



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel






def 					a(   A :					List[Any]				,  A :					Union[str, Any] )	-> Union[str, Any]:
					"""simple docstring"""





					a        =     UNetaDModel(**A )
					a        =     checkpoint["all_modules.0.W"].data
					a        =     checkpoint["all_modules.0.W"].data
					a        =     checkpoint["all_modules.1.weight"].data
					a        =     checkpoint["all_modules.1.bias"].data

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

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

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

					a        =     4

					def set_attention_weights(A :					List[str]				,  A :					Any				,  A :					List[str] ):
										a        =     old_checkpoint[f'''all_modules.{index}.NIN_0.W'''].data.T
										a        =     old_checkpoint[f'''all_modules.{index}.NIN_1.W'''].data.T
										a        =     old_checkpoint[f'''all_modules.{index}.NIN_2.W'''].data.T

										a        =     old_checkpoint[f'''all_modules.{index}.NIN_0.b'''].data
										a        =     old_checkpoint[f'''all_modules.{index}.NIN_1.b'''].data
										a        =     old_checkpoint[f'''all_modules.{index}.NIN_2.b'''].data

										a        =     old_checkpoint[f'''all_modules.{index}.NIN_3.W'''].data.T
										a        =     old_checkpoint[f'''all_modules.{index}.NIN_3.b'''].data

										a        =     old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
										a        =     old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

					def set_resnet_weights(A :					str				,  A :					List[str]				,  A :					Optional[Any] ):
										a        =     old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
										a        =     old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
										a        =     old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
										a        =     old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

										a        =     old_checkpoint[f'''all_modules.{index}.Conv_1.weight'''].data
										a        =     old_checkpoint[f'''all_modules.{index}.Conv_1.bias'''].data
										a        =     old_checkpoint[f'''all_modules.{index}.GroupNorm_1.weight'''].data
										a        =     old_checkpoint[f'''all_modules.{index}.GroupNorm_1.bias'''].data

										a        =     old_checkpoint[f'''all_modules.{index}.Dense_0.weight'''].data
										a        =     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:
															a        =     old_checkpoint[f'''all_modules.{index}.Conv_2.weight'''].data
															a        =     old_checkpoint[f'''all_modules.{index}.Conv_2.bias'''].data

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

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

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

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

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

					a        =     checkpoint[f'''all_modules.{module_index}.weight'''].data
					a        =     checkpoint[f'''all_modules.{module_index}.bias'''].data
					module_index += 1
					a        =     checkpoint[f'''all_modules.{module_index}.weight'''].data
					a        =     checkpoint[f'''all_modules.{module_index}.bias'''].data

					return new_model_architecture.state_dict()


if __name__ == "__main__":
				_lowercase:				int       =	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.",
				)

				_lowercase:				int       =	parser.parse_args()

				_lowercase:				Optional[Any]       =	torch.load(args.checkpoint_path, map_location="cpu")

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

				_lowercase:				Any       =	convert_ncsnpp_checkpoint(
				    checkpoint,
				    config,
				)

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

				_lowercase:				int       =	UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				try:
								_lowercase:				Any       =	ScoreSdeVeScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

								_lowercase:				Optional[Any]       =	ScoreSdeVePipeline(unet=model, scheduler=scheduler)
								pipe.save_pretrained(args.dump_path)
				except:  # noqa: E722
								model.save_pretrained(args.dump_path)





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

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left



@flax.struct.dataclass
class 			_lowercase							:





	"""simple docstring"""
	# setable values
	__A					=							None
	__A					=							None
	__A					=							None




	@classmethod
	def   UpperCamelCase_ (cls	):
						"""simple docstring"""

						return cls()




@dataclass
class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							42
	__A					=							42
	__A					=							None




class 			_lowercase							(     lowerCAmelCase,   lowerCAmelCase			):





	"""simple docstring"""




