
"""simple docstring"""


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

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''
			lowerCamelCase						      =	42
			lowerCamelCase						      =	None







def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase=0.9_99					,				__UpperCamelCase="cosine"					,				)       ->				Union[str, Any]:
	if alpha_transform_type == "cosine":

		def alpha_bar_fn(__UpperCamelCase ):
			return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2

	elif alpha_transform_type == "exp":

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

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

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







class 	lowerCamelCase__      (      __magic_name__				,			__magic_name__       ):





			'''simple docstring'''
			lowerCamelCase						      =	[e.name for e in KarrasDiffusionSchedulers]
			lowerCamelCase						      =	1



			@register_to_config
			def __init__(							self  ,  __UpperCAmelCase = 10_00  ,  __UpperCAmelCase = 0.0_0_0_1  ,  __UpperCAmelCase = 0.0_2  ,  __UpperCAmelCase = "linear"  ,  __UpperCAmelCase = None  ,  __UpperCAmelCase = False  ,  __UpperCAmelCase = "epsilon"  ,  __UpperCAmelCase = "linspace"  ,  __UpperCAmelCase = 0  ,  )				->  Optional[int]:
				if trained_betas is not None:
					_lowerCAmelCase			=torch.tensor(__UpperCAmelCase  ,  dtype=torch.floataa						)
				elif beta_schedule == "linear":
					_lowerCAmelCase			=torch.linspace(__UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  dtype=torch.floataa						)
				elif beta_schedule == "scaled_linear":
					# this schedule is very specific to the latent diffusion model.
					_lowerCAmelCase			=(
					    torch.linspace(beta_start**0.5  ,  beta_end**0.5  ,  __UpperCAmelCase  ,  dtype=torch.floataa						) ** 2
					)
				elif beta_schedule == "squaredcos_cap_v2":
					# Glide cosine schedule
					_lowerCAmelCase			=betas_for_alpha_bar(__UpperCAmelCase						)
				else:
					raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''						)

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

				_lowerCAmelCase			=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
				_lowerCAmelCase			=np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
				_lowerCAmelCase			=torch.from_numpy(__UpperCAmelCase						)

				# setable values
				_lowerCAmelCase			=None
				_lowerCAmelCase			=use_karras_sigmas
				self.set_timesteps(__UpperCAmelCase  ,  __UpperCAmelCase						)
				_lowerCAmelCase			=[]
				_lowerCAmelCase			=False



			@property
			def       _lowerCAmelCase	(							self						)				->  List[str]:
				# 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  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)				->  torch.FloatTensor:
				if isinstance(__UpperCAmelCase  ,  torch.Tensor						):
					_lowerCAmelCase			=timestep.to(self.timesteps.device						)
				_lowerCAmelCase			=(self.timesteps == timestep).nonzero().item()
				_lowerCAmelCase			=self.sigmas[step_index]
				_lowerCAmelCase			=sample / ((sigma**2 + 1) ** 0.5)
				_lowerCAmelCase			=True
				return sample



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)				->  Optional[Any]:

				def lms_derivative(__UpperCAmelCase						):
					_lowerCAmelCase			=1.0
					for k in range(__UpperCAmelCase						):
						if current_order == k:
							continue
						prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
					return prod

				_lowerCAmelCase			=integrate.quad(__UpperCAmelCase  ,  self.sigmas[t]  ,  self.sigmas[t + 1]  ,  epsrel=1e-4						)[0]

				return integrated_coeff



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase = None						)				->  Tuple:
				_lowerCAmelCase			=num_inference_steps

				# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
				if self.config.timestep_spacing == "linspace":
					_lowerCAmelCase			=np.linspace(0  ,  self.config.num_train_timesteps - 1  ,  __UpperCAmelCase  ,  dtype=__UpperCAmelCase						)[
					    ::-1
					].copy()
				elif self.config.timestep_spacing == "leading":
					_lowerCAmelCase			=self.config.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
					_lowerCAmelCase			=(np.arange(0  ,  __UpperCAmelCase						) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase						)
					timesteps += self.config.steps_offset
				elif self.config.timestep_spacing == "trailing":
					_lowerCAmelCase			=self.config.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
					_lowerCAmelCase			=(np.arange(self.config.num_train_timesteps  ,  0  ,  -step_ratio						)).round().copy().astype(__UpperCAmelCase						)
					timesteps -= 1
				else:
					raise ValueError(
					    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''						)

