
'''simple docstring'''







import argparse

import torch

from diffusers import UNetaDConditionModel
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					List[Any] , __SCREAMING_SNAKE_CASE   :					Tuple , __SCREAMING_SNAKE_CASE   :					Dict , __SCREAMING_SNAKE_CASE   :					List[Any]=None , __SCREAMING_SNAKE_CASE   :					List[Any]=None , __SCREAMING_SNAKE_CASE   :					int=None				):



						"""simple docstring"""





						assert isinstance(snake_case_ , 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():
																		lowercase_      :	Optional[int]						=			old_checkpoint[path]
																		lowercase_      :	int						=			old_tensor.shape[0] // 3

																		lowercase_      :	int						=			(-1, channels) if len(old_tensor.shape				) == 3 else (-1)

																		lowercase_      :	Optional[Any]						=			old_tensor.shape[0] // config["num_head_channels"] // 3

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

																		lowercase_      :	Optional[int]						=			query.reshape(snake_case_				)
																		lowercase_      :	int						=			key.reshape(snake_case_				)
																		lowercase_      :	str						=			value.reshape(snake_case_				)

						for path in paths:
												lowercase_      :	Any						=			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_      :	str						=			new_path.replace(replacement['''old'''] , replacement['''new''']				)

        # proj_attn.weight has to be converted from conv 1D to linear
												lowercase_      :	Dict						=			old_checkpoint[path["old"]]
												lowercase_      :	int						=			["proj_attn.weight"]
												lowercase_      :	Any						=			["proj_out.weight", "proj_in.weight"]
												if any(k in new_path for k in names				):
																		lowercase_      :	str						=			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_      :	List[str]						=			weight[:, :, 0]
												else:
																		lowercase_      :	Any						=			weight
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Any , __SCREAMING_SNAKE_CASE   :					Dict=0				):



						"""simple docstring"""





						lowercase_      :	str						=			[]
						for old_item in old_list:
												lowercase_      :	Optional[int]						=			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    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Dict , __SCREAMING_SNAKE_CASE   :					Optional[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_						(							__SCREAMING_SNAKE_CASE   :					str , __SCREAMING_SNAKE_CASE   :					Tuple=0				):



						"""simple docstring"""





						lowercase_      :	str						=			[]
						for old_item in old_list:
												mapping.append({'''old''': old_item, '''new''': old_item}				)

						return mapping
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Any , __SCREAMING_SNAKE_CASE   :					Union[str, Any]=0				):



						"""simple docstring"""





						lowercase_      :	Any						=			[]
						for old_item in old_list:
												lowercase_      :	List[str]						=			old_item.replace('''in_layers.0''' , '''norm1'''				)
												lowercase_      :	Union[str, Any]						=			new_item.replace('''in_layers.2''' , '''conv1'''				)

												lowercase_      :	int						=			new_item.replace('''out_layers.0''' , '''norm2'''				)
												lowercase_      :	int						=			new_item.replace('''out_layers.3''' , '''conv2'''				)

												lowercase_      :	List[str]						=			new_item.replace('''emb_layers.1''' , '''time_emb_proj'''				)
												lowercase_      :	Optional[int]						=			new_item.replace('''skip_connection''' , '''conv_shortcut'''				)

												lowercase_      :	List[Any]						=			shave_segments(snake_case_ , n_shave_prefix_segments=snake_case_				)

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

						return mapping
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Optional[Any] , __SCREAMING_SNAKE_CASE   :					Dict , __SCREAMING_SNAKE_CASE   :					Dict=None , __SCREAMING_SNAKE_CASE   :					Optional[Any]=False				):



						"""simple docstring"""





						lowercase_      :	Tuple						=			{}
						lowercase_      :	Dict						=			list(checkpoint.keys()				)

						lowercase_      :	List[str]						=			"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'''				):
																								lowercase_      :	Any						=			"model_ema." + "".join(key.split('''.'''				)[1:]				)
																								lowercase_      :	int						=			checkpoint.pop(snake_case_				)
						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:
																		lowercase_      :	int						=			checkpoint.pop(snake_case_				)

