




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






def    snake_case   (				*A     )				->				Optional[int]:




	'''simple docstring'''


	lowerCamelCase_				:						List[str]				  =				torch.cuda.device_count()
	print(f"Device count: {num_processes}"     )
	with patch_environment(
	    world_size=A		,  master_addr='127.0.01'		,  master_port='29500'		,  mixed_precision=args[1].mixed_precision     ):
		lowerCamelCase_				:						Optional[Any]				  =				PrepareForLaunch(A		,  distributed_type='MULTI_GPU'     )
		torch.multiprocessing.start_processes(A		,  args=A		,  nprocs=A		,  start_method='spawn'     )


if __name__ == "__main__":
							# Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
							# for cloud access setup instructions (if using on-demand hardware), and for API specifications.

							# on-demand GPU
							# gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
							UpperCamelCase__						=		rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
							gpu.up_if_not()

							# on-prem GPU
							# gpu = rh.cluster(
							#           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
							#       )

							# Set up remote function
							UpperCamelCase__						=		[
							    'pip:./',
							    'transformers',
							    'datasets',
							    'evaluate',
							    'tqdm',
							    'scipy',
							    'scikit-learn',
							    'tensorboard',
							    'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
							]
							UpperCamelCase__						=		rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

							# Define train args/config, run train function
							UpperCamelCase__						=		argparse.Namespace(cpu=False, mixed_precision='fp16')
							UpperCamelCase__						=		{'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
							launch_train_gpu(config, train_args, stream_logs=True)

							# Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
							# gpu.install_packages(reqs)
							# gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])



from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowercase_									=    logging.get_logger(__name__)


class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    lowerCamelCase						      =				['input_values', 'attention_mask']


    def __init__( self					:			Tuple,lowercase_					:			Optional[Any]=1,lowercase_					:			Union[str, Any]=1_6_0_0_0,lowercase_					:			str=1_2_8,lowercase_					:			Optional[Any]=1_0_2_4,lowercase_					:			Union[str, Any]=0.0,lowercase_					:			Any=True,lowercase_					:			List[Any]=-4.2_677_393,lowercase_					:			Union[str, Any]=4.5_689_974,lowercase_					:			Dict=False,**lowercase_					:			Dict,)->       int:





        '''simple docstring'''







        super().__init__(feature_size=lowercase_,sampling_rate=lowercase_,padding_value=lowercase_,**lowercase_						)
        A__        =  num_mel_bins
        A__        =  max_length
        A__        =  do_normalize
        A__        =  mean
        A__        =  std
        A__        =  return_attention_mask


    def 		snake_case__	( self					:			Any,lowercase_					:			np.ndarray,lowercase_					:			int,)->       np.ndarray:





        '''simple docstring'''







        A__        =  torch.from_numpy(lowercase_						).unsqueeze(0						)
        A__        =  ta_kaldi.fbank(
            lowercase_,htk_compat=lowercase_,sample_frequency=self.sampling_rate,use_energy=lowercase_,window_type='hanning',num_mel_bins=self.num_mel_bins,dither=0.0,frame_shift=1_0,)

        A__        =  fbank.shape[0]
        A__        =  max_length - n_frames

        # pad or truncate, depending on difference
        if difference > 0:
            A__        =  torch.nn.ZeroPadad((0, 0, 0, difference)						)
            A__        =  pad_module(lowercase_						)
        elif difference < 0:
            A__        =  fbank[0:max_length, :]

        A__        =  fbank.numpy()

        return fbank


    def 		snake_case__	( self					:			int,lowercase_					:			np.ndarray						)->       np.ndarray:





        '''simple docstring'''







        return (input_values - (self.mean)) / (self.std * 2)


    def __call__( self					:			str,lowercase_					:			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],lowercase_					:			Optional[int] = None,lowercase_					:			Optional[Union[str, TensorType]] = None,**lowercase_					:			str,)->       BatchFeature:





        '''simple docstring'''







        if sampling_rate is not None:
            if sampling_rate != self.sampling_rate:
                raise ValueError(
                    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                    F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
                    F' {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.'						)

        A__        =  isinstance(lowercase_,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}'						)
        A__        =  is_batched_numpy or (
            isinstance(lowercase_,(list, tuple)						) and (isinstance(raw_speech[0],(np.ndarray, tuple, list)						))
        )

        if is_batched:
            A__        =  [np.asarray(lowercase_,dtype=np.floataa						) for speech in raw_speech]
        elif not is_batched and not isinstance(lowercase_,np.ndarray						):
            A__        =  np.asarray(lowercase_,dtype=np.floataa						)
        elif isinstance(lowercase_,np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
            A__        =  raw_speech.astype(np.floataa						)

        # always return batch
        if not is_batched:
            A__        =  [raw_speech]

        # extract fbank features and pad/truncate to max_length
        A__        =  [self._extract_fbank_features(lowercase_,max_length=self.max_length						) for waveform in raw_speech]

        # convert into BatchFeature
        A__        =  BatchFeature({'input_values': features}						)

        # make sure list is in array format
        A__        =  padded_inputs.get('input_values'						)
        if isinstance(input_values[0],lowercase_						):
            A__        =  [np.asarray(lowercase_,dtype=np.floataa						) for feature in input_values]

        # normalization
        if self.do_normalize:
            A__        =  [self.normalize(lowercase_						) for feature in input_values]

        if return_tensors is not None:
            A__        =  padded_inputs.convert_to_tensors(lowercase_						)

        return padded_inputs

