





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


A__:  List[Any]        =     False, False, False




@dataclass
class     _a  :

      """simple docstring"""


      UpperCamelCase__   						=    None
      UpperCamelCase__   						=    True
      UpperCamelCase__   						=    True
      UpperCamelCase__   						=    None
      # Automatically constructed
      UpperCamelCase__   						=    """dict"""
      UpperCamelCase__   						=    pa.struct({"""bytes""": pa.binary(), """path""": pa.string()})
      UpperCamelCase__   						=    field(default="""Audio"""		,       init=UpperCamelCase__		,       repr=UpperCamelCase__)






      def __call__(						self:       Dict ):
          '''simple docstring'''


          return self.pa_type






      def        UpperCAmelCase_    (						self:       Optional[Any]     ,  __lowerCamelCase:       Union[str, Any] ):
          '''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(__lowerCamelCase     ,  __lowerCamelCase ):
              return {"bytes": None, "path": value}
          elif isinstance(__lowerCamelCase     ,  __lowerCamelCase ):
              return {"bytes": value, "path": None}
          elif "array" in value:
              # convert the audio array to wav bytes
              UpperCamelCase__:     Any     							=			BytesIO()
              sf.write(__lowerCamelCase     ,  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__:     Tuple     							=			np.frombuffer(value["bytes"]     ,  dtype=np.intaa ).astype(np.floataa ) / 3_2767
                  else:
                      UpperCamelCase__:     Tuple     							=			np.memmap(value["path"]     ,  dtype="h"     ,  mode="r" ).astype(np.floataa ) / 3_2767

                  UpperCamelCase__:     Union[str, Any]     							=			BytesIO(bytes() )
                  sf.write(__lowerCamelCase     ,  __lowerCamelCase     ,  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        UpperCAmelCase_    (						self:       int     ,  __lowerCamelCase:       int     ,  __lowerCamelCase:       int = None ):
          '''simple docstring'''


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

          UpperCamelCase__:     Any     							=			(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__:     Any     							=			xsplitext(__lowerCamelCase )[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__:     List[str]     							=			token_per_repo_id or {}
              UpperCamelCase__:     Tuple     							=			path.split("::" )[-1]
              try:
                  UpperCamelCase__:     List[Any]     							=			string_to_dict(__lowerCamelCase     ,  config.HUB_DATASETS_URL )['''repo_id''']
                  UpperCamelCase__:     int     							=			token_per_repo_id[repo_id]
              except (ValueError, KeyError):
                  UpperCamelCase__:     int     							=			None

              with xopen(__lowerCamelCase     ,  "rb"     ,  use_auth_token=__lowerCamelCase ) as f:
                  UpperCamelCase__:     Union[str, Any]     							=			sf.read(__lowerCamelCase )

          else:
              UpperCamelCase__:     Optional[int]     							=			sf.read(__lowerCamelCase )

          UpperCamelCase__:     List[str]     							=			array.T
          if self.mono:
              UpperCamelCase__:     str     							=			librosa.to_mono(__lowerCamelCase )
          if self.sampling_rate and self.sampling_rate != sampling_rate:
              UpperCamelCase__:     int     							=			librosa.resample(__lowerCamelCase     ,  orig_sr=__lowerCamelCase     ,  target_sr=self.sampling_rate )
              UpperCamelCase__:     List[str]     							=			self.sampling_rate

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






      def        UpperCAmelCase_    (						self:       Dict ):
          '''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        UpperCAmelCase_    (						self:       Optional[Any]     ,  __lowerCamelCase:       Any ):
          '''simple docstring'''


          if pa.types.is_string(storage.type ):
              UpperCamelCase__:     List[str]     							=			pa.array([None] * len(__lowerCamelCase )     ,  type=pa.binary() )
              UpperCamelCase__:     int     							=			pa.StructArray.from_arrays([bytes_array, storage]     ,  ["bytes", "path"]     ,  mask=storage.is_null() )
          elif pa.types.is_binary(storage.type ):
              UpperCamelCase__:     Optional[Any]     							=			pa.array([None] * len(__lowerCamelCase )     ,  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__:     Any     							=			pa.array([Audio().encode_example(__lowerCamelCase ) 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__:     List[str]     							=			storage.field("bytes" )
              else:
                  UpperCamelCase__:     int     							=			pa.array([None] * len(__lowerCamelCase )     ,  type=pa.binary() )
              if storage.type.get_field_index("path" ) >= 0:
                  UpperCamelCase__:     Optional[Any]     							=			storage.field("path" )
              else:
                  UpperCamelCase__:     Dict     							=			pa.array([None] * len(__lowerCamelCase )     ,  type=pa.string() )
              UpperCamelCase__:     int     							=			pa.StructArray.from_arrays([bytes_array, path_array]     ,  ["bytes", "path"]     ,  mask=storage.is_null() )
          return array_cast(__lowerCamelCase     ,  self.pa_type )






      def        UpperCAmelCase_    (						self:       Any     ,  __lowerCamelCase:       Union[str, Any] ):
          '''simple docstring'''


          @no_op_if_value_is_null
          def path_to_bytes(__lowerCamelCase:       Optional[Any] ):
              with xopen(__lowerCamelCase     ,  "rb" ) as f:
                  UpperCamelCase__:     Any     							=			f.read()
              return bytes_

          UpperCamelCase__:     Dict     							=			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__:     Tuple     							=			pa.array(
              [os.path.basename(__lowerCamelCase ) 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(__lowerCamelCase     ,  self.pa_type )






"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowerCAmelCase    :Optional[int]     =					logging.get_logger(__name__)
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     def __init__(					self       ,	*A       ,	**A		)							->					None:
         warnings.warn(
             '''The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
             ''' use DonutImageProcessor instead.'''       ,	A       ,	)
         super().__init__(*A       ,	**A		)


