




"""simple docstring"""





import argparse

import torch

from diffusers import UNetaDConditionModel

def 						lowerCAmelCase							(       lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase=None					,	lowerCamelCase=None					,	lowerCamelCase=None						):
						'''simple docstring'''


						assert isinstance(__lowerCamelCase					,	__lowerCamelCase						), "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():
																		snake_case__				:    Union[str, Any]								=    old_checkpoint[path]
																		snake_case__				:    Union[str, Any]								=    old_tensor.shape[0] // 3

																		snake_case__				:    Dict								=    (-1, channels) if len(old_tensor.shape						) == 3 else (-1)

																		snake_case__				:    List[Any]								=    old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

																		snake_case__				:    List[Any]								=    query.reshape(__lowerCamelCase						)
																		snake_case__				:    List[Any]								=    key.reshape(__lowerCamelCase						)
																		snake_case__				:    int								=    value.reshape(__lowerCamelCase						)

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

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

def 						lowerCAmelCase							(       lowerCamelCase					,	lowerCamelCase=0						):
						'''simple docstring'''


						snake_case__				:    Union[str, Any]								=    []
						for old_item in old_list:
												snake_case__				:    List[str]								=    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							(       lowerCamelCase					,	lowerCamelCase=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 						lowerCAmelCase							(       lowerCamelCase					,	lowerCamelCase=0						):
						'''simple docstring'''


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

						return mapping

def 						lowerCAmelCase							(       lowerCamelCase					,	lowerCamelCase=0						):
						'''simple docstring'''


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

												snake_case__				:    Optional[Any]								=    new_item.replace('''out_layers.0'''					,	'''norm2'''						)
												snake_case__				:    Any								=    new_item.replace('''out_layers.3'''					,	'''conv2'''						)

												snake_case__				:    Any								=    new_item.replace('''emb_layers.1'''					,	'''time_emb_proj'''						)
												snake_case__				:    Optional[Any]								=    new_item.replace('''skip_connection'''					,	'''conv_shortcut'''						)

												snake_case__				:    str								=    shave_segments(__lowerCamelCase					,	n_shave_prefix_segments=__lowerCamelCase						)

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

						return mapping

def 						lowerCAmelCase							(       lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase=None					,	lowerCamelCase=False						):
						'''simple docstring'''


						snake_case__				:    List[Any]								=    {}
						snake_case__				:    Dict								=    list(checkpoint.keys()						)

						snake_case__				:    Any								=    '''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'''						):
																								snake_case__				:    Dict								=    '''model_ema.''' + ''''''.join(key.split('''.'''						)[1:]						)
																								snake_case__				:    Union[str, Any]								=    checkpoint.pop(__lowerCamelCase						)
						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:
																		snake_case__				:    List[Any]								=    checkpoint.pop(__lowerCamelCase						)

						snake_case__				:    Optional[int]								=    {}

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

						snake_case__				:    List[str]								=    unet_state_dict['''input_blocks.0.0.weight''']
						snake_case__				:    Tuple								=    unet_state_dict['''input_blocks.0.0.bias''']

						snake_case__				:    Optional[int]								=    [v for v in unet_state_dict if v.startswith('''input_blocks.0.1'''						)]
						snake_case__				:    Tuple								=    renew_attention_paths(__lowerCamelCase						)
						snake_case__				:    List[str]								=    {'''old''': '''input_blocks.0.1''', '''new''': '''transformer_in'''}
						assign_to_checkpoint(__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase					,	additional_replacements=[meta_path]					,	config=__lowerCamelCase						)

						snake_case__				:    Any								=    unet_state_dict['''out.0.weight''']
						snake_case__				:    str								=    unet_state_dict['''out.0.bias''']
						snake_case__				:    Union[str, Any]								=    unet_state_dict['''out.2.weight''']
						snake_case__				:    Optional[int]								=    unet_state_dict['''out.2.bias''']

