"""simple docstring"""







import argparse

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)
#
# 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
#
########################################################################


_a  :  int=				16
_a  :  Optional[Any]=				32
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Accelerator    ,							UpperCAmelCase_					:       int = 16						)							->    Tuple:

	'''simple docstring'''





	__snake_case    :							Tuple   	=				AutoTokenizer.from_pretrained('bert-base-cased'						)
	__snake_case    :							Optional[Any]   	=				load_dataset('glue'    ,							'mrpc'						)

	def tokenize_function(UpperCAmelCase_					:       Any						):
		# max_length=None => use the model max length (it's actually the default)
		__snake_case    :							Any   	=				tokenizer(examples['sentence1']    ,							examples['sentence2']    ,							truncation=UpperCAmelCase_    ,							max_length=UpperCAmelCase_						)
		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    :							Any   	=				datasets.map(
		    UpperCAmelCase_    ,							batched=UpperCAmelCase_    ,							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    :							int   	=				tokenized_datasets.rename_column('label'    ,							'labels'						)

	def collate_fn(UpperCAmelCase_					:       Any						):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		__snake_case    :							List[Any]   	=				1_28 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			__snake_case    :							Optional[int]   	=				16
		elif accelerator.mixed_precision != "no":
			__snake_case    :							List[Any]   	=				8
		else:
			__snake_case    :							Any   	=				None

		return tokenizer.pad(
		    UpperCAmelCase_    ,							padding='longest'    ,							max_length=UpperCAmelCase_    ,							pad_to_multiple_of=UpperCAmelCase_    ,							return_tensors='pt'    ,							)

	# Instantiate dataloaders.
	__snake_case    :							Dict   	=				DataLoader(
	    tokenized_datasets['train']    ,							shuffle=UpperCAmelCase_    ,							collate_fn=UpperCAmelCase_    ,							batch_size=UpperCAmelCase_    ,							drop_last=UpperCAmelCase_						)
	__snake_case    :							str   	=				DataLoader(
	    tokenized_datasets['validation']    ,							shuffle=UpperCAmelCase_    ,							collate_fn=UpperCAmelCase_    ,							batch_size=UpperCAmelCase_    ,							drop_last=(accelerator.mixed_precision == 'fp8')    ,							)

	return train_dataloader, eval_dataloader
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple    ,							UpperCAmelCase_					:       Tuple						)							->    List[Any]:

	'''simple docstring'''





	__snake_case    :							str   	=				Accelerator(cpu=args.cpu    ,							mixed_precision=args.mixed_precision						)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	__snake_case    :							Optional[Any]   	=				config['lr']
	__snake_case    :							Any   	=				int(config['num_epochs']						)
	__snake_case    :							int   	=				int(config['seed']						)
	__snake_case    :							Tuple   	=				int(config['batch_size']						)

	__snake_case    :							Any   	=				evaluate.load('glue'    ,							'mrpc'						)

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

	set_seed(UpperCAmelCase_						)
	__snake_case	,       __snake_case    :							List[str]   	=				get_dataloaders(UpperCAmelCase_    ,							UpperCAmelCase_						)
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	__snake_case    :							Union[str, Any]   	=				AutoModelForSequenceClassification.from_pretrained('bert-base-cased'    ,							return_dict=UpperCAmelCase_						)

	# 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    :							Any   	=				model.to(accelerator.device						)
	# Instantiate optimizer
	__snake_case    :							Optional[Any]   	=				AdamW(params=model.parameters()    ,							lr=UpperCAmelCase_						)

	# Instantiate scheduler
	__snake_case    :							Dict   	=				get_linear_schedule_with_warmup(
	    optimizer=UpperCAmelCase_    ,							num_warmup_steps=1_00    ,							num_training_steps=(len(UpperCAmelCase_						) * 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    :							Any   	=				accelerator.prepare(
	    UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_						)

	# Now we train the model
	for epoch in range(UpperCAmelCase_						):
		model.train()
		for step, batch in enumerate(UpperCAmelCase_						):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device						)
			__snake_case    :							int   	=				model(**UpperCAmelCase_						)
			__snake_case    :							List[Any]   	=				outputs.loss
			__snake_case    :							Optional[Any]   	=				loss / gradient_accumulation_steps
			accelerator.backward(UpperCAmelCase_						)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

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

		__snake_case    :							Any   	=				metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F"epoch {epoch}:"    ,							UpperCAmelCase_						)
def 						__UpperCAmelCase       (					)							->    Any:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				argparse.ArgumentParser(description='Simple example of training script.'						)
	parser.add_argument(
	    '--mixed_precision'    ,							type=UpperCAmelCase_    ,							default=UpperCAmelCase_    ,							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.'						)
	__snake_case    :							Optional[int]   	=				parser.parse_args()
	__snake_case    :							int   	=				{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
	training_function(UpperCAmelCase_    ,							UpperCAmelCase_						)


if __name__ == "__main__":
	main()






