





import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_=1		) ->  Union[str, Any]:
		if n_shave_prefix_segments >= 0:
				return ".".join(path.split('''.'''		)[n_shave_prefix_segments:]		)
		else:
				return ".".join(path.split('''.'''		)[:n_shave_prefix_segments]		)


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_=0		) ->  int:
		lowerCamelCase :			List[Any]    		=[]
		for old_item in old_list:
				lowerCamelCase :			List[str]    		=old_item
				lowerCamelCase :			Union[str, Any]    		=new_item.replace('''block.'''				,							'''resnets.'''		)
				lowerCamelCase :			List[Any]    		=new_item.replace('''conv_shorcut'''				,							'''conv1'''		)
				lowerCamelCase :			List[Any]    		=new_item.replace('''in_shortcut'''				,							'''conv_shortcut'''		)
				lowerCamelCase :			Optional[Any]    		=new_item.replace('''temb_proj'''				,							'''time_emb_proj'''		)

				lowerCamelCase :			List[Any]    		=shave_segments(SCREAMING_SNAKE_CASE_				,							n_shave_prefix_segments=SCREAMING_SNAKE_CASE_		)

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

		return mapping


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_=0				,							SCREAMING_SNAKE_CASE_=False		) ->  List[Any]:
		lowerCamelCase :			List[str]    		=[]
		for old_item in old_list:
				lowerCamelCase :			Optional[int]    		=old_item

				# In `model.mid`, the layer is called `attn`.
				if not in_mid:
						lowerCamelCase :			int    		=new_item.replace('''attn'''				,							'''attentions'''		)
				lowerCamelCase :			List[str]    		=new_item.replace('''.k.'''				,							'''.key.'''		)
				lowerCamelCase :			List[str]    		=new_item.replace('''.v.'''				,							'''.value.'''		)
				lowerCamelCase :			Tuple    		=new_item.replace('''.q.'''				,							'''.query.'''		)

				lowerCamelCase :			Any    		=new_item.replace('''proj_out'''				,							'''proj_attn'''		)
				lowerCamelCase :			List[str]    		=new_item.replace('''norm'''				,							'''group_norm'''		)

				lowerCamelCase :			Optional[int]    		=shave_segments(SCREAMING_SNAKE_CASE_				,							n_shave_prefix_segments=SCREAMING_SNAKE_CASE_		)
				mapping.append({'''old''': old_item, '''new''': new_item}		)

		return mapping


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_=None				,							SCREAMING_SNAKE_CASE_=None				,							SCREAMING_SNAKE_CASE_=None		) ->  Optional[int]:
		assert isinstance(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		), "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():
						lowerCamelCase :			List[Any]    		=old_checkpoint[path]
						lowerCamelCase :			Optional[int]    		=old_tensor.shape[0] // 3

						lowerCamelCase :			Dict    		=(-1, channels) if len(old_tensor.shape		) == 3 else (-1)

						lowerCamelCase :			Optional[Any]    		=old_tensor.shape[0] // config.get('''num_head_channels'''				,							1		) // 3

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

						lowerCamelCase :			Optional[Any]    		=query.reshape(SCREAMING_SNAKE_CASE_		).squeeze()
						lowerCamelCase :			List[str]    		=key.reshape(SCREAMING_SNAKE_CASE_		).squeeze()
						lowerCamelCase :			Dict    		=value.reshape(SCREAMING_SNAKE_CASE_		).squeeze()

		for path in paths:
				lowerCamelCase :			Dict    		=path['''new''']

				if attention_paths_to_split is not None and new_path in attention_paths_to_split:
						continue

				lowerCamelCase :			Union[str, Any]    		=new_path.replace('''down.'''				,							'''down_blocks.'''		)
				lowerCamelCase :			Optional[int]    		=new_path.replace('''up.'''				,							'''up_blocks.'''		)

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

				if "attentions" in new_path:
						lowerCamelCase :			Any    		=old_checkpoint[path['''old''']].squeeze()
				else:
						lowerCamelCase :			int    		=old_checkpoint[path['''old''']]