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

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

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

						for i in range(1					,	__lowerCamelCase						):
												snake_case__				:    Optional[Any]								=    (i - 1) // (config['''layers_per_block'''] + 1)
												snake_case__				:    int								=    (i - 1) % (config['''layers_per_block'''] + 1)

												snake_case__				:    Optional[Any]								=    [
												    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
												]
												snake_case__				:    Optional[int]								=    [key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]
												snake_case__				:    Dict								=    [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:
																		snake_case__				:    Optional[Any]								=    unet_state_dict.pop(
																		    f'''input_blocks.{i}.op.weight'''						)
																		snake_case__				:    str								=    unet_state_dict.pop(
																		    f'''input_blocks.{i}.op.bias'''						)

												snake_case__				:    Tuple								=    renew_resnet_paths(__lowerCamelCase						)
												snake_case__				:    Dict								=    {'''old''': f'''input_blocks.{i}.0''', '''new''': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
												assign_to_checkpoint(
												    __lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase					,	additional_replacements=[meta_path]					,	config=__lowerCamelCase						)

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

												if len(__lowerCamelCase						):
																		snake_case__				:    Dict								=    renew_attention_paths(__lowerCamelCase						)
																		snake_case__				:    List[str]								=    {'''old''': f'''input_blocks.{i}.1''', '''new''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
																		assign_to_checkpoint(
																		    __lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase					,	additional_replacements=[meta_path]					,	config=__lowerCamelCase						)

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

						snake_case__				:    Tuple								=    middle_blocks[0]
						snake_case__				:    str								=    [key for key in resnet_a if '''temopral_conv''' in key]
						snake_case__				:    Dict								=    middle_blocks[1]
						snake_case__				:    Optional[int]								=    middle_blocks[2]
						snake_case__				:    Dict								=    middle_blocks[3]
						snake_case__				:    int								=    [key for key in resnet_a if '''temopral_conv''' in key]

						snake_case__				:    Union[str, Any]								=    renew_resnet_paths(__lowerCamelCase						)
						snake_case__				:    Optional[int]								=    {'''old''': '''middle_block.0''', '''new''': '''mid_block.resnets.0'''}
						assign_to_checkpoint(
						    __lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase					,	config=__lowerCamelCase					,	additional_replacements=[meta_path]						)

						snake_case__				:    int								=    renew_temp_conv_paths(__lowerCamelCase						)
						snake_case__				:    Union[str, Any]								=    {'''old''': '''middle_block.0.temopral_conv''', '''new''': '''mid_block.temp_convs.0'''}
						assign_to_checkpoint(
						    __lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase					,	config=__lowerCamelCase					,	additional_replacements=[meta_path]						)

						snake_case__				:    Dict								=    renew_resnet_paths(__lowerCamelCase						)
						snake_case__				:    Optional[int]								=    {'''old''': '''middle_block.3''', '''new''': '''mid_block.resnets.1'''}
						assign_to_checkpoint(
						    __lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase					,	config=__lowerCamelCase					,	additional_replacements=[meta_path]						)

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

						snake_case__				:    int								=    renew_attention_paths(__lowerCamelCase						)
						snake_case__				:    Union[str, Any]								=    {'''old''': '''middle_block.1''', '''new''': '''mid_block.attentions.0'''}
						assign_to_checkpoint(
						    __lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase					,	additional_replacements=[meta_path]					,	config=__lowerCamelCase						)

						snake_case__				:    Dict								=    renew_attention_paths(__lowerCamelCase						)
						snake_case__				:    str								=    {'''old''': '''middle_block.2''', '''new''': '''mid_block.temp_attentions.0'''}
						assign_to_checkpoint(
						    __lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase					,	additional_replacements=[meta_path]					,	config=__lowerCamelCase						)

						for i in range(__lowerCamelCase						):
												snake_case__				:    Dict								=    i // (config['''layers_per_block'''] + 1)
												snake_case__				:    Optional[Any]								=    i % (config['''layers_per_block'''] + 1)
												snake_case__				:    Dict								=    [shave_segments(__lowerCamelCase					,	2						) for name in output_blocks[i]]
												snake_case__				:    List[Any]								=    {}

