




import os
from string import ascii_letters

__A						:		int          =						ascii_letters + " \t\n"





def      UpperCAmelCase  (						)     ->     dict[str, None]:







				'''simple docstring'''


				__lowerCAmelCase					=  os.path.split(os.path.realpath(UpperCamelCase__   )   )
				__lowerCAmelCase					=  {}
				with open(path[0] + """/dictionary.txt"""   ) as dictionary_file:
								for word in dictionary_file.read().split("""\n"""   ):
												__lowerCAmelCase					=  None
				return english_words


__A						:		Union[str, Any]          =						load_dictionary()





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     float:







				'''simple docstring'''


				__lowerCAmelCase					=  message.upper()
				__lowerCAmelCase					=  remove_non_letters(UpperCamelCase__   )
				__lowerCAmelCase					=  message.split()
				__lowerCAmelCase					=  len([word for word in possible_words if word in ENGLISH_WORDS]   )
				return float(UpperCamelCase__   ) / len(UpperCamelCase__   )





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     str:







				'''simple docstring'''


				return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE   )





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__ = 20						,			UpperCamelCase__ = 85   )     ->     bool:







				'''simple docstring'''


				__lowerCAmelCase					=  get_english_count(UpperCamelCase__   ) * 1_00 >= word_percentage
				__lowerCAmelCase					=  len(remove_non_letters(UpperCamelCase__   )   )
				__lowerCAmelCase					=  (float(UpperCamelCase__   ) / len(UpperCamelCase__   )) * 1_00
				__lowerCAmelCase					=  message_letters_percentage >= letter_percentage
				return words_match and letters_match


if __name__ == "__main__":
	import doctest

	doctest.testmod()







import torch

from accelerate import Accelerator, DistributedType
class    _UpperCamelCase:

						def __enter__(					self		:			str			):
													if self.enabled:
																				_UpperCAmelCase  :			int	       =		self.model.no_sync()
																				self.model_sync_obj.__enter__()

													return self

						def __exit__(					self		:			str	,						_lowerCamelCase		:			Union[str, Any]	,						_lowerCamelCase		:			Tuple	,						_lowerCamelCase		:			Optional[Any]			):
													if self.enabled:
																				# Average all models on exit
																				self._sync_and_avg_model_params()
																				self.model_sync_obj.__exit__(_lowerCamelCase	,						_lowerCamelCase	,						_lowerCamelCase			)

						def __init__(					self		:			str	,						_lowerCamelCase		:			Accelerator	,						_lowerCamelCase		:			torch.nn.Module	,						_lowerCamelCase		:			int	,						_lowerCamelCase		:			bool = True			):
													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  :			Any	       =		enabled and accelerator.distributed_type != DistributedType.NO
													_UpperCAmelCase  :			str	       =		0
													if self.enabled:
																				_UpperCAmelCase  :			Tuple	       =		accelerator
																				_UpperCAmelCase  :			List[str]	       =		model
																				_UpperCAmelCase  :			Dict	       =		local_sgd_steps

						def 						a__ (					self		:			Optional[int]			):
													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 						a__ (					self		:			Tuple			):
													self.accelerator.wait_for_everyone()
													with self.accelerator.autocast():
																				for param in self.model.parameters():
																											_UpperCAmelCase  :			Optional[Any]	       =		self.accelerator.reduce(param.data	,						reduction="mean"			)




