






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

import numpy as np






def      _snake_case     (__lowercase     ,   __lowercase):
   UpperCamelCase_						=  f"""{sampling_rate}"""
   UpperCamelCase_						=  '1'
   UpperCamelCase_						=  'f32le'
   UpperCamelCase_						=  [
       'ffmpeg',
       '-i',
       'pipe:0',
       '-ac',
       ac,
       '-ar',
       ar,
       '-f',
       format_for_conversion,
       '-hide_banner',
       '-loglevel',
       'quiet',
       'pipe:1',
   ]

   try:
      with subprocess.Popen(__lowercase     ,   stdin=subprocess.PIPE     ,   stdout=subprocess.PIPE) as ffmpeg_process:
         UpperCamelCase_						=  ffmpeg_process.communicate(__lowercase)
   except FileNotFoundError as error:
      raise ValueError('ffmpeg was not found but is required to load audio files from filename') from error
   UpperCamelCase_						=  output_stream[0]
   UpperCamelCase_						=  np.frombuffer(__lowercase     ,   np.floataa)
   if audio.shape[0] == 0:
      raise ValueError('Malformed soundfile')
   return audio






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

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

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






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase = None     ,   __lowercase = None     ,   __lowercase = "f32le"     ,   ):
   if stream_chunk_s is not None:
      UpperCamelCase_						=  stream_chunk_s
   else:
      UpperCamelCase_						=  chunk_length_s

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

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

   UpperCamelCase_						=  int(round(sampling_rate * stride_length_s[0])) * size_of_sample
   UpperCamelCase_						=  int(round(sampling_rate * stride_length_s[1])) * size_of_sample
   UpperCamelCase_						=  datetime.datetime.now()
   UpperCamelCase_						=  datetime.timedelta(seconds=__lowercase)
   for item in chunk_bytes_iter(__lowercase     ,   __lowercase     ,   stride=(stride_left, stride_right)     ,   stream=__lowercase):
      # Put everything back in numpy scale
      UpperCamelCase_						=  np.frombuffer(item['raw']     ,   dtype=__lowercase)
      UpperCamelCase_						=  (
          item['stride'][0] // size_of_sample,
          item['stride'][1] // size_of_sample,
      )
      UpperCamelCase_						=  sampling_rate
      audio_time += delta
      if datetime.datetime.now() > audio_time + 10 * delta:
         # We're late !! SKIP
         continue
      yield item






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






def      _snake_case     (__lowercase     ,   __lowercase):
   UpperCamelCase_						=  2**24  # 16Mo
   try:
      with subprocess.Popen(__lowercase     ,   stdout=subprocess.PIPE     ,   bufsize=__lowercase) as ffmpeg_process:
         while True:
            UpperCamelCase_						=  ffmpeg_process.stdout.read(__lowercase)
            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 transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
 import datasets


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  @slow
  @require_torch
  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     UpperCamelCase_						=  EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'		,		'prajjwal1/bert-tiny'			)
     UpperCamelCase_						=  BertTokenizer.from_pretrained('bert-base-uncased'			)

     UpperCamelCase_						=  bertabert.config.encoder.vocab_size
     UpperCamelCase_						=  tokenizer.sep_token_id
     UpperCamelCase_						=  tokenizer.cls_token_id
     UpperCamelCase_						=  128

     UpperCamelCase_						=  datasets.load_dataset('cnn_dailymail'		,		'3.0.0'		,		split='train[:1%]'			)
     UpperCamelCase_						=  datasets.load_dataset('cnn_dailymail'		,		'3.0.0'		,		split='validation[:1%]'			)

     UpperCamelCase_						=  train_dataset.select(range(32			)			)
     UpperCamelCase_						=  val_dataset.select(range(16			)			)

     UpperCamelCase_						=  4

     def _map_to_encoder_decoder_inputs(_UpperCAmelCase			):
        # Tokenizer will automatically set [BOS] <text> [EOS]
        UpperCamelCase_						=  tokenizer(batch['article']		,		padding='max_length'		,		truncation=_UpperCAmelCase		,		max_length=512			)
        UpperCamelCase_						=  tokenizer(batch['highlights']		,		padding='max_length'		,		truncation=_UpperCAmelCase		,		max_length=128			)
        UpperCamelCase_						=  inputs.input_ids
        UpperCamelCase_						=  inputs.attention_mask

        UpperCamelCase_						=  outputs.input_ids
        UpperCamelCase_						=  outputs.input_ids.copy()
        UpperCamelCase_						=  [
            [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
        ]
        UpperCamelCase_						=  outputs.attention_mask

        assert all(len(_UpperCAmelCase			) == 512 for x in inputs.input_ids			)
        assert all(len(_UpperCAmelCase			) == 128 for x in outputs.input_ids			)

        return batch

     def _compute_metrics(_UpperCAmelCase			):
        UpperCamelCase_						=  pred.label_ids
        UpperCamelCase_						=  pred.predictions

        # all unnecessary tokens are removed
        UpperCamelCase_						=  tokenizer.batch_decode(_UpperCAmelCase		,		skip_special_tokens=_UpperCAmelCase			)
        UpperCamelCase_						=  tokenizer.batch_decode(_UpperCAmelCase		,		skip_special_tokens=_UpperCAmelCase			)

        UpperCamelCase_						=  sum([int(pred_str[i] == label_str[i]			) for i in range(len(_UpperCAmelCase			)			)]			) / len(_UpperCAmelCase			)

        return {"accuracy": accuracy}

     # map train dataset
     UpperCamelCase_						=  train_dataset.map(
         _map_to_encoder_decoder_inputs		,		batched=_UpperCAmelCase		,		batch_size=_UpperCAmelCase		,		remove_columns=['article', 'highlights']		,		)
     train_dataset.set_format(
         type='torch'		,		columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']		,		)

     # same for validation dataset
     UpperCamelCase_						=  val_dataset.map(
         _map_to_encoder_decoder_inputs		,		batched=_UpperCAmelCase		,		batch_size=_UpperCAmelCase		,		remove_columns=['article', 'highlights']		,		)
     val_dataset.set_format(
         type='torch'		,		columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']		,		)

     UpperCamelCase_						=  self.get_auto_remove_tmp_dir()

     UpperCamelCase_						=  SeqaSeqTrainingArguments(
         output_dir=_UpperCAmelCase		,		per_device_train_batch_size=_UpperCAmelCase		,		per_device_eval_batch_size=_UpperCAmelCase		,		predict_with_generate=_UpperCAmelCase		,		evaluation_strategy='steps'		,		do_train=_UpperCAmelCase		,		do_eval=_UpperCAmelCase		,		warmup_steps=0		,		eval_steps=2		,		logging_steps=2		,		)

     # instantiate trainer
     UpperCamelCase_						=  SeqaSeqTrainer(
         model=_UpperCAmelCase		,		args=_UpperCAmelCase		,		compute_metrics=_compute_metrics		,		train_dataset=_UpperCAmelCase		,		eval_dataset=_UpperCAmelCase		,		tokenizer=_UpperCAmelCase		,		)

     # start training
     trainer.train()





