





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

import numpy as np

def   a_   (			lowerCAmelCase_				:      bytes,     lowerCAmelCase_				:      int     ):
   __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(lowerCAmelCase_,     stdin=subprocess.PIPE,     stdout=subprocess.PIPE     ) as ffmpeg_process:
         __lowerCAmelCase							     =     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							     =     output_stream[0]
   __lowerCAmelCase							     =     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",     ):
   __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(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",     ):
   if stream_chunk_s is not None:
      __lowerCAmelCase							     =     stream_chunk_s
   else:
      __lowerCAmelCase							     =     chunk_length_s

   __lowerCAmelCase							     =     ffmpeg_microphone(lowerCAmelCase_,     lowerCAmelCase_,     format_for_conversion=lowerCAmelCase_     )
   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(lowerCAmelCase_,     (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=lowerCAmelCase_     )
   for item in chunk_bytes_iter(lowerCAmelCase_,     lowerCAmelCase_,     stride=(stride_left, stride_right),     stream=lowerCAmelCase_     ):
      # Put everything back in numpy scale
      __lowerCAmelCase							     =     np.frombuffer(item['raw'],     dtype=lowerCAmelCase_     )
      __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   a_   (			lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      int,     lowerCAmelCase_				:      Tuple[int, int],     lowerCAmelCase_				:      bool = False     ):
   __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(lowerCAmelCase_     ) < chunk_len:
         __lowerCAmelCase							     =     (_stride_left, 0)
         yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
      else:
         while len(lowerCAmelCase_     ) >= 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(lowerCAmelCase_     ) > stride_left:
      __lowerCAmelCase							     =     {'raw': acc, 'stride': (_stride_left, 0)}
      if stream:
         __lowerCAmelCase							     =     False
      yield item




def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      int     ):
   __lowerCAmelCase							     =     2**24  # 16Mo
   try:
      with subprocess.Popen(lowerCAmelCase_,     stdout=subprocess.PIPE,     bufsize=lowerCAmelCase_     ) as ffmpeg_process:
         while True:
            __lowerCAmelCase							     =     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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case  :   Optional[Any]						   =  {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   int						   =  ['FNetTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   Tuple						   =  ['FNetTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   List[str]						   =  [
         'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'FNetForMaskedLM',
         'FNetForMultipleChoice',
         'FNetForNextSentencePrediction',
         'FNetForPreTraining',
         'FNetForQuestionAnswering',
         'FNetForSequenceClassification',
         'FNetForTokenClassification',
         'FNetLayer',
         'FNetModel',
         'FNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

     _snake_case  :   str						   =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

