


'''simple docstring'''
from __future__ import annotations

from random import random
from typing import Generic, TypeVar

__snake_case											=					TypeVar('''KT''')
__snake_case											=					TypeVar('''VT''')
class        lowercase     (							Generic[KT, VT] ):

		"""simple docstring"""

		def __init__(		self   ,       UpperCamelCase_ = "root"   ,       UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			key
							UpperCamelCase__       :List[str]							 =			value
							UpperCamelCase__       :list[Node[KT, VT]]							 =			[]




		def __repr__(		self							):

							'''simple docstring'''


							return F'''Node({self.key}: {self.value})'''




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return len(self.forward							)



class        lowercase     (							Generic[KT, VT] ):

		"""simple docstring"""

		def __init__(		self   ,       UpperCamelCase_ = 0.5   ,       UpperCamelCase_ = 16							):

							'''simple docstring'''


							UpperCamelCase__       :Node[KT, VT]							 =			Node[KT, VT]()
							UpperCamelCase__       :List[str]							 =			0
							UpperCamelCase__       :Dict							 =			p
							UpperCamelCase__       :str							 =			max_level




		def __str__(		self							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			list(self							)

							if len(UpperCamelCase__							) == 0:
												return F'''SkipList(level={self.level})'''

							UpperCamelCase__       :Tuple							 =			max((len(str(UpperCamelCase__							)							) for item in items)   ,       default=4							)
							UpperCamelCase__       :Optional[int]							 =			max(UpperCamelCase__   ,       4							) + 4

							UpperCamelCase__       :Tuple							 =			self.head
							UpperCamelCase__       :int							 =			[]

							UpperCamelCase__       :Dict							 =			node.forward.copy()
							lines.append(F'''[{node.key}]'''.ljust(UpperCamelCase__   ,       '''-'''							) + '''* ''' * len(UpperCamelCase__							)							)
							lines.append(''' ''' * label_size + '''| ''' * len(UpperCamelCase__							)							)

							while len(node.forward							) != 0:
												UpperCamelCase__       :Any							 =			node.forward[0]

												lines.append(
												    F'''[{node.key}]'''.ljust(UpperCamelCase__   ,       '''-'''							)
												    + ''' '''.join(str(n.key							) if n.key == node.key else '''|''' for n in forwards							)							)
												lines.append(''' ''' * label_size + '''| ''' * len(UpperCamelCase__							)							)
												UpperCamelCase__       :str							 =			node.forward

							lines.append('''None'''.ljust(UpperCamelCase__							) + '''* ''' * len(UpperCamelCase__							)							)
							return F'''SkipList(level={self.level})\n''' + "\n".join(UpperCamelCase__							)




		def __iter__(		self							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			self.head

							while len(node.forward							) != 0:
												yield node.forward[0].key
												UpperCamelCase__       :Any							 =			node.forward[0]




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			1
							while random() < self.p and level < self.max_level:
												level += 1

							return level




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			[]

							UpperCamelCase__       :List[Any]							 =			self.head

							for i in reversed(range(self.level							)							):
												# i < node.level - When node level is lesser than `i` decrement `i`.
												# node.forward[i].key < key - Jumping to node with key value higher
												#                             or equal to searched key would result
												#                             in skipping searched key.
												while i < node.level and node.forward[i].key < key:
																	UpperCamelCase__       :Union[str, Any]							 =			node.forward[i]
												# Each leftmost node (relative to searched node) will potentially have to
												# be updated.
												update_vector.append(UpperCamelCase__							)

							update_vector.reverse()  # Note that we were inserting values in reverse order.

							# len(node.forward) != 0 - If current node doesn't contain any further
							#                          references then searched key is not present.
							# node.forward[0].key == key - Next node key should be equal to search key
							#                              if key is present.
							if len(node.forward							) != 0 and node.forward[0].key == key:
												return node.forward[0], update_vector
							else:
												return None, update_vector




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			self._locate_node(UpperCamelCase__							)

							if node is not None:
												for i, update_node in enumerate(UpperCamelCase__							):
																	# Remove or replace all references to removed node.
																	if update_node.level > i and update_node.forward[i].key == key:
																						if node.level > i:
																											UpperCamelCase__       :List[str]							 =			node.forward[i]
																						else:
																											UpperCamelCase__       :str							 =			update_node.forward[:i]




