






"""simple docstring"""



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



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

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

				lowercase_ : Union[str, Any]							       =   new_item.replace('''emb_layers.1'''		,					'''time_emb_proj'''	)
				lowercase_ : Dict							       =   new_item.replace('''skip_connection'''		,					'''conv_shortcut'''	)

				lowercase_ : Dict							       =   shave_segments(lowerCAmelCase_		,					n_shave_prefix_segments=lowerCAmelCase_	)

				mapping.append({'''old''': old_item, '''new''': new_item}	)

		return mapping



def 						lowercase    (				__snake_case				: int		,					__snake_case				: int=0	):
		lowercase_ : Optional[int]							       =   []
		for old_item in old_list:
				lowercase_ : Any							       =   old_item

				lowercase_ : Tuple							       =   new_item.replace('''norm.weight'''		,					'''group_norm.weight'''	)
				lowercase_ : Any							       =   new_item.replace('''norm.bias'''		,					'''group_norm.bias'''	)

				lowercase_ : Dict							       =   new_item.replace('''proj_out.weight'''		,					'''proj_attn.weight'''	)
				lowercase_ : List[Any]							       =   new_item.replace('''proj_out.bias'''		,					'''proj_attn.bias'''	)

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

				mapping.append({'''old''': old_item, '''new''': new_item}	)

		return mapping



def 						lowercase    (				__snake_case				: Any		,					__snake_case				: Union[str, Any]		,					__snake_case				: Optional[int]		,					__snake_case				: List[Any]=None		,					__snake_case				: Dict=None		,					__snake_case				: List[str]=None	):
		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():
						lowercase_ : Union[str, Any]							       =   old_checkpoint[path]
						lowercase_ : Optional[int]							       =   old_tensor.shape[0] // 3

						lowercase_ : Optional[Any]							       =   (-1, channels) if len(old_tensor.shape	) == 3 else (-1)

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

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

						lowercase_ : Any							       =   query.reshape(lowerCAmelCase_	)
						lowercase_ : Any							       =   key.reshape(lowerCAmelCase_	)
						lowercase_ : Union[str, Any]							       =   value.reshape(lowerCAmelCase_	)

		for path in paths:
				lowercase_ : Tuple							       =   path["""new"""]

				# These have already been assigned
				if attention_paths_to_split is not None and new_path in attention_paths_to_split:
						continue

				# Global renaming happens here
				lowercase_ : Any							       =   new_path.replace('''middle_block.0'''		,					'''mid_block.resnets.0'''	)
				lowercase_ : int							       =   new_path.replace('''middle_block.1'''		,					'''mid_block.attentions.0'''	)
				lowercase_ : int							       =   new_path.replace('''middle_block.2'''		,					'''mid_block.resnets.1'''	)

				if additional_replacements is not None:
						for replacement in additional_replacements:
								lowercase_ : Any							       =   new_path.replace(replacement['''old''']		,					replacement['''new''']	)

        # proj_attn.weight has to be converted from conv 1D to linear
				if "proj_attn.weight" in new_path:
						lowercase_ : str							       =   old_checkpoint[path["""old"""]][:, :, 0]
				else:
						lowercase_ : Optional[int]							       =   old_checkpoint[path["""old"""]]



def 						lowercase    (				__snake_case				: int		,					__snake_case				: Union[str, Any]	):
		lowercase_ : Union[str, Any]							       =   {}

		lowercase_ : Tuple							       =   checkpoint["""time_embed.0.weight"""]
		lowercase_ : List[str]							       =   checkpoint["""time_embed.0.bias"""]
		lowercase_ : Optional[int]							       =   checkpoint["""time_embed.2.weight"""]
		lowercase_ : Union[str, Any]							       =   checkpoint["""time_embed.2.bias"""]

		lowercase_ : Any							       =   checkpoint["""input_blocks.0.0.weight"""]
		lowercase_ : int							       =   checkpoint["""input_blocks.0.0.bias"""]

