





'''simple docstring'''






import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__A :  Tuple    							=				{
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1000,
    "block_out_channels": [32, 64],
    "attention_head_dim": 8,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__A :  Any    							=				{
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1000,
    "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__A :  str    							=				{
    "sample_size": 256,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "default",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__A :  Optional[int]    							=				{
    "num_train_timesteps": 40,
    "sigma_min": 0.0_0_2,
    "sigma_max": 80.0,
}

__A :  Union[str, Any]    							=				{
    "num_train_timesteps": 201,
    "sigma_min": 0.0_0_2,
    "sigma_max": 80.0,
}

__A :  Optional[Any]    							=				{
    "num_train_timesteps": 151,
    "sigma_min": 0.0_0_2,
    "sigma_max": 80.0,
}







def     UpperCamelCase_      (   A__ :       str ):
  '''simple docstring'''





  if isinstance(A__							,   A__ ):
    return v
  if v.lower() in ("yes", "true", "t", "y", "1"):
    return True
  elif v.lower() in ("no", "false", "f", "n", "0"):
    return False
  else:
    raise argparse.ArgumentTypeError("""boolean value expected""" )







def     UpperCamelCase_      (   A__ :       Tuple							,   A__ :       Tuple							,   A__ :       str							,   A__ :       Tuple							,   A__ :       Any=False ):
  '''simple docstring'''





  lowerCAmelCase_      :				Dict      =       checkpoint[f'{old_prefix}.in_layers.0.weight']
  lowerCAmelCase_      :				Any      =       checkpoint[f'{old_prefix}.in_layers.0.bias']
  lowerCAmelCase_      :				Tuple      =       checkpoint[f'{old_prefix}.in_layers.2.weight']
  lowerCAmelCase_      :				Optional[int]      =       checkpoint[f'{old_prefix}.in_layers.2.bias']
  lowerCAmelCase_      :				Optional[int]      =       checkpoint[f'{old_prefix}.emb_layers.1.weight']
  lowerCAmelCase_      :				List[Any]      =       checkpoint[f'{old_prefix}.emb_layers.1.bias']
  lowerCAmelCase_      :				Tuple      =       checkpoint[f'{old_prefix}.out_layers.0.weight']
  lowerCAmelCase_      :				Optional[Any]      =       checkpoint[f'{old_prefix}.out_layers.0.bias']
  lowerCAmelCase_      :				Union[str, Any]      =       checkpoint[f'{old_prefix}.out_layers.3.weight']
  lowerCAmelCase_      :				Optional[Any]      =       checkpoint[f'{old_prefix}.out_layers.3.bias']

  if has_skip:
    lowerCAmelCase_      :				Any      =       checkpoint[f'{old_prefix}.skip_connection.weight']
    lowerCAmelCase_      :				Union[str, Any]      =       checkpoint[f'{old_prefix}.skip_connection.bias']

  return new_checkpoint







def     UpperCamelCase_      (   A__ :       Dict							,   A__ :       Any							,   A__ :       int							,   A__ :       Optional[Any]							,   A__ :       Union[str, Any]=None ):
  '''simple docstring'''





  lowerCAmelCase_      :				str      =       checkpoint[f'{old_prefix}.qkv.weight'].chunk(3							,   dim=0 )
  lowerCAmelCase_      :				List[str]      =       checkpoint[f'{old_prefix}.qkv.bias'].chunk(3							,   dim=0 )

  lowerCAmelCase_      :				Any      =       checkpoint[f'{old_prefix}.norm.weight']
  lowerCAmelCase_      :				Union[str, Any]      =       checkpoint[f'{old_prefix}.norm.bias']

  lowerCAmelCase_      :				Any      =       weight_q.squeeze(-1 ).squeeze(-1 )
  lowerCAmelCase_      :				Tuple      =       bias_q.squeeze(-1 ).squeeze(-1 )
  lowerCAmelCase_      :				Any      =       weight_k.squeeze(-1 ).squeeze(-1 )
  lowerCAmelCase_      :				Optional[int]      =       bias_k.squeeze(-1 ).squeeze(-1 )
  lowerCAmelCase_      :				Dict      =       weight_v.squeeze(-1 ).squeeze(-1 )
  lowerCAmelCase_      :				int      =       bias_v.squeeze(-1 ).squeeze(-1 )

