


'''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__(    lowerCamelCase=2,					lowerCamelCase=True,					lowerCamelCase=False):
						__lowerCAmelCase											=					Accelerator(step_scheduler_with_optimizer=lowerCamelCase,					split_batches=lowerCamelCase)
						__lowerCAmelCase											=					torch.nn.Linear(2,					4)
						__lowerCAmelCase											=					torch.optim.AdamW(model.parameters(),					lr=1.0)
						__lowerCAmelCase											=					torch.optim.lr_scheduler.OneCycleLR(lowerCamelCase,					max_lr=0.01,					steps_per_epoch=2,					epochs=1)
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=					accelerator.prepare(lowerCamelCase,					lowerCamelCase,					lowerCamelCase)

						# 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__(    lowerCamelCase=2,					lowerCamelCase=True,					lowerCamelCase=False):
						__lowerCAmelCase											=					Accelerator(step_scheduler_with_optimizer=lowerCamelCase,					split_batches=lowerCamelCase)
						__lowerCAmelCase											=					torch.nn.Linear(2,					4)
						__lowerCAmelCase											=					torch.optim.AdamW(model.parameters(),					lr=1.0)
						__lowerCAmelCase											=					torch.optim.lr_scheduler.LambdaLR(lowerCamelCase,					lr_lambda=lambda lowerCamelCase: 1 - n / 1_0)
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=					accelerator.prepare(lowerCamelCase,					lowerCamelCase,					lowerCamelCase)

						# Optimizer has stepped
						__lowerCAmelCase											=					False
						scheduler.step()
						__lowerCAmelCase											=					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
						__lowerCAmelCase											=					True
						scheduler.step()
						if not step_scheduler_with_optimizer:
												__lowerCAmelCase											=					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__(    lowerCamelCase = 2):
						from transformers import get_linear_schedule_with_warmup

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

												__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=					accelerator.prepare(lowerCamelCase,					lowerCamelCase,					lowerCamelCase)

												for i in range(1_0 * num_steps):
																		with accelerator.accumulate(lowerCamelCase):
																								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    a__					(			unittest.TestCase   ):







				"""simple docstring"""



				def        _snake_case	(self		):
										debug_launcher(partial(__lowercase							,      num_processes=1		)							,      num_processes=1		)
										debug_launcher(partial(__lowercase							,      num_processes=1							,      split_batches=__lowercase		)							,      num_processes=1		)




				def        _snake_case	(self		):
										debug_launcher(partial(__lowercase							,      num_processes=1		)							,      num_processes=1		)
										debug_launcher(partial(__lowercase							,      num_processes=1							,      split_batches=__lowercase		)							,      num_processes=1		)




				def        _snake_case	(self		):
										debug_launcher(partial(__lowercase							,      num_processes=1							,      step_scheduler_with_optimizer=__lowercase		)							,      num_processes=1		)




				def        _snake_case	(self		):
										debug_launcher(partial(__lowercase							,      num_processes=1							,      step_scheduler_with_optimizer=__lowercase		)							,      num_processes=1		)




				def        _snake_case	(self		):
										AcceleratorState._reset_state(__lowercase		)
										debug_launcher(__lowercase		)
										debug_launcher(partial(__lowercase							,      num_processes=1							,      split_batches=__lowercase		)							,      num_processes=1		)




				def        _snake_case	(self		):
										AcceleratorState._reset_state(__lowercase		)
										debug_launcher(__lowercase		)
										debug_launcher(partial(__lowercase							,      num_processes=1							,      split_batches=__lowercase		)							,      num_processes=1		)




				def        _snake_case	(self		):
										AcceleratorState._reset_state(__lowercase		)
										debug_launcher(partial(__lowercase							,      step_scheduler_with_optimizer=__lowercase		)		)




				def        _snake_case	(self		):
										AcceleratorState._reset_state(__lowercase		)
										debug_launcher(partial(__lowercase							,      step_scheduler_with_optimizer=__lowercase		)		)




				@require_huggingface_suite
				def        _snake_case	(self		):
										AcceleratorState._reset_state(__lowercase		)
										debug_launcher(partial(__lowercase							,      num_processes=1		)		)
										debug_launcher(__lowercase		)





'''simple docstring'''

from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_UpperCAmelCase					:			Any				     =					TypeVar("""T""", bound=bool)


class    a__					(			Generic[T]   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase		):
										__lowerCAmelCase											=					value
										__lowerCAmelCase											=					None
										__lowerCAmelCase											=					None




				@property
				def        _snake_case	(self		):
										return self._value




				@staticmethod
				def        _snake_case	(__lowercase							,      __lowercase		):
										if not roota:
																return roota

										if not roota:
																return roota

										if roota.value > roota.value:
																__lowerCAmelCase							,       __lowerCAmelCase											=					roota, roota

										if random.choice([True, False]		):
																__lowerCAmelCase							,       __lowerCAmelCase											=					roota.right, roota.left

										__lowerCAmelCase											=					RandomizedHeapNode.merge(roota.left							,      __lowercase		)

										return roota


class    a__					(			Generic[T]   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase = ()		):
										__lowerCAmelCase											=					None

										if data:
																for item in data:
																						self.insert(__lowercase		)




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					RandomizedHeapNode.merge(self._root							,      RandomizedHeapNode(__lowercase		)		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.top()

										if self._root is None:
																return None

										__lowerCAmelCase											=					RandomizedHeapNode.merge(self._root.left							,      self._root.right		)

										return result




				def        _snake_case	(self		):
										if not self._root:
																raise IndexError('''Can\'t get top element for the empty heap.'''		)
										return self._root.value




				def        _snake_case	(self		):
										__lowerCAmelCase											=					None




				def        _snake_case	(self		):
										__lowerCAmelCase											=					[]
										while self:
																result.append(self.pop()		)

										return result




				def __bool__(self		):
										return self._root is not None


if __name__ == "__main__":
	import doctest

	doctest.testmod()

