





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

import numpy as np






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Dict      		=  F"""{sampling_rate}"""
   a__	:						List[str]      		=  "1"
   a__	:						Any      		=  "f32le"
   a__	:						Optional[int]      		=  [
       "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:
         a__	:						int      		=  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
   a__	:						int      		=  output_stream[0]
   a__	:						Any      		=  np.frombuffer(lowerCamelCase							, np.floataa   )
   if audio.shape[0] == 0:
      raise ValueError("Malformed soundfile"   )
   return audio






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase = "f32le"							, ):
   a__	:						int      		=  F"""{sampling_rate}"""
   a__	:						List[str]      		=  "1"
   if format_for_conversion == "s16le":
      a__	:						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__	:						int      		=  platform.system()
   if system == "Linux":
      a__	:						Tuple      		=  "alsa"
      a__	:						Any      		=  "default"
   elif system == "Darwin":
      a__	:						List[str]      		=  "avfoundation"
      a__	:						Any      		=  ":0"
   elif system == "Windows":
      a__	:						Any      		=  "dshow"
      a__	:						int      		=  "default"

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






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase = None							, lowerCamelCase = None							, lowerCamelCase = "f32le"							, ):
   if stream_chunk_s is not None:
      a__	:						int      		=  stream_chunk_s
   else:
      a__	:						str      		=  chunk_length_s

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

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

   a__	:						List[str]      		=  int(round(sampling_rate * stride_length_s[0]   )   ) * size_of_sample
   a__	:						List[Any]      		=  int(round(sampling_rate * stride_length_s[1]   )   ) * size_of_sample
   a__	:						Dict      		=  datetime.datetime.now()
   a__	:						Optional[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
      a__	:						Dict      		=  np.frombuffer(item["raw"]							, dtype=lowerCamelCase   )
      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 + 10 * delta:
         # We're late !! SKIP
         continue
      yield item






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase = False   ):
   a__	:						int      		=  b""
   a__		,     a__	:						Dict      		=  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__	:						str      		=  0
   for raw in iterator:
      acc += raw
      if stream and len(lowerCamelCase   ) < chunk_len:
         a__	:						int      		=  (_stride_left, 0)
         yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
      else:
         while len(lowerCamelCase   ) >= chunk_len:
            # We are flushing the accumulator
            a__	:						str      		=  (_stride_left, stride_right)
            a__	:						List[Any]      		=  {"raw": acc[:chunk_len], "stride": stride}
            if stream:
               a__	:						int      		=  False
            yield item
            a__	:						List[Any]      		=  stride_left
            a__	:						Tuple      		=  acc[chunk_len - stride_left - stride_right :]
    # Last chunk
   if len(lowerCamelCase   ) > stride_left:
      a__	:						int      		=  {"raw": acc, "stride": (_stride_left, 0)}
      if stream:
         a__	:						Any      		=  False
      yield item






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						List[Any]      		=  2**24  # 16Mo
   try:
      with subprocess.Popen(lowerCamelCase							, stdout=subprocess.PIPE							, bufsize=lowerCamelCase   ) as ffmpeg_process:
         while True:
            a__	:						Dict      		=  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








import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


SCREAMING_SNAKE_CASE__       :			List[Any]			      =						"""sshleifer/bart-tiny-random"""
SCREAMING_SNAKE_CASE__       :			Tuple			      =						"""patrickvonplaten/t5-tiny-random"""







@require_torch
class   __lowerCAmelCase			(					unittest.TestCase  ):





   @cached_property
   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      return AutoConfig.from_pretrained(snake_case      )





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__		,     *a__	:						int      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=1      )
      self.assertEqual(student.config.num_hidden_layers					,			1      )





   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      a__		,     *a__	:						int      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=snake_case      )





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__		,     *a__	:						Any      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=snake_case      )
      self.assertEqual(student.config.encoder_layers					,			1      )
      self.assertEqual(student.config.decoder_layers					,			self.teacher_config.encoder_layers      )





   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      a__		,     *a__	:						List[str]      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=1      )
      self.assertEqual(student.config.encoder_layers					,			1      )
      self.assertEqual(student.config.decoder_layers					,			1      )





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      with self.assertRaises(snake_case      ):
         create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=snake_case					,			d=snake_case      )

