

"""simple docstring"""






import argparse

import torch

from diffusers import UNetaDConditionModel
def 		A_	(   __UpperCamelCase     :				int       ,	__UpperCamelCase     :				int       ,	__UpperCamelCase     :				Tuple       ,	__UpperCamelCase     :				Optional[Any]=None       ,	__UpperCamelCase     :				Tuple=None       ,	__UpperCamelCase     :				List[Any]=None				):
		assert isinstance(__UpperCamelCase       ,	__UpperCamelCase				), "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():
						lowercase										=							old_checkpoint[path]
						lowercase										=							old_tensor.shape[0] // 3

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

						lowercase										=							old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

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

		for path in paths:
				lowercase										=							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:
								lowercase										=							new_path.replace(replacement['''old''']       ,	replacement['''new''']				)

        # proj_attn.weight has to be converted from conv 1D to linear
				lowercase										=							old_checkpoint[path['''old''']]
				lowercase										=							['''proj_attn.weight''']
				lowercase										=							['''proj_out.weight''', '''proj_in.weight''']
				if any(k in new_path for k in names				):
						lowercase										=							weight[:, :, 0]
				elif any(k in new_path for k in names_a				) and len(weight.shape				) > 2 and ".attentions." not in new_path:
						lowercase										=							weight[:, :, 0]
				else:
						lowercase										=							weight
def 		A_	(   __UpperCamelCase     :				Any       ,	__UpperCamelCase     :				Optional[int]=0				):
		lowercase										=							[]
		for old_item in old_list:
				lowercase										=							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 		A_	(   __UpperCamelCase     :				List[Any]       ,	__UpperCamelCase     :				Any=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 		A_	(   __UpperCamelCase     :				List[Any]       ,	__UpperCamelCase     :				Any=0				):
		lowercase										=							[]
		for old_item in old_list:
				mapping.append({'''old''': old_item, '''new''': old_item}				)

		return mapping
def 		A_	(   __UpperCamelCase     :				Tuple       ,	__UpperCamelCase     :				Optional[int]=0				):
		lowercase										=							[]
		for old_item in old_list:
				lowercase										=							old_item.replace('''in_layers.0'''       ,	'''norm1'''				)
				lowercase										=							new_item.replace('''in_layers.2'''       ,	'''conv1'''				)

				lowercase										=							new_item.replace('''out_layers.0'''       ,	'''norm2'''				)
				lowercase										=							new_item.replace('''out_layers.3'''       ,	'''conv2'''				)

				lowercase										=							new_item.replace('''emb_layers.1'''       ,	'''time_emb_proj'''				)
				lowercase										=							new_item.replace('''skip_connection'''       ,	'''conv_shortcut'''				)

				lowercase										=							shave_segments(__UpperCamelCase       ,	n_shave_prefix_segments=__UpperCamelCase				)

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

		return mapping
def 		A_	(   __UpperCamelCase     :				Union[str, Any]       ,	__UpperCamelCase     :				Optional[Any]       ,	__UpperCamelCase     :				str=None       ,	__UpperCamelCase     :				Union[str, Any]=False				):
		lowercase										=							{}
		lowercase										=							list(checkpoint.keys()				)

		lowercase										=							'''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				) > 1_00 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'''				):
								lowercase										=							'''model_ema.''' + ''''''.join(key.split('''.'''				)[1:]				)
								lowercase										=							checkpoint.pop(__UpperCamelCase				)
		else:
				if sum(k.startswith('''model_ema'''				) for k in keys				) > 1_00:
						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:
						lowercase										=							checkpoint.pop(__UpperCamelCase				)

		lowercase										=							{}

		lowercase										=							unet_state_dict['''time_embed.0.weight''']
		lowercase										=							unet_state_dict['''time_embed.0.bias''']
		lowercase										=							unet_state_dict['''time_embed.2.weight''']
		lowercase										=							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":
				lowercase										=							unet_state_dict['''label_emb.0.0.weight''']
				lowercase										=							unet_state_dict['''label_emb.0.0.bias''']
				lowercase										=							unet_state_dict['''label_emb.0.2.weight''']
				lowercase										=							unet_state_dict['''label_emb.0.2.bias''']
		else:
				raise NotImplementedError(f"""Not implemented `class_embed_type`: {config["class_embed_type"]}"""				)

		lowercase										=							unet_state_dict['''input_blocks.0.0.weight''']
		lowercase										=							unet_state_dict['''input_blocks.0.0.bias''']

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

		lowercase										=							unet_state_dict['''out.0.weight''']
		lowercase										=							unet_state_dict['''out.0.bias''']
		lowercase										=							unet_state_dict['''out.2.weight''']
		lowercase										=							unet_state_dict['''out.2.bias''']

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

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

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

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

				lowercase										=							[
				    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
				]
				lowercase										=							[key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]
				lowercase										=							[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:
						lowercase										=							unet_state_dict.pop(
						    f"""input_blocks.{i}.op.weight"""				)
						lowercase										=							unet_state_dict.pop(
						    f"""input_blocks.{i}.op.bias"""				)

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

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

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

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

		lowercase										=							middle_blocks[0]
		lowercase										=							[key for key in resnet_a if '''temopral_conv''' in key]
		lowercase										=							middle_blocks[1]
		lowercase										=							middle_blocks[2]
		lowercase										=							middle_blocks[3]
		lowercase										=							[key for key in resnet_a if '''temopral_conv''' in key]

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

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

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

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

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

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

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

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

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

						lowercase										=							renew_resnet_paths(__UpperCamelCase				)
						lowercase										=							renew_resnet_paths(__UpperCamelCase				)

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

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

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

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

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

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

						lowercase										=							[l for l in output_block_layers if '''temopral_conv''' in l]
						for path in temopral_conv_paths:
								lowercase										=							path.split('''temopral_conv.'''				)[-1]
								lowercase										=							'''.'''.join(['''output_blocks''', str(__UpperCamelCase				), str(__UpperCamelCase				), '''temopral_conv''', pruned_path]				)
								lowercase										=							'''.'''.join(['''up_blocks''', str(__UpperCamelCase				), '''temp_convs''', str(__UpperCamelCase				), pruned_path]				)
								lowercase										=							unet_state_dict[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('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
							__lowerCAmelCase				      =   parser.parse_args()

							__lowerCAmelCase				      =   torch.load(args.checkpoint_path, map_location='''cpu''')
							__lowerCAmelCase				      =   UNetaDConditionModel()

							__lowerCAmelCase				      =   convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

							__lowerCAmelCase				      =   set(unet.state_dict().keys()) - set(converted_ckpt.keys())
							__lowerCAmelCase				      =   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"""






from typing import Callable
def 		A_	(   __UpperCamelCase     :				Callable				):

		def wrapper(__UpperCamelCase     :				Optional[Any]				):
				lowercase										=							function_with_docstring.__doc__
				return target_function

		return wrapper