




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

import numpy as np

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	bytes     ,							lowercase     :	int					):


      '''simple docstring'''



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

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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	int     ,							lowercase     :	float     ,							lowercase     :	str = "f32le"     ,							):


      '''simple docstring'''



      lowerCamelCase_							=   f"""{sampling_rate}"""
      lowerCamelCase_							=   '1'
      if format_for_conversion == "s16le":
            lowerCamelCase_							=   2
      elif format_for_conversion == "f32le":
            lowerCamelCase_							=   4
      else:
            raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""					)

      lowerCamelCase_							=   platform.system()
      if system == "Linux":
            lowerCamelCase_							=   'alsa'
            lowerCamelCase_							=   'default'
      elif system == "Darwin":
            lowerCamelCase_							=   'avfoundation'
            lowerCamelCase_							=   ':0'
      elif system == "Windows":
            lowerCamelCase_							=   'dshow'
            lowerCamelCase_							=   'default'

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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	int     ,							lowercase     :	float     ,							lowercase     :	Optional[int] = None     ,							lowercase     :	Optional[Union[Tuple[float, float], float]] = None     ,							lowercase     :	str = "f32le"     ,							):


      '''simple docstring'''



      if stream_chunk_s is not None:
            lowerCamelCase_							=   stream_chunk_s
      else:
            lowerCamelCase_							=   chunk_length_s

      lowerCamelCase_							=   ffmpeg_microphone(lowercase     ,							lowercase     ,							format_for_conversion=lowercase					)
      if format_for_conversion == "s16le":
            lowerCamelCase_							=   np.intaa
            lowerCamelCase_							=   2
      elif format_for_conversion == "f32le":
            lowerCamelCase_							=   np.floataa
            lowerCamelCase_							=   4
      else:
            raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""					)

      if stride_length_s is None:
            lowerCamelCase_							=   chunk_length_s / 6
      lowerCamelCase_							=   int(round(sampling_rate * chunk_length_s					)					) * size_of_sample
      if isinstance(lowercase     ,							(int, float)					):
            lowerCamelCase_							=   [stride_length_s, stride_length_s]

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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Optional[int]     ,							lowercase     :	int     ,							lowercase     :	Tuple[int, int]     ,							lowercase     :	bool = False					):


      '''simple docstring'''



      lowerCamelCase_							=   b''
      lowerCamelCase_					,    lowerCamelCase_							=   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_							=   0
      for raw in iterator:
            acc += raw
            if stream and len(lowercase					) < chunk_len:
                  lowerCamelCase_							=   (_stride_left, 0)
                  yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
            else:
                  while len(lowercase					) >= chunk_len:
                        # We are flushing the accumulator
                        lowerCamelCase_							=   (_stride_left, stride_right)
                        lowerCamelCase_							=   {'raw': acc[:chunk_len], 'stride': stride}
                        if stream:
                              lowerCamelCase_							=   False
                        yield item
                        lowerCamelCase_							=   stride_left
                        lowerCamelCase_							=   acc[chunk_len - stride_left - stride_right :]
    # Last chunk
      if len(lowercase					) > stride_left:
            lowerCamelCase_							=   {'raw': acc, 'stride': (_stride_left, 0)}
            if stream:
                  lowerCamelCase_							=   False
            yield item






def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Optional[Any]     ,							lowercase     :	int					):


      '''simple docstring'''



      lowerCamelCase_							=   2**24  # 16Mo
      try:
            with subprocess.Popen(lowercase     ,							stdout=subprocess.PIPE     ,							bufsize=lowercase					) as ffmpeg_process:
                  while True:
                        lowerCamelCase_							=   ffmpeg_process.stdout.read(lowercase					)
                        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









from ..utils import is_flax_available, is_torch_available


if is_torch_available():
 from .autoencoder_kl import AutoencoderKL
 from .controlnet import ControlNetModel
 from .dual_transformer_ad import DualTransformeraDModel
 from .modeling_utils import ModelMixin
 from .prior_transformer import PriorTransformer
 from .ta_film_transformer import TaFilmDecoder
 from .transformer_ad import TransformeraDModel
 from .unet_ad import UNetaDModel
 from .unet_ad import UNetaDModel
 from .unet_ad_condition import UNetaDConditionModel
 from .unet_ad_condition import UNetaDConditionModel
 from .vq_model import VQModel

if is_flax_available():
 from .controlnet_flax import FlaxControlNetModel
 from .unet_ad_condition_flax import FlaxUNetaDConditionModel
 from .vae_flax import FlaxAutoencoderKL



