'''simple docstring'''





import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler




class        __A      :



    def __init__(self    :    List[str]   ,			__a    :    Any ):
         if isinstance(__a   ,			__a ):
              # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
              # modified it, it will not be accepted here again, since `auto` values would have been overridden
              UpperCAmelCase_           =   deepcopy(__a )
         elif os.path.exists(__a ):
              with io.open(__a   ,			"r"   ,			encoding="utf-8" ) as f:
                   UpperCAmelCase_           =   json.load(__a )
         else:
              try:
                   UpperCAmelCase_           =   baseaa.urlsafe_baadecode(__a ).decode("utf-8" )
                   UpperCAmelCase_           =   json.loads(__a )
              except (UnicodeDecodeError, AttributeError, ValueError):
                   raise ValueError(
                       f"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}""" )

         UpperCAmelCase_           =   config

         self.set_stage_and_offload()



    def 					_lowercase						(self    :    Optional[Any] ):
         # zero stage - this is done as early as possible, before model is created, to allow
         # ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
         # during ``zero.Init()`` which needs to know the dtype, and some other hparams.
         UpperCAmelCase_           =   self.get_value("zero_optimization.stage"   ,			-1 )

         # offload
         UpperCAmelCase_           =   False
         if self.is_zeroa() or self.is_zeroa():
              UpperCAmelCase_           =   set(["cpu", "nvme"] )
              UpperCAmelCase_           =   set(
                  [
                      self.get_value("zero_optimization.offload_optimizer.device" ),
                      self.get_value("zero_optimization.offload_param.device" ),
                  ] )
              if len(offload_devices & offload_devices_valid ) > 0:
                   UpperCAmelCase_           =   True



    def 					_lowercase						(self    :    Tuple   ,			__a    :    int ):
         UpperCAmelCase_           =   self.config

         # find the config node of interest if it exists
         UpperCAmelCase_           =   ds_key_long.split("." )
         UpperCAmelCase_           =   nodes.pop()
         for node in nodes:
              UpperCAmelCase_           =   config.get(__a )
              if config is None:
                   return None, ds_key

         return config, ds_key



    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    Union[str, Any]   ,			__a    :    List[Any]=None ):
         UpperCAmelCase_    , UpperCAmelCase_           =   self.find_config_node(__a )
         if config is None:
              return default
         return config.get(__a   ,			__a )



    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    List[str]   ,			__a    :    Tuple=False ):
         UpperCAmelCase_           =   self.config

         # find the config node of interest if it exists
         UpperCAmelCase_           =   ds_key_long.split("." )
         for node in nodes:
              UpperCAmelCase_           =   config
              UpperCAmelCase_           =   config.get(__a )
              if config is None:
                   if must_exist:
                        raise ValueError(f"""Can't find {ds_key_long} entry in the config: {self.config}""" )
                   else:
                        return

        # if found remove it
         if parent_config is not None:
              parent_config.pop(__a )



    def 					_lowercase						(self    :    Dict   ,			__a    :    Any ):
         UpperCAmelCase_           =   self.get_value(__a )
         return False if value is None else bool(__a )



    def 					_lowercase						(self    :    Dict   ,			__a    :    List[Any] ):
         UpperCAmelCase_           =   self.get_value(__a )
         return False if value is None else not bool(__a )



    def 					_lowercase						(self    :    Any ):
         return self._stage == 2



    def 					_lowercase						(self    :    Union[str, Any] ):
         return self._stage == 3






    def 					_lowercase						(self    :    Dict ):
         return self._offload




class        __A      :



    def __init__(self    :    Dict   ,			__a    :    List[str] ):
         UpperCAmelCase_           =   engine






    def 					_lowercase						(self    :    str   ,			__a    :    Tuple   ,			**__a    :    Tuple ):
         # runs backpropagation and handles mixed precision
         self.engine.backward(__a   ,			**__a )

         # Deepspeed's `engine.step` performs the following operations:
         # - gradient accumulation check
         # - gradient clipping
         # - optimizer step
         # - zero grad
         # - checking overflow
         # - lr_scheduler step (only if engine.lr_scheduler is not None)
         self.engine.step()
         # and this plugin overrides the above calls with no-ops when Accelerate runs under
         # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
         # training loop that works transparently under many training regimes.




class        __A      ( UpperCamelCase__	):



    def __init__(self    :    Union[str, Any]   ,			__a    :    int ):
         super().__init__(__a   ,			device_placement=__a   ,			scaler=__a )
         UpperCAmelCase_           =   hasattr(self.optimizer   ,			"overflow" )



    def 					_lowercase						(self    :    Dict   ,			__a    :    List[Any]=None ):
         pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed



    def 					_lowercase						(self    :    List[str] ):
         pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






    @property
    def 					_lowercase						(self    :    str ):
         if self.__has_overflow__:
              return self.optimizer.overflow
         return False




class        __A      ( UpperCamelCase__	):



    def __init__(self    :    Optional[int]   ,			__a    :    Any   ,			__a    :    int ):
         super().__init__(__a   ,			__a )






    def 					_lowercase						(self    :    List[str] ):
         pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




class        __A      :



    def __init__(self    :    Dict   ,			__a    :    Optional[int]   ,			__a    :    Dict=0.0_01   ,			__a    :    Any=0   ,			**__a    :    Dict ):
         UpperCAmelCase_           =   params
         UpperCAmelCase_           =   lr
         UpperCAmelCase_           =   weight_decay
         UpperCAmelCase_           =   kwargs




class        __A      :



    def __init__(self    :    Optional[int]   ,			__a    :    List[str]   ,			__a    :    List[str]=None   ,			__a    :    Dict=0   ,			**__a    :    Dict ):
         UpperCAmelCase_           =   optimizer
         UpperCAmelCase_           =   total_num_steps
         UpperCAmelCase_           =   warmup_num_steps
         UpperCAmelCase_           =   kwargs












