code: 
  
  
  
  '''simple docstring'''
  
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
  
  
  
  
  
  
  
  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:
  		__UpperCamelCase				      =  old_item
  		__UpperCamelCase				      =  new_item.replace("""block."""							,"""resnets."""       )
  		__UpperCamelCase				      =  new_item.replace("""conv_shorcut"""							,"""conv1"""       )
  		__UpperCamelCase				      =  new_item.replace("""in_shortcut"""							,"""conv_shortcut"""       )
  		__UpperCamelCase				      =  new_item.replace("""temb_proj"""							,"""time_emb_proj"""       )
  
  		__UpperCamelCase				      =  shave_segments(__A							,n_shave_prefix_segments=__A       )
  
  		mapping.append({"""old""": old_item, """new""": new_item}       )
  
  	return mapping
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A=0							,__A=False       ):
  
  
  
  	'''simple docstring'''
  
  
  
  	__UpperCamelCase				      =  []
  	for old_item in old_list:
  		__UpperCamelCase				      =  old_item
  
  		# In `model.mid`, the layer is called `attn`.
  		if not in_mid:
  			__UpperCamelCase				      =  new_item.replace("""attn"""							,"""attentions"""       )
  		__UpperCamelCase				      =  new_item.replace(""".k."""							,""".key."""       )
  		__UpperCamelCase				      =  new_item.replace(""".v."""							,""".value."""       )
  		__UpperCamelCase				      =  new_item.replace(""".q."""							,""".query."""       )
  
  		__UpperCamelCase				      =  new_item.replace("""proj_out"""							,"""proj_attn"""       )
  		__UpperCamelCase				      =  new_item.replace("""norm"""							,"""group_norm"""       )
  
  		__UpperCamelCase				      =  shave_segments(__A							,n_shave_prefix_segments=__A       )
  		mapping.append({"""old""": old_item, """new""": new_item}       )
  
  	return mapping
  
  
  
  
  
  
  
  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."
  
  	if attention_paths_to_split is not None:
  		if config is None:
  			raise ValueError("""Please specify the config if setting 'attention_paths_to_split' to 'True'."""       )
  
  		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.get("""num_head_channels"""							,1       ) // 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       ).squeeze()
  			__UpperCamelCase				      =  key.reshape(__A       ).squeeze()
  			__UpperCamelCase				      =  value.reshape(__A       ).squeeze()
  
  	for path in paths:
  		__UpperCamelCase				      =  path["""new"""]
  
  		if attention_paths_to_split is not None and new_path in attention_paths_to_split:
  			continue
  
  		__UpperCamelCase				      =  new_path.replace("""down."""							,"""down_blocks."""       )
  		__UpperCamelCase				      =  new_path.replace("""up."""							,"""up_blocks."""       )
  
  		if additional_replacements is not None:
  			for replacement in additional_replacements:
  				__UpperCamelCase				      =  new_path.replace(replacement["""old"""]							,replacement["""new"""]       )
  
  		if "attentions" in new_path:
  			__UpperCamelCase				      =  old_checkpoint[path["""old"""]].squeeze()
  		else:
  			__UpperCamelCase				      =  old_checkpoint[path["""old"""]]
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A       ):
  
  
  
  	'''simple docstring'''
  
  
  
  	__UpperCamelCase				      =  {}
  
  	__UpperCamelCase				      =  checkpoint["""temb.dense.0.weight"""]
  	__UpperCamelCase				      =  checkpoint["""temb.dense.0.bias"""]
  	__UpperCamelCase				      =  checkpoint["""temb.dense.1.weight"""]
  	__UpperCamelCase				      =  checkpoint["""temb.dense.1.bias"""]
  
  	__UpperCamelCase				      =  checkpoint["""norm_out.weight"""]
  	__UpperCamelCase				      =  checkpoint["""norm_out.bias"""]
  
  	__UpperCamelCase				      =  checkpoint["""conv_in.weight"""]
  	__UpperCamelCase				      =  checkpoint["""conv_in.bias"""]
  	__UpperCamelCase				      =  checkpoint["""conv_out.weight"""]
  	__UpperCamelCase				      =  checkpoint["""conv_out.bias"""]
  
  	__UpperCamelCase				      =  len({""".""".join(layer.split("""."""       )[:2]       ) for layer in checkpoint if """down""" in layer}       )
  	__UpperCamelCase				      =  {
  	    layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(__A       )
  	}
  
