
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   _A       (    SCREAMING_SNAKE_CASE__  :     Tuple=2					,  SCREAMING_SNAKE_CASE__  :     Optional[Any]=True					,  SCREAMING_SNAKE_CASE__  :     Optional[Any]=False	):
    UpperCamelCase	:int     							=       Accelerator(step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE__					,  split_batches=SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:Dict     							=       torch.nn.Linear(2					,  4	)
    UpperCamelCase	:Optional[Any]     							=       torch.optim.AdamW(model.parameters()					,  lr=1.0	)
    UpperCamelCase	:Union[str, Any]     							=       torch.optim.lr_scheduler.OneCycleLR(SCREAMING_SNAKE_CASE__					,  max_lr=0.01					,  steps_per_epoch=2					,  epochs=1	)
    UpperCamelCase	:int     							=       accelerator.prepare(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)

    # 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   _A       (    SCREAMING_SNAKE_CASE__  :     Dict=2					,  SCREAMING_SNAKE_CASE__  :     int=True					,  SCREAMING_SNAKE_CASE__  :     Any=False	):
    UpperCamelCase	:Any     							=       Accelerator(step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE__					,  split_batches=SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:Any     							=       torch.nn.Linear(2					,  4	)
    UpperCamelCase	:int     							=       torch.optim.AdamW(model.parameters()					,  lr=1.0	)
    UpperCamelCase	:str     							=       torch.optim.lr_scheduler.LambdaLR(SCREAMING_SNAKE_CASE__					,  lr_lambda=lambda SCREAMING_SNAKE_CASE__	: 1 - n / 10	)
    UpperCamelCase	:Tuple     							=       accelerator.prepare(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)

    # Optimizer has stepped
    UpperCamelCase	:Optional[int]     							=       False
    scheduler.step()
    UpperCamelCase	:Optional[int]     							=       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	:Optional[int]     							=       True
    scheduler.step()
    if not step_scheduler_with_optimizer:
        UpperCamelCase	:Optional[int]     							=       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   _A       (    SCREAMING_SNAKE_CASE__  :     int = 2	):
    from transformers import get_linear_schedule_with_warmup

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

        UpperCamelCase	:List[str]     							=       accelerator.prepare(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)

        for i in range(10 * num_steps	):
            with accelerator.accumulate(SCREAMING_SNAKE_CASE__	):
                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       UpperCAmelCase_						( unittest.TestCase			):



      """simple docstring"""
      def    UpperCAmelCase						(    self			)    ->				Tuple:
          debug_launcher(partial(SCREAMING_SNAKE_CASE_   ,    num_processes=1			)   ,    num_processes=1			)
          debug_launcher(partial(SCREAMING_SNAKE_CASE_   ,    num_processes=1   ,    split_batches=SCREAMING_SNAKE_CASE_			)   ,    num_processes=1			)
      def    UpperCAmelCase						(    self			)    ->				Optional[Any]:
          debug_launcher(partial(SCREAMING_SNAKE_CASE_   ,    num_processes=1			)   ,    num_processes=1			)
          debug_launcher(partial(SCREAMING_SNAKE_CASE_   ,    num_processes=1   ,    split_batches=SCREAMING_SNAKE_CASE_			)   ,    num_processes=1			)
      def    UpperCAmelCase						(    self			)    ->				Optional[int]:
          debug_launcher(partial(SCREAMING_SNAKE_CASE_   ,    num_processes=1   ,    step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE_			)   ,    num_processes=1			)
      def    UpperCAmelCase						(    self			)    ->				Tuple:
          debug_launcher(partial(SCREAMING_SNAKE_CASE_   ,    num_processes=1   ,    step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE_			)   ,    num_processes=1			)
      def    UpperCAmelCase						(    self			)    ->				Union[str, Any]:
          AcceleratorState._reset_state(SCREAMING_SNAKE_CASE_			)
          debug_launcher(SCREAMING_SNAKE_CASE_			)
          debug_launcher(partial(SCREAMING_SNAKE_CASE_   ,    num_processes=1   ,    split_batches=SCREAMING_SNAKE_CASE_			)   ,    num_processes=1			)
      def    UpperCAmelCase						(    self			)    ->				List[Any]:
          AcceleratorState._reset_state(SCREAMING_SNAKE_CASE_			)
          debug_launcher(SCREAMING_SNAKE_CASE_			)
          debug_launcher(partial(SCREAMING_SNAKE_CASE_   ,    num_processes=1   ,    split_batches=SCREAMING_SNAKE_CASE_			)   ,    num_processes=1			)
      def    UpperCAmelCase						(    self			)    ->				Optional[Any]:
          AcceleratorState._reset_state(SCREAMING_SNAKE_CASE_			)
          debug_launcher(partial(SCREAMING_SNAKE_CASE_   ,    step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE_			)			)
      def    UpperCAmelCase						(    self			)    ->				Dict:
          AcceleratorState._reset_state(SCREAMING_SNAKE_CASE_			)
          debug_launcher(partial(SCREAMING_SNAKE_CASE_   ,    step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE_			)			)




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













from __future__ import annotations

from enum import Enum







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Optional[Any]									=24
      UpperCamelCase_    : Any									=21
      UpperCamelCase_    : Union[str, Any]									=18
      UpperCamelCase_    : Tuple									=15
      UpperCamelCase_    : List[Any]									=12
      UpperCamelCase_    : Optional[Any]									=9
      UpperCamelCase_    : List[str]									=6
      UpperCamelCase_    : List[str]									=3
      UpperCamelCase_    : Any									=2
      UpperCamelCase_    : List[str]									=1
      UpperCamelCase_    : Optional[Any]									=-1
      UpperCamelCase_    : str									=-2
      UpperCamelCase_    : Any									=-3
      UpperCamelCase_    : Optional[int]									=-6
      UpperCamelCase_    : int									=-9
      UpperCamelCase_    : Optional[int]									=-12
      UpperCamelCase_    : Optional[Any]									=-15
      UpperCamelCase_    : str									=-18
      UpperCamelCase_    : int									=-21
      UpperCamelCase_    : Union[str, Any]									=-24







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=8
      UpperCamelCase_    : str									=7
      UpperCamelCase_    : List[str]									=6
      UpperCamelCase_    : List[str]									=5
      UpperCamelCase_    : int									=4
      UpperCamelCase_    : Optional[Any]									=3
      UpperCamelCase_    : Optional[Any]									=2
      UpperCamelCase_    : List[str]									=1







def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     str | SIUnit					,  SCREAMING_SNAKE_CASE__  :     str | SIUnit					,  ):
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:str     							=       SIUnit[known_prefix.lower()]
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:Any     							=       SIUnit[unknown_prefix.lower()]
    UpperCamelCase	:float     							=       known_amount * (
        10 ** (known_prefix.value - unknown_prefix.value)
    )
    return unknown_amount


def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     str | BinaryUnit					,  SCREAMING_SNAKE_CASE__  :     str | BinaryUnit					,  ):
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:Dict     							=       BinaryUnit[known_prefix.lower()]
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:List[str]     							=       BinaryUnit[unknown_prefix.lower()]
    UpperCamelCase	:float     							=       known_amount * (
        2 ** ((known_prefix.value - unknown_prefix.value) * 10)
    )
    return unknown_amount


if __name__ == "__main__":
 import doctest

 doctest.testmod()





