




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

import numpy as np
import torch

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


UpperCAmelCase_   							=      logging.get_logger(__name__)  # pylint: disable=invalid-name





@dataclass
class        lowerCamelCase__(   __UpperCamelCase):
      UpperCAmelCase__ :      torch.FloatTensor





class        lowerCamelCase__(   __UpperCamelCase					,	__UpperCamelCase):
      UpperCAmelCase__ :      List[str]       						=  1
      @register_to_config
      def __init__(							self:					List[Any]       ,			UpperCamelCase_:					int = 40       ,			UpperCamelCase_:					float = 0.002       ,			UpperCamelCase_:					float = 80.0       ,			UpperCamelCase_:					float = 0.5       ,			UpperCamelCase_:					float = 1.0       ,			UpperCamelCase_:					float = 7.0       ,			UpperCamelCase_:					bool = True       ,			):
             # standard deviation of the initial noise distribution
             __lowerCamelCase  =		sigma_max

             __lowerCamelCase  =		np.linspace(0       ,			1       ,			_lowerCAmelCase  )
             __lowerCamelCase  =		self._convert_to_karras(_lowerCAmelCase  )
             __lowerCamelCase  =		self.sigma_to_t(_lowerCAmelCase  )

             # setable values
             __lowerCamelCase  =		None
             __lowerCamelCase  =		torch.from_numpy(_lowerCAmelCase  )
             __lowerCamelCase  =		torch.from_numpy(_lowerCAmelCase  )
             __lowerCamelCase  =		False
             __lowerCamelCase  =		False
      def      lowerCAmelCase__		(							self:					Any       ,			UpperCamelCase_:					Optional[int]       ,			UpperCamelCase_:					Optional[Any]=None  ):
             if schedule_timesteps is None:
                    __lowerCamelCase  =		self.timesteps

             __lowerCamelCase  =		(schedule_timesteps == timestep).nonzero()
             return indices.item()
      def      lowerCAmelCase__		(							self:					Optional[int]       ,			UpperCamelCase_:					torch.FloatTensor       ,			UpperCamelCase_:					Union[float, torch.FloatTensor]  ):
             # Get sigma corresponding to timestep
             if isinstance(_lowerCAmelCase       ,			torch.Tensor  ):
                    __lowerCamelCase  =		timestep.to(self.timesteps.device  )
             __lowerCamelCase  =		self.index_for_timestep(_lowerCAmelCase  )
             __lowerCamelCase  =		self.sigmas[step_idx]

             __lowerCamelCase  =		sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

             __lowerCamelCase  =		True
             return sample
      def      lowerCAmelCase__		(							self:					Optional[Any]       ,			UpperCamelCase_:					Union[float, np.ndarray]  ):
             if not isinstance(_lowerCAmelCase       ,			np.ndarray  ):
                    __lowerCamelCase  =		np.array(_lowerCAmelCase       ,			dtype=np.floataa  )

             __lowerCamelCase  =		10_00 * 0.25 * np.log(sigmas + 1E-44  )

             return timesteps
      def      lowerCAmelCase__		(							self:					Tuple       ,			UpperCamelCase_:					Optional[int] = None       ,			UpperCamelCase_:					Union[str, torch.device] = None       ,			UpperCamelCase_:					Optional[List[int]] = None       ,			):
             if num_inference_steps is None and timesteps is None:
                    raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""  )

             if num_inference_steps is not None and timesteps is not None:
                    raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""  )

             # Follow DDPMScheduler custom timesteps logic
             if timesteps is not None:
                    for i in range(1       ,			len(_lowerCAmelCase  )  ):
                           if timesteps[i] >= timesteps[i - 1]:
                                  raise ValueError("""`timesteps` must be in descending order."""  )

                    if timesteps[0] >= self.config.num_train_timesteps:
                           raise ValueError(
                               F'`timesteps` must start before `self.config.train_timesteps`:'
                               F' {self.config.num_train_timesteps}.'  )

                    __lowerCamelCase  =		np.array(_lowerCAmelCase       ,			dtype=np.intaa  )
                    __lowerCamelCase  =		True
             else:
                    if num_inference_steps > self.config.num_train_timesteps:
                           raise ValueError(
                               F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                               F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                               F' maximal {self.config.num_train_timesteps} timesteps.'  )

