"""simple docstring"""




import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__=1					):






							"""simple docstring"""





							if n_shave_prefix_segments >= 0:
														return ".".join(path.split('.'					)[n_shave_prefix_segments:]					)
							else:
														return ".".join(path.split('.'					)[:n_shave_prefix_segments]					)






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__=0					):






							"""simple docstring"""





							A__						  =				[]
							for old_item in old_list:
														A__						  =				old_item
														A__						  =				new_item.replace('block.'							,     'resnets.'					)
														A__						  =				new_item.replace('conv_shorcut'							,     'conv1'					)
														A__						  =				new_item.replace('in_shortcut'							,     'conv_shortcut'					)
														A__						  =				new_item.replace('temb_proj'							,     'time_emb_proj'					)

														A__						  =				shave_segments(UpperCamelCase__							,     n_shave_prefix_segments=UpperCamelCase__					)

														mapping.append({'old': old_item, 'new': new_item}					)

							return mapping






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__=0							,     UpperCamelCase__=False					):






							"""simple docstring"""





							A__						  =				[]
							for old_item in old_list:
														A__						  =				old_item

														# In `model.mid`, the layer is called `attn`.
														if not in_mid:
																					A__						  =				new_item.replace('attn'							,     'attentions'					)
														A__						  =				new_item.replace('.k.'							,     '.key.'					)
														A__						  =				new_item.replace('.v.'							,     '.value.'					)
														A__						  =				new_item.replace('.q.'							,     '.query.'					)

														A__						  =				new_item.replace('proj_out'							,     'proj_attn'					)
														A__						  =				new_item.replace('norm'							,     'group_norm'					)

														A__						  =				shave_segments(UpperCamelCase__							,     n_shave_prefix_segments=UpperCamelCase__					)
														mapping.append({'old': old_item, 'new': new_item}					)

							return mapping






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__=None							,     UpperCamelCase__=None							,     UpperCamelCase__=None					):






							"""simple docstring"""





							assert isinstance(UpperCamelCase__							,     UpperCamelCase__					), "Paths should be a list of dicts containing 'old' and 'new' keys."

							if attention_paths_to_split is not None:
														if config is None:
																					raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'					)

														for path, path_map in attention_paths_to_split.items():
																					A__						  =				old_checkpoint[path]
																					A__						  =				old_tensor.shape[0] // 3

																					A__						  =				(-1, channels) if len(old_tensor.shape					) == 3 else (-1)

																					A__						  =				old_tensor.shape[0] // config.get('num_head_channels'							,     1					) // 3

																					A__						  =				old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]					)
																					A__  ,	A__  ,	A__						  =				old_tensor.split(channels // num_heads							,     dim=1					)

																					A__						  =				query.reshape(UpperCamelCase__					).squeeze()
																					A__						  =				key.reshape(UpperCamelCase__					).squeeze()
																					A__						  =				value.reshape(UpperCamelCase__					).squeeze()

							for path in paths:
														A__						  =				path['new']

														if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																					continue

														A__						  =				new_path.replace('down.'							,     'down_blocks.'					)
														A__						  =				new_path.replace('up.'							,     'up_blocks.'					)

														if additional_replacements is not None:
																					for replacement in additional_replacements:
																												A__						  =				new_path.replace(replacement['old']							,     replacement['new']					)

														if "attentions" in new_path:
																					A__						  =				old_checkpoint[path['old']].squeeze()
														else:
																					A__						  =				old_checkpoint[path['old']]






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				{}

							A__						  =				checkpoint['temb.dense.0.weight']
							A__						  =				checkpoint['temb.dense.0.bias']
							A__						  =				checkpoint['temb.dense.1.weight']
							A__						  =				checkpoint['temb.dense.1.bias']

							A__						  =				checkpoint['norm_out.weight']
							A__						  =				checkpoint['norm_out.bias']

							A__						  =				checkpoint['conv_in.weight']
							A__						  =				checkpoint['conv_in.bias']
							A__						  =				checkpoint['conv_out.weight']
							A__						  =				checkpoint['conv_out.bias']

