





'''simple docstring'''







import argparse

import torch

from diffusers import UNetaDConditionModel





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase					,	lowercase=None					,	lowercase=None					,	lowercase=None	)					->					Union[str, Any]:
			assert isinstance(lowercase					,	lowercase	), "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():
									__lowerCAmelCase											=				old_checkpoint[path]
									__lowerCAmelCase											=				old_tensor.shape[0] // 3

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

									__lowerCAmelCase											=				old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

									__lowerCAmelCase											=				query.reshape(lowercase	)
									__lowerCAmelCase											=				key.reshape(lowercase	)
									__lowerCAmelCase											=				value.reshape(lowercase	)

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

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

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

        # proj_attn.weight has to be converted from conv 1D to linear
						__lowerCAmelCase											=				old_checkpoint[path["""old"""]]
						__lowerCAmelCase											=				["""proj_attn.weight"""]
						__lowerCAmelCase											=				["""proj_out.weight""", """proj_in.weight"""]
						if any(k in new_path for k in names	):
									__lowerCAmelCase											=				weight[:, :, 0]
						elif any(k in new_path for k in names_a	) and len(weight.shape	) > 2 and ".attentions." not in new_path:
									__lowerCAmelCase											=				weight[:, :, 0]
						else:
									__lowerCAmelCase											=				weight





def    _lowerCAmelCase						(						lowercase					,	lowercase=0	)					->					Dict:
			__lowerCAmelCase											=				[]
			for old_item in old_list:
						__lowerCAmelCase											=				old_item

						#         new_item = new_item.replace('norm.weight', 'group_norm.weight')
						#         new_item = new_item.replace('norm.bias', 'group_norm.bias')

						#         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
						#         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

						#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

			return mapping





def    _lowerCAmelCase						(						lowercase					,	lowercase=1	)					->					Optional[Any]:
			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	)					->					Optional[int]:
			__lowerCAmelCase											=				[]
			for old_item in old_list:
						mapping.append({"""old""": old_item, """new""": old_item}	)

			return mapping





def    _lowerCAmelCase						(						lowercase					,	lowercase=0	)					->					Optional[int]:
			__lowerCAmelCase											=				[]
			for old_item in old_list:
						__lowerCAmelCase											=				old_item.replace("""in_layers.0"""					,	"""norm1"""	)
						__lowerCAmelCase											=				new_item.replace("""in_layers.2"""					,	"""conv1"""	)

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

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

						__lowerCAmelCase											=				shave_segments(lowercase					,	n_shave_prefix_segments=lowercase	)

						if "temopral_conv" not in old_item:
									mapping.append({"""old""": old_item, """new""": new_item}	)

			return mapping





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase=None					,	lowercase=False	)					->					int:
			__lowerCAmelCase											=				{}
			__lowerCAmelCase											=				list(checkpoint.keys()	)

			__lowerCAmelCase											=				"""model.diffusion_model."""

			# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
			if sum(k.startswith("""model_ema"""	) for k in keys	) > 100 and extract_ema:
						print(f'Checkpoint {path} has both EMA and non-EMA weights.'	)
						print(
						    """In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"""
						    """ weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."""	)
						for key in keys:
									if key.startswith("""model.diffusion_model"""	):
												__lowerCAmelCase											=				"""model_ema.""" + """""".join(key.split("""."""	)[1:]	)
												__lowerCAmelCase											=				checkpoint.pop(lowercase	)
			else:
						if sum(k.startswith("""model_ema"""	) for k in keys	) > 100:
									print(
									    """In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"""
									    """ weights (usually better for inference), please make sure to add the `--extract_ema` flag."""	)

						for key in keys:
									__lowerCAmelCase											=				checkpoint.pop(lowercase	)

			__lowerCAmelCase											=				{}

			__lowerCAmelCase											=				unet_state_dict["""time_embed.0.weight"""]
			__lowerCAmelCase											=				unet_state_dict["""time_embed.0.bias"""]
			__lowerCAmelCase											=				unet_state_dict["""time_embed.2.weight"""]
			__lowerCAmelCase											=				unet_state_dict["""time_embed.2.bias"""]

			if config["class_embed_type"] is None:
						# No parameters to port
						...
			elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
						__lowerCAmelCase											=				unet_state_dict["""label_emb.0.0.weight"""]
						__lowerCAmelCase											=				unet_state_dict["""label_emb.0.0.bias"""]
						__lowerCAmelCase											=				unet_state_dict["""label_emb.0.2.weight"""]
						__lowerCAmelCase											=				unet_state_dict["""label_emb.0.2.bias"""]
			else:
						raise NotImplementedError(f'Not implemented `class_embed_type`: {config["class_embed_type"]}'	)

