





import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)




def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =checkpoint

   UpperCAmelCase_           ={}

   UpperCAmelCase_           =vae_state_dict["encoder.conv_in.weight"]
   UpperCAmelCase_           =vae_state_dict["encoder.conv_in.bias"]
   UpperCAmelCase_           =vae_state_dict["encoder.conv_out.weight"]
   UpperCAmelCase_           =vae_state_dict["encoder.conv_out.bias"]
   UpperCAmelCase_           =vae_state_dict["encoder.norm_out.weight"]
   UpperCAmelCase_           =vae_state_dict["encoder.norm_out.bias"]

   UpperCAmelCase_           =vae_state_dict["decoder.conv_in.weight"]
   UpperCAmelCase_           =vae_state_dict["decoder.conv_in.bias"]
   UpperCAmelCase_           =vae_state_dict["decoder.conv_out.weight"]
   UpperCAmelCase_           =vae_state_dict["decoder.conv_out.bias"]
   UpperCAmelCase_           =vae_state_dict["decoder.norm_out.weight"]
   UpperCAmelCase_           =vae_state_dict["decoder.norm_out.bias"]

   UpperCAmelCase_           =vae_state_dict["quant_conv.weight"]
   UpperCAmelCase_           =vae_state_dict["quant_conv.bias"]
   UpperCAmelCase_           =vae_state_dict["post_quant_conv.weight"]
   UpperCAmelCase_           =vae_state_dict["post_quant_conv.bias"]

   # Retrieves the keys for the encoder down blocks only
   UpperCAmelCase_           =len({".".join(layer.split("."	)[:3]	) for layer in vae_state_dict if "encoder.down" in layer}	)
   UpperCAmelCase_           ={
       layer_id: [key for key in vae_state_dict if F'down.{layer_id}' in key] for layer_id in range(lowercase__	)
   }

   # Retrieves the keys for the decoder up blocks only
   UpperCAmelCase_           =len({".".join(layer.split("."	)[:3]	) for layer in vae_state_dict if "decoder.up" in layer}	)
   UpperCAmelCase_           ={
       layer_id: [key for key in vae_state_dict if F'up.{layer_id}' in key] for layer_id in range(lowercase__	)
   }

   for i in range(lowercase__	):
      UpperCAmelCase_           =[key for key in down_blocks[i] if F'down.{i}' in key and F'down.{i}.downsample' not in key]

      if F'encoder.down.{i}.downsample.conv.weight' in vae_state_dict:
         UpperCAmelCase_           =vae_state_dict.pop(
             F'encoder.down.{i}.downsample.conv.weight'	)
         UpperCAmelCase_           =vae_state_dict.pop(
             F'encoder.down.{i}.downsample.conv.bias'	)

      UpperCAmelCase_           =renew_vae_resnet_paths(lowercase__	)
      UpperCAmelCase_           ={"old": F'down.{i}.block', "new": F'down_blocks.{i}.resnets'}
      assign_to_checkpoint(lowercase__	,     lowercase__	,     lowercase__	,     additional_replacements=[meta_path]	,     config=lowercase__	)

   UpperCAmelCase_           =[key for key in vae_state_dict if "encoder.mid.block" in key]
   UpperCAmelCase_           =2
   for i in range(1	,     num_mid_res_blocks + 1	):
      UpperCAmelCase_           =[key for key in mid_resnets if F'encoder.mid.block_{i}' in key]

      UpperCAmelCase_           =renew_vae_resnet_paths(lowercase__	)
      UpperCAmelCase_           ={"old": F'mid.block_{i}', "new": F'mid_block.resnets.{i - 1}'}
      assign_to_checkpoint(lowercase__	,     lowercase__	,     lowercase__	,     additional_replacements=[meta_path]	,     config=lowercase__	)

   UpperCAmelCase_           =[key for key in vae_state_dict if "encoder.mid.attn" in key]
   UpperCAmelCase_           =renew_vae_attention_paths(lowercase__	)
   UpperCAmelCase_           ={"old": "mid.attn_1", "new": "mid_block.attentions.0"}
   assign_to_checkpoint(lowercase__	,     lowercase__	,     lowercase__	,     additional_replacements=[meta_path]	,     config=lowercase__	)
   conv_attn_to_linear(lowercase__	)

   for i in range(lowercase__	):
      UpperCAmelCase_           =num_up_blocks - 1 - i
      UpperCAmelCase_           =[
          key for key in up_blocks[block_id] if F'up.{block_id}' in key and F'up.{block_id}.upsample' not in key
      ]

      if F'decoder.up.{block_id}.upsample.conv.weight' in vae_state_dict:
         UpperCAmelCase_           =vae_state_dict[
             F'decoder.up.{block_id}.upsample.conv.weight'
         ]
         UpperCAmelCase_           =vae_state_dict[
             F'decoder.up.{block_id}.upsample.conv.bias'
         ]

