
'''simple docstring'''






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

import numpy as np







def 		A_							(						_lowerCAmelCase :     bytes					,						_lowerCAmelCase :     int				):



      """simple docstring"""




      _lowerCamelCase					: str		    = F'{sampling_rate}'
      _lowerCamelCase					: List[str]		    = "1"
      _lowerCamelCase					: Union[str, Any]		    = "f32le"
      _lowerCamelCase					: 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(_lowerCAmelCase					,						stdin=subprocess.PIPE					,						stdout=subprocess.PIPE				) as ffmpeg_process:
                  _lowerCamelCase					: Dict		    = 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					: List[Any]		    = output_stream[0]
      _lowerCamelCase					: Any		    = np.frombuffer(_lowerCAmelCase					,						np.floataa				)
      if audio.shape[0] == 0:
            raise ValueError("Malformed soundfile"				)
      return audio







def 		A_							(						_lowerCAmelCase :     int					,						_lowerCAmelCase :     float					,						_lowerCAmelCase :     str = "f32le"					,						):



      """simple docstring"""




      _lowerCamelCase					: str		    = F'{sampling_rate}'
      _lowerCamelCase					: Optional[int]		    = "1"
      if format_for_conversion == "s16le":
            _lowerCamelCase					: List[str]		    = 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					: List[Any]		    = platform.system()
      if system == "Linux":
            _lowerCamelCase					: Dict		    = "alsa"
            _lowerCamelCase					: int		    = "default"
      elif system == "Darwin":
            _lowerCamelCase					: str		    = "avfoundation"
            _lowerCamelCase					: Optional[int]		    = ":0"
      elif system == "Windows":
            _lowerCamelCase					: Tuple		    = "dshow"
            _lowerCamelCase					: Tuple		    = "default"

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







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



      """simple docstring"""




      if stream_chunk_s is not None:
            _lowerCamelCase					: Any		    = stream_chunk_s
      else:
            _lowerCamelCase					: int		    = chunk_length_s

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

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

      _lowerCamelCase					: Optional[Any]		    = int(round(sampling_rate * stride_length_s[0]				)				) * size_of_sample
      _lowerCamelCase					: Optional[int]		    = int(round(sampling_rate * stride_length_s[1]				)				) * size_of_sample
      _lowerCamelCase					: List[str]		    = datetime.datetime.now()
      _lowerCamelCase					: Union[str, 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					: Any		    = (
                item["stride"][0] // size_of_sample,
                item["stride"][1] // size_of_sample,
            )
            _lowerCamelCase					: Any		    = sampling_rate
            audio_time += delta
            if datetime.datetime.now() > audio_time + 10 * delta:
                  # We're late !! SKIP
                  continue
            yield item







def 		A_							(						_lowerCAmelCase :     Optional[int]					,						_lowerCAmelCase :     int					,						_lowerCAmelCase :     Tuple[int, int]					,						_lowerCAmelCase :     bool = False				):



      """simple docstring"""




      _lowerCamelCase					: Any		    = B""
      _lowerCamelCase    ,		_lowerCamelCase					: Optional[int]		    = 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					: Optional[int]		    = 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					: str		    = (_stride_left, stride_right)
                        _lowerCamelCase					: Optional[Any]		    = {"raw": acc[:chunk_len], "stride": stride}
                        if stream:
                              _lowerCamelCase					: Optional[int]		    = False
                        yield item
                        _lowerCamelCase					: Tuple		    = stride_left
                        _lowerCamelCase					: List[Any]		    = acc[chunk_len - stride_left - stride_right :]
    # Last chunk
      if len(_lowerCAmelCase				) > stride_left:
            _lowerCamelCase					: int		    = {"raw": acc, "stride": (_stride_left, 0)}
            if stream:
                  _lowerCamelCase					: Dict		    = False
            yield item







def 		A_							(						_lowerCAmelCase :     List[str]					,						_lowerCAmelCase :     int				):



      """simple docstring"""