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			self._locate_node(UpperCamelCase__							)
							if node is not None:
												UpperCamelCase__       :str							 =			value
							else:
												UpperCamelCase__       :Tuple							 =			self.random_level()

												if level > self.level:
																	# After level increase we have to add additional nodes to head.
																	for _ in range(self.level - 1   ,       UpperCamelCase__							):
																						update_vector.append(self.head							)
																	UpperCamelCase__       :Optional[int]							 =			level

												UpperCamelCase__       :Tuple							 =			Node(UpperCamelCase__   ,       UpperCamelCase__							)

												for i, update_node in enumerate(update_vector[:level]							):
																	# Change references to pass through new node.
																	if update_node.level > i:
																						new_node.forward.append(update_node.forward[i]							)

																	if update_node.level < i + 1:
																						update_node.forward.append(UpperCamelCase__							)
																	else:
																						UpperCamelCase__       :int							 =			new_node




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			self._locate_node(UpperCamelCase__							)

							if node is not None:
												return node.value

							return None



def       a				(      )  ->     Tuple:



					'''simple docstring'''






					UpperCamelCase__       :Optional[int]							 =			SkipList()
					skip_list.insert('''Key1'''			, 3       )
					skip_list.insert('''Key2'''			, 12       )
					skip_list.insert('''Key3'''			, 41       )
					skip_list.insert('''Key4'''			, -19       )

					UpperCamelCase__       :Tuple							 =			skip_list.head
					UpperCamelCase__       :Optional[int]							 =			{}
					while node.level != 0:
										UpperCamelCase__       :Dict							 =			node.forward[0]
										UpperCamelCase__       :Dict							 =			node.value

					assert len(__SCREAMING_SNAKE_CASE       ) == 4
					assert all_values["Key1"] == 3
					assert all_values["Key2"] == 12
					assert all_values["Key3"] == 41
					assert all_values["Key4"] == -19


def       a				(      )  ->     Tuple:



					'''simple docstring'''






					UpperCamelCase__       :Dict							 =			SkipList()
					skip_list.insert('''Key1'''			, 10       )
					skip_list.insert('''Key1'''			, 12       )

					skip_list.insert('''Key5'''			, 7       )
					skip_list.insert('''Key7'''			, 10       )
					skip_list.insert('''Key10'''			, 5       )

					skip_list.insert('''Key7'''			, 7       )
					skip_list.insert('''Key5'''			, 5       )
					skip_list.insert('''Key10'''			, 10       )

					UpperCamelCase__       :Optional[Any]							 =			skip_list.head
					UpperCamelCase__       :Tuple							 =			{}
					while node.level != 0:
										UpperCamelCase__       :Optional[int]							 =			node.forward[0]
										UpperCamelCase__       :List[Any]							 =			node.value

					if len(__SCREAMING_SNAKE_CASE       ) != 4:
										print()
					assert len(__SCREAMING_SNAKE_CASE       ) == 4
					assert all_values["Key1"] == 12
					assert all_values["Key7"] == 7
					assert all_values["Key5"] == 5
					assert all_values["Key10"] == 10


def       a				(      )  ->     Any:



					'''simple docstring'''






					UpperCamelCase__       :List[Any]							 =			SkipList()
					assert skip_list.find('''Some key'''       ) is None


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



					'''simple docstring'''






					UpperCamelCase__       :List[Any]							 =			SkipList()

					skip_list.insert('''Key2'''			, 20       )
					assert skip_list.find('''Key2'''       ) == 20

					skip_list.insert('''Some Key'''			, 10       )
					skip_list.insert('''Key2'''			, 8       )
					skip_list.insert('''V'''			, 13       )

					assert skip_list.find('''Y'''       ) is None
					assert skip_list.find('''Key2'''       ) == 8
					assert skip_list.find('''Some Key'''       ) == 10
					assert skip_list.find('''V'''       ) == 13


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



					'''simple docstring'''






					UpperCamelCase__       :List[str]							 =			SkipList()
					skip_list.delete('''Some key'''       )

					assert len(skip_list.head.forward       ) == 0


def       a				(      )  ->     List[Any]:



					'''simple docstring'''






					UpperCamelCase__       :str							 =			SkipList()

					skip_list.insert('''Key1'''			, 12       )
					skip_list.insert('''V'''			, 13       )
					skip_list.insert('''X'''			, 14       )
					skip_list.insert('''Key2'''			, 15       )

					skip_list.delete('''V'''       )
					skip_list.delete('''Key2'''       )

					assert skip_list.find('''V'''       ) is None
					assert skip_list.find('''Key2'''       ) is None


def       a				(      )  ->     Any:



					'''simple docstring'''






					UpperCamelCase__       :Optional[int]							 =			SkipList()

					skip_list.insert('''Key1'''			, 12       )
					skip_list.insert('''V'''			, 13       )
					skip_list.insert('''X'''			, 14       )
					skip_list.insert('''Key2'''			, 15       )

					skip_list.delete('''V'''       )
					assert skip_list.find('''V'''       ) is None
					assert skip_list.find('''X'''       ) == 14
					assert skip_list.find('''Key1'''       ) == 12
					assert skip_list.find('''Key2'''       ) == 15

					skip_list.delete('''X'''       )
					assert skip_list.find('''V'''       ) is None
					assert skip_list.find('''X'''       ) is None
					assert skip_list.find('''Key1'''       ) == 12
					assert skip_list.find('''Key2'''       ) == 15

					skip_list.delete('''Key1'''       )
					assert skip_list.find('''V'''       ) is None
					assert skip_list.find('''X'''       ) is None
					assert skip_list.find('''Key1'''       ) is None
					assert skip_list.find('''Key2'''       ) == 15

					skip_list.delete('''Key2'''       )
					assert skip_list.find('''V'''       ) is None
					assert skip_list.find('''X'''       ) is None
					assert skip_list.find('''Key1'''       ) is None
					assert skip_list.find('''Key2'''       ) is None


def       a				(      )  ->     List[str]:



					'''simple docstring'''






					UpperCamelCase__       :Dict							 =			SkipList()

					skip_list.insert('''Key1'''			, 12       )
					skip_list.insert('''V'''			, 13       )
					skip_list.insert('''X'''			, 142       )
					skip_list.insert('''Key2'''			, 15       )

					skip_list.delete('''X'''       )

					def traverse_keys(__a       ):
										yield node.key
										for forward_node in node.forward:
															yield from traverse_keys(__SCREAMING_SNAKE_CASE       )

					assert len(set(traverse_keys(skip_list.head       )       )       ) == 4


def       a				(      )  ->     Optional[int]:



					'''simple docstring'''






					def is_sorted(__a       ):
										return all(next_item >= item for item, next_item in zip(__SCREAMING_SNAKE_CASE			, lst[1:]       )       )

					UpperCamelCase__       :int							 =			SkipList()
					for i in range(10       ):
										skip_list.insert(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE       )
					assert is_sorted(list(__SCREAMING_SNAKE_CASE       )       )
					skip_list.delete(5       )
					skip_list.delete(8       )
					skip_list.delete(2       )
					assert is_sorted(list(__SCREAMING_SNAKE_CASE       )       )
					skip_list.insert(-12			, -12       )
					skip_list.insert(77			, 77       )
					assert is_sorted(list(__SCREAMING_SNAKE_CASE       )       )


def       a				(      )  ->     Optional[Any]:



					'''simple docstring'''






					for _ in range(100       ):
										# Repeat test 100 times due to the probabilistic nature of skip list
										# random values == random bugs
										test_insert()
										test_insert_overrides_existing_value()

										test_searching_empty_list_returns_none()
										test_search()

										test_deleting_item_from_empty_list_do_nothing()
										test_deleted_items_are_not_founded_by_find_method()
										test_delete_removes_only_given_key()
										test_delete_doesnt_leave_dead_nodes()

										test_iter_always_yields_sorted_values()


def       a				(      )  ->     Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__       :Tuple							 =			SkipList()
					skip_list.insert(2			, '''2'''       )
					skip_list.insert(4			, '''4'''       )
					skip_list.insert(6			, '''4'''       )
					skip_list.insert(4			, '''5'''       )
					skip_list.insert(8			, '''4'''       )
					skip_list.insert(9			, '''4'''       )

					skip_list.delete(4       )

					print(__SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()



'''simple docstring'''
import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__snake_case											=					16
__snake_case											=					32


def       a				(      __a			, __a = 16			, __a = "bert-base-cased"       )  ->     Any:



					'''simple docstring'''






					UpperCamelCase__       :List[str]							 =			AutoTokenizer.from_pretrained(__a       )
					UpperCamelCase__       :List[Any]							 =			load_dataset('''glue'''			, '''mrpc'''       )

					def tokenize_function(__a       ):
										# max_length=None => use the model max length (it's actually the default)
										UpperCamelCase__       :Tuple							 =			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
					UpperCamelCase__       :Optional[int]							 =			datasets.map(
					    __a			, batched=__a			, remove_columns=['''idx''', '''sentence1''', '''sentence2''']			, load_from_cache_file=__a       )

