



'''simple docstring'''





import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      np.array:
  __lowerCamelCase       :       Any           = F'{sampling_rate}'
  __lowerCamelCase       :       List[str]           = '1'
  __lowerCamelCase       :       int           = 'f32le'
  __lowerCamelCase       :       Dict           = [
      'ffmpeg',
      '-i',
      'pipe:0',
      '-ac',
      ac,
      '-ar',
      ar,
      '-f',
      format_for_conversion,
      '-hide_banner',
      '-loglevel',
      'quiet',
      'pipe:1',
  ]

  try:
    with subprocess.Popen(_lowerCAmelCase       ,stdin=subprocess.PIPE       ,stdout=subprocess.PIPE       ) as ffmpeg_process:
      __lowerCamelCase       :       Tuple           = ffmpeg_process.communicate(_lowerCAmelCase       )
  except FileNotFoundError as error:
    raise ValueError('ffmpeg was not found but is required to load audio files from filename'       ) from error
  __lowerCamelCase       :       Any           = output_stream[0]
  __lowerCamelCase       :       Union[str, Any]           = np.frombuffer(_lowerCAmelCase       ,np.floataa       )
  if audio.shape[0] == 0:
    raise ValueError('Malformed soundfile'       )
  return audio

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = "f32le"       ,)      ->      Dict:
  __lowerCamelCase       :       Optional[Any]           = F'{sampling_rate}'
  __lowerCamelCase       :       Optional[int]           = '1'
  if format_for_conversion == "s16le":
    __lowerCamelCase       :       List[Any]           = 2
  elif format_for_conversion == "f32le":
    __lowerCamelCase       :       Tuple           = 4
  else:
    raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'       )

  __lowerCamelCase       :       Any           = platform.system()
  if system == "Linux":
    __lowerCamelCase       :       Tuple           = 'alsa'
    __lowerCamelCase       :       Optional[Any]           = 'default'
  elif system == "Darwin":
    __lowerCamelCase       :       Union[str, Any]           = 'avfoundation'
    __lowerCamelCase       :       Tuple           = ':0'
  elif system == "Windows":
    __lowerCamelCase       :       List[str]           = 'dshow'
    __lowerCamelCase       :       Optional[Any]           = 'default'

  __lowerCamelCase       :       Optional[int]           = [
      'ffmpeg',
      '-f',
      format_,
      '-i',
      input_,
      '-ac',
      ac,
      '-ar',
      ar,
      '-f',
      format_for_conversion,
      '-fflags',
      'nobuffer',
      '-hide_banner',
      '-loglevel',
      'quiet',
      'pipe:1',
  ]
  __lowerCamelCase       :       List[str]           = int(round(sampling_rate * chunk_length_s       )       ) * size_of_sample
  __lowerCamelCase       :       int           = _ffmpeg_stream(_lowerCAmelCase       ,_lowerCAmelCase       )
  for item in iterator:
    yield item

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,_lowerCAmelCase = "f32le"       ,)      ->      List[str]:
  if stream_chunk_s is not None:
    __lowerCamelCase       :       int           = stream_chunk_s
  else:
    __lowerCamelCase       :       List[Any]           = chunk_length_s

  __lowerCamelCase       :       Dict           = ffmpeg_microphone(_lowerCAmelCase       ,_lowerCAmelCase       ,format_for_conversion=_lowerCAmelCase       )
  if format_for_conversion == "s16le":
    __lowerCamelCase       :       List[str]           = np.intaa
    __lowerCamelCase       :       Union[str, Any]           = 2
  elif format_for_conversion == "f32le":
    __lowerCamelCase       :       Union[str, Any]           = np.floataa
    __lowerCamelCase       :       Optional[Any]           = 4
  else:
    raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'       )

  if stride_length_s is None:
    __lowerCamelCase       :       Any           = chunk_length_s / 6
  __lowerCamelCase       :       List[str]           = int(round(sampling_rate * chunk_length_s       )       ) * size_of_sample
  if isinstance(_lowerCAmelCase       ,(int, float)       ):
    __lowerCamelCase       :       Tuple           = [stride_length_s, stride_length_s]

  __lowerCamelCase       :       Union[str, Any]           = int(round(sampling_rate * stride_length_s[0]       )       ) * size_of_sample
  __lowerCamelCase       :       Optional[Any]           = int(round(sampling_rate * stride_length_s[1]       )       ) * size_of_sample
  __lowerCamelCase       :       Dict           = datetime.datetime.now()
  __lowerCamelCase       :       Any           = datetime.timedelta(seconds=_lowerCAmelCase       )
  for item in chunk_bytes_iter(_lowerCAmelCase       ,_lowerCAmelCase       ,stride=(stride_left, stride_right)       ,stream=_lowerCAmelCase       ):
    # Put everything back in numpy scale
    __lowerCamelCase       :       Optional[int]           = np.frombuffer(item['raw']       ,dtype=_lowerCAmelCase       )
    __lowerCamelCase       :       Tuple           = (
        item['stride'][0] // size_of_sample,
        item['stride'][1] // size_of_sample,
    )
    __lowerCamelCase       :       Optional[int]           = sampling_rate
    audio_time += delta
    if datetime.datetime.now() > audio_time + 10 * delta:
      # We're late !! SKIP
      continue
    yield item

