


'''simple docstring'''
import argparse
import json

import torch

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




def      __snake_case       (       lowerCamelCase_ :				Union[str, Any]	,  lowerCamelCase_ :				List[Any]=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      __snake_case       (       lowerCamelCase_ :				int	,  lowerCamelCase_ :				Union[str, Any]=0						):
				'''simple docstring'''




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

								__magic_name__	  = shave_segments(lowerCamelCase_	,  n_shave_prefix_segments=lowerCamelCase_						)

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

				return mapping




def      __snake_case       (       lowerCamelCase_ :				str	,  lowerCamelCase_ :				Union[str, Any]=0	,  lowerCamelCase_ :				List[str]=False						):
				'''simple docstring'''




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

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

								__magic_name__	  = new_item.replace("proj_out"	,  "proj_attn"						)
								__magic_name__	  = new_item.replace("norm"	,  "group_norm"						)

								__magic_name__	  = shave_segments(lowerCamelCase_	,  n_shave_prefix_segments=lowerCamelCase_						)
								mapping.append({"old": old_item, "new": new_item}						)

				return mapping




def      __snake_case       (       lowerCamelCase_ :				Any	,  lowerCamelCase_ :				int	,  lowerCamelCase_ :				List[Any]	,  lowerCamelCase_ :				List[Any]=None	,  lowerCamelCase_ :				Union[str, Any]=None	,  lowerCamelCase_ :				List[str]=None						):
				'''simple docstring'''




				assert isinstance(lowerCamelCase_	,  lowerCamelCase_						), "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():
												__magic_name__	  = old_checkpoint[path]
												__magic_name__	  = old_tensor.shape[0] // 3

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

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

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

												__magic_name__	  = query.reshape(lowerCamelCase_						).squeeze()
												__magic_name__	  = key.reshape(lowerCamelCase_						).squeeze()
												__magic_name__	  = value.reshape(lowerCamelCase_						).squeeze()

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

								if attention_paths_to_split is not None and new_path in attention_paths_to_split:
												continue

								__magic_name__	  = new_path.replace("down."	,  "down_blocks."						)
								__magic_name__	  = new_path.replace("up."	,  "up_blocks."						)

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

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




def      __snake_case       (       lowerCamelCase_ :				Optional[int]	,  lowerCamelCase_ :				Dict						):
				'''simple docstring'''




				__magic_name__	  = {}

				__magic_name__	  = checkpoint["temb.dense.0.weight"]
				__magic_name__	  = checkpoint["temb.dense.0.bias"]
				__magic_name__	  = checkpoint["temb.dense.1.weight"]
				__magic_name__	  = checkpoint["temb.dense.1.bias"]

				__magic_name__	  = checkpoint["norm_out.weight"]
				__magic_name__	  = checkpoint["norm_out.bias"]

				__magic_name__	  = checkpoint["conv_in.weight"]
				__magic_name__	  = checkpoint["conv_in.bias"]
				__magic_name__	  = checkpoint["conv_out.weight"]
				__magic_name__	  = checkpoint["conv_out.bias"]

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

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

				for i in range(lowerCamelCase_						):
								__magic_name__	  = (i - 1) // (config["layers_per_block"] + 1)

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

												if num_blocks > 0:
																for j in range(config["layers_per_block"]						):
																				__magic_name__	  = renew_resnet_paths(blocks[j]						)
																				assign_to_checkpoint(lowerCamelCase_	,  lowerCamelCase_	,  lowerCamelCase_						)

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

												if num_attn > 0:
																for j in range(config["layers_per_block"]						):
																				__magic_name__	  = renew_attention_paths(attns[j]						)
																				assign_to_checkpoint(lowerCamelCase_	,  lowerCamelCase_	,  lowerCamelCase_	,  config=lowerCamelCase_						)

				__magic_name__	  = [key for key in checkpoint if "mid.block_1" in key]
				__magic_name__	  = [key for key in checkpoint if "mid.block_2" in key]
				__magic_name__	  = [key for key in checkpoint if "mid.attn_1" in key]

				# Mid new 2
				__magic_name__	  = renew_resnet_paths(lowerCamelCase_						)
				assign_to_checkpoint(
				    lowerCamelCase_	,  lowerCamelCase_	,  lowerCamelCase_	,  additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]	,  )

				__magic_name__	  = renew_resnet_paths(lowerCamelCase_						)
				assign_to_checkpoint(
				    lowerCamelCase_	,  lowerCamelCase_	,  lowerCamelCase_	,  additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]	,  )

				__magic_name__	  = renew_attention_paths(lowerCamelCase_	,  in_mid=lowerCamelCase_						)
				assign_to_checkpoint(
				    lowerCamelCase_	,  lowerCamelCase_	,  lowerCamelCase_	,  additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]	,  )

				for i in range(lowerCamelCase_						):
								__magic_name__	  = num_up_blocks - 1 - i

