
"""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


__UpperCAmelCase       =  logging.get_logger(__name__)




class 			_SCREAMING_SNAKE_CASE					(			A__							):
			UpperCAmelCase_  :Tuple				 =						["audio_values", "audio_mask"]






			def __init__(					self       ,   __A=2048       ,   __A=1       ,   __A=[16, 16]       ,   __A=128       ,   __A=4_4100       ,   __A=86       ,   __A=2048       ,   __A=0.0       ,   **__A       ,   )			->     int:
						super().__init__(
						    feature_size=__A       ,   sampling_rate=__A       ,   padding_value=__A       ,   **__A       ,   )

						lowerCAmelCase_    :str			     =  spectrogram_length
						lowerCAmelCase_    :Union[str, Any]			     =  num_channels
						lowerCAmelCase_    :List[Any]			     =  patch_size
						lowerCAmelCase_    :Dict			     =  feature_size // self.patch_size[1]
						lowerCAmelCase_    :int			     =  n_fft
						lowerCAmelCase_    :Optional[Any]			     =  sampling_rate // hop_length_to_sampling_rate
						lowerCAmelCase_    :List[Any]			     =  sampling_rate
						lowerCAmelCase_    :List[Any]			     =  padding_value
						lowerCAmelCase_    :Dict			     =  mel_filter_bank(
						    num_frequency_bins=1 + n_fft // 2       ,   num_mel_filters=__A       ,   min_frequency=0.0       ,   max_frequency=2_2_0_5_0.0       ,   sampling_rate=__A       ,   norm="""slaney"""       ,   mel_scale="""slaney"""       ,   ).T






			def  __lowerCAmelCase (					self       ,   __A       )			->     np.ndarray:
						lowerCAmelCase_    :Dict			     =  spectrogram(
						    __A       ,   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=8_0.0       ,   )
						lowerCAmelCase_    :Dict			     =  log_spec[:, :-1]
						lowerCAmelCase_    :Union[str, Any]			     =  log_spec - 2_0.0
						lowerCAmelCase_    :Union[str, Any]			     =  np.clip(log_spec / 4_0.0       ,   -2.0       ,   0.0       ) + 1.0
						return log_spec






			def __call__(					self       ,   __A       ,   __A = None       ,   __A = True       ,   __A = None       ,   __A = False       ,   __A = False       ,   **__A       ,   )			->     BatchFeature:

						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."""       )

						lowerCAmelCase_    :Tuple			     =  isinstance(__A       ,   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}"""       )
						lowerCAmelCase_    :Tuple			     =  is_batched_numpy or (
						    isinstance(__A       ,   (list, tuple)       ) and (isinstance(raw_speech[0]       ,   (np.ndarray, tuple, list)       ))
						)
						if is_batched:
									lowerCAmelCase_    :Tuple			     =  [np.asarray([speech]       ,   dtype=np.floataa       ).T for speech in raw_speech]
						elif not is_batched and not isinstance(__A       ,   np.ndarray       ):
									lowerCAmelCase_    :List[Any]			     =  np.asarray(__A       ,   dtype=np.floataa       )
						elif isinstance(__A       ,   np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
									lowerCAmelCase_    :Union[str, Any]			     =  raw_speech.astype(np.floataa       )
						# always return batch
						if not is_batched:
									lowerCAmelCase_    :Union[str, Any]			     =  [np.asarray([raw_speech]       ).T]

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

						# Create audio attention mask
						lowerCAmelCase_    :Dict			     =  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:
									lowerCAmelCase_    :Dict			     =  [
									    (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
									]
									lowerCAmelCase_    :str			     =  np.array(__A       ).astype(np.floataa       )

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

						# return as BatchFeature
						if return_attention_mask:
									lowerCAmelCase_    :Tuple			     =  {"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
						else:
									lowerCAmelCase_    :Optional[int]			     =  {"""audio_values""": padded_audio_features}

						lowerCAmelCase_    :Union[str, Any]			     =  BatchFeature(data=__A       ,   tensor_type=__A       )
						return encoded_inputs










import socket
def 			_a (      ):


		"""simple docstring"""




		lowercase__            =      socket.socket(socket.AF_INET						,				socket.SOCK_STREAM     )
		lowercase__            =      socket.gethostname()
		lowercase__            =      1_23_12

		sock.connect((host, port)     )
		sock.send(B'''Hello server!'''     )

		with open('''Received_file'''						,				'''wb'''     ) as out_file:
				print('''File opened'''     )
				print('''Receiving data...'''     )
				while True:
						lowercase__            =      sock.recv(10_24     )
						if not data:
								break
						out_file.write(SCREAMING_SNAKE_CASE     )

		print('''Successfully received the file'''     )
		sock.close()
		print('''Connection closed'''     )


if __name__ == "__main__":
			main()


