

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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase   :					Dict        =    checkpoint

     _lowerCAmelCase   :					Union[str, Any]        =    {}

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

     _lowerCAmelCase   :					Optional[Any]        =    vae_state_dict["decoder.conv_in.weight"]
     _lowerCAmelCase   :					Dict        =    vae_state_dict["decoder.conv_in.bias"]
     _lowerCAmelCase   :					Dict        =    vae_state_dict["decoder.conv_out.weight"]
     _lowerCAmelCase   :					Tuple        =    vae_state_dict["decoder.conv_out.bias"]
     _lowerCAmelCase   :					Any        =    vae_state_dict["decoder.norm_out.weight"]
     _lowerCAmelCase   :					str        =    vae_state_dict["decoder.norm_out.bias"]

     _lowerCAmelCase   :					List[str]        =    vae_state_dict["quant_conv.weight"]
     _lowerCAmelCase   :					Tuple        =    vae_state_dict["quant_conv.bias"]
     _lowerCAmelCase   :					int        =    vae_state_dict["post_quant_conv.weight"]
     _lowerCAmelCase   :					int        =    vae_state_dict["post_quant_conv.bias"]

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

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

     for i in range(SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Tuple        =    [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   :					List[str]        =    vae_state_dict.pop(
                   f'''encoder.down.{i}.downsample.conv.weight'''				)
               _lowerCAmelCase   :					List[Any]        =    vae_state_dict.pop(
                   f'''encoder.down.{i}.downsample.conv.bias'''				)

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

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

          _lowerCAmelCase   :					Dict        =    renew_vae_resnet_paths(SCREAMING_SNAKE_CASE				)
          _lowerCAmelCase   :					Any        =    {"old": f'''mid.block_{i}''', "new": f'''mid_block.resnets.{i - 1}'''}
          assign_to_checkpoint(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							additional_replacements=[meta_path]		,							config=SCREAMING_SNAKE_CASE				)

     _lowerCAmelCase   :					Optional[Any]        =    [key for key in vae_state_dict if "encoder.mid.attn" in key]
     _lowerCAmelCase   :					List[Any]        =    renew_vae_attention_paths(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Optional[int]        =    {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
     assign_to_checkpoint(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							additional_replacements=[meta_path]		,							config=SCREAMING_SNAKE_CASE				)
     conv_attn_to_linear(SCREAMING_SNAKE_CASE				)

     for i in range(SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Union[str, Any]        =    num_up_blocks - 1 - i
          _lowerCAmelCase   :					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:
               _lowerCAmelCase   :					Tuple        =    vae_state_dict[
                   f'''decoder.up.{block_id}.upsample.conv.weight'''
               ]
               _lowerCAmelCase   :					Optional[Any]        =    vae_state_dict[
                   f'''decoder.up.{block_id}.upsample.conv.bias'''
               ]

          _lowerCAmelCase   :					List[str]        =    renew_vae_resnet_paths(SCREAMING_SNAKE_CASE				)
          _lowerCAmelCase   :					Tuple        =    {"old": f'''up.{block_id}.block''', "new": f'''up_blocks.{i}.resnets'''}
          assign_to_checkpoint(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							additional_replacements=[meta_path]		,							config=SCREAMING_SNAKE_CASE				)

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

          _lowerCAmelCase   :					Optional[Any]        =    renew_vae_resnet_paths(SCREAMING_SNAKE_CASE				)
          _lowerCAmelCase   :					Dict        =    {"old": f'''mid.block_{i}''', "new": f'''mid_block.resnets.{i - 1}'''}
          assign_to_checkpoint(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							additional_replacements=[meta_path]		,							config=SCREAMING_SNAKE_CASE				)

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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							)       ->      Optional[int]:

     """simple docstring"""







     _lowerCAmelCase   :					List[str]        =    requests.get(
         " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"				)
     _lowerCAmelCase   :					Dict        =    io.BytesIO(r.content				)

     _lowerCAmelCase   :					Any        =    OmegaConf.load(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Optional[Any]        =    512
     _lowerCAmelCase   :					Any        =    "cuda" if torch.cuda.is_available() else "cpu"
     if checkpoint_path.endswith("safetensors"				):
          from safetensors import safe_open

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

     # Convert the VAE model.
     _lowerCAmelCase   :					Tuple        =    create_vae_diffusers_config(SCREAMING_SNAKE_CASE		,							image_size=SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					int        =    custom_convert_ldm_vae_checkpoint(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)

     _lowerCAmelCase   :					List[str]        =    AutoencoderKL(**SCREAMING_SNAKE_CASE				)
     vae.load_state_dict(SCREAMING_SNAKE_CASE				)
     vae.save_pretrained(SCREAMING_SNAKE_CASE				)


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)









from typing import TYPE_CHECKING

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


__UpperCAmelCase    				=     {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     ['YolosFeatureExtractor']
  __UpperCAmelCase    				=     ['YolosImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'YolosForObjectDetection',
      'YolosModel',
      'YolosPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_yolos import YolosFeatureExtractor
    from .image_processing_yolos import YolosImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_yolos import (
        YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
        YolosForObjectDetection,
        YolosModel,
        YolosPreTrainedModel,
    )


else:
  import sys

  __UpperCAmelCase    				=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






