


"""simple docstring"""
class      _UpperCAmelCase					:






		'''simple docstring'''

		def __init__(self							,			a_   ):





			'''simple docstring'''

			__snake_case      :		List[Any]         =							val
			# Number of nodes in left subtree
			__snake_case      :		Dict         =							0
			__snake_case      :		Optional[Any]         =							None
			__snake_case      :		Dict         =							None
			__snake_case      :		List[Any]         =							None



		def 							SCREAMING_SNAKE_CASE    (self							,			a_   ):





			'''simple docstring'''

			assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

			if self.val < other.val:
				__snake_case      :		str         =							self.right
				__snake_case      :		Union[str, Any]         =							None
				if self.right:
					__snake_case      :		List[Any]         =							other
				__snake_case      :		Optional[int]         =							other
				__snake_case      :		Union[str, Any]         =							self.left_tree_size * 2 + 1
				return self
			else:
				__snake_case      :		Optional[Any]         =							other.right
				__snake_case      :		Any         =							None
				if other.right:
					__snake_case      :		Union[str, Any]         =							self
				__snake_case      :		int         =							self
				__snake_case      :		List[Any]         =							other.left_tree_size * 2 + 1
				return other



class      _UpperCAmelCase					:






		'''simple docstring'''

		def __init__(self							,			a_=None							,			a_=None							,			a_=0   ):





			'''simple docstring'''

			__snake_case      :		str         =							heap_size
			__snake_case      :		Optional[int]         =							bottom_root
			__snake_case      :		Any         =							min_node



		def 							SCREAMING_SNAKE_CASE    (self							,			a_   ):





			'''simple docstring'''

			if other.size == 0:
				return None
			if self.size == 0:
				__snake_case      :		Any         =							other.size
				__snake_case      :		str         =							other.bottom_root
				__snake_case      :		Optional[int]         =							other.min_node
				return None
			# Update size
			__snake_case      :		List[str]         =							self.size + other.size

			# Update min.node
			if self.min_node.val > other.min_node.val:
				__snake_case      :		Dict         =							other.min_node
			# Merge

			# Order roots by left_subtree_size
			__snake_case      :		Optional[int]         =							[]
			__snake_case      ,				__snake_case      :		List[str]         =							self.bottom_root, other.bottom_root
			while i or j:
				if i and ((not j) or i.left_tree_size < j.left_tree_size):
					combined_roots_list.append((i, True)   )
					__snake_case      :		Optional[Any]         =							i.parent
				else:
					combined_roots_list.append((j, False)   )
					__snake_case      :		List[str]         =							j.parent
        # Insert links between them
			for i in range(len(a_   ) - 1   ):
				if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
					__snake_case      :		List[Any]         =							combined_roots_list[i + 1][0]
					__snake_case      :		Optional[Any]         =							combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
			__snake_case      :		List[Any]         =							combined_roots_list[0][0]
			while i.parent:
				if (
				    (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
				) or (
				    i.left_tree_size == i.parent.left_tree_size
				    and i.left_tree_size != i.parent.parent.left_tree_size
				):
					# Neighbouring Nodes
					__snake_case      :		Union[str, Any]         =							i.left
					__snake_case      :		int         =							i.parent.parent

					# Merging trees
					__snake_case      :		Optional[Any]         =							i.merge_trees(i.parent   )

					# Updating links
					__snake_case      :		Dict         =							previous_node
					__snake_case      :		Optional[Any]         =							next_node
					if previous_node:
						__snake_case      :		List[str]         =							i
					if next_node:
						__snake_case      :		Any         =							i
				else:
					__snake_case      :		Optional[Any]         =							i.parent
        # Updating self.bottom_root
			while i.left:
				__snake_case      :		Union[str, Any]         =							i.left
			__snake_case      :		int         =							i

			# Update other
			__snake_case      :		Tuple         =							self.size
			__snake_case      :		Optional[int]         =							self.bottom_root
			__snake_case      :		List[str]         =							self.min_node

			# Return the merged heap
			return self



		def 							SCREAMING_SNAKE_CASE    (self							,			a_   ):





			'''simple docstring'''

			if self.size == 0:
				__snake_case      :		str         =							Node(a_   )
				__snake_case      :		List[Any]         =							1
				__snake_case      :		Optional[Any]         =							self.bottom_root
			else:
				# Create new node
				__snake_case      :		List[Any]         =							Node(a_   )

				# Update size
				self.size += 1

				# update min_node
				if val < self.min_node.val:
					__snake_case      :		Union[str, Any]         =							new_node
				# Put new_node as a bottom_root in heap
				__snake_case      :		str         =							new_node
				__snake_case      :		List[str]         =							self.bottom_root
				__snake_case      :		List[Any]         =							new_node