							A__						  =				len({'.'.join(layer.split('.'					)[:2]					) for layer in checkpoint if 'down' in layer}					)
							A__						  =				{
							    layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(UpperCamelCase__					)
							}

							A__						  =				len({'.'.join(layer.split('.'					)[:2]					) for layer in checkpoint if 'up' in layer}					)
							A__						  =				{layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(UpperCamelCase__					)}

							for i in range(UpperCamelCase__					):
														A__						  =				(i - 1) // (config['layers_per_block'] + 1)

														if any('downsample' in layer for layer in down_blocks[i]					):
																					A__						  =				checkpoint[
																					    F'''down.{i}.downsample.op.weight'''
																					]
																					A__						  =				checkpoint[F'''down.{i}.downsample.op.bias''']
														#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
														#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

														if any('block' in layer for layer in down_blocks[i]					):
																					A__						  =				len(
																					    {'.'.join(shave_segments(UpperCamelCase__							,     2					).split('.'					)[:2]					) for layer in down_blocks[i] if 'block' in layer}					)
																					A__						  =				{
																					    layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
																					    for layer_id in range(UpperCamelCase__					)
																					}

																					if num_blocks > 0:
																												for j in range(config['layers_per_block']					):
																																			A__						  =				renew_resnet_paths(blocks[j]					)
																																			assign_to_checkpoint(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__					)

														if any('attn' in layer for layer in down_blocks[i]					):
																					A__						  =				len(
																					    {'.'.join(shave_segments(UpperCamelCase__							,     2					).split('.'					)[:2]					) for layer in down_blocks[i] if 'attn' in layer}					)
																					A__						  =				{
																					    layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
																					    for layer_id in range(UpperCamelCase__					)
																					}

																					if num_attn > 0:
																												for j in range(config['layers_per_block']					):
																																			A__						  =				renew_attention_paths(attns[j]					)
																																			assign_to_checkpoint(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     config=UpperCamelCase__					)

							A__						  =				[key for key in checkpoint if 'mid.block_1' in key]
							A__						  =				[key for key in checkpoint if 'mid.block_2' in key]
							A__						  =				[key for key in checkpoint if 'mid.attn_1' in key]

							# Mid new 2
							A__						  =				renew_resnet_paths(UpperCamelCase__					)
							assign_to_checkpoint(
							    UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]							,     )

							A__						  =				renew_resnet_paths(UpperCamelCase__					)
							assign_to_checkpoint(
							    UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]							,     )

							A__						  =				renew_attention_paths(UpperCamelCase__							,     in_mid=UpperCamelCase__					)
							assign_to_checkpoint(
							    UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]							,     )

							for i in range(UpperCamelCase__					):
														A__						  =				num_up_blocks - 1 - i

														if any('upsample' in layer for layer in up_blocks[i]					):
																					A__						  =				checkpoint[
																					    F'''up.{i}.upsample.conv.weight'''
																					]
																					A__						  =				checkpoint[F'''up.{i}.upsample.conv.bias''']

														if any('block' in layer for layer in up_blocks[i]					):
																					A__						  =				len(
																					    {'.'.join(shave_segments(UpperCamelCase__							,     2					).split('.'					)[:2]					) for layer in up_blocks[i] if 'block' in layer}					)
																					A__						  =				{
																					    layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(UpperCamelCase__					)
																					}

																					if num_blocks > 0:
																												for j in range(config['layers_per_block'] + 1					):
																																			A__						  =				{'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
																																			A__						  =				renew_resnet_paths(blocks[j]					)
																																			assign_to_checkpoint(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     additional_replacements=[replace_indices]					)

														if any('attn' in layer for layer in up_blocks[i]					):
																					A__						  =				len(
																					    {'.'.join(shave_segments(UpperCamelCase__							,     2					).split('.'					)[:2]					) for layer in up_blocks[i] if 'attn' in layer}					)
																					A__						  =				{
																					    layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(UpperCamelCase__					)
																					}

																					if num_attn > 0:
																												for j in range(config['layers_per_block'] + 1					):
																																			A__						  =				{'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
																																			A__						  =				renew_attention_paths(attns[j]					)
																																			assign_to_checkpoint(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     additional_replacements=[replace_indices]					)