						lowercase_      :	str						=			{}

						lowercase_      :	Optional[Any]						=			unet_state_dict["time_embed.0.weight"]
						lowercase_      :	Any						=			unet_state_dict["time_embed.0.bias"]
						lowercase_      :	Optional[int]						=			unet_state_dict["time_embed.2.weight"]
						lowercase_      :	Optional[Any]						=			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_      :	Any						=			unet_state_dict["label_emb.0.0.weight"]
												lowercase_      :	List[str]						=			unet_state_dict["label_emb.0.0.bias"]
												lowercase_      :	int						=			unet_state_dict["label_emb.0.2.weight"]
												lowercase_      :	Union[str, Any]						=			unet_state_dict["label_emb.0.2.bias"]
						else:
												raise NotImplementedError(F'''Not implemented `class_embed_type`: {config['class_embed_type']}'''				)

						lowercase_      :	List[str]						=			unet_state_dict["input_blocks.0.0.weight"]
						lowercase_      :	Dict						=			unet_state_dict["input_blocks.0.0.bias"]

						lowercase_      :	Union[str, Any]						=			[v for v in unet_state_dict if v.startswith('''input_blocks.0.1'''				)]
						lowercase_      :	Tuple						=			renew_attention_paths(snake_case_				)
						lowercase_      :	Optional[int]						=			{"old": "input_blocks.0.1", "new": "transformer_in"}
						assign_to_checkpoint(snake_case_ , snake_case_ , snake_case_ , additional_replacements=[meta_path] , config=snake_case_				)

						lowercase_      :	str						=			unet_state_dict["out.0.weight"]
						lowercase_      :	Optional[int]						=			unet_state_dict["out.0.bias"]
						lowercase_      :	Dict						=			unet_state_dict["out.2.weight"]
						lowercase_      :	Any						=			unet_state_dict["out.2.bias"]

						# Retrieves the keys for the input blocks only
						lowercase_      :	Optional[int]						=			len({'''.'''.join(layer.split('''.'''				)[:2]				) for layer in unet_state_dict if '''input_blocks''' in layer}				)
						lowercase_      :	str						=			{
						    layer_id: [key for key in unet_state_dict if F'''input_blocks.{layer_id}''' in key]
						    for layer_id in range(snake_case_				)
						}

						# Retrieves the keys for the middle blocks only
						lowercase_      :	List[Any]						=			len({'''.'''.join(layer.split('''.'''				)[:2]				) for layer in unet_state_dict if '''middle_block''' in layer}				)
						lowercase_      :	Any						=			{
						    layer_id: [key for key in unet_state_dict if F'''middle_block.{layer_id}''' in key]
						    for layer_id in range(snake_case_				)
						}

						# Retrieves the keys for the output blocks only
						lowercase_      :	Tuple						=			len({'''.'''.join(layer.split('''.'''				)[:2]				) for layer in unet_state_dict if '''output_blocks''' in layer}				)
						lowercase_      :	Optional[Any]						=			{
						    layer_id: [key for key in unet_state_dict if F'''output_blocks.{layer_id}''' in key]
						    for layer_id in range(snake_case_				)
						}

						for i in range(1 , snake_case_				):
												lowercase_      :	Optional[int]						=			(i - 1) // (config["layers_per_block"] + 1)
												lowercase_      :	List[str]						=			(i - 1) % (config["layers_per_block"] + 1)

												lowercase_      :	str						=			[
												    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_      :	Optional[Any]						=			[key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]
												lowercase_      :	List[Any]						=			[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_      :	Any						=			unet_state_dict.pop(
																		    F'''input_blocks.{i}.op.weight'''				)
																		lowercase_      :	Tuple						=			unet_state_dict.pop(
																		    F'''input_blocks.{i}.op.bias'''				)

												lowercase_      :	List[str]						=			renew_resnet_paths(snake_case_				)
												lowercase_      :	Any						=			{"old": F'''input_blocks.{i}.0''', "new": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
												assign_to_checkpoint(
												    snake_case_ , snake_case_ , snake_case_ , additional_replacements=[meta_path] , config=snake_case_				)

												lowercase_      :	str						=			[key for key in resnets if "temopral_conv" in key]
												lowercase_      :	List[str]						=			renew_temp_conv_paths(snake_case_				)
												lowercase_      :	Optional[int]						=			{
												    "old": F'''input_blocks.{i}.0.temopral_conv''',
												    "new": F'''down_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
												}
												assign_to_checkpoint(
												    snake_case_ , snake_case_ , snake_case_ , additional_replacements=[meta_path] , config=snake_case_				)