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			:     Dict  ,		__lowerCamelCase			:     int		):
  __UpperCAmelCase							:	List[Any]           =  checkpoint

  __UpperCAmelCase							:	Tuple           =  {}

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

  __UpperCAmelCase							:	Dict           =  vae_state_dict["""decoder.conv_in.weight"""]
  __UpperCAmelCase							:	Tuple           =  vae_state_dict["""decoder.conv_in.bias"""]
  __UpperCAmelCase							:	Optional[int]           =  vae_state_dict["""decoder.conv_out.weight"""]
  __UpperCAmelCase							:	Optional[int]           =  vae_state_dict["""decoder.conv_out.bias"""]
  __UpperCAmelCase							:	Optional[Any]           =  vae_state_dict["""decoder.norm_out.weight"""]
  __UpperCAmelCase							:	Union[str, Any]           =  vae_state_dict["""decoder.norm_out.bias"""]

  __UpperCAmelCase							:	Optional[int]           =  vae_state_dict["""quant_conv.weight"""]
  __UpperCAmelCase							:	int           =  vae_state_dict["""quant_conv.bias"""]
  __UpperCAmelCase							:	Union[str, Any]           =  vae_state_dict["""post_quant_conv.weight"""]
  __UpperCAmelCase							:	Any           =  vae_state_dict["""post_quant_conv.bias"""]

  # Retrieves the keys for the encoder down blocks only
  __UpperCAmelCase							:	int           =  len({""".""".join(layer.split("""."""		)[:3]		) for layer in vae_state_dict if """encoder.down""" in layer}		)
  __UpperCAmelCase							:	Optional[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							:	Dict           =  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							:	List[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							:	Optional[Any]           =  vae_state_dict.pop(
          f"""encoder.down.{i}.downsample.conv.weight"""		)
      __UpperCAmelCase							:	int           =  vae_state_dict.pop(
          f"""encoder.down.{i}.downsample.conv.bias"""		)

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

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

    __UpperCAmelCase							:	Tuple           =  renew_vae_resnet_paths(__lowerCamelCase		)
    __UpperCAmelCase							:	Tuple           =  {"""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							:	List[Any]           =  [key for key in vae_state_dict if """encoder.mid.attn""" in key]
  __UpperCAmelCase							:	str           =  renew_vae_attention_paths(__lowerCamelCase		)
  __UpperCAmelCase							:	List[str]           =  {"""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							:	Optional[Any]           =  num_up_blocks - 1 - i
    __UpperCAmelCase							:	Union[str, 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							:	int           =  vae_state_dict[
          f"""decoder.up.{block_id}.upsample.conv.weight"""
      ]
      __UpperCAmelCase							:	Dict           =  vae_state_dict[
          f"""decoder.up.{block_id}.upsample.conv.bias"""
      ]

    __UpperCAmelCase							:	Dict           =  renew_vae_resnet_paths(__lowerCamelCase		)
    __UpperCAmelCase							:	Optional[Any]           =  {"""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							:	Tuple           =  [key for key in vae_state_dict if """decoder.mid.block""" in key]
  __UpperCAmelCase							:	Union[str, Any]           =  2
  for i in range(1  ,		num_mid_res_blocks + 1		):
    __UpperCAmelCase							:	Dict           =  [key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key]

    __UpperCAmelCase							:	List[Any]           =  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							:	Dict           =  [key for key in vae_state_dict if """decoder.mid.attn""" in key]
  __UpperCAmelCase							:	List[Any]           =  renew_vae_attention_paths(__lowerCamelCase		)
  __UpperCAmelCase							:	List[str]           =  {"""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  ,		):
  # Only support V1
  __UpperCAmelCase							:	Optional[int]           =  requests.get(
      """ https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"""		)
  __UpperCAmelCase							:	Optional[int]           =  io.BytesIO(r.content		)

  __UpperCAmelCase							:	Dict           =  OmegaConf.load(__lowerCamelCase		)
  __UpperCAmelCase							:	str           =  512
  __UpperCAmelCase							:	Any           =  """cuda""" if torch.cuda.is_available() else """cpu"""
  if checkpoint_path.endswith("""safetensors"""		):
    from safetensors import safe_open

    __UpperCAmelCase							:	List[Any]           =  {}
    with safe_open(__lowerCamelCase  ,		framework="""pt"""  ,		device="""cpu"""		) as f:
      for key in f.keys():
        __UpperCAmelCase							:	str           =  f.get_tensor(__lowerCamelCase		)
  else:
    __UpperCAmelCase							:	Optional[int]           =  torch.load(__lowerCamelCase  ,		map_location=__lowerCamelCase		)["""state_dict"""]

  # Convert the VAE model.
  __UpperCAmelCase							:	Optional[int]           =  create_vae_diffusers_config(__lowerCamelCase  ,		image_size=__lowerCamelCase		)
  __UpperCAmelCase							:	Union[str, Any]           =  custom_convert_ldm_vae_checkpoint(__lowerCamelCase  ,		__lowerCamelCase		)

  __UpperCAmelCase							:	Optional[int]           =  AutoencoderKL(**__lowerCamelCase		)
  vae.load_state_dict(__lowerCamelCase		)
  vae.save_pretrained(__lowerCamelCase		)


if __name__ == "__main__":
       a		:       List[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.")

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

       vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)


