






import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase   ,		lowerCAmelCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		str								=						1


     @register_to_config
     def __init__(					self							:						int       ,			UpperCAmelCase_							:						int = 1000       ,			UpperCAmelCase_							:						Optional[Union[np.ndarray, List[float]]] = None   ):
       # set `betas`, `alphas`, `timesteps`
       self.set_timesteps(UpperCAmelCase_   )

       # standard deviation of the initial noise distribution
       SCREAMING_SNAKE_CASE							:   Tuple						    =     1.0

       # For now we only support F-PNDM, i.e. the runge-kutta method
       # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
       # mainly at formula (9), (12), (13) and the Algorithm 2.
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     4

       # running values
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     []


     def 					_A							(					self							:						Tuple       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						Union[str, torch.device] = None   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     num_inference_steps
       SCREAMING_SNAKE_CASE							:   Dict						    =     torch.linspace(1       ,			0       ,			num_inference_steps + 1   )[:-1]
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     torch.cat([steps, torch.tensor([0.0]   )]   )

       if self.config.trained_betas is not None:
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     torch.tensor(self.config.trained_betas       ,			dtype=torch.floataa   )
       else:
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     torch.sin(steps * math.pi / 2   ) ** 2

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     (1.0 - self.betas**2) ** 0.5

       SCREAMING_SNAKE_CASE							:   str						    =     (torch.atana(self.betas       ,			self.alphas   ) / math.pi * 2)[:-1]
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     timesteps.to(UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Any						    =     []


     def 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						torch.FloatTensor       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						torch.FloatTensor       ,			UpperCAmelCase_							:						bool = True       ,			):
       if self.num_inference_steps is None:
         raise ValueError(
             "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"   )

       SCREAMING_SNAKE_CASE							:   List[str]						    =     (self.timesteps == timestep).nonzero().item()
       SCREAMING_SNAKE_CASE							:   str						    =     timestep_index + 1

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
       self.ets.append(UpperCAmelCase_   )

       if len(self.ets   ) == 1:
         SCREAMING_SNAKE_CASE							:   List[str]						    =     self.ets[-1]
       elif len(self.ets   ) == 2:
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     (3 * self.ets[-1] - self.ets[-2]) / 2
       elif len(self.ets   ) == 3:
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
       else:
         SCREAMING_SNAKE_CASE							:   List[str]						    =     (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

       SCREAMING_SNAKE_CASE							:   Dict						    =     self._get_prev_sample(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )

       if not return_dict:
         return (prev_sample,)

       return SchedulerOutput(prev_sample=UpperCAmelCase_   )


     def 					_A							(					self							:						str       ,			UpperCAmelCase_							:						torch.FloatTensor       ,			*UpperCAmelCase_							:						Optional[int]       ,			**UpperCAmelCase_							:						List[Any]   ):
       return sample


     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						List[str]       ,			UpperCAmelCase_							:						Tuple       ,			UpperCAmelCase_							:						List[Any]       ,			UpperCAmelCase_							:						int   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.alphas[timestep_index]
       SCREAMING_SNAKE_CASE							:   str						    =     self.betas[timestep_index]

       SCREAMING_SNAKE_CASE							:   List[str]						    =     self.alphas[prev_timestep_index]
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.betas[prev_timestep_index]

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     (sample - sigma * ets) / max(UpperCAmelCase_       ,			1E-8   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     next_alpha * pred + ets * next_sigma

       return prev_sample






     def __len__(					self							:						List[Any]   ):
       return self.config.num_train_timesteps








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

import torch_xla.distributed.xla_multiprocessing as xmp

def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      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=__magic_name__		,    default=1		,    help="Number of TPU cores to use (1 or 8)."						)

				# positional
				parser.add_argument(
				    "training_script"		,    type=__magic_name__		,    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=__magic_name__						)

				return parser.parse_args()

def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      parse_args()

				# Import training_script as a module.
				lowercase         =      Path(args.training_script						)
				sys.path.append(str(script_fpath.parent.resolve()						)						)
				lowercase         =      script_fpath.stem
				lowercase         =      importlib.import_module(__magic_name__						)

				# Patch sys.argv
				lowercase         =      [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()