      UpperCAmelCase_           =renew_vae_resnet_paths(lowercase__	)
      UpperCAmelCase_           ={"old": F'up.{block_id}.block', "new": F'up_blocks.{i}.resnets'}
      assign_to_checkpoint(lowercase__	,     lowercase__	,     lowercase__	,     additional_replacements=[meta_path]	,     config=lowercase__	)

   UpperCAmelCase_           =[key for key in vae_state_dict if "decoder.mid.block" in key]
   UpperCAmelCase_           =2
   for i in range(1	,     num_mid_res_blocks + 1	):
      UpperCAmelCase_           =[key for key in mid_resnets if F'decoder.mid.block_{i}' in key]

      UpperCAmelCase_           =renew_vae_resnet_paths(lowercase__	)
      UpperCAmelCase_           ={"old": F'mid.block_{i}', "new": F'mid_block.resnets.{i - 1}'}
      assign_to_checkpoint(lowercase__	,     lowercase__	,     lowercase__	,     additional_replacements=[meta_path]	,     config=lowercase__	)

   UpperCAmelCase_           =[key for key in vae_state_dict if "decoder.mid.attn" in key]
   UpperCAmelCase_           =renew_vae_attention_paths(lowercase__	)
   UpperCAmelCase_           ={"old": "mid.attn_1", "new": "mid_block.attentions.0"}
   assign_to_checkpoint(lowercase__	,     lowercase__	,     lowercase__	,     additional_replacements=[meta_path]	,     config=lowercase__	)
   conv_attn_to_linear(lowercase__	)
   return new_checkpoint




def      a__			(       lowercase__	,     lowercase__	,     ):






   '''simple docstring'''






   UpperCAmelCase_           =requests.get(
       " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"	)
   UpperCAmelCase_           =io.BytesIO(r.content	)

   UpperCAmelCase_           =OmegaConf.load(lowercase__	)
   UpperCAmelCase_           =5_1_2
   UpperCAmelCase_           ="cuda" if torch.cuda.is_available() else "cpu"
   if checkpoint_path.endswith("safetensors"	):
      from safetensors import safe_open

      UpperCAmelCase_           ={}
      with safe_open(lowercase__	,     framework="pt"	,     device="cpu"	) as f:
         for key in f.keys():
            UpperCAmelCase_           =f.get_tensor(lowercase__	)
   else:
      UpperCAmelCase_           =torch.load(lowercase__	,     map_location=lowercase__	)["state_dict"]

   # Convert the VAE model.
   UpperCAmelCase_           =create_vae_diffusers_config(lowercase__	,     image_size=lowercase__	)
   UpperCAmelCase_           =custom_convert_ldm_vae_checkpoint(lowercase__	,     lowercase__	)

   UpperCAmelCase_           =AutoencoderKL(**lowercase__	)
   vae.load_state_dict(lowercase__	)
   vae.save_pretrained(lowercase__	)


if __name__ == "__main__":
     __lowercase	: Tuple       =argparse.ArgumentParser()

     parser.add_argument("""--vae_pt_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")
     parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")

     __lowercase	: Optional[int]       =parser.parse_args()

     vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)











from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_   :      Optional[int]			=   TypeVar('''T''')



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		return (position - 1) // 2



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		return (2 * position) + 1



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		return (2 * position) + 2

class 		snake_case_   (							Generic[T]						):







	'''simple docstring'''




	def __init__(						self							:     Any   )       -> None:

			'''simple docstring'''




			__lowercase											=     []
			__lowercase											=     {}
			__lowercase											=     0




	def __len__(						self							:     Optional[Any]   )       -> int:

			'''simple docstring'''




			return self.elements




	def __repr__(						self							:     Optional[Any]   )       -> str:

			'''simple docstring'''




			return str(self.heap   )




	def    UpperCAmelCase			(						self							:     Union[str, Any]   )       -> bool:

			'''simple docstring'''




			return self.elements == 0




	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			self.heap.append((elem, weight)   )
			__lowercase											=     self.elements
			self.elements += 1
			self._bubble_up(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Dict   )       -> T:

			'''simple docstring'''




			if self.elements > 1:
					self._swap_nodes(0					,      self.elements - 1   )
			__lowercase						,      __lowercase											=     self.heap.pop()
			del self.position_map[elem]
			self.elements -= 1
			if self.elements > 0:
					__lowercase						,      __lowercase											=     self.heap[0]
					self._bubble_down(__lowerCamelCase   )
			return elem




	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.position_map[elem]
			__lowercase											=     (elem, weight)
			if position > 0:
					__lowercase											=     get_parent_position(__lowerCamelCase   )
					__lowercase						,      __lowercase											=     self.heap[parent_position]
					if parent_weight > weight:
							self._bubble_up(__lowerCamelCase   )
					else:
							self._bubble_down(__lowerCamelCase   )
			else:
					self._bubble_down(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Tuple					,      __lowerCamelCase							:     T   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.position_map[elem]
			if curr_pos == 0:
					return None
			__lowercase											=     get_parent_position(__lowerCamelCase   )
			__lowercase						,      __lowercase											=     self.heap[curr_pos]
			__lowercase						,      __lowercase											=     self.heap[parent_position]
			if parent_weight > weight:
					self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
					return self._bubble_up(__lowerCamelCase   )
			return None




	def    UpperCAmelCase			(						self							:     List[str]					,      __lowerCamelCase							:     T   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.position_map[elem]
			__lowercase						,      __lowercase											=     self.heap[curr_pos]
			__lowercase											=     get_child_left_position(__lowerCamelCase   )
			__lowercase											=     get_child_right_position(__lowerCamelCase   )
			if child_left_position < self.elements and child_right_position < self.elements:
					__lowercase						,      __lowercase											=     self.heap[child_left_position]
					__lowercase						,      __lowercase											=     self.heap[child_right_position]
					if child_right_weight < child_left_weight and child_right_weight < weight:
							self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
							return self._bubble_down(__lowerCamelCase   )
			if child_left_position < self.elements:
					__lowercase						,      __lowercase											=     self.heap[child_left_position]
					if child_left_weight < weight:
							self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
							return self._bubble_down(__lowerCamelCase   )
			else:
					return None
			if child_right_position < self.elements:
					__lowercase						,      __lowercase											=     self.heap[child_right_position]
					if child_right_weight < weight:
							self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
							return self._bubble_down(__lowerCamelCase   )
			return None






	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     int					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.heap[nodea_pos][0]
			__lowercase											=     self.heap[nodea_pos][0]
			__lowercase						,      __lowercase											=     (
			    self.heap[nodea_pos],
			    self.heap[nodea_pos],
			)
			__lowercase											=     nodea_pos
			__lowercase											=     nodea_pos





class 		snake_case_   (							Generic[T]						):







	'''simple docstring'''




	def __init__(						self							:     Optional[Any]   )       -> None:

			'''simple docstring'''




			__lowercase											=     {}
			__lowercase											=     0




	def __repr__(						self							:     Union[str, Any]   )       -> str:

			'''simple docstring'''




			return str(self.connections   )




	def __len__(						self							:     int   )       -> int:

			'''simple docstring'''




			return self.nodes




	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     T   )       -> None:

			'''simple docstring'''




			if node not in self.connections:
					__lowercase											=     {}
					self.nodes += 1






	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			self.add_node(__lowerCamelCase   )
			self.add_node(__lowerCamelCase   )
			__lowercase											=     weight
			__lowercase											=     weight





def  SCREAMING_SNAKE_CASE				(      snake_case						,   )		->    tuple[dict[T, int], dict[T, T | None]]:
		__lowercase											=     {node: maxsize for node in graph.connections}
		__lowercase											=     {node: None for node in graph.connections}

		__lowercase											=     MinPriorityQueue()
		for node, weight in dist.items():
				priority_queue.push(snake_case						,   snake_case							)

		if priority_queue.is_empty():
				return dist, parent

		# initialization
		__lowercase											=     priority_queue.extract_min()
		__lowercase											=     0
		for neighbour in graph.connections[node]:
				if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
						__lowercase											=     dist[node] + graph.connections[node][neighbour]
						priority_queue.update_key(snake_case						,   dist[neighbour]							)
						__lowercase											=     node

    # running prim's algorithm
		while not priority_queue.is_empty():
				__lowercase											=     priority_queue.extract_min()
				for neighbour in graph.connections[node]:
						if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
								__lowercase											=     dist[node] + graph.connections[node][neighbour]
								priority_queue.update_key(snake_case						,   dist[neighbour]							)
								__lowercase											=     node
		return dist, parent




