
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						int:
			# The longer word should come first
			if len(SCREAMING_SNAKE_CASE_	) < len(SCREAMING_SNAKE_CASE_	):
						return levenshtein_distance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)

			if len(SCREAMING_SNAKE_CASE_	) == 0:
						return len(SCREAMING_SNAKE_CASE_	)

			lowerCAmelCase__ : List[Any]   =						list(range(len(SCREAMING_SNAKE_CASE_	) + 1	)	)

			for i, ca in enumerate(SCREAMING_SNAKE_CASE_	):
						lowerCAmelCase__ : Union[str, Any]   =						[i + 1]

						for j, ca in enumerate(SCREAMING_SNAKE_CASE_	):
									# Calculate insertions, deletions and substitutions
									lowerCAmelCase__ : Any   =						previous_row[j + 1] + 1
									lowerCAmelCase__ : List[Any]   =						current_row[j] + 1
									lowerCAmelCase__ : Any   =						previous_row[j] + (ca != ca)

									# Get the minimum to append to the current row
									current_row.append(min(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)	)

						# Store the previous row
						lowerCAmelCase__ : Dict   =						current_row

			# Returns the last element (distance)
			return previous_row[-1]


if __name__ == "__main__":
					lowerCamelCase__        =						input("""Enter the first word:\n""").strip()
					lowerCamelCase__        =						input("""Enter the second word:\n""").strip()

					lowerCamelCase__        =						levenshtein_distance(first_word, second_word)
					print(F"""Levenshtein distance between {first_word} and {second_word} is {result}""")

import torch

from accelerate import Accelerator, DistributedType







class 	A__				:






				def __enter__(		self   : Optional[Any]  ):






							'''simple docstring'''







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

							return self






				def __exit__(		self   : int       ,	a   : int       ,	a   : Optional[Any]       ,	a   : str  ):






							'''simple docstring'''







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






				def __init__(		self   : List[str]       ,	a   : Accelerator       ,	a   : torch.nn.Module       ,	a   : int       ,	a   : bool = 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)'  )
							lowerCAmelCase__ : Union[str, Any]   =						enabled and accelerator.distributed_type != DistributedType.NO
							lowerCAmelCase__ : str   =						0
							if self.enabled:
										lowerCAmelCase__ : Dict   =						accelerator
										lowerCAmelCase__ : Dict   =						model
										lowerCAmelCase__ : Optional[int]   =						local_sgd_steps






				def 				_lowerCamelCase						(		self   : Optional[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 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







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