






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 				__lowerCamelCase     (							__lowerCAmelCase							:					Union[str, Any]					,							__lowerCAmelCase							:					Any							)     ->  Tuple:
   __UpperCamelCase	:			str					=					checkpoint

   __UpperCamelCase	:			Any					=					{}

   __UpperCamelCase	:			Optional[Any]					=					vae_state_dict["""encoder.conv_in.weight"""]
   __UpperCamelCase	:			List[Any]					=					vae_state_dict["""encoder.conv_in.bias"""]
   __UpperCamelCase	:			Optional[int]					=					vae_state_dict["""encoder.conv_out.weight"""]
   __UpperCamelCase	:			Dict					=					vae_state_dict["""encoder.conv_out.bias"""]
   __UpperCamelCase	:			Union[str, Any]					=					vae_state_dict["""encoder.norm_out.weight"""]
   __UpperCamelCase	:			Dict					=					vae_state_dict["""encoder.norm_out.bias"""]

   __UpperCamelCase	:			Optional[int]					=					vae_state_dict["""decoder.conv_in.weight"""]
   __UpperCamelCase	:			Optional[Any]					=					vae_state_dict["""decoder.conv_in.bias"""]
   __UpperCamelCase	:			List[str]					=					vae_state_dict["""decoder.conv_out.weight"""]
   __UpperCamelCase	:			int					=					vae_state_dict["""decoder.conv_out.bias"""]
   __UpperCamelCase	:			int					=					vae_state_dict["""decoder.norm_out.weight"""]
   __UpperCamelCase	:			Tuple					=					vae_state_dict["""decoder.norm_out.bias"""]

   __UpperCamelCase	:			Union[str, Any]					=					vae_state_dict["""quant_conv.weight"""]
   __UpperCamelCase	:			List[str]					=					vae_state_dict["""quant_conv.bias"""]
   __UpperCamelCase	:			List[Any]					=					vae_state_dict["""post_quant_conv.weight"""]
   __UpperCamelCase	:			str					=					vae_state_dict["""post_quant_conv.bias"""]

   # Retrieves the keys for the encoder down blocks only
   __UpperCamelCase	:			List[str]					=					len({""".""".join(layer.split("""."""							)[:3]							) for layer in vae_state_dict if """encoder.down""" in layer}							)
   __UpperCamelCase	:			Any					=					{
       layer_id: [key for key in vae_state_dict if f'down.{layer_id}' in key] for layer_id in range(__lowerCAmelCase							)
   }

   # Retrieves the keys for the decoder up blocks only
   __UpperCamelCase	:			Tuple					=					len({""".""".join(layer.split("""."""							)[:3]							) for layer in vae_state_dict if """decoder.up""" in layer}							)
   __UpperCamelCase	:			Optional[int]					=					{
       layer_id: [key for key in vae_state_dict if f'up.{layer_id}' in key] for layer_id in range(__lowerCAmelCase							)
   }

   for i in range(__lowerCAmelCase							):
      __UpperCamelCase	:			Union[str, Any]					=					[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	:			Dict					=					vae_state_dict.pop(
             f'encoder.down.{i}.downsample.conv.weight'							)
         __UpperCamelCase	:			Optional[Any]					=					vae_state_dict.pop(
             f'encoder.down.{i}.downsample.conv.bias'							)

      __UpperCamelCase	:			Any					=					renew_vae_resnet_paths(__lowerCAmelCase							)
      __UpperCamelCase	:			int					=					{"""old""": f'down.{i}.block', """new""": f'down_blocks.{i}.resnets'}
      assign_to_checkpoint(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							additional_replacements=[meta_path]					,							config=__lowerCAmelCase							)

   __UpperCamelCase	:			str					=					[key for key in vae_state_dict if """encoder.mid.block""" in key]
   __UpperCamelCase	:			Optional[int]					=					2
   for i in range(1					,							num_mid_res_blocks + 1							):
      __UpperCamelCase	:			Optional[Any]					=					[key for key in mid_resnets if f'encoder.mid.block_{i}' in key]

      __UpperCamelCase	:			Optional[int]					=					renew_vae_resnet_paths(__lowerCAmelCase							)
      __UpperCamelCase	:			int					=					{"""old""": f'mid.block_{i}', """new""": f'mid_block.resnets.{i - 1}'}
      assign_to_checkpoint(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							additional_replacements=[meta_path]					,							config=__lowerCAmelCase							)