      _lowerCamelCase					: str		    = 2**24  # 16Mo
      try:
            with subprocess.Popen(_lowerCAmelCase					,						stdout=subprocess.PIPE					,						bufsize=_lowerCAmelCase				) as ffmpeg_process:
                  while True:
                        _lowerCamelCase					: str		    = 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'''





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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer






@dataclass
class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 lowercase_					:    torch.FloatTensor






class 							UpperCAmelCase_							(    A					,				A				):

 '''simple docstring'''







 @register_to_config
 def __init__(					self		:  List[str]			,				snake_case__		:  int = 3			,				snake_case__		:  int = 3			,				snake_case__		:  Tuple[str] = ("DownEncoderBlock2D",)			,				snake_case__		:  Tuple[str] = ("UpDecoderBlock2D",)			,				snake_case__		:  Tuple[int] = (64,)			,				snake_case__		:  int = 1			,				snake_case__		:  str = "silu"			,				snake_case__		:  int = 3			,				snake_case__		:  int = 32			,				snake_case__		:  int = 2_56			,				snake_case__		:  int = 32			,				snake_case__		:  Optional[int] = None			,				snake_case__		:  float = 0.18215			,				snake_case__		:  str = "group"			,				):



   '''simple docstring'''




   super().__init__()

   # pass init params to Encoder
   UpperCAmelCase__      :					Tuple				       =							Encoder(
       in_channels=snake_case__			,				out_channels=snake_case__			,				down_block_types=snake_case__			,				block_out_channels=snake_case__			,				layers_per_block=snake_case__			,				act_fn=snake_case__			,				norm_num_groups=snake_case__			,				double_z=snake_case__			,				)

   UpperCAmelCase__      :					str				       =							vq_embed_dim if vq_embed_dim is not None else latent_channels

   UpperCAmelCase__      :					Tuple				       =							nn.Convad(snake_case__			,				snake_case__			,				1 )
   UpperCAmelCase__      :					int				       =							VectorQuantizer(snake_case__			,				snake_case__			,				beta=0.25			,				remap=snake_case__			,				sane_index_shape=snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							nn.Convad(snake_case__			,				snake_case__			,				1 )

   # pass init params to Decoder
   UpperCAmelCase__      :					Union[str, Any]				       =							Decoder(
       in_channels=snake_case__			,				out_channels=snake_case__			,				up_block_types=snake_case__			,				block_out_channels=snake_case__			,				layers_per_block=snake_case__			,				act_fn=snake_case__			,				norm_num_groups=snake_case__			,				norm_type=snake_case__			,				)







 @apply_forward_hook
 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				snake_case__		:  torch.FloatTensor			,				snake_case__		:  bool = True ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							self.encoder(snake_case__ )
   UpperCAmelCase__      :					List[str]				       =							self.quant_conv(snake_case__ )

   if not return_dict:
     return (h,)

   return VQEncoderOutput(latents=snake_case__ )







 @apply_forward_hook
 def 							UpperCamelCase  (					self		:  List[str]			,				snake_case__		:  torch.FloatTensor			,				snake_case__		:  bool = False			,				snake_case__		:  bool = True ):



   '''simple docstring'''




   if not force_not_quantize:
     UpperCAmelCase__       ,			UpperCAmelCase__       ,			UpperCAmelCase__      :					Any				       =							self.quantize(snake_case__ )
   else:
     UpperCAmelCase__      :					Tuple				       =							h
   UpperCAmelCase__      :					int				       =							self.post_quant_conv(snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							self.decoder(snake_case__			,				quant if self.config.norm_type == "spatial" else None )

   if not return_dict:
     return (dec,)

   return DecoderOutput(sample=snake_case__ )







 def 							UpperCamelCase  (					self		:  Any			,				snake_case__		:  torch.FloatTensor			,				snake_case__		:  bool = True ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							sample
   UpperCAmelCase__      :					int				       =							self.encode(snake_case__ ).latents
   UpperCAmelCase__      :					Any				       =							self.decode(snake_case__ ).sample

   if not return_dict:
     return (dec,)

   return DecoderOutput(sample=snake_case__ )



