




				def   a__(	self    :				Tuple   )->  str:

										"""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    :				Tuple   )->  Optional[int]:

										"""simple docstring"""




										return




				@unittest.skip(reason='''Bit does not output attentions'''   )
				def   a__(	self    :				int   )->  Any:

										"""simple docstring"""




										pass




				@unittest.skip(reason='''Bit does not use inputs_embeds'''   )
				def   a__(	self    :				Union[str, Any]   )->  Optional[Any]:

										"""simple docstring"""




										pass




				@unittest.skip(reason='''Bit does not support input and output embeddings'''   )
				def   a__(	self    :				List[str]   )->  Dict:

										"""simple docstring"""




										pass




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

										"""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   )




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

										"""simple docstring"""




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




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

										"""simple docstring"""




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




				def   a__(	self    :				List[str]   )->  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(config=lowerCAmelCase   )
																for name, module in model.named_modules():
																						if isinstance(lowerCAmelCase   ,					(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]   )->  str:

										"""simple docstring"""




										def check_hidden_states_output(lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				str   ):
																UpperCAmelCase         =			model_class(lowerCAmelCase   )
																model.to(lowerCAmelCase   )
																model.eval()

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

																UpperCAmelCase         =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																UpperCAmelCase         =			self.model_tester.num_stages
																self.assertEqual(len(lowerCAmelCase   )   ,					expected_num_stages + 1   )

																# Bit'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]   ,					)

										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()
										UpperCAmelCase         =			['''preactivation''', '''bottleneck''']
										for model_class in self.all_model_classes:
																for layer_type in layers_type:
																						UpperCAmelCase         =			layer_type
																						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   )




				@unittest.skip(reason='''Bit does not use feedforward chunking'''   )
				def   a__(	self    :				Optional[Any]   )->  Tuple:

										"""simple docstring"""




										pass




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

										"""simple docstring"""




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





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

										"""simple docstring"""




										for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																UpperCAmelCase         =			BitModel.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    :				List[Any]   )->  List[Any]:

										"""simple docstring"""




										return (
										    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ) if is_vision_available() else None
										)





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

										"""simple docstring"""




										UpperCAmelCase         =			BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).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([[-0.6526, -0.5263, -1.4398]]   ).to(lowerCAmelCase   )

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







@require_torch
class  UpperCamelCase__(							lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   Optional[Any]							= (BitBackbone,) if is_torch_available() else ()
				__magic_name__       :   int							= BitConfig

				__magic_name__       :   Union[str, Any]							= False




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

										"""simple docstring"""




										UpperCAmelCase         =			BitModelTester(self   )









'''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()





