



import argparse

import torch

from diffusers import UNetaDConditionModel





def 						a__     (						_UpperCamelCase     :	Tuple	,_UpperCamelCase     :	str	,_UpperCamelCase     :	int	,_UpperCamelCase     :	Tuple=None	,_UpperCamelCase     :	int=None	,_UpperCamelCase     :	Union[str, 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():
									__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(_UpperCamelCase							)
									__lowerCamelCase  							=      key.reshape(_UpperCamelCase							)
									__lowerCamelCase  							=      value.reshape(_UpperCamelCase							)

			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 						a__     (						_UpperCamelCase     :	Any	,_UpperCamelCase     :	Tuple=0							):
			__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 						a__     (						_UpperCamelCase     :	Dict	,_UpperCamelCase     :	Optional[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     :	Dict	,_UpperCamelCase     :	Optional[int]=0							):
			__lowerCamelCase  							=      []
			for old_item in old_list:
						mapping.append({'''old''': old_item, '''new''': old_item}							)

			return mapping





def 						a__     (						_UpperCamelCase     :	Optional[int]	,_UpperCamelCase     :	Optional[int]=0							):
			__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(_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     :	List[str]	,_UpperCamelCase     :	List[Any]	,_UpperCamelCase     :	int=None	,_UpperCamelCase     :	str=False							):
			__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							) > 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'''							):
												__lowerCamelCase  							=      '''model_ema.''' + ''''''.join(key.split('''.'''							)[1:]							)
												__lowerCamelCase  							=      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:
									__lowerCamelCase  							=      checkpoint.pop(_UpperCamelCase							)

			__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(_UpperCamelCase							)
			__lowerCamelCase  							=      {'''old''': '''input_blocks.0.1''', '''new''': '''transformer_in'''}
			assign_to_checkpoint(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,additional_replacements=[meta_path]	,config=_UpperCamelCase							)

			__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(_UpperCamelCase							)
			}

			# 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(_UpperCamelCase							)
			}

			# 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(_UpperCamelCase							)
			}

			for i in range(1	,_UpperCamelCase							):
						__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(_UpperCamelCase							)
						__lowerCamelCase  							=      {'''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							)

						__lowerCamelCase  							=      [key for key in resnets if '''temopral_conv''' in key]
						__lowerCamelCase  							=      renew_temp_conv_paths(_UpperCamelCase							)
						__lowerCamelCase  							=      {
						    '''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							):
									__lowerCamelCase  							=      renew_attention_paths(_UpperCamelCase							)
									__lowerCamelCase  							=      {'''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							):
									__lowerCamelCase  							=      renew_attention_paths(_UpperCamelCase							)
									__lowerCamelCase  							=      {
									    '''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							)

			__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(_UpperCamelCase							)
			__lowerCamelCase  							=      {'''old''': '''middle_block.0''', '''new''': '''mid_block.resnets.0'''}
			assign_to_checkpoint(
			    _UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase	,config=_UpperCamelCase	,additional_replacements=[meta_path]							)

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

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

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

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

			__lowerCamelCase  							=      renew_attention_paths(_UpperCamelCase							)
			__lowerCamelCase  							=      {'''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							):
						__lowerCamelCase  							=      i // (config['''layers_per_block'''] + 1)
						__lowerCamelCase  							=      i % (config['''layers_per_block'''] + 1)
						__lowerCamelCase  							=      [shave_segments(_UpperCamelCase	,2							) for name in output_blocks[i]]
						__lowerCamelCase  							=      {}

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

						if len(_UpperCamelCase							) > 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(_UpperCamelCase							)
									__lowerCamelCase  							=      renew_resnet_paths(_UpperCamelCase							)

									__lowerCamelCase  							=      {'''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							)

									__lowerCamelCase  							=      [key for key in resnets if '''temopral_conv''' in key]
									__lowerCamelCase  							=      renew_temp_conv_paths(_UpperCamelCase							)
									__lowerCamelCase  							=      {
									    '''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							)

									__lowerCamelCase  							=      {k: sorted(_UpperCamelCase							) 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(_UpperCamelCase							) == 2:
															__lowerCamelCase  							=      []

									if len(_UpperCamelCase							):
												__lowerCamelCase  							=      renew_attention_paths(_UpperCamelCase							)
												__lowerCamelCase  							=      {
												    '''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							):
												__lowerCamelCase  							=      renew_attention_paths(_UpperCamelCase							)
												__lowerCamelCase  							=      {
												    '''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:
									__lowerCamelCase  							=      renew_resnet_paths(_UpperCamelCase	,n_shave_prefix_segments=1							)
									for path in resnet_0_paths:
												__lowerCamelCase  							=      '''.'''.join(['''output_blocks''', str(_UpperCamelCase							), path['''old''']]							)
												__lowerCamelCase  							=      '''.'''.join(['''up_blocks''', str(_UpperCamelCase							), '''resnets''', str(_UpperCamelCase							), 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(_UpperCamelCase							), str(_UpperCamelCase							), '''temopral_conv''', pruned_path]							)
												__lowerCamelCase  							=      '''.'''.join(['''up_blocks''', str(_UpperCamelCase							), '''temp_convs''', str(_UpperCamelCase							), pruned_path]							)
												__lowerCamelCase  							=      unet_state_dict[old_path]

			return new_checkpoint


if __name__ == "__main__":
		a_								=   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_								=   parser.parse_args()

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

		a_								=   convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

		a_								=   set(unet.state_dict().keys()) - set(converted_ckpt.keys())
		a_								=   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 						a__     (						_UpperCamelCase     :	int							):
   if not isinstance(_UpperCamelCase	,_UpperCamelCase							):
      __lowerCamelCase  							=      (
          '''operation can not be conducted on a object of type '''
          F"""{type(_UpperCamelCase							).__name__}"""
      )
      raise TypeError(_UpperCamelCase							)
   __lowerCamelCase  							=      ''''''
   for _ in range(0	,32							):
      bit_string += str(number % 2							)
      __lowerCamelCase  							=      number >> 1
   return bit_string





def 						a__     (						_UpperCamelCase     :	int							):
   if number < 0:
      raise ValueError('''the value of input must be positive'''							)
   elif isinstance(_UpperCamelCase	,_UpperCamelCase							):
      raise TypeError('''Input value must be a \'int\' type'''							)
   elif isinstance(_UpperCamelCase	,_UpperCamelCase							):
      raise TypeError('''\'<\' not supported between instances of \'str\' and \'int\''''							)
   __lowerCamelCase  							=      0
   # iterator over [1 to 32],since we are dealing with 32 bit integer
   for _ in range(1	,33							):
      # left shift the bits by unity
      __lowerCamelCase  							=      result << 1
      # get the end bit
      __lowerCamelCase  							=      number % 2
      # right shift the bits by unity
      __lowerCamelCase  							=      number >> 1
      # add that bit to our ans
      __lowerCamelCase  							=      result | end_bit
   return get_reverse_bit_string(_UpperCamelCase							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()






