





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

import numpy as np



def A_	(		A__		,      A__				)  ->		np.array:
       a__						:	Optional[Any] =				F'{sampling_rate}'
       a__						:	Optional[int] =				'1'
       a__						:	Any =				'f32le'
       a__						:	Dict =				[
           '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:
                     a__						:	List[str] =				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
       a__						:	Union[str, Any] =				output_stream[0]
       a__						:	Any =				np.frombuffer(A__		,      np.floataa				)
       if audio.shape[0] == 0:
              raise ValueError('Malformed soundfile'				)
       return audio



def A_	(		A__		,      A__		,      A__ = "f32le"		,      )  ->		int:
       a__						:	Optional[int] =				F'{sampling_rate}'
       a__						:	Optional[Any] =				'1'
       if format_for_conversion == "s16le":
              a__						:	List[str] =				2
       elif format_for_conversion == "f32le":
              a__						:	int =				4
       else:
              raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'				)

       a__						:	List[Any] =				platform.system()
       if system == "Linux":
              a__						:	Any =				'alsa'
              a__						:	Optional[Any] =				'default'
       elif system == "Darwin":
              a__						:	Union[str, Any] =				'avfoundation'
              a__						:	Any =				':0'
       elif system == "Windows":
              a__						:	Union[str, Any] =				'dshow'
              a__						:	Optional[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__						:	List[Any] =				int(round(sampling_rate * chunk_length_s				)				) * size_of_sample
       a__						:	str =				_ffmpeg_stream(A__		,      A__				)
       for item in iterator:
              yield item



def A_	(		A__		,      A__		,      A__ = None		,      A__ = None		,      A__ = "f32le"		,      )  ->		Union[str, Any]:
       if stream_chunk_s is not None:
              a__						:	Union[str, Any] =				stream_chunk_s
       else:
              a__						:	Dict =				chunk_length_s

       a__						:	Tuple =				ffmpeg_microphone(A__		,      A__		,      format_for_conversion=A__				)
       if format_for_conversion == "s16le":
              a__						:	str =				np.intaa
              a__						:	Optional[int] =				2
       elif format_for_conversion == "f32le":
              a__						:	Tuple =				np.floataa
              a__						:	Optional[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__						:	int =				int(round(sampling_rate * chunk_length_s				)				) * size_of_sample
       if isinstance(A__		,      (int, float)				):
              a__						:	Any =				[stride_length_s, stride_length_s]

       a__						:	Optional[Any] =				int(round(sampling_rate * stride_length_s[0]				)				) * size_of_sample
       a__						:	str =				int(round(sampling_rate * stride_length_s[1]				)				) * size_of_sample
       a__						:	List[str] =				datetime.datetime.now()
       a__						:	Tuple =				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
              a__						:	str =				np.frombuffer(item['raw']		,      dtype=A__				)
              a__						:	List[str] =				(
                  item['stride'][0] // size_of_sample,
                  item['stride'][1] // size_of_sample,
              )
              a__						:	Optional[int] =				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				)  ->		Tuple:
       a__						:	Optional[Any] =				B''
       a__ ,							a__						:	List[str] =				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__						:	Optional[Any] =				0
       for raw in iterator:
              acc += raw
              if stream and len(A__				) < chunk_len:
                     a__						:	Any =				(_stride_left, 0)
                     yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
              else:
                     while len(A__				) >= chunk_len:
                            # We are flushing the accumulator
                            a__						:	int =				(_stride_left, stride_right)
                            a__						:	Any =				{'raw': acc[:chunk_len], 'stride': stride}
                            if stream:
                                   a__						:	int =				False
                            yield item
                            a__						:	Union[str, Any] =				stride_left
                            a__						:	int =				acc[chunk_len - stride_left - stride_right :]
    # Last chunk
       if len(A__				) > stride_left:
              a__						:	str =				{'raw': acc, 'stride': (_stride_left, 0)}
              if stream:
                     a__						:	List[str] =				False
              yield item



def A_	(		A__		,      A__				)  ->		Tuple:
       a__						:	Union[str, Any] =				2**24  # 16Mo
       try:
              with subprocess.Popen(A__		,      stdout=subprocess.PIPE		,      bufsize=A__				) as ffmpeg_process:
                     while True:
                            a__						:	str =				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





import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
       # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
       # setup instructions, if using on-demand hardware

       # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
       # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
       # Throw an error if user passes both BYO and on-demand cluster args
       # Otherwise, use default values
       snake_case_			  = argparse.ArgumentParser()
       parser.add_argument('''--user''', type=str, default='''ubuntu''')
       parser.add_argument('''--host''', type=str, default='''localhost''')
       parser.add_argument('''--key_path''', type=str, default=None)
       parser.add_argument('''--instance''', type=str, default='''V100:1''')
       parser.add_argument('''--provider''', type=str, default='''cheapest''')
       parser.add_argument('''--use_spot''', type=bool, default=False)
       parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
       snake_case_  ,		snake_case_			  = parser.parse_known_args()
       if args.host != "localhost":
              if args.instance != "V100:1" or args.provider != "cheapest":
                     raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
              snake_case_			  = rh.cluster(
                  name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
              )
       else:
              snake_case_			  = rh.cluster(
                  name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
              )
       snake_case_			  = args.example.rsplit('''/''', 1)[0]

       # Set up remote environment
       cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
       # Note transformers is copied into the home directory on the remote machine, so we can install from there
       cluster.run([F'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
       cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

       # Run example. You can bypass the CLI wrapper and paste your own code here.
       cluster.run([F'''python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}'''])

       # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
       # from my_script... import train
       # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
       # launch_train_gpu = rh.function(fn=train,
       #                                system=gpu,
       #                                reqs=reqs,
       #                                name='train_bert_glue')
       #
       # We can pass in arguments just like we would to a function:
       # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
       #                  stream_logs=True)

