



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      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__			):


      """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(UpperCAmelCase__			)
      }

      # 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(UpperCAmelCase__			)
      }

      for i in range(UpperCAmelCase__			):
            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(UpperCAmelCase__			)
            lowerCamelCase             =		{"old": F"""down.{i}.block""", "new": F"""down_blocks.{i}.resnets"""}
            assign_to_checkpoint(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , additional_replacements=[meta_path] , config=UpperCAmelCase__			)

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

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

      for i in range(UpperCAmelCase__			):
            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(UpperCAmelCase__			)
            lowerCamelCase             =		{"old": F"""up.{block_id}.block""", "new": F"""up_blocks.{i}.resnets"""}
            assign_to_checkpoint(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , additional_replacements=[meta_path] , config=UpperCAmelCase__			)

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

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

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , ):


      """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(UpperCAmelCase__			)
      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(UpperCAmelCase__ , framework="pt" , device="cpu"			) as f:
                  for key in f.keys():
                        lowerCamelCase             =		f.get_tensor(UpperCAmelCase__			)
      else:
            lowerCamelCase             =		torch.load(UpperCAmelCase__ , map_location=UpperCAmelCase__			)["state_dict"]

      # Convert the VAE model.
      lowerCamelCase             =		create_vae_diffusers_config(UpperCAmelCase__ , image_size=UpperCAmelCase__			)
      lowerCamelCase             =		custom_convert_ldm_vae_checkpoint(UpperCAmelCase__ , UpperCAmelCase__			)

      lowerCamelCase             =		AutoencoderKL(**UpperCAmelCase__			)
      vae.load_state_dict(UpperCAmelCase__			)
      vae.save_pretrained(UpperCAmelCase__			)


if __name__ == "__main__":
     a_    :  Dict						  =		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_    :  int						  =		parser.parse_args()

     vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


a_    :  Tuple						  =		{'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_    :  Union[str, Any]						  =		[
         'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
         'UniSpeechForCTC',
         'UniSpeechForPreTraining',
         'UniSpeechForSequenceClassification',
         'UniSpeechModel',
         'UniSpeechPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_unispeech import (
              UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
              UniSpeechForCTC,
              UniSpeechForPreTraining,
              UniSpeechForSequenceClassification,
              UniSpeechModel,
              UniSpeechPreTrainedModel,
          )

else:
     import sys

     a_    :  Optional[Any]						  =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)