				_lowerCAmelCase			=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
				_lowerCAmelCase			=np.log(__UpperCAmelCase						)
				_lowerCAmelCase			=np.interp(__UpperCAmelCase  ,  np.arange(0  ,  len(__UpperCAmelCase						)						)  ,  __UpperCAmelCase						)

				if self.use_karras_sigmas:
					_lowerCAmelCase			=self._convert_to_karras(in_sigmas=__UpperCAmelCase						)
					_lowerCAmelCase			=np.array([self._sigma_to_t(__UpperCAmelCase  ,  __UpperCAmelCase						) for sigma in sigmas]						)

				_lowerCAmelCase			=np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)

				_lowerCAmelCase			=torch.from_numpy(__UpperCAmelCase						).to(device=__UpperCAmelCase						)
				if str(__UpperCAmelCase						).startswith("""mps"""						):
					# mps does not support float64
					_lowerCAmelCase			=torch.from_numpy(__UpperCAmelCase						).to(__UpperCAmelCase  ,  dtype=torch.floataa						)
				else:
					_lowerCAmelCase			=torch.from_numpy(__UpperCAmelCase						).to(device=__UpperCAmelCase						)

				_lowerCAmelCase			=[]



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)				->  str:
				# get log sigma
				_lowerCAmelCase			=np.log(__UpperCAmelCase						)

				# get distribution
				_lowerCAmelCase			=log_sigma - log_sigmas[:, np.newaxis]

				# get sigmas range
				_lowerCAmelCase			=np.cumsum((dists >= 0)  ,  axis=0						).argmax(axis=0						).clip(max=log_sigmas.shape[0] - 2						)
				_lowerCAmelCase			=low_idx + 1

				_lowerCAmelCase			=log_sigmas[low_idx]
				_lowerCAmelCase			=log_sigmas[high_idx]

				# interpolate sigmas
				_lowerCAmelCase			=(low - log_sigma) / (low - high)
				_lowerCAmelCase			=np.clip(__UpperCAmelCase  ,  0  ,  1						)

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



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase						)				->  torch.FloatTensor:
				_lowerCAmelCase			=in_sigmas[-1].item()
				_lowerCAmelCase			=in_sigmas[0].item()

				_lowerCAmelCase			=7.0  # 7.0 is the value used in the paper
				_lowerCAmelCase			=np.linspace(0  ,  1  ,  self.num_inference_steps						)
				_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  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase = 4  ,  __UpperCAmelCase = True  ,  )				->  Union[LMSDiscreteSchedulerOutput, Tuple]:
				if not self.is_scale_input_called:
					warnings.warn(
					    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
					    """See `StableDiffusionPipeline` for a usage example."""						)

				if isinstance(__UpperCAmelCase  ,  torch.Tensor						):
					_lowerCAmelCase			=timestep.to(self.timesteps.device						)
				_lowerCAmelCase			=(self.timesteps == timestep).nonzero().item()
				_lowerCAmelCase			=self.sigmas[step_index]

				# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
				if self.config.prediction_type == "epsilon":
					_lowerCAmelCase			=sample - sigma * model_output
				elif self.config.prediction_type == "v_prediction":
					# * c_out + input * c_skip
					_lowerCAmelCase			=model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
				elif self.config.prediction_type == "sample":
					_lowerCAmelCase			=model_output
				else:
					raise ValueError(
					    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''						)

				# 2. Convert to an ODE derivative
				_lowerCAmelCase			=(sample - pred_original_sample) / sigma
				self.derivatives.append(__UpperCAmelCase						)
				if len(self.derivatives						) > order:
					self.derivatives.pop(0						)

				# 3. Compute linear multistep coefficients
				_lowerCAmelCase			=min(step_index + 1  ,  __UpperCAmelCase						)
				_lowerCAmelCase			=[self.get_lms_coefficient(__UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase						) for curr_order in range(__UpperCAmelCase						)]

				# 4. Compute previous sample based on the derivatives path
				_lowerCAmelCase			=sample + sum(
				    coeff * derivative for coeff, derivative in zip(__UpperCAmelCase  ,  reversed(self.derivatives						)						)						)

				if not return_dict:
					return (prev_sample,)

				return LMSDiscreteSchedulerOutput(prev_sample=__UpperCAmelCase  ,  pred_original_sample=__UpperCAmelCase						)



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __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(__UpperCAmelCase						):
					# 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						)				->  Any:
				return self.config.num_train_timesteps





"""simple docstring"""


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase )       ->				Dict:
	_lowerCAmelCase			=UNetaDModel(**__UpperCamelCase )
	_lowerCAmelCase			=checkpoint["""all_modules.0.W"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.0.W"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.1.weight"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.1.bias"""].data