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

   for i in range(__lowerCAmelCase							):
      __UpperCamelCase	:			List[Any]					=					num_up_blocks - 1 - i
      __UpperCamelCase	:			Any					=					[
          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	:			Tuple					=					vae_state_dict[
             f'decoder.up.{block_id}.upsample.conv.weight'
         ]
         __UpperCamelCase	:			int					=					vae_state_dict[
             f'decoder.up.{block_id}.upsample.conv.bias'
         ]

      __UpperCamelCase	:			str					=					renew_vae_resnet_paths(__lowerCAmelCase							)
      __UpperCamelCase	:			str					=					{"""old""": f'up.{block_id}.block', """new""": f'up_blocks.{i}.resnets'}
      assign_to_checkpoint(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							additional_replacements=[meta_path]					,							config=__lowerCAmelCase							)

   __UpperCamelCase	:			Union[str, Any]					=					[key for key in vae_state_dict if """decoder.mid.block""" in key]
   __UpperCamelCase	:			Any					=					2
   for i in range(1					,							num_mid_res_blocks + 1							):
      __UpperCamelCase	:			List[str]					=					[key for key in mid_resnets if f'decoder.mid.block_{i}' in key]

      __UpperCamelCase	:			Tuple					=					renew_vae_resnet_paths(__lowerCAmelCase							)
      __UpperCamelCase	:			Any					=					{"""old""": f'mid.block_{i}', """new""": f'mid_block.resnets.{i - 1}'}
      assign_to_checkpoint(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							additional_replacements=[meta_path]					,							config=__lowerCAmelCase							)

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


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					str					,							)     ->  Optional[Any]:
   # Only support V1
   __UpperCamelCase	:			Any					=					requests.get(
       """ https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"""							)
   __UpperCamelCase	:			Any					=					io.BytesIO(r.content							)

   __UpperCamelCase	:			Optional[int]					=					OmegaConf.load(__lowerCAmelCase							)
   __UpperCamelCase	:			Tuple					=					512
   __UpperCamelCase	:			Dict					=					"""cuda""" if torch.cuda.is_available() else """cpu"""
   if checkpoint_path.endswith("""safetensors"""							):
      from safetensors import safe_open

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

   # Convert the VAE model.
   __UpperCamelCase	:			int					=					create_vae_diffusers_config(__lowerCAmelCase					,							image_size=__lowerCAmelCase							)
   __UpperCamelCase	:			int					=					custom_convert_ldm_vae_checkpoint(__lowerCAmelCase					,							__lowerCAmelCase							)

   __UpperCamelCase	:			Dict					=					AutoencoderKL(**__lowerCAmelCase							)
   vae.load_state_dict(__lowerCAmelCase							)
   vae.save_pretrained(__lowerCAmelCase							)


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

       UpperCamelCase    							=      parser.parse_args()

       vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)











from __future__ import annotations

from typing import Any







class 		_A						:

  def __init__(    self						:	List[Any]   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	float = 0			):
     """simple docstring"""

     __UpperCamelCase	,     __UpperCamelCase	:			Tuple					=					row, column
     __UpperCamelCase	:			Tuple					=					[[default_value for c in range(lowerCamelCase__			)] for r in range(lowerCamelCase__			)]

  def __str__(    self						:	List[Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					f'Matrix consist of {self.row} rows and {self.column} columns\n'

     # Make string identifier
     __UpperCamelCase	:			Optional[int]					=					0
     for row_vector in self.array:
        for obj in row_vector:
           __UpperCamelCase	:			int					=					max(lowerCamelCase__   ,				len(str(lowerCamelCase__			)			)			)
     __UpperCamelCase	:			Union[str, Any]					=					f'%{max_element_length}s'

     # Make string and return
     def single_line(lowerCamelCase__						:	list[float]			) -> str:
        nonlocal string_format_identifier
        __UpperCamelCase	:			List[Any]					=					"""["""
        line += ", ".join(string_format_identifier % (obj,) for obj in row_vector			)
        line += "]"
        return line

     s += "\n".join(single_line(lowerCamelCase__			) for row_vector in self.array			)
     return s

  def __repr__(    self						:	Any			):
     """simple docstring"""

     return str(self			)

  def    a					(    self						:	str   ,				lowerCamelCase__						:	tuple[int, int]			):
     """simple docstring"""

     if not (isinstance(lowerCamelCase__   ,				(list, tuple)			) and len(lowerCamelCase__			) == 2):
        return False
     elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
        return False
     else:
        return True

