

'''simple docstring'''






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

import numpy as np


def 						_A	(       A__	,							A__ ):






 """simple docstring"""



 __lowercase				    =					F"{sampling_rate}"
 __lowercase				    =					'''1'''
 __lowercase				    =					'''f32le'''
 __lowercase				    =					[
     '''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:
   __lowercase				    =					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
 __lowercase				    =					output_stream[0]
 __lowercase				    =					np.frombuffer(A__	,							np.floataa )
 if audio.shape[0] == 0:
  raise ValueError('''Malformed soundfile''' )
 return audio


def 						_A	(       A__	,							A__	,							A__ = "f32le"	,							):






 """simple docstring"""



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

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

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


def 						_A	(       A__	,							A__	,							A__ = None	,							A__ = None	,							A__ = "f32le"	,							):






 """simple docstring"""



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

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

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

 __lowercase				    =					int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
 __lowercase				    =					int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
 __lowercase				    =					datetime.datetime.now()
 __lowercase				    =					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
  __lowercase				    =					np.frombuffer(item['''raw''']	,							dtype=A__ )
  __lowercase				    =					(
      item['''stride'''][0] // size_of_sample,
      item['''stride'''][1] // size_of_sample,
  )
  __lowercase				    =					sampling_rate
  audio_time += delta
  if datetime.datetime.now() > audio_time + 10 * delta:
   # We're late !! SKIP
   continue
  yield item


def 						_A	(       A__	,							A__	,							A__	,							A__ = False ):






 """simple docstring"""



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






def 						_A	(       A__	,							A__ ):






 """simple docstring"""



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




'''simple docstring'''



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


_UpperCamelCase						:  Optional[int]							       =				logging.get_logger(__name__)

_UpperCamelCase						:  Tuple							       =				{
    "google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       List[Any]				=     '''canine'''

  def __init__(				self      , _SCREAMING_SNAKE_CASE=7_68      , _SCREAMING_SNAKE_CASE=12      , _SCREAMING_SNAKE_CASE=12      , _SCREAMING_SNAKE_CASE=30_72      , _SCREAMING_SNAKE_CASE="gelu"      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE=1_63_84      , _SCREAMING_SNAKE_CASE=16      , _SCREAMING_SNAKE_CASE=0.02      , _SCREAMING_SNAKE_CASE=1e-12      , _SCREAMING_SNAKE_CASE=0      , _SCREAMING_SNAKE_CASE=0Xe0_00      , _SCREAMING_SNAKE_CASE=0Xe0_01      , _SCREAMING_SNAKE_CASE=4      , _SCREAMING_SNAKE_CASE=4      , _SCREAMING_SNAKE_CASE=8      , _SCREAMING_SNAKE_CASE=1_63_84      , _SCREAMING_SNAKE_CASE=1_28      , **_SCREAMING_SNAKE_CASE      , ):





      '''simple docstring'''

      super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE      , bos_token_id=_SCREAMING_SNAKE_CASE      , eos_token_id=_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							max_position_embeddings
      lowerCAmelCase 						=							hidden_size
      lowerCAmelCase 						=							num_hidden_layers
      lowerCAmelCase 						=							num_attention_heads
      lowerCAmelCase 						=							intermediate_size
      lowerCAmelCase 						=							hidden_act
      lowerCAmelCase 						=							hidden_dropout_prob
      lowerCAmelCase 						=							attention_probs_dropout_prob
      lowerCAmelCase 						=							initializer_range
      lowerCAmelCase 						=							type_vocab_size
      lowerCAmelCase 						=							layer_norm_eps

      # Character config:
      lowerCAmelCase 						=							downsampling_rate
      lowerCAmelCase 						=							upsampling_kernel_size
      lowerCAmelCase 						=							num_hash_functions
      lowerCAmelCase 						=							num_hash_buckets
      lowerCAmelCase 						=							local_transformer_stride





