


'''simple docstring'''

from collections import namedtuple

lowerCamelCase							:     Optional[int]								=			namedtuple('from_to', 'from_ to')

lowerCamelCase							:     int								=			{
    'millimeter': 'mm',
    'centimeter': 'cm',
    'meter': 'm',
    'kilometer': 'km',
    'inch': 'in',
    'inche': 'in',  # Trailing 's' has been stripped off
    'feet': 'ft',
    'foot': 'ft',
    'yard': 'yd',
    'mile': 'mi',
}

lowerCamelCase							:     int								=			{
    'mm': from_to(0.0_0_1, 1_000),
    'cm': from_to(0.0_1, 100),
    'm': from_to(1, 1),
    'km': from_to(1_000, 0.0_0_1),
    'in': from_to(0.0_2_5_4, 3_9.3_7_0_1),
    'ft': from_to(0.3_0_4_8, 3.2_8_0_8_4),
    'yd': from_to(0.9_1_4_4, 1.0_9_3_6_1),
    'mi': from_to(1_6_0_9.3_4, 0.0_0_0_6_2_1_3_7_1),
}

def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> float:






       """simple docstring"""







       lowercase__       =				from_type.lower().rstrip('''s''' )
       lowercase__       =				TYPE_CONVERSION.get(A						,				A )
       lowercase__       =				to_type.lower().rstrip('''s''' )
       lowercase__       =				TYPE_CONVERSION.get(A						,				A )
       if new_from not in METRIC_CONVERSION:
              lowercase__       =				(
                  f"Invalid 'from_type' value: {from_type!r}.\n"
                  f"Conversion abbreviations are: {', '.join(A )}"
              )
              raise ValueError(A )
       if new_to not in METRIC_CONVERSION:
              lowercase__       =				(
                  f"Invalid 'to_type' value: {to_type!r}.\n"
                  f"Conversion abbreviations are: {', '.join(A )}"
              )
              raise ValueError(A )
       return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
 import doctest

 doctest.testmod()








'''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'''				)




