




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
		import jax
		import jax.numpy as jnp

		from transformers.modeling_flax_pytorch_utils import (
		    convert_pytorch_state_dict_to_flax,
		    load_flax_weights_in_pytorch_model,
		)
		from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
		import torch




class    snake_case__							:
				"""simple docstring"""



				def __init__(				self    :					int ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					Optional[int]=14 ,				__lowerCamelCase    :					Any=7 ,				__lowerCamelCase    :					List[str]=True ,				__lowerCamelCase    :					str=True ,				__lowerCamelCase    :					Optional[Any]=False ,				__lowerCamelCase    :					int=True ,				__lowerCamelCase    :					str=99 ,				__lowerCamelCase    :					str=32 ,				__lowerCamelCase    :					Tuple=4 ,				__lowerCamelCase    :					Any=4 ,				__lowerCamelCase    :					int=[[["global", "local"], 2]] ,				__lowerCamelCase    :					str=37 ,				__lowerCamelCase    :					int="gelu" ,				__lowerCamelCase    :					Tuple=0.1 ,				__lowerCamelCase    :					int=0.1 ,				__lowerCamelCase    :					Union[str, Any]=5_12 ,				__lowerCamelCase    :					Any=7 ,				__lowerCamelCase    :					Any=0.02 ,				) ->					Optional[Any]:
							a   =      parent
							a   =      batch_size
							a   =      seq_length
							a   =      is_training
							a   =      use_input_mask
							a   =      use_token_type_ids
							a   =      use_labels
							a   =      vocab_size
							a   =      hidden_size
							a   =      num_hidden_layers
							a   =      num_attention_heads
							a   =      attention_types
							a   =      intermediate_size
							a   =      hidden_act
							a   =      hidden_dropout_prob
							a   =      attention_probs_dropout_prob
							a   =      max_position_embeddings
							a   =      window_size
							a   =      initializer_range
							a   =      None
							a   =      vocab_size - 1
							a   =      vocab_size - 1
							a   =      vocab_size - 1





				def 							__UpperCAmelCase							(				self    :					Any							) ->					Union[str, Any]:
							a   =      ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size							)

							a   =      None
							if self.use_input_mask:
										a   =      random_attention_mask([self.batch_size, self.seq_length]							)

							a   =      GPTNeoConfig(
							    vocab_size=self.vocab_size ,				hidden_size=self.hidden_size ,				num_layers=self.num_hidden_layers ,				num_heads=self.num_attention_heads ,				max_position_embeddings=self.max_position_embeddings ,				use_cache=__lowerCamelCase ,				bos_token_id=self.bos_token_id ,				eos_token_id=self.eos_token_id ,				pad_token_id=self.pad_token_id ,				window_size=self.window_size ,				attention_types=self.attention_types ,				)

							return (config, input_ids, input_mask)





				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Union[str, Any]:
							a   =      self.prepare_config_and_inputs()
							a   ,		a   ,		a   =      config_and_inputs
							a   =      {"input_ids": input_ids, "attention_mask": attention_mask}
							return config, inputs_dict





				def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					Any							) ->					str:
							a   =      20
							a   =      model_class_name(__lowerCamelCase							)

							a   =      model.init_cache(input_ids.shape[0] ,				__lowerCamelCase							)
							a   =      jnp.ones((input_ids.shape[0], max_decoder_length) ,				dtype="i4"							)

							a   =      jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1							)[None, :] ,				(input_ids.shape[0], input_ids.shape[-1] - 1)							)
							a   =      model(
							    input_ids[:, :-1] ,				attention_mask=__lowerCamelCase ,				past_key_values=__lowerCamelCase ,				position_ids=__lowerCamelCase ,				)

							a   =      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,				dtype="i4"							)
							a   =      model(
							    input_ids[:, -1:] ,				attention_mask=__lowerCamelCase ,				past_key_values=outputs_cache.past_key_values ,				position_ids=__lowerCamelCase ,				)

							a   =      model(__lowerCamelCase							)

							a   =      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
							self.parent.assertTrue(diff < 1e-3 ,				msg=f"""Max diff is {diff}"""							)