  	__UpperCamelCase				      =  len({""".""".join(layer.split("""."""       )[:2]       ) for layer in checkpoint if """up""" in layer}       )
  	__UpperCamelCase				      =  {layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(__A       )}
  
  	for i in range(__A       ):
  		__UpperCamelCase				      =  (i - 1) // (config["""layers_per_block"""] + 1)
  
  		if any("""downsample""" in layer for layer in down_blocks[i]       ):
  			__UpperCamelCase				      =  checkpoint[
  			    f"down.{i}.downsample.op.weight"
  			]
  			__UpperCamelCase				      =  checkpoint[f"down.{i}.downsample.op.bias"]
  		#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
  		#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']
  
  		if any("""block""" in layer for layer in down_blocks[i]       ):
  			__UpperCamelCase				      =  len(
  			    {""".""".join(shave_segments(__A							,2       ).split("""."""       )[:2]       ) for layer in down_blocks[i] if """block""" in layer}       )
  			__UpperCamelCase				      =  {
  			    layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
  			    for layer_id in range(__A       )
  			}
  
  			if num_blocks > 0:
  				for j in range(config["""layers_per_block"""]       ):
  					__UpperCamelCase				      =  renew_resnet_paths(blocks[j]       )
  					assign_to_checkpoint(__A							,__A							,__A       )
  
  		if any("""attn""" in layer for layer in down_blocks[i]       ):
  			__UpperCamelCase				      =  len(
  			    {""".""".join(shave_segments(__A							,2       ).split("""."""       )[:2]       ) for layer in down_blocks[i] if """attn""" in layer}       )
  			__UpperCamelCase				      =  {
  			    layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
  			    for layer_id in range(__A       )
  			}
  
  			if num_attn > 0:
  				for j in range(config["""layers_per_block"""]       ):
  					__UpperCamelCase				      =  renew_attention_paths(attns[j]       )
  					assign_to_checkpoint(__A							,__A							,__A							,config=__A       )
  
  	__UpperCamelCase				      =  [key for key in checkpoint if """mid.block_1""" in key]
  	__UpperCamelCase				      =  [key for key in checkpoint if """mid.block_2""" in key]
  	__UpperCamelCase				      =  [key for key in checkpoint if """mid.attn_1""" in key]
  
  	# Mid new 2
  	__UpperCamelCase				      =  renew_resnet_paths(__A       )
  	assign_to_checkpoint(
  	    __A							,__A							,__A							,additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]							,)
  
  	__UpperCamelCase				      =  renew_resnet_paths(__A       )
  	assign_to_checkpoint(
  	    __A							,__A							,__A							,additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]							,)
  
  	__UpperCamelCase				      =  renew_attention_paths(__A							,in_mid=__A       )
  	assign_to_checkpoint(
  	    __A							,__A							,__A							,additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]							,)
  
  	for i in range(__A       ):
  		__UpperCamelCase				      =  num_up_blocks - 1 - i
  
  		if any("""upsample""" in layer for layer in up_blocks[i]       ):
  			__UpperCamelCase				      =  checkpoint[
  			    f"up.{i}.upsample.conv.weight"
  			]
  			__UpperCamelCase				      =  checkpoint[f"up.{i}.upsample.conv.bias"]
  
  		if any("""block""" in layer for layer in up_blocks[i]       ):
  			__UpperCamelCase				      =  len(
  			    {""".""".join(shave_segments(__A							,2       ).split("""."""       )[:2]       ) for layer in up_blocks[i] if """block""" in layer}       )
  			__UpperCamelCase				      =  {
  			    layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(__A       )
  			}
  
  			if num_blocks > 0:
  				for j in range(config["""layers_per_block"""] + 1       ):
  					__UpperCamelCase				      =  {"""old""": f"up_blocks.{i}", """new""": f"up_blocks.{block_id}"}
  					__UpperCamelCase				      =  renew_resnet_paths(blocks[j]       )
  					assign_to_checkpoint(__A							,__A							,__A							,additional_replacements=[replace_indices]       )
  
  		if any("""attn""" in layer for layer in up_blocks[i]       ):
  			__UpperCamelCase				      =  len(
  			    {""".""".join(shave_segments(__A							,2       ).split("""."""       )[:2]       ) for layer in up_blocks[i] if """attn""" in layer}       )
  			__UpperCamelCase				      =  {
  			    layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(__A       )
  			}
  
  			if num_attn > 0:
  				for j in range(config["""layers_per_block"""] + 1       ):
  					__UpperCamelCase				      =  {"""old""": f"up_blocks.{i}", """new""": f"up_blocks.{block_id}"}
  					__UpperCamelCase				      =  renew_attention_paths(attns[j]       )
  					assign_to_checkpoint(__A							,__A							,__A							,additional_replacements=[replace_indices]       )
  