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

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

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

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

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

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




def      __snake_case       (       lowerCamelCase_ :				str	,  lowerCamelCase_ :				Dict						):
				'''simple docstring'''




				__magic_name__	  = {}

				__magic_name__	  = checkpoint["encoder.norm_out.weight"]
				__magic_name__	  = checkpoint["encoder.norm_out.bias"]

				__magic_name__	  = checkpoint["encoder.conv_in.weight"]
				__magic_name__	  = checkpoint["encoder.conv_in.bias"]
				__magic_name__	  = checkpoint["encoder.conv_out.weight"]
				__magic_name__	  = checkpoint["encoder.conv_out.bias"]

				__magic_name__	  = checkpoint["decoder.norm_out.weight"]
				__magic_name__	  = checkpoint["decoder.norm_out.bias"]

				__magic_name__	  = checkpoint["decoder.conv_in.weight"]
				__magic_name__	  = checkpoint["decoder.conv_in.bias"]
				__magic_name__	  = checkpoint["decoder.conv_out.weight"]
				__magic_name__	  = checkpoint["decoder.conv_out.bias"]

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

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

				for i in range(lowerCamelCase_						):
								__magic_name__	  = (i - 1) // (config["layers_per_block"] + 1)

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

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

												if num_blocks > 0:
																for j in range(config["layers_per_block"]						):
																				__magic_name__	  = renew_resnet_paths(blocks[j]						)
																				assign_to_checkpoint(lowerCamelCase_	,  lowerCamelCase_	,  lowerCamelCase_						)

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

												if num_attn > 0:
																for j in range(config["layers_per_block"]						):
																				__magic_name__	  = renew_attention_paths(attns[j]						)
																				assign_to_checkpoint(lowerCamelCase_	,  lowerCamelCase_	,  lowerCamelCase_	,  config=lowerCamelCase_						)

				__magic_name__	  = [key for key in checkpoint if "mid.block_1" in key]
				__magic_name__	  = [key for key in checkpoint if "mid.block_2" in key]
				__magic_name__	  = [key for key in checkpoint if "mid.attn_1" in key]

				# Mid new 2
				__magic_name__	  = renew_resnet_paths(lowerCamelCase_						)
				assign_to_checkpoint(
				    lowerCamelCase_	,  lowerCamelCase_	,  lowerCamelCase_	,  additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]	,  )

				__magic_name__	  = renew_resnet_paths(lowerCamelCase_						)
				assign_to_checkpoint(
				    lowerCamelCase_	,  lowerCamelCase_	,  lowerCamelCase_	,  additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]	,  )

				__magic_name__	  = renew_attention_paths(lowerCamelCase_	,  in_mid=lowerCamelCase_						)
				assign_to_checkpoint(
				    lowerCamelCase_	,  lowerCamelCase_	,  lowerCamelCase_	,  additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]	,  )

				for i in range(lowerCamelCase_						):
								__magic_name__	  = num_up_blocks - 1 - i

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

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

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

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

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

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

				return new_checkpoint


if __name__ == "__main__":
						__magic_name__   :   Union[str, Any]  							=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.')

						__magic_name__   :   Union[str, Any]  							=parser.parse_args()
						__magic_name__   :   Optional[int]  							=torch.load(args.checkpoint_path)

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

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

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

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

												__magic_name__   :   Tuple  							=DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

												__magic_name__   :   Optional[Any]  							=DDPMPipeline(unet=model, scheduler=scheduler)
												pipe.save_pretrained(args.dump_path)







'''simple docstring'''
from __future__ import annotations




def      __snake_case       (       lowerCamelCase_ :				list[list[int]]	,  lowerCamelCase_ :				list[list[int]]						):
				'''simple docstring'''




				assert len(lowerCamelCase_						) == len(lowerCamelCase_						)

				__magic_name__	  = len(lowerCamelCase_						)
				__magic_name__	  = list(range(lowerCamelCase_						)						)
				__magic_name__	  = [-1] * n  # who the donor has donated to
				__magic_name__	  = [-1] * n  # who the recipient has received from
				__magic_name__	  = [0] * n

				while unmatched_donors:
								__magic_name__	  = unmatched_donors[0]
								__magic_name__	  = donor_pref[donor]
								__magic_name__	  = donor_preference[num_donations[donor]]
								num_donations[donor] += 1
								__magic_name__	  = recipient_pref[recipient]
								__magic_name__	  = rec_record[recipient]

								if prev_donor != -1:
												if rec_preference.index(lowerCamelCase_						) > rec_preference.index(lowerCamelCase_						):
																__magic_name__	  = donor
																__magic_name__	  = recipient
																unmatched_donors.append(lowerCamelCase_						)
																unmatched_donors.remove(lowerCamelCase_						)
								else:
												__magic_name__	  = donor
												__magic_name__	  = recipient
												unmatched_donors.remove(lowerCamelCase_						)
				return donor_record