							A__						  =				{k.replace('mid_new_2'							,     'mid_block'					): v for k, v in new_checkpoint.items()}
							return new_checkpoint






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				{}

							A__						  =				checkpoint['encoder.norm_out.weight']
							A__						  =				checkpoint['encoder.norm_out.bias']

							A__						  =				checkpoint['encoder.conv_in.weight']
							A__						  =				checkpoint['encoder.conv_in.bias']
							A__						  =				checkpoint['encoder.conv_out.weight']
							A__						  =				checkpoint['encoder.conv_out.bias']

							A__						  =				checkpoint['decoder.norm_out.weight']
							A__						  =				checkpoint['decoder.norm_out.bias']

							A__						  =				checkpoint['decoder.conv_in.weight']
							A__						  =				checkpoint['decoder.conv_in.bias']
							A__						  =				checkpoint['decoder.conv_out.weight']
							A__						  =				checkpoint['decoder.conv_out.bias']

							A__						  =				len({'.'.join(layer.split('.'					)[:3]					) for layer in checkpoint if 'down' in layer}					)
							A__						  =				{
							    layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(UpperCamelCase__					)
							}

							A__						  =				len({'.'.join(layer.split('.'					)[:3]					) for layer in checkpoint if 'up' in layer}					)
							A__						  =				{layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(UpperCamelCase__					)}

							for i in range(UpperCamelCase__					):
														A__						  =				(i - 1) // (config['layers_per_block'] + 1)

														if any('downsample' in layer for layer in down_blocks[i]					):
																					A__						  =				checkpoint[
																					    F'''encoder.down.{i}.downsample.conv.weight'''
																					]
																					A__						  =				checkpoint[
																					    F'''encoder.down.{i}.downsample.conv.bias'''
																					]

														if any('block' in layer for layer in down_blocks[i]					):
																					A__						  =				len(
																					    {'.'.join(shave_segments(UpperCamelCase__							,     3					).split('.'					)[:3]					) for layer in down_blocks[i] if 'block' in layer}					)
																					A__						  =				{
																					    layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
																					    for layer_id in range(UpperCamelCase__					)
																					}

																					if num_blocks > 0:
																												for j in range(config['layers_per_block']					):
																																			A__						  =				renew_resnet_paths(blocks[j]					)
																																			assign_to_checkpoint(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__					)

														if any('attn' in layer for layer in down_blocks[i]					):
																					A__						  =				len(
																					    {'.'.join(shave_segments(UpperCamelCase__							,     3					).split('.'					)[:3]					) for layer in down_blocks[i] if 'attn' in layer}					)
																					A__						  =				{
																					    layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
																					    for layer_id in range(UpperCamelCase__					)
																					}

																					if num_attn > 0:
																												for j in range(config['layers_per_block']					):
																																			A__						  =				renew_attention_paths(attns[j]					)
																																			assign_to_checkpoint(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     config=UpperCamelCase__					)

							A__						  =				[key for key in checkpoint if 'mid.block_1' in key]
							A__						  =				[key for key in checkpoint if 'mid.block_2' in key]
							A__						  =				[key for key in checkpoint if 'mid.attn_1' in key]

							# Mid new 2
							A__						  =				renew_resnet_paths(UpperCamelCase__					)
							assign_to_checkpoint(
							    UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]							,     )

							A__						  =				renew_resnet_paths(UpperCamelCase__					)
							assign_to_checkpoint(
							    UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]							,     )

