

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[Any]		,				SCREAMING_SNAKE_CASE_:							List[str]=1					)	->					str:




				'''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     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]		,				SCREAMING_SNAKE_CASE_:							Any=0					)	->					Dict:




				'''simple docstring'''





				A__						=	[]
				for old_item in old_list:
								A__						=	old_item.replace("in_layers.0"		,				"norm1"					)
								A__						=	new_item.replace("in_layers.2"		,				"conv1"					)

								A__						=	new_item.replace("out_layers.0"		,				"norm2"					)
								A__						=	new_item.replace("out_layers.3"		,				"conv2"					)

								A__						=	new_item.replace("emb_layers.1"		,				"time_emb_proj"					)
								A__						=	new_item.replace("skip_connection"		,				"conv_shortcut"					)

								A__						=	shave_segments(SCREAMING_SNAKE_CASE_		,				n_shave_prefix_segments=SCREAMING_SNAKE_CASE_					)

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

				return mapping



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]		,				SCREAMING_SNAKE_CASE_:							Tuple=0					)	->					Dict:




				'''simple docstring'''





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

								A__						=	new_item.replace("norm.weight"		,				"group_norm.weight"					)
								A__						=	new_item.replace("norm.bias"		,				"group_norm.bias"					)

								A__						=	new_item.replace("proj_out.weight"		,				"proj_attn.weight"					)
								A__						=	new_item.replace("proj_out.bias"		,				"proj_attn.bias"					)

								A__						=	shave_segments(SCREAMING_SNAKE_CASE_		,				n_shave_prefix_segments=SCREAMING_SNAKE_CASE_					)

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

				return mapping



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]		,				SCREAMING_SNAKE_CASE_:							Optional[Any]		,				SCREAMING_SNAKE_CASE_:							Optional[Any]		,				SCREAMING_SNAKE_CASE_:							int=None		,				SCREAMING_SNAKE_CASE_:							Tuple=None		,				SCREAMING_SNAKE_CASE_:							List[str]=None					)	->					int:




				'''simple docstring'''





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

				# Splits the attention layers into three variables.
				if attention_paths_to_split is not None:
								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["num_head_channels"] // 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(SCREAMING_SNAKE_CASE_					)
												A__						=	key.reshape(SCREAMING_SNAKE_CASE_					)
												A__						=	value.reshape(SCREAMING_SNAKE_CASE_					)

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

								# These have already been assigned
								if attention_paths_to_split is not None and new_path in attention_paths_to_split:
												continue

								# Global renaming happens here
								A__						=	new_path.replace("middle_block.0"		,				"mid_block.resnets.0"					)
								A__						=	new_path.replace("middle_block.1"		,				"mid_block.attentions.0"					)
								A__						=	new_path.replace("middle_block.2"		,				"mid_block.resnets.1"					)

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

        # proj_attn.weight has to be converted from conv 1D to linear
								if "proj_attn.weight" in new_path:
												A__						=	old_checkpoint[path["old"]][:, :, 0]
								else:
												A__						=	old_checkpoint[path["old"]]



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple		,				SCREAMING_SNAKE_CASE_:							str					)	->					Union[str, Any]:




				'''simple docstring'''





				A__						=	{}

				A__						=	checkpoint["time_embed.0.weight"]
				A__						=	checkpoint["time_embed.0.bias"]
				A__						=	checkpoint["time_embed.2.weight"]
				A__						=	checkpoint["time_embed.2.bias"]

				A__						=	checkpoint["input_blocks.0.0.weight"]
				A__						=	checkpoint["input_blocks.0.0.bias"]

				A__						=	checkpoint["out.0.weight"]
				A__						=	checkpoint["out.0.bias"]
				A__						=	checkpoint["out.2.weight"]
				A__						=	checkpoint["out.2.bias"]

				# Retrieves the keys for the input blocks only
				A__						=	len({".".join(layer.split("."					)[:2]					) for layer in checkpoint if "input_blocks" in layer}					)
				A__						=	{
				    layer_id: [key for key in checkpoint if F'input_blocks.{layer_id}' in key]
				    for layer_id in range(SCREAMING_SNAKE_CASE_					)
				}

				# Retrieves the keys for the middle blocks only
				A__						=	len({".".join(layer.split("."					)[:2]					) for layer in checkpoint if "middle_block" in layer}					)
				A__						=	{
				    layer_id: [key for key in checkpoint if F'middle_block.{layer_id}' in key]
				    for layer_id in range(SCREAMING_SNAKE_CASE_					)
				}

