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_ (     __A,  __A							)     ->				int:







      '''simple docstring'''


      UpperCAmelCase__								=  checkpoint

      UpperCAmelCase__								=  {}

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

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

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

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

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

      for i in range(__A							):
            UpperCAmelCase__								=  [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__								=  vae_state_dict.pop(
                      f"""encoder.down.{i}.downsample.conv.weight"""							)
                  UpperCAmelCase__								=  vae_state_dict.pop(
                      f"""encoder.down.{i}.downsample.conv.bias"""							)

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

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

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

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

      for i in range(__A							):
            UpperCAmelCase__								=  num_up_blocks - 1 - i
            UpperCAmelCase__								=  [
                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__								=  vae_state_dict[
                      f"""decoder.up.{block_id}.upsample.conv.weight"""
                  ]
                  UpperCAmelCase__								=  vae_state_dict[
                      f"""decoder.up.{block_id}.upsample.conv.bias"""
                  ]

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

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

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

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




def    lowerCAmelCase_ (     __A,  __A,  )     ->				Optional[int]:







      '''simple docstring'''


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

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

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

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

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


if __name__ == "__main__":
      UpperCamelCase__											=					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__											=					parser.parse_args()

      vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)










"""simple docstring"""



# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     42

 # setable values
 _lowerCamelCase        =     42
 _lowerCamelCase        =     42
 _lowerCamelCase        =     None

 @classmethod
 def 					UpperCamelCase__(    cls					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     return cls(common=__lowerCamelCase					,						init_noise_sigma=__lowerCamelCase					,						timesteps=__lowerCamelCase  )






@dataclass
class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     42






class 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     [e.name for e in FlaxKarrasDiffusionSchedulers]

 _lowerCamelCase        =     42

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return True

 @register_to_config
 def __init__(    self					,						__lowerCamelCase = 1000					,						__lowerCamelCase = 0.0_0_0_1					,						__lowerCamelCase = 0.0_2					,						__lowerCamelCase = "linear"					,						__lowerCamelCase = None					,						__lowerCamelCase = "fixed_small"					,						__lowerCamelCase = True					,						__lowerCamelCase = "epsilon"					,						__lowerCamelCase = jnp.floataa					,						):



     '''simple docstring'''




     __A    :						Tuple						 =     dtype

 def 					UpperCamelCase__(    self					,						__lowerCamelCase = None  ):



     '''simple docstring'''




     if common is None:
         __A    :						Tuple						 =     CommonSchedulerState.create(self  )

     # standard deviation of the initial noise distribution
     __A    :						Tuple						 =     jnp.array(1.0					,						dtype=self.dtype  )

     __A    :						Optional[int]						 =     jnp.arange(0					,						self.config.num_train_timesteps  ).round()[::-1]

     return DDPMSchedulerState.create(
         common=__lowerCamelCase					,						init_noise_sigma=__lowerCamelCase					,						timesteps=__lowerCamelCase					,						)

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = None  ):



     '''simple docstring'''




     return sample

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = ()  ):



     '''simple docstring'''




     __A    :						Optional[Any]						 =     self.config.num_train_timesteps // num_inference_steps
     # creates integer timesteps by multiplying by ratio
     # rounding to avoid issues when num_inference_step is power of 3
     __A    :						Optional[Any]						 =     (jnp.arange(0					,						__lowerCamelCase  ) * step_ratio).round()[::-1]

     return state.replace(
         num_inference_steps=__lowerCamelCase					,						timesteps=__lowerCamelCase					,						)

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase=None					,						__lowerCamelCase=None  ):



     '''simple docstring'''




     __A    :						int						 =     state.common.alphas_cumprod[t]
     __A    :						List[str]						 =     jnp.where(t > 0					,						state.common.alphas_cumprod[t - 1]					,						jnp.array(1.0					,						dtype=self.dtype  )  )

     # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
     # and sample from it to get previous sample
     # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
     __A    :						str						 =     (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

     if variance_type is None:
         __A    :						Dict						 =     self.config.variance_type

     # hacks - were probably added for training stability
     if variance_type == "fixed_small":
         __A    :						List[Any]						 =     jnp.clip(__lowerCamelCase					,						a_min=1e-2_0  )
     # for rl-diffuser https://arxiv.org/abs/2205.09991
     elif variance_type == "fixed_small_log":
         __A    :						Optional[Any]						 =     jnp.log(jnp.clip(__lowerCamelCase					,						a_min=1e-2_0  )  )
     elif variance_type == "fixed_large":
         __A    :						Tuple						 =     state.common.betas[t]
     elif variance_type == "fixed_large_log":
         # Glide max_log
         __A    :						Union[str, Any]						 =     jnp.log(state.common.betas[t]  )
     elif variance_type == "learned":
         return predicted_variance
     elif variance_type == "learned_range":
         __A    :						Optional[Any]						 =     variance
         __A    :						Optional[Any]						 =     state.common.betas[t]
         __A    :						Any						 =     (predicted_variance + 1) / 2
         __A    :						Union[str, Any]						 =     frac * max_log + (1 - frac) * min_log

     return variance

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = None					,						__lowerCamelCase = True					,						):



     '''simple docstring'''




     __A    :						Optional[int]						 =     timestep

     if key is None:
         __A    :						List[Any]						 =     jax.random.PRNGKey(0  )

     if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
         __A ,       __A    :						Tuple						 =     jnp.split(__lowerCamelCase					,						sample.shape[1]					,						axis=1  )
     else:
         __A    :						List[str]						 =     None

     # 1. compute alphas, betas
     __A    :						Dict						 =     state.common.alphas_cumprod[t]
     __A    :						int						 =     jnp.where(t > 0					,						state.common.alphas_cumprod[t - 1]					,						jnp.array(1.0					,						dtype=self.dtype  )  )
     __A    :						Tuple						 =     1 - alpha_prod_t
     __A    :						Optional[int]						 =     1 - alpha_prod_t_prev

     # 2. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
     if self.config.prediction_type == "epsilon":
         __A    :						Optional[Any]						 =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif self.config.prediction_type == "sample":
         __A    :						Any						 =     model_output
     elif self.config.prediction_type == "v_prediction":
         __A    :						str						 =     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
     else:
         raise ValueError(
             F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
             ''' for the FlaxDDPMScheduler.'''  )

     # 3. Clip "predicted x_0"
     if self.config.clip_sample:
         __A    :						str						 =     jnp.clip(__lowerCamelCase					,						-1					,						1  )

     # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     __A    :						Optional[Any]						 =     (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
     __A    :						Union[str, Any]						 =     state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

     # 5. Compute predicted previous sample µ_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     __A    :						Optional[Any]						 =     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

     # 6. Add noise
     def random_variance():
         __A    :						List[Any]						 =     jax.random.split(__lowerCamelCase					,						num=1  )
         __A    :						List[str]						 =     jax.random.normal(__lowerCamelCase					,						shape=model_output.shape					,						dtype=self.dtype  )
         return (self._get_variance(__lowerCamelCase					,						__lowerCamelCase					,						predicted_variance=__lowerCamelCase  ) ** 0.5) * noise

     __A    :						Optional[Any]						 =     jnp.where(t > 0					,						random_variance()					,						jnp.zeros(model_output.shape					,						dtype=self.dtype  )  )

     __A    :						Any						 =     pred_prev_sample + variance

     if not return_dict:
         return (pred_prev_sample, state)

     return FlaxDDPMSchedulerOutput(prev_sample=__lowerCamelCase					,						state=__lowerCamelCase  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




     return add_noise_common(state.common					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




     return get_velocity_common(state.common					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  )







 def __len__(    self  ):



     '''simple docstring'''




     return self.config.num_train_timesteps




