code: 
  
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  import torch
  
  from diffusers import UNetaDConditionModel
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A							,__A=None							,__A=None							,__A=None       ):
  
  
  
  	'''simple docstring'''
  
  
  
  	assert isinstance(__A							,__A       ), "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():
  			__UpperCamelCase				      =  old_checkpoint[path]
  			__UpperCamelCase				      =  old_tensor.shape[0] // 3
  
  			__UpperCamelCase				      =  (-1, channels) if len(old_tensor.shape       ) == 3 else (-1)
  
  			__UpperCamelCase				      =  old_tensor.shape[0] // config["""num_head_channels"""] // 3
  
  			__UpperCamelCase				      =  old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]       )
  			__UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  old_tensor.split(channels // num_heads							,dim=1       )
  
  			__UpperCamelCase				      =  query.reshape(__A       )
  			__UpperCamelCase				      =  key.reshape(__A       )
  			__UpperCamelCase				      =  value.reshape(__A       )
  
  	for path in paths:
  		__UpperCamelCase				      =  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:
  				__UpperCamelCase				      =  new_path.replace(replacement["""old"""]							,replacement["""new"""]       )
  
  				    # proj_attn.weight has to be converted from conv 1D to linear
  		__UpperCamelCase				      =  old_checkpoint[path["""old"""]]
  		__UpperCamelCase				      =  ["""proj_attn.weight"""]
  		__UpperCamelCase				      =  ["""proj_out.weight""", """proj_in.weight"""]
  		if any(k in new_path for k in names       ):
  			__UpperCamelCase				      =  weight[:, :, 0]
  		elif any(k in new_path for k in names_a       ) and len(weight.shape       ) > 2 and ".attentions." not in new_path:
  			__UpperCamelCase				      =  weight[:, :, 0]
  		else:
  			__UpperCamelCase				      =  weight
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A=0       ):
  
  
  
  	'''simple docstring'''
  
  
  
  	__UpperCamelCase				      =  []
  	for old_item in old_list:
  		__UpperCamelCase				      =  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 _lowercase   (  __A							,__A=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 _lowercase   (  __A							,__A=0       ):
  
  
  
  	'''simple docstring'''
  
  
  
  	__UpperCamelCase				      =  []
  	for old_item in old_list:
  		mapping.append({"""old""": old_item, """new""": old_item}       )
  
  	return mapping
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A=0       ):
  
  
  
  	'''simple docstring'''
  
  
  
  	__UpperCamelCase				      =  []
  	for old_item in old_list:
  		__UpperCamelCase				      =  old_item.replace("""in_layers.0"""							,"""norm1"""       )
  		__UpperCamelCase				      =  new_item.replace("""in_layers.2"""							,"""conv1"""       )
  
  		__UpperCamelCase				      =  new_item.replace("""out_layers.0"""							,"""norm2"""       )
  		__UpperCamelCase				      =  new_item.replace("""out_layers.3"""							,"""conv2"""       )
  
  		__UpperCamelCase				      =  new_item.replace("""emb_layers.1"""							,"""time_emb_proj"""       )
  		__UpperCamelCase				      =  new_item.replace("""skip_connection"""							,"""conv_shortcut"""       )
  
  		__UpperCamelCase				      =  shave_segments(__A							,n_shave_prefix_segments=__A       )
  
  		if "temopral_conv" not in old_item:
  			mapping.append({"""old""": old_item, """new""": new_item}       )
  
  	return mapping
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A=None							,__A=False       ):
  
  
  
  	'''simple docstring'''
  
  
  
  	__UpperCamelCase				      =  {}
  	__UpperCamelCase				      =  list(checkpoint.keys()       )
  
  	__UpperCamelCase				      =  """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"""       ):
  				__UpperCamelCase				      =  """model_ema.""" + """""".join(key.split("""."""       )[1:]       )
  				__UpperCamelCase				      =  checkpoint.pop(__A       )
  	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:
  			__UpperCamelCase				      =  checkpoint.pop(__A       )
  
