

import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE

class    _lowercase				(					__snake_case			):
   _lowercase     :       Dict  					=    ''
   _lowercase     :       str  					=    (
       None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
   )
   _lowercase     :       str  					=    None  # compression type in fsspec. ex: "gzip"
   _lowercase     :       str  					=    None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz

   def __init__(				self	:    Optional[Any]			,						lowerCamelCase__	:    List[str] = ""			,						lowerCamelCase__	:    Tuple = None			,						lowerCamelCase__	:    Optional[int] = None			,						**lowerCamelCase__	:    Optional[int]       ) ->			Union[str, Any]:
         """simple docstring"""





         super().__init__(self			,						**A_       )
         # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
         A_							       =		fsspec.open(
             A_			,						mode='''rb'''			,						protocol=A_			,						compression=self.compression			,						client_kwargs={
                 '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
                 '''trust_env''': True,  # Enable reading proxy env variables.
                 **(target_options or {}).pop('''client_kwargs'''			,						{}       ),  # To avoid issues if it was already passed.
             }			,						**(target_options or {})			,						)
         A_							       =		os.path.basename(self.file.path.split('''::'''       )[0]       )
         A_							       =		(
             self.compressed_name[: self.compressed_name.rindex('''.'''       )]
             if "." in self.compressed_name
             else self.compressed_name
         )
         A_							       =		None

   @classmethod
   def     UpperCamelCase					(				cls	:    Union[str, Any]			,						lowerCamelCase__	:    Dict       ) ->			Dict:
         """simple docstring"""





         return super()._strip_protocol(A_       ).lstrip('''/'''       )

   def     UpperCamelCase					(				self	:    List[str]       ) ->			Tuple:
         """simple docstring"""





         if self.dir_cache is None:
               A_							       =		{**self.file.fs.info(self.file.path       ), "name": self.uncompressed_name}
               A_							       =		{f["name"]: f}

   def     UpperCamelCase					(				self	:    Union[str, Any]			,						lowerCamelCase__	:    Optional[int]       ) ->			Dict:
         """simple docstring"""





         return self.file.open().read()





   def     UpperCamelCase					(				self	:    List[Any]			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    str = "rb"			,						lowerCamelCase__	:    int=None			,						lowerCamelCase__	:    Dict=True			,						lowerCamelCase__	:    Dict=None			,						**lowerCamelCase__	:    List[str]			,						) ->			List[str]:
         """simple docstring"""





         A_							       =		self._strip_protocol(A_       )
         if mode != "rb":
               raise ValueError(F"Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'"       )
         return self.file.open()



class    _lowercase				(					__snake_case			):
   _lowercase     :       str  					=    'bz2'
   _lowercase     :       Optional[int]  					=    'bz2'
   _lowercase     :       List[str]  					=    '.bz2'



class    _lowercase				(					__snake_case			):
   _lowercase     :       List[str]  					=    'gzip'
   _lowercase     :       Optional[Any]  					=    'gzip'
   _lowercase     :       Optional[int]  					=    '.gz'



class    _lowercase				(					__snake_case			):
   _lowercase     :       Dict  					=    'lz4'
   _lowercase     :       Dict  					=    'lz4'
   _lowercase     :       List[str]  					=    '.lz4'



class    _lowercase				(					__snake_case			):
   _lowercase     :       str  					=    'xz'
   _lowercase     :       Optional[int]  					=    'xz'
   _lowercase     :       Tuple  					=    '.xz'



class    _lowercase				(					__snake_case			):
   _lowercase     :       Tuple  					=    'zstd'
   _lowercase     :       List[str]  					=    'zstd'
   _lowercase     :       Dict  					=    '.zst'

   def __init__(				self	:    str			,						lowerCamelCase__	:    Optional[Any]			,						lowerCamelCase__	:    int = "rb"			,						lowerCamelCase__	:    Optional[Any] = None			,						lowerCamelCase__	:    Dict = None			,						lowerCamelCase__	:    Dict = DEFAULT_BLOCK_SIZE			,						**lowerCamelCase__	:    Optional[Any]			,						) ->			str:
         """simple docstring"""





         super().__init__(
             fo=A_			,						mode=A_			,						target_protocol=A_			,						target_options=A_			,						block_size=A_			,						**A_			,						)
         # We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
         #
         # File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
         #     out.close = close
         # AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
         #
         # see https://github.com/intake/filesystem_spec/issues/725
         A_							       =		self.file.__enter__

         class    _lowercase				:

            def __init__(				self	:    str			,						lowerCamelCase__	:    str       ) ->			Any:
                  """simple docstring"""





