
'''simple docstring'''


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


snake_case_			:	Optional[int] 	=       False, False, False


@dataclass
class     lowercase__						:






   '''simple docstring'''







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







   def __call__(      self ):

      '''simple docstring'''







      return self.pa_type







   def    UpperCAmelCase					(      self			, lowerCamelCase__ ):

      '''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(_UpperCAmelCase			, _UpperCAmelCase ):
         return {"bytes": None, "path": value}
      elif isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
         return {"bytes": value, "path": None}
      elif "array" in value:
         # convert the audio array to wav bytes
         UpperCamelCase	  =	BytesIO()
         sf.write(_UpperCAmelCase			, 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	  =	np.frombuffer(value['''bytes''']			, dtype=np.intaa ).astype(np.floataa ) / 3_2_7_6_7
            else:
               UpperCamelCase	  =	np.memmap(value['''path''']			, dtype='''h'''			, mode='''r''' ).astype(np.floataa ) / 3_2_7_6_7

            UpperCamelCase	  =	BytesIO(bytes() )
            sf.write(_UpperCAmelCase			, _UpperCAmelCase			, 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			, lowerCamelCase__			, lowerCamelCase__ = None ):

      '''simple docstring'''







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

      UpperCamelCase	  =	(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	  =	xsplitext(_UpperCAmelCase )[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	  =	token_per_repo_id or {}
         UpperCamelCase	  =	path.split('''::''' )[-1]
         try:
            UpperCamelCase	  =	string_to_dict(_UpperCAmelCase			, config.HUB_DATASETS_URL )['repo_id']
            UpperCamelCase	  =	token_per_repo_id[repo_id]
         except (ValueError, KeyError):
            UpperCamelCase	  =	None

         with xopen(_UpperCAmelCase			, '''rb'''			, use_auth_token=_UpperCAmelCase ) as f:
            UpperCamelCase	  =	sf.read(_UpperCAmelCase )

      else:
         UpperCamelCase	  =	sf.read(_UpperCAmelCase )

      UpperCamelCase	  =	array.T
      if self.mono:
         UpperCamelCase	  =	librosa.to_mono(_UpperCAmelCase )
      if self.sampling_rate and self.sampling_rate != sampling_rate:
         UpperCamelCase	  =	librosa.resample(_UpperCAmelCase			, orig_sr=_UpperCAmelCase			, target_sr=self.sampling_rate )
         UpperCamelCase	  =	self.sampling_rate

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







   def    UpperCAmelCase					(      self ):

      '''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			, lowerCamelCase__ ):

      '''simple docstring'''







      if pa.types.is_string(storage.type ):
         UpperCamelCase	  =	pa.array([None] * len(_UpperCAmelCase )			, type=pa.binary() )
         UpperCamelCase	  =	pa.StructArray.from_arrays([bytes_array, storage]			, ['''bytes''', '''path''']			, mask=storage.is_null() )
      elif pa.types.is_binary(storage.type ):
         UpperCamelCase	  =	pa.array([None] * len(_UpperCAmelCase )			, type=pa.string() )
         UpperCamelCase	  =	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	  =	pa.array([Audio().encode_example(_UpperCAmelCase ) 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	  =	storage.field('''bytes''' )
         else:
            UpperCamelCase	  =	pa.array([None] * len(_UpperCAmelCase )			, type=pa.binary() )
         if storage.type.get_field_index('''path''' ) >= 0:
            UpperCamelCase	  =	storage.field('''path''' )
         else:
            UpperCamelCase	  =	pa.array([None] * len(_UpperCAmelCase )			, type=pa.string() )
         UpperCamelCase	  =	pa.StructArray.from_arrays([bytes_array, path_array]			, ['''bytes''', '''path''']			, mask=storage.is_null() )
      return array_cast(_UpperCAmelCase			, self.pa_type )







   def    UpperCAmelCase					(      self			, lowerCamelCase__ ):

      '''simple docstring'''







      @no_op_if_value_is_null
      def path_to_bytes(lowerCamelCase__ ):
         with xopen(_UpperCAmelCase			, '''rb''' ) as f:
            UpperCamelCase	  =	f.read()
         return bytes_

      UpperCamelCase	  =	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	  =	pa.array(
          [os.path.basename(_UpperCAmelCase ) if path is not None else None for path in storage.field('''path''' ).to_pylist()]			, type=pa.string()			, )
      UpperCamelCase	  =	pa.StructArray.from_arrays([bytes_array, path_array]			, ['''bytes''', '''path''']			, mask=bytes_array.is_null() )
      return array_cast(_UpperCAmelCase			, self.pa_type )












'''simple docstring'''




import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


lowercase__       : Dict         =       logging.getLogger(__name__)


@dataclass
@add_start_docstrings(TrainingArguments.__doc__				)
class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			field(default=a__   ,						metadata={'''help''': '''Whether to use SortishSampler or not.'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': (
                 '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `max_length` value of the model configuration.'''
             )
         }   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': (
                 '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `num_beams` value of the model configuration.'''
             )
         }   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }   ,						)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			super().to_dict()
          for k, v in d.items():
               if isinstance(_UpperCAmelCase		,      _UpperCAmelCase):
                    __A		:					List[Any]						=			v.to_dict()
          return d