				# Consecutively merge roots with same left_tree_size
				while (
				    self.bottom_root.parent
				    and self.bottom_root.left_tree_size
				    == self.bottom_root.parent.left_tree_size
				):
					# Next node
					__snake_case      :		Optional[int]         =							self.bottom_root.parent.parent

					# Merge
					__snake_case      :		List[Any]         =							self.bottom_root.merge_trees(self.bottom_root.parent   )

					# Update Links
					__snake_case      :		List[Any]         =							next_node
					__snake_case      :		Optional[Any]         =							None
					if next_node:
						__snake_case      :		List[Any]         =							self.bottom_root



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			return self.min_node.val



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			return self.size == 0



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[Any]         =							self.min_node.val

			# Last element in heap corner case
			if self.size == 1:
				# Update size
				__snake_case      :		Union[str, Any]         =							0

				# Update bottom root
				__snake_case      :		Tuple         =							None

				# Update min_node
				__snake_case      :		Optional[int]         =							None

				return min_value
			# No right subtree corner case
			# The structure of the tree implies that this should be the bottom root
			# and there is at least one other root
			if self.min_node.right is None:
				# Update size
				self.size -= 1

				# Update bottom root
				__snake_case      :		List[str]         =							self.bottom_root.parent
				__snake_case      :		int         =							None

				# Update min_node
				__snake_case      :		Union[str, Any]         =							self.bottom_root
				__snake_case      :		Any         =							self.bottom_root.parent
				while i:
					if i.val < self.min_node.val:
						__snake_case      :		Dict         =							i
					__snake_case      :		str         =							i.parent
				return min_value
			# General case
			# Find the BinomialHeap of the right subtree of min_node
			__snake_case      :		List[Any]         =							self.min_node.right
			__snake_case      :		str         =							None
			__snake_case      :		Tuple         =							bottom_of_new
			__snake_case      :		Optional[Any]         =							1

			# Size, min_node and bottom_root
			while bottom_of_new.left:
				__snake_case      :		str         =							size_of_new * 2 + 1
				__snake_case      :		Union[str, Any]         =							bottom_of_new.left
				if bottom_of_new.val < min_of_new.val:
					__snake_case      :		Dict         =							bottom_of_new
        # Corner case of single root on top left path
			if (not self.min_node.left) and (not self.min_node.parent):
				__snake_case      :		List[str]         =							size_of_new
				__snake_case      :		str         =							bottom_of_new
				__snake_case      :		Optional[Any]         =							min_of_new
				# print("Single root, multiple nodes case")
				return min_value
			# Remaining cases
			# Construct heap of right subtree
			__snake_case      :		Optional[int]         =							BinomialHeap(
			    bottom_root=a_							,			min_node=a_							,			heap_size=a_   )

			# Update size
			__snake_case      :		List[str]         =							self.size - 1 - size_of_new

			# Neighbour nodes
			__snake_case      :		Dict         =							self.min_node.left
			__snake_case      :		Dict         =							self.min_node.parent

			# Initialize new bottom_root and min_node
			__snake_case      :		List[Any]         =							previous_node or next_node
			__snake_case      :		Union[str, Any]         =							next_node

			# Update links of previous_node and search below for new min_node and
			# bottom_root
			if previous_node:
				__snake_case      :		Optional[int]         =							next_node

				# Update bottom_root and search for min_node below
				__snake_case      :		Optional[Any]         =							previous_node
				__snake_case      :		str         =							previous_node
				while self.bottom_root.left:
					__snake_case      :		Any         =							self.bottom_root.left
					if self.bottom_root.val < self.min_node.val:
						__snake_case      :		Tuple         =							self.bottom_root
			if next_node:
				__snake_case      :		Optional[int]         =							previous_node

				# Search for new min_node above min_node
				__snake_case      :		Tuple         =							next_node
				while i:
					if i.val < self.min_node.val:
						__snake_case      :		List[str]         =							i
					__snake_case      :		int         =							i.parent
        # Merge heaps
			self.merge_heaps(a_   )

			return min_value



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Dict         =							self.bottom_root
			while top_root.parent:
				__snake_case      :		Optional[int]         =							top_root.parent
			# preorder
			__snake_case      :		Dict         =							[]
			self.__traversal(a_							,			a_   )
			return heap_pre_order



		def 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_							,			a_=0   ):





			'''simple docstring'''

			if curr_node:
				preorder.append((curr_node.val, level)   )
				self.__traversal(curr_node.left							,			a_							,			level + 1   )
				self.__traversal(curr_node.right							,			a_							,			level + 1   )
			else:
				preorder.append(('''#''', level)   )



		def __str__(self   ):