  	__UpperCamelCase				      =  {k.replace("""mid_new_2"""							,"""mid_block"""       ): v for k, v in new_checkpoint.items()}
  	return new_checkpoint
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A       ):
  
  
  
  	'''simple docstring'''
  
  
  
  	__UpperCamelCase				      =  {}
  
  	__UpperCamelCase				      =  checkpoint["""encoder.norm_out.weight"""]
  	__UpperCamelCase				      =  checkpoint["""encoder.norm_out.bias"""]
  
  	__UpperCamelCase				      =  checkpoint["""encoder.conv_in.weight"""]
  	__UpperCamelCase				      =  checkpoint["""encoder.conv_in.bias"""]
  	__UpperCamelCase				      =  checkpoint["""encoder.conv_out.weight"""]
  	__UpperCamelCase				      =  checkpoint["""encoder.conv_out.bias"""]
  
  	__UpperCamelCase				      =  checkpoint["""decoder.norm_out.weight"""]
  	__UpperCamelCase				      =  checkpoint["""decoder.norm_out.bias"""]
  
  	__UpperCamelCase				      =  checkpoint["""decoder.conv_in.weight"""]
  	__UpperCamelCase				      =  checkpoint["""decoder.conv_in.bias"""]
  	__UpperCamelCase				      =  checkpoint["""decoder.conv_out.weight"""]
  	__UpperCamelCase				      =  checkpoint["""decoder.conv_out.bias"""]
  
  	__UpperCamelCase				      =  len({""".""".join(layer.split("""."""       )[:3]       ) for layer in checkpoint if """down""" in layer}       )
  	__UpperCamelCase				      =  {
  	    layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(__A       )
  	}
  
  	__UpperCamelCase				      =  len({""".""".join(layer.split("""."""       )[:3]       ) for layer in checkpoint if """up""" in layer}       )
  	__UpperCamelCase				      =  {layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(__A       )}
  
  	for i in range(__A       ):
  		__UpperCamelCase				      =  (i - 1) // (config["""layers_per_block"""] + 1)
  
  		if any("""downsample""" in layer for layer in down_blocks[i]       ):
  			__UpperCamelCase				      =  checkpoint[
  			    f"encoder.down.{i}.downsample.conv.weight"
  			]
  			__UpperCamelCase				      =  checkpoint[
  			    f"encoder.down.{i}.downsample.conv.bias"
  			]
  
  		if any("""block""" in layer for layer in down_blocks[i]       ):
  			__UpperCamelCase				      =  len(
  			    {""".""".join(shave_segments(__A							,3       ).split("""."""       )[:3]       ) for layer in down_blocks[i] if """block""" in layer}       )
  			__UpperCamelCase				      =  {
  			    layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
  			    for layer_id in range(__A       )
  			}
  
  			if num_blocks > 0:
  				for j in range(config["""layers_per_block"""]       ):
  					__UpperCamelCase				      =  renew_resnet_paths(blocks[j]       )
  					assign_to_checkpoint(__A							,__A							,__A       )
  
  		if any("""attn""" in layer for layer in down_blocks[i]       ):
  			__UpperCamelCase				      =  len(
  			    {""".""".join(shave_segments(__A							,3       ).split("""."""       )[:3]       ) for layer in down_blocks[i] if """attn""" in layer}       )
  			__UpperCamelCase				      =  {
  			    layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
  			    for layer_id in range(__A       )
  			}
  
  			if num_attn > 0:
  				for j in range(config["""layers_per_block"""]       ):
  					__UpperCamelCase				      =  renew_attention_paths(attns[j]       )
  					assign_to_checkpoint(__A							,__A							,__A							,config=__A       )
  
  	__UpperCamelCase				      =  [key for key in checkpoint if """mid.block_1""" in key]
  	__UpperCamelCase				      =  [key for key in checkpoint if """mid.block_2""" in key]
  	__UpperCamelCase				      =  [key for key in checkpoint if """mid.attn_1""" in key]
  
  	# Mid new 2
  	__UpperCamelCase				      =  renew_resnet_paths(__A       )
  	assign_to_checkpoint(
  	    __A							,__A							,__A							,additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]							,)
  
  	__UpperCamelCase				      =  renew_resnet_paths(__A       )
  	assign_to_checkpoint(
  	    __A							,__A							,__A							,additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]							,)
  
