






"""simple docstring"""
import math
from collections import defaultdict
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 KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__=0.999   ,   snake_case__="cosine"   ,   ):






 if alpha_transform_type == "cosine":

  def alpha_bar_fn(snake_case__					):
   return math.cos((t + 0.008) / 1.008 * math.pi / 2					) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(snake_case__					):
   return math.exp(t * -12.0					)

 else:
  raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""					)

 A_		:						Union[str, Any]	    =   []
 for i in range(_snake_case					):
  A_		:						Optional[int]	    =   i / num_diffusion_timesteps
  A_		:						Optional[int]	    =   (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(_snake_case					) / alpha_bar_fn(_snake_case					)   ,   _snake_case					)					)
 return torch.tensor(_snake_case   ,   dtype=torch.floataa					)




class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE						):





 """simple docstring"""




 _A       :				Optional[int]    =				[e.name for e in KarrasDiffusionSchedulers]
 _A       :				List[Any]    =				2





 @register_to_config
 def __init__(self     ,     lowerCAmelCase_ = 1000     ,     lowerCAmelCase_ = 0.00085     ,     lowerCAmelCase_ = 0.012     ,     lowerCAmelCase_ = "linear"     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = "epsilon"     ,     lowerCAmelCase_ = "linspace"     ,     lowerCAmelCase_ = 0     ,     ):
  if trained_betas is not None:
   A_		:						Tuple	    =   torch.tensor(lowerCAmelCase_     ,     dtype=torch.floataa							)
  elif beta_schedule == "linear":
   A_		:						Union[str, Any]	    =   torch.linspace(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     dtype=torch.floataa							)
  elif beta_schedule == "scaled_linear":
   # this schedule is very specific to the latent diffusion model.
   A_		:						List[str]	    =   (
       torch.linspace(beta_start**0.5     ,     beta_end**0.5     ,     lowerCAmelCase_     ,     dtype=torch.floataa							) ** 2
   )
  elif beta_schedule == "squaredcos_cap_v2":
   # Glide cosine schedule
   A_		:						Any	    =   betas_for_alpha_bar(lowerCAmelCase_							)
  else:
   raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""							)

  A_		:						Union[str, Any]	    =   1.0 - self.betas
  A_		:						Tuple	    =   torch.cumprod(self.alphas     ,     dim=0							)

  #  set all values
  self.set_timesteps(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)





 def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=None							):
  if schedule_timesteps is None:
   A_		:						str	    =   self.timesteps

  A_		:						List[Any]	    =   (schedule_timesteps == timestep).nonzero()

  # The sigma index that is taken for the **very** first `step`
  # is always the second index (or the last index if there is only 1)
  # This way we can ensure we don't accidentally skip a sigma in
  # case we start in the middle of the denoising schedule (e.g. for image-to-image)
  if len(self._index_counter							) == 0:
   A_		:						int	    =   1 if len(lowerCAmelCase_							) > 1 else 0
  else:
   A_		:						Dict	    =   timestep.cpu().item() if torch.is_tensor(lowerCAmelCase_							) else timestep
   A_		:						List[Any]	    =   self._index_counter[timestep_int]

  return indices[pos].item()





 @property
 def    lowerCamelCase(self							):
  # standard deviation of the initial noise distribution
  if self.config.timestep_spacing in ["linspace", "trailing"]:
   return self.sigmas.max()

  return (self.sigmas.max() ** 2 + 1) ** 0.5





 def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     ):
  A_		:						Optional[int]	    =   self.index_for_timestep(lowerCAmelCase_							)

  if self.state_in_first_order:
   A_		:						Dict	    =   self.sigmas[step_index]
  else:
   A_		:						int	    =   self.sigmas_interpol[step_index]

  A_		:						Optional[int]	    =   sample / ((sigma**2 + 1) ** 0.5)
  return sample





 def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     ):
  A_		:						int	    =   num_inference_steps

  A_		:						Optional[Any]	    =   num_train_timesteps or self.config.num_train_timesteps

  # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
  if self.config.timestep_spacing == "linspace":
   A_		:						Dict	    =   np.linspace(0     ,     num_train_timesteps - 1     ,     lowerCAmelCase_     ,     dtype=lowerCAmelCase_							)[::-1].copy()
  elif self.config.timestep_spacing == "leading":
   A_		:						str	    =   num_train_timesteps // self.num_inference_steps
   # creates integer timesteps by multiplying by ratio
   # casting to int to avoid issues when num_inference_step is power of 3
   A_		:						int	    =   (np.arange(0     ,     lowerCAmelCase_							) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase_							)
   timesteps += self.config.steps_offset
  elif self.config.timestep_spacing == "trailing":
   A_		:						int	    =   num_train_timesteps / self.num_inference_steps
   # creates integer timesteps by multiplying by ratio
   # casting to int to avoid issues when num_inference_step is power of 3
   A_		:						Tuple	    =   (np.arange(lowerCAmelCase_     ,     0     ,     -step_ratio							)).round().copy().astype(lowerCAmelCase_							)
   timesteps -= 1
  else:
   raise ValueError(
       f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'."""							)