				def 							__UpperCAmelCase							(				self    :					List[str] ,				__lowerCamelCase    :					Optional[int] ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Tuple							) ->					List[str]:
							a   =      20
							a   =      model_class_name(__lowerCamelCase							)

							a   =      jnp.concatenate(
							    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])							)] ,				axis=-1 ,				)

							a   =      model.init_cache(input_ids.shape[0] ,				__lowerCamelCase							)
							a   =      jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1							)[None, :] ,				(input_ids.shape[0], input_ids.shape[-1] - 1)							)

							a   =      model(
							    input_ids[:, :-1] ,				attention_mask=__lowerCamelCase ,				past_key_values=__lowerCamelCase ,				position_ids=__lowerCamelCase ,				)
							a   =      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,				dtype="i4"							)
							a   =      model(
							    input_ids[:, -1:] ,				past_key_values=outputs_cache.past_key_values ,				attention_mask=__lowerCamelCase ,				position_ids=__lowerCamelCase ,				)

							a   =      model(__lowerCamelCase ,				attention_mask=__lowerCamelCase							)

							a   =      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
							self.parent.assertTrue(diff < 1e-3 ,				msg=f"""Max diff is {diff}"""							)




@require_flax
class    snake_case__							(_UpperCamelCase   ,	_UpperCamelCase   ,	unittest.TestCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[str]										= (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
				SCREAMING_SNAKE_CASE_	:							Tuple										= (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Tuple:
							a   =      FlaxGPTNeoModelTester(self							)





				def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					Any:
							for model_class_name in self.all_model_classes:
										a   ,		a   ,		a   =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.check_use_cache_forward(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Optional[Any]:
							for model_class_name in self.all_model_classes:
										a   ,		a   ,		a   =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.check_use_cache_forward_with_attn_mask(
										    __lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase							)





				@slow
				def 							__UpperCAmelCase							(				self    :					str							) ->					Union[str, Any]:
							a   =      GPTaTokenizer.from_pretrained("gpt2" ,				pad_token="<|endoftext|>" ,				padding_side="left"							)
							a   =      tokenizer(["Hello this is a long string", "Hey"] ,				return_tensors="np" ,				padding=__lowerCamelCase ,				truncation=__lowerCamelCase							)

							a   =      FlaxGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M"							)
							a   =      False
							a   =      model.config.eos_token_id

							a   =      jax.jit(model.generate							)

							a   =      jit_generate(
							    inputs["input_ids"] ,				attention_mask=inputs["attention_mask"] ,				pad_token_id=tokenizer.pad_token_id							).sequences

							a   =      tokenizer.batch_decode(__lowerCamelCase ,				skip_special_tokens=__lowerCamelCase							)

							a   =      [
							    "Hello this is a long string of text.\n\nI'm trying to get the text of the",
							    "Hey, I'm a little late to the party. I'm going to",
							]

							self.assertListEqual(__lowerCamelCase ,				__lowerCamelCase							)





				@is_pt_flax_cross_test
				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Optional[Any]:
							a   ,		a   =      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__							):
													# prepare inputs
													a   =      self._prepare_for_class(__lowerCamelCase ,				__lowerCamelCase							)
													a   =      {k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

													# load corresponding PyTorch class
													a   =      model_class.__name__[4:]  # Skip the "Flax" at the beginning
													a   =      getattr(__lowerCamelCase ,				__lowerCamelCase							)

													a   ,		a   =      pt_inputs["input_ids"].shape
													a   =      np.random.randint(0 ,				seq_length - 1 ,				size=(batch_size,)							)
													for batch_idx, start_index in enumerate(__lowerCamelCase							):
																a   =      0
																a   =      1
																a   =      0
																a   =      1
													a   =      pt_model_class(__lowerCamelCase							).eval()
													a   =      model_class(__lowerCamelCase ,				dtype=jnp.floataa							)

													a   =      convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,				__lowerCamelCase							)
													a   =      fx_state

													with torch.no_grad():
																a   =      pt_model(**__lowerCamelCase							).to_tuple()

