import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
  from .features import FeatureType


UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_			    =							False, False, False




@dataclass
class  __magic_name__  :





   """simple docstring"""




   lowerCAmelCase      :    Optional[int]  			=   None
   lowerCAmelCase      :    bool  			=   True
   lowerCAmelCase      :    bool  			=   True
   lowerCAmelCase      :    Optional[str]  			=   None
   # Automatically constructed
   lowerCAmelCase      :    ClassVar[str]  			=   "dict"
   lowerCAmelCase      :    ClassVar[Any]  			=   pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}    )
   lowerCAmelCase      :    str  			=   field(default='''Audio'''      ,	init=__a      ,	repr=__a    )



   def __call__(	self					:  Tuple							):



    """simple docstring"""



    return self.pa_type



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  Union[str, bytes, dict]							):



    """simple docstring"""



    try:
     import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
    except ImportError as err:
     raise ImportError('''To support encoding audio data, please install \'soundfile\'.'''							) from err
    if isinstance(_lowercase		,  _lowercase							):
     return {"bytes": None, "path": value}
    elif isinstance(_lowercase		,  _lowercase							):
     return {"bytes": value, "path": None}
    elif "array" in value:
     # convert the audio array to wav bytes
     _UpperCamelCase:     List[str]							= BytesIO()
     sf.write(_lowercase		,  value['''array''']		,  value['''sampling_rate''']		,  format='''wav'''							)
     return {"bytes": buffer.getvalue(), "path": None}
    elif value.get('''path'''							) is not None and os.path.isfile(value['''path''']							):
     # we set "bytes": None to not duplicate the data if they're already available locally
     if value["path"].endswith('''pcm'''							):
      # "PCM" only has raw audio bytes
      if value.get('''sampling_rate'''							) is None:
       # At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
       raise KeyError('''To use PCM files, please specify a \'sampling_rate\' in Audio object'''							)
      if value.get('''bytes'''							):
       # If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
       _UpperCamelCase:     Optional[Any]							= np.frombuffer(value['''bytes''']		,  dtype=np.intaa							).astype(np.floataa							) / 32_767
      else:
       _UpperCamelCase:     Optional[int]							= np.memmap(value['''path''']		,  dtype='''h'''		,  mode='''r'''							).astype(np.floataa							) / 32_767

      _UpperCamelCase:     List[Any]							= BytesIO(bytes()							)
      sf.write(_lowercase		,  _lowercase		,  value['''sampling_rate''']		,  format='''wav'''							)
      return {"bytes": buffer.getvalue(), "path": None}
     else:
      return {"bytes": None, "path": value.get('''path'''							)}
    elif value.get('''bytes'''							) is not None or value.get('''path'''							) is not None:
     # store the audio bytes, and path is used to infer the audio format using the file extension
     return {"bytes": value.get('''bytes'''							), "path": value.get('''path'''							)}
    else:
     raise ValueError(
         f"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}."""							)



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  dict		,  _lowercase					:  Optional[Dict[str, Union[str, bool, None]]] = None							):



    """simple docstring"""



    if not self.decode:
     raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.'''							)

    _UpperCamelCase	,							_UpperCamelCase:     Dict							= (value['''path'''], BytesIO(value['''bytes''']							)) if value['''bytes'''] is not None else (value['''path'''], None)
    if path is None and file is None:
     raise ValueError(f"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}."""							)

    try:
     import librosa
     import soundfile as sf
    except ImportError as err:
     raise ImportError('''To support decoding audio files, please install \'librosa\' and \'soundfile\'.'''							) from err

    _UpperCamelCase:     Dict							= xsplitext(_lowercase							)[1][1:].lower() if path is not None else None
    if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
     raise RuntimeError(
         '''Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '''
         '''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '''							)
    elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
     raise RuntimeError(
         '''Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '''
         '''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '''							)

    if file is None:
     _UpperCamelCase:     Any							= token_per_repo_id or {}
     _UpperCamelCase:     Any							= path.split('''::'''							)[-1]
     try:
      _UpperCamelCase:     Optional[int]							= string_to_dict(_lowercase		,  config.HUB_DATASETS_URL							)['''repo_id''']
      _UpperCamelCase:     Tuple							= token_per_repo_id[repo_id]
     except (ValueError, KeyError):
      _UpperCamelCase:     Any							= None

     with xopen(_lowercase		,  '''rb'''		,  use_auth_token=_lowercase							) as f:
      _UpperCamelCase	,							_UpperCamelCase:     str							= sf.read(_lowercase							)

    else:
     _UpperCamelCase	,							_UpperCamelCase:     Any							= sf.read(_lowercase							)