  A_		:						List[str]	    =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
  A_		:						Tuple	    =   torch.from_numpy(np.log(lowerCAmelCase_							)							).to(lowerCAmelCase_							)

  A_		:						Optional[Any]	    =   np.interp(lowerCAmelCase_     ,     np.arange(0     ,     len(lowerCAmelCase_							)							)     ,     lowerCAmelCase_							)
  A_		:						List[str]	    =   np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
  A_		:						Dict	    =   torch.from_numpy(lowerCAmelCase_							).to(device=lowerCAmelCase_							)

  # interpolate sigmas
  A_		:						Union[str, Any]	    =   sigmas.log().lerp(sigmas.roll(1							).log()     ,     0.5							).exp()

  A_		:						Optional[int]	    =   torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2							), sigmas[-1:]]							)
  A_		:						str	    =   torch.cat(
      [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2							), sigmas_interpol[-1:]]							)

  if str(lowerCAmelCase_							).startswith("""mps"""							):
   # mps does not support float64
   A_		:						int	    =   torch.from_numpy(lowerCAmelCase_							).to(lowerCAmelCase_     ,     dtype=torch.floataa							)
  else:
   A_		:						str	    =   torch.from_numpy(lowerCAmelCase_							).to(lowerCAmelCase_							)

  # interpolate timesteps
  A_		:						Optional[int]	    =   self.sigma_to_t(lowerCAmelCase_							).to(lowerCAmelCase_     ,     dtype=timesteps.dtype							)
  A_		:						List[str]	    =   torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])     ,     dim=-1							).flatten()

  A_		:						Union[str, Any]	    =   torch.cat([timesteps[:1], interleaved_timesteps]							)

  A_		:						Optional[int]	    =   None

  # for exp beta schedules, such as the one for `pipeline_shap_e.py`
  # we need an index counter
  A_		:						Union[str, Any]	    =   defaultdict(lowerCAmelCase_							)





 def    lowerCamelCase(self     ,     lowerCAmelCase_							):
  # get log sigma
  A_		:						str	    =   sigma.log()

  # get distribution
  A_		:						Optional[Any]	    =   log_sigma - self.log_sigmas[:, None]

  # get sigmas range
  A_		:						List[Any]	    =   dists.ge(0							).cumsum(dim=0							).argmax(dim=0							).clamp(max=self.log_sigmas.shape[0] - 2							)
  A_		:						List[Any]	    =   low_idx + 1

  A_		:						Optional[int]	    =   self.log_sigmas[low_idx]
  A_		:						Union[str, Any]	    =   self.log_sigmas[high_idx]

  # interpolate sigmas
  A_		:						Tuple	    =   (low - log_sigma) / (low - high)
  A_		:						Any	    =   w.clamp(0     ,     1							)

  # transform interpolation to time range
  A_		:						Optional[Any]	    =   (1 - w) * low_idx + w * high_idx
  A_		:						List[Any]	    =   t.view(sigma.shape							)
  return t





 @property
 def    lowerCamelCase(self							):
  return self.sample is None





 def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = True     ,     ):
  A_		:						List[str]	    =   self.index_for_timestep(lowerCAmelCase_							)

  # advance index counter by 1
  A_		:						Dict	    =   timestep.cpu().item() if torch.is_tensor(lowerCAmelCase_							) else timestep
  self._index_counter[timestep_int] += 1

  if self.state_in_first_order:
   A_		:						int	    =   self.sigmas[step_index]
   A_		:						List[Any]	    =   self.sigmas_interpol[step_index + 1]
   A_		:						Union[str, Any]	    =   self.sigmas[step_index + 1]
  else:
   # 2nd order / KDPM2's method
   A_		:						Any	    =   self.sigmas[step_index - 1]
   A_		:						Tuple	    =   self.sigmas_interpol[step_index]
   A_		:						Tuple	    =   self.sigmas[step_index]

  # currently only gamma=0 is supported. This usually works best anyways.
  # We can support gamma in the future but then need to scale the timestep before
  # passing it to the model which requires a change in API
  A_		:						Union[str, Any]	    =   0
  A_		:						Tuple	    =   sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

  # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
  if self.config.prediction_type == "epsilon":
   A_		:						int	    =   sigma_hat if self.state_in_first_order else sigma_interpol
   A_		:						Tuple	    =   sample - sigma_input * model_output
  elif self.config.prediction_type == "v_prediction":
   A_		:						Tuple	    =   sigma_hat if self.state_in_first_order else sigma_interpol
   A_		:						Any	    =   model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
       sample / (sigma_input**2 + 1)
   )
  elif self.config.prediction_type == "sample":
   raise NotImplementedError("""prediction_type not implemented yet: sample"""							)
  else:
   raise ValueError(
       f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""							)

  if self.state_in_first_order:
   # 2. Convert to an ODE derivative for 1st order
   A_		:						Tuple	    =   (sample - pred_original_sample) / sigma_hat
   # 3. delta timestep
   A_		:						List[Any]	    =   sigma_interpol - sigma_hat

   # store for 2nd order step
   A_		:						Any	    =   sample
  else:
   # DPM-Solver-2
   # 2. Convert to an ODE derivative for 2nd order
   A_		:						List[str]	    =   (sample - pred_original_sample) / sigma_interpol

   # 3. delta timestep
   A_		:						Optional[int]	    =   sigma_next - sigma_hat

   A_		:						Optional[int]	    =   self.sample
   A_		:						str	    =   None

  A_		:						int	    =   sample + derivative * dt

  if not return_dict:
   return (prev_sample,)

  return SchedulerOutput(prev_sample=lowerCAmelCase_							)





 def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     ):
  # Make sure sigmas and timesteps have the same device and dtype as original_samples
  A_		:						List[Any]	    =   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
   A_		:						List[Any]	    =   self.timesteps.to(original_samples.device     ,     dtype=torch.floataa							)
   A_		:						int	    =   timesteps.to(original_samples.device     ,     dtype=torch.floataa							)
  else:
   A_		:						Optional[int]	    =   self.timesteps.to(original_samples.device							)
   A_		:						List[str]	    =   timesteps.to(original_samples.device							)

  A_		:						Dict	    =   [self.index_for_timestep(lowerCAmelCase_     ,     lowerCAmelCase_							) for t in timesteps]

  A_		:						Optional[int]	    =   sigmas[step_indices].flatten()
  while len(sigma.shape							) < len(original_samples.shape							):
   A_		:						Any	    =   sigma.unsqueeze(-1							)

  A_		:						str	    =   original_samples + noise * sigma
  return noisy_samples





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









"""simple docstring"""
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						str	    =   OmegaConf.load(snake_case__					)
	A_		:						List[str]	    =   torch.load(snake_case__   ,   map_location="""cpu"""					)["""model"""]
	A_		:						Any	    =   list(state_dict.keys()					)

	# extract state_dict for VQVAE
	A_		:						Any	    =   {}
	A_		:						Union[str, Any]	    =   """first_stage_model."""
	for key in keys:
		if key.startswith(snake_case__					):
			A_		:						List[Any]	    =   state_dict[key]

    # extract state_dict for UNetLDM
	A_		:						Tuple	    =   {}
	A_		:						Dict	    =   """model.diffusion_model."""
	for key in keys:
		if key.startswith(snake_case__					):
			A_		:						Optional[Any]	    =   state_dict[key]

	A_		:						List[Any]	    =   config.model.params.first_stage_config.params
	A_		:						Tuple	    =   config.model.params.unet_config.params

	A_		:						Any	    =   VQModel(**snake_case__					).eval()
	vqvae.load_state_dict(snake_case__					)

	A_		:						Optional[int]	    =   UNetLDMModel(**snake_case__					).eval()
	unet.load_state_dict(snake_case__					)

	A_		:						List[str]	    =   DDIMScheduler(
	    timesteps=config.model.params.timesteps   ,   beta_schedule="""scaled_linear"""   ,   beta_start=config.model.params.linear_start   ,   beta_end=config.model.params.linear_end   ,   clip_sample=snake_case__   ,   )

	A_		:						str	    =   LDMPipeline(snake_case__   ,   snake_case__   ,   snake_case__					)
	pipeline.save_pretrained(snake_case__					)


if __name__ == "__main__":
							_lowerCAmelCase         =							argparse.ArgumentParser()
							parser.add_argument("--checkpoint_path", type=str, required=True)
							parser.add_argument("--config_path", type=str, required=True)
							parser.add_argument("--output_path", type=str, required=True)
							_lowerCAmelCase         =							parser.parse_args()

							convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)