				# Retrieves the keys for the output blocks only
				A__						=	len({".".join(layer.split("."					)[:2]					) for layer in checkpoint if "output_blocks" in layer}					)
				A__						=	{
				    layer_id: [key for key in checkpoint if F'output_blocks.{layer_id}' in key]
				    for layer_id in range(SCREAMING_SNAKE_CASE_					)
				}

				for i in range(1		,				SCREAMING_SNAKE_CASE_					):
								A__						=	(i - 1) // (config["num_res_blocks"] + 1)
								A__						=	(i - 1) % (config["num_res_blocks"] + 1)

								A__						=	[key for key in input_blocks[i] if F'input_blocks.{i}.0' in key]
								A__						=	[key for key in input_blocks[i] if F'input_blocks.{i}.1' in key]

								if F'input_blocks.{i}.0.op.weight' in checkpoint:
												A__						=	checkpoint[
												    F'input_blocks.{i}.0.op.weight'
												]
												A__						=	checkpoint[
												    F'input_blocks.{i}.0.op.bias'
												]
												continue

								A__						=	renew_resnet_paths(SCREAMING_SNAKE_CASE_					)
								A__						=	{"old": F'input_blocks.{i}.0', "new": F'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
								A__						=	{"old": "resnets.2.op", "new": "downsamplers.0.op"}
								assign_to_checkpoint(
								    SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				additional_replacements=[meta_path, resnet_op]		,				config=SCREAMING_SNAKE_CASE_					)

								if len(SCREAMING_SNAKE_CASE_					):
												A__						=	renew_attention_paths(SCREAMING_SNAKE_CASE_					)
												A__						=	{
												    "old": F'input_blocks.{i}.1',
												    "new": F'down_blocks.{block_id}.attentions.{layer_in_block_id}',
												}
												A__						=	{
												    F'input_blocks.{i}.1.qkv.bias': {
												        "key": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
												        "query": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
												        "value": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
												    },
												    F'input_blocks.{i}.1.qkv.weight': {
												        "key": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
												        "query": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
												        "value": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
												    },
												}
												assign_to_checkpoint(
												    SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				additional_replacements=[meta_path]		,				attention_paths_to_split=SCREAMING_SNAKE_CASE_		,				config=SCREAMING_SNAKE_CASE_		,				)

				A__						=	middle_blocks[0]
				A__						=	middle_blocks[1]
				A__						=	middle_blocks[2]

				A__						=	renew_resnet_paths(SCREAMING_SNAKE_CASE_					)
				assign_to_checkpoint(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				config=SCREAMING_SNAKE_CASE_					)

				A__						=	renew_resnet_paths(SCREAMING_SNAKE_CASE_					)
				assign_to_checkpoint(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				config=SCREAMING_SNAKE_CASE_					)

				A__						=	renew_attention_paths(SCREAMING_SNAKE_CASE_					)
				A__						=	{
				    "middle_block.1.qkv.bias": {
				        "key": "mid_block.attentions.0.key.bias",
				        "query": "mid_block.attentions.0.query.bias",
				        "value": "mid_block.attentions.0.value.bias",
				    },
				    "middle_block.1.qkv.weight": {
				        "key": "mid_block.attentions.0.key.weight",
				        "query": "mid_block.attentions.0.query.weight",
				        "value": "mid_block.attentions.0.value.weight",
				    },
				}
				assign_to_checkpoint(
				    SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				attention_paths_to_split=SCREAMING_SNAKE_CASE_		,				config=SCREAMING_SNAKE_CASE_					)

				for i in range(SCREAMING_SNAKE_CASE_					):
								A__						=	i // (config["num_res_blocks"] + 1)
								A__						=	i % (config["num_res_blocks"] + 1)
								A__						=	[shave_segments(SCREAMING_SNAKE_CASE_		,				2					) for name in output_blocks[i]]
								A__						=	{}

								for layer in output_block_layers:
												A__			, A__						=	layer.split("."					)[0], shave_segments(SCREAMING_SNAKE_CASE_		,				1					)
												if layer_id in output_block_list:
																output_block_list[layer_id].append(SCREAMING_SNAKE_CASE_					)
												else:
																A__						=	[layer_name]

								if len(SCREAMING_SNAKE_CASE_					) > 1:
												A__						=	[key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
												A__						=	[key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]

												A__						=	renew_resnet_paths(SCREAMING_SNAKE_CASE_					)
												A__						=	renew_resnet_paths(SCREAMING_SNAKE_CASE_					)

