






"""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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str		,					__snake_case			:  List[Any]    ):





  '''simple docstring'''





  lowercase							    =       checkpoint

  lowercase							    =       {}

  lowercase							    =       vae_state_dict['encoder.conv_in.weight']
  lowercase							    =       vae_state_dict['encoder.conv_in.bias']
  lowercase							    =       vae_state_dict['encoder.conv_out.weight']
  lowercase							    =       vae_state_dict['encoder.conv_out.bias']
  lowercase							    =       vae_state_dict['encoder.norm_out.weight']
  lowercase							    =       vae_state_dict['encoder.norm_out.bias']

  lowercase							    =       vae_state_dict['decoder.conv_in.weight']
  lowercase							    =       vae_state_dict['decoder.conv_in.bias']
  lowercase							    =       vae_state_dict['decoder.conv_out.weight']
  lowercase							    =       vae_state_dict['decoder.conv_out.bias']
  lowercase							    =       vae_state_dict['decoder.norm_out.weight']
  lowercase							    =       vae_state_dict['decoder.norm_out.bias']

  lowercase							    =       vae_state_dict['quant_conv.weight']
  lowercase							    =       vae_state_dict['quant_conv.bias']
  lowercase							    =       vae_state_dict['post_quant_conv.weight']
  lowercase							    =       vae_state_dict['post_quant_conv.bias']

  # Retrieves the keys for the encoder down blocks only
  lowercase							    =       len({'.'.join(layer.split('.'    )[:3]    ) for layer in vae_state_dict if 'encoder.down' in layer}    )
  lowercase							    =       {
      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
  lowercase							    =       len({'.'.join(layer.split('.'    )[:3]    ) for layer in vae_state_dict if 'decoder.up' in layer}    )
  lowercase							    =       {
      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    ):
    lowercase							    =       [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:
      lowercase							    =       vae_state_dict.pop(
          f'encoder.down.{i}.downsample.conv.weight'    )
      lowercase							    =       vae_state_dict.pop(
          f'encoder.down.{i}.downsample.conv.bias'    )

    lowercase							    =       renew_vae_resnet_paths(__lowercase    )
    lowercase							    =       {'old': f'down.{i}.block', 'new': f'down_blocks.{i}.resnets'}
    assign_to_checkpoint(__lowercase		,					__lowercase		,					__lowercase		,					additional_replacements=[meta_path]		,					config=__lowercase    )

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

    lowercase							    =       renew_vae_resnet_paths(__lowercase    )
    lowercase							    =       {'old': f'mid.block_{i}', 'new': f'mid_block.resnets.{i - 1}'}
    assign_to_checkpoint(__lowercase		,					__lowercase		,					__lowercase		,					additional_replacements=[meta_path]		,					config=__lowercase    )

  lowercase							    =       [key for key in vae_state_dict if 'encoder.mid.attn' in key]
  lowercase							    =       renew_vae_attention_paths(__lowercase    )
  lowercase							    =       {'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    ):
    lowercase							    =       num_up_blocks - 1 - i
    lowercase							    =       [
        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:
      lowercase							    =       vae_state_dict[
          f'decoder.up.{block_id}.upsample.conv.weight'
      ]
      lowercase							    =       vae_state_dict[
          f'decoder.up.{block_id}.upsample.conv.bias'
      ]

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

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

    lowercase							    =       renew_vae_resnet_paths(__lowercase    )
    lowercase							    =       {'old': f'mid.block_{i}', 'new': f'mid_block.resnets.{i - 1}'}
    assign_to_checkpoint(__lowercase		,					__lowercase		,					__lowercase		,					additional_replacements=[meta_path]		,					config=__lowercase    )

  lowercase							    =       [key for key in vae_state_dict if 'decoder.mid.attn' in key]
  lowercase							    =       renew_vae_attention_paths(__lowercase    )
  lowercase							    =       {'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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str		,					__snake_case			:  str		,					):





  '''simple docstring'''





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

  lowercase							    =       OmegaConf.load(__lowercase    )
  lowercase							    =       5_12
  lowercase							    =       'cuda' if torch.cuda.is_available() else 'cpu'
  if checkpoint_path.endswith('safetensors'    ):
    from safetensors import safe_open

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

  # Convert the VAE model.
  lowercase							    =       create_vae_diffusers_config(__lowercase		,					image_size=__lowercase    )
  lowercase							    =       custom_convert_ldm_vae_checkpoint(__lowercase		,					__lowercase    )

  lowercase							    =       AutoencoderKL(**__lowercase    )
  vae.load_state_dict(__lowercase    )
  vae.save_pretrained(__lowercase    )


if __name__ == "__main__":
    _UpperCamelCase		:  Union[str, Any]						=		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		:  Any						=		parser.parse_args()

    vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)









'''simple docstring'''




from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


__SCREAMING_SNAKE_CASE     :int       =				logging.get_logger(__name__)







class   A_	:
 _lowerCamelCase  : str
 _lowerCamelCase  : str           =							None




 @staticmethod
 def      lowercase	(					):
      raise NotImplementedError




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    Optional[int]	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    List[Any]    ):
      raise NotImplementedError




 def      lowercase	(					self       :    Any	,			snake_case_       :    int    ):
      raise NotImplementedError




 def      lowercase	(					self       :    List[str]    ):
      if not self.is_available():
           raise RuntimeError(
               f'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'    )




 @classmethod
 def      lowercase	(					cls       :    List[Any]    ):
      return f'`pip install {cls.pip_package or cls.name}`'







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""optuna"""




 @staticmethod
 def      lowercase	(					):
      return is_optuna_available()




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    Any	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Tuple    ):
      return run_hp_search_optuna(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    int	,			snake_case_       :    Optional[int]    ):
      return default_hp_space_optuna(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Any           =							"""ray"""
 _lowerCamelCase  : Tuple           =							"""'ray[tune]'"""




 @staticmethod
 def      lowercase	(					):
      return is_ray_available()




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    Any	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    List[str]    ):
      return run_hp_search_ray(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    str    ):
      return default_hp_space_ray(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""sigopt"""




 @staticmethod
 def      lowercase	(					):
      return is_sigopt_available()




 def      lowercase	(					self       :    Any	,			snake_case_       :    int	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Dict    ):
      return run_hp_search_sigopt(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Dict	,			snake_case_       :    Optional[Any]    ):
      return default_hp_space_sigopt(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Optional[int]           =							"""wandb"""




 @staticmethod
 def      lowercase	(					):
      return is_wandb_available()




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    Optional[Any]	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Optional[Any]    ):
      return run_hp_search_wandb(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    Union[str, Any]    ):
      return default_hp_space_wandb(snake_case_    )





__SCREAMING_SNAKE_CASE     :Dict       =				{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def 	UpperCAmelCase_							(					)		->       str:


     '''simple docstring'''



     _UpperCAmelCase     							=   [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
     if len(__lowercase       ) > 0:
          _UpperCAmelCase     							=   available_backends[0].name
          if len(__lowercase       ) > 1:
               logger.info(
                   f'{len(__lowercase       )} hyperparameter search backends available. Using {name} as the default.'       )
          return name
     raise RuntimeError(
         "No hyperparameter search backend available.\n"
         + "\n".join(
             f' - To install {backend.name} run {backend.pip_install()}'
             for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()       )       )