	_lowerCAmelCase			=checkpoint["""all_modules.2.weight"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.2.bias"""].data

	_lowerCAmelCase			=checkpoint["""all_modules.3.weight"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.3.bias"""].data

	_lowerCAmelCase			=checkpoint[list(checkpoint.keys() )[-4]].data
	_lowerCAmelCase			=checkpoint[list(checkpoint.keys() )[-3]].data
	_lowerCAmelCase			=checkpoint[list(checkpoint.keys() )[-2]].data
	_lowerCAmelCase			=checkpoint[list(checkpoint.keys() )[-1]].data

	_lowerCAmelCase			=4

	def set_attention_weights(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase ):
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

	def set_resnet_weights(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase ):
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Dense_0.bias'''].data

		if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
			_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
			_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

	for i, block in enumerate(new_model_architecture.downsample_blocks ):
		_lowerCAmelCase			=hasattr(__UpperCamelCase					,				"""attentions""" )
		for j in range(len(block.resnets ) ):
			set_resnet_weights(block.resnets[j]					,				__UpperCamelCase					,				__UpperCamelCase )
			module_index += 1
			if has_attentions:
				set_attention_weights(block.attentions[j]					,				__UpperCamelCase					,				__UpperCamelCase )
				module_index += 1

		if hasattr(__UpperCamelCase					,				"""downsamplers""" ) and block.downsamplers is not None:
			set_resnet_weights(block.resnet_down					,				__UpperCamelCase					,				__UpperCamelCase )
			module_index += 1
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.Conv_0.bias'''].data
			module_index += 1

	set_resnet_weights(new_model_architecture.mid_block.resnets[0]					,				__UpperCamelCase					,				__UpperCamelCase )
	module_index += 1
	set_attention_weights(new_model_architecture.mid_block.attentions[0]					,				__UpperCamelCase					,				__UpperCamelCase )
	module_index += 1
	set_resnet_weights(new_model_architecture.mid_block.resnets[1]					,				__UpperCamelCase					,				__UpperCamelCase )
	module_index += 1

	for i, block in enumerate(new_model_architecture.up_blocks ):
		_lowerCAmelCase			=hasattr(__UpperCamelCase					,				"""attentions""" )
		for j in range(len(block.resnets ) ):
			set_resnet_weights(block.resnets[j]					,				__UpperCamelCase					,				__UpperCamelCase )
			module_index += 1
		if has_attentions:
			set_attention_weights(
			    block.attentions[0]					,				__UpperCamelCase					,				__UpperCamelCase )  # why can there only be a single attention layer for up?
			module_index += 1

		if hasattr(__UpperCamelCase					,				"""resnet_up""" ) and block.resnet_up is not None:
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.weight'''].data
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.bias'''].data
			module_index += 1
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.weight'''].data
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.bias'''].data
			module_index += 1
			set_resnet_weights(block.resnet_up					,				__UpperCamelCase					,				__UpperCamelCase )
			module_index += 1

	_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.weight'''].data
	_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.bias'''].data
	module_index += 1
	_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.weight'''].data
	_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.bias'''].data

	return new_model_architecture.state_dict()


if __name__ == "__main__":
			__A 		= argparse.ArgumentParser()

			parser.add_argument(
			    '--checkpoint_path',
			    default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin',
			    type=str,
			    required=False,
			    help='Path to the checkpoint to convert.',
			)

			parser.add_argument(
			    '--config_file',
			    default='/Users/arthurzucker/Work/diffusers/ArthurZ/config.json',
			    type=str,
			    required=False,
			    help='The config json file corresponding to the architecture.',
			)

			parser.add_argument(
			    '--dump_path',
			    default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt',
			    type=str,
			    required=False,
			    help='Path to the output model.',
			)

			__A 		= parser.parse_args()

			__A 		= torch.load(args.checkpoint_path, map_location='cpu')

			with open(args.config_file) as f:
						__A 		= json.loads(f.read())

			__A 		= convert_ncsnpp_checkpoint(
			    checkpoint,
			    config,
			)

			if "sde" in config:
						del config["sde"]

			__A 		= UNetaDModel(**config)
			model.load_state_dict(converted_checkpoint)

			try:
						__A 		= ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

						__A 		= ScoreSdeVePipeline(unet=model, scheduler=scheduler)
						pipe.save_pretrained(args.dump_path)
			except:  # noqa: E722
						model.save_pretrained(args.dump_path)