                  A_							       =		file_

            def __enter__(				self	:    List[Any]       ) ->			str:
                  """simple docstring"""





                  self._file.__enter__()
                  return self

            def __exit__(				self	:    Optional[Any]			,						*lowerCamelCase__	:    str			,						**lowerCamelCase__	:    List[Any]       ) ->			Union[str, Any]:
                  """simple docstring"""





                  self._file.__exit__(*A_			,						**A_       )

            def __iter__(				self	:    Optional[Any]       ) ->			Optional[int]:
                  """simple docstring"""





                  return iter(self._file       )

            def     UpperCamelCase					(				self	:    Dict       ) ->			Dict:
                  """simple docstring"""





                  return next(self._file       )





            def __getattr__(				self	:    int			,						lowerCamelCase__	:    str       ) ->			int:
                  """simple docstring"""





                  return getattr(self._file			,						A_       )

         def fixed_enter(*lowerCamelCase__	:    Dict			,						**lowerCamelCase__	:    Optional[Any]       ):
               return WrappedFile(_enter(*A_			,						**A_       )       )

         A_							       =		fixed_enter












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 PaddingStrategy, TensorType, logging


__lowerCamelCase			:	List[str]					    =	logging.get_logger(__name__)



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :List[str]		       =  ['input_features', 'attention_mask']





      def __init__(					self			,				A_=80			,				A_=1_6000			,				A_=80			,				A_=0.0			,				A_=True			,				A_=True			,				A_=True			,				**A_			,				):
       '''simple docstring'''




       super().__init__(feature_size=A_			,				sampling_rate=A_			,				padding_value=A_			,				**A_  )
       UpperCamelCase      :   List[Any]			      =  num_mel_bins
       UpperCamelCase      :   Any			      =  do_ceptral_normalize
       UpperCamelCase      :   int			      =  normalize_means
       UpperCamelCase      :   Tuple			      =  normalize_vars
       UpperCamelCase      :   List[Any]			      =  True





      def 			__UpperCamelCase(					self			,				A_			,				):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
       UpperCamelCase      :   Optional[Any]			      =  torch.from_numpy(A_  ).unsqueeze(0  )
       UpperCamelCase      :   Dict			      =  ta_kaldi.fbank(A_			,				num_mel_bins=self.num_mel_bins			,				sample_frequency=self.sampling_rate  )
       return features.numpy()





      @staticmethod
      def 			__UpperCamelCase(					A_			,				A_			,				A_ = True			,				A_ = True			,				A_ = 0.0			,				):
       '''simple docstring'''




       if normalize_means:
        UpperCamelCase      :   str			      =  x[:input_length].mean(axis=0  )
        UpperCamelCase      :   Any			      =  np.subtract(A_			,				A_  )
       if normalize_vars:
        UpperCamelCase      :   List[str]			      =  x[:input_length].std(axis=0  )
        UpperCamelCase      :   Optional[int]			      =  np.divide(A_			,				A_  )

       if input_length < x.shape[0]:
        UpperCamelCase      :   Dict			      =  padding_value

       # make sure array is in float32
       UpperCamelCase      :   Union[str, Any]			      =  x.astype(np.floataa  )

       return x





      def 			__UpperCamelCase(					self			,				A_			,				A_ = None  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[Any]			      =  attention_mask.sum(-1  ) if attention_mask is not None else [x.shape[0] for x in input_features]
       return [
           self.utterance_cmvn(A_			,				A_			,				self.normalize_means			,				self.normalize_vars			,				self.padding_value  )
           for x, n in zip(A_			,				A_  )
       ]





      def __call__(					self			,				A_			,				A_ = False			,				A_ = None			,				A_ = False			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				**A_			,				):
       '''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."  )

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

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

       # always return batch
       if not is_batched:
        UpperCamelCase      :   Optional[int]			      =  [raw_speech]

       # extract fbank features
       UpperCamelCase      :   Optional[int]			      =  [self._extract_fbank_features(A_  ) for waveform in raw_speech]

       # convert into correct format for padding
       UpperCamelCase      :   List[Any]			      =  BatchFeature({"input_features": features}  )

       UpperCamelCase      :   Any			      =  self.pad(
           A_			,				padding=A_			,				max_length=A_			,				truncation=A_			,				pad_to_multiple_of=A_			,				return_attention_mask=A_			,				**A_			,				)

       # make sure list is in array format
       UpperCamelCase      :   Optional[int]			      =  padded_inputs.get("input_features"  )
       if isinstance(input_features[0]			,				A_  ):
        UpperCamelCase      :   str			      =  [np.asarray(A_			,				dtype=np.floataa  ) for feature in input_features]

       UpperCamelCase      :   Optional[int]			      =  padded_inputs.get("attention_mask"  )
       if attention_mask is not None:
        UpperCamelCase      :   Optional[int]			      =  [np.asarray(A_			,				dtype=np.intaa  ) for array in attention_mask]

       # Utterance-level cepstral mean and variance normalization
       if self.do_ceptral_normalize:
        UpperCamelCase      :   List[Any]			      =  (
            np.array(A_			,				dtype=np.intaa  )
            if self._get_padding_strategies(A_			,				max_length=A_  ) is not PaddingStrategy.DO_NOT_PAD
            else None
        )
        UpperCamelCase      :   int			      =  self.normalize(
            padded_inputs["input_features"]			,				attention_mask=A_  )

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

       return padded_inputs