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

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

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

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

		for i in range(1		,					lowerCAmelCase_	):
				lowercase_ : Any							       =   (i - 1) // (config["""num_res_blocks"""] + 1)
				lowercase_ : Any							       =   (i - 1) % (config["""num_res_blocks"""] + 1)

				lowercase_ : Any							       =   [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
				lowercase_ : Any							       =   [key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]

				if F'''input_blocks.{i}.0.op.weight''' in checkpoint:
						lowercase_ : Dict							       =   checkpoint[
						    F'''input_blocks.{i}.0.op.weight'''
						]
						lowercase_ : Tuple							       =   checkpoint[
						    F'''input_blocks.{i}.0.op.bias'''
						]
						continue

				lowercase_ : Optional[Any]							       =   renew_resnet_paths(lowerCAmelCase_	)
				lowercase_ : Optional[Any]							       =   {"""old""": F'''input_blocks.{i}.0''', """new""": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
				lowercase_ : Optional[int]							       =   {"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
				assign_to_checkpoint(
				    lowerCAmelCase_		,					lowerCAmelCase_		,					lowerCAmelCase_		,					additional_replacements=[meta_path, resnet_op]		,					config=lowerCAmelCase_	)

				if len(lowerCAmelCase_	):
						lowercase_ : Optional[Any]							       =   renew_attention_paths(lowerCAmelCase_	)
						lowercase_ : Optional[Any]							       =   {
						    """old""": F'''input_blocks.{i}.1''',
						    """new""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
						}
						lowercase_ : int							       =   {
						    F'''input_blocks.{i}.1.qkv.bias''': {
						        """key""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
						        """query""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
						        """value""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
						    },
						    F'''input_blocks.{i}.1.qkv.weight''': {
						        """key""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
						        """query""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
						        """value""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
						    },
						}
						assign_to_checkpoint(
						    lowerCAmelCase_		,					lowerCAmelCase_		,					lowerCAmelCase_		,					additional_replacements=[meta_path]		,					attention_paths_to_split=lowerCAmelCase_		,					config=lowerCAmelCase_		,					)

		lowercase_ : int							       =   middle_blocks[0]
		lowercase_ : Dict							       =   middle_blocks[1]
		lowercase_ : Optional[int]							       =   middle_blocks[2]

		lowercase_ : Tuple							       =   renew_resnet_paths(lowerCAmelCase_	)
		assign_to_checkpoint(lowerCAmelCase_		,					lowerCAmelCase_		,					lowerCAmelCase_		,					config=lowerCAmelCase_	)

		lowercase_ : int							       =   renew_resnet_paths(lowerCAmelCase_	)
		assign_to_checkpoint(lowerCAmelCase_		,					lowerCAmelCase_		,					lowerCAmelCase_		,					config=lowerCAmelCase_	)

		lowercase_ : str							       =   renew_attention_paths(lowerCAmelCase_	)
		lowercase_ : List[Any]							       =   {
		    """middle_block.1.qkv.bias""": {
		        """key""": """mid_block.attentions.0.key.bias""",
		        """query""": """mid_block.attentions.0.query.bias""",
		        """value""": """mid_block.attentions.0.value.bias""",
		    },
		    """middle_block.1.qkv.weight""": {
		        """key""": """mid_block.attentions.0.key.weight""",
		        """query""": """mid_block.attentions.0.query.weight""",
		        """value""": """mid_block.attentions.0.value.weight""",
		    },
		}
		assign_to_checkpoint(
		    lowerCAmelCase_		,					lowerCAmelCase_		,					lowerCAmelCase_		,					attention_paths_to_split=lowerCAmelCase_		,					config=lowerCAmelCase_	)

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

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

				if len(lowerCAmelCase_	) > 1:
						lowercase_ : Tuple							       =   [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
						lowercase_ : Any							       =   [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

						lowercase_ : Dict							       =   renew_resnet_paths(lowerCAmelCase_	)
						lowercase_ : Union[str, Any]							       =   renew_resnet_paths(lowerCAmelCase_	)

						lowercase_ : Dict							       =   {"""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_	)

						if ["conv.weight", "conv.bias"] in output_block_list.values():
								lowercase_ : List[Any]							       =   list(output_block_list.values()	).index(['''conv.weight''', '''conv.bias''']	)
								lowercase_ : Union[str, Any]							       =   checkpoint[
								    F'''output_blocks.{i}.{index}.conv.weight'''
								]
								lowercase_ : str							       =   checkpoint[
								    F'''output_blocks.{i}.{index}.conv.bias'''
								]