												A__						=	{"old": F'output_blocks.{i}.0', "new": F'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
												assign_to_checkpoint(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				additional_replacements=[meta_path]		,				config=SCREAMING_SNAKE_CASE_					)

												if ["conv.weight", "conv.bias"] in output_block_list.values():
																A__						=	list(output_block_list.values()					).index(["conv.weight", "conv.bias"]					)
																A__						=	checkpoint[
																    F'output_blocks.{i}.{index}.conv.weight'
																]
																A__						=	checkpoint[
																    F'output_blocks.{i}.{index}.conv.bias'
																]

																# Clear attentions as they have been attributed above.
																if len(SCREAMING_SNAKE_CASE_					) == 2:
																				A__						=	[]

												if len(SCREAMING_SNAKE_CASE_					):
																A__						=	renew_attention_paths(SCREAMING_SNAKE_CASE_					)
																A__						=	{
																    "old": F'output_blocks.{i}.1',
																    "new": F'up_blocks.{block_id}.attentions.{layer_in_block_id}',
																}
																A__						=	{
																    F'output_blocks.{i}.1.qkv.bias': {
																        "key": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
																        "query": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
																        "value": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
																    },
																    F'output_blocks.{i}.1.qkv.weight': {
																        "key": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
																        "query": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
																        "value": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
																    },
																}
																assign_to_checkpoint(
																    SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				additional_replacements=[meta_path]		,				attention_paths_to_split=to_split if any("qkv" in key for key in attentions					) else None		,				config=SCREAMING_SNAKE_CASE_		,				)
								else:
												A__						=	renew_resnet_paths(SCREAMING_SNAKE_CASE_		,				n_shave_prefix_segments=1					)
												for path in resnet_0_paths:
																A__						=	".".join(["output_blocks", str(SCREAMING_SNAKE_CASE_					), path["old"]]					)
																A__						=	".".join(["up_blocks", str(SCREAMING_SNAKE_CASE_					), "resnets", str(SCREAMING_SNAKE_CASE_					), path["new"]]					)

																A__						=	checkpoint[old_path]

				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())

	lowerCAmelCase__		      =       convert_ldm_checkpoint(checkpoint, config)

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

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

	try:
		lowerCAmelCase__		      =       DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
		lowerCAmelCase__		      =       VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

		lowerCAmelCase__		      =       LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
		pipe.save_pretrained(args.dump_path)
	except:  # noqa: E722
		model.save_pretrained(args.dump_path)







import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


_A     =			'''scheduler_config.json'''


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   1
		__snake_case									=   2
		__snake_case									=   3
		__snake_case									=   4
		__snake_case									=   5
		__snake_case									=   6
		__snake_case									=   7
		__snake_case									=   8
		__snake_case									=   9
		__snake_case									=   10
		__snake_case									=   11
		__snake_case									=   12
		__snake_case									=   13
		__snake_case									=   14







@dataclass
class  A		(  __UpperCAmelCase     ):
		__snake_case									=   42







class  A		:
		__snake_case									=   SCHEDULER_CONFIG_NAME
		__snake_case									=   []
		__snake_case									=   True



		@classmethod
		def        SCREAMING_SNAKE_CASE__			(     cls,	UpperCamelCase__ = None,	UpperCamelCase__ = None,	UpperCamelCase__=False,	**UpperCamelCase__,	):







								"""simple docstring"""



								lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_      			=  cls.load_config(
								    pretrained_model_name_or_path=UpperCamelCase__,	subfolder=UpperCamelCase__,	return_unused_kwargs=UpperCamelCase__,	return_commit_hash=UpperCamelCase__,	**UpperCamelCase__,	)
								return cls.from_config(UpperCamelCase__,	return_unused_kwargs=UpperCamelCase__,	**UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = False,	**UpperCamelCase__   ):







								"""simple docstring"""



								self.save_config(save_directory=UpperCamelCase__,	push_to_hub=UpperCamelCase__,	**UpperCamelCase__   )



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return self._get_compatibles()







		@classmethod
		def        SCREAMING_SNAKE_CASE__			(     cls   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  list(set([cls.__name__] + cls._compatibles   )   )
								lowerCAmelCase_      			=  importlib.import_module(__name__.split('''.'''   )[0]   )
								lowerCAmelCase_      			=  [
								    getattr(UpperCamelCase__,	UpperCamelCase__   ) for c in compatible_classes_str if hasattr(UpperCamelCase__,	UpperCamelCase__   )
								]
								return compatible_classes




