


'''simple docstring'''

import argparse
import json

import torch

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

def _SCREAMING_SNAKE_CASE				(A  ,			A=1	)	->	int:






							"""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 _SCREAMING_SNAKE_CASE				(A  ,			A=0	)	->	Optional[int]:






							"""simple docstring"""







							lowercase__				      =			[]
							for old_item in old_list:
														lowercase__				      =			old_item
														lowercase__				      =			new_item.replace('''block.'''  ,			'''resnets.'''	)
														lowercase__				      =			new_item.replace('''conv_shorcut'''  ,			'''conv1'''	)
														lowercase__				      =			new_item.replace('''in_shortcut'''  ,			'''conv_shortcut'''	)
														lowercase__				      =			new_item.replace('''temb_proj'''  ,			'''time_emb_proj'''	)

														lowercase__				      =			shave_segments(A  ,			n_shave_prefix_segments=A	)

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

							return mapping

def _SCREAMING_SNAKE_CASE				(A  ,			A=0  ,			A=False	)	->	str:






							"""simple docstring"""







							lowercase__				      =			[]
							for old_item in old_list:
														lowercase__				      =			old_item

														# In `model.mid`, the layer is called `attn`.
														if not in_mid:
																					lowercase__				      =			new_item.replace('''attn'''  ,			'''attentions'''	)
														lowercase__				      =			new_item.replace('''.k.'''  ,			'''.key.'''	)
														lowercase__				      =			new_item.replace('''.v.'''  ,			'''.value.'''	)
														lowercase__				      =			new_item.replace('''.q.'''  ,			'''.query.'''	)

														lowercase__				      =			new_item.replace('''proj_out'''  ,			'''proj_attn'''	)
														lowercase__				      =			new_item.replace('''norm'''  ,			'''group_norm'''	)

														lowercase__				      =			shave_segments(A  ,			n_shave_prefix_segments=A	)
														mapping.append({'''old''': old_item, '''new''': new_item}	)

							return mapping

def _SCREAMING_SNAKE_CASE				(A  ,			A  ,			A  ,			A=None  ,			A=None  ,			A=None	)	->	int:






							"""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():
																					lowercase__				      =			old_checkpoint[path]
																					lowercase__				      =			old_tensor.shape[0] // 3

																					lowercase__				      =			(-1, channels) if len(old_tensor.shape	) == 3 else (-1)

																					lowercase__				      =			old_tensor.shape[0] // config.get('''num_head_channels'''  ,			1	) // 3

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

																					lowercase__				      =			query.reshape(A	).squeeze()
																					lowercase__				      =			key.reshape(A	).squeeze()
																					lowercase__				      =			value.reshape(A	).squeeze()

							for path in paths:
														lowercase__				      =			path['''new''']

														if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																					continue

														lowercase__				      =			new_path.replace('''down.'''  ,			'''down_blocks.'''	)
														lowercase__				      =			new_path.replace('''up.'''  ,			'''up_blocks.'''	)

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

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

def _SCREAMING_SNAKE_CASE				(A  ,			A	)	->	List[Any]:






							"""simple docstring"""







							lowercase__				      =			{}

							lowercase__				      =			checkpoint['''temb.dense.0.weight''']
							lowercase__				      =			checkpoint['''temb.dense.0.bias''']
							lowercase__				      =			checkpoint['''temb.dense.1.weight''']
							lowercase__				      =			checkpoint['''temb.dense.1.bias''']

							lowercase__				      =			checkpoint['''norm_out.weight''']
							lowercase__				      =			checkpoint['''norm_out.bias''']

							lowercase__				      =			checkpoint['''conv_in.weight''']
							lowercase__				      =			checkpoint['''conv_in.bias''']
							lowercase__				      =			checkpoint['''conv_out.weight''']
							lowercase__				      =			checkpoint['''conv_out.bias''']

							lowercase__				      =			len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''down''' in layer}	)
							lowercase__				      =			{
							    layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(A	)
							}

							lowercase__				      =			len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''up''' in layer}	)
							lowercase__				      =			{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	):
														lowercase__				      =			(i - 1) // (config['''layers_per_block'''] + 1)

