

"""simple docstring"""
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


A									=						logging.get_logger(__name__)

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    ['input_values', 'attention_mask']



  def __init__( self					: List[Any]       ,       snake_case					: List[str]=1       ,       snake_case					: List[Any]=1_6000       ,       snake_case					: Dict=128       ,       snake_case					: Dict=1024       ,       snake_case					: Dict=0.0       ,       snake_case					: Optional[int]=True       ,       snake_case					: int=-4.267_7393       ,       snake_case					: List[Any]=4.568_9974       ,       snake_case					: List[str]=False       ,       **snake_case					: Dict       ,       )	->			Any:


         '''simple docstring'''





         super().__init__(feature_size=snake_case       ,       sampling_rate=snake_case       ,       padding_value=snake_case       ,       **snake_case      )
         __magic_name__			:		List[Any]        =     num_mel_bins
         __magic_name__			:		Any        =     max_length
         __magic_name__			:		Any        =     do_normalize
         __magic_name__			:		Dict        =     mean
         __magic_name__			:		Tuple        =     std
         __magic_name__			:		List[str]        =     return_attention_mask



  def 							_UpperCAmelCase      ( self					: List[str]       ,       snake_case					: np.ndarray       ,       snake_case					: int       ,       )	->			np.ndarray:


         '''simple docstring'''





         __magic_name__			:		Dict        =     torch.from_numpy(snake_case      ).unsqueeze(0      )
         __magic_name__			:		Optional[int]        =     ta_kaldi.fbank(
             snake_case       ,       htk_compat=snake_case       ,       sample_frequency=self.sampling_rate       ,       use_energy=snake_case       ,       window_type='''hanning'''       ,       num_mel_bins=self.num_mel_bins       ,       dither=0.0       ,       frame_shift=10       ,       )

         __magic_name__			:		Optional[int]        =     fbank.shape[0]
         __magic_name__			:		Any        =     max_length - n_frames

         # pad or truncate, depending on difference
         if difference > 0:
                __magic_name__			:		Dict        =     torch.nn.ZeroPadad((0, 0, 0, difference)      )
                __magic_name__			:		Optional[int]        =     pad_module(snake_case      )
         elif difference < 0:
                __magic_name__			:		Union[str, Any]        =     fbank[0:max_length, :]

         __magic_name__			:		Union[str, Any]        =     fbank.numpy()

         return fbank



  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: np.ndarray      )	->			np.ndarray:


         '''simple docstring'''





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




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

         __magic_name__			:		List[str]        =     isinstance(snake_case       ,       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}"""      )
         __magic_name__			:		List[Any]        =     is_batched_numpy or (
             isinstance(snake_case       ,       (list, tuple)      ) and (isinstance(raw_speech[0]       ,       (np.ndarray, tuple, list)      ))
         )

         if is_batched:
                __magic_name__			:		Any        =     [np.asarray(snake_case       ,       dtype=np.floataa      ) for speech in raw_speech]
         elif not is_batched and not isinstance(snake_case       ,       np.ndarray      ):
                __magic_name__			:		Union[str, Any]        =     np.asarray(snake_case       ,       dtype=np.floataa      )
         elif isinstance(snake_case       ,       np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
                __magic_name__			:		Union[str, Any]        =     raw_speech.astype(np.floataa      )

         # always return batch
         if not is_batched:
                __magic_name__			:		List[str]        =     [raw_speech]

         # extract fbank features and pad/truncate to max_length
         __magic_name__			:		Optional[int]        =     [self._extract_fbank_features(snake_case       ,       max_length=self.max_length      ) for waveform in raw_speech]

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

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

         # normalization
         if self.do_normalize:
                __magic_name__			:		int        =     [self.normalize(snake_case      ) for feature in input_values]

         if return_tensors is not None:
                __magic_name__			:		Tuple        =     padded_inputs.convert_to_tensors(snake_case      )

         return padded_inputs



"""simple docstring"""
#
# Sort large text files in a minimum amount of memory
#
import argparse
import os

class        _UpperCamelCase	:


  """simple docstring"""





  snake_case_        =    'block_{0}.dat'



  def __init__( self					: int       ,       snake_case					: int      )	->			List[str]:


         '''simple docstring'''





         __magic_name__			:		Optional[int]        =     filename
         __magic_name__			:		Any        =     []



  def 							_UpperCAmelCase      ( self					: Optional[Any]       ,       snake_case					: int       ,       snake_case					: Any      )	->			List[Any]:


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     self.BLOCK_FILENAME_FORMAT.format(snake_case      )
         with open(snake_case       ,       '''w'''      ) as file:
                file.write(snake_case      )
         self.block_filenames.append(snake_case      )



  def 							_UpperCAmelCase      ( self					: str      )	->			List[str]:


         '''simple docstring'''





         return self.block_filenames



  def 							_UpperCAmelCase      ( self					: Union[str, Any]       ,       snake_case					: Dict       ,       snake_case					: int=None      )	->			Union[str, Any]:


         '''simple docstring'''





         __magic_name__			:		List[str]        =     0
         with open(self.filename      ) as file:
                while True:
                       __magic_name__			:		Tuple        =     file.readlines(snake_case      )

                       if lines == []:
                              break

                       if sort_key is None:
                              lines.sort()
                       else:
                              lines.sort(key=snake_case      )

                       self.write_block(''''''.join(snake_case      )       ,       snake_case      )
                       i += 1




  def 							_UpperCAmelCase      ( self					: Tuple      )	->			Optional[int]:


         '''simple docstring'''





         map(os.remove       ,       self.block_filenames      )

class        _UpperCamelCase	:


  """simple docstring"""





  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: Dict      )	->			int:


         '''simple docstring'''





         __magic_name__			:		Any        =     -1
         __magic_name__			:		Union[str, Any]        =     None

         for i in range(len(snake_case      )      ):
                if min_str is None or choices[i] < min_str:
                       __magic_name__			:		Dict        =     i

         return min_index

class        _UpperCamelCase	:


  """simple docstring"""





  def __init__( self					: str       ,       snake_case					: Optional[int]      )	->			Any:


         '''simple docstring'''





         __magic_name__			:		Dict        =     files
         __magic_name__			:		List[str]        =     set()
         __magic_name__			:		Any        =     len(snake_case      )
         __magic_name__			:		int        =     {i: None for i in range(self.num_buffers      )}



  def 							_UpperCAmelCase      ( self					: List[str]      )	->			Optional[Any]:


         '''simple docstring'''





         return {
             i: self.buffers[i] for i in range(self.num_buffers      ) if i not in self.empty
         }



  def 							_UpperCAmelCase      ( self					: int      )	->			int:


         '''simple docstring'''





         for i in range(self.num_buffers      ):
                if self.buffers[i] is None and i not in self.empty:
                       __magic_name__			:		int        =     self.files[i].readline()

                       if self.buffers[i] == "":
                              self.empty.add(snake_case      )
                              self.files[i].close()

         if len(self.empty      ) == self.num_buffers:
                return False

         return True




  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: Optional[Any]      )	->			Optional[Any]:


         '''simple docstring'''





         __magic_name__			:		List[str]        =     self.buffers[index]
         __magic_name__			:		Dict        =     None

         return value

class        _UpperCamelCase	:


  """simple docstring"""





  def __init__( self					: Any       ,       snake_case					: str      )	->			Dict:


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     merge_strategy



  def 							_UpperCAmelCase      ( self					: Optional[int]       ,       snake_case					: int       ,       snake_case					: Dict       ,       snake_case					: Dict      )	->			Union[str, Any]:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     FilesArray(self.get_file_handles(snake_case       ,       snake_case      )      )
         with open(snake_case       ,       '''w'''       ,       snake_case      ) as outfile:
                while buffers.refresh():
                       __magic_name__			:		List[str]        =     self.merge_strategy.select(buffers.get_dict()      )
                       outfile.write(buffers.unshift(snake_case      )      )




  def 							_UpperCAmelCase      ( self					: Union[str, Any]       ,       snake_case					: List[str]       ,       snake_case					: Optional[int]      )	->			Union[str, Any]:


         '''simple docstring'''





         __magic_name__			:		str        =     {}

         for i in range(len(snake_case      )      ):
                __magic_name__			:		Any        =     open(filenames[i]       ,       '''r'''       ,       snake_case      )  # noqa: UP015

         return files

class        _UpperCamelCase	:


  """simple docstring"""





  def __init__( self					: Optional[Any]       ,       snake_case					: Union[str, Any]      )	->			Optional[int]:


         '''simple docstring'''





         __magic_name__			:		int        =     block_size



  def 							_UpperCAmelCase      ( self					: str       ,       snake_case					: Optional[int]       ,       snake_case					: Dict=None      )	->			Optional[Any]:


         '''simple docstring'''





         __magic_name__			:		Tuple        =     self.get_number_blocks(snake_case       ,       self.block_size      )
         __magic_name__			:		str        =     FileSplitter(snake_case      )
         splitter.split(self.block_size       ,       snake_case      )

         __magic_name__			:		int        =     FileMerger(NWayMerge()      )
         __magic_name__			:		Any        =     self.block_size / (num_blocks + 1)
         merger.merge(splitter.get_block_filenames()       ,       filename + '''.out'''       ,       snake_case      )

         splitter.cleanup()




  def 							_UpperCAmelCase      ( self					: Tuple       ,       snake_case					: List[str]       ,       snake_case					: Union[str, Any]      )	->			Tuple:


         '''simple docstring'''





         return (os.stat(snake_case      ).st_size / block_size) + 1







def       UpperCamelCase_					(     lowerCamelCase							:					Union[str, Any]   )						->     Union[str, Any]:



       """simple docstring"""







       if string[-1].lower() == "k":
              return int(string[:-1]   ) * 1024
       elif string[-1].lower() == "m":
              return int(string[:-1]   ) * 1024 * 1024
       elif string[-1].lower() == "g":
              return int(string[:-1]   ) * 1024 * 1024 * 1024
       else:
              return int(lowerCamelCase   )







def       UpperCamelCase_					(     )						->     Optional[Any]:



       """simple docstring"""







       __magic_name__			:		Tuple        =     argparse.ArgumentParser()
       parser.add_argument(
           '''-m'''     , '''--mem'''     , help='''amount of memory to use for sorting'''     , default='''100M'''   )
       parser.add_argument(
           '''filename'''     , metavar='''<filename>'''     , nargs=1     , help='''name of file to sort'''   )
       __magic_name__			:		List[str]        =     parser.parse_args()

       __magic_name__			:		Any        =     ExternalSort(parse_memory(args.mem   )   )
       sorter.sort(args.filename[0]   )


if __name__ == "__main__":
 main()