  lowerCAmelCase_      :				List[Any]      =       (
      checkpoint[f'{old_prefix}.proj_out.weight'].squeeze(-1 ).squeeze(-1 )
  )
  lowerCAmelCase_      :				Dict      =       checkpoint[f'{old_prefix}.proj_out.bias'].squeeze(-1 ).squeeze(-1 )

  return new_checkpoint







def     UpperCamelCase_      (   A__ :       str							,   A__ :       List[Any] ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[Any]      =       torch.load(A__							,   map_location="""cpu""" )
  lowerCAmelCase_      :				Tuple      =       {}

  lowerCAmelCase_      :				Optional[Any]      =       checkpoint["""time_embed.0.weight"""]
  lowerCAmelCase_      :				List[str]      =       checkpoint["""time_embed.0.bias"""]
  lowerCAmelCase_      :				Union[str, Any]      =       checkpoint["""time_embed.2.weight"""]
  lowerCAmelCase_      :				Dict      =       checkpoint["""time_embed.2.bias"""]

  if unet_config["num_class_embeds"] is not None:
    lowerCAmelCase_      :				List[Any]      =       checkpoint["""label_emb.weight"""]

  lowerCAmelCase_      :				List[str]      =       checkpoint["""input_blocks.0.0.weight"""]
  lowerCAmelCase_      :				Any      =       checkpoint["""input_blocks.0.0.bias"""]

  lowerCAmelCase_      :				int      =       unet_config["""down_block_types"""]
  lowerCAmelCase_      :				List[Any]      =       unet_config["""layers_per_block"""]
  lowerCAmelCase_      :				int      =       unet_config["""attention_head_dim"""]
  lowerCAmelCase_      :				Dict      =       unet_config["""block_out_channels"""]
  lowerCAmelCase_      :				Optional[Any]      =       1
  lowerCAmelCase_      :				Tuple      =       channels_list[0]

  for i, layer_type in enumerate(A__ ):
    lowerCAmelCase_      :				Optional[int]      =       channels_list[i]
    lowerCAmelCase_      :				Union[str, Any]      =       current_channels != prev_channels
    if layer_type == "ResnetDownsampleBlock2D":
      for j in range(A__ ):
        lowerCAmelCase_      :				List[Any]      =       f'down_blocks.{i}.resnets.{j}'
        lowerCAmelCase_      :				Union[str, Any]      =       f'input_blocks.{current_layer}.0'
        lowerCAmelCase_      :				Union[str, Any]      =       True if j == 0 and downsample_block_has_skip else False
        lowerCAmelCase_      :				Dict      =       convert_resnet(A__							,   A__							,   A__							,   A__							,   has_skip=A__ )
        current_layer += 1

    elif layer_type == "AttnDownBlock2D":
      for j in range(A__ ):
        lowerCAmelCase_      :				Dict      =       f'down_blocks.{i}.resnets.{j}'
        lowerCAmelCase_      :				Tuple      =       f'input_blocks.{current_layer}.0'
        lowerCAmelCase_      :				str      =       True if j == 0 and downsample_block_has_skip else False
        lowerCAmelCase_      :				List[str]      =       convert_resnet(A__							,   A__							,   A__							,   A__							,   has_skip=A__ )
        lowerCAmelCase_      :				Union[str, Any]      =       f'down_blocks.{i}.attentions.{j}'
        lowerCAmelCase_      :				List[str]      =       f'input_blocks.{current_layer}.1'
        lowerCAmelCase_      :				Optional[int]      =       convert_attention(
            A__							,   A__							,   A__							,   A__							,   A__ )
        current_layer += 1

    if i != len(A__ ) - 1:
      lowerCAmelCase_      :				Union[str, Any]      =       f'down_blocks.{i}.downsamplers.0'
      lowerCAmelCase_      :				str      =       f'input_blocks.{current_layer}.0'
      lowerCAmelCase_      :				Optional[int]      =       convert_resnet(A__							,   A__							,   A__							,   A__ )
      current_layer += 1