							A__						  =				renew_attention_paths(UpperCamelCase__							,     in_mid=UpperCamelCase__					)
							assign_to_checkpoint(
							    UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]							,     )

							for i in range(UpperCamelCase__					):
														A__						  =				num_up_blocks - 1 - i

														if any('upsample' in layer for layer in up_blocks[i]					):
																					A__						  =				checkpoint[
																					    F'''decoder.up.{i}.upsample.conv.weight'''
																					]
																					A__						  =				checkpoint[
																					    F'''decoder.up.{i}.upsample.conv.bias'''
																					]

														if any('block' in layer for layer in up_blocks[i]					):
																					A__						  =				len(
																					    {'.'.join(shave_segments(UpperCamelCase__							,     3					).split('.'					)[:3]					) for layer in up_blocks[i] if 'block' in layer}					)
																					A__						  =				{
																					    layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(UpperCamelCase__					)
																					}

																					if num_blocks > 0:
																												for j in range(config['layers_per_block'] + 1					):
																																			A__						  =				{'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
																																			A__						  =				renew_resnet_paths(blocks[j]					)
																																			assign_to_checkpoint(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     additional_replacements=[replace_indices]					)

														if any('attn' in layer for layer in up_blocks[i]					):
																					A__						  =				len(
																					    {'.'.join(shave_segments(UpperCamelCase__							,     3					).split('.'					)[:3]					) for layer in up_blocks[i] if 'attn' in layer}					)
																					A__						  =				{
																					    layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(UpperCamelCase__					)
																					}

																					if num_attn > 0:
																												for j in range(config['layers_per_block'] + 1					):
																																			A__						  =				{'old': F'''up_blocks.{i}''', 'new': F'''up_blocks.{block_id}'''}
																																			A__						  =				renew_attention_paths(attns[j]					)
																																			assign_to_checkpoint(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     additional_replacements=[replace_indices]					)

							A__						  =				{k.replace('mid_new_2'							,     'mid_block'					): v for k, v in new_checkpoint.items()}
							A__						  =				checkpoint['quant_conv.weight']
							A__						  =				checkpoint['quant_conv.bias']
							if "quantize.embedding.weight" in checkpoint:
														A__						  =				checkpoint['quantize.embedding.weight']
							A__						  =				checkpoint['post_quant_conv.weight']
							A__						  =				checkpoint['post_quant_conv.bias']

							return new_checkpoint


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

				parser.add_argument(
				    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
				)

				parser.add_argument(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The config json file corresponding to the architecture.",
				)

				parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

				__lowerCamelCase									=	parser.parse_args()
				__lowerCamelCase									=	torch.load(args.checkpoint_path)

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

				# unet case
				__lowerCamelCase									=	{key.split(".")[0] for key in checkpoint.keys()}
				if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
								__lowerCamelCase									=	convert_vq_autoenc_checkpoint(checkpoint, config)
				else:
								__lowerCamelCase									=	convert_ddpm_checkpoint(checkpoint, config)

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

				if config["_class_name"] == "VQModel":
								__lowerCamelCase									=	VQModel(**config)
								model.load_state_dict(converted_checkpoint)
								model.save_pretrained(args.dump_path)
				elif config["_class_name"] == "AutoencoderKL":
								__lowerCamelCase									=	AutoencoderKL(**config)
								model.load_state_dict(converted_checkpoint)
								model.save_pretrained(args.dump_path)
				else:
								__lowerCamelCase									=	UNetaDModel(**config)
								model.load_state_dict(converted_checkpoint)

								__lowerCamelCase									=	DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

								__lowerCamelCase									=	DDPMPipeline(unet=model, scheduler=scheduler)
								pipe.save_pretrained(args.dump_path)






"""simple docstring"""




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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)
@flax.struct.dataclass
class      UpperCamelCase__:
						lowerCAmelCase__	:    CommonSchedulerState

						# setable values
						lowerCAmelCase__	:    jnp.ndarray
						lowerCAmelCase__	:    jnp.ndarray
						lowerCAmelCase__	:    jnp.ndarray
						lowerCAmelCase__	:    Optional[int]    				=		None

						# running values
						lowerCAmelCase__	:    Optional[jnp.ndarray]    				=		None





						@classmethod
						def 					snake_case__						(							cls		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					Optional[Any]:
													return cls(common=__UpperCAmelCase		,init_noise_sigma=__UpperCAmelCase		,timesteps=__UpperCAmelCase		,sigmas=__UpperCAmelCase  )