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

						if len(lowerCAmelCase_	):
								lowercase_ : Union[str, Any]							       =   renew_attention_paths(lowerCAmelCase_	)
								lowercase_ : Tuple							       =   {
								    """old""": F'''output_blocks.{i}.1''',
								    """new""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
								}
								lowercase_ : Dict							       =   {
								    F'''output_blocks.{i}.1.qkv.bias''': {
								        """key""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
								        """query""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
								        """value""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
								    },
								    F'''output_blocks.{i}.1.qkv.weight''': {
								        """key""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
								        """query""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
								        """value""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
								    },
								}
								assign_to_checkpoint(
								    lowerCAmelCase_		,					lowerCAmelCase_		,					lowerCAmelCase_		,					additional_replacements=[meta_path]		,					attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions	) else None		,					config=lowerCAmelCase_		,					)
				else:
						lowercase_ : List[Any]							       =   renew_resnet_paths(lowerCAmelCase_		,					n_shave_prefix_segments=1	)
						for path in resnet_0_paths:
								lowercase_ : Optional[Any]							       =   """.""".join(['''output_blocks''', str(lowerCAmelCase_	), path['''old''']]	)
								lowercase_ : str							       =   """.""".join(['''up_blocks''', str(lowerCAmelCase_	), '''resnets''', str(lowerCAmelCase_	), path['''new''']]	)

								lowercase_ : Optional[Any]							       =   checkpoint[old_path]

		return new_checkpoint


if __name__ == "__main__":
						__A		:       Tuple  				=		argparse.ArgumentParser()

						parser.add_argument(
						    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
						)

						parser.add_argument(
						    '''--config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help='''The config json file corresponding to the architecture.''',
						)

						parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

						__A		:       List[str]  				=		parser.parse_args()

						__A		:       Any  				=		torch.load(args.checkpoint_path)

						with open(args.config_file) as f:
												__A		:       Union[str, Any]  				=		json.loads(f.read())

						__A		:       List[Any]  				=		convert_ldm_checkpoint(checkpoint, config)

						if "ldm" in config:
												del config["ldm"]

						__A		:       List[str]  				=		UNetaDModel(**config)
						model.load_state_dict(converted_checkpoint)

						try:
												__A		:       Union[str, Any]  				=		DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
												__A		:       str  				=		VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

												__A		:       Any  				=		LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
												pipe.save_pretrained(args.dump_path)
						except:  # noqa: E722
												model.save_pretrained(args.dump_path)








'''simple docstring'''






class 							lowercase       :


 """simple docstring"""


 def __init__(							self							)		->   List[str]:
        _UpperCAmelCase  :						int			     = 0
        _UpperCAmelCase  :						Union[str, Any]			     = 0
        _UpperCAmelCase  :						Optional[int]			     = {}







 def _snake_case (							self  ,a_							)		->   Optional[Any]:
        if vertex not in self.adjacency:
               _UpperCAmelCase  :						int			     = {}
               self.num_vertices += 1







 def _snake_case (							self  ,a_  ,a_  ,a_							)		->   int:
        self.add_vertex(a_							)
        self.add_vertex(a_							)

        if head == tail:
               return

        _UpperCAmelCase  :						List[Any]			     = weight
        _UpperCAmelCase  :						Dict			     = weight







 def _snake_case (							self							)		->   Dict:
        _UpperCAmelCase  :						Optional[int]			     = self.get_edges()
        for edge in edges:
               _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						Dict			     = edge
               edges.remove((tail, head, weight)							)
        for i in range(len(a_							)							):
               _UpperCAmelCase  :						str			     = list(edges[i]							)

        edges.sort(key=lambda a_							: e[2]							)
        for i in range(len(a_							) - 1							):
               if edges[i][2] >= edges[i + 1][2]:
                      _UpperCAmelCase  :						Optional[Any]			     = edges[i][2] + 1
        for edge in edges:
               _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						Union[str, Any]			     = edge
               _UpperCAmelCase  :						str			     = weight
               _UpperCAmelCase  :						List[str]			     = weight







