






"""simple docstring"""
from manim import *







class 			UpperCamelCase_       (UpperCAmelCase_     ):





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			List[Any]:
				UpperCAmelCase_     : int			     =					Rectangle(height=0.5	,	width=0.5						)
				UpperCAmelCase_     : Tuple			     =					Rectangle(height=0.4_6	,	width=0.4_6						).set_stroke(width=0						)

				UpperCAmelCase_     : Any			     =					[mem.copy() for i in range(6						)]
				UpperCAmelCase_     : List[Any]			     =					[mem.copy() for i in range(6						)]
				UpperCAmelCase_     : List[Any]			     =					VGroup(*__UpperCAmelCase						).arrange(__UpperCAmelCase	,	buff=0						)
				UpperCAmelCase_     : Union[str, Any]			     =					VGroup(*__UpperCAmelCase						).arrange(__UpperCAmelCase	,	buff=0						)
				UpperCAmelCase_     : Tuple			     =					VGroup(__UpperCAmelCase	,	__UpperCAmelCase						).arrange(__UpperCAmelCase	,	buff=0						)
				UpperCAmelCase_     : Union[str, Any]			     =					Text("CPU"	,	font_size=24						)
				UpperCAmelCase_     : Tuple			     =					Group(__UpperCAmelCase	,	__UpperCAmelCase						).arrange(__UpperCAmelCase	,	buff=0.5	,	aligned_edge=__UpperCAmelCase						)
				cpu.move_to([-2.5, -0.5, 0]						)
				self.add(__UpperCAmelCase						)

				UpperCAmelCase_     : Optional[int]			     =					[mem.copy() for i in range(1						)]
				UpperCAmelCase_     : Optional[Any]			     =					VGroup(*__UpperCAmelCase						).arrange(__UpperCAmelCase	,	buff=0						)
				UpperCAmelCase_     : Optional[Any]			     =					Text("GPU"	,	font_size=24						)
				UpperCAmelCase_     : Optional[Any]			     =					Group(__UpperCAmelCase	,	__UpperCAmelCase						).arrange(__UpperCAmelCase	,	buff=0.5	,	aligned_edge=__UpperCAmelCase						)
				gpu.align_to(__UpperCAmelCase	,	__UpperCAmelCase						)
				gpu.set_x(gpu.get_x() - 1						)

				self.add(__UpperCAmelCase						)

				UpperCAmelCase_     : Any			     =					[mem.copy() for i in range(6						)]
				UpperCAmelCase_     : Tuple			     =					VGroup(*__UpperCAmelCase						).arrange(__UpperCAmelCase	,	buff=0						)

				UpperCAmelCase_     : str			     =					Text("Model"	,	font_size=24						)
				UpperCAmelCase_     : Optional[int]			     =					Group(__UpperCAmelCase	,	__UpperCAmelCase						).arrange(__UpperCAmelCase	,	buff=0.5	,	aligned_edge=__UpperCAmelCase						)
				model.move_to([3, -1.0, 0]						)

				self.play(
				    Create(__UpperCAmelCase	,	run_time=1						)	,	Create(__UpperCAmelCase	,	run_time=1						)	,	Create(__UpperCAmelCase	,	run_time=1						)	,	)

				UpperCAmelCase_     : str			     =					MarkupText(
				    f"""First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM."""	,	font_size=24	,	)

				UpperCAmelCase_     : Dict			     =					Square(side_length=2.2						)
				key.move_to([-5, 2, 0]						)

				UpperCAmelCase_     : Optional[Any]			     =					MarkupText(
				    f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""	,	font_size=18	,	)

				key_text.move_to([-5, 2.4, 0]						)

				step_a.move_to([2, 2, 0]						)
				self.play(Write(__UpperCAmelCase	,	run_time=2.5						)	,	Write(__UpperCAmelCase						)	,	Write(__UpperCAmelCase						)						)

				self.add(__UpperCAmelCase						)

				UpperCAmelCase_     : Union[str, Any]			     =					[]
				UpperCAmelCase_     : Any			     =					[]
				UpperCAmelCase_     : Optional[Any]			     =					[]
				for i, rect in enumerate(__UpperCAmelCase						):
							UpperCAmelCase_     : int			     =					Rectangle(height=0.4_6	,	width=0.4_6						).set_stroke(width=0.0						).set_fill(__UpperCAmelCase	,	opacity=0.7						)
							cpu_target.move_to(__UpperCAmelCase						)
							cpu_target.generate_target()
							UpperCAmelCase_     : Union[str, Any]			     =					0.4_6 / 4
							UpperCAmelCase_     : List[str]			     =					0.4_6 / 3

							if i == 0:
										cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT						)	,	buff=0.0_2	,	direction=__UpperCAmelCase						)
										cpu_target.target.set_x(cpu_target.target.get_x() + 0.1						)
							elif i == 3:
										cpu_target.target.next_to(cpu_targs[0].target	,	direction=__UpperCAmelCase	,	buff=0.0						)
							else:
										cpu_target.target.next_to(cpu_targs[i - 1].target	,	direction=__UpperCAmelCase	,	buff=0.0						)
							cpu_targs.append(__UpperCAmelCase						)

							first_animations.append(rect.animate(run_time=0.5						).set_stroke(__UpperCAmelCase						)						)
							second_animations.append(MoveToTarget(__UpperCAmelCase	,	run_time=1.5						)						)

				self.play(*__UpperCAmelCase						)
				self.play(*__UpperCAmelCase						)

				self.wait()




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     ):


   def 			lowercase_      (self     :						Union[str, Any]		)						->				str:
         """simple docstring"""



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


   @require_multi_gpu
   def 			lowercase_      (self     :						List[str]		)						->				Any:
         """simple docstring"""



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


   @require_multi_gpu
   def 			lowercase_      (self     :						str		)						->				str:
         """simple docstring"""



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


   @require_multi_gpu
   def 			lowercase_      (self     :						Tuple		)						->				int:
         """simple docstring"""



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





   @require_multi_gpu
   def 			lowercase_      (self     :						Dict		)						->				str:
         """simple docstring"""



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


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

      UpperCamelCase__											=					''

      UpperCamelCase__											=					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)."

      UpperCamelCase__											=					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."
      UpperCamelCase__											=					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)



