"""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 	UpperCamelCase    (					lowercase		):
    UpperCAmelCase       :   int					=	0
    UpperCAmelCase       :   bool					=	False
    UpperCAmelCase       :   float					=	3.0







class 	UpperCamelCase    (					unittest.TestCase		):



    def       _lowercase  (self       :   Optional[Any])					->			Tuple:
     # 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       _lowercase  (self       :   Union[str, Any])					->			Union[str, Any]:
     # If no defaults are changed, `to_kwargs` returns an empty dict.
     __snake_case    :							Any   	=				GradScalerKwargs(init_scale=10_24			,						growth_factor=2)
     AcceleratorState._reset_state()
     __snake_case    :							List[str]   	=				Accelerator(mixed_precision='fp16'			,						kwargs_handlers=[scaler_handler])
     print(accelerator.use_fpaa)
     __snake_case    :							Dict   	=				accelerator.scaler

     # Check the kwargs have been applied
     self.assertEqual(scaler._init_scale			,						10_24.0)
     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			,						20_00)
     self.assertEqual(scaler._enabled			,						_A)






    @require_multi_gpu
    def       _lowercase  (self       :   str)					->			Union[str, Any]:
     __snake_case    :							Any   	=				['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__)]
     execute_subprocess_async(_A			,						env=os.environ.copy())


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

 # Check the values changed in kwargs
 _a  :  str=				""
 _a  :  List[Any]=				model.bucket_bytes_cap // (1_024 * 1_024)
 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"""







import math
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       list    ,							UpperCAmelCase_					:       int						)							->    int:

	'''simple docstring'''





	__snake_case    :							List[str]   	=				len(UpperCAmelCase_						)
	__snake_case    :							List[Any]   	=				int(math.floor(math.sqrt(UpperCAmelCase_						)						)						)
	__snake_case    :							Any   	=				0
	while arr[min(UpperCAmelCase_    ,							UpperCAmelCase_						) - 1] < x:
		__snake_case    :							Tuple   	=				step
		step += int(math.floor(math.sqrt(UpperCAmelCase_						)						)						)
		if prev >= n:
			return -1

	while arr[prev] < x:
		__snake_case    :							Union[str, Any]   	=				prev + 1
		if prev == min(UpperCAmelCase_    ,							UpperCAmelCase_						):
			return -1
	if arr[prev] == x:
		return prev
	return -1


if __name__ == "__main__":
	_a  :  str=				input("Enter numbers separated by a comma:\n").strip()
	_a  :  int=				[int(item) for item in user_input.split(",")]
	_a  :  Optional[Any]=				int(input("Enter the number to be searched:\n"))
	_a  :  Tuple=				jump_search(arr, x)
	if res == -1:
		print("Number not found!")
	else:
		print(f'''Number {x} is at index {res}''')