			__lowerCAmelCase											=				unet_state_dict["""input_blocks.0.0.weight"""]
			__lowerCAmelCase											=				unet_state_dict["""input_blocks.0.0.bias"""]

			__lowerCAmelCase											=				[v for v in unet_state_dict if v.startswith("""input_blocks.0.1"""	)]
			__lowerCAmelCase											=				renew_attention_paths(lowercase	)
			__lowerCAmelCase											=				{"""old""": """input_blocks.0.1""", """new""": """transformer_in"""}
			assign_to_checkpoint(lowercase					,	lowercase					,	lowercase					,	additional_replacements=[meta_path]					,	config=lowercase	)

			__lowerCAmelCase											=				unet_state_dict["""out.0.weight"""]
			__lowerCAmelCase											=				unet_state_dict["""out.0.bias"""]
			__lowerCAmelCase											=				unet_state_dict["""out.2.weight"""]
			__lowerCAmelCase											=				unet_state_dict["""out.2.bias"""]

			# Retrieves the keys for the input blocks only
			__lowerCAmelCase											=				len({""".""".join(layer.split("""."""	)[:2]	) for layer in unet_state_dict if """input_blocks""" in layer}	)
			__lowerCAmelCase											=				{
			    layer_id: [key for key in unet_state_dict if f'input_blocks.{layer_id}' in key]
			    for layer_id in range(lowercase	)
			}

			# Retrieves the keys for the middle blocks only
			__lowerCAmelCase											=				len({""".""".join(layer.split("""."""	)[:2]	) for layer in unet_state_dict if """middle_block""" in layer}	)
			__lowerCAmelCase											=				{
			    layer_id: [key for key in unet_state_dict if f'middle_block.{layer_id}' in key]
			    for layer_id in range(lowercase	)
			}

			# Retrieves the keys for the output blocks only
			__lowerCAmelCase											=				len({""".""".join(layer.split("""."""	)[:2]	) for layer in unet_state_dict if """output_blocks""" in layer}	)
			__lowerCAmelCase											=				{
			    layer_id: [key for key in unet_state_dict if f'output_blocks.{layer_id}' in key]
			    for layer_id in range(lowercase	)
			}

			for i in range(1					,	lowercase	):
						__lowerCAmelCase											=				(i - 1) // (config["""layers_per_block"""] + 1)
						__lowerCAmelCase											=				(i - 1) % (config["""layers_per_block"""] + 1)

						__lowerCAmelCase											=				[
						    key for key in input_blocks[i] if f'input_blocks.{i}.0' in key and f'input_blocks.{i}.0.op' not in key
						]
						__lowerCAmelCase											=				[key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]
						__lowerCAmelCase											=				[key for key in input_blocks[i] if f'input_blocks.{i}.2' in key]

						if f'input_blocks.{i}.op.weight' in unet_state_dict:
									__lowerCAmelCase											=				unet_state_dict.pop(
									    f'input_blocks.{i}.op.weight'	)
									__lowerCAmelCase											=				unet_state_dict.pop(
									    f'input_blocks.{i}.op.bias'	)

						__lowerCAmelCase											=				renew_resnet_paths(lowercase	)
						__lowerCAmelCase											=				{"""old""": f'input_blocks.{i}.0', """new""": f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
						assign_to_checkpoint(
						    lowercase					,	lowercase					,	lowercase					,	additional_replacements=[meta_path]					,	config=lowercase	)

						__lowerCAmelCase											=				[key for key in resnets if """temopral_conv""" in key]
						__lowerCAmelCase											=				renew_temp_conv_paths(lowercase	)
						__lowerCAmelCase											=				{
						    """old""": f'input_blocks.{i}.0.temopral_conv',
						    """new""": f'down_blocks.{block_id}.temp_convs.{layer_in_block_id}',
						}
						assign_to_checkpoint(
						    lowercase					,	lowercase					,	lowercase					,	additional_replacements=[meta_path]					,	config=lowercase	)

						if len(lowercase	):
									__lowerCAmelCase											=				renew_attention_paths(lowercase	)
									__lowerCAmelCase											=				{"""old""": f'input_blocks.{i}.1', """new""": f'down_blocks.{block_id}.attentions.{layer_in_block_id}'}
									assign_to_checkpoint(
									    lowercase					,	lowercase					,	lowercase					,	additional_replacements=[meta_path]					,	config=lowercase	)

						if len(lowercase	):
									__lowerCAmelCase											=				renew_attention_paths(lowercase	)
									__lowerCAmelCase											=				{
									    """old""": f'input_blocks.{i}.2',
									    """new""": f'down_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
									}
									assign_to_checkpoint(
									    lowercase					,	lowercase					,	lowercase					,	additional_replacements=[meta_path]					,	config=lowercase	)

