from __future__ import annotations

from typing import Generic, TypeVar

lowerCAmelCase__  	=      TypeVar("T")
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	pass

class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	pass

class 	_a  (							Generic[T]					):



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_ = 10       ):
		_lowercase   =[]
		_lowercase   =limit


	def __bool__(   self       ):
		return bool(self.stack       )


	def __str__(   self       ):
		return str(self.stack       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		if len(self.stack       ) >= self.limit:
			raise StackOverflowError
		self.stack.append(lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		if not self.stack:
			raise StackUnderflowError
		return self.stack.pop()


	def __lowerCAmelCase      (   self       ):
		if not self.stack:
			raise StackUnderflowError
		return self.stack[-1]


	def __lowerCAmelCase      (   self       ):
		return not bool(self.stack       )


	def __lowerCAmelCase      (   self       ):
		return self.size() == self.limit


	def __lowerCAmelCase      (   self       ):
		return len(self.stack       )


	def __contains__(   self       ,  lowerCAmelCase_       ):
		return item in self.stack

def 	__lowerCamelCase							(				)      ->       None:
	_lowercase   =Stack(10		)
	assert bool(__a		) is False
	assert stack.is_empty() is True
	assert stack.is_full() is False
	assert str(__a		) == "[]"

	try:
		_lowercase   =stack.pop()
		raise AssertionError  # This should not happen
	except StackUnderflowError:
		assert True  # This should happen

	try:
		_lowercase   =stack.peek()
		raise AssertionError  # This should not happen
	except StackUnderflowError:
		assert True  # This should happen

	for i in range(10		):
		assert stack.size() == i
		stack.push(__a		)

	assert bool(__a		)
	assert not stack.is_empty()
	assert stack.is_full()
	assert str(__a		) == str(list(range(10		)		)		)
	assert stack.pop() == 9
	assert stack.peek() == 8

	stack.push(100		)
	assert str(__a		) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100]		)

	try:
		stack.push(200		)
		raise AssertionError  # This should not happen
	except StackOverflowError:
		assert True  # This should happen

	assert not stack.is_empty()
	assert stack.size() == 10

	assert 5 in stack
	assert 55 not in stack


if __name__ == "__main__":
						test_stack()












import torch

from accelerate import Accelerator, DistributedType

class        snake_case_				:







   """simple docstring"""



   def __enter__(     self:						int						)	->		Tuple:


       """simple docstring"""



       if self.enabled:
           _lowercase     			=						self.model.no_sync()
           self.model_sync_obj.__enter__()

       return self

   def __exit__(     self:						str ,							snake_case__:						Tuple ,							snake_case__:						int ,							snake_case__:						Tuple						)	->		Any:


       """simple docstring"""



       if self.enabled:
           # Average all models on exit
           self._sync_and_avg_model_params()
           self.model_sync_obj.__exit__(snake_case__ ,							snake_case__ ,							snake_case__						)

   def __init__(     self:						int ,							snake_case__:						Accelerator ,							snake_case__:						torch.nn.Module ,							snake_case__:						int ,							snake_case__:						bool = True						)	->		Optional[Any]:


       """simple docstring"""



       if accelerator.distributed_type not in [
           DistributedType.NO,
           DistributedType.MULTI_CPU,
           DistributedType.MULTI_GPU,
       ]:
           raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'						)
       _lowercase     			=						enabled and accelerator.distributed_type != DistributedType.NO
       _lowercase     			=						0
       if self.enabled:
           _lowercase     			=						accelerator
           _lowercase     			=						model
           _lowercase     			=						local_sgd_steps

   def       SCREAMING_SNAKE_CASE__					(     self:						str						)	->		List[Any]:


       """simple docstring"""



       self.num_steps += 1
       if not self.enabled:
           return

       if self.num_steps % self.local_sgd_steps == 0:
           self._sync_and_avg_model_params()

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		Dict:


       """simple docstring"""



       self.accelerator.wait_for_everyone()
       with self.accelerator.autocast():
           for param in self.model.parameters():
               _lowercase     			=						self.accelerator.reduce(param.data ,							reduction='mean'						)