                    __lowerCamelCase  =		num_inference_steps

                    __lowerCamelCase  =		self.config.num_train_timesteps // self.num_inference_steps
                    __lowerCamelCase  =		(np.arange(0       ,			_lowerCAmelCase  ) * step_ratio).round()[::-1].copy().astype(np.intaa  )
                    __lowerCamelCase  =		False

             # Map timesteps to Karras sigmas directly for multistep sampling
             # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
             __lowerCamelCase  =		self.config.num_train_timesteps
             __lowerCamelCase  =		timesteps[::-1].copy()
             __lowerCamelCase  =		ramp / (num_train_timesteps - 1)
             __lowerCamelCase  =		self._convert_to_karras(_lowerCAmelCase  )
             __lowerCamelCase  =		self.sigma_to_t(_lowerCAmelCase  )

             __lowerCamelCase  =		np.concatenate([sigmas, [self.sigma_min]]  ).astype(np.floataa  )
             __lowerCamelCase  =		torch.from_numpy(_lowerCAmelCase  ).to(device=_lowerCAmelCase  )

             if str(_lowerCAmelCase  ).startswith("""mps"""  ):
                    # mps does not support float64
                    __lowerCamelCase  =		torch.from_numpy(_lowerCAmelCase  ).to(_lowerCAmelCase       ,			dtype=torch.floataa  )
             else:
                    __lowerCamelCase  =		torch.from_numpy(_lowerCAmelCase  ).to(device=_lowerCAmelCase  )
      def      lowerCAmelCase__		(							self:					List[Any]       ,			UpperCamelCase_:					Union[str, Any]  ):
             __lowerCamelCase  =		self.config.sigma_min
             __lowerCamelCase  =		self.config.sigma_max

             __lowerCamelCase  =		self.config.rho
             __lowerCamelCase  =		sigma_min ** (1 / rho)
             __lowerCamelCase  =		sigma_max ** (1 / rho)
             __lowerCamelCase  =		(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
             return sigmas
      def      lowerCAmelCase__		(							self:					int       ,			UpperCamelCase_:					Any  ):
             __lowerCamelCase  =		self.config.sigma_data

             __lowerCamelCase  =		sigma_data**2 / (sigma**2 + sigma_data**2)
             __lowerCamelCase  =		sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
             return c_skip, c_out
      def      lowerCAmelCase__		(							self:					Any       ,			UpperCamelCase_:					Any  ):
             __lowerCamelCase  =		self.config.sigma_min
             __lowerCamelCase  =		self.config.sigma_data

             __lowerCamelCase  =		sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
             __lowerCamelCase  =		(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
             return c_skip, c_out
      def      lowerCAmelCase__		(							self:					List[str]       ,			UpperCamelCase_:					torch.FloatTensor       ,			UpperCamelCase_:					Union[float, torch.FloatTensor]       ,			UpperCamelCase_:					torch.FloatTensor       ,			UpperCamelCase_:					Optional[torch.Generator] = None       ,			UpperCamelCase_:					bool = True       ,			):

             if (
                 isinstance(_lowerCAmelCase       ,			_lowerCAmelCase  )
                 or isinstance(_lowerCAmelCase       ,			torch.IntTensor  )
                 or isinstance(_lowerCAmelCase       ,			torch.LongTensor  )
             ):
                    raise ValueError(
                        (
                            """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
                            F' `{self.__class__}.step()` is not supported. Make sure to pass'
                            """ one of the `scheduler.timesteps` as a timestep."""
                        )       ,			)

             if not self.is_scale_input_called:
                    logger.warning(
                        """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
                        """See `StableDiffusionPipeline` for a usage example."""  )

             if isinstance(_lowerCAmelCase       ,			torch.Tensor  ):
                    __lowerCamelCase  =		timestep.to(self.timesteps.device  )

             __lowerCamelCase  =		self.config.sigma_min
             __lowerCamelCase  =		self.config.sigma_max

             __lowerCamelCase  =		self.index_for_timestep(_lowerCAmelCase  )

             # sigma_next corresponds to next_t in original implementation
             __lowerCamelCase  =		self.sigmas[step_index]
             if step_index + 1 < self.config.num_train_timesteps:
                    __lowerCamelCase  =		self.sigmas[step_index + 1]
             else:
                    # Set sigma_next to sigma_min
                    __lowerCamelCase  =		self.sigmas[-1]

