






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin




@dataclass
class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		torch.FloatTensor
       a    :		torch.FloatTensor
       a    :		Optional[torch.FloatTensor]									=None







class     _lowerCAmelCase(	UpperCAmelCase_							,     UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Tuple									=2





       @register_to_config
       def __init__( self  ,      _lowerCamelCase = 0.0_2  ,      _lowerCamelCase = 1_0_0  ,      _lowerCamelCase = 1.0_0_7  ,      _lowerCamelCase = 8_0  ,      _lowerCamelCase = 0.0_5  ,      _lowerCamelCase = 5_0  ,      ):
         # standard deviation of the initial noise distribution
         UpperCamelCase_: Tuple   				=    sigma_max

         # setable values
         UpperCamelCase_: int   				=    None
         UpperCamelCase_: np.IntTensor   				=    None
         UpperCamelCase_: torch.FloatTensor   				=    None  # sigma(t_i)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None							):
         return sample





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None							):
         UpperCamelCase_: Dict   				=    num_inference_steps
         UpperCamelCase_: Dict   				=    np.arange(0  ,      self.num_inference_steps							)[::-1].copy()
         UpperCamelCase_: Dict   				=    torch.from_numpy(_lowerCamelCase							).to(_lowerCamelCase							)
         UpperCamelCase_: Tuple   				=    [
             (
                 self.config.sigma_max**2
                 * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
             )
             for i in self.timesteps
         ]
         UpperCamelCase_: List[str]   				=    torch.tensor(_lowerCamelCase  ,      dtype=torch.floataa  ,      device=_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None							):
         if self.config.s_min <= sigma <= self.config.s_max:
           UpperCamelCase_: List[Any]   				=    min(self.config.s_churn / self.num_inference_steps  ,      2**0.5 - 1							)
         else:
           UpperCamelCase_: Union[str, Any]   				=    0

         # sample eps ~ N(0, S_noise^2 * I)
         UpperCamelCase_: Any   				=    self.config.s_noise * randn_tensor(sample.shape  ,      generator=_lowerCamelCase							).to(sample.device							)
         UpperCamelCase_: Any   				=    sigma + gamma * sigma
         UpperCamelCase_: List[str]   				=    sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

         return sample_hat, sigma_hat





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = True  ,      ):
         UpperCamelCase_: Optional[int]   				=    sample_hat + sigma_hat * model_output
         UpperCamelCase_: Dict   				=    (sample_hat - pred_original_sample) / sigma_hat
         UpperCamelCase_: Tuple   				=    sample_hat + (sigma_prev - sigma_hat) * derivative

         if not return_dict:
           return (sample_prev, derivative)

         return KarrasVeOutput(
             prev_sample=_lowerCamelCase  ,      derivative=_lowerCamelCase  ,      pred_original_sample=_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = True  ,      ):
         UpperCamelCase_: Union[str, Any]   				=    sample_prev + sigma_prev * model_output
         UpperCamelCase_: List[str]   				=    (sample_prev - pred_original_sample) / sigma_prev
         UpperCamelCase_: Optional[int]   				=    sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

         if not return_dict:
           return (sample_prev, derivative)

         return KarrasVeOutput(
             prev_sample=_lowerCamelCase  ,      derivative=_lowerCamelCase  ,      pred_original_sample=_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         raise NotImplementedError()







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

import numpy as np

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    np.array:
  UpperCamelCase_: Dict   				=    F'''{sampling_rate}'''
  UpperCamelCase_: Any   				=    '1'
  UpperCamelCase_: Any   				=    'f32le'
  UpperCamelCase_: Union[str, Any]   				=    [
      'ffmpeg',
      '-i',
      'pipe:0',
      '-ac',
      ac,
      '-ar',
      ar,
      '-f',
      format_for_conversion,
      '-hide_banner',
      '-loglevel',
      'quiet',
      'pipe:1',
  ]

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

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = "f32le"     ,   )	->    Tuple:
  UpperCamelCase_: Any   				=    F'''{sampling_rate}'''
  UpperCamelCase_: Union[str, Any]   				=    '1'
  if format_for_conversion == "s16le":
    UpperCamelCase_: Optional[Any]   				=    2
  elif format_for_conversion == "f32le":
    UpperCamelCase_: Any   				=    4
  else:
    raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''  )

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

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

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = "f32le"     ,   )	->    Any:
  if stream_chunk_s is not None:
    UpperCamelCase_: List[Any]   				=    stream_chunk_s
  else:
    UpperCamelCase_: Dict   				=    chunk_length_s

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

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

  UpperCamelCase_: Any   				=    int(round(sampling_rate * stride_length_s[0]  )  ) * size_of_sample
  UpperCamelCase_: Dict   				=    int(round(sampling_rate * stride_length_s[1]  )  ) * size_of_sample
  UpperCamelCase_: Optional[int]   				=    datetime.datetime.now()
  UpperCamelCase_: Optional[int]   				=    datetime.timedelta(seconds=UpperCAmelCase__  )
  for item in chunk_bytes_iter(UpperCAmelCase__     ,   UpperCAmelCase__     ,   stride=(stride_left, stride_right)     ,   stream=UpperCAmelCase__  ):
    # Put everything back in numpy scale
    UpperCamelCase_: Tuple   				=    np.frombuffer(item['raw']     ,   dtype=UpperCAmelCase__  )
    UpperCamelCase_: Optional[int]   				=    (
        item['stride'][0] // size_of_sample,
        item['stride'][1] // size_of_sample,
    )
    UpperCamelCase_: int   				=    sampling_rate
    audio_time += delta
    if datetime.datetime.now() > audio_time + 1_0 * delta:
      # We're late !! SKIP
      continue
    yield item

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

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  UpperCamelCase_: Any   				=    2**2_4  # 16Mo
  try:
    with subprocess.Popen(UpperCAmelCase__     ,   stdout=subprocess.PIPE     ,   bufsize=UpperCAmelCase__  ) as ffmpeg_process:
      while True:
        UpperCamelCase_: Any   				=    ffmpeg_process.stdout.read(UpperCAmelCase__  )
        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