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					UpperCamelCase__       :Optional[int]							 =			tokenized_datasets.rename_column('''label'''			, '''labels'''       )

					def collate_fn(__a       ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										if accelerator.distributed_type == DistributedType.TPU:
															return tokenizer.pad(__a			, padding='''max_length'''			, max_length=128			, return_tensors='''pt'''       )
										return tokenizer.pad(__a			, padding='''longest'''			, return_tensors='''pt'''       )

					# Instantiate dataloaders.
					UpperCamelCase__       :Dict							 =			DataLoader(
					    tokenized_datasets['''train''']			, shuffle=__a			, collate_fn=__a			, batch_size=__a       )
					UpperCamelCase__       :str							 =			DataLoader(
					    tokenized_datasets['''validation''']			, shuffle=__a			, collate_fn=__a			, batch_size=__a       )

					return train_dataloader, eval_dataloader


def       a				(      __a			, __a			, __a			, __a       )  ->     str:



					'''simple docstring'''






					model.eval()
					UpperCamelCase__       :List[str]							 =			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       )
										with torch.no_grad():
															UpperCamelCase__       :int							 =			model(**__a       )
										UpperCamelCase__       :Tuple							 =			outputs.logits.argmax(dim=-1       )
										# It is slightly faster to call this once, than multiple times
										UpperCamelCase__ ,    UpperCamelCase__       :int							 =			accelerator.gather(
										    (predictions, batch['''labels'''])       )  # If we are in a multiprocess environment, the last batch has duplicates
										if accelerator.use_distributed:
															if step == len(__a       ) - 1:
																				UpperCamelCase__       :Union[str, Any]							 =			predictions[: len(eval_dataloader.dataset       ) - samples_seen]
																				UpperCamelCase__       :List[str]							 =			references[: len(eval_dataloader.dataset       ) - samples_seen]
															else:
																				samples_seen += references.shape[0]
										metric.add_batch(
										    predictions=__a			, references=__a			, )

					UpperCamelCase__       :Union[str, Any]							 =			metric.compute()
					return eval_metric["accuracy"]


def       a				(      __a			, __a       )  ->     List[Any]:



					'''simple docstring'''






					UpperCamelCase__       :Any							 =			Accelerator()

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					UpperCamelCase__       :Any							 =			config['''lr''']
					UpperCamelCase__       :Optional[int]							 =			int(config['''num_epochs''']       )
					UpperCamelCase__       :List[Any]							 =			int(config['''seed''']       )
					UpperCamelCase__       :List[Any]							 =			int(config['''batch_size''']       )
					UpperCamelCase__       :List[Any]							 =			args.model_name_or_path

					set_seed(__a       )
					UpperCamelCase__ ,    UpperCamelCase__       :Any							 =			get_dataloaders(__a			, __a			, __a       )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					UpperCamelCase__       :Union[str, Any]							 =			AutoModelForSequenceClassification.from_pretrained(__a			, return_dict=__a       )

					# Instantiate optimizer
					UpperCamelCase__       :Any							 =			(
					    AdamW
					    if accelerator.state.deepspeed_plugin is None
					    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
					    else DummyOptim
					)
					UpperCamelCase__       :Optional[Any]							 =			optimizer_cls(params=model.parameters()			, lr=__a       )

					if accelerator.state.deepspeed_plugin is not None:
										UpperCamelCase__       :Tuple							 =			accelerator.state.deepspeed_plugin.deepspeed_config[
										    '''gradient_accumulation_steps'''
										]
					else:
										UpperCamelCase__       :Dict							 =			1
					UpperCamelCase__       :Tuple							 =			(len(__a       ) * num_epochs) // gradient_accumulation_steps

					# Instantiate scheduler
					if (
					    accelerator.state.deepspeed_plugin is None
					    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
					):
										UpperCamelCase__       :Any							 =			get_linear_schedule_with_warmup(
										    optimizer=__a			, num_warmup_steps=0			, num_training_steps=__a			, )
					else:
										UpperCamelCase__       :Any							 =			DummyScheduler(__a			, total_num_steps=__a			, warmup_num_steps=0       )

					# 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__       :Dict							 =			accelerator.prepare(
					    __a			, __a			, __a			, __a			, __a       )