@dataclass
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    LMSDiscreteSchedulerState






class      UpperCamelCase__(      __A		,    __A			):
						lowerCAmelCase__	:    List[str]    				=		[e.name for e in FlaxKarrasDiffusionSchedulers]

						lowerCAmelCase__	:    jnp.dtype





						@property
						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													return True





						@register_to_config
						def __init__(							self		,__UpperCAmelCase = 10_00		,__UpperCAmelCase = 0.0_0_0_1		,__UpperCAmelCase = 0.0_2		,__UpperCAmelCase = "linear"		,__UpperCAmelCase = None		,__UpperCAmelCase = "epsilon"		,__UpperCAmelCase = jnp.floataa		,)       ->					str:
													A__						  =				dtype





						def 					snake_case__						(							self		,__UpperCAmelCase = None  )       ->					LMSDiscreteSchedulerState:
													if common is None:
																				A__						  =				CommonSchedulerState.create(self  )

													A__						  =				jnp.arange(0		,self.config.num_train_timesteps  ).round()[::-1]
													A__						  =				((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

													# standard deviation of the initial noise distribution
													A__						  =				sigmas.max()

													return LMSDiscreteSchedulerState.create(
													    common=__UpperCAmelCase		,init_noise_sigma=__UpperCAmelCase		,timesteps=__UpperCAmelCase		,sigmas=__UpperCAmelCase		,)





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					jnp.ndarray:
													((A__)  ,	)						  =				jnp.where(state.timesteps == timestep		,size=1  )
													A__						  =				step_index[0]

													A__						  =				state.sigmas[step_index]
													A__						  =				sample / ((sigma**2 + 1) ** 0.5)
													return sample





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					Optional[int]:

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

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

													return integrated_coeff





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = ()  )       ->					LMSDiscreteSchedulerState:
													A__						  =				jnp.linspace(self.config.num_train_timesteps - 1		,0		,__UpperCAmelCase		,dtype=self.dtype  )

													A__						  =				jnp.floor(__UpperCAmelCase  ).astype(jnp.intaa  )
													A__						  =				jnp.ceil(__UpperCAmelCase  ).astype(jnp.intaa  )

													A__						  =				jnp.mod(__UpperCAmelCase		,1.0  )

													A__						  =				((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
													A__						  =				(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
													A__						  =				jnp.concatenate([sigmas, jnp.array([0.0]		,dtype=self.dtype  )]  )

													A__						  =				timesteps.astype(jnp.intaa  )

													# initial running values
													A__						  =				jnp.zeros((0,) + shape		,dtype=self.dtype  )

													return state.replace(
													    timesteps=__UpperCAmelCase		,sigmas=__UpperCAmelCase		,num_inference_steps=__UpperCAmelCase		,derivatives=__UpperCAmelCase		,)





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = 4		,__UpperCAmelCase = True		,)       ->					Union[FlaxLMSSchedulerOutput, Tuple]:
													if state.num_inference_steps is None:
																				raise ValueError(
																				    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'  )

													A__						  =				state.sigmas[timestep]

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													if self.config.prediction_type == "epsilon":
																				A__						  =				sample - sigma * model_output
													elif self.config.prediction_type == "v_prediction":
																				# * c_out + input * c_skip
																				A__						  =				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
													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
													A__						  =				(sample - pred_original_sample) / sigma
													A__						  =				state.replace(derivatives=jnp.append(state.derivatives		,__UpperCAmelCase  )  )
													if len(state.derivatives  ) > order:
																				A__						  =				state.replace(derivatives=jnp.delete(state.derivatives		,0  )  )

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

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

													if not return_dict:
																				return (prev_sample, state)

													return FlaxLMSSchedulerOutput(prev_sample=__UpperCAmelCase		,state=__UpperCAmelCase  )





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,)       ->					jnp.ndarray:
													A__						  =				state.sigmas[timesteps].flatten()
													A__						  =				broadcast_to_shape_from_left(__UpperCAmelCase		,noise.shape  )

													A__						  =				original_samples + noise * sigma

													return noisy_samples





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





