



'''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)










'''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''')





