

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 	UpperCAmelCase__		(	A__						,					A__	):






		"""simple docstring"""


		a									= 1






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

							# standard deviation of the initial noise distribution
							SCREAMING_SNAKE_CASE__   =   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__   =   4

							# running values
							SCREAMING_SNAKE_CASE__   =   []






		def 				lowercase_		(							self						:		Any     ,       __lowerCamelCase						:		int     ,       __lowerCamelCase						:		Union[str, torch.device] = None )		->							Dict:
							SCREAMING_SNAKE_CASE__   =   num_inference_steps
							SCREAMING_SNAKE_CASE__   =   torch.linspace(1     ,       0     ,       num_inference_steps + 1 )[:-1]
							SCREAMING_SNAKE_CASE__   =   torch.cat([steps, torch.tensor([0.0] )] )

							if self.config.trained_betas is not None:
												SCREAMING_SNAKE_CASE__   =   torch.tensor(self.config.trained_betas     ,       dtype=torch.floataa )
							else:
												SCREAMING_SNAKE_CASE__   =   torch.sin(steps * math.pi / 2 ) ** 2

							SCREAMING_SNAKE_CASE__   =   (1.0 - self.betas**2) ** 0.5

							SCREAMING_SNAKE_CASE__   =   (torch.atana(self.betas     ,       self.alphas ) / math.pi * 2)[:-1]
							SCREAMING_SNAKE_CASE__   =   timesteps.to(__lowerCamelCase )

							SCREAMING_SNAKE_CASE__   =   []






		def 				lowercase_		(							self						:		Any     ,       __lowerCamelCase						:		torch.FloatTensor     ,       __lowerCamelCase						:		int     ,       __lowerCamelCase						:		torch.FloatTensor     ,       __lowerCamelCase						:		bool = True     ,       )		->							Union[SchedulerOutput, Tuple]:
							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__   =   (self.timesteps == timestep).nonzero().item()
							SCREAMING_SNAKE_CASE__   =   timestep_index + 1

							SCREAMING_SNAKE_CASE__   =   sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
							self.ets.append(__lowerCamelCase )

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

							SCREAMING_SNAKE_CASE__   =   self._get_prev_sample(__lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase )

							if not return_dict:
												return (prev_sample,)

							return SchedulerOutput(prev_sample=__lowerCamelCase )






		def 				lowercase_		(							self						:		List[str]     ,       __lowerCamelCase						:		torch.FloatTensor     ,       *__lowerCamelCase						:		Any     ,       **__lowerCamelCase						:		Optional[Any] )		->							torch.FloatTensor:
							return sample






		def 				lowercase_		(							self						:		int     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		Tuple     ,       __lowerCamelCase						:		str     ,       __lowerCamelCase						:		Union[str, Any] )		->							str:
							SCREAMING_SNAKE_CASE__   =   self.alphas[timestep_index]
							SCREAMING_SNAKE_CASE__   =   self.betas[timestep_index]

							SCREAMING_SNAKE_CASE__   =   self.alphas[prev_timestep_index]
							SCREAMING_SNAKE_CASE__   =   self.betas[prev_timestep_index]

							SCREAMING_SNAKE_CASE__   =   (sample - sigma * ets) / max(__lowerCamelCase     ,       1e-8 )
							SCREAMING_SNAKE_CASE__   =   next_alpha * pred + ets * next_sigma

							return prev_sample







		def __len__(							self						:		str )		->							Union[str, Any]:
							return self.config.num_train_timesteps





from argparse import ArgumentParser

from .env import EnvironmentCommand
def     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   ArgumentParser('''Diffusers CLI tool'''				,		usage='''diffusers-cli <command> [<args>]'''    )
     SCREAMING_SNAKE_CASE__   =   parser.add_subparsers(help='''diffusers-cli command helpers'''    )

     # Register commands
     EnvironmentCommand.register_subcommand(_A    )

     # Let's go
     SCREAMING_SNAKE_CASE__   =   parser.parse_args()

     if not hasattr(_A				,		'''func'''    ):
          parser.print_help()
          exit(1    )

     # Run
     SCREAMING_SNAKE_CASE__   =   args.func(_A    )
     service.run()


if __name__ == "__main__":
       main()