    _UpperCamelCase:     Any							= array.T
    if self.mono:
     _UpperCamelCase:     str							= librosa.to_mono(_lowercase							)
    if self.sampling_rate and self.sampling_rate != sampling_rate:
     _UpperCamelCase:     str							= librosa.resample(_lowercase		,  orig_sr=_lowercase		,  target_sr=self.sampling_rate							)
     _UpperCamelCase:     List[str]							= self.sampling_rate

    return {"path": path, "array": array, "sampling_rate": sampling_rate}



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    from .features import Value

    if self.decode:
     raise ValueError('''Cannot flatten a decoded Audio feature.'''							)
    return {
        "bytes": Value('''binary'''							),
        "path": Value('''string'''							),
    }



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  Union[pa.StringArray, pa.StructArray]							):



    """simple docstring"""



    if pa.types.is_string(storage.type							):
     _UpperCamelCase:     Dict							= pa.array([None] * len(_lowercase							)		,  type=pa.binary()							)
     _UpperCamelCase:     Optional[int]							= pa.StructArray.from_arrays([bytes_array, storage]		,  ['''bytes''', '''path''']		,  mask=storage.is_null()							)
    elif pa.types.is_binary(storage.type							):
     _UpperCamelCase:     Union[str, Any]							= pa.array([None] * len(_lowercase							)		,  type=pa.string()							)
     _UpperCamelCase:     int							= pa.StructArray.from_arrays([storage, path_array]		,  ['''bytes''', '''path''']		,  mask=storage.is_null()							)
    elif pa.types.is_struct(storage.type							) and storage.type.get_all_field_indices('''array'''							):
     _UpperCamelCase:     int							= pa.array([Audio().encode_example(_lowercase							) if x is not None else None for x in storage.to_pylist()]							)
    elif pa.types.is_struct(storage.type							):
     if storage.type.get_field_index('''bytes'''							) >= 0:
      _UpperCamelCase:     Union[str, Any]							= storage.field('''bytes'''							)
     else:
      _UpperCamelCase:     Optional[int]							= pa.array([None] * len(_lowercase							)		,  type=pa.binary()							)
     if storage.type.get_field_index('''path'''							) >= 0:
      _UpperCamelCase:     List[str]							= storage.field('''path'''							)
     else:
      _UpperCamelCase:     Tuple							= pa.array([None] * len(_lowercase							)		,  type=pa.string()							)
     _UpperCamelCase:     Dict							= pa.StructArray.from_arrays([bytes_array, path_array]		,  ['''bytes''', '''path''']		,  mask=storage.is_null()							)
    return array_cast(_lowercase		,  self.pa_type							)



   def   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  pa.StructArray							):



    """simple docstring"""



    @no_op_if_value_is_null
    def path_to_bytes(_lowercase					:  Dict							):
     with xopen(_lowercase		,  '''rb'''							) as f:
      _UpperCamelCase:     Tuple							= f.read()
     return bytes_

    _UpperCamelCase:     Optional[int]							= pa.array(
        [
            (path_to_bytes(x['''path''']							) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
            for x in storage.to_pylist()
        ]		,  type=pa.binary()		,  )
    _UpperCamelCase:     int							= pa.array(
        [os.path.basename(_lowercase							) if path is not None else None for path in storage.field('''path'''							).to_pylist()]		,  type=pa.string()		,  )
    _UpperCamelCase:     Optional[Any]							= pa.StructArray.from_arrays([bytes_array, path_array]		,  ['''bytes''', '''path''']		,  mask=bytes_array.is_null()							)
    return array_cast(_lowercase		,  self.pa_type							)
import warnings

from ..trainer import Trainer
from ..utils import logging


UpperCAmelCase_			    =							logging.get_logger(__name__)




class  __magic_name__  (		__a    ):





   """simple docstring"""




   def __init__(	self					:  List[Any]		,  _lowercase					:  int=None		,  **_lowercase					:  Optional[Any]							):



    """simple docstring"""



    warnings.warn(
        '''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
        '''instead.'''		,  _lowercase		,  )
    super().__init__(args=_lowercase		,  **_lowercase							)