														if any('''downsample''' in layer for layer in down_blocks[i]	):
																					lowercase__				      =			checkpoint[
																					    f"down.{i}.downsample.op.weight"
																					]
																					lowercase__				      =			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]	):
																					lowercase__				      =			len(
																					    {'''.'''.join(shave_segments(A  ,			2	).split('''.'''	)[:2]	) for layer in down_blocks[i] if '''block''' in layer}	)
																					lowercase__				      =			{
																					    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''']	):
																																			lowercase__				      =			renew_resnet_paths(blocks[j]	)
																																			assign_to_checkpoint(A  ,			A  ,			A	)

														if any('''attn''' in layer for layer in down_blocks[i]	):
																					lowercase__				      =			len(
																					    {'''.'''.join(shave_segments(A  ,			2	).split('''.'''	)[:2]	) for layer in down_blocks[i] if '''attn''' in layer}	)
																					lowercase__				      =			{
																					    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''']	):
																																			lowercase__				      =			renew_attention_paths(attns[j]	)
																																			assign_to_checkpoint(A  ,			A  ,			A  ,			config=A	)

							lowercase__				      =			[key for key in checkpoint if '''mid.block_1''' in key]
							lowercase__				      =			[key for key in checkpoint if '''mid.block_2''' in key]
							lowercase__				      =			[key for key in checkpoint if '''mid.attn_1''' in key]

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

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

							lowercase__				      =			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	):
														lowercase__				      =			num_up_blocks - 1 - i

														if any('''upsample''' in layer for layer in up_blocks[i]	):
																					lowercase__				      =			checkpoint[
																					    f"up.{i}.upsample.conv.weight"
																					]
																					lowercase__				      =			checkpoint[f"up.{i}.upsample.conv.bias"]

														if any('''block''' in layer for layer in up_blocks[i]	):
																					lowercase__				      =			len(
																					    {'''.'''.join(shave_segments(A  ,			2	).split('''.'''	)[:2]	) for layer in up_blocks[i] if '''block''' in layer}	)
																					lowercase__				      =			{
																					    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	):
																																			lowercase__				      =			{'''old''': f"up_blocks.{i}", '''new''': f"up_blocks.{block_id}"}
																																			lowercase__				      =			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]	):
																					lowercase__				      =			len(
																					    {'''.'''.join(shave_segments(A  ,			2	).split('''.'''	)[:2]	) for layer in up_blocks[i] if '''attn''' in layer}	)
																					lowercase__				      =			{
																					    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	):
																																			lowercase__				      =			{'''old''': f"up_blocks.{i}", '''new''': f"up_blocks.{block_id}"}
																																			lowercase__				      =			renew_attention_paths(attns[j]	)
																																			assign_to_checkpoint(A  ,			A  ,			A  ,			additional_replacements=[replace_indices]	)

							lowercase__				      =			{k.replace('''mid_new_2'''  ,			'''mid_block'''	): v for k, v in new_checkpoint.items()}
							return new_checkpoint

def _SCREAMING_SNAKE_CASE				(A  ,			A	)	->	List[str]:






							"""simple docstring"""







							lowercase__				      =			{}

							lowercase__				      =			checkpoint['''encoder.norm_out.weight''']
							lowercase__				      =			checkpoint['''encoder.norm_out.bias''']

							lowercase__				      =			checkpoint['''encoder.conv_in.weight''']
							lowercase__				      =			checkpoint['''encoder.conv_in.bias''']
							lowercase__				      =			checkpoint['''encoder.conv_out.weight''']
							lowercase__				      =			checkpoint['''encoder.conv_out.bias''']

							lowercase__				      =			checkpoint['''decoder.norm_out.weight''']
							lowercase__				      =			checkpoint['''decoder.norm_out.bias''']

							lowercase__				      =			checkpoint['''decoder.conv_in.weight''']
							lowercase__				      =			checkpoint['''decoder.conv_in.bias''']
							lowercase__				      =			checkpoint['''decoder.conv_out.weight''']
							lowercase__				      =			checkpoint['''decoder.conv_out.bias''']

							lowercase__				      =			len({'''.'''.join(layer.split('''.'''	)[:3]	) for layer in checkpoint if '''down''' in layer}	)
							lowercase__				      =			{
							    layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(A	)
							}

							lowercase__				      =			len({'''.'''.join(layer.split('''.'''	)[:3]	) for layer in checkpoint if '''up''' in layer}	)
							lowercase__				      =			{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	):
														lowercase__				      =			(i - 1) // (config['''layers_per_block'''] + 1)

