
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 			UpperCamelCase					(    __lowerCamelCase     : Dict				,					__lowerCamelCase     : Optional[int]    ):
       snake_case       :   str							=      checkpoint

       snake_case       :   Tuple							=      {}

       snake_case       :   str							=      vae_state_dict["encoder.conv_in.weight"]
       snake_case       :   int							=      vae_state_dict["encoder.conv_in.bias"]
       snake_case       :   Optional[int]							=      vae_state_dict["encoder.conv_out.weight"]
       snake_case       :   Any							=      vae_state_dict["encoder.conv_out.bias"]
       snake_case       :   Tuple							=      vae_state_dict["encoder.norm_out.weight"]
       snake_case       :   Union[str, Any]							=      vae_state_dict["encoder.norm_out.bias"]

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

       snake_case       :   str							=      vae_state_dict["quant_conv.weight"]
       snake_case       :   Any							=      vae_state_dict["quant_conv.bias"]
       snake_case       :   List[str]							=      vae_state_dict["post_quant_conv.weight"]
       snake_case       :   Tuple							=      vae_state_dict["post_quant_conv.bias"]

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

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

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

              snake_case       :   Dict							=      renew_vae_resnet_paths(__lowerCamelCase    )
              snake_case       :   Optional[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    )

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

              snake_case       :   Tuple							=      renew_vae_resnet_paths(__lowerCamelCase    )
              snake_case       :   Tuple							=      {"old": f"""up.{block_id}.block""", "new": f"""up_blocks.{i}.resnets"""}
              assign_to_checkpoint(__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase				,					additional_replacements=[meta_path]				,					config=__lowerCamelCase    )

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

              snake_case       :   Optional[Any]							=      renew_vae_resnet_paths(__lowerCamelCase    )
              snake_case       :   List[str]							=      {"old": f"""mid.block_{i}""", "new": f"""mid_block.resnets.{i - 1}"""}
              assign_to_checkpoint(__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase				,					additional_replacements=[meta_path]				,					config=__lowerCamelCase    )

       snake_case       :   Tuple							=      [key for key in vae_state_dict if "decoder.mid.attn" in key]
       snake_case       :   Optional[int]							=      renew_vae_attention_paths(__lowerCamelCase    )
       snake_case       :   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 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : str				,					):
       snake_case       :   Optional[int]							=      requests.get(
           " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"    )
       snake_case       :   Union[str, Any]							=      io.BytesIO(r.content    )

       snake_case       :   Optional[int]							=      OmegaConf.load(__lowerCamelCase    )
       snake_case       :   str							=      512
       snake_case       :   str							=      "cuda" if torch.cuda.is_available() else "cpu"
       if checkpoint_path.endswith("safetensors"    ):
              from safetensors import safe_open

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

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

       snake_case       :   Any							=      AutoencoderKL(**__lowerCamelCase    )
       vae.load_state_dict(__lowerCamelCase    )
       vae.save_pretrained(__lowerCamelCase    )


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

   __lowerCamelCase							=       parser.parse_args()

   vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)











from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __init__(				self					:  str						,							A_					:  TransformeraDModel						,							A_					:  AutoencoderKL						,							A_					:  KarrasDiffusionSchedulers						,							A_					:  Optional[Dict[int, str]] = None						,							)   ->	str:

       """simple docstring"""






       super().__init__()
       self.register_modules(transformer=A_						,							vae=A_						,							scheduler=A_  )

       # create a imagenet -> id dictionary for easier use
       lowerCamelCase_		     =    {}
       if idalabel is not None:
             for key, value in idalabel.items():
                   for label in value.split(','  ):
                         lowerCamelCase_		     =    int(A_  )
             lowerCamelCase_		     =    dict(sorted(self.labels.items()  )  )

 def 				a__ (				self					:  Optional[int]						,							A_					:  Union[str, List[str]]  )   ->	List[int]:

       """simple docstring"""






       if not isinstance(A_						,							A_  ):
             lowerCamelCase_		     =    list(A_  )

       for l in label:
             if l not in self.labels:
                   raise ValueError(
                       f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""  )

       return [self.labels[l] for l in label]