  	__UpperCamelCase				      =  {}
  
  	__UpperCamelCase				      =  unet_state_dict["""time_embed.0.weight"""]
  	__UpperCamelCase				      =  unet_state_dict["""time_embed.0.bias"""]
  	__UpperCamelCase				      =  unet_state_dict["""time_embed.2.weight"""]
  	__UpperCamelCase				      =  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":
  		__UpperCamelCase				      =  unet_state_dict["""label_emb.0.0.weight"""]
  		__UpperCamelCase				      =  unet_state_dict["""label_emb.0.0.bias"""]
  		__UpperCamelCase				      =  unet_state_dict["""label_emb.0.2.weight"""]
  		__UpperCamelCase				      =  unet_state_dict["""label_emb.0.2.bias"""]
  	else:
  		raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}"       )
  
  	__UpperCamelCase				      =  unet_state_dict["""input_blocks.0.0.weight"""]
  	__UpperCamelCase				      =  unet_state_dict["""input_blocks.0.0.bias"""]
  
  	__UpperCamelCase				      =  [v for v in unet_state_dict if v.startswith("""input_blocks.0.1"""       )]
  	__UpperCamelCase				      =  renew_attention_paths(__A       )
  	__UpperCamelCase				      =  {"""old""": """input_blocks.0.1""", """new""": """transformer_in"""}
  	assign_to_checkpoint(__A							,__A							,__A							,additional_replacements=[meta_path]							,config=__A       )
  
  	__UpperCamelCase				      =  unet_state_dict["""out.0.weight"""]
  	__UpperCamelCase				      =  unet_state_dict["""out.0.bias"""]
  	__UpperCamelCase				      =  unet_state_dict["""out.2.weight"""]
  	__UpperCamelCase				      =  unet_state_dict["""out.2.bias"""]
  
  	# Retrieves the keys for the input blocks only
  	__UpperCamelCase				      =  len({""".""".join(layer.split("""."""       )[:2]       ) for layer in unet_state_dict if """input_blocks""" in layer}       )
  	__UpperCamelCase				      =  {
  	    layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
  	    for layer_id in range(__A       )
  	}
  
  	# Retrieves the keys for the middle blocks only
  	__UpperCamelCase				      =  len({""".""".join(layer.split("""."""       )[:2]       ) for layer in unet_state_dict if """middle_block""" in layer}       )
  	__UpperCamelCase				      =  {
  	    layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
  	    for layer_id in range(__A       )
  	}
  
  	# Retrieves the keys for the output blocks only
  	__UpperCamelCase				      =  len({""".""".join(layer.split("""."""       )[:2]       ) for layer in unet_state_dict if """output_blocks""" in layer}       )
  	__UpperCamelCase				      =  {
  	    layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
  	    for layer_id in range(__A       )
  	}
  
  	for i in range(1							,__A       ):
  		__UpperCamelCase				      =  (i - 1) // (config["""layers_per_block"""] + 1)
  		__UpperCamelCase				      =  (i - 1) % (config["""layers_per_block"""] + 1)
  
  		__UpperCamelCase				      =  [
  		    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
  		]
  		__UpperCamelCase				      =  [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
  		__UpperCamelCase				      =  [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:
  			__UpperCamelCase				      =  unet_state_dict.pop(
  			    f"input_blocks.{i}.op.weight"       )
  			__UpperCamelCase				      =  unet_state_dict.pop(
  			    f"input_blocks.{i}.op.bias"       )
  
  		__UpperCamelCase				      =  renew_resnet_paths(__A       )
  		__UpperCamelCase				      =  {"""old""": f"input_blocks.{i}.0", """new""": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
  		assign_to_checkpoint(
  		    __A							,__A							,__A							,additional_replacements=[meta_path]							,config=__A       )
  
  		__UpperCamelCase				      =  [key for key in resnets if """temopral_conv""" in key]
  		__UpperCamelCase				      =  renew_temp_conv_paths(__A       )
  		__UpperCamelCase				      =  {
  		    """old""": f"input_blocks.{i}.0.temopral_conv",
  		    """new""": f"down_blocks.{block_id}.temp_convs.{layer_in_block_id}",
  		}
  		assign_to_checkpoint(
  		    __A							,__A							,__A							,additional_replacements=[meta_path]							,config=__A       )
  
  		if len(__A       ):
  			__UpperCamelCase				      =  renew_attention_paths(__A       )
  			__UpperCamelCase				      =  {"""old""": f"input_blocks.{i}.1", """new""": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
  			assign_to_checkpoint(
  			    __A							,__A							,__A							,additional_replacements=[meta_path]							,config=__A       )
  
  		if len(__A       ):
  			__UpperCamelCase				      =  renew_attention_paths(__A       )
  			__UpperCamelCase				      =  {
  			    """old""": f"input_blocks.{i}.2",
  			    """new""": f"down_blocks.{block_id}.temp_attentions.{layer_in_block_id}",
  			}
  			assign_to_checkpoint(
  			    __A							,__A							,__A							,additional_replacements=[meta_path]							,config=__A       )
  