"""simple docstring"""







from typing import Any

class 	UpperCamelCase    :



				def __init__(self       :   List[str]			,						_A       :   Any)					->			int:
					__snake_case    :							Any   	=				data
					__snake_case    :							Dict   	=				None






				def __repr__(self       :   Tuple)					->			str:
					return f"Node({self.data})"







class 	UpperCamelCase    :



				def __init__(self       :   Union[str, Any])					->			Union[str, Any]:
					__snake_case    :							Any   	=				None



				def __iter__(self       :   Tuple)					->			Any:
					__snake_case    :							List[str]   	=				self.head
					while node:
						yield node.data
						__snake_case    :							Any   	=				node.next



				def __len__(self       :   str)					->			int:
					return sum(1 for _ in self)



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



				def __getitem__(self       :   List[Any]			,						_A       :   int)					->			Any:
					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       :   int			,						_A       :   int			,						_A       :   Any)					->			None:
					if not 0 <= index < len(self):
						raise ValueError('list index out of range.')
					__snake_case    :							Optional[int]   	=				self.head
					for _ in range(_A):
						__snake_case    :							Any   	=				current.next
					__snake_case    :							Dict   	=				data



				def       _lowercase  (self       :   List[Any]			,						_A       :   Any)					->			None:
					self.insert_nth(len(self)			,						_A)



				def       _lowercase  (self       :   List[str]			,						_A       :   Any)					->			None:
					self.insert_nth(0			,						_A)



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



				def       _lowercase  (self       :   Optional[int])					->			None:  # print every node data
					print(self)



				def       _lowercase  (self       :   Optional[Any])					->			Any:
					return self.delete_nth(0)



				def       _lowercase  (self       :   List[str])					->			Any:  # delete from tail
					return self.delete_nth(len(self) - 1)



				def       _lowercase  (self       :   int			,						_A       :   int = 0)					->			Any:
					if not 0 <= index <= len(self) - 1:  # test if index is valid
						raise IndexError('List index out of range.')
					__snake_case    :							int   	=				self.head  # default first node
					if index == 0:
						__snake_case    :							Any   	=				self.head.next
					else:
						__snake_case    :							List[Any]   	=				self.head
						for _ in range(index - 1):
							__snake_case    :							List[str]   	=				temp.next
						__snake_case    :							Union[str, Any]   	=				temp.next
						__snake_case    :							str   	=				temp.next.next
					return delete_node.data



				def       _lowercase  (self       :   str)					->			bool:
					return self.head is None






				def       _lowercase  (self       :   Tuple)					->			None:
					__snake_case    :							List[Any]   	=				None
					__snake_case    :							Optional[Any]   	=				self.head

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







def 						__UpperCAmelCase       (					)							->    None:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				LinkedList()
	assert linked_list.is_empty() is True
	assert str(UpperCAmelCase_						) == ""

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

	linked_list.insert_head(0						)
	linked_list.insert_tail(11						)
	assert str(UpperCAmelCase_						) == "->".join(str(UpperCAmelCase_						) 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(UpperCAmelCase_						) == 9
	assert str(UpperCAmelCase_						) == "->".join(str(UpperCAmelCase_						) 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						):
		__snake_case    :							Tuple   	=				-i
	assert all(linked_list[i] == -i for i in range(0    ,							9						)						) is True

	linked_list.reverse()
	assert str(UpperCAmelCase_						) == "->".join(str(UpperCAmelCase_						) for i in range(-8    ,							1						)						)
def 						__UpperCAmelCase       (					)							->    None:

	'''simple docstring'''





	__snake_case    :							str   	=				[
	    -9,
	    1_00,
	    Node(77_34_51_12						),
	    'dlrow olleH',
	    7,
	    55_55,
	    0,
	    -192.55_555,
	    'Hello, world!',
	    77.9,
	    Node(10						),
	    None,
	    None,
	    12.20,
	]
	__snake_case    :							Optional[Any]   	=				LinkedList()

	for i in test_input:
		linked_list.insert_tail(UpperCAmelCase_						)

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

	# Delete the head
	__snake_case    :							int   	=				linked_list.delete_head()
	assert result == -9
	assert (
	    str(UpperCAmelCase_						) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
	    "Hello, world!->77.9->Node(10)->None->None->12.2"
	)

	# Delete the tail
	__snake_case    :							Any   	=				linked_list.delete_tail()
	assert result == 12.2
	assert (
	    str(UpperCAmelCase_						) == "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
	__snake_case    :							Tuple   	=				linked_list.delete_nth(10						)
	assert result is None
	assert (
	    str(UpperCAmelCase_						) == "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(UpperCAmelCase_						)
	    == "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(UpperCAmelCase_						)
	assert (
	    str(UpperCAmelCase_						)
	    == "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(UpperCAmelCase_						)
	    == "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()

	__snake_case    :							int   	=				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(UpperCAmelCase_						)
	print('\nReading/changing Node data using indexing:'						)
	print(F"Element at Position 1: {linked_list[1]}"						)
	__snake_case    :							Optional[Any]   	=				input('Enter New Value: '						).strip()
	print('New list:'						)
	print(UpperCAmelCase_						)
	print(F"length of linked_list is : {len(UpperCAmelCase_						)}"						)


if __name__ == "__main__":
	main()





