



"""simple docstring"""






# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig
def      A      (			_A,     _A,     _A=0							):




					"""simple docstring"""
					# Format the message.
					if name is None:
										snake_case_						:Union[str, Any]								=						None
					else:
										snake_case_						:int								=						"." * max(0,     spaces - 2							) + "# {:" + str(50 - spaces							) + "s}"
										snake_case_						:Optional[int]								=						fmt.format(_A							)

					# Print and recurse (if needed).
					if isinstance(_A,     _A							):
										if msg is not None:
															print(_A							)
										for k in val.keys():
															recursive_print(_A,     val[k],     spaces + 2							)
					elif isinstance(_A,     torch.Tensor							):
										print(_A,     ":",     val.size()							)
					else:
										print(_A,     ":",     _A							)
def      A      (			_A,     _A,     _A,     _A,     _A							):




					"""simple docstring"""
					# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
					# for compatibility with later versions of NVIDIA Megatron-LM.
					# The inverse operation is performed inside Megatron-LM to read checkpoints:
					# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
					# If param is the weight tensor of the self-attention block, the returned tensor
					# will have to be transposed one more time to be read by HuggingFace BERT.
					snake_case_						:str								=						param.size()
					if checkpoint_version == 1.0:
										# version 1.0 stores [num_heads * hidden_size * num_splits, :]
										snake_case_						:int								=						(num_heads, hidden_size, num_splits) + input_shape[1:]
										snake_case_						:List[Any]								=						param.view(*_A							)
										snake_case_						:Optional[Any]								=						param.transpose(0,     2							)
										snake_case_						:Tuple								=						param.transpose(1,     2							).contiguous()
					elif checkpoint_version >= 2.0:
										# other versions store [num_heads * num_splits * hidden_size, :]
										snake_case_						:str								=						(num_heads, num_splits, hidden_size) + input_shape[1:]
										snake_case_						:Any								=						param.view(*_A							)
										snake_case_						:int								=						param.transpose(0,     1							).contiguous()
					snake_case_						:Tuple								=						param.view(*_A							)
					return param
def      A      (			_A,     _A,     _A							):




					"""simple docstring"""
					# The converted output model.
					snake_case_						:List[Any]								=						{}

					# old versions did not store training args
					snake_case_						:Union[str, Any]								=						input_state_dict.get("args",     _A							)
					if ds_args is not None:
										# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
										# from pprint import pprint
										# pprint(vars(ds_args))

										snake_case_						:Union[str, Any]								=						ds_args.tokenizer_type
										snake_case_						:List[Any]								=						ds_args.padded_vocab_size
										snake_case_						:str								=						ds_args.max_position_embeddings
										snake_case_						:Any								=						ds_args.hidden_size
										snake_case_						:str								=						ds_args.num_layers
										snake_case_						:List[str]								=						ds_args.num_attention_heads
										snake_case_						:Tuple								=						ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
										# pprint(config)

					# The number of heads.
					snake_case_						:Union[str, Any]								=						config.num_attention_heads
					# The hidden_size per head.
					snake_case_						:int								=						config.hidden_size // heads
					# Megatron-LM checkpoint version
					if "checkpoint_version" in input_state_dict.keys():
										snake_case_						:Dict								=						input_state_dict["checkpoint_version"]
					else:
										snake_case_						:Optional[Any]								=						0.0

					# The model.
					snake_case_						:Dict								=						input_state_dict["model"]
					# The language model.
					snake_case_						:str								=						model["language_model"]
					# The embeddings.
					snake_case_						:Optional[Any]								=						lm["embedding"]

					# The word embeddings.
					snake_case_						:Tuple								=						embeddings["word_embeddings"]["weight"]
					# Truncate the embedding table to vocab_size rows.
					snake_case_						:Any								=						word_embeddings[: config.vocab_size, :]
					# Store the word embeddings.
					snake_case_						:Optional[int]								=						word_embeddings