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  int:
		lowerCamelCase :			Dict    		={}

		lowerCamelCase :			List[Any]    		=checkpoint['''temb.dense.0.weight''']
		lowerCamelCase :			str    		=checkpoint['''temb.dense.0.bias''']
		lowerCamelCase :			Union[str, Any]    		=checkpoint['''temb.dense.1.weight''']
		lowerCamelCase :			List[Any]    		=checkpoint['''temb.dense.1.bias''']

		lowerCamelCase :			Tuple    		=checkpoint['''norm_out.weight''']
		lowerCamelCase :			Union[str, Any]    		=checkpoint['''norm_out.bias''']

		lowerCamelCase :			str    		=checkpoint['''conv_in.weight''']
		lowerCamelCase :			str    		=checkpoint['''conv_in.bias''']
		lowerCamelCase :			int    		=checkpoint['''conv_out.weight''']
		lowerCamelCase :			Tuple    		=checkpoint['''conv_out.bias''']

		lowerCamelCase :			Tuple    		=len({'''.'''.join(layer.split('''.'''		)[:2]		) for layer in checkpoint if '''down''' in layer}		)
		lowerCamelCase :			List[Any]    		={
		    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(SCREAMING_SNAKE_CASE_		)
		}

		lowerCamelCase :			List[Any]    		=len({'''.'''.join(layer.split('''.'''		)[:2]		) for layer in checkpoint if '''up''' in layer}		)
		lowerCamelCase :			str    		={layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(SCREAMING_SNAKE_CASE_		)}

		for i in range(SCREAMING_SNAKE_CASE_		):
				lowerCamelCase :			Dict    		=(i - 1) // (config['''layers_per_block'''] + 1)

				if any('''downsample''' in layer for layer in down_blocks[i]		):
						lowerCamelCase :			int    		=checkpoint[
						    F"down.{i}.downsample.op.weight"
						]
						lowerCamelCase :			Tuple    		=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]		):
						lowerCamelCase :			int    		=len(
						    {'''.'''.join(shave_segments(SCREAMING_SNAKE_CASE_				,							2		).split('''.'''		)[:2]		) for layer in down_blocks[i] if '''block''' in layer}		)
						lowerCamelCase :			Any    		={
						    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
						    for layer_id in range(SCREAMING_SNAKE_CASE_		)
						}

						if num_blocks > 0:
								for j in range(config['''layers_per_block''']		):
										lowerCamelCase :			List[Any]    		=renew_resnet_paths(blocks[j]		)
										assign_to_checkpoint(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)

				if any('''attn''' in layer for layer in down_blocks[i]		):
						lowerCamelCase :			List[str]    		=len(
						    {'''.'''.join(shave_segments(SCREAMING_SNAKE_CASE_				,							2		).split('''.'''		)[:2]		) for layer in down_blocks[i] if '''attn''' in layer}		)
						lowerCamelCase :			Dict    		={
						    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
						    for layer_id in range(SCREAMING_SNAKE_CASE_		)
						}

						if num_attn > 0:
								for j in range(config['''layers_per_block''']		):
										lowerCamelCase :			List[str]    		=renew_attention_paths(attns[j]		)
										assign_to_checkpoint(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							config=SCREAMING_SNAKE_CASE_		)

		lowerCamelCase :			Optional[Any]    		=[key for key in checkpoint if '''mid.block_1''' in key]
		lowerCamelCase :			List[str]    		=[key for key in checkpoint if '''mid.block_2''' in key]
		lowerCamelCase :			Any    		=[key for key in checkpoint if '''mid.attn_1''' in key]

