code: 
  
  
  '''simple docstring'''
  
  import numpy as np
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> Any:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				np.array(A )
         lowercase__       =				np.array(A )
  
         lowercase__       =				abs(predict - actual )
         lowercase__       =				difference.mean()
  
         return score
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> Optional[Any]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				np.array(A )
         lowercase__       =				np.array(A )
  
         lowercase__       =				predict - actual
         lowercase__       =				np.square(A )
  
         lowercase__       =				square_diff.mean()
         return score
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> Optional[Any]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				np.array(A )
         lowercase__       =				np.array(A )
  
         lowercase__       =				predict - actual
         lowercase__       =				np.square(A )
         lowercase__       =				square_diff.mean()
         lowercase__       =				np.sqrt(A )
         return score
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> Tuple:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				np.array(A )
         lowercase__       =				np.array(A )
  
         lowercase__       =				np.log(predict + 1 )
         lowercase__       =				np.log(actual + 1 )
  
         lowercase__       =				log_predict - log_actual
         lowercase__       =				np.square(A )
         lowercase__       =				square_diff.mean()
  
         lowercase__       =				np.sqrt(A )
  
         return score
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> Optional[Any]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				np.array(A )
         lowercase__       =				np.array(A )
  
         lowercase__       =				predict - actual
         lowercase__       =				np.sum(A ) / len(A )
         lowercase__       =				np.sum(A ) / len(A )
         # print(numerator, denumerator)
         lowercase__       =				float(A ) / denumerator * 100
  
         return score
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> Tuple:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         return np.mean(np.array(A ) == np.array(A ) )
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  import torch
  
  from accelerate import Accelerator, DistributedType
  class 						__lowerCAmelCase		:
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def __enter__(self					:					List[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if self.enabled:
                      lowercase__       =				self.model.no_sync()
                      self.model_sync_obj.__enter__()
  
               return self
  
  
  
        def __exit__(self					:					List[str]     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					Optional[int]     ,		UpperCamelCase					:					Optional[int]				):
  
               '''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					:					Dict     ,		UpperCamelCase					:					Accelerator     ,		UpperCamelCase					:					torch.nn.Module     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					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)'''				)
               lowercase__       =				enabled and accelerator.distributed_type != DistributedType.NO
               lowercase__       =				0
               if self.enabled:
                      lowercase__       =				accelerator
                      lowercase__       =				model
                      lowercase__       =				local_sgd_steps
  
  
  
        def    UpperCamelCase__	(self					:					List[str]				):
  
               '''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    UpperCamelCase__	(self					:					Any				):
  
               '''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'''				)
  
  
  
  
  