  def __getitem__(    self						:	Union[str, Any]   ,				lowerCamelCase__						:	tuple[int, int]			):
     """simple docstring"""

     assert self.validate_indicies(lowerCamelCase__			)
     return self.array[loc[0]][loc[1]]

  def __setitem__(    self						:	Union[str, Any]   ,				lowerCamelCase__						:	tuple[int, int]   ,				lowerCamelCase__						:	float			):
     """simple docstring"""

     assert self.validate_indicies(lowerCamelCase__			)
     __UpperCamelCase	:			str					=					value

  def __add__(    self						:	int   ,				lowerCamelCase__						:	Matrix			):
     """simple docstring"""

     assert isinstance(lowerCamelCase__   ,				lowerCamelCase__			)
     assert self.row == another.row and self.column == another.column

     # Add
     __UpperCamelCase	:			Tuple					=					Matrix(self.row   ,				self.column			)
     for r in range(self.row			):
        for c in range(self.column			):
           __UpperCamelCase	:			Optional[int]					=					self[r, c] + another[r, c]
     return result

  def __neg__(    self						:	Optional[int]			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					Matrix(self.row   ,				self.column			)
     for r in range(self.row			):
        for c in range(self.column			):
           __UpperCamelCase	:			Any					=					-self[r, c]
     return result

  def __sub__(    self						:	Tuple   ,				lowerCamelCase__						:	Matrix			):
     """simple docstring"""

     return self + (-another)

  def __mul__(    self						:	Tuple   ,				lowerCamelCase__						:	int | float | Matrix			):
     """simple docstring"""

     if isinstance(lowerCamelCase__   ,				(int, float)			):  # Scalar multiplication
        __UpperCamelCase	:			List[str]					=					Matrix(self.row   ,				self.column			)
        for r in range(self.row			):
           for c in range(self.column			):
              __UpperCamelCase	:			Tuple					=					self[r, c] * another
        return result
     elif isinstance(lowerCamelCase__   ,				lowerCamelCase__			):  # Matrix multiplication
        assert self.column == another.row
        __UpperCamelCase	:			List[Any]					=					Matrix(self.row   ,				another.column			)
        for r in range(self.row			):
           for c in range(another.column			):
              for i in range(self.column			):
                 result[r, c] += self[r, i] * another[i, c]
        return result
     else:
        __UpperCamelCase	:			Any					=					f'Unsupported type given for another ({type(lowerCamelCase__			)})'
        raise TypeError(lowerCamelCase__			)

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					Matrix(self.column   ,				self.row			)
     for r in range(self.row			):
        for c in range(self.column			):
           __UpperCamelCase	:			str					=					self[r, c]
     return result

  def    a					(    self						:	Any   ,				lowerCamelCase__						:	Matrix   ,				lowerCamelCase__						:	Matrix			):
     """simple docstring"""

     assert isinstance(lowerCamelCase__   ,				lowerCamelCase__			) and isinstance(lowerCamelCase__   ,				lowerCamelCase__			)
     assert self.row == self.column == u.row == v.row  # u, v should be column vector
     assert u.column == v.column == 1  # u, v should be column vector

     # Calculate
     __UpperCamelCase	:			Optional[int]					=					v.transpose()
     __UpperCamelCase	:			Tuple					=					(v_t * self * u)[0, 0] + 1
     if numerator_factor == 0:
        return None  # It's not invertable
     return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":


       def 				__lowerCamelCase     (							)     ->  None:
          # a^(-1)
          __UpperCamelCase	:			str					=					Matrix(3					,							3					,							0							)
          for i in range(3							):
             __UpperCamelCase	:			List[str]					=					1
          print(f'a^(-1) is {ainv}'							)
          # u, v
          __UpperCamelCase	:			Any					=					Matrix(3					,							1					,							0							)
          __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	:			Any					=					1, 2, -3
          __UpperCamelCase	:			int					=					Matrix(3					,							1					,							0							)
          __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	:			Dict					=					4, -2, 5
          print(f'u is {u}'							)
          print(f'v is {v}'							)
          print(f'uv^T is {u * v.transpose()}'							)
          # Sherman Morrison
          print(f'(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowerCAmelCase					,							__lowerCAmelCase							)}'							)


       def 				__lowerCamelCase     (							)     ->  None:
          import doctest

          doctest.testmod()



       testa()