		# Mid new 2
		lowerCamelCase :			List[Any]    		=renew_resnet_paths(SCREAMING_SNAKE_CASE_		)
		assign_to_checkpoint(
		    SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]				,							)

		lowerCamelCase :			Tuple    		=renew_resnet_paths(SCREAMING_SNAKE_CASE_		)
		assign_to_checkpoint(
		    SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]				,							)

		lowerCamelCase :			Dict    		=renew_attention_paths(SCREAMING_SNAKE_CASE_				,							in_mid=SCREAMING_SNAKE_CASE_		)
		assign_to_checkpoint(
		    SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]				,							)

		for i in range(SCREAMING_SNAKE_CASE_		):
				lowerCamelCase :			Dict    		=num_up_blocks - 1 - i

				if any('''upsample''' in layer for layer in up_blocks[i]		):
						lowerCamelCase :			str    		=checkpoint[
						    F"up.{i}.upsample.conv.weight"
						]
						lowerCamelCase :			Optional[Any]    		=checkpoint[F"up.{i}.upsample.conv.bias"]

				if any('''block''' in layer for layer in up_blocks[i]		):
						lowerCamelCase :			Optional[Any]    		=len(
						    {'''.'''.join(shave_segments(SCREAMING_SNAKE_CASE_				,							2		).split('''.'''		)[:2]		) for layer in up_blocks[i] if '''block''' in layer}		)
						lowerCamelCase :			int    		={
						    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(SCREAMING_SNAKE_CASE_		)
						}

						if num_blocks > 0:
								for j in range(config['''layers_per_block'''] + 1		):
										lowerCamelCase :			Dict    		={'''old''': F"up_blocks.{i}", '''new''': F"up_blocks.{block_id}"}
										lowerCamelCase :			Union[str, Any]    		=renew_resnet_paths(blocks[j]		)
										assign_to_checkpoint(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							additional_replacements=[replace_indices]		)

				if any('''attn''' in layer for layer in up_blocks[i]		):
						lowerCamelCase :			str    		=len(
						    {'''.'''.join(shave_segments(SCREAMING_SNAKE_CASE_				,							2		).split('''.'''		)[:2]		) for layer in up_blocks[i] if '''attn''' in layer}		)
						lowerCamelCase :			Dict    		={
						    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(SCREAMING_SNAKE_CASE_		)
						}

						if num_attn > 0:
								for j in range(config['''layers_per_block'''] + 1		):
										lowerCamelCase :			List[str]    		={'''old''': F"up_blocks.{i}", '''new''': F"up_blocks.{block_id}"}
										lowerCamelCase :			Dict    		=renew_attention_paths(attns[j]		)
										assign_to_checkpoint(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							additional_replacements=[replace_indices]		)

		lowerCamelCase :			Optional[int]    		={k.replace('''mid_new_2'''				,							'''mid_block'''		): v for k, v in new_checkpoint.items()}
		return new_checkpoint


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Optional[int]:
		lowerCamelCase :			Dict    		={}

		lowerCamelCase :			str    		=checkpoint['''encoder.norm_out.weight''']
		lowerCamelCase :			Optional[Any]    		=checkpoint['''encoder.norm_out.bias''']

		lowerCamelCase :			Optional[int]    		=checkpoint['''encoder.conv_in.weight''']
		lowerCamelCase :			List[str]    		=checkpoint['''encoder.conv_in.bias''']
		lowerCamelCase :			Dict    		=checkpoint['''encoder.conv_out.weight''']
		lowerCamelCase :			Dict    		=checkpoint['''encoder.conv_out.bias''']

		lowerCamelCase :			Union[str, Any]    		=checkpoint['''decoder.norm_out.weight''']
		lowerCamelCase :			int    		=checkpoint['''decoder.norm_out.bias''']

		lowerCamelCase :			Union[str, Any]    		=checkpoint['''decoder.conv_in.weight''']
		lowerCamelCase :			int    		=checkpoint['''decoder.conv_in.bias''']
		lowerCamelCase :			List[Any]    		=checkpoint['''decoder.conv_out.weight''']
		lowerCamelCase :			str    		=checkpoint['''decoder.conv_out.bias''']

		lowerCamelCase :			int    		=len({'''.'''.join(layer.split('''.'''		)[:3]		) for layer in checkpoint if '''down''' in layer}		)
		lowerCamelCase :			Optional[Any]    		={
		    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(SCREAMING_SNAKE_CASE_		)
		}

