import logging
import re

import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor


__lowerCamelCase      :			Dict							       =		logging.getLogger(__name__)

__lowerCamelCase      :			str							       =		50  # max width of layer names
__lowerCamelCase      :			int							       =		70  # max width of quantizer names



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[int]     )							-> int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				parser.add_argument_group("""quant_trainer arguments"""     )
  group.add_argument("""--wprec"""			,					type=__UpperCamelCase			,					default=8			,					help="""weight precision"""     )
  group.add_argument("""--aprec"""			,					type=__UpperCamelCase			,					default=8			,					help="""activation precision"""     )
  group.add_argument("""--quant-per-tensor"""			,					action="""store_true"""			,					help="""per tensor weight scaling"""     )
  group.add_argument("""--quant-disable"""			,					action="""store_true"""			,					help="""disable all quantizers"""     )
  group.add_argument("""--quant-disable-embeddings"""			,					action="""store_true"""			,					help="""disable all embeddings quantizers"""     )
  group.add_argument("""--quant-disable-keyword"""			,					type=__UpperCamelCase			,					nargs="""+"""			,					help="""disable quantizers by keyword"""     )
  group.add_argument("""--quant-disable-layer-module"""			,					type=__UpperCamelCase			,					help="""disable quantizers by keyword under layer."""     )
  group.add_argument("""--quant-enable-layer-module"""			,					type=__UpperCamelCase			,					help="""enable quantizers by keyword under layer"""     )
  group.add_argument("""--calibrator"""			,					default="""max"""			,					help="""which quantization range calibrator to use"""     )
  group.add_argument("""--percentile"""			,					default=__UpperCamelCase			,					type=__UpperCamelCase			,					help="""percentile for PercentileCalibrator"""     )
  group.add_argument("""--fuse-qkv"""			,					action="""store_true"""			,					help="""use the same scale factor for qkv"""     )
  group.add_argument("""--clip-gelu"""			,					metavar="""N"""			,					type=__UpperCamelCase			,					help="""clip gelu output maximum value to N"""     )
  group.add_argument(
      """--recalibrate-weights"""			,					action="""store_true"""			,					help=(
          """recalibrate weight amaxes by taking the max of the weights."""
          """ amaxes will be computed with the current quantization granularity (axis)."""
      )			,					)



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Any     )							-> List[Any]:







  """simple docstring"""





  if args.calibrator == "max":
    SCREAMING_SNAKE_CASE__      							=				"""max"""
  elif args.calibrator == "percentile":
    if args.percentile is None:
      raise ValueError("""Specify --percentile when using percentile calibrator"""     )
    SCREAMING_SNAKE_CASE__      							=				"""histogram"""
  elif args.calibrator == "mse":
    SCREAMING_SNAKE_CASE__      							=				"""histogram"""
  else:
    raise ValueError(f"""Invalid calibrator {args.calibrator}"""     )

  SCREAMING_SNAKE_CASE__      							=				QuantDescriptor(num_bits=args.aprec			,					calib_method=__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				QuantDescriptor(num_bits=args.wprec			,					axis=(None if args.quant_per_tensor else (0,))     )
  quant_nn.QuantLinear.set_default_quant_desc_input(__UpperCamelCase     )
  quant_nn.QuantLinear.set_default_quant_desc_weight(__UpperCamelCase     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	str			,					__UpperCamelCase   :	str			,					__UpperCamelCase   :	Dict=False			,					__UpperCamelCase   :	str=False     )							-> Tuple:







  """simple docstring"""





  logger.info("""Configuring Model for Quantization"""     )
  logger.info(f"""using quantization package {pytorch_quantization.__file__}"""     )

  if not calib:
    if args.quant_disable_embeddings:
      set_quantizer_by_name(__UpperCamelCase			,					["""embeddings"""]			,					which="""weight"""			,					_disabled=__UpperCamelCase     )

    if args.quant_disable:
      set_quantizer_by_name(__UpperCamelCase			,					[""""""]			,					_disabled=__UpperCamelCase     )

    if args.quant_disable_keyword:
      set_quantizer_by_name(__UpperCamelCase			,					args.quant_disable_keyword			,					_disabled=__UpperCamelCase     )

