

from argparse import ArgumentParser

from .env import EnvironmentCommand

def       __snake_case						( ):



      """simple docstring"""

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

      # Register commands
      EnvironmentCommand.register_subcommand(snake_case__    )

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

      if not hasattr(snake_case__				,"func"    ):
            parser.print_help()
            exit(1    )

      # Run
      A_						  =				args.func(snake_case__    )
      service.run()


if __name__ == "__main__":
      main()







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

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput




@dataclass
class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					torch.FloatTensor
       lowerCamelCase				:					torch.FloatTensor




class       snake_case__ (				lowercase_						,     lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					int								=			1



       @register_to_config
       def __init__(				self      ,    a__ = 20_00      ,    a__ = 0.15      ,    a__ = 0.01      ,    a__ = 13_48.0      ,    a__ = 1e-5      ,    a__ = 1      ,    )			->   Any:






            '''simple docstring'''





            __snake_case      :int 							=    sigma_max

            # setable values
            __snake_case      :Any 							=    None

            self.set_sigmas(a__      ,    a__      ,    a__      ,    a__     )



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   torch.FloatTensor:






            '''simple docstring'''





            return sample



       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = None     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

            __snake_case      :int 							=    torch.linspace(1      ,    a__      ,    a__      ,    device=a__     )



       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = None      ,    a__ = None     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    sigma_min if sigma_min is not None else self.config.sigma_min
            __snake_case      :Any 							=    sigma_max if sigma_max is not None else self.config.sigma_max
            __snake_case      :Any 							=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
            if self.timesteps is None:
                 self.set_timesteps(a__      ,    a__     )

            __snake_case      :int 							=    sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
            __snake_case      :List[str] 							=    torch.exp(torch.linspace(math.log(a__     )      ,    math.log(a__     )      ,    a__     )     )
            __snake_case      :Any 							=    torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]     )



       def        __lowercase    (				self      ,    a__      ,    a__     )			->   Tuple:






            '''simple docstring'''





            return torch.where(
                timesteps == 0      ,    torch.zeros_like(t.to(timesteps.device     )     )      ,    self.discrete_sigmas[timesteps - 1].to(timesteps.device     )      ,    )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    a__ = None      ,    a__ = True      ,    )			->   Union[SdeVeOutput, Tuple]:






            '''simple docstring'''





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

            __snake_case      :Optional[int] 							=    timestep * torch.ones(
                sample.shape[0]      ,    device=sample.device     )  # torch.repeat_interleave(timestep, sample.shape[0])
            __snake_case      :Tuple 							=    (timestep * (len(self.timesteps     ) - 1)).long()

            # mps requires indices to be in the same device, so we use cpu as is the default with cuda
            __snake_case      :str 							=    timesteps.to(self.discrete_sigmas.device     )

            __snake_case      :str 							=    self.discrete_sigmas[timesteps].to(sample.device     )
            __snake_case      :Union[str, Any] 							=    self.get_adjacent_sigma(a__      ,    a__     ).to(sample.device     )
            __snake_case      :Dict 							=    torch.zeros_like(a__     )
            __snake_case      :str 							=    (sigma**2 - adjacent_sigma**2) ** 0.5

            # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
            # also equation 47 shows the analog from SDE models to ancestral sampling methods
            __snake_case      :Optional[Any] 							=    diffusion.flatten()
            while len(diffusion.shape     ) < len(sample.shape     ):
                 __snake_case      :int 							=    diffusion.unsqueeze(-1     )
            __snake_case      :Optional[Any] 							=    drift - diffusion**2 * model_output

            #  equation 6: sample noise for the diffusion term of
            __snake_case      :str 							=    randn_tensor(
                sample.shape      ,    layout=sample.layout      ,    generator=a__      ,    device=sample.device      ,    dtype=sample.dtype     )
            __snake_case      :str 							=    sample - drift  # subtract because `dt` is a small negative timestep
            # TODO is the variable diffusion the correct scaling term for the noise?
            __snake_case      :str 							=    prev_sample_mean + diffusion * noise  # add impact of diffusion field g

            if not return_dict:
                 return (prev_sample, prev_sample_mean)

            return SdeVeOutput(prev_sample=a__      ,    prev_sample_mean=a__     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__ = None      ,    a__ = True      ,    )			->   Union[SchedulerOutput, Tuple]:






            '''simple docstring'''





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

            # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
            # sample noise for correction
            __snake_case      :Any 							=    randn_tensor(sample.shape      ,    layout=sample.layout      ,    generator=a__     ).to(sample.device     )

            # compute step size from the model_output, the noise, and the snr
            __snake_case      :List[Any] 							=    torch.norm(model_output.reshape(model_output.shape[0]      ,    -1     )      ,    dim=-1     ).mean()
            __snake_case      :Tuple 							=    torch.norm(noise.reshape(noise.shape[0]      ,    -1     )      ,    dim=-1     ).mean()
            __snake_case      :Dict 							=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
            __snake_case      :int 							=    step_size * torch.ones(sample.shape[0]     ).to(sample.device     )
            # self.repeat_scalar(step_size, sample.shape[0])

            # compute corrected sample: model_output term and noise term
            __snake_case      :Tuple 							=    step_size.flatten()
            while len(step_size.shape     ) < len(sample.shape     ):
                 __snake_case      :Optional[int] 							=    step_size.unsqueeze(-1     )
            __snake_case      :List[str] 							=    sample + step_size * model_output
            __snake_case      :Tuple 							=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

            if not return_dict:
                 return (prev_sample,)

            return SchedulerOutput(prev_sample=a__     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    )			->   torch.FloatTensor:






            '''simple docstring'''





            __snake_case      :Tuple 							=    timesteps.to(original_samples.device     )
            __snake_case      :Optional[Any] 							=    self.discrete_sigmas.to(original_samples.device     )[timesteps]
            __snake_case      :int 							=    (
                noise * sigmas[:, None, None, None]
                if noise is not None
                else torch.randn_like(a__     ) * sigmas[:, None, None, None]
            )
            __snake_case      :str 							=    noise + original_samples
            return noisy_samples



       def __len__(				self     )			->   Optional[int]:






            '''simple docstring'''





            return self.config.num_train_timesteps