			'''simple docstring'''

			if self.is_empty():
				return ""
			__snake_case      :		List[Any]         =							self.pre_order()

			return "\n".join(('''-''' * level + str(a_   )) for value, level in preorder_heap   )


# Unit Tests
if __name__ == "__main__":
			import doctest

			doctest.testmod()








import argparse
import json

import torch

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







def _a					(			SCREAMING_SNAKE_CASE						:     Any       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]=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 _a					(			SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     str=0						)       ->       Any:

 """simple docstring"""






 __lowerCAmelCase:  List[str]        = []
 for old_item in old_list:
  __lowerCAmelCase:  Optional[Any]        = old_item
  __lowerCAmelCase:  List[str]        = new_item.replace('block.'       ,   'resnets.'						)
  __lowerCAmelCase:  Dict        = new_item.replace('conv_shorcut'       ,   'conv1'						)
  __lowerCAmelCase:  Optional[Any]        = new_item.replace('in_shortcut'       ,   'conv_shortcut'						)
  __lowerCAmelCase:  str        = new_item.replace('temb_proj'       ,   'time_emb_proj'						)

  __lowerCAmelCase:  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						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     Dict=0       ,   SCREAMING_SNAKE_CASE						:     str=False						)       ->       Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:  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:  Any        = new_item.replace('attn'       ,   'attentions'						)
  __lowerCAmelCase:  Union[str, Any]        = new_item.replace('.k.'       ,   '.key.'						)
  __lowerCAmelCase:  int        = new_item.replace('.v.'       ,   '.value.'						)
  __lowerCAmelCase:  Dict        = new_item.replace('.q.'       ,   '.query.'						)

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

  __lowerCAmelCase:  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						:     Dict       ,   SCREAMING_SNAKE_CASE						:     str       ,   SCREAMING_SNAKE_CASE						:     Dict       ,   SCREAMING_SNAKE_CASE						:     Any=None       ,   SCREAMING_SNAKE_CASE						:     Union[str, Any]=None       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]=None						)       ->       Union[str, Any]:

 """simple docstring"""






 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:  Tuple        = old_tensor.shape[0] // 3

   __lowerCAmelCase:  List[Any]        = (-1, channels) if len(old_tensor.shape						) == 3 else (-1)

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

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

   __lowerCAmelCase:  Union[str, Any]        = query.reshape(SCREAMING_SNAKE_CASE						).squeeze()
   __lowerCAmelCase:  Dict        = key.reshape(SCREAMING_SNAKE_CASE						).squeeze()
   __lowerCAmelCase:  List[Any]        = value.reshape(SCREAMING_SNAKE_CASE						).squeeze()

 for path in paths:
  __lowerCAmelCase:  Optional[Any]        = path['new']

  if attention_paths_to_split is not None and new_path in attention_paths_to_split:
   continue

  __lowerCAmelCase:  Optional[int]        = new_path.replace('down.'       ,   'down_blocks.'						)
  __lowerCAmelCase:  List[Any]        = new_path.replace('up.'       ,   'up_blocks.'						)

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

  if "attentions" in new_path:
   __lowerCAmelCase:  Optional[Any]        = old_checkpoint[path['old']].squeeze()
  else:
   __lowerCAmelCase:  List[Any]        = old_checkpoint[path['old']]







def _a					(			SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     Tuple						)       ->       List[str]:

 """simple docstring"""






 __lowerCAmelCase:  Tuple        = {}

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

 __lowerCAmelCase:  Tuple        = checkpoint['norm_out.weight']
 __lowerCAmelCase:  Dict        = checkpoint['norm_out.bias']

 __lowerCAmelCase:  Dict        = checkpoint['conv_in.weight']
 __lowerCAmelCase:  List[str]        = checkpoint['conv_in.bias']
 __lowerCAmelCase:  str        = checkpoint['conv_out.weight']
 __lowerCAmelCase:  Tuple        = checkpoint['conv_out.bias']

 __lowerCAmelCase:  List[Any]        = len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'down' in layer}						)
 __lowerCAmelCase:  Union[str, Any]        = {
     layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE						)
 }

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

  if any('downsample' in layer for layer in down_blocks[i]						):
   __lowerCAmelCase:  List[Any]        = 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:  Optional[Any]        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   2						).split('.'						)[:2]						) 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:  int        = 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:  Optional[int]        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   2						).split('.'						)[:2]						) 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:  List[Any]        = renew_attention_paths(attns[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   config=SCREAMING_SNAKE_CASE						)

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

 # Mid new 2
 __lowerCAmelCase:  Union[str, 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:  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_2', 'new': 'resnets.1'}]       ,   )