    lowerCAmelCase_      :				List[str]      =       current_channels

  # hardcoded the mid-block for now
  lowerCAmelCase_      :				str      =       """mid_block.resnets.0"""
  lowerCAmelCase_      :				Optional[Any]      =       """middle_block.0"""
  lowerCAmelCase_      :				int      =       convert_resnet(A__							,   A__							,   A__							,   A__ )
  lowerCAmelCase_      :				Dict      =       """mid_block.attentions.0"""
  lowerCAmelCase_      :				str      =       """middle_block.1"""
  lowerCAmelCase_      :				List[Any]      =       convert_attention(A__							,   A__							,   A__							,   A__							,   A__ )
  lowerCAmelCase_      :				Optional[int]      =       """mid_block.resnets.1"""
  lowerCAmelCase_      :				Tuple      =       """middle_block.2"""
  lowerCAmelCase_      :				Tuple      =       convert_resnet(A__							,   A__							,   A__							,   A__ )

  lowerCAmelCase_      :				Any      =       0
  lowerCAmelCase_      :				Tuple      =       unet_config["""up_block_types"""]

  for i, layer_type in enumerate(A__ ):
    if layer_type == "ResnetUpsampleBlock2D":
      for j in range(layers_per_block + 1 ):
        lowerCAmelCase_      :				Optional[Any]      =       f'up_blocks.{i}.resnets.{j}'
        lowerCAmelCase_      :				str      =       f'output_blocks.{current_layer}.0'
        lowerCAmelCase_      :				Dict      =       convert_resnet(A__							,   A__							,   A__							,   A__							,   has_skip=A__ )
        current_layer += 1

      if i != len(A__ ) - 1:
        lowerCAmelCase_      :				Any      =       f'up_blocks.{i}.upsamplers.0'
        lowerCAmelCase_      :				Dict      =       f'output_blocks.{current_layer-1}.1'
        lowerCAmelCase_      :				List[Any]      =       convert_resnet(A__							,   A__							,   A__							,   A__ )
    elif layer_type == "AttnUpBlock2D":
      for j in range(layers_per_block + 1 ):
        lowerCAmelCase_      :				str      =       f'up_blocks.{i}.resnets.{j}'
        lowerCAmelCase_      :				Optional[int]      =       f'output_blocks.{current_layer}.0'
        lowerCAmelCase_      :				Any      =       convert_resnet(A__							,   A__							,   A__							,   A__							,   has_skip=A__ )
        lowerCAmelCase_      :				Tuple      =       f'up_blocks.{i}.attentions.{j}'
        lowerCAmelCase_      :				Dict      =       f'output_blocks.{current_layer}.1'
        lowerCAmelCase_      :				Union[str, Any]      =       convert_attention(
            A__							,   A__							,   A__							,   A__							,   A__ )
        current_layer += 1

      if i != len(A__ ) - 1:
        lowerCAmelCase_      :				Optional[int]      =       f'up_blocks.{i}.upsamplers.0'
        lowerCAmelCase_      :				Tuple      =       f'output_blocks.{current_layer-1}.2'
        lowerCAmelCase_      :				str      =       convert_resnet(A__							,   A__							,   A__							,   A__ )

  lowerCAmelCase_      :				Union[str, Any]      =       checkpoint["""out.0.weight"""]
  lowerCAmelCase_      :				Any      =       checkpoint["""out.0.bias"""]
  lowerCAmelCase_      :				str      =       checkpoint["""out.2.weight"""]
  lowerCAmelCase_      :				List[Any]      =       checkpoint["""out.2.bias"""]

  return new_checkpoint


if __name__ == "__main__":
  __A :  Optional[Any]    							=				argparse.ArgumentParser()

  parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")
  parser.add_argument(
      "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."
  )
  parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")

  __A :  Optional[int]    							=				parser.parse_args()
  __A :  List[Any]    							=				strabool(args.class_cond)

  __A :  str    							=				os.path.basename(args.unet_path)
  print(F'''Checkpoint: {ckpt_name}''')

  # Get U-Net config
  if "imagenet64" in ckpt_name:
    __A :  List[str]    							=				IMAGENET_64_UNET_CONFIG
  elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
    __A :  int    							=				LSUN_256_UNET_CONFIG
  elif "test" in ckpt_name:
    __A :  Union[str, Any]    							=				TEST_UNET_CONFIG
  else:
    raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

  if not args.class_cond:
    __A :  List[str]    							=				None

  __A :  Dict    							=				con_pt_to_diffuser(args.unet_path, unet_config)

  __A :  Tuple    							=				UNetaDModel(**unet_config)
  image_unet.load_state_dict(converted_unet_ckpt)

  # Get scheduler config
  if "cd" in ckpt_name or "test" in ckpt_name:
    __A :  int    							=				CD_SCHEDULER_CONFIG
  elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
    __A :  Union[str, Any]    							=				CT_IMAGENET_64_SCHEDULER_CONFIG
  elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
    __A :  Optional[int]    							=				CT_LSUN_256_SCHEDULER_CONFIG
  else:
    raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

