






import torch

from accelerate import Accelerator, DistributedType




class 							__A	:





				"""simple docstring"""







				def __enter__(					self       ):




									"""simple docstring"""

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

									return self


				def __exit__(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

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


				def __init__(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__ = True       ):




									"""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)'       )
									__UpperCamelCase :   Tuple									=enabled and accelerator.distributed_type != DistributedType.NO
									__UpperCamelCase :   Optional[int]									=0
									if self.enabled:
														__UpperCamelCase :   Any									=accelerator
														__UpperCamelCase :   Any									=model
														__UpperCamelCase :   List[Any]									=local_sgd_steps


				def  __lowercase				(					self       ):




									"""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  __lowercase				(					self       ):




									"""simple docstring"""

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








"""simple docstring"""
from functools import lru_cache






@lru_cache
def    A__							(     UpperCamelCase			):
							if not isinstance(UpperCamelCase   ,    UpperCamelCase			):
														raise TypeError("k must be an integer."			)
							if k < 0:
														raise ValueError("k integer must be greater or equal to zero."			)
							if k == 0:
														return 0
							elif k == 1:
														return 1
							else:
														return fibonacci(k - 1			) + fibonacci(k - 2			)






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    len(UpperCamelCase			)
							# Find m such that F_m >= n where F_i is the i_th fibonacci number.
							A						=    0
							while True:
														if fibonacci(UpperCamelCase			) >= len_list:
																					A						=    i
																					break
														i += 1
							A						=    0
							while fibb_k > 0:
														A						=    min(
														    offset + fibonacci(fibb_k - 1			)   ,    len_list - 1			)  # Prevent out of range
														A						=    arr[index_k]
														if item_k_1 == val:
																					return index_k
														elif val < item_k_1:
																					fibb_k -= 1
														elif val > item_k_1:
																					offset += fibonacci(fibb_k - 1			)
																					fibb_k -= 2
							else:
														return -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()