 __lowerCAmelCase:  Tuple        = 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:  Tuple        = checkpoint[
       f'''up.{i}.upsample.conv.weight'''
   ]
   __lowerCAmelCase:  Any        = checkpoint[f'''up.{i}.upsample.conv.bias''']

  if any('block' in layer for layer in up_blocks[i]						):
   __lowerCAmelCase:  Optional[int]        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   2						).split('.'						)[:2]						) for layer in up_blocks[i] if 'block' in layer}						)
   __lowerCAmelCase:  Tuple        = {
       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:  int        = 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:  Optional[Any]        = {
       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:  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:  Union[str, Any]        = {k.replace('mid_new_2'       ,   'mid_block'						): v for k, v in new_checkpoint.items()}
 return new_checkpoint







def _a					(			SCREAMING_SNAKE_CASE						:     Dict       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]						)       ->       Any:

 """simple docstring"""






 __lowerCAmelCase:  int        = {}

 __lowerCAmelCase:  List[Any]        = checkpoint['encoder.norm_out.weight']
 __lowerCAmelCase:  str        = checkpoint['encoder.norm_out.bias']

 __lowerCAmelCase:  Any        = checkpoint['encoder.conv_in.weight']
 __lowerCAmelCase:  str        = checkpoint['encoder.conv_in.bias']
 __lowerCAmelCase:  str        = checkpoint['encoder.conv_out.weight']
 __lowerCAmelCase:  List[str]        = checkpoint['encoder.conv_out.bias']

 __lowerCAmelCase:  Tuple        = checkpoint['decoder.norm_out.weight']
 __lowerCAmelCase:  int        = checkpoint['decoder.norm_out.bias']

 __lowerCAmelCase:  Optional[int]        = checkpoint['decoder.conv_in.weight']
 __lowerCAmelCase:  List[Any]        = checkpoint['decoder.conv_in.bias']
 __lowerCAmelCase:  int        = checkpoint['decoder.conv_out.weight']
 __lowerCAmelCase:  Tuple        = checkpoint['decoder.conv_out.bias']

 __lowerCAmelCase:  Optional[int]        = len({'.'.join(layer.split('.'						)[:3]						) for layer in checkpoint if 'down' in layer}						)
 __lowerCAmelCase:  int        = {
     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:  Optional[int]        = {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:  Tuple        = checkpoint[
       f'''encoder.down.{i}.downsample.conv.weight'''
   ]
   __lowerCAmelCase:  Any        = checkpoint[
       f'''encoder.down.{i}.downsample.conv.bias'''
   ]

  if any('block' in layer for layer in down_blocks[i]						):
   __lowerCAmelCase:  Tuple        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   3						).split('.'						)[:3]						) for layer in down_blocks[i] if 'block' in layer}						)
   __lowerCAmelCase:  List[str]        = {
       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       ,   3						).split('.'						)[:3]						) for layer in down_blocks[i] if 'attn' in layer}						)
   __lowerCAmelCase:  Optional[int]        = {
       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:  int        = renew_attention_paths(attns[j]						)
     assign_to_checkpoint(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   config=SCREAMING_SNAKE_CASE						)

 __lowerCAmelCase:  str        = [key for key in checkpoint if 'mid.block_1' in key]
 __lowerCAmelCase:  List[Any]        = [key for key in checkpoint if 'mid.block_2' in key]
 __lowerCAmelCase:  Tuple        = [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:  str        = 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:  Any        = 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:  List[Any]        = checkpoint[
       f'''decoder.up.{i}.upsample.conv.weight'''
   ]
   __lowerCAmelCase:  Dict        = checkpoint[
       f'''decoder.up.{i}.upsample.conv.bias'''
   ]

  if any('block' in layer for layer in up_blocks[i]						):
   __lowerCAmelCase:  Tuple        = len(
       {'.'.join(shave_segments(SCREAMING_SNAKE_CASE       ,   3						).split('.'						)[:3]						) for layer in up_blocks[i] if 'block' in layer}						)
   __lowerCAmelCase:  Optional[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:  Any        = {'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
     __lowerCAmelCase:  List[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:  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:  int        = {'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()}
 __lowerCAmelCase:  List[str]        = checkpoint['quant_conv.weight']
 __lowerCAmelCase:  int        = checkpoint['quant_conv.bias']
 if "quantize.embedding.weight" in checkpoint:
  __lowerCAmelCase:  str        = checkpoint['quantize.embedding.weight']
 __lowerCAmelCase:  Optional[int]        = checkpoint['post_quant_conv.weight']
 __lowerCAmelCase:  Optional[Any]        = checkpoint['post_quant_conv.bias']

 return new_checkpoint


if __name__ == "__main__":
  _a									=  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									=  parser.parse_args()
  _a									=  torch.load(args.checkpoint_path)

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

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

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

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

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

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





