'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase						: Optional[Any]						=						{
    'configuration_mobilenet_v2': [
        'MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV2Config',
        'MobileNetV2OnnxConfig',
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase						: Optional[Any]						=						['MobileNetV2FeatureExtractor']
						_lowercase						: Union[str, Any]						=						['MobileNetV2ImageProcessor']


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase						: Dict						=						[
						    'MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'MobileNetV2ForImageClassification',
						    'MobileNetV2ForSemanticSegmentation',
						    'MobileNetV2Model',
						    'MobileNetV2PreTrainedModel',
						    'load_tf_weights_in_mobilenet_v2',
						]


if TYPE_CHECKING:
						from .configuration_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileNetVaConfig,
						    MobileNetVaOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
												from .image_processing_mobilenet_va import MobileNetVaImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilenet_va import (
												    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileNetVaForImageClassification,
												    MobileNetVaForSemanticSegmentation,
												    MobileNetVaModel,
												    MobileNetVaPreTrainedModel,
												    load_tf_weights_in_mobilenet_va,
												)


else:
						import sys

						_lowercase						: Union[str, Any]						=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






'''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 	lowerCAmelCase_				(					snake_case_       : List[Any]					,    snake_case_       : int			)    ->      Optional[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(snake_case_			)
     }

     # 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(snake_case_			)
     }

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

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

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

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

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

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





def 	lowerCAmelCase_				(					snake_case_       : str					,    snake_case_       : str					,    )    ->      Dict:


     '''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(snake_case_			)
     UpperCAmelCase_           =   5_12
     UpperCAmelCase_           =   "cuda" if torch.cuda.is_available() else "cpu"
     if checkpoint_path.endswith("safetensors"			):
          from safetensors import safe_open

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

     # Convert the VAE model.
     UpperCAmelCase_           =   create_vae_diffusers_config(snake_case_					,    image_size=snake_case_			)
     UpperCAmelCase_           =   custom_convert_ldm_vae_checkpoint(snake_case_					,    snake_case_			)

     UpperCAmelCase_           =   AutoencoderKL(**snake_case_			)
     vae.load_state_dict(snake_case_			)
     vae.save_pretrained(snake_case_			)


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     Optional[int]  =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.')

    SCREAMING_SNAKE_CASE_:     str  =parser.parse_args()

    vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)