					# The position embeddings.
					snake_case_						:Tuple								=						embeddings["position_embeddings"]["weight"]
					assert pos_embeddings.size(0							) == config.max_position_embeddings and pos_embeddings.size(1							) == config.hidden_size
					# Store the position embeddings.
					snake_case_						:Tuple								=						pos_embeddings

					# The token-type embeddings.
					snake_case_						:Tuple								=						embeddings["tokentype_embeddings"]["weight"]
					# Store the position embeddings.
					snake_case_						:List[Any]								=						tokentype_embeddings

					# The transformer.
					snake_case_						:Tuple								=						lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

					# The regex to extract layer names.
					snake_case_						:Optional[Any]								=						re.compile(R"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"							)

					# The simple map of names for "automated" rules.
					snake_case_						:Union[str, Any]								=						{
					    "attention.dense": ".attention.output.dense.",
					    "self_attention.dense": ".attention.output.dense.",
					    "mlp.dense_h_to_4h": ".intermediate.dense.",
					    "mlp.dense_4h_to_h": ".output.dense.",
					}

					# Keep track of the attention/query/value tensor.
					snake_case_						:Optional[int]								=						None

					# Extract the layers.
					for key, val in transformer.items():
										# Match the name.
										snake_case_						:int								=						layer_re.match(_A							)

										# Stop if that's not a layer
										if m is None:
															break

										# The index of the layer.
										snake_case_						:List[str]								=						int(m.group(1							)							)
										# The name of the operation.
										snake_case_						:Tuple								=						m.group(2							)
										# Is it a weight or a bias?
										snake_case_						:Tuple								=						m.group(3							)

										# The name of the layer.
										snake_case_						:Optional[Any]								=						F'''bert.encoder.layer.{layer_idx}'''

										# For layernorm(s), simply store the layer norm.
										if op_name.endswith("layernorm"							):
															snake_case_						:Optional[int]								=						"attention.ln" if op_name.startswith("input"							) else "ln"
															snake_case_						:Any								=						val

										# Transpose the QKV matrix.
										elif (
										    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
										) and weight_or_bias == "weight":
															# Make sure the QKV pointer is nil.
															assert attention_qkv_weight is None, ""

															snake_case_						:int								=						fix_query_key_value_ordering(_A,     _A,     3,     _A,     _A							)
															# Store the tensor as we need the bias as well to interleave QKV and biases.
															snake_case_						:str								=						out_val

										# Transpose the bias.
										elif (
										    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
										) and weight_or_bias == "bias":
															# Make sure we read the weight tensor.
															assert attention_qkv_weight is not None, ""

															# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
															snake_case_						:Optional[int]								=						attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
															snake_case_						:str								=						attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
															snake_case_						:Any								=						attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

															snake_case_						:Dict								=						fix_query_key_value_ordering(_A,     _A,     3,     _A,     _A							)
															# Split the bias.
															snake_case_						:List[str]								=						out_val[0 * config.hidden_size : 1 * config.hidden_size]
															snake_case_						:List[Any]								=						out_val[1 * config.hidden_size : 2 * config.hidden_size]
															snake_case_						:List[Any]								=						out_val[2 * config.hidden_size : 3 * config.hidden_size]

															# Store.
															snake_case_						:Any								=						q
															snake_case_						:int								=						q_bias
															snake_case_						:Dict								=						k
															snake_case_						:Optional[Any]								=						k_bias
															snake_case_						:Union[str, Any]								=						v
															snake_case_						:List[Any]								=						v_bias

															# Clear the stored tensor.
															snake_case_						:int								=						None

										# Copy weights and biases as is.
										elif weight_or_bias in ["weight", "bias"]:
															snake_case_						:Optional[Any]								=						megatron_to_transformers[op_name]
															snake_case_						:str								=						val

    # The final layernorm.
					snake_case_						:List[str]								=						transformer["final_layernorm.weight"]
					snake_case_						:int								=						transformer["final_layernorm.bias"]

					# The pooler.
					snake_case_						:List[str]								=						lm["pooler"]

					# Store the matrix and the bias.
					snake_case_						:List[Any]								=						pooler["dense.weight"]
					snake_case_						:List[Any]								=						pooler["dense.bias"]