    if args.quant_disable_layer_module:
      set_quantizer_by_name(__UpperCamelCase			,					[r"""layer.\d+.""" + args.quant_disable_layer_module]			,					_disabled=__UpperCamelCase     )

    if args.quant_enable_layer_module:
      set_quantizer_by_name(__UpperCamelCase			,					[r"""layer.\d+.""" + args.quant_enable_layer_module]			,					_disabled=__UpperCamelCase     )

    if args.recalibrate_weights:
      recalibrate_weights(__UpperCamelCase     )

    if args.fuse_qkv:
      fuse_qkv(__UpperCamelCase			,					__UpperCamelCase     )

  if args.clip_gelu:
    clip_gelu(__UpperCamelCase			,					args.clip_gelu     )

  # if args.local_rank in [-1, 0] and not calib:
  print_quant_summary(__UpperCamelCase     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Union[str, Any]     )							-> List[str]:







  """simple docstring"""





  logger.info("""Enabling Calibration"""     )
  for name, module in model.named_modules():
    if name.endswith("""_quantizer"""     ):
      if module._calibrator is not None:
        module.disable_quant()
        module.enable_calib()
      else:
        module.disable()
      logger.info(f"""{name:80}: {module}"""     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Union[str, Any]			,					__UpperCamelCase   :	List[str]     )							-> Optional[int]:







  """simple docstring"""





  logger.info("""Loading calibrated amax"""     )
  for name, module in model.named_modules():
    if name.endswith("""_quantizer"""     ):
      if module._calibrator is not None:
        if isinstance(module._calibrator			,					calib.MaxCalibrator     ):
          module.load_calib_amax()
        else:
          module.load_calib_amax("""percentile"""			,					percentile=args.percentile     )
        module.enable_quant()
        module.disable_calib()
      else:
        module.enable()
  model.cuda()
  print_quant_summary(__UpperCamelCase     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Any			,					__UpperCamelCase   :	int     )							-> Tuple:







  """simple docstring"""





  def fusea(__UpperCamelCase   :	Any			,					__UpperCamelCase   :	List[str]			,					__UpperCamelCase   :	Tuple     ):
    for mod in [qq, qk, qv]:
      if not hasattr(__UpperCamelCase			,					"""_amax"""     ):
        print("""          WARNING: NO AMAX BUFFER"""     )
        return
    SCREAMING_SNAKE_CASE__      							=				qq._amax.detach().item()
    SCREAMING_SNAKE_CASE__      							=				qk._amax.detach().item()
    SCREAMING_SNAKE_CASE__      							=				qv._amax.detach().item()

    SCREAMING_SNAKE_CASE__      							=				max(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase     )
    qq._amax.fill_(__UpperCamelCase     )
    qk._amax.fill_(__UpperCamelCase     )
    qv._amax.fill_(__UpperCamelCase     )
    logger.info(f"""          q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}"""     )

  for name, mod in model.named_modules():
    if name.endswith(""".attention.self"""     ):
      logger.info(f"""FUSE_QKV: {name:{name_width}}"""     )
      fusea(mod.matmul_q_input_quantizer			,					mod.matmul_k_input_quantizer			,					mod.matmul_v_input_quantizer     )
      if args.quant_per_tensor:
        fusea(mod.query._weight_quantizer			,					mod.key._weight_quantizer			,					mod.value._weight_quantizer     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Dict			,					__UpperCamelCase   :	Optional[int]     )							-> Optional[int]:







  """simple docstring"""





  for name, mod in model.named_modules():
    if name.endswith(""".output.dense"""     ) and not name.endswith("""attention.output.dense"""     ):
      SCREAMING_SNAKE_CASE__      							=				mod._input_quantizer._amax.data.detach().item()
      mod._input_quantizer._amax.data.detach().clamp_(max=__UpperCamelCase     )
      SCREAMING_SNAKE_CASE__      							=				mod._input_quantizer._amax.data.detach().item()
      logger.info(f"""CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}"""     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> int:







  """simple docstring"""





  for name, mod in model.named_modules():
    if hasattr(__UpperCamelCase			,					"""_weight_quantizer"""     ) and mod._weight_quantizer.axis is not None:
      SCREAMING_SNAKE_CASE__      							=				mod.weight.shape[0]
      SCREAMING_SNAKE_CASE__      							=				mod._weight_quantizer._amax.detach()
      SCREAMING_SNAKE_CASE__      							=				torch.ones(__UpperCamelCase			,					dtype=amax.dtype			,					device=amax.device     ) * amax
      print(f"""expanding {name} {amax} -> {mod._weight_quantizer._amax}"""     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[Any]     )							-> Dict:







  """simple docstring"""





  for name, mod in model.named_modules():
    if hasattr(__UpperCamelCase			,					"""_weight_quantizer"""     ):
      if not hasattr(mod.weight_quantizer			,					"""_amax"""     ):
        print("""RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER"""     )
        continue

      # determine which axes to reduce across
      # e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
      SCREAMING_SNAKE_CASE__      							=				set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis     )
      SCREAMING_SNAKE_CASE__      							=				set(range(len(mod.weight.size()     )     )     ) - axis_set
      SCREAMING_SNAKE_CASE__      							=				pytorch_quantization.utils.reduce_amax(mod.weight			,					axis=__UpperCamelCase			,					keepdims=__UpperCamelCase     ).detach()
      logger.info(f"""RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}"""     )
      SCREAMING_SNAKE_CASE__      							=				amax



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int			,					__UpperCamelCase   :	str=25			,					__UpperCamelCase   :	Optional[int]=1_80			,					__UpperCamelCase   :	List[Any]=None     )							-> List[str]:







  """simple docstring"""





  if ignore is None:
    SCREAMING_SNAKE_CASE__      							=				[]
  elif not isinstance(__UpperCamelCase			,					__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				[ignore]

  SCREAMING_SNAKE_CASE__      							=				0
  for name, mod in model.named_modules():
    if not hasattr(__UpperCamelCase			,					"""weight"""     ):
      continue
    SCREAMING_SNAKE_CASE__      							=				max(__UpperCamelCase			,					len(__UpperCamelCase     )     )

  for name, mod in model.named_modules():
    SCREAMING_SNAKE_CASE__      							=				getattr(__UpperCamelCase			,					"""_input_quantizer"""			,					__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__      							=				getattr(__UpperCamelCase			,					"""_weight_quantizer"""			,					__UpperCamelCase     )
    if not hasattr(__UpperCamelCase			,					"""weight"""     ):
      continue
    if type(__UpperCamelCase     ) in ignore:
      continue
    if [True for s in ignore if type(__UpperCamelCase     ) is str and s in name]:
      continue
    SCREAMING_SNAKE_CASE__      							=				f"""Act:{input_q.extra_repr()}"""
    SCREAMING_SNAKE_CASE__      							=				f"""Wgt:{weight_q.extra_repr()}"""
    SCREAMING_SNAKE_CASE__      							=				f"""{name:{name_width}} {act_str} {wgt_str}"""
    if len(__UpperCamelCase     ) <= line_width:
      logger.info(__UpperCamelCase     )
    else:
      logger.info(f"""{name:{name_width}} {act_str}"""     )
      logger.info(f"""{'  ':{name_width}} {wgt_str}"""     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> List[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				0
  for name, mod in model.named_modules():
    if isinstance(__UpperCamelCase			,					pytorch_quantization.nn.TensorQuantizer     ):
      print(f"""{name:80} {mod}"""     )
      count += 1
  print(f"""{count} TensorQuantizers found in model"""     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]			,					__UpperCamelCase   :	Any			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	Any			,					__UpperCamelCase   :	Optional[Any]     )							-> List[str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				getattr(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase     )
  if quantizer_mod is not None:
    assert hasattr(__UpperCamelCase			,					__UpperCamelCase     )
    setattr(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase     )
  else:
    logger.warning(f"""{name} has no {quantizer}"""     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Any			,					__UpperCamelCase   :	Any			,					__UpperCamelCase   :	int="both"			,					**__UpperCamelCase   :	Dict     )							-> int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				f"""Warning: changing {which} quantizers of {name:{qname_width}}"""
  for k, v in kwargs.items():
    s += f""" {k}={v}"""
    if which in ["input", "both"]:
      set_quantizer(__UpperCamelCase			,					__UpperCamelCase			,					"""_input_quantizer"""			,					__UpperCamelCase			,					__UpperCamelCase     )
    if which in ["weight", "both"]:
      set_quantizer(__UpperCamelCase			,					__UpperCamelCase			,					"""_weight_quantizer"""			,					__UpperCamelCase			,					__UpperCamelCase     )
  logger.info(__UpperCamelCase     )






def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]			,					__UpperCamelCase   :	List[str]			,					**__UpperCamelCase   :	Dict     )							-> Dict:







  """simple docstring"""





  for name, mod in model.named_modules():
    if hasattr(__UpperCamelCase			,					"""_input_quantizer"""     ) or hasattr(__UpperCamelCase			,					"""_weight_quantizer"""     ):
      for n in names:
        if re.search(__UpperCamelCase			,					__UpperCamelCase     ):
          set_quantizers(__UpperCamelCase			,					__UpperCamelCase			,					**__UpperCamelCase     )
    elif name.endswith("""_quantizer"""     ):
      for n in names:
        if re.search(__UpperCamelCase			,					__UpperCamelCase     ):
          SCREAMING_SNAKE_CASE__      							=				f"""Warning: changing {name:{name_width}}"""
          for k, v in kwargs.items():
            s += f""" {k}={v}"""
            setattr(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase     )
          logger.info(__UpperCamelCase     )

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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	str     )							-> Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				checkpoint

  SCREAMING_SNAKE_CASE__      							=				{}

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

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

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

  # Retrieves the keys for the encoder down blocks only
  SCREAMING_SNAKE_CASE__      							=				len({""".""".join(layer.split("""."""     )[:3]     ) for layer in vae_state_dict if """encoder.down""" in layer}     )
  SCREAMING_SNAKE_CASE__      							=				{
      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
  SCREAMING_SNAKE_CASE__      							=				len({""".""".join(layer.split("""."""     )[:3]     ) for layer in vae_state_dict if """decoder.up""" in layer}     )
  SCREAMING_SNAKE_CASE__      							=				{
      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     ):
    SCREAMING_SNAKE_CASE__      							=				[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:
      SCREAMING_SNAKE_CASE__      							=				vae_state_dict.pop(
          f"""encoder.down.{i}.downsample.conv.weight"""     )
      SCREAMING_SNAKE_CASE__      							=				vae_state_dict.pop(
          f"""encoder.down.{i}.downsample.conv.bias"""     )

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

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

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

  SCREAMING_SNAKE_CASE__      							=				[key for key in vae_state_dict if """encoder.mid.attn""" in key]
  SCREAMING_SNAKE_CASE__      							=				renew_vae_attention_paths(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				{"""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     ):
    SCREAMING_SNAKE_CASE__      							=				num_up_blocks - 1 - i
    SCREAMING_SNAKE_CASE__      							=				[
        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:
      SCREAMING_SNAKE_CASE__      							=				vae_state_dict[
          f"""decoder.up.{block_id}.upsample.conv.weight"""
      ]
      SCREAMING_SNAKE_CASE__      							=				vae_state_dict[
          f"""decoder.up.{block_id}.upsample.conv.bias"""
      ]

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

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

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

  SCREAMING_SNAKE_CASE__      							=				[key for key in vae_state_dict if """decoder.mid.attn""" in key]
  SCREAMING_SNAKE_CASE__      							=				renew_vae_attention_paths(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				{"""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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	str			,					__UpperCamelCase   :	str			,					)							-> Optional[int]:







  """simple docstring"""





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

  SCREAMING_SNAKE_CASE__      							=				OmegaConf.load(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				5_12
  SCREAMING_SNAKE_CASE__      							=				"""cuda""" if torch.cuda.is_available() else """cpu"""
  if checkpoint_path.endswith("""safetensors"""     ):
    from safetensors import safe_open

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

  # Convert the VAE model.
  SCREAMING_SNAKE_CASE__      							=				create_vae_diffusers_config(__UpperCamelCase			,					image_size=__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				custom_convert_ldm_vae_checkpoint(__UpperCamelCase			,					__UpperCamelCase     )

  SCREAMING_SNAKE_CASE__      							=				AutoencoderKL(**__UpperCamelCase     )
  vae.load_state_dict(__UpperCamelCase     )
  vae.save_pretrained(__UpperCamelCase     )


if __name__ == "__main__":
   __lowerCamelCase      :			List[Any]							       =		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      :			Optional[int]							       =		parser.parse_args()

   vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