             # Get scalings for boundary conditions
             __lowerCamelCase  =		self.get_scalings_for_boundary_condition(_lowerCAmelCase  )

             # 1. Denoise model output using boundary conditions
             __lowerCamelCase  =		c_out * model_output + c_skip * sample
             if self.config.clip_denoised:
                    __lowerCamelCase  =		denoised.clamp(-1       ,			1  )

             # 2. Sample z ~ N(0, s_noise^2 * I)
             # Noise is not used for onestep sampling.
             if len(self.timesteps  ) > 1:
                    __lowerCamelCase  =		randn_tensor(
                        model_output.shape       ,			dtype=model_output.dtype       ,			device=model_output.device       ,			generator=_lowerCAmelCase  )
             else:
                    __lowerCamelCase  =		torch.zeros_like(_lowerCAmelCase  )
             __lowerCamelCase  =		noise * self.config.s_noise

             __lowerCamelCase  =		sigma_next.clamp(min=_lowerCAmelCase       ,			max=_lowerCAmelCase  )

             # 3. Return noisy sample
             # tau = sigma_hat, eps = sigma_min
             __lowerCamelCase  =		denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

             if not return_dict:
                    return (prev_sample,)

             return CMStochasticIterativeSchedulerOutput(prev_sample=_lowerCAmelCase  )
      def      lowerCAmelCase__		(							self:					str       ,			UpperCamelCase_:					torch.FloatTensor       ,			UpperCamelCase_:					torch.FloatTensor       ,			UpperCamelCase_:					torch.FloatTensor       ,			):
             # Make sure sigmas and timesteps have the same device and dtype as original_samples
             __lowerCamelCase  =		self.sigmas.to(device=original_samples.device       ,			dtype=original_samples.dtype  )
             if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCAmelCase  ):
                    # mps does not support float64
                    __lowerCamelCase  =		self.timesteps.to(original_samples.device       ,			dtype=torch.floataa  )
                    __lowerCamelCase  =		timesteps.to(original_samples.device       ,			dtype=torch.floataa  )
             else:
                    __lowerCamelCase  =		self.timesteps.to(original_samples.device  )
                    __lowerCamelCase  =		timesteps.to(original_samples.device  )

             __lowerCamelCase  =		[(schedule_timesteps == t).nonzero().item() for t in timesteps]

             __lowerCamelCase  =		sigmas[step_indices].flatten()
             while len(sigma.shape  ) < len(original_samples.shape  ):
                    __lowerCamelCase  =		sigma.unsqueeze(-1  )

             __lowerCamelCase  =		original_samples + noise * sigma
             return noisy_samples




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


import torch
from torch import autograd







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			,			_lowerCAmelCase					:     bool			):
           __snake_case       :				Optional[int] 			=    inputs.numel()
           __snake_case       :				str 			=    int(0.005 * nb_elems			) + 1
           if sigmoid:
               __snake_case       :				Union[str, Any] 			=    (torch.sigmoid(_lowerCAmelCase			) > threshold).type(inputs.type()			)
           else:
               __snake_case       :				Any 			=    (inputs > threshold).type(inputs.type()			)
           if mask.sum() < nb_min:
               # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
               __snake_case       :				Dict 			=    inputs.flatten().kthvalue(max(nb_elems - nb_min			,			1			)			).values
               __snake_case       :				List[str] 			=    (inputs > k_threshold).type(inputs.type()			)
           return mask





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     str			,			_lowerCAmelCase					:     Tuple			):
           return gradOutput, None, None







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				str 			=    inputs.clone()
           __snake_case			, __snake_case       :				Dict 			=    inputs.flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    int(threshold * inputs.numel()			)

           # flat_out and mask access the same memory.
           __snake_case       :				Optional[int] 			=    mask.flatten()
           __snake_case       :				Dict 			=    0
           __snake_case       :				Optional[Any] 			=    1
           return mask





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     Optional[Any]			):
           return gradOutput, None







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				List[str] 			=    inputs.clone()
           __snake_case			, __snake_case       :				str 			=    inputs.abs().flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				List[Any] 			=    int(threshold * inputs.numel()			)

           # flat_out and mask access the same memory.
           __snake_case       :				List[Any] 			=    mask.flatten()
           __snake_case       :				int 			=    0
           __snake_case       :				Optional[int] 			=    1
           return mask



