

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   lowerCamelCase__       (       UpperCamelCase__     :    Dict    ,      UpperCamelCase__     :    Optional[int]=0.999    ,      UpperCamelCase__     :    str="cosine"    ,      )     ->	Dict:



					'''simple docstring'''




					if alpha_transform_type == "cosine":

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

					elif alpha_transform_type == "exp":

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

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

					_snake_case													=			[]
					for i in range(lowerCamelCase_  ):
										_snake_case													=			i / num_diffusion_timesteps
										_snake_case													=			(i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(lowerCamelCase_  ) / alpha_bar_fn(lowerCamelCase_  )    ,      lowerCamelCase_  )  )
					return torch.tensor(lowerCamelCase_    ,      dtype=torch.floataa  )




class      UpperCamelCase_						(					lowerCamelCase__							,					lowerCamelCase__   ):
					lowerCAmelCase_							=		[e.name for e in KarrasDiffusionSchedulers]
					lowerCAmelCase_							=		2




					@register_to_config
					def __init__(						self			,  lowerCAmelCase_ = 1000			,  lowerCAmelCase_ = 0.0_00_85			,  lowerCAmelCase_ = 0.0_12			,  lowerCAmelCase_ = "linear"			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = "epsilon"			,  lowerCAmelCase_ = "linspace"			,  lowerCAmelCase_ = 0			,  )						->							Optional[Any]:






										if trained_betas is not None:
															_snake_case													=			torch.tensor(__lowerCamelCase			,  dtype=torch.floataa					)
										elif beta_schedule == "linear":
															_snake_case													=			torch.linspace(__lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  dtype=torch.floataa					)
										elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															_snake_case													=			(
															    torch.linspace(beta_start**0.5			,  beta_end**0.5			,  __lowerCamelCase			,  dtype=torch.floataa					) ** 2
															)
										elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															_snake_case													=			betas_for_alpha_bar(__lowerCamelCase					)
										else:
															raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''					)

										_snake_case													=			1.0 - self.betas
										_snake_case													=			torch.cumprod(self.alphas			,  dim=0					)

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




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None					)						->							str:






										if schedule_timesteps is None:
															_snake_case													=			self.timesteps

										_snake_case													=			(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:
															_snake_case													=			1 if len(__lowerCamelCase					) > 1 else 0
										else:
															_snake_case													=			timestep.cpu().item() if torch.is_tensor(__lowerCamelCase					) else timestep
															_snake_case													=			self._index_counter[timestep_int]

										return indices[pos].item()




					@property
					def 		lowerCAmelCase     (						self					)						->							Any:






										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_			,  )						->							torch.FloatTensor:
										_snake_case													=			self.index_for_timestep(__lowerCamelCase					)

										if self.state_in_first_order:
															_snake_case													=			self.sigmas[step_index]
										else:
															_snake_case													=			self.sigmas_interpol[step_index]

										_snake_case													=			sample / ((sigma**2 + 1) ** 0.5)
										return sample




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  )						->							Union[str, Any]:
										_snake_case													=			num_inference_steps

										_snake_case													=			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":
															_snake_case													=			np.linspace(0			,  num_train_timesteps - 1			,  __lowerCamelCase			,  dtype=__lowerCamelCase					)[::-1].copy()
										elif self.config.timestep_spacing == "leading":
															_snake_case													=			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
															_snake_case													=			(np.arange(0			,  __lowerCamelCase					) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase					)
															timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
															_snake_case													=			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
															_snake_case													=			(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\'.'''					)

										_snake_case													=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
										_snake_case													=			torch.from_numpy(np.log(__lowerCamelCase					)					).to(__lowerCamelCase					)

										_snake_case													=			np.interp(__lowerCamelCase			,  np.arange(0			,  len(__lowerCamelCase					)					)			,  __lowerCamelCase					)
										_snake_case													=			np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
										_snake_case													=			torch.from_numpy(__lowerCamelCase					).to(device=__lowerCamelCase					)

										# interpolate sigmas
										_snake_case													=			sigmas.log().lerp(sigmas.roll(1					).log()			,  0.5					).exp()

										_snake_case													=			torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2					), sigmas[-1:]]					)
										_snake_case													=			torch.cat(
										    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2					), sigmas_interpol[-1:]]					)

										if str(__lowerCamelCase					).startswith('mps'					):
															# mps does not support float64
															_snake_case													=			torch.from_numpy(__lowerCamelCase					).to(__lowerCamelCase			,  dtype=torch.floataa					)
										else:
															_snake_case													=			torch.from_numpy(__lowerCamelCase					).to(__lowerCamelCase					)

										# interpolate timesteps
										_snake_case													=			self.sigma_to_t(__lowerCamelCase					).to(__lowerCamelCase			,  dtype=timesteps.dtype					)
										_snake_case													=			torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])			,  dim=-1					).flatten()

										_snake_case													=			torch.cat([timesteps[:1], interleaved_timesteps]					)