			__lowerCAmelCase											=				middle_blocks[0]
			__lowerCAmelCase											=				[key for key in resnet_a if """temopral_conv""" in key]
			__lowerCAmelCase											=				middle_blocks[1]
			__lowerCAmelCase											=				middle_blocks[2]
			__lowerCAmelCase											=				middle_blocks[3]
			__lowerCAmelCase											=				[key for key in resnet_a if """temopral_conv""" in key]

			__lowerCAmelCase											=				renew_resnet_paths(lowercase	)
			__lowerCAmelCase											=				{"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
			assign_to_checkpoint(
			    lowercase					,	lowercase					,	lowercase					,	config=lowercase					,	additional_replacements=[meta_path]	)

			__lowerCAmelCase											=				renew_temp_conv_paths(lowercase	)
			__lowerCAmelCase											=				{"""old""": """middle_block.0.temopral_conv""", """new""": """mid_block.temp_convs.0"""}
			assign_to_checkpoint(
			    lowercase					,	lowercase					,	lowercase					,	config=lowercase					,	additional_replacements=[meta_path]	)

			__lowerCAmelCase											=				renew_resnet_paths(lowercase	)
			__lowerCAmelCase											=				{"""old""": """middle_block.3""", """new""": """mid_block.resnets.1"""}
			assign_to_checkpoint(
			    lowercase					,	lowercase					,	lowercase					,	config=lowercase					,	additional_replacements=[meta_path]	)

			__lowerCAmelCase											=				renew_temp_conv_paths(lowercase	)
			__lowerCAmelCase											=				{"""old""": """middle_block.3.temopral_conv""", """new""": """mid_block.temp_convs.1"""}
			assign_to_checkpoint(
			    lowercase					,	lowercase					,	lowercase					,	config=lowercase					,	additional_replacements=[meta_path]	)

			__lowerCAmelCase											=				renew_attention_paths(lowercase	)
			__lowerCAmelCase											=				{"""old""": """middle_block.1""", """new""": """mid_block.attentions.0"""}
			assign_to_checkpoint(
			    lowercase					,	lowercase					,	lowercase					,	additional_replacements=[meta_path]					,	config=lowercase	)

			__lowerCAmelCase											=				renew_attention_paths(lowercase	)
			__lowerCAmelCase											=				{"""old""": """middle_block.2""", """new""": """mid_block.temp_attentions.0"""}
			assign_to_checkpoint(
			    lowercase					,	lowercase					,	lowercase					,	additional_replacements=[meta_path]					,	config=lowercase	)

			for i in range(lowercase	):
						__lowerCAmelCase											=				i // (config["""layers_per_block"""] + 1)
						__lowerCAmelCase											=				i % (config["""layers_per_block"""] + 1)
						__lowerCAmelCase											=				[shave_segments(lowercase					,	2	) for name in output_blocks[i]]
						__lowerCAmelCase											=				{}

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

						if len(lowercase	) > 1:
									__lowerCAmelCase											=				[key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
									__lowerCAmelCase											=				[key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]
									__lowerCAmelCase											=				[key for key in output_blocks[i] if f'output_blocks.{i}.2' in key]

									__lowerCAmelCase											=				renew_resnet_paths(lowercase	)
									__lowerCAmelCase											=				renew_resnet_paths(lowercase	)

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

									__lowerCAmelCase											=				[key for key in resnets if """temopral_conv""" in key]
									__lowerCAmelCase											=				renew_temp_conv_paths(lowercase	)
									__lowerCAmelCase											=				{
									    """old""": f'output_blocks.{i}.0.temopral_conv',
									    """new""": f'up_blocks.{block_id}.temp_convs.{layer_in_block_id}',
									}
									assign_to_checkpoint(
									    lowercase					,	lowercase					,	lowercase					,	additional_replacements=[meta_path]					,	config=lowercase	)

