
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        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							Dict	=						inspect.getfile(accelerate.test_utils      )
							UpperCamelCase_:							List[Any]	=						os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ["""scripts""", """test_script.py"""]      )
							UpperCamelCase_:							int	=						os.path.sep.join(
							    mod_file.split(os.path.sep      )[:-1] + ["""scripts""", """test_distributed_data_loop.py"""]      )
							UpperCamelCase_:							List[Any]	=						os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ["""scripts""", """test_ops.py"""]      )

			@require_multi_gpu
			def       lowerCAmelCase__	(       self :       Any      ):
							print(f'''Found {torch.cuda.device_count()} devices.'''      )
							UpperCamelCase_:							Optional[Any]	=						["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
							with patch_environment(omp_num_threads=1      ):
											execute_subprocess_async(snake_case_						,	env=os.environ.copy()      )

			@require_multi_gpu
			def       lowerCAmelCase__	(       self :       Dict      ):
							print(f'''Found {torch.cuda.device_count()} devices.'''      )
							UpperCamelCase_:							Tuple	=						["""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(snake_case_						,	env=os.environ.copy()      )

			@require_multi_gpu
			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Tuple	=						["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__      )]
							with patch_environment(omp_num_threads=1      ):
											execute_subprocess_async(snake_case_						,	env=os.environ.copy()      )




			@require_multi_gpu
			def       lowerCAmelCase__	(       self :       Tuple      ):
							print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''      )
							UpperCamelCase_:							Optional[int]	=						["""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(snake_case_						,	env=os.environ.copy()      )


if __name__ == "__main__":
							lowerCamelCase_						: int         =  Accelerator()
							lowerCamelCase_						: Any         =  (accelerator.state.process_index + 2, 10)
							lowerCamelCase_						: Tuple         =  torch.randint(0, 10, shape).to(accelerator.device)

							lowerCamelCase_						: Optional[int]         =  """"""

							lowerCamelCase_						: int         =  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_						: str         =  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_						: List[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)








from manim import *

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Dict	=						Rectangle(height=0.5						,	width=0.5      )
							UpperCamelCase_:							Dict	=						Rectangle(height=0.46						,	width=0.46      ).set_stroke(width=0      )

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

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

							self.add(snake_case_      )

							UpperCamelCase_:							Dict	=						[mem.copy() for i in range(6      )]
							UpperCamelCase_:							List[str]	=						VGroup(*snake_case_      ).arrange(snake_case_						,	buff=0      )

							UpperCamelCase_:							Any	=						Text("""Model"""						,	font_size=24      )
							UpperCamelCase_:							Optional[Any]	=						Group(snake_case_						,	snake_case_      ).arrange(snake_case_						,	buff=0.5						,	aligned_edge=snake_case_      )
							model.move_to([3, -1.0, 0]      )

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

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

							UpperCamelCase_:							Optional[Any]	=						Square(side_length=2.2      )
							key.move_to([-5, 2, 0]      )

							UpperCamelCase_:							Union[str, 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(snake_case_						,	run_time=2.5      )						,	Write(snake_case_      )						,	Write(snake_case_      )      )

							self.add(snake_case_      )

							UpperCamelCase_:							Union[str, Any]	=						[]
							UpperCamelCase_:							Union[str, Any]	=						[]
							UpperCamelCase_:							Tuple	=						[]
							for i, rect in enumerate(snake_case_      ):
											UpperCamelCase_:							Tuple	=						Rectangle(height=0.46						,	width=0.46      ).set_stroke(width=0.0      ).set_fill(snake_case_						,	opacity=0.7      )
											cpu_target.move_to(snake_case_      )
											cpu_target.generate_target()
											UpperCamelCase_:							int	=						0.46 / 4
											UpperCamelCase_:							Optional[int]	=						0.46 / 3

											if i == 0:
															cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT      )						,	buff=0.02						,	direction=snake_case_      )
															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=snake_case_						,	buff=0.0      )
											else:
															cpu_target.target.next_to(cpu_targs[i - 1].target						,	direction=snake_case_						,	buff=0.0      )
											cpu_targs.append(snake_case_      )

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

							self.play(*snake_case_      )
							self.play(*snake_case_      )

							self.wait()