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = False       )      ->      str:
  __lowerCamelCase       :       Optional[int]           = b''
  __lowerCamelCase       ,__lowerCamelCase       :       Any           = stride
  if stride_left + stride_right >= chunk_len:
    raise ValueError(
        F'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}'       )
  __lowerCamelCase       :       str           = 0
  for raw in iterator:
    acc += raw
    if stream and len(_lowerCAmelCase       ) < chunk_len:
      __lowerCamelCase       :       Any           = (_stride_left, 0)
      yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
    else:
      while len(_lowerCAmelCase       ) >= chunk_len:
        # We are flushing the accumulator
        __lowerCamelCase       :       Any           = (_stride_left, stride_right)
        __lowerCamelCase       :       Optional[int]           = {'raw': acc[:chunk_len], 'stride': stride}
        if stream:
          __lowerCamelCase       :       List[str]           = False
        yield item
        __lowerCamelCase       :       Tuple           = stride_left
        __lowerCamelCase       :       Union[str, Any]           = acc[chunk_len - stride_left - stride_right :]
    # Last chunk
  if len(_lowerCAmelCase       ) > stride_left:
    __lowerCamelCase       :       Tuple           = {'raw': acc, 'stride': (_stride_left, 0)}
    if stream:
      __lowerCamelCase       :       List[str]           = False
    yield item


def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Tuple:
  __lowerCamelCase       :       int           = 2**24  # 16Mo
  try:
    with subprocess.Popen(_lowerCAmelCase       ,stdout=subprocess.PIPE       ,bufsize=_lowerCAmelCase       ) as ffmpeg_process:
      while True:
        __lowerCamelCase       :       Union[str, Any]           = ffmpeg_process.stdout.read(_lowerCAmelCase       )
        if raw == b"":
          break
        yield raw
  except FileNotFoundError as error:
    raise ValueError('ffmpeg was not found but is required to stream audio files from filename'       ) from error











'''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        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Dict:
  __lowerCamelCase       :       Any           = checkpoint

  __lowerCamelCase       :       List[str]           = {}

  __lowerCamelCase       :       List[str]           = vae_state_dict['encoder.conv_in.weight']
  __lowerCamelCase       :       Dict           = vae_state_dict['encoder.conv_in.bias']
  __lowerCamelCase       :       Optional[Any]           = 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       :       Dict           = vae_state_dict['decoder.conv_in.weight']
  __lowerCamelCase       :       List[str]           = vae_state_dict['decoder.conv_in.bias']
  __lowerCamelCase       :       Tuple           = vae_state_dict['decoder.conv_out.weight']
  __lowerCamelCase       :       List[str]           = vae_state_dict['decoder.conv_out.bias']
  __lowerCamelCase       :       Dict           = vae_state_dict['decoder.norm_out.weight']
  __lowerCamelCase       :       Union[str, Any]           = vae_state_dict['decoder.norm_out.bias']

  __lowerCamelCase       :       Any           = vae_state_dict['quant_conv.weight']
  __lowerCamelCase       :       Union[str, Any]           = vae_state_dict['quant_conv.bias']
  __lowerCamelCase       :       Tuple           = vae_state_dict['post_quant_conv.weight']
  __lowerCamelCase       :       List[Any]           = vae_state_dict['post_quant_conv.bias']

  # Retrieves the keys for the encoder down blocks only
  __lowerCamelCase       :       str           = 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(_lowerCAmelCase       )
  }

  # Retrieves the keys for the decoder up blocks only
  __lowerCamelCase       :       Dict           = len({'.'.join(layer.split('.'       )[:3]       ) for layer in vae_state_dict if 'decoder.up' in layer}       )
  __lowerCamelCase       :       Dict           = {
      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       ):
    __lowerCamelCase       :       List[str]           = [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       :       Any           = vae_state_dict.pop(
          F'encoder.down.{i}.downsample.conv.weight'       )
      __lowerCamelCase       :       Any           = vae_state_dict.pop(
          F'encoder.down.{i}.downsample.conv.bias'       )

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

  __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       :       int           = [key for key in mid_resnets if F'encoder.mid.block_{i}' in key]

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

  __lowerCamelCase       :       Optional[Any]           = [key for key in vae_state_dict if 'encoder.mid.attn' in key]
  __lowerCamelCase       :       int           = renew_vae_attention_paths(_lowerCAmelCase       )
  __lowerCamelCase       :       Optional[Any]           = {'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       ):
    __lowerCamelCase       :       Optional[Any]           = num_up_blocks - 1 - i
    __lowerCamelCase       :       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       :       Optional[int]           = 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       :       Tuple           = renew_vae_resnet_paths(_lowerCAmelCase       )
    __lowerCamelCase       :       Any           = {'old': F'up.{block_id}.block', 'new': F'up_blocks.{i}.resnets'}
    assign_to_checkpoint(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,additional_replacements=[meta_path]       ,config=_lowerCAmelCase       )

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

  __lowerCamelCase       :       Any           = [key for key in vae_state_dict if 'decoder.mid.attn' in key]
  __lowerCamelCase       :       List[str]           = renew_vae_attention_paths(_lowerCAmelCase       )
  __lowerCamelCase       :       Optional[Any]           = {'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        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,)      ->      List[Any]:
  # Only support V1
  __lowerCamelCase       :       Any           = requests.get(
      ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'       )
  __lowerCamelCase       :       str           = io.BytesIO(r.content       )

  __lowerCamelCase       :       Any           = OmegaConf.load(_lowerCAmelCase       )
  __lowerCamelCase       :       List[Any]           = 512
  __lowerCamelCase       :       Dict           = 'cuda' if torch.cuda.is_available() else 'cpu'
  if checkpoint_path.endswith('safetensors'       ):
    from safetensors import safe_open

    __lowerCamelCase       :       Optional[Any]           = {}
    with safe_open(_lowerCAmelCase       ,framework='pt'       ,device='cpu'       ) as f:
      for key in f.keys():
        __lowerCamelCase       :       int           = f.get_tensor(_lowerCAmelCase       )
  else:
    __lowerCamelCase       :       int           = torch.load(_lowerCAmelCase       ,map_location=_lowerCAmelCase       )['state_dict']

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

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


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)






