




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        __SCREAMING_SNAKE_CASE (	lowercase_=2       ,  lowercase_=True       ,  lowercase_=False   )							->						Tuple:

    """simple docstring"""


    __UpperCamelCase			=    Accelerator(step_scheduler_with_optimizer=lowercase_       ,  split_batches=lowercase_   )
    __UpperCamelCase			=    torch.nn.Linear(2       ,  4   )
    __UpperCamelCase			=    torch.optim.AdamW(model.parameters()       ,  lr=1.0   )
    __UpperCamelCase			=    torch.optim.lr_scheduler.OneCycleLR(lowercase_       ,  max_lr=0.01       ,  steps_per_epoch=2       ,  epochs=1   )
    __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    accelerator.prepare(lowercase_       ,  lowercase_       ,  lowercase_   )

    # 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        __SCREAMING_SNAKE_CASE (	lowercase_=2       ,  lowercase_=True       ,  lowercase_=False   )							->						Optional[int]:

    """simple docstring"""


    __UpperCamelCase			=    Accelerator(step_scheduler_with_optimizer=lowercase_       ,  split_batches=lowercase_   )
    __UpperCamelCase			=    torch.nn.Linear(2       ,  4   )
    __UpperCamelCase			=    torch.optim.AdamW(model.parameters()       ,  lr=1.0   )
    __UpperCamelCase			=    torch.optim.lr_scheduler.LambdaLR(lowercase_       ,  lr_lambda=lambda lowercase_   : 1 - n / 10   )
    __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    accelerator.prepare(lowercase_       ,  lowercase_       ,  lowercase_   )

    # Optimizer has stepped
    __UpperCamelCase			=    False
    scheduler.step()
    __UpperCamelCase			=    1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
    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
    __UpperCamelCase			=    True
    scheduler.step()
    if not step_scheduler_with_optimizer:
        __UpperCamelCase			=    1 - 2 / 10
    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        __SCREAMING_SNAKE_CASE (	lowercase_ = 2   )							->						Union[str, Any]:

    """simple docstring"""


    from transformers import get_linear_schedule_with_warmup

    __UpperCamelCase			=    [1, 2, 4]
    for num_steps in steps:
        __UpperCamelCase			=    GradientAccumulationPlugin(num_steps=lowercase_       ,  adjust_scheduler=num_steps > 1   )
        __UpperCamelCase			=    Accelerator(gradient_accumulation_plugin=lowercase_   )
        __UpperCamelCase			=    torch.nn.Linear(2       ,  4   )
        __UpperCamelCase			=    torch.optim.AdamW(model.parameters()       ,  lr=10.0   )
        __UpperCamelCase			=    get_linear_schedule_with_warmup(optimizer=lowercase_       ,  num_warmup_steps=0       ,  num_training_steps=20   )

        __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    accelerator.prepare(lowercase_       ,  lowercase_       ,  lowercase_   )

        for i in range(10 * num_steps   ):
            with accelerator.accumulate(lowercase_   ):
                optimizer.step()
                scheduler.step()

            if i == (10 * 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 ):

      """simple docstring"""

      def    snake_case						(  self       :			List[str]					):
          debug_launcher(partial(snake_case			,							num_processes=1					)			,							num_processes=1					)
          debug_launcher(partial(snake_case			,							num_processes=1			,							split_batches=snake_case					)			,							num_processes=1					)







      def    snake_case						(  self       :			Optional[int]					):
          debug_launcher(partial(snake_case			,							num_processes=1					)			,							num_processes=1					)
          debug_launcher(partial(snake_case			,							num_processes=1			,							split_batches=snake_case					)			,							num_processes=1					)







      def    snake_case						(  self       :			str					):
          debug_launcher(partial(snake_case			,							num_processes=1			,							step_scheduler_with_optimizer=snake_case					)			,							num_processes=1					)







      def    snake_case						(  self       :			int					):
          debug_launcher(partial(snake_case			,							num_processes=1			,							step_scheduler_with_optimizer=snake_case					)			,							num_processes=1					)







      def    snake_case						(  self       :			Optional[int]					):
          AcceleratorState._reset_state(snake_case					)
          debug_launcher(snake_case					)
          debug_launcher(partial(snake_case			,							num_processes=1			,							split_batches=snake_case					)			,							num_processes=1					)







      def    snake_case						(  self       :			Dict					):
          AcceleratorState._reset_state(snake_case					)
          debug_launcher(snake_case					)
          debug_launcher(partial(snake_case			,							num_processes=1			,							split_batches=snake_case					)			,							num_processes=1					)







      def    snake_case						(  self       :			int					):
          AcceleratorState._reset_state(snake_case					)
          debug_launcher(partial(snake_case			,							step_scheduler_with_optimizer=snake_case					)					)







      def    snake_case						(  self       :			List[Any]					):
          AcceleratorState._reset_state(snake_case					)
          debug_launcher(partial(snake_case			,							step_scheduler_with_optimizer=snake_case					)					)







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












class 			_lowerCamelCase    :

      """simple docstring"""

      def __init__(  self       :			Optional[Any]			,							snake_case       :			int					):
          __UpperCamelCase			=    data
          __UpperCamelCase			=    42
          __UpperCamelCase			=    42







def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						None:

    """simple docstring"""


    __UpperCamelCase			=    0
    __UpperCamelCase			=    x




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						None:

    """simple docstring"""


    __UpperCamelCase		,   __UpperCamelCase			=    find_set(lowercase_   ), find_set(lowercase_   )
    if x == y:
        return

    elif x.rank > y.rank:
        __UpperCamelCase			=    x
    else:
        __UpperCamelCase			=    y
        if x.rank == y.rank:
            y.rank += 1




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						Node:

    """simple docstring"""


    if x != x.parent:
        __UpperCamelCase			=    find_set(x.parent   )
    return x.parent




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						set:

    """simple docstring"""


    __UpperCamelCase			=    ({0, 1, 2}, {3, 4, 5})
    for s in sets:
        if node.data in s:
            return s
    __UpperCamelCase			=    F"{node.data} is not in {sets}"
    raise ValueError(lowercase_   )




def        __SCREAMING_SNAKE_CASE (	)							->						None:

    """simple docstring"""


    __UpperCamelCase			=    [Node(lowercase_   ) for i in range(6   )]
    for v in vertex:
        make_set(lowercase_   )

    union_set(vertex[0]       ,  vertex[1]   )
    union_set(vertex[1]       ,  vertex[2]   )
    union_set(vertex[3]       ,  vertex[4]   )
    union_set(vertex[3]       ,  vertex[5]   )

    for nodea in vertex:
        for nodea in vertex:
            if find_python_set(lowercase_   ).isdisjoint(find_python_set(lowercase_   )   ):
                assert find_set(lowercase_   ) != find_set(lowercase_   )
            else:
                assert find_set(lowercase_   ) == find_set(lowercase_   )


if __name__ == "__main__":
 test_disjoint_set()






