





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        __snake_case       (						lowerCAmelCase_=2   ,       lowerCAmelCase_=True   ,       lowerCAmelCase_=False  )							->	Optional[int]:
    SCREAMING_SNAKE_CASE__       							= Accelerator(step_scheduler_with_optimizer=lowerCAmelCase_   ,       split_batches=lowerCAmelCase_  )
    SCREAMING_SNAKE_CASE__       							= torch.nn.Linear(2   ,       4  )
    SCREAMING_SNAKE_CASE__       							= torch.optim.AdamW(model.parameters()   ,       lr=1.0  )
    SCREAMING_SNAKE_CASE__       							= torch.optim.lr_scheduler.OneCycleLR(lowerCAmelCase_   ,       max_lr=0.01   ,       steps_per_epoch=2   ,       epochs=1  )
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= 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        __snake_case       (						lowerCAmelCase_=2   ,       lowerCAmelCase_=True   ,       lowerCAmelCase_=False  )							->	Dict:
    SCREAMING_SNAKE_CASE__       							= Accelerator(step_scheduler_with_optimizer=lowerCAmelCase_   ,       split_batches=lowerCAmelCase_  )
    SCREAMING_SNAKE_CASE__       							= torch.nn.Linear(2   ,       4  )
    SCREAMING_SNAKE_CASE__       							= torch.optim.AdamW(model.parameters()   ,       lr=1.0  )
    SCREAMING_SNAKE_CASE__       							= torch.optim.lr_scheduler.LambdaLR(lowerCAmelCase_   ,       lr_lambda=lambda lowerCAmelCase_  : 1 - n / 1_0  )
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= accelerator.prepare(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )

    # Optimizer has stepped
    SCREAMING_SNAKE_CASE__       							= False
    scheduler.step()
    SCREAMING_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
    SCREAMING_SNAKE_CASE__       							= True
    scheduler.step()
    if not step_scheduler_with_optimizer:
        SCREAMING_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        __snake_case       (						lowerCAmelCase_ = 2  )							->	List[Any]:
    from transformers import get_linear_schedule_with_warmup

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

        SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= 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 				__snake_case  (     unittest.TestCase						):


    '''simple docstring'''







    def    lowercase_				(					self    ):





        '''simple docstring'''

        debug_launcher(partial(A_      ,							num_processes=1    )      ,							num_processes=1    )
        debug_launcher(partial(A_      ,							num_processes=1      ,							split_batches=A_    )      ,							num_processes=1    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        debug_launcher(partial(A_      ,							num_processes=1    )      ,							num_processes=1    )
        debug_launcher(partial(A_      ,							num_processes=1      ,							split_batches=A_    )      ,							num_processes=1    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        debug_launcher(partial(A_      ,							num_processes=1      ,							step_scheduler_with_optimizer=A_    )      ,							num_processes=1    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        debug_launcher(partial(A_      ,							num_processes=1      ,							step_scheduler_with_optimizer=A_    )      ,							num_processes=1    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        AcceleratorState._reset_state(A_    )
        debug_launcher(A_    )
        debug_launcher(partial(A_      ,							num_processes=1      ,							split_batches=A_    )      ,							num_processes=1    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        AcceleratorState._reset_state(A_    )
        debug_launcher(A_    )
        debug_launcher(partial(A_      ,							num_processes=1      ,							split_batches=A_    )      ,							num_processes=1    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        AcceleratorState._reset_state(A_    )
        debug_launcher(partial(A_      ,							step_scheduler_with_optimizer=A_    )    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        AcceleratorState._reset_state(A_    )
        debug_launcher(partial(A_      ,							step_scheduler_with_optimizer=A_    )    )




    @require_huggingface_suite
    def    lowercase_				(					self    ):





        '''simple docstring'''

        AcceleratorState._reset_state(A_    )
        debug_launcher(partial(A_      ,							num_processes=1    )    )
        debug_launcher(A_    )







from __future__ import annotations

from collections.abc import Generator


def        __snake_case       (						lowerCAmelCase_  )							->	Generator[int, None, None]:
    if not isinstance(lowerCAmelCase_   ,       lowerCAmelCase_  ) or n < 1:
        raise Exception('''Sequence only defined for positive integers'''  )

    yield n
    while n != 1:
        if n % 2 == 0:
            n //= 2
        else:
            SCREAMING_SNAKE_CASE__       							= 3 * n + 1
        yield n


def        __snake_case       (						)							->	str:
    SCREAMING_SNAKE_CASE__       							= 4_3
    SCREAMING_SNAKE_CASE__       							= tuple(collatz_sequence(lowerCAmelCase_  )  )
    print(lowerCAmelCase_  )
    print(f'''Collatz sequence from {n} took {len(lowerCAmelCase_  )} steps.'''  )


if __name__ == "__main__":
  main()
