



'''simple docstring'''






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

import numpy as np
def 				lowerCamelCase__	(						_A			,      _A    ):
  a						: Dict					       =					f"""{sampling_rate}"""
  a						: int					       =					'1'
  a						: Tuple					       =					'f32le'
  a						: int					       =					[
      '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:
      a						: Dict					       =					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
  a						: Union[str, Any]					       =					output_stream[0]
  a						: Dict					       =					np.frombuffer(_A			,      np.floataa    )
  if audio.shape[0] == 0:
    raise ValueError('Malformed soundfile'    )
  return audio
def 				lowerCamelCase__	(						_A			,      _A			,      _A = "f32le"			,      ):
  a						: Union[str, Any]					       =					f"""{sampling_rate}"""
  a						: Any					       =					'1'
  if format_for_conversion == "s16le":
    a						: Optional[int]					       =					2
  elif format_for_conversion == "f32le":
    a						: Optional[int]					       =					4
  else:
    raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""    )

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

  a						: Optional[Any]					       =					[
      'ffmpeg',
      '-f',
      format_,
      '-i',
      input_,
      '-ac',
      ac,
      '-ar',
      ar,
      '-f',
      format_for_conversion,
      '-fflags',
      'nobuffer',
      '-hide_banner',
      '-loglevel',
      'quiet',
      'pipe:1',
  ]
  a						: Dict					       =					int(round(sampling_rate * chunk_length_s    )    ) * size_of_sample
  a						: Optional[int]					       =					_ffmpeg_stream(_A			,      _A    )
  for item in iterator:
    yield item
def 				lowerCamelCase__	(						_A			,      _A			,      _A = None			,      _A = None			,      _A = "f32le"			,      ):
  if stream_chunk_s is not None:
    a						: Union[str, Any]					       =					stream_chunk_s
  else:
    a						: str					       =					chunk_length_s

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

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

  a						: Any					       =					int(round(sampling_rate * stride_length_s[0]    )    ) * size_of_sample
  a						: Optional[Any]					       =					int(round(sampling_rate * stride_length_s[1]    )    ) * size_of_sample
  a						: List[Any]					       =					datetime.datetime.now()
  a						: 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
    a						: List[str]					       =					np.frombuffer(item['raw']			,      dtype=_A    )
    a						: List[Any]					       =					(
        item['stride'][0] // size_of_sample,
        item['stride'][1] // size_of_sample,
    )
    a						: str					       =					sampling_rate
    audio_time += delta
    if datetime.datetime.now() > audio_time + 10 * delta:
      # We're late !! SKIP
      continue
    yield item
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A = False    ):
  a						: int					       =					B''
  a       ,     a						: str					       =					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}"""    )
  a						: Dict					       =					0
  for raw in iterator:
    acc += raw
    if stream and len(_A    ) < chunk_len:
      a						: List[Any]					       =					(_stride_left, 0)
      yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
    else:
      while len(_A    ) >= chunk_len:
        # We are flushing the accumulator
        a						: Optional[int]					       =					(_stride_left, stride_right)
        a						: Tuple					       =					{'raw': acc[:chunk_len], 'stride': stride}
        if stream:
          a						: int					       =					False
        yield item
        a						: Dict					       =					stride_left
        a						: List[Any]					       =					acc[chunk_len - stride_left - stride_right :]
    # Last chunk
  if len(_A    ) > stride_left:
    a						: Optional[int]					       =					{'raw': acc, 'stride': (_stride_left, 0)}
    if stream:
      a						: int					       =					False
    yield item

def 				lowerCamelCase__	(						_A			,      _A    ):
  a						: int					       =					2**24  # 16Mo
  try:
    with subprocess.Popen(_A			,      stdout=subprocess.PIPE			,      bufsize=_A    ) as ffmpeg_process:
      while True:
        a						: Dict					       =					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




'''simple docstring'''






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
     lowerCAmelCase: Any							=						argparse.ArgumentParser()

     parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

     parser.add_argument(
         '--txt2img_unclip',
         default='kakaobrain/karlo-v1-alpha',
         type=str,
         required=False,
         help='The pretrained txt2img unclip.',
     )

     lowerCAmelCase: Optional[int]							=						parser.parse_args()

     lowerCAmelCase: List[Any]							=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

     lowerCAmelCase: Optional[Any]							=						CLIPImageProcessor()
     lowerCAmelCase: Tuple							=						CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

     lowerCAmelCase: List[str]							=						UnCLIPImageVariationPipeline(
         decoder=txtaimg.decoder,
         text_encoder=txtaimg.text_encoder,
         tokenizer=txtaimg.tokenizer,
         text_proj=txtaimg.text_proj,
         feature_extractor=feature_extractor,
         image_encoder=image_encoder,
         super_res_first=txtaimg.super_res_first,
         super_res_last=txtaimg.super_res_last,
         decoder_scheduler=txtaimg.decoder_scheduler,
         super_res_scheduler=txtaimg.super_res_scheduler,
     )

     imgaimg.save_pretrained(args.dump_path)