






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

  # Optimizer has stepped
  UpperCamelCase_:					Optional[Any]          =				False
  scheduler.step()
  UpperCamelCase_:					List[Any]          =				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
  UpperCamelCase_:					Optional[Any]          =				True
  scheduler.step()
  if not step_scheduler_with_optimizer:
    UpperCamelCase_:					Tuple          =				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					(UpperCAmelCase__ = 2  )       ->				int:
  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_:					int          =				Accelerator(gradient_accumulation_plugin=SCREAMING_SNAKE_CASE__  )
    UpperCamelCase_:					int          =				torch.nn.Linear(2	,    4  )
    UpperCamelCase_:					List[str]          =				torch.optim.AdamW(model.parameters()	,    lr=10.0  )
    UpperCamelCase_:					str          =				get_linear_schedule_with_warmup(optimizer=SCREAMING_SNAKE_CASE__	,    num_warmup_steps=0	,    num_training_steps=2_0  )

    UpperCamelCase_:					Any          =				accelerator.prepare(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  )

    for i in range(1_0 * num_steps  ):
      with accelerator.accumulate(SCREAMING_SNAKE_CASE__  ):
        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					):





       """simple docstring"""







       def 			_a    (					self							):
         debug_launcher(partial(UpperCamelCase__  ,			num_processes=1							)  ,			num_processes=1							)
         debug_launcher(partial(UpperCamelCase__  ,			num_processes=1  ,			split_batches=UpperCamelCase__							)  ,			num_processes=1							)





       def 			_a    (					self							):
         debug_launcher(partial(UpperCamelCase__  ,			num_processes=1							)  ,			num_processes=1							)
         debug_launcher(partial(UpperCamelCase__  ,			num_processes=1  ,			split_batches=UpperCamelCase__							)  ,			num_processes=1							)





       def 			_a    (					self							):
         debug_launcher(partial(UpperCamelCase__  ,			num_processes=1  ,			step_scheduler_with_optimizer=UpperCamelCase__							)  ,			num_processes=1							)





       def 			_a    (					self							):
         debug_launcher(partial(UpperCamelCase__  ,			num_processes=1  ,			step_scheduler_with_optimizer=UpperCamelCase__							)  ,			num_processes=1							)





       def 			_a    (					self							):
         AcceleratorState._reset_state(UpperCamelCase__							)
         debug_launcher(UpperCamelCase__							)
         debug_launcher(partial(UpperCamelCase__  ,			num_processes=1  ,			split_batches=UpperCamelCase__							)  ,			num_processes=1							)





       def 			_a    (					self							):
         AcceleratorState._reset_state(UpperCamelCase__							)
         debug_launcher(UpperCamelCase__							)
         debug_launcher(partial(UpperCamelCase__  ,			num_processes=1  ,			split_batches=UpperCamelCase__							)  ,			num_processes=1							)





       def 			_a    (					self							):
         AcceleratorState._reset_state(UpperCamelCase__							)
         debug_launcher(partial(UpperCamelCase__  ,			step_scheduler_with_optimizer=UpperCamelCase__							)							)





       def 			_a    (					self							):
         AcceleratorState._reset_state(UpperCamelCase__							)
         debug_launcher(partial(UpperCamelCase__  ,			step_scheduler_with_optimizer=UpperCamelCase__							)							)





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

'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__ = 100      ,    )   ->				float:


		'''simple docstring'''


		snake_case   :    int			       =		x_start
		snake_case   :    Tuple			       =		fnc(SCREAMING_SNAKE_CASE__       )
		snake_case   :    Dict			       =		0.0

		for _ in range(SCREAMING_SNAKE_CASE__       ):
				# Approximates small segments of curve as linear and solve
				# for trapezoidal area
				snake_case   :    str			       =		(x_end - x_start) / steps + xa
				snake_case   :    str			       =		fnc(SCREAMING_SNAKE_CASE__       )
				area += abs(fxa + fxa       ) * (xa - xa) / 2

				# Increment step
				snake_case   :    Any			       =		xa
				snake_case   :    List[str]			       =		fxa
		return area


if __name__ == "__main__":




						def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				str:


								'''simple docstring'''


								return x**3

						print("f(x) = x^3")
						print("The area between the curve, x = -10, x = 10 and the x axis is:")
						lowercase__		    =	1_0
						while i <= 1_0_0_0_0_0:
												lowercase__		    =	trapezoidal_area(f, -5, 5, i)
												print(f"with {i} steps: {area}")
												i *= 1_0


