




'''simple docstring'''






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


_lowerCAmelCase      	=					logging.get_logger(__name__)

_lowerCAmelCase      	=					{
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}



class 					A		( SCREAMING_SNAKE_CASE_		):




				'''simple docstring'''





				A      	=				"switch_transformers"
				A      	=				["past_key_values"]
				A      	=				{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
				def __init__(self ,  _UpperCAmelCase=3_2_1_2_8 ,  _UpperCAmelCase=7_6_8 ,  _UpperCAmelCase=6_4 ,  _UpperCAmelCase=2_0_4_8 ,  _UpperCAmelCase=6_4 ,  _UpperCAmelCase=1_2 ,  _UpperCAmelCase=3 ,  _UpperCAmelCase=1_2 ,  _UpperCAmelCase=3 ,  _UpperCAmelCase=1_2 ,  _UpperCAmelCase=8 ,  _UpperCAmelCase=False ,  _UpperCAmelCase=0.01 ,  _UpperCAmelCase="float32" ,  _UpperCAmelCase=False ,  _UpperCAmelCase=3_2 ,  _UpperCAmelCase=1_2_8 ,  _UpperCAmelCase=0.1 ,  _UpperCAmelCase=1E-6 ,  _UpperCAmelCase=0.001 ,  _UpperCAmelCase=0.001 ,  _UpperCAmelCase=1.0 ,  _UpperCAmelCase="relu" ,  _UpperCAmelCase=True ,  _UpperCAmelCase=False ,  _UpperCAmelCase=True ,  _UpperCAmelCase=0 ,  _UpperCAmelCase=1 ,  **_UpperCAmelCase ,  )	->       int:
							__UpperCamelCase      :			List[Any]									=   vocab_size
							__UpperCamelCase      :			List[str]									=   d_model
							__UpperCamelCase      :			Any									=   d_kv
							__UpperCamelCase      :			Optional[int]									=   d_ff

							__UpperCamelCase      :			str									=   num_sparse_encoder_layers

							__UpperCamelCase      :			Dict									=   num_layers
							__UpperCamelCase      :			Union[str, Any]									=   (
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							__UpperCamelCase      :			Dict									=   num_sparse_decoder_layers

							# This tells us, each how many encoder layer we'll have to set a sparse layer.
							if self.num_sparse_encoder_layers > 0:
										__UpperCamelCase      :			Optional[int]									=   self.num_layers // self.num_sparse_encoder_layers
							else:
										__UpperCamelCase      :			Optional[Any]									=   self.num_layers  # HACK: this will create 0 sparse layers

							# This tells us, each how many encoder layer we'll have to set a sparse layer.
							if self.num_sparse_decoder_layers > 0:
										__UpperCamelCase      :			Union[str, Any]									=   self.num_decoder_layers // self.num_sparse_decoder_layers
							else:
										__UpperCamelCase      :			Tuple									=   self.num_decoder_layers  # HACK: this will create 0 sparse layers

							__UpperCamelCase      :			int									=   num_heads
							__UpperCamelCase      :			Any									=   num_experts
							__UpperCamelCase      :			List[str]									=   expert_capacity
							__UpperCamelCase      :			List[Any]									=   router_bias
							__UpperCamelCase      :			int									=   router_jitter_noise
							if router_dtype not in ["float32", "float16", "bfloat16"]:
										raise ValueError(f"`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}"			)
							__UpperCamelCase      :			Optional[Any]									=   router_dtype

							__UpperCamelCase      :			Optional[int]									=   router_ignore_padding_tokens
							__UpperCamelCase      :			Union[str, Any]									=   relative_attention_num_buckets
							__UpperCamelCase      :			List[Any]									=   relative_attention_max_distance

							__UpperCamelCase      :			Optional[int]									=   dropout_rate
							__UpperCamelCase      :			str									=   layer_norm_epsilon
							__UpperCamelCase      :			List[Any]									=   initializer_factor
							__UpperCamelCase      :			Dict									=   feed_forward_proj
							__UpperCamelCase      :			List[Any]									=   use_cache
							__UpperCamelCase      :			Optional[Any]									=   add_router_probs

							__UpperCamelCase      :			Union[str, Any]									=   router_z_loss_coef
							__UpperCamelCase      :			Dict									=   router_aux_loss_coef

							__UpperCamelCase      :			Dict									=   self.feed_forward_proj.split("-"			)
							__UpperCamelCase      :			Union[str, Any]									=   act_info[-1]
							__UpperCamelCase      :			List[str]									=   act_info[0] == 'gated'

							if len(_a			) > 1 and act_info[0] != "gated" or len(_a			) > 2:
										raise ValueError(
										    f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
										    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
										    "\'gated-gelu\' or \'relu\'"			)

							# for backwards compatibility
							if feed_forward_proj == "gated-gelu":
										__UpperCamelCase      :			int									=   'gelu_new'

							super().__init__(
							    pad_token_id=_a ,  eos_token_id=_a ,  is_encoder_decoder=_a ,  **_a ,  )








'''simple docstring'''





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

import numpy as np

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      np.array:
  __lowerCamelCase       :       Any           = F'{sampling_rate}'
  __lowerCamelCase       :       List[str]           = '1'
  __lowerCamelCase       :       int           = 'f32le'
  __lowerCamelCase       :       Dict           = [
      '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       :       Tuple           = 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       :       Any           = output_stream[0]
  __lowerCamelCase       :       Union[str, Any]           = np.frombuffer(_lowerCAmelCase       ,np.floataa       )
  if audio.shape[0] == 0:
    raise ValueError('Malformed soundfile'       )
  return audio

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = "f32le"       ,)      ->      Dict:
  __lowerCamelCase       :       Optional[Any]           = F'{sampling_rate}'
  __lowerCamelCase       :       Optional[int]           = '1'
  if format_for_conversion == "s16le":
    __lowerCamelCase       :       List[Any]           = 2
  elif format_for_conversion == "f32le":
    __lowerCamelCase       :       Tuple           = 4
  else:
    raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'       )

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

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

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,_lowerCAmelCase = "f32le"       ,)      ->      List[str]:
  if stream_chunk_s is not None:
    __lowerCamelCase       :       int           = stream_chunk_s
  else:
    __lowerCamelCase       :       List[Any]           = chunk_length_s

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

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

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

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = False       )      ->      str:
  __lowerCamelCase       :       Optional[int]           = b''
  __lowerCamelCase       ,__lowerCamelCase       :       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}'       )
  __lowerCamelCase       :       str           = 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       :       Any           = (_stride_left, stride_right)
        __lowerCamelCase       :       Optional[int]           = {'raw': acc[:chunk_len], 'stride': stride}
        if stream:
          __lowerCamelCase       :       List[str]           = False
        yield item
        __lowerCamelCase       :       Tuple           = stride_left
        __lowerCamelCase       :       Union[str, Any]           = acc[chunk_len - stride_left - stride_right :]
    # Last chunk
  if len(_lowerCAmelCase       ) > stride_left:
    __lowerCamelCase       :       Tuple           = {'raw': acc, 'stride': (_stride_left, 0)}
    if stream:
      __lowerCamelCase       :       List[str]           = False
    yield item


def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Tuple:
  __lowerCamelCase       :       int           = 2**24  # 16Mo
  try:
    with subprocess.Popen(_lowerCAmelCase       ,stdout=subprocess.PIPE       ,bufsize=_lowerCAmelCase       ) as ffmpeg_process:
      while True:
        __lowerCamelCase       :       Union[str, Any]           = 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






