



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		,__snake_case		,__snake_case						:Dict						=					False, False, False

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



       def __call__(		self							:					Optional[Any]):


           '''simple docstring'''

           return self.pa_type



       def 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					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(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE):
               return {"bytes": None, "path": value}
           elif isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE):
               return {"bytes": value, "path": None}
           elif "array" in value:
               # convert the audio array to wav bytes
               __a							=    BytesIO()
               sf.write(__SCREAMING_SNAKE_CASE					,		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!)
                       __a							=    np.frombuffer(value['''bytes''']					,		dtype=np.intaa).astype(np.floataa) / 32_767
                   else:
                       __a							=    np.memmap(value['''path''']					,		dtype='''h'''					,		mode='''r''').astype(np.floataa) / 32_767

                   __a							=    BytesIO(bytes())
                   sf.write(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		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							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					dict					,		__SCREAMING_SNAKE_CASE							:					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.''')

           __a	,       __a							=    (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

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

               with xopen(__SCREAMING_SNAKE_CASE					,		'''rb'''					,		use_auth_token=__SCREAMING_SNAKE_CASE) as f:
                   __a	,       __a							=    sf.read(__SCREAMING_SNAKE_CASE)

           else:
               __a	,       __a							=    sf.read(__SCREAMING_SNAKE_CASE)

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

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



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''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]					,		__SCREAMING_SNAKE_CASE							:					Union[pa.StringArray, pa.StructArray]):


           '''simple docstring'''

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





       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					pa.StructArray):


           '''simple docstring'''

           @no_op_if_value_is_null
           def path_to_bytes(__SCREAMING_SNAKE_CASE							:					str):
               with xopen(__SCREAMING_SNAKE_CASE					,		'''rb''') as f:
                   __a							=    f.read()
               return bytes_

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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case						:Tuple						=					{
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case						:Optional[int]						=					[
     '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LukeForEntityClassification''',
     '''LukeForEntityPairClassification''',
     '''LukeForEntitySpanClassification''',
     '''LukeForMultipleChoice''',
     '''LukeForQuestionAnswering''',
     '''LukeForSequenceClassification''',
     '''LukeForTokenClassification''',
     '''LukeForMaskedLM''',
     '''LukeModel''',
     '''LukePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
 from .tokenization_luke import LukeTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_luke import (
      LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
      LukeForEntityClassification,
      LukeForEntityPairClassification,
      LukeForEntitySpanClassification,
      LukeForMaskedLM,
      LukeForMultipleChoice,
      LukeForQuestionAnswering,
      LukeForSequenceClassification,
      LukeForTokenClassification,
      LukeModel,
      LukePreTrainedModel,
  )

else:
 import sys

 __snake_case						:Dict						=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





