






"""simple docstring"""
import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


lowerCamelCase_       			=   logging.get_logger(__name__)







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       ['''input_values''', '''attention_mask''']





	def __init__(   self      :				Optional[int]			,			lowerCAmelCase_      :				int = 1			,			lowerCAmelCase_      :				int = 16_000			,			lowerCAmelCase_      :				float = 0.0			,			lowerCAmelCase_      :				bool = False			,			lowerCAmelCase_      :				int = 80			,			lowerCAmelCase_      :				int = 16			,			lowerCAmelCase_      :				int = 64			,			lowerCAmelCase_      :				str = "hann_window"			,			lowerCAmelCase_      :				float = 1.0			,			lowerCAmelCase_      :				float = 80			,			lowerCAmelCase_      :				float = 7_600			,			lowerCAmelCase_      :				float = 1e-10			,			lowerCAmelCase_      :				int = 2			,			lowerCAmelCase_      :				bool = True			,			**lowerCAmelCase_      :				List[str]			,			)		->    List[Any]:
				super().__init__(feature_size=lowerCAmelCase_			,			sampling_rate=lowerCAmelCase_			,			padding_value=lowerCAmelCase_			,			**lowerCAmelCase_		)
				UpperCAmelCase_  :				Tuple      =      do_normalize
				UpperCAmelCase_  :				Tuple      =      return_attention_mask

				UpperCAmelCase_  :				List[Any]      =      num_mel_bins
				UpperCAmelCase_  :				str      =      hop_length
				UpperCAmelCase_  :				List[Any]      =      win_length
				UpperCAmelCase_  :				List[str]      =      win_function
				UpperCAmelCase_  :				Optional[Any]      =      frame_signal_scale
				UpperCAmelCase_  :				int      =      fmin
				UpperCAmelCase_  :				Union[str, Any]      =      fmax
				UpperCAmelCase_  :				Optional[int]      =      mel_floor
				UpperCAmelCase_  :				Optional[Any]      =      reduction_factor

				UpperCAmelCase_  :				List[str]      =      win_length * sampling_rate // 1_000
				UpperCAmelCase_  :				List[str]      =      hop_length * sampling_rate // 1_000
				UpperCAmelCase_  :				Optional[Any]      =      optimal_fft_length(self.sample_size		)
				UpperCAmelCase_  :				Union[str, Any]      =      (self.n_fft // 2) + 1

				UpperCAmelCase_  :				Tuple      =      window_function(window_length=self.sample_size			,			name=self.win_function			,			periodic=lowerCAmelCase_		)

				UpperCAmelCase_  :				Optional[Any]      =      mel_filter_bank(
				    num_frequency_bins=self.n_freqs			,			num_mel_filters=self.num_mel_bins			,			min_frequency=self.fmin			,			max_frequency=self.fmax			,			sampling_rate=self.sampling_rate			,			norm="slaney"			,			mel_scale="slaney"			,			)

				if frame_signal_scale != 1.0:
							warnings.warn(
							    "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"			,			lowerCAmelCase_			,			)
				if reduction_factor != 2.0:
							warnings.warn(
							    "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"			,			lowerCAmelCase_			,			)





	@staticmethod
	# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
	def   _SCREAMING_SNAKE_CASE   (   lowerCAmelCase_      :				List[np.ndarray]			,			lowerCAmelCase_      :				List[np.ndarray]			,			lowerCAmelCase_      :				float = 0.0		)		->    List[np.ndarray]:
				if attention_mask is not None:
							UpperCAmelCase_  :				Optional[Any]      =      np.array(lowerCAmelCase_			,			np.intaa		)
							UpperCAmelCase_  :				List[Any]      =      []

							for vector, length in zip(lowerCAmelCase_			,			attention_mask.sum(-1		)		):
										UpperCAmelCase_  :				Optional[Any]      =      (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7		)
										if length < normed_slice.shape[0]:
													UpperCAmelCase_  :				str      =      padding_value

										normed_input_values.append(lowerCAmelCase_		)
				else:
							UpperCAmelCase_  :				str      =      [(x - x.mean()) / np.sqrt(x.var() + 1e-7		) for x in input_values]

				return normed_input_values





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				np.ndarray			,			)		->    np.ndarray:
				UpperCAmelCase_  :				Union[str, Any]      =      spectrogram(
				    lowerCAmelCase_			,			window=self.window			,			frame_length=self.sample_size			,			hop_length=self.sample_stride			,			fft_length=self.n_fft			,			mel_filters=self.mel_filters			,			mel_floor=self.mel_floor			,			log_mel="log10"			,			)
				return log_mel_spec.T





	def __call__(   self      :				Any			,			lowerCAmelCase_      :				Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,			lowerCAmelCase_      :				Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,			lowerCAmelCase_      :				Union[bool, str, PaddingStrategy] = False			,			lowerCAmelCase_      :				Optional[int] = None			,			lowerCAmelCase_      :				bool = False			,			lowerCAmelCase_      :				Optional[int] = None			,			lowerCAmelCase_      :				Optional[bool] = None			,			lowerCAmelCase_      :				Optional[Union[str, TensorType]] = None			,			lowerCAmelCase_      :				Optional[int] = None			,			**lowerCAmelCase_      :				Dict			,			)		->    BatchFeature:
				if audio is None and audio_target is None:
							raise ValueError("You must provide either `audio` or `audio_target` values."		)

