



"""simple docstring"""


import argparse
import json

import torch

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

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_=1		):
						if n_shave_prefix_segments >= 0:
												return ".".join(path.split('.'		)[n_shave_prefix_segments:]		)
						else:
												return ".".join(path.split('.'		)[:n_shave_prefix_segments]		)

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_=0		):
						UpperCAmelCase    =				[]
						for old_item in old_list:
												UpperCAmelCase    =				old_item
												UpperCAmelCase    =				new_item.replace('block.'	,			'resnets.'		)
												UpperCAmelCase    =				new_item.replace('conv_shorcut'	,			'conv1'		)
												UpperCAmelCase    =				new_item.replace('in_shortcut'	,			'conv_shortcut'		)
												UpperCAmelCase    =				new_item.replace('temb_proj'	,			'time_emb_proj'		)

												UpperCAmelCase    =				shave_segments(lowercase_	,			n_shave_prefix_segments=lowercase_		)

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

						return mapping

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_=0	,			lowercase_=False		):
						UpperCAmelCase    =				[]
						for old_item in old_list:
												UpperCAmelCase    =				old_item

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

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

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

						return mapping

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_	,			lowercase_	,			lowercase_=None	,			lowercase_=None	,			lowercase_=None		):
						assert isinstance(lowercase_	,			lowercase_		), "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():
																		UpperCAmelCase    =				old_checkpoint[path]
																		UpperCAmelCase    =				old_tensor.shape[0] // 3

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

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

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

																		UpperCAmelCase    =				query.reshape(lowercase_		).squeeze()
																		UpperCAmelCase    =				key.reshape(lowercase_		).squeeze()
																		UpperCAmelCase    =				value.reshape(lowercase_		).squeeze()

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

												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

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

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

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

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_		):
						UpperCAmelCase    =				{}

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

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

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

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

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

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

												if any('downsample' in layer for layer in down_blocks[i]		):
																		UpperCAmelCase    =				checkpoint[
																		    F"""down.{i}.downsample.op.weight"""
																		]
																		UpperCAmelCase    =				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]		):
																		UpperCAmelCase    =				len(
																		    {'.'.join(shave_segments(lowercase_	,			2		).split('.'		)[:2]		) for layer in down_blocks[i] if 'block' in layer}		)
																		UpperCAmelCase    =				{
																		    layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
																		    for layer_id in range(lowercase_		)
																		}

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

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

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

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

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

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

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

						for i in range(lowercase_		):
												UpperCAmelCase    =				num_up_blocks - 1 - i

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

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

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

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

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

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

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_		):
						UpperCAmelCase    =				{}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

						for i in range(lowercase_		):
												UpperCAmelCase    =				num_up_blocks - 1 - i

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

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

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

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

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

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

						return new_checkpoint


if __name__ == "__main__":
				snake_case_         = 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.""")

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

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

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

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

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

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

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










"""simple docstring"""


from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

snake_case_         = TypeVar("""T""", bound=bool)







class    A_    (  Generic[T]       ):



						"""simple docstring"""






						def __init__(      self				:str     ,						lowercase_				:T      )       ->    None:
												UpperCAmelCase    =				value
												UpperCAmelCase    =				None
												UpperCAmelCase    =				None






						@property
						def 			UpperCAmelCase__  (      self				:Any      )       ->    T:
												return self._value






						@staticmethod
						def 			UpperCAmelCase__  (      lowercase_				:RandomizedHeapNode[T] | None     ,						lowercase_				:RandomizedHeapNode[T] | None      )       ->    RandomizedHeapNode[T] | None:
												if not roota:
																		return roota

												if not roota:
																		return roota

												if roota.value > roota.value:
																		UpperCAmelCase   ,					UpperCAmelCase    =				roota, roota

												if random.choice([True, False]      ):
																		UpperCAmelCase   ,					UpperCAmelCase    =				roota.right, roota.left

												UpperCAmelCase    =				RandomizedHeapNode.merge(roota.left     ,						lowercase_      )

												return roota







class    A_    (  Generic[T]       ):



						"""simple docstring"""






						def __init__(      self				:Optional[Any]     ,						lowercase_				:Iterable[T] | None = ()      )       ->    None:
												UpperCAmelCase    =				None

												if data:
																		for item in data:
																								self.insert(lowercase_      )






						def 			UpperCAmelCase__  (      self				:Dict     ,						lowercase_				:T      )       ->    None:
												UpperCAmelCase    =				RandomizedHeapNode.merge(self._root     ,						RandomizedHeapNode(lowercase_      )      )






						def 			UpperCAmelCase__  (      self				:Dict      )       ->    T | None:
												UpperCAmelCase    =				self.top()

												if self._root is None:
																		return None

												UpperCAmelCase    =				RandomizedHeapNode.merge(self._root.left     ,						self._root.right      )

												return result






						def 			UpperCAmelCase__  (      self				:Tuple      )       ->    T:
												if not self._root:
																		raise IndexError('Can\'t get top element for the empty heap.'      )
												return self._root.value






						def 			UpperCAmelCase__  (      self				:Dict      )       ->    None:
												UpperCAmelCase    =				None






						def 			UpperCAmelCase__  (      self				:Union[str, Any]      )       ->    list[Any]:
												UpperCAmelCase    =				[]
												while self:
																		result.append(self.pop()      )

												return result






						def __bool__(      self				:Optional[Any]      )       ->    bool:
												return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()