		lowerCamelCase :			str    		=len({'''.'''.join(layer.split('''.'''		)[:3]		) for layer in checkpoint if '''up''' in layer}		)
		lowerCamelCase :			str    		={layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(SCREAMING_SNAKE_CASE_		)}

		for i in range(SCREAMING_SNAKE_CASE_		):
				lowerCamelCase :			List[str]    		=(i - 1) // (config['''layers_per_block'''] + 1)

				if any('''downsample''' in layer for layer in down_blocks[i]		):
						lowerCamelCase :			Union[str, Any]    		=checkpoint[
						    F"encoder.down.{i}.downsample.conv.weight"
						]
						lowerCamelCase :			Dict    		=checkpoint[
						    F"encoder.down.{i}.downsample.conv.bias"
						]

				if any('''block''' in layer for layer in down_blocks[i]		):
						lowerCamelCase :			List[Any]    		=len(
						    {'''.'''.join(shave_segments(SCREAMING_SNAKE_CASE_				,							3		).split('''.'''		)[:3]		) for layer in down_blocks[i] if '''block''' in layer}		)
						lowerCamelCase :			Union[str, Any]    		={
						    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
						    for layer_id in range(SCREAMING_SNAKE_CASE_		)
						}

						if num_blocks > 0:
								for j in range(config['''layers_per_block''']		):
										lowerCamelCase :			List[str]    		=renew_resnet_paths(blocks[j]		)
										assign_to_checkpoint(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)

				if any('''attn''' in layer for layer in down_blocks[i]		):
						lowerCamelCase :			Union[str, Any]    		=len(
						    {'''.'''.join(shave_segments(SCREAMING_SNAKE_CASE_				,							3		).split('''.'''		)[:3]		) for layer in down_blocks[i] if '''attn''' in layer}		)
						lowerCamelCase :			Union[str, Any]    		={
						    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
						    for layer_id in range(SCREAMING_SNAKE_CASE_		)
						}

						if num_attn > 0:
								for j in range(config['''layers_per_block''']		):
										lowerCamelCase :			Tuple    		=renew_attention_paths(attns[j]		)
										assign_to_checkpoint(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							config=SCREAMING_SNAKE_CASE_		)

		lowerCamelCase :			List[str]    		=[key for key in checkpoint if '''mid.block_1''' in key]
		lowerCamelCase :			Tuple    		=[key for key in checkpoint if '''mid.block_2''' in key]
		lowerCamelCase :			Union[str, Any]    		=[key for key in checkpoint if '''mid.attn_1''' in key]

		# Mid new 2
		lowerCamelCase :			Optional[int]    		=renew_resnet_paths(SCREAMING_SNAKE_CASE_		)
		assign_to_checkpoint(
		    SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]				,							)

		lowerCamelCase :			Dict    		=renew_resnet_paths(SCREAMING_SNAKE_CASE_		)
		assign_to_checkpoint(
		    SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]				,							)

		lowerCamelCase :			str    		=renew_attention_paths(SCREAMING_SNAKE_CASE_				,							in_mid=SCREAMING_SNAKE_CASE_		)
		assign_to_checkpoint(
		    SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]				,							)

		for i in range(SCREAMING_SNAKE_CASE_		):
				lowerCamelCase :			Union[str, Any]    		=num_up_blocks - 1 - i

				if any('''upsample''' in layer for layer in up_blocks[i]		):
						lowerCamelCase :			List[str]    		=checkpoint[
						    F"decoder.up.{i}.upsample.conv.weight"
						]
						lowerCamelCase :			Any    		=checkpoint[
						    F"decoder.up.{i}.upsample.conv.bias"
						]

				if any('''block''' in layer for layer in up_blocks[i]		):
						lowerCamelCase :			str    		=len(
						    {'''.'''.join(shave_segments(SCREAMING_SNAKE_CASE_				,							3		).split('''.'''		)[:3]		) for layer in up_blocks[i] if '''block''' in layer}		)
						lowerCamelCase :			List[str]    		={
						    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(SCREAMING_SNAKE_CASE_		)
						}