													a   =      fx_model(**__lowerCamelCase							).to_tuple()
													self.assertEqual(len(__lowerCamelCase							) ,				len(__lowerCamelCase							) ,				"Output lengths differ between Flax and PyTorch"							)
													for fx_output, pt_output in zip(__lowerCamelCase ,				__lowerCamelCase							):
																self.assert_almost_equals(fx_output[:, -1] ,				pt_output[:, -1].numpy() ,				4e-2							)

													with tempfile.TemporaryDirectory() as tmpdirname:
																pt_model.save_pretrained(__lowerCamelCase							)
																a   =      model_class.from_pretrained(__lowerCamelCase ,				from_pt=__lowerCamelCase							)

													a   =      fx_model_loaded(**__lowerCamelCase							).to_tuple()
													self.assertEqual(
													    len(__lowerCamelCase							) ,				len(__lowerCamelCase							) ,				"Output lengths differ between Flax and PyTorch"							)
													for fx_output_loaded, pt_output in zip(__lowerCamelCase ,				__lowerCamelCase							):
																self.assert_almost_equals(fx_output_loaded[:, -1] ,				pt_output[:, -1].numpy() ,				4e-2							)





				@is_pt_flax_cross_test
				def 							__UpperCAmelCase							(				self    :					int							) ->					Any:
							a   ,		a   =      self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__							):
													# prepare inputs
													a   =      self._prepare_for_class(__lowerCamelCase ,				__lowerCamelCase							)
													a   =      {k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

													# load corresponding PyTorch class
													a   =      model_class.__name__[4:]  # Skip the "Flax" at the beginning
													a   =      getattr(__lowerCamelCase ,				__lowerCamelCase							)

													a   =      pt_model_class(__lowerCamelCase							).eval()
													a   =      model_class(__lowerCamelCase ,				dtype=jnp.floataa							)

													a   =      load_flax_weights_in_pytorch_model(__lowerCamelCase ,				fx_model.params							)
													a   ,		a   =      pt_inputs["input_ids"].shape
													a   =      np.random.randint(0 ,				seq_length - 1 ,				size=(batch_size,)							)
													for batch_idx, start_index in enumerate(__lowerCamelCase							):
																a   =      0
																a   =      1
																a   =      0
																a   =      1

													# make sure weights are tied in PyTorch
													pt_model.tie_weights()

													with torch.no_grad():
																a   =      pt_model(**__lowerCamelCase							).to_tuple()

													a   =      fx_model(**__lowerCamelCase							).to_tuple()
													self.assertEqual(len(__lowerCamelCase							) ,				len(__lowerCamelCase							) ,				"Output lengths differ between Flax and PyTorch"							)
													for fx_output, pt_output in zip(__lowerCamelCase ,				__lowerCamelCase							):
																self.assert_almost_equals(fx_output[:, -1] ,				pt_output[:, -1].numpy() ,				4e-2							)

													with tempfile.TemporaryDirectory() as tmpdirname:
																fx_model.save_pretrained(__lowerCamelCase							)
																a   =      pt_model_class.from_pretrained(__lowerCamelCase ,				from_flax=__lowerCamelCase							)

													with torch.no_grad():
																a   =      pt_model_loaded(**__lowerCamelCase							).to_tuple()

													self.assertEqual(
													    len(__lowerCamelCase							) ,				len(__lowerCamelCase							) ,				"Output lengths differ between Flax and PyTorch"							)
													for fx_output, pt_output in zip(__lowerCamelCase ,				__lowerCamelCase							):
																self.assert_almost_equals(fx_output[:, -1] ,				pt_output[:, -1].numpy() ,				4e-2							)





				@slow
				def 							__UpperCAmelCase							(				self    :					Tuple							) ->					str:
							for model_class_name in self.all_model_classes:
										a   =      model_class_name.from_pretrained("EleutherAI/gpt-neo-125M"							)
										a   =      model(np.ones((1, 1)							)							)
										self.assertIsNotNone(__lowerCamelCase							)












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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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		:    List[Any]	      =		16
__lowerCAmelCase		:    List[Any]	      =		32




def       __magic_name__    (			A		: List[Any],  A		: List[Any]       ):






