"""simple docstring"""


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
 lowerCAmelCase_:					int     =					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")
 lowerCAmelCase_    ,							lowerCAmelCase_:					str     =					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")
  lowerCAmelCase_:					List[str]     =					rh.cluster(
      name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
  )
 else:
  lowerCAmelCase_:					List[str]     =					rh.cluster(
      name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
  )
 lowerCAmelCase_:					Optional[int]     =					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)





"""simple docstring"""


from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


lowerCAmelCase_:					Union[str, Any]     =					logging.get_logger(__name__)






class    a__    (   _a	):
   snake_case_							=      ["audio_values", "audio_mask"]


   def __init__(     self,				_UpperCAmelCase=2048,				_UpperCAmelCase=1,				_UpperCAmelCase=[16, 16],				_UpperCAmelCase=128,				_UpperCAmelCase=4_4100,				_UpperCAmelCase=86,				_UpperCAmelCase=2048,				_UpperCAmelCase=0.0,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(
            feature_size=_UpperCAmelCase,				sampling_rate=_UpperCAmelCase,				padding_value=_UpperCAmelCase,				**_UpperCAmelCase,				)

        lowercase__				 =							spectrogram_length
        lowercase__				 =							num_channels
        lowercase__				 =							patch_size
        lowercase__				 =							feature_size // self.patch_size[1]
        lowercase__				 =							n_fft
        lowercase__				 =							sampling_rate // hop_length_to_sampling_rate
        lowercase__				 =							sampling_rate
        lowercase__				 =							padding_value
        lowercase__				 =							mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2,				num_mel_filters=_UpperCAmelCase,				min_frequency=0.0,				max_frequency=22_050.0,				sampling_rate=_UpperCAmelCase,				norm="slaney",				mel_scale="slaney",				).T


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							spectrogram(
            _UpperCAmelCase,				window_function(self.n_fft,				"hann"  ),				frame_length=self.n_fft,				hop_length=self.hop_length,				power=2.0,				mel_filters=self.mel_filters.T,				log_mel="dB",				db_range=80.0,				)
        lowercase__				 =							log_spec[:, :-1]
        lowercase__				 =							log_spec - 20.0
        lowercase__				 =							np.clip(log_spec / 40.0,				-2.0,				0.0  ) + 1.0
        return log_spec


   def __call__(     self,				_UpperCAmelCase,				_UpperCAmelCase = None,				_UpperCAmelCase = True,				_UpperCAmelCase = None,				_UpperCAmelCase = False,				_UpperCAmelCase = False,				**_UpperCAmelCase,				):
        '''simple docstring'''



        if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                  raise ValueError(
                      "This feature extractor is set to support sampling rate"
                      F''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
                      F''' with {self.sampling_rate} and not {sampling_rate}.'''  )
        else:
             logger.warning(
                 "It is strongly recommended to pass the `sampling_rate` argument to this function. "
                 "Failing to do so can result in silent errors that might be hard to debug."  )

        lowercase__				 =							isinstance(_UpperCAmelCase,				np.ndarray  ) and len(raw_speech.shape  ) > 1
        if is_batched_numpy and len(raw_speech.shape  ) > 2:
             raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''  )
        lowercase__				 =							is_batched_numpy or (
            isinstance(_UpperCAmelCase,				(list, tuple)  ) and (isinstance(raw_speech[0],				(np.ndarray, tuple, list)  ))
        )
        if is_batched:
             lowercase__				 =							[np.asarray([speech],				dtype=np.floataa  ).T for speech in raw_speech]
        elif not is_batched and not isinstance(_UpperCAmelCase,				np.ndarray  ):
             lowercase__				 =							np.asarray(_UpperCAmelCase,				dtype=np.floataa  )
        elif isinstance(_UpperCAmelCase,				np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
             lowercase__				 =							raw_speech.astype(np.floataa  )
        # always return batch
        if not is_batched:
             lowercase__				 =							[np.asarray([raw_speech]  ).T]

        # Convert audio signals to log mel spectrograms, truncate by time axis
        lowercase__				 =							[
            self._np_extract_fbank_features(waveform.squeeze()  ).T[: self.spectrogram_length] for waveform in raw_speech
        ]
        if isinstance(audio_features[0],				_UpperCAmelCase  ):
             lowercase__				 =							[np.asarray(_UpperCAmelCase,				dtype=np.floataa  ) for feature in audio_features]

        # Create audio attention mask
        lowercase__				 =							max(
            [ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len for feature in audio_features]  )  # The maximum number of audio patches in a batch
        if return_attention_mask:
             lowercase__				 =							[
                 (ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [1]
                 + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [0]
                 for feature in audio_features
             ]
             lowercase__				 =							np.array(_UpperCAmelCase  ).astype(np.floataa  )

        # convert into correct format for padding
        lowercase__				 =							max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
        lowercase__				 =							np.ones([len(_UpperCAmelCase  ), 1, max_time_len, self.feature_size]  ).astype(np.floataa  )
        lowercase__				 =							padded_audio_features * self.padding_value
        for i in range(len(_UpperCAmelCase  )  ):
             lowercase__				 =							audio_features[i]
             lowercase__				 =							feature

        # return as BatchFeature
        if return_attention_mask:
             lowercase__				 =							{"audio_values": padded_audio_features, "audio_mask": audio_mask}
        else:
             lowercase__				 =							{"audio_values": padded_audio_features}

        lowercase__				 =							BatchFeature(data=_UpperCAmelCase,				tensor_type=_UpperCAmelCase  )
        return encoded_inputs