												for layer in output_block_layers:
																		snake_case__	,		snake_case__				:    Any								=    layer.split('''.'''						)[0], shave_segments(__lowerCamelCase					,	1						)
																		if layer_id in output_block_list:
																								output_block_list[layer_id].append(__lowerCamelCase						)
																		else:
																								snake_case__				:    Optional[int]								=    [layer_name]

												if len(__lowerCamelCase						) > 1:
																		snake_case__				:    Tuple								=    [key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
																		snake_case__				:    str								=    [key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]
																		snake_case__				:    str								=    [key for key in output_blocks[i] if f'''output_blocks.{i}.2''' in key]

																		snake_case__				:    List[Any]								=    renew_resnet_paths(__lowerCamelCase						)
																		snake_case__				:    str								=    renew_resnet_paths(__lowerCamelCase						)

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

																		snake_case__				:    Tuple								=    [key for key in resnets if '''temopral_conv''' in key]
																		snake_case__				:    Optional[int]								=    renew_temp_conv_paths(__lowerCamelCase						)
																		snake_case__				:    List[Any]								=    {
																		    '''old''': f'''output_blocks.{i}.0.temopral_conv''',
																		    '''new''': f'''up_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
																		}
																		assign_to_checkpoint(
																		    __lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase					,	additional_replacements=[meta_path]					,	config=__lowerCamelCase						)

																		snake_case__				:    Union[str, Any]								=    {k: sorted(__lowerCamelCase						) for k, v in output_block_list.items()}
																		if ["conv.bias", "conv.weight"] in output_block_list.values():
																								snake_case__				:    List[str]								=    list(output_block_list.values()						).index(['''conv.bias''', '''conv.weight''']						)
																								snake_case__				:    Union[str, Any]								=    unet_state_dict[
																								    f'''output_blocks.{i}.{index}.conv.weight'''
																								]
																								snake_case__				:    List[str]								=    unet_state_dict[
																								    f'''output_blocks.{i}.{index}.conv.bias'''
																								]

																								# Clear attentions as they have been attributed above.
																								if len(__lowerCamelCase						) == 2:
																														snake_case__				:    Dict								=    []

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

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

																		snake_case__				:    int								=    [l for l in output_block_layers if '''temopral_conv''' in l]
																		for path in temopral_conv_paths:
																								snake_case__				:    List[str]								=    path.split('''temopral_conv.'''						)[-1]
																								snake_case__				:    str								=    '''.'''.join(['''output_blocks''', str(__lowerCamelCase						), str(__lowerCamelCase						), '''temopral_conv''', pruned_path]						)
																								snake_case__				:    int								=    '''.'''.join(['''up_blocks''', str(__lowerCamelCase						), '''temp_convs''', str(__lowerCamelCase						), pruned_path]						)
																								snake_case__				:    List[str]								=    unet_state_dict[old_path]

						return new_checkpoint


if __name__ == "__main__":
							_lowercase							=			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							=			parser.parse_args()

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

							_lowercase							=			convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

							_lowercase							=			set(unet.state_dict().keys()) - set(converted_ckpt.keys())
							_lowercase							=			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 							lowerCAmelCase(	__lowerCamelCase = 10**6						):

					if n <= 0:
										raise ValueError('Please enter an integer greater than 0'						)

					__a							=   list(range(n + 1						)						)
					for number in range(2      ,				n + 1						):
										if phi[number] == number:
															phi[number] -= 1
															for multiple in range(number * 2      ,				n + 1      ,				__lowerCamelCase						):
																				__a							=   (phi[multiple] // number) * (number - 1)

					__a							=   1
					for number in range(1      ,				n + 1						):
										if (answer / phi[answer]) < (number / phi[number]):
															__a							=   number

					return answer


if __name__ == "__main__":
	print(solution())