												if len(snake_case_				):
																		lowercase_      :	Any						=			renew_attention_paths(snake_case_				)
																		lowercase_      :	List[Any]						=			{"old": F'''input_blocks.{i}.1''', "new": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
																		assign_to_checkpoint(
																		    snake_case_ , snake_case_ , snake_case_ , additional_replacements=[meta_path] , config=snake_case_				)

												if len(snake_case_				):
																		lowercase_      :	Any						=			renew_attention_paths(snake_case_				)
																		lowercase_      :	List[Any]						=			{
																		    "old": F'''input_blocks.{i}.2''',
																		    "new": F'''down_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
																		}
																		assign_to_checkpoint(
																		    snake_case_ , snake_case_ , snake_case_ , additional_replacements=[meta_path] , config=snake_case_				)

						lowercase_      :	Optional[Any]						=			middle_blocks[0]
						lowercase_      :	Optional[Any]						=			[key for key in resnet_a if "temopral_conv" in key]
						lowercase_      :	str						=			middle_blocks[1]
						lowercase_      :	Optional[int]						=			middle_blocks[2]
						lowercase_      :	List[str]						=			middle_blocks[3]
						lowercase_      :	Union[str, Any]						=			[key for key in resnet_a if "temopral_conv" in key]

						lowercase_      :	Optional[Any]						=			renew_resnet_paths(snake_case_				)
						lowercase_      :	List[Any]						=			{"old": "middle_block.0", "new": "mid_block.resnets.0"}
						assign_to_checkpoint(
						    snake_case_ , snake_case_ , snake_case_ , config=snake_case_ , additional_replacements=[meta_path]				)

						lowercase_      :	str						=			renew_temp_conv_paths(snake_case_				)
						lowercase_      :	Optional[int]						=			{"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
						assign_to_checkpoint(
						    snake_case_ , snake_case_ , snake_case_ , config=snake_case_ , additional_replacements=[meta_path]				)

						lowercase_      :	int						=			renew_resnet_paths(snake_case_				)
						lowercase_      :	List[Any]						=			{"old": "middle_block.3", "new": "mid_block.resnets.1"}
						assign_to_checkpoint(
						    snake_case_ , snake_case_ , snake_case_ , config=snake_case_ , additional_replacements=[meta_path]				)

						lowercase_      :	int						=			renew_temp_conv_paths(snake_case_				)
						lowercase_      :	List[str]						=			{"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
						assign_to_checkpoint(
						    snake_case_ , snake_case_ , snake_case_ , config=snake_case_ , additional_replacements=[meta_path]				)

						lowercase_      :	Any						=			renew_attention_paths(snake_case_				)
						lowercase_      :	int						=			{"old": "middle_block.1", "new": "mid_block.attentions.0"}
						assign_to_checkpoint(
						    snake_case_ , snake_case_ , snake_case_ , additional_replacements=[meta_path] , config=snake_case_				)

						lowercase_      :	Any						=			renew_attention_paths(snake_case_				)
						lowercase_      :	List[str]						=			{"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
						assign_to_checkpoint(
						    snake_case_ , snake_case_ , snake_case_ , additional_replacements=[meta_path] , config=snake_case_				)

						for i in range(snake_case_				):
												lowercase_      :	Any						=			i // (config["layers_per_block"] + 1)
												lowercase_      :	Union[str, Any]						=			i % (config["layers_per_block"] + 1)
												lowercase_      :	Optional[int]						=			[shave_segments(snake_case_ , 2				) for name in output_blocks[i]]
												lowercase_      :	Dict						=			{}

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

												if len(snake_case_				) > 1:
																		lowercase_      :	List[str]						=			[key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
																		lowercase_      :	List[str]						=			[key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]
																		lowercase_      :	Dict						=			[key for key in output_blocks[i] if F'''output_blocks.{i}.2''' in key]

																		lowercase_      :	List[Any]						=			renew_resnet_paths(snake_case_				)
																		lowercase_      :	int						=			renew_resnet_paths(snake_case_				)