										_snake_case													=			None

										# for exp beta schedules, such as the one for `pipeline_shap_e.py`
										# we need an index counter
										_snake_case													=			defaultdict(__lowerCamelCase					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Tuple:
										_snake_case													=			sigma.log()

										# get distribution
										_snake_case													=			log_sigma - self.log_sigmas[:, None]

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

										_snake_case													=			self.log_sigmas[low_idx]
										_snake_case													=			self.log_sigmas[high_idx]

										# interpolate sigmas
										_snake_case													=			(low - log_sigma) / (low - high)
										_snake_case													=			w.clamp(0			,  1					)

										# transform interpolation to time range
										_snake_case													=			(1 - w) * low_idx + w * high_idx
										_snake_case													=			t.view(sigma.shape					)
										return t




					@property
					def 		lowerCAmelCase     (						self					)						->							Tuple:
										return self.sample is None




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = True			,  )						->							Union[SchedulerOutput, Tuple]:
										_snake_case													=			self.index_for_timestep(__lowerCamelCase					)

										# advance index counter by 1
										_snake_case													=			timestep.cpu().item() if torch.is_tensor(__lowerCamelCase					) else timestep
										self._index_counter[timestep_int] += 1

										if self.state_in_first_order:
															_snake_case													=			self.sigmas[step_index]
															_snake_case													=			self.sigmas_interpol[step_index + 1]
															_snake_case													=			self.sigmas[step_index + 1]
										else:
															# 2nd order / KDPM2's method
															_snake_case													=			self.sigmas[step_index - 1]
															_snake_case													=			self.sigmas_interpol[step_index]
															_snake_case													=			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
										_snake_case													=			0
										_snake_case													=			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":
															_snake_case													=			sigma_hat if self.state_in_first_order else sigma_interpol
															_snake_case													=			sample - sigma_input * model_output
										elif self.config.prediction_type == "v_prediction":
															_snake_case													=			sigma_hat if self.state_in_first_order else sigma_interpol
															_snake_case													=			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
															_snake_case													=			(sample - pred_original_sample) / sigma_hat
															# 3. delta timestep
															_snake_case													=			sigma_interpol - sigma_hat

															# store for 2nd order step
															_snake_case													=			sample
										else:
															# DPM-Solver-2
															# 2. Convert to an ODE derivative for 2nd order
															_snake_case													=			(sample - pred_original_sample) / sigma_interpol

															# 3. delta timestep
															_snake_case													=			sigma_next - sigma_hat

															_snake_case													=			self.sample
															_snake_case													=			None

										_snake_case													=			sample + derivative * dt

										if not return_dict:
															return (prev_sample,)

										return SchedulerOutput(prev_sample=__lowerCamelCase					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  )						->							torch.FloatTensor:
										_snake_case													=			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
															_snake_case													=			self.timesteps.to(original_samples.device			,  dtype=torch.floataa					)
															_snake_case													=			timesteps.to(original_samples.device			,  dtype=torch.floataa					)
										else:
															_snake_case													=			self.timesteps.to(original_samples.device					)
															_snake_case													=			timesteps.to(original_samples.device					)

										_snake_case													=			[self.index_for_timestep(__lowerCamelCase			,  __lowerCamelCase					) for t in timesteps]

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

										_snake_case													=			original_samples + noise * sigma
										return noisy_samples






					def __len__(						self					)						->							Optional[Any]:
										return self.config.num_train_timesteps










import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict    ,      UpperCamelCase__     :    List[str]    ,      UpperCamelCase__     :    Dict  )     ->	List[Any]:



					'''simple docstring'''




					_snake_case													=			OmegaConf.load(UpperCamelCase__  )
					_snake_case													=			torch.load(UpperCamelCase__    ,      map_location='cpu'  )['model']
					_snake_case													=			list(state_dict.keys()  )

					# extract state_dict for VQVAE
					_snake_case													=			{}
					_snake_case													=			'first_stage_model.'
					for key in keys:
										if key.startswith(UpperCamelCase__  ):
															_snake_case													=			state_dict[key]

    # extract state_dict for UNetLDM
					_snake_case													=			{}
					_snake_case													=			'model.diffusion_model.'
					for key in keys:
										if key.startswith(UpperCamelCase__  ):
															_snake_case													=			state_dict[key]

					_snake_case													=			config.model.params.first_stage_config.params
					_snake_case													=			config.model.params.unet_config.params

					_snake_case													=			VQModel(**UpperCamelCase__  ).eval()
					vqvae.load_state_dict(UpperCamelCase__  )

					_snake_case													=			UNetLDMModel(**UpperCamelCase__  ).eval()
					unet.load_state_dict(UpperCamelCase__  )

					_snake_case													=			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=UpperCamelCase__    ,      )

					_snake_case													=			LDMPipeline(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__  )
					pipeline.save_pretrained(UpperCamelCase__  )


if __name__ == "__main__":
					UpperCAmelCase_ 							=       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)
					UpperCAmelCase_ 							=       parser.parse_args()

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