  __A :  Optional[Any]    							=				CMStochasticIterativeScheduler(**scheduler_config)

  __A :  Tuple    							=				ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
  consistency_model.save_pretrained(args.dump_path)







'''simple docstring'''






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

import numpy as np







def     UpperCamelCase_      (   A__ :       bytes							,   A__ :       int ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       f'{sampling_rate}'
  lowerCAmelCase_      :				str      =       """1"""
  lowerCAmelCase_      :				Optional[int]      =       """f32le"""
  lowerCAmelCase_      :				Any      =       [
      """ffmpeg""",
      """-i""",
      """pipe:0""",
      """-ac""",
      ac,
      """-ar""",
      ar,
      """-f""",
      format_for_conversion,
      """-hide_banner""",
      """-loglevel""",
      """quiet""",
      """pipe:1""",
  ]

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







def     UpperCamelCase_      (   A__ :       int							,   A__ :       float							,   A__ :       str = "f32le"							,   ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       f'{sampling_rate}'
  lowerCAmelCase_      :				Any      =       """1"""
  if format_for_conversion == "s16le":
    lowerCAmelCase_      :				Optional[Any]      =       2
  elif format_for_conversion == "f32le":
    lowerCAmelCase_      :				Union[str, Any]      =       4
  else:
    raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

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

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







def     UpperCamelCase_      (   A__ :       int							,   A__ :       float							,   A__ :       Optional[int] = None							,   A__ :       Optional[Union[Tuple[float, float], float]] = None							,   A__ :       str = "f32le"							,   ):
  '''simple docstring'''





  if stream_chunk_s is not None:
    lowerCAmelCase_      :				Union[str, Any]      =       stream_chunk_s
  else:
    lowerCAmelCase_      :				Tuple      =       chunk_length_s

  lowerCAmelCase_      :				List[Any]      =       ffmpeg_microphone(A__							,   A__							,   format_for_conversion=A__ )
  if format_for_conversion == "s16le":
    lowerCAmelCase_      :				Tuple      =       np.intaa
    lowerCAmelCase_      :				List[Any]      =       2
  elif format_for_conversion == "f32le":
    lowerCAmelCase_      :				Dict      =       np.floataa
    lowerCAmelCase_      :				int      =       4
  else:
    raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

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

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







def     UpperCamelCase_      (   A__ :       Any							,   A__ :       int							,   A__ :       Tuple[int, int]							,   A__ :       bool = False ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[str]      =       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_      :				Union[str, Any]      =       0
  for raw in iterator:
    acc += raw
    if stream and len(A__ ) < chunk_len:
      lowerCAmelCase_      :				Dict      =       (_stride_left, 0)
      yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
    else:
      while len(A__ ) >= chunk_len:
        # We are flushing the accumulator
        lowerCAmelCase_      :				Optional[Any]      =       (_stride_left, stride_right)
        lowerCAmelCase_      :				List[str]      =       {"""raw""": acc[:chunk_len], """stride""": stride}
        if stream:
          lowerCAmelCase_      :				List[Any]      =       False
        yield item
        lowerCAmelCase_      :				str      =       stride_left
        lowerCAmelCase_      :				str      =       acc[chunk_len - stride_left - stride_right :]
    # Last chunk
  if len(A__ ) > stride_left:
    lowerCAmelCase_      :				Tuple      =       {"""raw""": acc, """stride""": (_stride_left, 0)}
    if stream:
      lowerCAmelCase_      :				Optional[Any]      =       False
    yield item







def     UpperCamelCase_      (   A__ :       List[str]							,   A__ :       int ):
  '''simple docstring'''





  lowerCAmelCase_      :				Dict      =       2**24  # 16Mo
  try:
    with subprocess.Popen(A__							,   stdout=subprocess.PIPE							,   bufsize=A__ ) as ffmpeg_process:
      while True:
        lowerCAmelCase_      :				Union[str, Any]      =       ffmpeg_process.stdout.read(A__ )
        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



