






"""simple docstring"""




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

import numpy as np

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Tuple             =	F"""{sampling_rate}"""
  _a  :       List[str]             =	"""1"""
  _a  :       Any             =	"""f32le"""
  _a  :       Tuple             =	[
      """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  :       List[Any]             =	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  :       str             =	output_stream[0]
  _a  :       Optional[Any]             =	np.frombuffer(A_     ,   np.floataa				)
  if audio.shape[0] == 0:
    raise ValueError("""Malformed soundfile"""				)
  return audio

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__ = "f32le"     ,   ):



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

  _a  :       List[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  :       List[Any]             =	_ffmpeg_stream(A_     ,   A_				)
  for item in iterator:
    yield item

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__ = None     ,   UpperCamelCase__ = None     ,   UpperCamelCase__ = "f32le"     ,   ):



  '''simple docstring'''





  if stream_chunk_s is not None:
    _a  :       int             =	stream_chunk_s
  else:
    _a  :       List[Any]             =	chunk_length_s

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

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

  _a  :       Tuple             =	int(round(sampling_rate * stride_length_s[0]				)				) * size_of_sample
  _a  :       Dict             =	int(round(sampling_rate * stride_length_s[1]				)				) * size_of_sample
  _a  :       Tuple             =	datetime.datetime.now()
  _a  :       Optional[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  :       Dict             =	np.frombuffer(item["""raw"""]     ,   dtype=A_				)
    _a  :       int             =	(
        item["""stride"""][0] // size_of_sample,
        item["""stride"""][1] // size_of_sample,
    )
    _a  :       int             =	sampling_rate
    audio_time += delta
    if datetime.datetime.now() > audio_time + 1_0 * delta:
      # We're late !! SKIP
      continue
    yield item

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__ = False				):



  '''simple docstring'''
  _a  :       Tuple             =	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  :       int             =	0
  for raw in iterator:
    acc += raw
    if stream and len(A_				) < chunk_len:
      _a  :       Optional[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  :       int             =	(_stride_left, stride_right)
        _a  :       Tuple             =	{"""raw""": acc[:chunk_len], """stride""": stride}
        if stream:
          _a  :       Dict             =	False
        yield item
        _a  :       List[str]             =	stride_left
        _a  :       str             =	acc[chunk_len - stride_left - stride_right :]
    # Last chunk
  if len(A_				) > stride_left:
    _a  :       Union[str, Any]             =	{"""raw""": acc, """stride""": (_stride_left, 0)}
    if stream:
      _a  :       int             =	False
    yield item



def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       List[str]             =	2**2_4  # 16Mo
  try:
    with subprocess.Popen(A_     ,   stdout=subprocess.PIPE     ,   bufsize=A_				) as ffmpeg_process:
      while True:
        _a  :       int             =	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



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase			:     Union[str, Any]           =   logging.get_logger(__name__)

__lowerCAmelCase			:     Tuple           =   {
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		"""pegasus"""
  a__           =		["""past_key_values"""]
  a__           =		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  def __init__( self			: Optional[int]			,							UpperCamelCase__			: Optional[int]=5_0265			,							UpperCamelCase__			: Optional[int]=1024			,							UpperCamelCase__			: Any=12			,							UpperCamelCase__			: Union[str, Any]=4096			,							UpperCamelCase__			: Any=16			,							UpperCamelCase__			: Union[str, Any]=12			,							UpperCamelCase__			: List[str]=4096			,							UpperCamelCase__			: Tuple=16			,							UpperCamelCase__			: Optional[int]=0.0			,							UpperCamelCase__			: List[Any]=0.0			,							UpperCamelCase__			: List[str]=True			,							UpperCamelCase__			: List[Any]=True			,							UpperCamelCase__			: List[Any]="gelu"			,							UpperCamelCase__			: List[Any]=1024			,							UpperCamelCase__			: Optional[Any]=0.1			,							UpperCamelCase__			: str=0.0			,							UpperCamelCase__			: Any=0.0			,							UpperCamelCase__			: Union[str, Any]=0.02			,							UpperCamelCase__			: Any=0			,							UpperCamelCase__			: int=False			,							UpperCamelCase__			: Any=0			,							UpperCamelCase__			: List[str]=1			,							UpperCamelCase__			: Tuple=1			,							**UpperCamelCase__			: Union[str, Any]			,							)			->	str:







    """simple docstring"""






    __magic_name__				     =       vocab_size
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       d_model
    __magic_name__				     =       encoder_ffn_dim
    __magic_name__				     =       encoder_layers
    __magic_name__				     =       encoder_attention_heads
    __magic_name__				     =       decoder_ffn_dim
    __magic_name__				     =       decoder_layers
    __magic_name__				     =       decoder_attention_heads
    __magic_name__				     =       dropout
    __magic_name__				     =       attention_dropout
    __magic_name__				     =       activation_dropout
    __magic_name__				     =       activation_function
    __magic_name__				     =       init_std
    __magic_name__				     =       encoder_layerdrop
    __magic_name__				     =       decoder_layerdrop
    __magic_name__				     =       use_cache
    __magic_name__				     =       encoder_layers
    __magic_name__				     =       scale_embedding  # scale factor will be sqrt(d_model) if True
    super().__init__(
        pad_token_id=UpperCamelCase__			,							eos_token_id=UpperCamelCase__			,							is_encoder_decoder=UpperCamelCase__			,							decoder_start_token_id=UpperCamelCase__			,							forced_eos_token_id=UpperCamelCase__			,							**UpperCamelCase__			,							)
  @property
  def 							_lowercase							( self			: List[Any]    )			->	int:







    """simple docstring"""






    return self.encoder_attention_heads





  @property
  def 							_lowercase							( self			: Dict    )			->	int:







    """simple docstring"""






    return self.d_model

