
"""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       __a	(						__lowerCamelCase=2,						__lowerCamelCase=True,						__lowerCamelCase=False    ):
   UpperCAmelCase_   :   str     	=       Accelerator(step_scheduler_with_optimizer=__lowerCamelCase,						split_batches=__lowerCamelCase    )
   UpperCAmelCase_   :   int     	=       torch.nn.Linear(2,						4    )
   UpperCAmelCase_   :   Union[str, Any]     	=       torch.optim.AdamW(model.parameters(),						lr=1.0    )
   UpperCAmelCase_   :   Dict     	=       torch.optim.lr_scheduler.OneCycleLR(__lowerCamelCase,						max_lr=0.01,						steps_per_epoch=2,						epochs=1    )
   UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :   Any     	=       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       __a	(						__lowerCamelCase=2,						__lowerCamelCase=True,						__lowerCamelCase=False    ):
   UpperCAmelCase_   :   List[Any]     	=       Accelerator(step_scheduler_with_optimizer=__lowerCamelCase,						split_batches=__lowerCamelCase    )
   UpperCAmelCase_   :   Union[str, Any]     	=       torch.nn.Linear(2,						4    )
   UpperCAmelCase_   :   int     	=       torch.optim.AdamW(model.parameters(),						lr=1.0    )
   UpperCAmelCase_   :   Any     	=       torch.optim.lr_scheduler.LambdaLR(__lowerCamelCase,						lr_lambda=lambda __lowerCamelCase    : 1 - n / 10    )
   UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :   Union[str, Any]     	=       accelerator.prepare(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )

   # Optimizer has stepped
   UpperCAmelCase_   :   int     	=       False
   scheduler.step()
   UpperCAmelCase_   :   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[Any]     	=       True
   scheduler.step()
   if not step_scheduler_with_optimizer:
      UpperCAmelCase_   :   Dict     	=       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	(						__lowerCamelCase = 2    ):
   from transformers import get_linear_schedule_with_warmup

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

      UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[Any]     	=       accelerator.prepare(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )

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




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     debug_launcher(partial(lowercase_							,  num_processes=1    )							,  num_processes=1    )
     debug_launcher(partial(lowercase_							,  num_processes=1							,  split_batches=lowercase_    )							,  num_processes=1    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     debug_launcher(partial(lowercase_							,  num_processes=1    )							,  num_processes=1    )
     debug_launcher(partial(lowercase_							,  num_processes=1							,  split_batches=lowercase_    )							,  num_processes=1    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     debug_launcher(partial(lowercase_							,  num_processes=1							,  step_scheduler_with_optimizer=lowercase_    )							,  num_processes=1    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     debug_launcher(partial(lowercase_							,  num_processes=1							,  step_scheduler_with_optimizer=lowercase_    )							,  num_processes=1    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     AcceleratorState._reset_state(lowercase_    )
     debug_launcher(lowercase_    )
     debug_launcher(partial(lowercase_							,  num_processes=1							,  split_batches=lowercase_    )							,  num_processes=1    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     AcceleratorState._reset_state(lowercase_    )
     debug_launcher(lowercase_    )
     debug_launcher(partial(lowercase_							,  num_processes=1							,  split_batches=lowercase_    )							,  num_processes=1    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     AcceleratorState._reset_state(lowercase_    )
     debug_launcher(partial(lowercase_							,  step_scheduler_with_optimizer=lowercase_    )    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     AcceleratorState._reset_state(lowercase_    )
     debug_launcher(partial(lowercase_							,  step_scheduler_with_optimizer=lowercase_    )    )







  @require_huggingface_suite
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     AcceleratorState._reset_state(lowercase_    )
     debug_launcher(partial(lowercase_							,  num_processes=1    )    )
     debug_launcher(lowercase_    )










"""simple docstring"""





import numpy as np



def      __lowercase       (     a			:					Union[str, Any]							,		a			:					Optional[Any]							,		a			:					List[str]			)       ->       Dict:
      __snake_case   :     Dict	      =n_draws * class_counts / class_counts.sum()
      # floored means we don't overshoot n_samples, but probably undershoot
      __snake_case   :     int	      =np.floor(a			)
      # we add samples according to how much "left over" probability
      # they had, until we arrive at n_samples
      __snake_case   :     Dict	      =int(n_draws - floored.sum()			)
      if need_to_add > 0:
            __snake_case   :     List[str]	      =continuous - floored
            __snake_case   :     str	      =np.sort(np.unique(a			)			)[::-1]
            # add according to remainder, but break ties
            # randomly to avoid biases
            for value in values:
                  ((__snake_case)      ,					)   :     Tuple	      =np.where(remainder == value			)
                  # if we need_to_add less than what's in inds
                  # we draw randomly from them.
                  # if we need to add more, we add them all and
                  # go to the next value
                  __snake_case   :     int	      =min(len(a			)							,		a			)
                  __snake_case   :     Any	      =rng.choice(a							,		size=a							,		replace=a			)
                  floored[inds] += 1
                  need_to_add -= add_now
                  if need_to_add == 0:
                        break
      return floored.astype(np.intaa			)



def      __lowercase       (     a			:					Any							,		a			:					str							,		a			:					Dict							,		a			:					Union[str, Any]							,		a			:					Union[str, Any]=10			)       ->       Any:
      __snake_case      ,					__snake_case   :     int	      =np.unique(a							,		return_inverse=a			)
      __snake_case   :     Dict	      =classes.shape[0]
      __snake_case   :     int	      =np.bincount(a			)
      if np.min(a			) < 2:
            raise ValueError('''Minimum class count error'''			)
      if n_train < n_classes:
            raise ValueError(
                '''The train_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_train, n_classes)			)
      if n_test < n_classes:
            raise ValueError(
                '''The test_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_test, n_classes)			)
      __snake_case   :     str	      =np.split(np.argsort(a							,		kind='''mergesort'''			)							,		np.cumsum(a			)[:-1]			)
      for _ in range(a			):
            __snake_case   :     str	      =approximate_mode(a							,		a							,		a			)
            __snake_case   :     List[str]	      =class_counts - n_i
            __snake_case   :     Union[str, Any]	      =approximate_mode(a							,		a							,		a			)

            __snake_case   :     int	      =[]
            __snake_case   :     Union[str, Any]	      =[]

            for i in range(a			):
                  __snake_case   :     Union[str, Any]	      =rng.permutation(class_counts[i]			)
                  __snake_case   :     Dict	      =class_indices[i].take(a							,		mode='''clip'''			)
                  train.extend(perm_indices_class_i[: n_i[i]]			)
                  test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]			)
            __snake_case   :     Any	      =rng.permutation(a			)
            __snake_case   :     int	      =rng.permutation(a			)

            yield train, test


