

'''simple docstring'''

import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin

def     __magic_name__       ( A=2					,   A=True					,   A=False							)			->		Union[str, Any]:
	snake_case							 =				Accelerator(step_scheduler_with_optimizer=A					,   split_batches=A							)
	snake_case							 =				torch.nn.Linear(2					,   4							)
	snake_case							 =				torch.optim.AdamW(model.parameters()					,   lr=1.0							)
	snake_case							 =				torch.optim.lr_scheduler.OneCycleLR(A					,   max_lr=0.01					,   steps_per_epoch=2					,   epochs=1							)
	snake_case , snake_case , snake_case							 =				accelerator.prepare(A					,   A					,   A							)

	# Optimizer has stepped
	scheduler.step()
	if step_scheduler_with_optimizer or (num_processes == 1):
		assert (
		    scheduler.scheduler.last_epoch == num_processes
		), F'''Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'''
	else:
		assert (
		    scheduler.scheduler.last_epoch != num_processes
		), F'''Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'''

def     __magic_name__       ( A=2					,   A=True					,   A=False							)			->		Dict:
	snake_case							 =				Accelerator(step_scheduler_with_optimizer=A					,   split_batches=A							)
	snake_case							 =				torch.nn.Linear(2					,   4							)
	snake_case							 =				torch.optim.AdamW(model.parameters()					,   lr=1.0							)
	snake_case							 =				torch.optim.lr_scheduler.LambdaLR(A					,   lr_lambda=lambda A							: 1 - n / 1_0							)
	snake_case , snake_case , snake_case							 =				accelerator.prepare(A					,   A					,   A							)

	# Optimizer has stepped
	snake_case							 =				False
	scheduler.step()
	snake_case							 =				1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 1_0
	assert (
	    scheduler.get_last_lr()[0] == expected_lr
	), F'''Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''

	# Optimizer has not stepped
	snake_case							 =				True
	scheduler.step()
	if not step_scheduler_with_optimizer:
		snake_case							 =				1 - 2 / 1_0
	assert (
	    scheduler.get_last_lr()[0] == expected_lr
	), F'''Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''

def     __magic_name__       ( A = 2							)			->		Any:
	from transformers import get_linear_schedule_with_warmup

	snake_case							 =				[1, 2, 4]
	for num_steps in steps:
		snake_case							 =				GradientAccumulationPlugin(num_steps=A					,   adjust_scheduler=num_steps > 1							)
		snake_case							 =				Accelerator(gradient_accumulation_plugin=A							)
		snake_case							 =				torch.nn.Linear(2					,   4							)
		snake_case							 =				torch.optim.AdamW(model.parameters()					,   lr=10.0							)
		snake_case							 =				get_linear_schedule_with_warmup(optimizer=A					,   num_warmup_steps=0					,   num_training_steps=2_0							)

		snake_case , snake_case , snake_case							 =				accelerator.prepare(A					,   A					,   A							)

		for i in range(1_0 * num_steps							):
			with accelerator.accumulate(A							):
				optimizer.step()
				scheduler.step()

			if i == (1_0 * num_steps - 2):
				assert (
				    scheduler.get_last_lr()[0] != 0
				), F'''Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'''
		assert (
		    scheduler.get_last_lr()[0] == 0
		), F'''Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'''
		GradientState._reset_state()







@require_cpu
class lowerCamelCase      (						unittest.TestCase							):

				def 				_lowerCamelCase						(					self   )						->       Optional[Any]:
					debug_launcher(partial(lowercase_,	num_processes=1   ),	num_processes=1   )
					debug_launcher(partial(lowercase_,	num_processes=1,	split_batches=lowercase_   ),	num_processes=1   )

				def 				_lowerCamelCase						(					self   )						->       str:
					debug_launcher(partial(lowercase_,	num_processes=1   ),	num_processes=1   )
					debug_launcher(partial(lowercase_,	num_processes=1,	split_batches=lowercase_   ),	num_processes=1   )

				def 				_lowerCamelCase						(					self   )						->       Optional[Any]:
					debug_launcher(partial(lowercase_,	num_processes=1,	step_scheduler_with_optimizer=lowercase_   ),	num_processes=1   )

				def 				_lowerCamelCase						(					self   )						->       Tuple:
					debug_launcher(partial(lowercase_,	num_processes=1,	step_scheduler_with_optimizer=lowercase_   ),	num_processes=1   )

				def 				_lowerCamelCase						(					self   )						->       Optional[Any]:
					AcceleratorState._reset_state(lowercase_   )
					debug_launcher(lowercase_   )
					debug_launcher(partial(lowercase_,	num_processes=1,	split_batches=lowercase_   ),	num_processes=1   )

				def 				_lowerCamelCase						(					self   )						->       Optional[Any]:
					AcceleratorState._reset_state(lowercase_   )
					debug_launcher(lowercase_   )
					debug_launcher(partial(lowercase_,	num_processes=1,	split_batches=lowercase_   ),	num_processes=1   )

				def 				_lowerCamelCase						(					self   )						->       Tuple:
					AcceleratorState._reset_state(lowercase_   )
					debug_launcher(partial(lowercase_,	step_scheduler_with_optimizer=lowercase_   )   )

				def 				_lowerCamelCase						(					self   )						->       Dict:
					AcceleratorState._reset_state(lowercase_   )
					debug_launcher(partial(lowercase_,	step_scheduler_with_optimizer=lowercase_   )   )






				@require_huggingface_suite
				def 				_lowerCamelCase						(					self   )						->       str:
					AcceleratorState._reset_state(lowercase_   )
					debug_launcher(partial(lowercase_,	num_processes=1   )   )
					debug_launcher(lowercase_   )








'''simple docstring'''

lowerCAmelCase_						   =			{0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

lowerCAmelCase_						   =			{0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}

def     __magic_name__       ( A					,   A					,   A							)			->		list:
	snake_case							 =				True
	snake_case							 =				[]

	for neighbour in graph[vert]:
		if not visited[neighbour]:
			connected_verts += dfs(A					,   A					,   A							)

	return [vert, *connected_verts]

def     __magic_name__       ( A							)			->		list:
	snake_case							 =				len(A							)
	snake_case							 =				graph_size * [False]
	snake_case							 =				[]

	for i in range(A							):
		if not visited[i]:
			snake_case							 =				dfs(A					,   A					,   A							)
			components_list.append(A							)

	return components_list


if __name__ == "__main__":
						import doctest

						doctest.testmod()