  	__UpperCamelCase				      =  middle_blocks[0]
  	__UpperCamelCase				      =  [key for key in resnet_a if """temopral_conv""" in key]
  	__UpperCamelCase				      =  middle_blocks[1]
  	__UpperCamelCase				      =  middle_blocks[2]
  	__UpperCamelCase				      =  middle_blocks[3]
  	__UpperCamelCase				      =  [key for key in resnet_a if """temopral_conv""" in key]
  
  	__UpperCamelCase				      =  renew_resnet_paths(__A       )
  	__UpperCamelCase				      =  {"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
  	assign_to_checkpoint(
  	    __A							,__A							,__A							,config=__A							,additional_replacements=[meta_path]       )
  
  	__UpperCamelCase				      =  renew_temp_conv_paths(__A       )
  	__UpperCamelCase				      =  {"""old""": """middle_block.0.temopral_conv""", """new""": """mid_block.temp_convs.0"""}
  	assign_to_checkpoint(
  	    __A							,__A							,__A							,config=__A							,additional_replacements=[meta_path]       )
  
  	__UpperCamelCase				      =  renew_resnet_paths(__A       )
  	__UpperCamelCase				      =  {"""old""": """middle_block.3""", """new""": """mid_block.resnets.1"""}
  	assign_to_checkpoint(
  	    __A							,__A							,__A							,config=__A							,additional_replacements=[meta_path]       )
  
  	__UpperCamelCase				      =  renew_temp_conv_paths(__A       )
  	__UpperCamelCase				      =  {"""old""": """middle_block.3.temopral_conv""", """new""": """mid_block.temp_convs.1"""}
  	assign_to_checkpoint(
  	    __A							,__A							,__A							,config=__A							,additional_replacements=[meta_path]       )
  
  	__UpperCamelCase				      =  renew_attention_paths(__A       )
  	__UpperCamelCase				      =  {"""old""": """middle_block.1""", """new""": """mid_block.attentions.0"""}
  	assign_to_checkpoint(
  	    __A							,__A							,__A							,additional_replacements=[meta_path]							,config=__A       )
  
  	__UpperCamelCase				      =  renew_attention_paths(__A       )
  	__UpperCamelCase				      =  {"""old""": """middle_block.2""", """new""": """mid_block.temp_attentions.0"""}
  	assign_to_checkpoint(
  	    __A							,__A							,__A							,additional_replacements=[meta_path]							,config=__A       )
  
  	for i in range(__A       ):
  		__UpperCamelCase				      =  i // (config["""layers_per_block"""] + 1)
  		__UpperCamelCase				      =  i % (config["""layers_per_block"""] + 1)
  		__UpperCamelCase				      =  [shave_segments(__A							,2       ) for name in output_blocks[i]]
  		__UpperCamelCase				      =  {}
  
  		for layer in output_block_layers:
  			__UpperCamelCase      ,		__UpperCamelCase				      =  layer.split("""."""       )[0], shave_segments(__A							,1       )
  			if layer_id in output_block_list:
  				output_block_list[layer_id].append(__A       )
  			else:
  				__UpperCamelCase				      =  [layer_name]
  
  		if len(__A       ) > 1:
  			__UpperCamelCase				      =  [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
  			__UpperCamelCase				      =  [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
  			__UpperCamelCase				      =  [key for key in output_blocks[i] if f"output_blocks.{i}.2" in key]
  
  			__UpperCamelCase				      =  renew_resnet_paths(__A       )
  			__UpperCamelCase				      =  renew_resnet_paths(__A       )
  
  			__UpperCamelCase				      =  {"""old""": f"output_blocks.{i}.0", """new""": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
  			assign_to_checkpoint(
  			    __A							,__A							,__A							,additional_replacements=[meta_path]							,config=__A       )
  