  	__UpperCamelCase				      =  renew_attention_paths(__A							,in_mid=__A       )
  	assign_to_checkpoint(
  	    __A							,__A							,__A							,additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]							,)
  
  	for i in range(__A       ):
  		__UpperCamelCase				      =  num_up_blocks - 1 - i
  
  		if any("""upsample""" in layer for layer in up_blocks[i]       ):
  			__UpperCamelCase				      =  checkpoint[
  			    f"decoder.up.{i}.upsample.conv.weight"
  			]
  			__UpperCamelCase				      =  checkpoint[
  			    f"decoder.up.{i}.upsample.conv.bias"
  			]
  
  		if any("""block""" in layer for layer in up_blocks[i]       ):
  			__UpperCamelCase				      =  len(
  			    {""".""".join(shave_segments(__A							,3       ).split("""."""       )[:3]       ) for layer in up_blocks[i] if """block""" in layer}       )
  			__UpperCamelCase				      =  {
  			    layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(__A       )
  			}
  
  			if num_blocks > 0:
  				for j in range(config["""layers_per_block"""] + 1       ):
  					__UpperCamelCase				      =  {"""old""": f"up_blocks.{i}", """new""": f"up_blocks.{block_id}"}
  					__UpperCamelCase				      =  renew_resnet_paths(blocks[j]       )
  					assign_to_checkpoint(__A							,__A							,__A							,additional_replacements=[replace_indices]       )
  
  		if any("""attn""" in layer for layer in up_blocks[i]       ):
  			__UpperCamelCase				      =  len(
  			    {""".""".join(shave_segments(__A							,3       ).split("""."""       )[:3]       ) for layer in up_blocks[i] if """attn""" in layer}       )
  			__UpperCamelCase				      =  {
  			    layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(__A       )
  			}
  
  			if num_attn > 0:
  				for j in range(config["""layers_per_block"""] + 1       ):
  					__UpperCamelCase				      =  {"""old""": f"up_blocks.{i}", """new""": f"up_blocks.{block_id}"}
  					__UpperCamelCase				      =  renew_attention_paths(attns[j]       )
  					assign_to_checkpoint(__A							,__A							,__A							,additional_replacements=[replace_indices]       )
  
  	__UpperCamelCase				      =  {k.replace("""mid_new_2"""							,"""mid_block"""       ): v for k, v in new_checkpoint.items()}
  	__UpperCamelCase				      =  checkpoint["""quant_conv.weight"""]
  	__UpperCamelCase				      =  checkpoint["""quant_conv.bias"""]
  	if "quantize.embedding.weight" in checkpoint:
  		__UpperCamelCase				      =  checkpoint["""quantize.embedding.weight"""]
  	__UpperCamelCase				      =  checkpoint["""post_quant_conv.weight"""]
  	__UpperCamelCase				      =  checkpoint["""post_quant_conv.bias"""]
  
  	return new_checkpoint
  
  
  if __name__ == "__main__":
  	a__						:			Dict										=			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__						:			Optional[int]										=			parser.parse_args()
  	a__						:			Optional[int]										=			torch.load(args.checkpoint_path)
  
  	with open(args.config_file) as f:
  		a__						:			int										=			json.loads(f.read())
  
  	# unet case
  	a__						:			int										=			{key.split('.')[0] for key in checkpoint.keys()}
  	if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
  		a__						:			List[str]										=			convert_vq_autoenc_checkpoint(checkpoint, config)
  	else:
  		a__						:			List[str]										=			convert_ddpm_checkpoint(checkpoint, config)
  
  	if "ddpm" in config:
  		del config["ddpm"]
  
  	if config["_class_name"] == "VQModel":
  		a__						:			Tuple										=			VQModel(**config)
  		model.load_state_dict(converted_checkpoint)
  		model.save_pretrained(args.dump_path)
  	elif config["_class_name"] == "AutoencoderKL":
  		a__						:			int										=			AutoencoderKL(**config)
  		model.load_state_dict(converted_checkpoint)
  		model.save_pretrained(args.dump_path)
  	else:
  		a__						:			Tuple										=			UNetaDModel(**config)
  		model.load_state_dict(converted_checkpoint)
  
  		a__						:			Optional[int]										=			DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
  
  		a__						:			str										=			DDPMPipeline(unet=model, scheduler=scheduler)
  		pipe.save_pretrained(args.dump_path)
  
  
  
  
  
  
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''')
  
  
  
  
  
  