			'''simple docstring'''







			if args.with_tracking:
						a   =      Accelerator(
						    cpu=args.cpu,  mixed_precision=args.mixed_precision,  log_with="all",  project_dir=args.project_dir       )
			else:
						a   =      Accelerator(cpu=args.cpu,  mixed_precision=args.mixed_precision       )

			if hasattr(args.checkpointing_steps,  "isdigit"       ):
						if args.checkpointing_steps == "epoch":
									a   =      args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									a   =      int(args.checkpointing_steps       )
						else:
									raise ValueError(
									    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""       )
			else:
						a   =      None
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			a   =      config["lr"]
			a   =      int(config["num_epochs"]       )
			a   =      int(config["seed"]       )
			a   =      int(config["batch_size"]       )

			# We need to initialize the trackers we use, and also store our configuration
			if args.with_tracking:
						a   =      os.path.split(A       )[-1].split("."       )[0]
						accelerator.init_trackers(A,  A       )

			a   =      AutoTokenizer.from_pretrained("bert-base-cased"       )
			a   =      load_dataset("glue",  "mrpc"       )
			a   =      evaluate.load("glue",  "mrpc"       )

			def tokenize_function(A		: Tuple       ):
						# max_length=None => use the model max length (it's actually the default)
						a   =      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():
						a   =      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
			a   =      tokenized_datasets.rename_column("label",  "labels"       )

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

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

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

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

			set_seed(A       )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			a   =      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).
			a   =      model.to(accelerator.device       )

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

			# Instantiate scheduler
			a   =      get_linear_schedule_with_warmup(
			    optimizer=A,  num_warmup_steps=100,  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.
			a   ,		a   ,		a   ,		a   ,		a   =      accelerator.prepare(
			    A,  A,  A,  A,  A       )

			# We need to keep track of how many total steps we have iterated over
			a   =      0
			# We also need to keep track of the stating epoch so files are named properly
			a   =      0

			# Potentially load in the weights and states from a previous save
			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       )
									a   =      os.path.basename(args.resume_from_checkpoint       )
						else:
									# Get the most recent checkpoint
									a   =      [f.name for f in os.scandir(os.getcwd()       ) if f.is_dir()]
									dirs.sort(key=os.path.getctime       )
									a   =      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						a   =      os.path.splitext(A       )[0]

						if "epoch" in training_difference:
									a   =      int(training_difference.replace("epoch_",  ""       )       ) + 1
									a   =      None
						else:
									a   =      int(training_difference.replace("step_",  ""       )       )
									a   =      resume_step // len(A       )
									resume_step -= starting_epoch * len(A       )

    # Now we train the model
			for epoch in range(A,  A       ):
						model.train()
						if args.with_tracking:
									a   =      0
						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
									a   =      accelerator.skip_first_batches(A,  A       )
									overall_step += resume_step
						else:
									# After the first iteration though, we need to go back to the original dataloader
									a   =      train_dataloader
						for step, batch in enumerate(A       ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device       )
									a   =      model(**A       )
									a   =      outputs.loss
									a   =      loss / gradient_accumulation_steps
									# We keep track of the loss at each epoch
									if args.with_tracking:
												total_loss += loss.detach().float()
									accelerator.backward(A       )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

									if isinstance(A,  A       ):
												a   =      F"""step_{overall_step}"""
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		a   =      os.path.join(args.output_dir,  A       )
															accelerator.save_state(A       )

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

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

						if checkpointing_steps == "epoch":
									a   =      F"""epoch_{epoch}"""
									if args.output_dir is not None:
												a   =      os.path.join(args.output_dir,  A       )
									accelerator.save_state(A       )

			if args.with_tracking:
						accelerator.end_training()




def       __magic_name__    (			):






			'''simple docstring'''







			a   =      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(
			    "--checkpointing_steps",  type=A,  default=A,  help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",  )
			parser.add_argument(
			    "--resume_from_checkpoint",  type=A,  default=A,  help="If the training should continue from a checkpoint folder.",  )
			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(
			    "--output_dir",  type=A,  default=".",  help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",  )
			parser.add_argument(
			    "--project_dir",  type=A,  default="logs",  help="Location on where to store experiment tracking logs` and relevent project information",  )
			a   =      parser.parse_args()
			a   =      {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(A,  A       )


if __name__ == "__main__":
		main()