  			__UpperCamelCase				      =  [key for key in resnets if """temopral_conv""" in key]
  			__UpperCamelCase				      =  renew_temp_conv_paths(__A       )
  			__UpperCamelCase				      =  {
  			    """old""": f"output_blocks.{i}.0.temopral_conv",
  			    """new""": f"up_blocks.{block_id}.temp_convs.{layer_in_block_id}",
  			}
  			assign_to_checkpoint(
  			    __A							,__A							,__A							,additional_replacements=[meta_path]							,config=__A       )
  
  			__UpperCamelCase				      =  {k: sorted(__A       ) for k, v in output_block_list.items()}
  			if ["conv.bias", "conv.weight"] in output_block_list.values():
  				__UpperCamelCase				      =  list(output_block_list.values()       ).index(["""conv.bias""", """conv.weight"""]       )
  				__UpperCamelCase				      =  unet_state_dict[
  				    f"output_blocks.{i}.{index}.conv.weight"
  				]
  				__UpperCamelCase				      =  unet_state_dict[
  				    f"output_blocks.{i}.{index}.conv.bias"
  				]
  
  				# Clear attentions as they have been attributed above.
  				if len(__A       ) == 2:
  					__UpperCamelCase				      =  []
  
  			if len(__A       ):
  				__UpperCamelCase				      =  renew_attention_paths(__A       )
  				__UpperCamelCase				      =  {
  				    """old""": f"output_blocks.{i}.1",
  				    """new""": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
  				}
  				assign_to_checkpoint(
  				    __A							,__A							,__A							,additional_replacements=[meta_path]							,config=__A       )
  
  			if len(__A       ):
  				__UpperCamelCase				      =  renew_attention_paths(__A       )
  				__UpperCamelCase				      =  {
  				    """old""": f"output_blocks.{i}.2",
  				    """new""": f"up_blocks.{block_id}.temp_attentions.{layer_in_block_id}",
  				}
  				assign_to_checkpoint(
  				    __A							,__A							,__A							,additional_replacements=[meta_path]							,config=__A       )
  		else:
  			__UpperCamelCase				      =  renew_resnet_paths(__A							,n_shave_prefix_segments=1       )
  			for path in resnet_0_paths:
  				__UpperCamelCase				      =  """.""".join(["""output_blocks""", str(__A       ), path["""old"""]]       )
  				__UpperCamelCase				      =  """.""".join(["""up_blocks""", str(__A       ), """resnets""", str(__A       ), path["""new"""]]       )
  				__UpperCamelCase				      =  unet_state_dict[old_path]
  
  			__UpperCamelCase				      =  [l for l in output_block_layers if """temopral_conv""" in l]
  			for path in temopral_conv_paths:
  				__UpperCamelCase				      =  path.split("""temopral_conv."""       )[-1]
  				__UpperCamelCase				      =  """.""".join(["""output_blocks""", str(__A       ), str(__A       ), """temopral_conv""", pruned_path]       )
  				__UpperCamelCase				      =  """.""".join(["""up_blocks""", str(__A       ), """temp_convs""", str(__A       ), pruned_path]       )
  				__UpperCamelCase				      =  unet_state_dict[old_path]
  
  	return new_checkpoint
  
  
  if __name__ == "__main__":
  	a__						:			str										=			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__						:			List[Any]										=			parser.parse_args()
  
  	a__						:			int										=			torch.load(args.checkpoint_path, map_location='cpu')
  	a__						:			int										=			UNetaDConditionModel()
  
  	a__						:			List[str]										=			convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)
  
  	a__						:			List[Any]										=			set(unet.state_dict().keys()) - set(converted_ckpt.keys())
  	a__						:			List[str]										=			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 --
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  from .stack import Stack
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  Stack()
   __UpperCamelCase				      =  {"""(""": """)""", """[""": """]""", """{""": """}"""}
   for bracket in parentheses:
    if bracket in bracket_pairs:
     stack.push(__A       )
    elif bracket in (")", "]", "}"):
     if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
      return False
   return stack.is_empty()
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()
  
   a__						:			List[Any]										=			['((()))', '((())', '(()))']
   print('Balanced parentheses demonstration:\n')
   for example in examples:
    a__						:			int										=			'' if balanced_parentheses(example) else 'not '
    print(f'''{example} is {not_str}balanced''')
  
  
  
  
  
  