


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 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= inspect.getfile(accelerate.test_utils   )
       snake_case__						= os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_script.py''']   )
       snake_case__						= os.path.sep.join(
           mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_distributed_data_loop.py''']   )
       snake_case__						= os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_ops.py''']   )

     @require_multi_gpu
     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       print(F"""Found {torch.cuda.device_count()} devices."""   )
       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(_a     ,		env=os.environ.copy()   )

     @require_multi_gpu
     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       print(F"""Found {torch.cuda.device_count()} devices."""   )
       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(_a     ,		env=os.environ.copy()   )

     @require_multi_gpu
     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       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(_a     ,		env=os.environ.copy()   )







     @require_multi_gpu
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       print(F"""Found {torch.cuda.device_count()} devices, using 2 devices only"""   )
       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(_a     ,		env=os.environ.copy()   )


if __name__ == "__main__":
   lowerCamelCase__							:						Optional[int]							     =   Accelerator()
   lowerCamelCase__							:						Union[str, Any]							     =   (accelerator.state.process_index + 2, 1_0)
   lowerCamelCase__							:						List[str]							     =   torch.randint(0, 1_0, shape).to(accelerator.device)

   lowerCamelCase__							:						Union[str, Any]							     =   """"""

   lowerCamelCase__							:						str							     =   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__							:						Optional[int]							     =   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__							:						str							     =   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"""




from __future__ import annotations

from collections.abc import Callable



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a = 100,				):
		SCREAMING_SNAKE_CASE_    			=			x_start
		SCREAMING_SNAKE_CASE_    			=			fnc(__a     )
		SCREAMING_SNAKE_CASE_    			=			0.0
		for _ in range(__a     ):
				# Approximates small segments of curve as linear and solve
				# for trapezoidal area
				SCREAMING_SNAKE_CASE_    			=			(x_end - x_start) / steps + xa
				SCREAMING_SNAKE_CASE_    			=			fnc(__a     )
				area += abs(fxa + fxa     ) * (xa - xa) / 2
				# Increment step
				SCREAMING_SNAKE_CASE_    			=			xa
				SCREAMING_SNAKE_CASE_    			=			fxa
		return area


if __name__ == "__main__":



						def 							_lowerCamelCase			(			__a     ):
								return x**3 + x**2

						print('f(x) = x^3 + x^2')
						print('The area between the curve, x = -5, x = 5 and the x axis is:')
						lowerCAmelCase__				=					10
						while i <= 100000:
												print(f'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
												i *= 10