
# 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		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__		):
							lowercase_       :       str       					=1




							@register_to_config
							def __init__(				self   ,A__=2_0_0_0   ,A__=0.1   ,A__=2_0   ,A__=1E-3):
												lowercase							=						None
												lowercase							=						None
												lowercase							=						None




							def  A__      (				self   ,A__   ,A__ = None):
												lowercase							=						torch.linspace(1   ,self.config.sampling_eps   ,A__   ,device=A__)




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__=None):
												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
												lowercase							=						(
												    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
												)
												lowercase							=						torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff))
												lowercase							=						std.flatten()
												while len(std.shape) < len(score.shape):
																	lowercase							=						std.unsqueeze(-1)
												lowercase							=						-score / std

												# compute
												lowercase							=						-1.0 / len(self.timesteps)

												lowercase							=						self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
												lowercase							=						beta_t.flatten()
												while len(beta_t.shape) < len(x.shape):
																	lowercase							=						beta_t.unsqueeze(-1)
												lowercase							=						-0.5 * beta_t * x

												lowercase							=						torch.sqrt(A__)
												lowercase							=						drift - diffusion**2 * score
												lowercase							=						x + drift * dt

												# add noise
												lowercase							=						randn_tensor(x.shape   ,layout=x.layout   ,generator=A__   ,device=x.device   ,dtype=x.dtype)
												lowercase							=						x_mean + diffusion * math.sqrt(-dt) * noise

												return x, x_mean





							def __len__(				self):
												return self.config.num_train_timesteps






"""simple docstring"""




import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   ArgumentParser(
							    description=(
							        """PyTorch TPU distributed training launch """
							        """helper utility that will spawn up """
							        """multiple distributed processes"""
							    )						)

							# Optional arguments for the launch helper
							parser.add_argument("""--num_cores"""   ,type=_snake_case   ,default=1   ,help="""Number of TPU cores to use (1 or 8)."""						)

							# positional
							parser.add_argument(
							    """training_script"""   ,type=_snake_case   ,help=(
							        """The full path to the single TPU training """
							        """program/script to be launched in parallel, """
							        """followed by all the arguments for the """
							        """training script"""
							    )   ,)

							# rest from the training program
							parser.add_argument("""training_script_args"""   ,nargs=_snake_case						)

							return parser.parse_args()

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       int           =   parse_args()

							# Import training_script as a module.
							SCREAMING_SNAKE_CASE__						:       Dict           =   Path(args.training_script						)
							sys.path.append(str(script_fpath.parent.resolve()						)						)
							SCREAMING_SNAKE_CASE__						:       int           =   script_fpath.stem
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   importlib.import_module(_snake_case						)

							# Patch sys.argv
							SCREAMING_SNAKE_CASE__						:       str           =   [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores						)]
							xmp.spawn(mod._mp_fn   ,args=()   ,nprocs=args.num_cores						)


if __name__ == "__main__":
					main()