														if any('''downsample''' in layer for layer in down_blocks[i]	):
																					lowercase__				      =			checkpoint[
																					    f"encoder.down.{i}.downsample.conv.weight"
																					]
																					lowercase__				      =			checkpoint[
																					    f"encoder.down.{i}.downsample.conv.bias"
																					]

														if any('''block''' in layer for layer in down_blocks[i]	):
																					lowercase__				      =			len(
																					    {'''.'''.join(shave_segments(A  ,			3	).split('''.'''	)[:3]	) for layer in down_blocks[i] if '''block''' in layer}	)
																					lowercase__				      =			{
																					    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''']	):
																																			lowercase__				      =			renew_resnet_paths(blocks[j]	)
																																			assign_to_checkpoint(A  ,			A  ,			A	)

														if any('''attn''' in layer for layer in down_blocks[i]	):
																					lowercase__				      =			len(
																					    {'''.'''.join(shave_segments(A  ,			3	).split('''.'''	)[:3]	) for layer in down_blocks[i] if '''attn''' in layer}	)
																					lowercase__				      =			{
																					    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''']	):
																																			lowercase__				      =			renew_attention_paths(attns[j]	)
																																			assign_to_checkpoint(A  ,			A  ,			A  ,			config=A	)

							lowercase__				      =			[key for key in checkpoint if '''mid.block_1''' in key]
							lowercase__				      =			[key for key in checkpoint if '''mid.block_2''' in key]
							lowercase__				      =			[key for key in checkpoint if '''mid.attn_1''' in key]

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

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

							lowercase__				      =			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	):
														lowercase__				      =			num_up_blocks - 1 - i

														if any('''upsample''' in layer for layer in up_blocks[i]	):
																					lowercase__				      =			checkpoint[
																					    f"decoder.up.{i}.upsample.conv.weight"
																					]
																					lowercase__				      =			checkpoint[
																					    f"decoder.up.{i}.upsample.conv.bias"
																					]

														if any('''block''' in layer for layer in up_blocks[i]	):
																					lowercase__				      =			len(
																					    {'''.'''.join(shave_segments(A  ,			3	).split('''.'''	)[:3]	) for layer in up_blocks[i] if '''block''' in layer}	)
																					lowercase__				      =			{
																					    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	):
																																			lowercase__				      =			{'''old''': f"up_blocks.{i}", '''new''': f"up_blocks.{block_id}"}
																																			lowercase__				      =			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]	):
																					lowercase__				      =			len(
																					    {'''.'''.join(shave_segments(A  ,			3	).split('''.'''	)[:3]	) for layer in up_blocks[i] if '''attn''' in layer}	)
																					lowercase__				      =			{
																					    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	):
																																			lowercase__				      =			{'''old''': f"up_blocks.{i}", '''new''': f"up_blocks.{block_id}"}
																																			lowercase__				      =			renew_attention_paths(attns[j]	)
																																			assign_to_checkpoint(A  ,			A  ,			A  ,			additional_replacements=[replace_indices]	)

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

							return new_checkpoint


if __name__ == "__main__":
	lowerCamelCase							:       List[str]  						=     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.')

	lowerCamelCase							:       Tuple  						=     parser.parse_args()
	lowerCamelCase							:       List[Any]  						=     torch.load(args.checkpoint_path)

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

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

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

	if config["_class_name"] == "VQModel":
		lowerCamelCase							:       int  						=     VQModel(**config)
		model.load_state_dict(converted_checkpoint)
		model.save_pretrained(args.dump_path)
	elif config["_class_name"] == "AutoencoderKL":
		lowerCamelCase							:       Optional[Any]  						=     AutoencoderKL(**config)
		model.load_state_dict(converted_checkpoint)
		model.save_pretrained(args.dump_path)
	else:
		lowerCamelCase							:       Tuple  						=     UNetaDModel(**config)
		model.load_state_dict(converted_checkpoint)

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

		lowerCamelCase							:       Optional[int]  						=     DDPMPipeline(unet=model, scheduler=scheduler)
		pipe.save_pretrained(args.dump_path)








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase							:       str  						=     {'configuration_nat': ['NAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NatConfig']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:       Tuple  						=     [
     'NAT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'NatForImageClassification',
     'NatModel',
     'NatPreTrainedModel',
     'NatBackbone',
 ]

if TYPE_CHECKING:
 from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nat import (
      NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      NatBackbone,
      NatForImageClassification,
      NatModel,
      NatPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase							:       Optional[Any]  						=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