					# We need to keep track of how many total steps we have iterated over
					UpperCamelCase__       :Tuple							 =			0
					# We also need to keep track of the stating epoch so files are named properly
					UpperCamelCase__       :Optional[Any]							 =			0
					UpperCamelCase__       :Optional[int]							 =			evaluate.load('''glue'''			, '''mrpc'''       )
					UpperCamelCase__       :List[Any]							 =			num_epochs

					if args.partial_train_epoch is not None:
										UpperCamelCase__       :Optional[Any]							 =			args.partial_train_epoch

					if args.resume_from_checkpoint:
										accelerator.load_state(args.resume_from_checkpoint       )
										UpperCamelCase__       :Dict							 =			args.resume_from_checkpoint.split('''epoch_'''       )[1]
										UpperCamelCase__       :Tuple							 =			''''''
										for char in epoch_string:
															if char.isdigit():
																				state_epoch_num += char
															else:
																				break
										UpperCamelCase__       :Any							 =			int(__a       ) + 1
										UpperCamelCase__       :Dict							 =			evaluation_loop(__a			, __a			, __a			, __a       )
										accelerator.print('''resumed checkpoint performance:'''			, __a       )
										accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''			, lr_scheduler.get_lr()[0]       )
										accelerator.print('''resumed optimizers\'s lr:'''			, optimizer.param_groups[0]['''lr''']       )
										with open(os.path.join(args.output_dir			, f'''state_{starting_epoch-1}.json'''       )			, '''r'''       ) as f:
															UpperCamelCase__       :Optional[int]							 =			json.load(__a       )
															assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
															assert (
															    resumed_state["lr"] == lr_scheduler.get_lr()[0]
															), "Scheduler learning rate mismatch, loading from checkpoint failed"
															assert (
															    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
															), "Optimizer learning rate mismatch, loading from checkpoint failed"
															assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
															return

    # Now we train the model
					UpperCamelCase__       :Optional[Any]							 =			{}
					for epoch in range(__a			, __a       ):
										model.train()
										for step, batch in enumerate(__a       ):
															UpperCamelCase__       :Optional[int]							 =			model(**__a       )
															UpperCamelCase__       :Optional[int]							 =			outputs.loss
															UpperCamelCase__       :str							 =			loss / gradient_accumulation_steps
															accelerator.backward(__a       )
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1
										UpperCamelCase__       :Union[str, Any]							 =			f'''epoch_{epoch}'''
										UpperCamelCase__       :List[Any]							 =			os.path.join(args.output_dir			, __a       )
										accelerator.save_state(__a       )
										UpperCamelCase__       :List[Any]							 =			evaluation_loop(__a			, __a			, __a			, __a       )
										UpperCamelCase__       :int							 =			accuracy
										UpperCamelCase__       :List[Any]							 =			lr_scheduler.get_lr()[0]
										UpperCamelCase__       :Any							 =			optimizer.param_groups[0]['''lr''']
										UpperCamelCase__       :int							 =			epoch
										UpperCamelCase__       :Tuple							 =			overall_step
										accelerator.print(f'''epoch {epoch}:'''			, __a       )

										accelerator.wait_for_everyone()
										if accelerator.is_main_process:
															with open(os.path.join(args.output_dir			, f'''state_{epoch}.json'''       )			, '''w'''       ) as f:
																				json.dump(__a			, __a       )


def       a				(      )  ->     Tuple:



					'''simple docstring'''






					UpperCamelCase__       :List[Any]							 =			argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''       )
					parser.add_argument(
					    '''--model_name_or_path'''			, type=__a			, default='''bert-base-cased'''			, help='''Path to pretrained model or model identifier from huggingface.co/models.'''			, required=__a			, )
					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(
					    '''--resume_from_checkpoint'''			, type=__a			, default=__a			, help='''If the training should continue from a checkpoint folder.'''			, )
					parser.add_argument(
					    '''--partial_train_epoch'''			, type=__a			, default=__a			, help='''If passed, the training will stop after this number of epochs.'''			, )
					parser.add_argument(
					    '''--num_epochs'''			, type=__a			, default=2			, help='''Number of train epochs.'''			, )
					UpperCamelCase__       :Optional[int]							 =			parser.parse_args()
					UpperCamelCase__       :List[str]							 =			{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

					training_function(__a			, __a       )


if __name__ == "__main__":
		main()