 def __str__(							self							)		->   Any:
        _UpperCAmelCase  :						List[Any]			     = """"""
        for tail in self.adjacency:
               for head in self.adjacency[tail]:
                      _UpperCAmelCase  :						List[str]			     = self.adjacency[head][tail]
                      string += f'''{head} -> {tail} == {weight}\n'''
        return string.rstrip("""\n"""							)







 def _snake_case (							self							)		->   str:
        _UpperCAmelCase  :						int			     = []
        for tail in self.adjacency:
               for head in self.adjacency[tail]:
                      output.append((tail, head, self.adjacency[head][tail])							)
        return output







 def _snake_case (							self							)		->   Optional[int]:
        return self.adjacency.keys()







 @staticmethod
 def _snake_case (							a_=None  ,a_=None							)		->   Tuple:
        _UpperCAmelCase  :						List[Any]			     = Graph()
        if vertices is None:
               _UpperCAmelCase  :						List[str]			     = []
        if edges is None:
               _UpperCAmelCase  :						Optional[Any]			     = []
        for vertex in vertices:
               g.add_vertex(a_							)
        for edge in edges:
               g.add_edge(*a_							)
        return g



 class 							lowercase       :


  """simple docstring"""


  def __init__(							self							)		->   int:
         _UpperCAmelCase  :						List[str]			     = {}
         _UpperCAmelCase  :						int			     = {}







  def __len__(							self							)		->   Tuple:
         return len(self.parent							)







  def _snake_case (							self  ,a_							)		->   str:
         if item in self.parent:
                return self.find(a_							)

         _UpperCAmelCase  :						Optional[Any]			     = item
         _UpperCAmelCase  :						List[Any]			     = 0
         return item







  def _snake_case (							self  ,a_							)		->   List[str]:
         if item not in self.parent:
                return self.make_set(a_							)
         if item != self.parent[item]:
                _UpperCAmelCase  :						List[Any]			     = self.find(self.parent[item]							)
         return self.parent[item]







  def _snake_case (							self  ,a_  ,a_							)		->   Union[str, Any]:
         _UpperCAmelCase  :						Any			     = self.find(a_							)
         _UpperCAmelCase  :						List[str]			     = self.find(a_							)

         if roota == roota:
                return roota

         if self.rank[roota] > self.rank[roota]:
                _UpperCAmelCase  :						Any			     = roota
                return roota

         if self.rank[roota] < self.rank[roota]:
                _UpperCAmelCase  :						Any			     = roota
                return roota

         if self.rank[roota] == self.rank[roota]:
                self.rank[roota] += 1
                _UpperCAmelCase  :						List[str]			     = roota
                return roota
         return None







 @staticmethod
 def _snake_case (							a_							)		->   List[Any]:
        _UpperCAmelCase  :						int			     = graph.num_vertices

        _UpperCAmelCase  :						int			     = Graph.UnionFind()
        _UpperCAmelCase  :						Optional[int]			     = []
        while num_components > 1:
               _UpperCAmelCase  :						int			     = {}
               for vertex in graph.get_vertices():
                      _UpperCAmelCase  :						Union[str, Any]			     = -1

               _UpperCAmelCase  :						Tuple			     = graph.get_edges()
               for edge in edges:
                      _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						str			     = edge
                      edges.remove((tail, head, weight)							)
               for edge in edges:
                      _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						Optional[Any]			     = edge
                      _UpperCAmelCase  :						Any			     = union_find.find(a_							)
                      _UpperCAmelCase  :						Any			     = union_find.find(a_							)
                      if seta != seta:
                             if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                                    _UpperCAmelCase  :						Tuple			     = [head, tail, weight]

                             if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                                    _UpperCAmelCase  :						List[str]			     = [head, tail, weight]
               for vertex in cheap_edge:
                      if cheap_edge[vertex] != -1:
                             _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						str			     = cheap_edge[vertex]
                             if union_find.find(a_							) != union_find.find(a_							):
                                    union_find.union(a_  ,a_							)
                                    mst_edges.append(cheap_edge[vertex]							)
                                    _UpperCAmelCase  :						Tuple			     = num_components - 1
        _UpperCAmelCase  :						Optional[int]			     = Graph.build(edges=a_							)
        return mst



