


"""simple docstring"""



import argparse

import torch

from diffusers import UNetaDConditionModel
def     UpperCAmelCase    (					_lowercase   :      Optional[int] ,      _lowercase   :      int ,      _lowercase   :      Optional[int] ,      _lowercase   :      Optional[int]=None ,      _lowercase   :      Optional[Any]=None ,      _lowercase   :      int=None  )	->   Dict:


	"""simple docstring"""




	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     UpperCAmelCase    (					_lowercase   :      int ,      _lowercase   :      str=0  )	->   Tuple:


	"""simple docstring"""




	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     UpperCAmelCase    (					_lowercase   :      Optional[Any] ,      _lowercase   :      Union[str, Any]=1  )	->   Optional[Any]:


	"""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     UpperCAmelCase    (					_lowercase   :      Any ,      _lowercase   :      str=0  )	->   str:


	"""simple docstring"""




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

	return mapping
def     UpperCAmelCase    (					_lowercase   :      str ,      _lowercase   :      str=0  )	->   int:


	"""simple docstring"""




	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     UpperCAmelCase    (					_lowercase   :      Optional[int] ,      _lowercase   :      Dict ,      _lowercase   :      Optional[int]=None ,      _lowercase   :      Optional[int]=False  )	->   Any:


	"""simple docstring"""




	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_0_0 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  ) > 1_0_0:
			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__":
		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 --



"""simple docstring"""



def     UpperCAmelCase    (					_lowercase   :      list[int]  )	->   int:


	"""simple docstring"""




	lowerCAmelCase_			       = len(_lowercase  )
	if n in (0, 1):
		return 0
	return sum(query * (n - i - 1) for i, query in enumerate(sorted(_lowercase  )  )  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()