									__lowerCAmelCase											=				{k: sorted(lowercase	) for k, v in output_block_list.items()}
									if ["conv.bias", "conv.weight"] in output_block_list.values():
												__lowerCAmelCase											=				list(output_block_list.values()	).index(["""conv.bias""", """conv.weight"""]	)
												__lowerCAmelCase											=				unet_state_dict[
												    f'output_blocks.{i}.{index}.conv.weight'
												]
												__lowerCAmelCase											=				unet_state_dict[
												    f'output_blocks.{i}.{index}.conv.bias'
												]

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

									if len(lowercase	):
												__lowerCAmelCase											=				renew_attention_paths(lowercase	)
												__lowerCAmelCase											=				{
												    """old""": f'output_blocks.{i}.1',
												    """new""": f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
												}
												assign_to_checkpoint(
												    lowercase					,	lowercase					,	lowercase					,	additional_replacements=[meta_path]					,	config=lowercase	)

									if len(lowercase	):
												__lowerCAmelCase											=				renew_attention_paths(lowercase	)
												__lowerCAmelCase											=				{
												    """old""": f'output_blocks.{i}.2',
												    """new""": f'up_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
												}
												assign_to_checkpoint(
												    lowercase					,	lowercase					,	lowercase					,	additional_replacements=[meta_path]					,	config=lowercase	)
						else:
									__lowerCAmelCase											=				renew_resnet_paths(lowercase					,	n_shave_prefix_segments=1	)
									for path in resnet_0_paths:
												__lowerCAmelCase											=				""".""".join(["""output_blocks""", str(lowercase	), path["""old"""]]	)
												__lowerCAmelCase											=				""".""".join(["""up_blocks""", str(lowercase	), """resnets""", str(lowercase	), path["""new"""]]	)
												__lowerCAmelCase											=				unet_state_dict[old_path]

									__lowerCAmelCase											=				[l for l in output_block_layers if """temopral_conv""" in l]
									for path in temopral_conv_paths:
												__lowerCAmelCase											=				path.split("""temopral_conv."""	)[-1]
												__lowerCAmelCase											=				""".""".join(["""output_blocks""", str(lowercase	), str(lowercase	), """temopral_conv""", pruned_path]	)
												__lowerCAmelCase											=				""".""".join(["""up_blocks""", str(lowercase	), """temp_convs""", str(lowercase	), pruned_path]	)
												__lowerCAmelCase											=				unet_state_dict[old_path]

			return new_checkpoint


if __name__ == "__main__":
	_a					: List[str]      =    argparse.ArgumentParser()

	parser.add_argument(
	    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
	)
	parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
	_a					: Any      =    parser.parse_args()

	_a					: Optional[int]      =    torch.load(args.checkpoint_path, map_location="""cpu""")
	_a					: List[str]      =    UNetaDConditionModel()

	_a					: str      =    convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

	_a					: int      =    set(unet.state_dict().keys()) - set(converted_ckpt.keys())
	_a					: str      =    set(converted_ckpt.keys()) - set(unet.state_dict().keys())

	assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

	# load state_dict
	unet.load_state_dict(converted_ckpt)

	unet.save_pretrained(args.dump_path)

	# -- finish converting the unet --








'''simple docstring'''







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp





def    _lowerCAmelCase						(						)					->					List[str]:
			__lowerCAmelCase											=				ArgumentParser(
			    description=(
			        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
			    )	)

			# Optional arguments for the launch helper
			parser.add_argument("""--num_cores"""					,	type=lowercase					,	default=1					,	help="""Number of TPU cores to use (1 or 8)."""	)

			# positional
			parser.add_argument(
			    """training_script"""					,	type=lowercase					,	help=(
			        """The full path to the single TPU training """
			        """program/script to be launched in parallel, """
			        """followed by all the arguments for the """
			        """training script"""
			    )					,	)

			# rest from the training program
			parser.add_argument("""training_script_args"""					,	nargs=lowercase	)

			return parser.parse_args()





def    _lowerCAmelCase						(						)					->					Any:
			__lowerCAmelCase											=				parse_args()

			# Import training_script as a module.
			__lowerCAmelCase											=				Path(args.training_script	)
			sys.path.append(str(script_fpath.parent.resolve()	)	)
			__lowerCAmelCase											=				script_fpath.stem
			__lowerCAmelCase											=				importlib.import_module(lowercase	)

			# Patch sys.argv
			__lowerCAmelCase											=				[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores	)]

			xmp.spawn(mod._mp_fn					,	args=()					,	nprocs=args.num_cores	)


if __name__ == "__main__":
	main()