				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 audio 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."		)

				if audio is not None:
							UpperCAmelCase_  :				str      =      self._process_audio(
							    lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			**lowerCAmelCase_			,			)
				else:
							UpperCAmelCase_  :				Any      =      None

				if audio_target is not None:
							UpperCAmelCase_  :				int      =      self._process_audio(
							    lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			**lowerCAmelCase_			,			)

							if inputs is None:
										return inputs_target
							else:
										UpperCAmelCase_  :				List[str]      =      inputs_target["input_values"]
										UpperCAmelCase_  :				Dict      =      inputs_target.get("attention_mask"		)
										if decoder_attention_mask is not None:
													UpperCAmelCase_  :				str      =      decoder_attention_mask

				return inputs





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any			,			lowerCAmelCase_      :				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,			lowerCAmelCase_      :				bool = False			,			lowerCAmelCase_      :				Union[bool, str, PaddingStrategy] = False			,			lowerCAmelCase_      :				Optional[int] = None			,			lowerCAmelCase_      :				bool = False			,			lowerCAmelCase_      :				Optional[int] = None			,			lowerCAmelCase_      :				Optional[bool] = None			,			lowerCAmelCase_      :				Optional[Union[str, TensorType]] = None			,			**lowerCAmelCase_      :				Union[str, Any]			,			)		->    BatchFeature:
				UpperCAmelCase_  :				List[str]      =      isinstance(lowerCAmelCase_			,			np.ndarray		) and len(speech.shape		) > 1
				if is_batched_numpy and len(speech.shape		) > 2:
							raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""		)
				UpperCAmelCase_  :				str      =      is_batched_numpy or (
				    isinstance(lowerCAmelCase_			,			(list, tuple)		) and (isinstance(speech[0]			,			(np.ndarray, tuple, list)		))
				)

				if is_batched:
							UpperCAmelCase_  :				Optional[Any]      =      [np.asarray(lowerCAmelCase_			,			dtype=np.floataa		) for speech in speech]
				elif not is_batched and not isinstance(lowerCAmelCase_			,			np.ndarray		):
							UpperCAmelCase_  :				Union[str, Any]      =      np.asarray(lowerCAmelCase_			,			dtype=np.floataa		)
				elif isinstance(lowerCAmelCase_			,			np.ndarray		) and speech.dtype is np.dtype(np.floataa		):
							UpperCAmelCase_  :				Optional[int]      =      speech.astype(np.floataa		)

				# always return batch
				if not is_batched:
							UpperCAmelCase_  :				Dict      =      [speech]

				# needed to make pad() work on spectrogram inputs
				UpperCAmelCase_  :				Optional[int]      =      self.feature_size

				# convert into correct format for padding
				if is_target:
							UpperCAmelCase_  :				str      =      [self._extract_mel_features(lowerCAmelCase_		) for waveform in speech]
							UpperCAmelCase_  :				Optional[Any]      =      BatchFeature({"input_values": features}		)
							UpperCAmelCase_  :				Optional[int]      =      self.num_mel_bins
				else:
							UpperCAmelCase_  :				Union[str, Any]      =      BatchFeature({"input_values": speech}		)

				UpperCAmelCase_  :				List[Any]      =      self.pad(
				    lowerCAmelCase_			,			padding=lowerCAmelCase_			,			max_length=lowerCAmelCase_			,			truncation=lowerCAmelCase_			,			pad_to_multiple_of=lowerCAmelCase_			,			return_attention_mask=lowerCAmelCase_			,			**lowerCAmelCase_			,			)

				UpperCAmelCase_  :				Any      =      feature_size_hack

				# convert input values to correct format
				UpperCAmelCase_  :				List[Any]      =      padded_inputs["input_values"]
				if not isinstance(input_values[0]			,			np.ndarray		):
							UpperCAmelCase_  :				Union[str, Any]      =      [np.asarray(lowerCAmelCase_			,			dtype=np.floataa		) for array in input_values]
				elif (
				    not isinstance(lowerCAmelCase_			,			np.ndarray		)
				    and isinstance(input_values[0]			,			np.ndarray		)
				    and input_values[0].dtype is np.dtype(np.floataa		)
				):
							UpperCAmelCase_  :				Any      =      [array.astype(np.floataa		) for array in input_values]
				elif isinstance(lowerCAmelCase_			,			np.ndarray		) and input_values.dtype is np.dtype(np.floataa		):
							UpperCAmelCase_  :				int      =      input_values.astype(np.floataa		)

				# convert attention_mask to correct format
				UpperCAmelCase_  :				Any      =      padded_inputs.get("attention_mask"		)
				if attention_mask is not None:
							UpperCAmelCase_  :				List[str]      =      [np.asarray(lowerCAmelCase_			,			dtype=np.intaa		) for array in attention_mask]

				# zero-mean and unit-variance normalization
				if not is_target and self.do_normalize:
							UpperCAmelCase_  :				Union[str, Any]      =      (
							    attention_mask
							    if self._get_padding_strategies(lowerCAmelCase_			,			max_length=lowerCAmelCase_		) is not PaddingStrategy.DO_NOT_PAD
							    else None
							)
							UpperCAmelCase_  :				Any      =      self.zero_mean_unit_var_norm(
							    padded_inputs["input_values"]			,			attention_mask=lowerCAmelCase_			,			padding_value=self.padding_value		)

				if return_tensors is not None:
							UpperCAmelCase_  :				List[str]      =      padded_inputs.convert_to_tensors(lowerCAmelCase_		)

				return padded_inputs





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Dict[str, Any]:
				UpperCAmelCase_  :				Union[str, Any]      =      super().to_dict()

				# Don't serialize these as they are derived from the other properties.
				UpperCAmelCase_  :				Dict      =      ["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
				for name in names:
							if name in output:
										del output[name]

				return output











"""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_       			=   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_       			=   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_       			=   rh.cluster(
						    name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
						)
			else:
						lowerCamelCase_       			=   rh.cluster(
						    name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
						)
			lowerCamelCase_       			=   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)



