


'''simple docstring'''
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      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				inspect.getfile(accelerate.test_utils							)
							UpperCamelCase__     :Union[str, Any]							      =				os.path.sep.join(mod_file.split(os.path.sep							)[:-1] + ['''scripts''', '''test_script.py''']							)
							UpperCamelCase__     :Optional[int]							      =				os.path.sep.join(
							    mod_file.split(os.path.sep							)[:-1] + ['''scripts''', '''test_distributed_data_loop.py''']							)
							UpperCamelCase__     :Optional[int]							      =				os.path.sep.join(mod_file.split(os.path.sep							)[:-1] + ['''scripts''', '''test_ops.py''']							)




		@require_multi_gpu
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							print(F'''Found {torch.cuda.device_count()} devices.'''							)
							UpperCamelCase__     :Any							      =				['''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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							print(F'''Found {torch.cuda.device_count()} devices.'''							)
							UpperCamelCase__     :List[Any]							      =				['''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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				['''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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only'''							)
							UpperCamelCase__     :Tuple							      =				['''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__":
		__snake_case							   =  Accelerator()
		__snake_case							   =  (accelerator.state.process_index + 2, 10)
		__snake_case							   =  torch.randint(0, 10, shape).to(accelerator.device)

		__snake_case							   =  ''''''

		__snake_case							   =  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)."

		__snake_case							   =  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."
		__snake_case							   =  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'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class      lowercase			(							A__			,	A__      ):

		"""simple docstring"""

		_a									=       1




		@register_to_config
		def __init__(	self					,     UpperCamelCase_=2000					,     UpperCamelCase_=0.1					,     UpperCamelCase_=20					,     UpperCamelCase_=1e-3							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				None
							UpperCamelCase__     :Any							      =				None
							UpperCamelCase__     :List[str]							      =				None




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				torch.linspace(1					,     self.config.sampling_eps					,     UpperCamelCase_					,     device=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_=None							):

							'''simple docstring'''


							if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

							# TODO(Patrick) better comments + non-PyTorch
							# postprocess model score
							UpperCamelCase__     :Optional[int]							      =				(
							    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
							)
							UpperCamelCase__     :Tuple							      =				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
							UpperCamelCase__     :Tuple							      =				std.flatten()
							while len(std.shape							) < len(score.shape							):
												UpperCamelCase__     :int							      =				std.unsqueeze(-1							)
							UpperCamelCase__     :List[Any]							      =				-score / std

							# compute
							UpperCamelCase__     :List[str]							      =				-1.0 / len(self.timesteps							)

							UpperCamelCase__     :Optional[int]							      =				self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
							UpperCamelCase__     :Any							      =				beta_t.flatten()
							while len(beta_t.shape							) < len(x.shape							):
												UpperCamelCase__     :Any							      =				beta_t.unsqueeze(-1							)
							UpperCamelCase__     :Optional[int]							      =				-0.5 * beta_t * x

							UpperCamelCase__     :int							      =				torch.sqrt(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				drift - diffusion**2 * score
							UpperCamelCase__     :Dict							      =				x + drift * dt

							# add noise
							UpperCamelCase__     :List[Any]							      =				randn_tensor(x.shape					,     layout=x.layout					,     generator=UpperCamelCase_					,     device=x.device					,     dtype=x.dtype							)
							UpperCamelCase__     :int							      =				x_mean + diffusion * math.sqrt(-dt							) * noise

							return x, x_mean




		def __len__(	self							):

							'''simple docstring'''


							return self.config.num_train_timesteps