




"""simple docstring"""




import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler






@dataclass
class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				__UpperCamelCase    						=		0
				__UpperCamelCase    						=		False
				__UpperCamelCase    						=		3.0






class 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							# If no defaults are changed, `to_kwargs` returns an empty dict.
							self.assertDictEqual(MockClass().to_kwargs()	,				{}				)
							self.assertDictEqual(MockClass(a=2				).to_kwargs()	,				{"""a""": 2}				)
							self.assertDictEqual(MockClass(a=2	,				b=_a				).to_kwargs()	,				{"""a""": 2, """b""": True}				)
							self.assertDictEqual(MockClass(a=2	,				c=2.25				).to_kwargs()	,				{"""a""": 2, """c""": 2.25}				)





				@require_cuda
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							# If no defaults are changed, `to_kwargs` returns an empty dict.
							lowerCamelCase       =      GradScalerKwargs(init_scale=1_024	,				growth_factor=2				)
							AcceleratorState._reset_state()
							lowerCamelCase       =      Accelerator(mixed_precision="""fp16"""	,				kwargs_handlers=[scaler_handler]				)
							print(accelerator.use_fpaa				)
							lowerCamelCase       =      accelerator.scaler

							# Check the kwargs have been applied
							self.assertEqual(scaler._init_scale	,				1_024.0				)
							self.assertEqual(scaler._growth_factor	,				2.0				)

							# Check the other values are at the default
							self.assertEqual(scaler._backoff_factor	,				0.5				)
							self.assertEqual(scaler._growth_interval	,				2_000				)
							self.assertEqual(scaler._enabled	,				_a				)





				@require_multi_gpu
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      ["""torchrun""", f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__				)]
							execute_subprocess_async(_a	,				env=os.environ.copy()				)


if __name__ == "__main__":
					lowerCAmelCase							:    List[Any]  							=							DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
					lowerCAmelCase							:    Union[str, Any]  							=							Accelerator(kwargs_handlers=[ddp_scaler])
					lowerCAmelCase							:    Optional[int]  							=							torch.nn.Linear(100, 200)
					lowerCAmelCase							:    Any  							=							accelerator.prepare(model)

					# Check the values changed in kwargs
					lowerCAmelCase							:    Optional[Any]  							=							""""""
					lowerCAmelCase							:    int  							=							model.bucket_bytes_cap // (1024 * 1024)
					if observed_bucket_cap_map != 15:
										error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
					if model.find_unused_parameters is not True:
										error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

					# Check the values of the defaults
					if model.dim != 0:
										error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
					if model.broadcast_buffers is not True:
										error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
					if model.gradient_as_bucket_view is not False:
										error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

					# Raise error at the end to make sure we don't stop at the first failure.
					if len(error_msg) > 0:
										raise ValueError(error_msg)









"""simple docstring"""




def 					a__				(       snake_case__       )		->      list:
			if n_term == "":
						return []
			lowerCamelCase       =      []
			for temp in range(int(snake_case__       )       ):
						series.append(F'1/{temp + 1}' if series else """1"""       )
			return series


if __name__ == "__main__":
					lowerCAmelCase							:    Optional[int]  							=							input("""Enter the last number (nth term) of the Harmonic Series""")
					print("""Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n""")
					print(harmonic_series(nth_term))



