


'''simple docstring'''






import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
__A :  Any    							=				logging.getLogger()







def     UpperCamelCase_      (   A__ :       Path							,   A__ :       list ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       """\n""".join(A__ )
  Path(A__ ).open("""w""" ).writelines(A__ )


__A :  Optional[int]    							=				"patrickvonplaten/t5-tiny-random"
__A :  Optional[int]    							=				"sshleifer/bart-tiny-random"
__A :  Dict    							=				"sshleifer/tiny-mbart"

__A :  Tuple    							=				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __lowercase       (       self   :  Optional[int]   ,						lowerCamelCase   :  str		)       ->  Any:
      lowerCAmelCase_      :				Tuple      =       Path(self.get_auto_remove_tmp_dir()		) / """utest_input.source"""
      lowerCAmelCase_      :				str      =       input_file_name.parent / """utest_output.txt"""
      assert not output_file_name.exists()
      lowerCAmelCase_      :				List[str]      =       [""" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."""]
      _dump_articles(lowerCamelCase   ,						lowerCamelCase		)

      lowerCAmelCase_      :				List[str]      =       str(Path(self.get_auto_remove_tmp_dir()		) / """scores.json"""		)
      lowerCAmelCase_      :				Union[str, Any]      =       """translation_en_to_de""" if model == T5_TINY else """summarization"""
      lowerCAmelCase_      :				Any      =       F'\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            '.split()

      with patch.object(lowerCamelCase   ,						"""argv"""   ,						lowerCamelCase		):
        run_generate()
        assert Path(lowerCamelCase		).exists()
        # os.remove(Path(output_file_name))

    def __lowercase       (       self   :  Optional[int]		)       ->  List[str]:
      self.run_eval_tester(lowerCamelCase		)

    @parameterized.expand([BART_TINY, MBART_TINY]		)
    @slow
    def __lowercase       (       self   :  str   ,						lowerCamelCase   :  str		)       ->  Optional[int]:
      self.run_eval_tester(lowerCamelCase		)





    @parameterized.expand([T5_TINY, MBART_TINY]		)
    @slow
    def __lowercase       (       self   :  List[Any]   ,						lowerCamelCase   :  Tuple		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Tuple      =       Path(self.get_auto_remove_tmp_dir()		) / """utest_input.source"""
      lowerCAmelCase_      :				List[Any]      =       input_file_name.parent / """utest_output.txt"""
      assert not output_file_name.exists()

      lowerCAmelCase_      :				Union[str, Any]      =       {
          """en""": ["""Machine learning is great, isn't it?""", """I like to eat bananas""", """Tomorrow is another great day!"""],
          """de""": [
              """Maschinelles Lernen ist großartig, oder?""",
              """Ich esse gerne Bananen""",
              """Morgen ist wieder ein toller Tag!""",
          ],
      }

      lowerCAmelCase_      :				Dict      =       Path(self.get_auto_remove_tmp_dir()		)
      lowerCAmelCase_      :				Any      =       str(tmp_dir / """scores.json"""		)
      lowerCAmelCase_      :				List[Any]      =       str(tmp_dir / """val.target"""		)
      _dump_articles(lowerCamelCase   ,						text["""en"""]		)
      _dump_articles(lowerCamelCase   ,						text["""de"""]		)
      lowerCAmelCase_      :				str      =       """translation_en_to_de""" if model == T5_TINY else """summarization"""
      lowerCAmelCase_      :				int      =       F'\n            run_eval_search.py\n            {model}\n            {str(lowerCamelCase		)}\n            {str(lowerCamelCase		)}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            '.split()
      testargs.extend(["""--search""", """num_beams=1:2 length_penalty=0.9:1.0"""]		)

      with patch.object(lowerCamelCase   ,						"""argv"""   ,						lowerCamelCase		):
        with CaptureStdout() as cs:
          run_search()
        lowerCAmelCase_      :				Optional[int]      =       [""" num_beams | length_penalty""", model, """Best score args"""]
        lowerCAmelCase_      :				Tuple      =       ["""Info"""]
        if "translation" in task:
          expected_strings.append("""bleu"""		)
        else:
          expected_strings.extend(lowerCamelCase		)
        for w in expected_strings:
          assert w in cs.out
        for w in un_expected_strings:
          assert w not in cs.out
        assert Path(lowerCamelCase		).exists()
        os.remove(Path(lowerCamelCase		)		)







'''simple docstring'''






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

import numpy as np







def     UpperCamelCase_      (   A__ :       bytes							,   A__ :       int ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       f'{sampling_rate}'
  lowerCAmelCase_      :				str      =       """1"""
  lowerCAmelCase_      :				Optional[int]      =       """f32le"""
  lowerCAmelCase_      :				Any      =       [
      """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:
      lowerCAmelCase_      :				Optional[int]      =       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
  lowerCAmelCase_      :				Optional[Any]      =       output_stream[0]
  lowerCAmelCase_      :				Optional[int]      =       np.frombuffer(A__							,   np.floataa )
  if audio.shape[0] == 0:
    raise ValueError("""Malformed soundfile""" )
  return audio







def     UpperCamelCase_      (   A__ :       int							,   A__ :       float							,   A__ :       str = "f32le"							,   ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       f'{sampling_rate}'
  lowerCAmelCase_      :				Any      =       """1"""
  if format_for_conversion == "s16le":
    lowerCAmelCase_      :				Optional[Any]      =       2
  elif format_for_conversion == "f32le":
    lowerCAmelCase_      :				Union[str, Any]      =       4
  else:
    raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

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

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







def     UpperCamelCase_      (   A__ :       int							,   A__ :       float							,   A__ :       Optional[int] = None							,   A__ :       Optional[Union[Tuple[float, float], float]] = None							,   A__ :       str = "f32le"							,   ):
  '''simple docstring'''





  if stream_chunk_s is not None:
    lowerCAmelCase_      :				Union[str, Any]      =       stream_chunk_s
  else:
    lowerCAmelCase_      :				Tuple      =       chunk_length_s

  lowerCAmelCase_      :				List[Any]      =       ffmpeg_microphone(A__							,   A__							,   format_for_conversion=A__ )
  if format_for_conversion == "s16le":
    lowerCAmelCase_      :				Tuple      =       np.intaa
    lowerCAmelCase_      :				List[Any]      =       2
  elif format_for_conversion == "f32le":
    lowerCAmelCase_      :				Dict      =       np.floataa
    lowerCAmelCase_      :				int      =       4
  else:
    raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

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

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







def     UpperCamelCase_      (   A__ :       Any							,   A__ :       int							,   A__ :       Tuple[int, int]							,   A__ :       bool = False ):
  '''simple docstring'''





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







def     UpperCamelCase_      (   A__ :       List[str]							,   A__ :       int ):
  '''simple docstring'''





  lowerCAmelCase_      :				Dict      =       2**24  # 16Mo
  try:
    with subprocess.Popen(A__							,   stdout=subprocess.PIPE							,   bufsize=A__ ) as ffmpeg_process:
      while True:
        lowerCAmelCase_      :				Union[str, Any]      =       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