						if num_blocks > 0:
								for j in range(config['''layers_per_block'''] + 1		):
										lowerCamelCase :			List[str]    		={'''old''': F"up_blocks.{i}", '''new''': F"up_blocks.{block_id}"}
										lowerCamelCase :			Optional[Any]    		=renew_resnet_paths(blocks[j]		)
										assign_to_checkpoint(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							additional_replacements=[replace_indices]		)

				if any('''attn''' in layer for layer in up_blocks[i]		):
						lowerCamelCase :			Any    		=len(
						    {'''.'''.join(shave_segments(SCREAMING_SNAKE_CASE_				,							3		).split('''.'''		)[:3]		) for layer in up_blocks[i] if '''attn''' in layer}		)
						lowerCamelCase :			int    		={
						    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(SCREAMING_SNAKE_CASE_		)
						}

						if num_attn > 0:
								for j in range(config['''layers_per_block'''] + 1		):
										lowerCamelCase :			Optional[Any]    		={'''old''': F"up_blocks.{i}", '''new''': F"up_blocks.{block_id}"}
										lowerCamelCase :			Union[str, Any]    		=renew_attention_paths(attns[j]		)
										assign_to_checkpoint(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							additional_replacements=[replace_indices]		)

		lowerCamelCase :			Optional[Any]    		={k.replace('''mid_new_2'''				,							'''mid_block'''		): v for k, v in new_checkpoint.items()}
		lowerCamelCase :			List[Any]    		=checkpoint['''quant_conv.weight''']
		lowerCamelCase :			Optional[Any]    		=checkpoint['''quant_conv.bias''']
		if "quantize.embedding.weight" in checkpoint:
				lowerCamelCase :			Optional[int]    		=checkpoint['''quantize.embedding.weight''']
		lowerCamelCase :			Optional[int]    		=checkpoint['''post_quant_conv.weight''']
		lowerCamelCase :			int    		=checkpoint['''post_quant_conv.bias''']

		return new_checkpoint


if __name__ == "__main__":
				snake_case_				=      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.''')

				snake_case_				=      parser.parse_args()
				snake_case_				=      torch.load(args.checkpoint_path)

				with open(args.config_file) as f:
								snake_case_				=      json.loads(f.read())

				# unet case
				snake_case_				=      {key.split('''.''')[0] for key in checkpoint.keys()}
				if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
								snake_case_				=      convert_vq_autoenc_checkpoint(checkpoint, config)
				else:
								snake_case_				=      convert_ddpm_checkpoint(checkpoint, config)

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

				if config["_class_name"] == "VQModel":
								snake_case_				=      VQModel(**config)
								model.load_state_dict(converted_checkpoint)
								model.save_pretrained(args.dump_path)
				elif config["_class_name"] == "AutoencoderKL":
								snake_case_				=      AutoencoderKL(**config)
								model.load_state_dict(converted_checkpoint)
								model.save_pretrained(args.dump_path)
				else:
								snake_case_				=      UNetaDModel(**config)
								model.load_state_dict(converted_checkpoint)

								snake_case_				=      DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

								snake_case_				=      DDPMPipeline(unet=model, scheduler=scheduler)
								pipe.save_pretrained(args.dump_path)








def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Dict:
		assert b != 0, "This cannot accept modulo that is == 0"
		if n == 0:
				return 1

		if n % 2 == 1:
				return (bin_exp_mod(SCREAMING_SNAKE_CASE_				,							n - 1				,							SCREAMING_SNAKE_CASE_		) * a) % b

		lowerCamelCase :			Tuple    		=bin_exp_mod(SCREAMING_SNAKE_CASE_				,							n / 2				,							SCREAMING_SNAKE_CASE_		)
		return (r * r) % b


if __name__ == "__main__":
				try:
								snake_case_				=      int(input('''Enter Base : ''').strip())
								snake_case_				=      int(input('''Enter Power : ''').strip())
								snake_case_				=      int(input('''Enter Modulo : ''').strip())
				except ValueError:
								print('''Invalid literal for integer''')

				print(bin_exp_mod(BASE, POWER, MODULO))

