


'''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


_lowerCAmelCase  :		int		     =	16
_lowerCAmelCase  :		Any		     =	32

def 			__UpperCamelCase    (    _A    :				Accelerator					,   _A    :				int = 16					,   _A    :				str = "bert-base-cased"				)    ->      Any:




	"""simple docstring"""





	lowerCAmelCase					:    List[str]      							= AutoTokenizer.from_pretrained(_A				)
	lowerCAmelCase					:    Union[str, Any]      							= load_dataset('glue'					,   'mrpc'				)

	def tokenize_function(_A    :				Any				):
		# max_length=None => use the model max length (it's actually the default)
		lowerCAmelCase					:    Union[str, 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
	lowerCAmelCase					:    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
	lowerCAmelCase					:    str      							= 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.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(_A					,   padding='max_length'					,   max_length=1_28					,   return_tensors='pt'				)
		return tokenizer.pad(_A					,   padding='longest'					,   return_tensors='pt'				)

	# Instantiate dataloaders.
	lowerCAmelCase					:    str      							= DataLoader(
	    tokenized_datasets['train']					,   shuffle=_A					,   collate_fn=_A					,   batch_size=_A				)
	lowerCAmelCase					:    List[str]      							= DataLoader(
	    tokenized_datasets['validation']					,   shuffle=_A					,   collate_fn=_A					,   batch_size=_A				)

	return train_dataloader, eval_dataloader

def 			__UpperCamelCase    (    _A    :				Optional[int]					,   _A    :				List[Any]					,   _A    :				Dict					,   _A    :				Optional[Any]				)    ->      int:




	"""simple docstring"""





	model.eval()
	lowerCAmelCase					:    Optional[Any]      							= 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():
			lowerCAmelCase					:    List[str]      							= model(**_A				)
		lowerCAmelCase					:    List[str]      							= outputs.logits.argmax(dim=-1				)
		# It is slightly faster to call this once, than multiple times
		lowerCAmelCase							,   lowerCAmelCase					:    Dict      							= 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:
				lowerCAmelCase					:    Tuple      							= predictions[: len(eval_dataloader.dataset				) - samples_seen]
				lowerCAmelCase					:    Optional[Any]      							= references[: len(eval_dataloader.dataset				) - samples_seen]
			else:
				samples_seen += references.shape[0]
		metric.add_batch(
		    predictions=_A					,   references=_A					,   )

	lowerCAmelCase					:    Any      							= metric.compute()
	return eval_metric["accuracy"]

def 			__UpperCamelCase    (    _A    :				Optional[Any]					,   _A    :				Optional[int]				)    ->      str:




	"""simple docstring"""





	lowerCAmelCase					:    Tuple      							= Accelerator()

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

	set_seed(_A				)
	lowerCAmelCase							,   lowerCAmelCase					:    str      							= get_dataloaders(_A					,   _A					,   _A				)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	lowerCAmelCase					:    int      							= AutoModelForSequenceClassification.from_pretrained(_A					,   return_dict=_A				)

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

	if accelerator.state.deepspeed_plugin is not None:
		lowerCAmelCase					:    Union[str, Any]      							= accelerator.state.deepspeed_plugin.deepspeed_config[
		    'gradient_accumulation_steps'
		]
	else:
		lowerCAmelCase					:    Optional[int]      							= 1
	lowerCAmelCase					:    Any      							= (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
	):
		lowerCAmelCase					:    int      							= get_linear_schedule_with_warmup(
		    optimizer=_A					,   num_warmup_steps=0					,   num_training_steps=_A					,   )
	else:
		lowerCAmelCase					:    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.
	lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					:    Optional[Any]      							= accelerator.prepare(
	    _A					,   _A					,   _A					,   _A					,   _A				)

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

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

	if args.resume_from_checkpoint:
		accelerator.load_state(args.resume_from_checkpoint				)
		lowerCAmelCase					:    List[Any]      							= args.resume_from_checkpoint.split('epoch_'				)[1]
		lowerCAmelCase					:    Optional[int]      							= ''
		for char in epoch_string:
			if char.isdigit():
				state_epoch_num += char
			else:
				break
		lowerCAmelCase					:    Any      							= int(_A				) + 1
		lowerCAmelCase					:    str      							= 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:
			lowerCAmelCase					:    Any      							= 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
	lowerCAmelCase					:    Optional[Any]      							= {}
	for epoch in range(_A					,   _A				):
		model.train()
		for step, batch in enumerate(_A				):
			lowerCAmelCase					:    List[Any]      							= model(**_A				)
			lowerCAmelCase					:    str      							= outputs.loss
			lowerCAmelCase					:    Any      							= loss / gradient_accumulation_steps
			accelerator.backward(_A				)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1
		lowerCAmelCase					:    Optional[int]      							= F"epoch_{epoch}"
		lowerCAmelCase					:    str      							= os.path.join(args.output_dir					,   _A				)
		accelerator.save_state(_A				)
		lowerCAmelCase					:    List[str]      							= evaluation_loop(_A					,   _A					,   _A					,   _A				)
		lowerCAmelCase					:    Tuple      							= accuracy
		lowerCAmelCase					:    Tuple      							= lr_scheduler.get_lr()[0]
		lowerCAmelCase					:    Optional[Any]      							= optimizer.param_groups[0]['lr']
		lowerCAmelCase					:    int      							= epoch
		lowerCAmelCase					:    List[str]      							= 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 			__UpperCamelCase    (    )    ->      Any:




	"""simple docstring"""





	lowerCAmelCase					:    str      							= 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.'					,   )
	lowerCAmelCase					:    Tuple      							= parser.parse_args()
	lowerCAmelCase					:    List[str]      							= {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

	training_function(_A					,   _A				)


if __name__ == "__main__":
				main()




'''simple docstring'''




from typing import Any



class  lowerCAmelCase				:



	def __init__(					self ,					snake_case__    ):
		lowerCAmelCase					:    Optional[int]      							= data
		lowerCAmelCase					:    Optional[Any]      							= None



	def __repr__(					self    ):
		return f"Node({self.data})"



class  lowerCAmelCase				:



	def __init__(					self    ):
		lowerCAmelCase					:    Dict      							= None



	def __iter__(					self    ):
		lowerCAmelCase					:    Optional[Any]      							= self.head
		while node:
			yield node.data
			lowerCAmelCase					:    Optional[int]      							= node.next



	def __len__(					self    ):
		return sum(1 for _ in self    )



	def __repr__(					self    ):
		return "->".join([str(snake_case__    ) for item in self]    )



	def __getitem__(					self ,					snake_case__    ):
		if not 0 <= index < len(self    ):
			raise ValueError('list index out of range.'    )
		for i, node in enumerate(self    ):
			if i == index:
				return node
		return None



	def __setitem__(					self ,					snake_case__ ,					snake_case__    ):
		if not 0 <= index < len(self    ):
			raise ValueError('list index out of range.'    )
		lowerCAmelCase					:    Any      							= self.head
		for _ in range(snake_case__    ):
			lowerCAmelCase					:    List[str]      							= current.next
		lowerCAmelCase					:    int      							= data



	def 				lowercase     (					self ,					snake_case__    ):
		self.insert_nth(len(self    ) ,					snake_case__    )



	def 				lowercase     (					self ,					snake_case__    ):
		self.insert_nth(0 ,					snake_case__    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__    ):
		if not 0 <= index <= len(self    ):
			raise IndexError('list index out of range'    )
		lowerCAmelCase					:    List[str]      							= Node(snake_case__    )
		if self.head is None:
			lowerCAmelCase					:    int      							= new_node
		elif index == 0:
			lowerCAmelCase					:    List[Any]      							= self.head  # link new_node to head
			lowerCAmelCase					:    List[Any]      							= new_node
		else:
			lowerCAmelCase					:    List[Any]      							= self.head
			for _ in range(index - 1    ):
				lowerCAmelCase					:    Union[str, Any]      							= temp.next
			lowerCAmelCase					:    Any      							= temp.next
			lowerCAmelCase					:    str      							= new_node



	def 				lowercase     (					self    ):  # print every node data
		print(self    )



	def 				lowercase     (					self    ):
		return self.delete_nth(0    )



	def 				lowercase     (					self    ):  # delete from tail
		return self.delete_nth(len(self    ) - 1    )



	def 				lowercase     (					self ,					snake_case__ = 0    ):
		if not 0 <= index <= len(self    ) - 1:  # test if index is valid
			raise IndexError('List index out of range.'    )
		lowerCAmelCase					:    List[str]      							= self.head  # default first node
		if index == 0:
			lowerCAmelCase					:    Tuple      							= self.head.next
		else:
			lowerCAmelCase					:    Dict      							= self.head
			for _ in range(index - 1    ):
				lowerCAmelCase					:    Tuple      							= temp.next
			lowerCAmelCase					:    Dict      							= temp.next
			lowerCAmelCase					:    Tuple      							= temp.next.next
		return delete_node.data



	def 				lowercase     (					self    ):
		return self.head is None



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= None
		lowerCAmelCase					:    Any      							= self.head

		while current:
			# Store the current node's next node.
			lowerCAmelCase					:    List[str]      							= current.next
			# Make the current node's next point backwards
			lowerCAmelCase					:    int      							= prev
			# Make the previous node be the current node
			lowerCAmelCase					:    int      							= current
			# Make the current node the next node (to progress iteration)
			lowerCAmelCase					:    Optional[Any]      							= next_node
		# Return prev in order to put the head at the end
		lowerCAmelCase					:    List[Any]      							= prev


def 			__UpperCamelCase    (    )    ->      None:




	"""simple docstring"""





	lowerCAmelCase					:    Tuple      							= LinkedList()
	assert linked_list.is_empty() is True
	assert str(_A				) == ""

	try:
		linked_list.delete_head()
		raise AssertionError  # This should not happen.
	except IndexError:
		assert True  # This should happen.

	try:
		linked_list.delete_tail()
		raise AssertionError  # This should not happen.
	except IndexError:
		assert True  # This should happen.

	for i in range(10				):
		assert len(_A				) == i
		linked_list.insert_nth(_A					,   i + 1				)
	assert str(_A				) == "->".join(str(_A				) for i in range(1					,   11				)				)

	linked_list.insert_head(0				)
	linked_list.insert_tail(11				)
	assert str(_A				) == "->".join(str(_A				) for i in range(0					,   12				)				)

	assert linked_list.delete_head() == 0
	assert linked_list.delete_nth(9				) == 10
	assert linked_list.delete_tail() == 11
	assert len(_A				) == 9
	assert str(_A				) == "->".join(str(_A				) for i in range(1					,   10				)				)

	assert all(linked_list[i] == i + 1 for i in range(0					,   9				)				) is True

	for i in range(0					,   9				):
		lowerCAmelCase					:    Optional[Any]      							= -i
	assert all(linked_list[i] == -i for i in range(0					,   9				)				) is True

	linked_list.reverse()
	assert str(_A				) == "->".join(str(_A				) for i in range(-8					,   1				)				)

def 			__UpperCamelCase    (    )    ->      None:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[int]      							= [
	    -9,
	    1_00,
	    Node(77_34_51_12				),
	    'dlrow olleH',
	    7,
	    55_55,
	    0,
	    -1_92.5_55_55,
	    'Hello, world!',
	    77.9,
	    Node(10				),
	    None,
	    None,
	    12.20,
	]
	lowerCAmelCase					:    Dict      							= LinkedList()

	for i in test_input:
		linked_list.insert_tail(_A				)

	# Check if it's empty or not
	assert linked_list.is_empty() is False
	assert (
	    str(_A				) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
	    "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
	)

	# Delete the head
	lowerCAmelCase					:    Optional[Any]      							= linked_list.delete_head()
	assert result == -9
	assert (
	    str(_A				) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
	    "Hello, world!->77.9->Node(10)->None->None->12.2"
	)

	# Delete the tail
	lowerCAmelCase					:    List[str]      							= linked_list.delete_tail()
	assert result == 12.2
	assert (
	    str(_A				) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
	    "Hello, world!->77.9->Node(10)->None->None"
	)

	# Delete a node in specific location in linked list
	lowerCAmelCase					:    List[str]      							= linked_list.delete_nth(10				)
	assert result is None
	assert (
	    str(_A				) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
	    "Hello, world!->77.9->Node(10)->None"
	)

	# Add a Node instance to its head
	linked_list.insert_head(Node('Hello again, world!'				)				)
	assert (
	    str(_A				)
	    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
	    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
	)

	# Add None to its tail
	linked_list.insert_tail(_A				)
	assert (
	    str(_A				)
	    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
	    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
	)

	# Reverse the linked list
	linked_list.reverse()
	assert (
	    str(_A				)
	    == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
	    "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
	)

def 			__UpperCamelCase    (    )    ->      List[Any]:




	"""simple docstring"""





	from doctest import testmod

	testmod()

	lowerCAmelCase					:    Optional[Any]      							= LinkedList()
	linked_list.insert_head(input('Inserting 1st at head '				).strip()				)
	linked_list.insert_head(input('Inserting 2nd at head '				).strip()				)
	print('\nPrint list:'				)
	linked_list.print_list()
	linked_list.insert_tail(input('\nInserting 1st at tail '				).strip()				)
	linked_list.insert_tail(input('Inserting 2nd at tail '				).strip()				)
	print('\nPrint list:'				)
	linked_list.print_list()
	print('\nDelete head'				)
	linked_list.delete_head()
	print('Delete tail'				)
	linked_list.delete_tail()
	print('\nPrint list:'				)
	linked_list.print_list()
	print('\nReverse linked list'				)
	linked_list.reverse()
	print('\nPrint list:'				)
	linked_list.print_list()
	print('\nString representation of linked list:'				)
	print(_A				)
	print('\nReading/changing Node data using indexing:'				)
	print(F"Element at Position 1: {linked_list[1]}"				)
	lowerCAmelCase					:    Tuple      							= input('Enter New Value: '				).strip()
	print('New list:'				)
	print(_A				)
	print(F"length of linked_list is : {len(_A				)}"				)


if __name__ == "__main__":
				main()