					# The LM head from Megatron (for RACE).
					snake_case_						:int								=						model["lm_head"]

					# The transform matrix.
					snake_case_						:Any								=						lm_head["dense.weight"]
					snake_case_						:Optional[int]								=						lm_head["dense.bias"]

					# The transform LN.
					snake_case_						:Optional[Any]								=						lm_head["layernorm.weight"]
					snake_case_						:Any								=						lm_head["layernorm.bias"]

					# For the decoder, we replicate the weights.
					snake_case_						:Optional[int]								=						word_embeddings
					snake_case_						:List[Any]								=						lm_head["bias"]

					# The classifier from Megatron (for MLNI).
					snake_case_						:Union[str, Any]								=						model["binary_head"]

					# Store the classifier.
					snake_case_						:List[Any]								=						binary_head["weight"]
					snake_case_						:Optional[Any]								=						binary_head["bias"]

					# It should be done!
					return output_state_dict
def      A      (			):




					"""simple docstring"""
					# Create the argument parser.
					snake_case_						:Union[str, Any]								=						argparse.ArgumentParser()
					parser.add_argument("--print-checkpoint-structure",     action="store_true"							)
					parser.add_argument("path_to_checkpoint",     type=_A,     help="Path to the ZIP file containing the checkpoint"							)
					parser.add_argument(
					    "--config_file",     default="",     type=_A,     help="An optional config json file describing the pre-trained model.",     )
					snake_case_						:Optional[Any]								=						parser.parse_args()

					# Extract the basename.
					snake_case_						:List[str]								=						os.path.dirname(args.path_to_checkpoint							)

					# Load the model.
					# the .zip is very optional, let's keep it for backward compatibility
					print(F'''Extracting PyTorch state dictionary from "{args.path_to_checkpoint}"'''							)
					if args.path_to_checkpoint.endswith(".zip"							):
										with zipfile.ZipFile(args.path_to_checkpoint,     "r"							) as checkpoint:
															with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"							) as pytorch_dict:
																				snake_case_						:Optional[Any]								=						torch.load(_A,     map_location="cpu"							)
					else:
										snake_case_						:Optional[int]								=						torch.load(args.path_to_checkpoint,     map_location="cpu"							)

					if args.config_file == "":
										# Default config of megatron-bert 345m
										snake_case_						:str								=						MegatronBertConfig()

										# different megatron-bert-*-345m models have different vocab sizes, so override the default
										# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
										snake_case_						:Any								=						input_state_dict["model"]["lm_head"]["bias"].numel()
					else:
										snake_case_						:Optional[int]								=						MegatronBertConfig.from_json_file(args.config_file							)

					# Convert.
					print("Converting"							)
					snake_case_						:List[Any]								=						convert_megatron_checkpoint(_A,     _A,     _A							)

					# Print the structure of converted state dict.
					if args.print_checkpoint_structure:
										recursive_print(_A,     _A							)

					# Store the config to file.
					print("Saving config"							)
					config.save_pretrained(_A							)

					# Store the state_dict to file.
					snake_case_						:Tuple								=						os.path.join(_A,     "pytorch_model.bin"							)
					print(F'''Saving checkpoint to "{output_checkpoint_file}"'''							)
					torch.save(_A,     _A							)


####################################################################################################

if __name__ == "__main__":
				main()

####################################################################################################






"""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
#
# 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
#
########################################################################


__UpperCAmelCase      :      Dict   					=					16
__UpperCAmelCase      :      List[Any]   					=					32
def      A      (			_A,     _A							):




					"""simple docstring"""
					# Initialize accelerator
					if args.with_tracking:
										snake_case_						:Union[str, Any]								=						Accelerator(
										    cpu=args.cpu,     mixed_precision=args.mixed_precision,     log_with="all",     project_dir=args.project_dir							)
					else:
										snake_case_						:Tuple								=						Accelerator(cpu=args.cpu,     mixed_precision=args.mixed_precision							)

