





'''simple docstring'''


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(SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       List[Any]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   checkpoint

    _lowerCAmelCase						      =   {}

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

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

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

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

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

    for i in range(SCREAMING_SNAKE_CASE_     ):
        _lowerCAmelCase						      =   [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:
            _lowerCAmelCase						      =   vae_state_dict.pop(
                F'''encoder.down.{i}.downsample.conv.weight'''     )
            _lowerCAmelCase						      =   vae_state_dict.pop(
                F'''encoder.down.{i}.downsample.conv.bias'''     )

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

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

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

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

    for i in range(SCREAMING_SNAKE_CASE_     ):
        _lowerCAmelCase						      =   num_up_blocks - 1 - i
        _lowerCAmelCase						      =   [
            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:
            _lowerCAmelCase						      =   vae_state_dict[
                F'''decoder.up.{block_id}.upsample.conv.weight'''
            ]
            _lowerCAmelCase						      =   vae_state_dict[
                F'''decoder.up.{block_id}.upsample.conv.bias'''
            ]

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

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

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

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


def 						__a(SCREAMING_SNAKE_CASE_	:       str		,							SCREAMING_SNAKE_CASE_	:       str		,							):



    '''simple docstring'''





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

    _lowerCAmelCase						      =   OmegaConf.load(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   512
    _lowerCAmelCase						      =   "cuda" if torch.cuda.is_available() else "cpu"
    if checkpoint_path.endswith("safetensors"     ):
        from safetensors import safe_open

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

    # Convert the VAE model.
    _lowerCAmelCase						      =   create_vae_diffusers_config(SCREAMING_SNAKE_CASE_		,							image_size=SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   custom_convert_ldm_vae_checkpoint(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )

    _lowerCAmelCase						      =   AutoencoderKL(**SCREAMING_SNAKE_CASE_     )
    vae.load_state_dict(SCREAMING_SNAKE_CASE_     )
    vae.save_pretrained(SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE									=				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.")

       _SCREAMING_SNAKE_CASE									=				parser.parse_args()

       vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)









'''simple docstring'''

import numpy as np







class __a			:





   def __init__(       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							(0, 0)
      __SCREAMING_SNAKE_CASE   				=							None
      __SCREAMING_SNAKE_CASE   				=							0
      __SCREAMING_SNAKE_CASE   				=							0
      __SCREAMING_SNAKE_CASE   				=							0





   def __eq__(       self					:      int					,lowerCamelCase					:      List[str]     ):

      '''simple docstring'''

      return self.position == cell.position





   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      print(self.position     )







class __a			:





   def __init__(       self					:      int					,lowerCamelCase					:      Optional[int]=(5, 5)     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							np.zeros(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							world_size[0]
      __SCREAMING_SNAKE_CASE   				=							world_size[1]





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      print(self.w     )





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[
          (-1, -1),
          (-1, 0),
          (-1, 1),
          (0, -1),
          (0, 1),
          (1, -1),
          (1, 0),
          (1, 1),
      ]
      __SCREAMING_SNAKE_CASE   				=							cell.position[0]
      __SCREAMING_SNAKE_CASE   				=							cell.position[1]
      __SCREAMING_SNAKE_CASE   				=							[]
      for n in neughbour_cord:
         __SCREAMING_SNAKE_CASE   				=							current_x + n[0]
         __SCREAMING_SNAKE_CASE   				=							current_y + n[1]
         if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
            __SCREAMING_SNAKE_CASE   				=							Cell()
            __SCREAMING_SNAKE_CASE   				=							(x, y)
            __SCREAMING_SNAKE_CASE   				=							cell
            neighbours.append(lowerCamelCase     )
      return neighbours





def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Any:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							[]
   __SCREAMING_SNAKE_CASE   				=							[]
   _open.append(__UpperCAmelCase   )

   while _open:
      __SCREAMING_SNAKE_CASE   				=							np.argmin([n.f for n in _open]   )
      __SCREAMING_SNAKE_CASE   				=							_open[min_f]
      _closed.append(_open.pop(__UpperCAmelCase   )   )
      if current == goal:
         break
      for n in world.get_neigbours(__UpperCAmelCase   ):
         for c in _closed:
            if c == n:
               continue
         __SCREAMING_SNAKE_CASE   				=							current.g + 1
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							n.position
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							goal.position
         __SCREAMING_SNAKE_CASE   				=							(ya - ya) ** 2 + (xa - xa) ** 2
         __SCREAMING_SNAKE_CASE   				=							n.h + n.g

         for c in _open:
            if c == n and c.f < n.f:
               continue
         _open.append(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							[]
   while current.parent is not None:
      path.append(current.position   )
      __SCREAMING_SNAKE_CASE   				=							current.parent
   path.append(current.position   )
   return path[::-1]


if __name__ == "__main__":
    a   	=    Gridworld()
    # Start position and goal
    a   	=    Cell()
    a   	=    (0, 0)
    a   	=    Cell()
    a   	=    (4, 4)
    print(F'''path from {start.position} to {goal.position}''')
    a   	=    astar(world, start, goal)
    #   Just for visual reasons.
    for i in s:
        a   	=    1
    print(world.w)