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return True




	@register_to_config
	def __init__(self	, lowerCamelCase_ = 2000	, lowerCamelCase_ = 0.15	, lowerCamelCase_ = 0.01	, lowerCamelCase_ = 1348.0	, lowerCamelCase_ = 1E-5	, lowerCamelCase_ = 1	, ):
						"""simple docstring"""

						pass




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     ScoreSdeVeSchedulerState.create()
						return self.set_sigmas(
						    lowerCamelCase_	, self.config.num_train_timesteps	, self.config.sigma_min	, self.config.sigma_max	, self.config.sampling_eps	, )




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = ()	, lowerCamelCase_ = None	):
						"""simple docstring"""

						a        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps

						a        =     jnp.linspace(1	, lowerCamelCase_	, lowerCamelCase_	)
						return state.replace(timesteps=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, ):
						"""simple docstring"""

						a        =     sigma_min if sigma_min is not None else self.config.sigma_min
						a        =     sigma_max if sigma_max is not None else self.config.sigma_max
						a        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps
						if state.timesteps is None:
											a        =     self.set_timesteps(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	)

						a        =     jnp.exp(jnp.linspace(jnp.log(lowerCamelCase_	)	, jnp.log(lowerCamelCase_	)	, lowerCamelCase_	)	)
						a        =     jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]	)

						return state.replace(discrete_sigmas=lowerCamelCase_	, sigmas=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						return jnp.where(timesteps == 0	, jnp.zeros_like(lowerCamelCase_	)	, state.discrete_sigmas[timesteps - 1]	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = True	, ):
						"""simple docstring"""

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

						a        =     timestep * jnp.ones(
						    sample.shape[0]	, )
						a        =     (timestep * (len(state.timesteps	) - 1)).long()

						a        =     state.discrete_sigmas[timesteps]
						a        =     self.get_adjacent_sigma(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	)
						a        =     jnp.zeros_like(lowerCamelCase_	)
						a        =     (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
						a        =     diffusion.flatten()
						a        =     broadcast_to_shape_from_left(lowerCamelCase_	, sample.shape	)
						a        =     drift - diffusion**2 * model_output

						#  equation 6: sample noise for the diffusion term of
						a        =     random.split(lowerCamelCase_	, num=1	)
						a        =     random.normal(key=lowerCamelCase_	, shape=sample.shape	)
						a        =     sample - drift  # subtract because `dt` is a small negative timestep
						# TODO is the variable diffusion the correct scaling term for the noise?
						a        =     prev_sample_mean + diffusion * noise  # add impact of diffusion field g

						if not return_dict:
											return (prev_sample, prev_sample_mean, state)

						return FlaxSdeVeOutput(prev_sample=lowerCamelCase_	, prev_sample_mean=lowerCamelCase_	, state=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = True	, ):
						"""simple docstring"""

						if state.timesteps is None:
											raise ValueError(
											    "`state.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
						a        =     random.split(lowerCamelCase_	, num=1	)
						a        =     random.normal(key=lowerCamelCase_	, shape=sample.shape	)

						# compute step size from the model_output, the noise, and the snr
						a        =     jnp.linalg.norm(lowerCamelCase_	)
						a        =     jnp.linalg.norm(lowerCamelCase_	)
						a        =     (self.config.snr * noise_norm / grad_norm) ** 2 * 2
						a        =     step_size * jnp.ones(sample.shape[0]	)

						# compute corrected sample: model_output term and noise term
						a        =     step_size.flatten()
						a        =     broadcast_to_shape_from_left(lowerCamelCase_	, sample.shape	)
						a        =     sample + step_size * model_output
						a        =     prev_sample_mean + ((step_size * 2) ** 0.5) * noise

						if not return_dict:
											return (prev_sample, state)

						return FlaxSdeVeOutput(prev_sample=lowerCamelCase_	, state=lowerCamelCase_	)




	def __len__(self	):
						"""simple docstring"""

						return self.config.num_train_timesteps
