




import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment







class   A(						unittest.TestCase   ):



 '''simple docstring'''


 def 				a__ (				self					:  str  )   ->	Optional[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    inspect.getfile(accelerate.test_utils  )
       lowerCamelCase_		     =    os.path.sep.join(mod_file.split(os.path.sep  )[:-1] + ['scripts', 'test_script.py']  )
       lowerCamelCase_		     =    os.path.sep.join(
           mod_file.split(os.path.sep  )[:-1] + ['scripts', 'test_distributed_data_loop.py']  )
       lowerCamelCase_		     =    os.path.sep.join(mod_file.split(os.path.sep  )[:-1] + ['scripts', 'test_ops.py']  )

 @require_multi_gpu
 def 				a__ (				self					:  List[Any]  )   ->	Any:

       """simple docstring"""






       print(f"""Found {torch.cuda.device_count()} devices."""  )
       lowerCamelCase_		     =    ['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
       with patch_environment(omp_num_threads=1  ):
             execute_subprocess_async(_a						,							env=os.environ.copy()  )

 @require_multi_gpu
 def 				a__ (				self					:  Tuple  )   ->	List[Any]:

       """simple docstring"""






       print(f"""Found {torch.cuda.device_count()} devices."""  )
       lowerCamelCase_		     =    ['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
       print(f"""Command: {cmd}"""  )
       with patch_environment(omp_num_threads=1  ):
             execute_subprocess_async(_a						,							env=os.environ.copy()  )

 @require_multi_gpu
 def 				a__ (				self					:  List[str]  )   ->	List[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    ['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__  )]
       with patch_environment(omp_num_threads=1  ):
             execute_subprocess_async(_a						,							env=os.environ.copy()  )





 @require_multi_gpu
 def 				a__ (				self					:  str  )   ->	Any:

       """simple docstring"""






       print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""  )
       lowerCamelCase_		     =    ['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
       with patch_environment(omp_num_threads=1						,							cuda_visible_devices='0,1'  ):
             execute_subprocess_async(_a						,							env=os.environ.copy()  )


if __name__ == "__main__":
 lowerCamelCase				:		List[Any]     =   Accelerator()
 lowerCamelCase				:		List[Any]     =   (accelerator.state.process_index + 2, 10)
 lowerCamelCase				:		Dict     =   torch.randint(0, 10, shape).to(accelerator.device)

 lowerCamelCase				:		Tuple     =   ""

 lowerCamelCase				:		Optional[Any]     =   accelerator.pad_across_processes(tensor)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
  error_msg += "Padding was not done with the right value (0)."

 lowerCamelCase				:		Union[str, Any]     =   accelerator.pad_across_processes(tensor, pad_first=True)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 lowerCamelCase				:		Union[str, Any]     =   accelerator.state.num_processes - accelerator.state.process_index - 1
 if not torch.equal(tensora[index:], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[:index] == 0):
  error_msg += "Padding was not done with the right value (0)."

 # 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        lowercase							(		lowerCAmelCase__ :     list    ,					lowerCAmelCase__ :     int			) ->      int:
		__a           =			len(lowerCAmelCase__			)
		__a           =			int(math.floor(math.sqrt(lowerCAmelCase__			)			)			)
		__a           =			0
		while arr[min(lowerCAmelCase__    ,					lowerCAmelCase__			) - 1] < x:
				__a           =			step
				step += int(math.floor(math.sqrt(lowerCAmelCase__			)			)			)
				if prev >= n:
						return -1

		while arr[prev] < x:
				__a           =			prev + 1
				if prev == min(lowerCAmelCase__    ,					lowerCAmelCase__			):
						return -1
		if arr[prev] == x:
				return prev
		return -1


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