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

																		lowercase_      :	List[str]						=			[key for key in resnets if "temopral_conv" in key]
																		lowercase_      :	Dict						=			renew_temp_conv_paths(snake_case_				)
																		lowercase_      :	Optional[int]						=			{
																		    "old": F'''output_blocks.{i}.0.temopral_conv''',
																		    "new": F'''up_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
																		}
																		assign_to_checkpoint(
																		    snake_case_ , snake_case_ , snake_case_ , additional_replacements=[meta_path] , config=snake_case_				)

																		lowercase_      :	Dict						=			{k: sorted(snake_case_				) for k, v in output_block_list.items()}
																		if ["conv.bias", "conv.weight"] in output_block_list.values():
																								lowercase_      :	str						=			list(output_block_list.values()				).index(['''conv.bias''', '''conv.weight''']				)
																								lowercase_      :	Dict						=			unet_state_dict[
																								    F'''output_blocks.{i}.{index}.conv.weight'''
																								]
																								lowercase_      :	Optional[Any]						=			unet_state_dict[
																								    F'''output_blocks.{i}.{index}.conv.bias'''
																								]

																								# Clear attentions as they have been attributed above.
																								if len(snake_case_				) == 2:
																														lowercase_      :	str						=			[]

																		if len(snake_case_				):
																								lowercase_      :	Optional[int]						=			renew_attention_paths(snake_case_				)
																								lowercase_      :	Any						=			{
																								    "old": F'''output_blocks.{i}.1''',
																								    "new": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
																								}
																								assign_to_checkpoint(
																								    snake_case_ , snake_case_ , snake_case_ , additional_replacements=[meta_path] , config=snake_case_				)

																		if len(snake_case_				):
																								lowercase_      :	str						=			renew_attention_paths(snake_case_				)
																								lowercase_      :	List[Any]						=			{
																								    "old": F'''output_blocks.{i}.2''',
																								    "new": F'''up_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
																								}
																								assign_to_checkpoint(
																								    snake_case_ , snake_case_ , snake_case_ , additional_replacements=[meta_path] , config=snake_case_				)
												else:
																		lowercase_      :	Tuple						=			renew_resnet_paths(snake_case_ , n_shave_prefix_segments=1				)
																		for path in resnet_0_paths:
																								lowercase_      :	str						=			".".join(['''output_blocks''', str(snake_case_				), path['''old''']]				)
																								lowercase_      :	int						=			".".join(['''up_blocks''', str(snake_case_				), '''resnets''', str(snake_case_				), path['''new''']]				)
																								lowercase_      :	Dict						=			unet_state_dict[old_path]

																		lowercase_      :	Tuple						=			[l for l in output_block_layers if "temopral_conv" in l]
																		for path in temopral_conv_paths:
																								lowercase_      :	Tuple						=			path.split('''temopral_conv.'''				)[-1]
																								lowercase_      :	str						=			".".join(['''output_blocks''', str(snake_case_				), str(snake_case_				), '''temopral_conv''', pruned_path]				)
																								lowercase_      :	Dict						=			".".join(['''up_blocks''', str(snake_case_				), '''temp_convs''', str(snake_case_				), pruned_path]				)
																								lowercase_      :	Any						=			unet_state_dict[old_path]

						return new_checkpoint


if __name__ == "__main__":
							_lowercase					:    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("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
							_lowercase					:    str       				=			parser.parse_args()

							_lowercase					:    str       				=			torch.load(args.checkpoint_path, map_location="cpu")
							_lowercase					:    Tuple       				=			UNetaDConditionModel()

							_lowercase					:    List[Any]       				=			convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

							_lowercase					:    int       				=			set(unet.state_dict().keys()) - set(converted_ckpt.keys())
							_lowercase					:    Tuple       				=			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 --





def      __UpperCAmelCase    (  snake_case_							:		str				):



	'''simple docstring'''



	UpperCAmelCase:					Tuple 						=     str(snake_case_				).strip()
	if not bin_string:
		raise ValueError("Empty string was passed to the function"				)
	UpperCAmelCase:					List[Any] 						=     bin_string[0] == "-"
	if is_negative:
		UpperCAmelCase:					Optional[int] 						=     bin_string[1:]
	if not all(char in "01" for char in bin_string				):
		raise ValueError("Non-binary value was passed to the function"				)
	UpperCAmelCase:					List[str] 						=     0
	for char in bin_string:
		UpperCAmelCase:					Union[str, Any] 						=     2 * decimal_number + int(snake_case_				)
	return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
	from doctest import testmod

	testmod()