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

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

					snake_case_						:Any								=						AutoTokenizer.from_pretrained("bert-base-cased"							)
					snake_case_						:Any								=						load_dataset("glue",     "mrpc"							)
					snake_case_						:Optional[Any]								=						evaluate.load("glue",     "mrpc"							)

					def tokenize_function(_A							):
										# max_length=None => use the model max length (it's actually the default)
										snake_case_						:Any								=						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():
										snake_case_						:int								=						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
					snake_case_						:Any								=						tokenized_datasets.rename_column("label",     "labels"							)

					# If the batch size is too big we use gradient accumulation
					snake_case_						:List[Any]								=						1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										snake_case_						:Optional[int]								=						batch_size // MAX_GPU_BATCH_SIZE
										snake_case_						:List[str]								=						MAX_GPU_BATCH_SIZE

					def collate_fn(_A							):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										snake_case_						:Optional[int]								=						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":
															snake_case_						:Union[str, Any]								=						16
										elif accelerator.mixed_precision != "no":
															snake_case_						:List[Any]								=						8
										else:
															snake_case_						:Any								=						None

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

					# Instantiate dataloaders.
					snake_case_						:str								=						DataLoader(
					    tokenized_datasets["train"],     shuffle=_A,     collate_fn=_A,     batch_size=_A							)
					snake_case_						:Any								=						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)
					snake_case_						:Optional[Any]								=						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).
					snake_case_						:str								=						model.to(accelerator.device							)

					# Instantiate optimizer
					snake_case_						:List[Any]								=						AdamW(params=model.parameters(),     lr=_A							)

					# Instantiate scheduler
					snake_case_						:Tuple								=						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.
					snake_case_	,  snake_case_	,  snake_case_	,  snake_case_	,  snake_case_						:Optional[Any]								=						accelerator.prepare(
					    _A,     _A,     _A,     _A,     _A							)

					# We need to keep track of how many total steps we have iterated over
					snake_case_						:Optional[int]								=						0
					# We also need to keep track of the stating epoch so files are named properly
					snake_case_						:int								=						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							)
															snake_case_						:str								=						os.path.basename(args.resume_from_checkpoint							)
										else:
															# Get the most recent checkpoint
															snake_case_						:int								=						[f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
															dirs.sort(key=os.path.getctime							)
															snake_case_						:Tuple								=						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										snake_case_						:Optional[int]								=						os.path.splitext(_A							)[0]

										if "epoch" in training_difference:
															snake_case_						:Union[str, Any]								=						int(training_difference.replace("epoch_",     ""							)							) + 1
															snake_case_						:List[str]								=						None
										else:
															snake_case_						:Tuple								=						int(training_difference.replace("step_",     ""							)							)
															snake_case_						:Optional[int]								=						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:
															snake_case_						:Optional[Any]								=						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
															snake_case_						:Dict								=						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
															snake_case_						:Optional[Any]								=						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							)
															snake_case_						:Union[str, Any]								=						model(**_A							)
															snake_case_						:str								=						outputs.loss
															snake_case_						:int								=						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							):
																				snake_case_						:List[str]								=						F'''step_{overall_step}'''
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														snake_case_						:Tuple								=						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():
																				snake_case_						:str								=						model(**_A							)
															snake_case_						:str								=						outputs.logits.argmax(dim=-1							)
															snake_case_	,  snake_case_						:List[str]								=						accelerator.gather_for_metrics((predictions, batch["labels"])							)
															metric.add_batch(
															    predictions=_A,     references=_A,     )

										snake_case_						:int								=						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":
															snake_case_						:str								=						F'''epoch_{epoch}'''
															if args.output_dir is not None:
																				snake_case_						:Any								=						os.path.join(args.output_dir,     _A							)
															accelerator.save_state(_A							)

					if args.with_tracking:
										accelerator.end_training()
def      A      (			):




					"""simple docstring"""
					snake_case_						:str								=						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",     )
					snake_case_						:Optional[int]								=						parser.parse_args()
					snake_case_						:Optional[Any]								=						{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(_A,     _A							)


if __name__ == "__main__":
				main()

