
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     lowerCamelCase      (       unittest.TestCase					):







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







				"""simple docstring"""






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







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







				"""simple docstring"""






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







	@require_multi_gpu
	def  snake_case__     (     self      :Optional[int]			)		->      Optional[Any]:







				"""simple docstring"""






				print(f"""Found {torch.cuda.device_count()} devices."""			)
				SCREAMING_SNAKE_CASE     						= ['''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(lowercase   ,     env=os.environ.copy()			)







	@require_multi_gpu
	def  snake_case__     (     self      :Dict			)		->      Dict:







				"""simple docstring"""






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







	@require_multi_gpu
	def  snake_case__     (     self      :List[Any]			)		->      Union[str, Any]:







				"""simple docstring"""






				print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""			)
				SCREAMING_SNAKE_CASE     						= ['''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(lowercase   ,     env=os.environ.copy()			)


if __name__ == "__main__":
							__lowerCAmelCase					   =       Accelerator()
							__lowerCAmelCase					   =       (accelerator.state.process_index + 2, 1_0)
							__lowerCAmelCase					   =       torch.randint(0, 1_0, shape).to(accelerator.device)

							__lowerCAmelCase					   =       ''

							__lowerCAmelCase					   =       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					   =       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					   =       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)

# Function to print upper half of diamond (pyramid)


def    a      ( a	)     ->Optional[Any]:




			'''simple docstring'''


			for i in range(0		,						a	):
						for _ in range(0		,						n - i - 1	):  # printing spaces
									print(''' '''		,						end=''''''	)
						for _ in range(0		,						i + 1	):  # printing stars
									print('''* '''		,						end=''''''	)
						print()


def    a      ( a	)     ->Union[str, Any]:




			'''simple docstring'''


			for i in range(a		,						0		,						-1	):
						for _ in range(a		,						0		,						-1	):  # printing stars
									print('''* '''		,						end=''''''	)
						print()
						for _ in range(n - i + 1		,						0		,						-1	):  # printing spaces
									print(''' '''		,						end=''''''	)


def    a      ( a	)     ->Optional[int]:




			'''simple docstring'''


			if n <= 0:
						print('''       ...       ....        nothing printing :('''	)
						return
			floyd(a	)  # upper half
			reverse_floyd(a	)  # lower half


if __name__ == "__main__":
							print(R'| /\ | |- |  |-  |--| |\  /| |-')
							print(R'|/  \| |- |_ |_  |__| | \/ | |_')
							__lowerCAmelCase					   =       1
							while K:
														__lowerCAmelCase					   =       int(input('enter the number and , and see the magic : '))
														print()
														pretty_print(user_number)
														__lowerCAmelCase					   =       int(input('press 0 to exit... and 1 to continue...'))

							print('Good Bye...')