 @torch.no_grad()
 def __call__(				self					:  Any						,							A_					:  List[int]						,							A_					:  float = 4.0						,							A_					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None						,							A_					:  int = 50						,							A_					:  Optional[str] = "pil"						,							A_					:  bool = True						,							)   ->	Union[ImagePipelineOutput, Tuple]:

       """simple docstring"""






       lowerCamelCase_		     =    len(A_  )
       lowerCamelCase_		     =    self.transformer.config.sample_size
       lowerCamelCase_		     =    self.transformer.config.in_channels

       lowerCamelCase_		     =    randn_tensor(
           shape=(batch_size, latent_channels, latent_size, latent_size)						,							generator=A_						,							device=self.device						,							dtype=self.transformer.dtype						,							)
       lowerCamelCase_		     =    torch.cat([latents] * 2  ) if guidance_scale > 1 else latents

       lowerCamelCase_		     =    torch.tensor(A_						,							device=self.device  ).reshape(-1  )
       lowerCamelCase_		     =    torch.tensor([1000] * batch_size						,							device=self.device  )
       lowerCamelCase_		     =    torch.cat([class_labels, class_null]						,							0  ) if guidance_scale > 1 else class_labels

       # set step values
       self.scheduler.set_timesteps(A_  )

       for t in self.progress_bar(self.scheduler.timesteps  ):
             if guidance_scale > 1:
                   lowerCamelCase_		     =    latent_model_input[: len(A_  ) // 2]
                   lowerCamelCase_		     =    torch.cat([half, half]						,							dim=0  )
             lowerCamelCase_		     =    self.scheduler.scale_model_input(A_						,							A_  )

             lowerCamelCase_		     =    t
             if not torch.is_tensor(A_  ):
                   # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                   # This would be a good case for the `match` statement (Python 3.10+)
                   lowerCamelCase_		     =    latent_model_input.device.type == 'mps'
                   if isinstance(A_						,							A_  ):
                         lowerCamelCase_		     =    torch.floataa if is_mps else torch.floataa
                   else:
                         lowerCamelCase_		     =    torch.intaa if is_mps else torch.intaa
                   lowerCamelCase_		     =    torch.tensor([timesteps]						,							dtype=A_						,							device=latent_model_input.device  )
             elif len(timesteps.shape  ) == 0:
                   lowerCamelCase_		     =    timesteps[None].to(latent_model_input.device  )
             # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
             lowerCamelCase_		     =    timesteps.expand(latent_model_input.shape[0]  )
             # predict noise model_output
             lowerCamelCase_		     =    self.transformer(
                 A_						,							timestep=A_						,							class_labels=A_  ).sample

             # perform guidance
             if guidance_scale > 1:
                   lowerCamelCase_     ,				lowerCamelCase_		     =    noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                   lowerCamelCase_     ,				lowerCamelCase_		     =    torch.split(A_						,							len(A_  ) // 2						,							dim=0  )

                   lowerCamelCase_		     =    uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                   lowerCamelCase_		     =    torch.cat([half_eps, half_eps]						,							dim=0  )

                   lowerCamelCase_		     =    torch.cat([eps, rest]						,							dim=1  )

             # learned sigma
             if self.transformer.config.out_channels // 2 == latent_channels:
                   lowerCamelCase_     ,				lowerCamelCase_		     =    torch.split(A_						,							A_						,							dim=1  )
             else:
                   lowerCamelCase_		     =    noise_pred

             # compute previous image: x_t -> x_t-1
             lowerCamelCase_		     =    self.scheduler.step(A_						,							A_						,							A_  ).prev_sample

       if guidance_scale > 1:
             lowerCamelCase_     ,				lowerCamelCase_		     =    latent_model_input.chunk(2						,							dim=0  )
       else:
             lowerCamelCase_		     =    latent_model_input

       lowerCamelCase_		     =    1 / self.vae.config.scaling_factor * latents
       lowerCamelCase_		     =    self.vae.decode(A_  ).sample

       lowerCamelCase_		     =    (samples / 2 + 0.5).clamp(0						,							1  )

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       lowerCamelCase_		     =    samples.cpu().permute(0						,							2						,							3						,							1  ).float().numpy()

       if output_type == "pil":
             lowerCamelCase_		     =    self.numpy_to_pil(A_  )

       if not return_dict:
             return (samples,)

       return ImagePipelineOutput(images=A_  )



