

import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

_snake_case						: Optional[int]											=							{
    'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
    'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/config.json',
}





class __SCREAMING_SNAKE_CASE     (	_snake_case		):
    SCREAMING_SNAKE_CASE__      ="""xlnet"""
    SCREAMING_SNAKE_CASE__      =["""mems"""]
    SCREAMING_SNAKE_CASE__      ={
        """n_token""": """vocab_size""",  # Backward compatibility
        """hidden_size""": """d_model""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }






    def __init__(       self,		_a=3_20_00,		_a=10_24,		_a=24,		_a=16,		_a=40_96,		_a="gelu",		_a=True,		_a="bi",		_a=0.02,		_a=1E-1_2,		_a=0.1,		_a=5_12,		_a=None,		_a=True,		_a=False,		_a=False,		_a=-1,		_a=False,		_a="last",		_a=True,		_a="tanh",		_a=0.1,		_a=5,		_a=5,		_a=5,		_a=1,		_a=2,		**_a,		)   ->					Optional[Any]:
       __SCREAMING_SNAKE_CASE       =				vocab_size
       __SCREAMING_SNAKE_CASE       =				d_model
       __SCREAMING_SNAKE_CASE       =				n_layer
       __SCREAMING_SNAKE_CASE       =				n_head
       if d_model % n_head != 0:
          raise ValueError(f'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0''' )
       if "d_head" in kwargs:
          if kwargs["d_head"] != d_model // n_head:
             raise ValueError(
                 f'''`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})''' )
       __SCREAMING_SNAKE_CASE       =				d_model // n_head
       __SCREAMING_SNAKE_CASE       =				ff_activation
       __SCREAMING_SNAKE_CASE       =				d_inner
       __SCREAMING_SNAKE_CASE       =				untie_r
       __SCREAMING_SNAKE_CASE       =				attn_type

       __SCREAMING_SNAKE_CASE       =				initializer_range
       __SCREAMING_SNAKE_CASE       =				layer_norm_eps

       __SCREAMING_SNAKE_CASE       =				dropout
       __SCREAMING_SNAKE_CASE       =				mem_len
       __SCREAMING_SNAKE_CASE       =				reuse_len
       __SCREAMING_SNAKE_CASE       =				bi_data
       __SCREAMING_SNAKE_CASE       =				clamp_len
       __SCREAMING_SNAKE_CASE       =				same_length

       __SCREAMING_SNAKE_CASE       =				summary_type
       __SCREAMING_SNAKE_CASE       =				summary_use_proj
       __SCREAMING_SNAKE_CASE       =				summary_activation
       __SCREAMING_SNAKE_CASE       =				summary_last_dropout
       __SCREAMING_SNAKE_CASE       =				start_n_top
       __SCREAMING_SNAKE_CASE       =				end_n_top

       __SCREAMING_SNAKE_CASE       =				bos_token_id
       __SCREAMING_SNAKE_CASE       =				pad_token_id
       __SCREAMING_SNAKE_CASE       =				eos_token_id

       if "use_cache" in kwargs:
          warnings.warn(
              "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
              " instead.",		__lowerCamelCase,		)
          __SCREAMING_SNAKE_CASE       =				kwargs['''use_cache''']

       __SCREAMING_SNAKE_CASE       =				use_mems_eval
       __SCREAMING_SNAKE_CASE       =				use_mems_train
       super().__init__(pad_token_id=__lowerCamelCase,		bos_token_id=__lowerCamelCase,		eos_token_id=__lowerCamelCase,		**__lowerCamelCase )






    @property
    def 						__lowerCAmelCase    (       self )   ->					List[str]:
       logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
       return -1






    @max_position_embeddings.setter
    def 						__lowerCAmelCase    (       self,		_a )   ->					Optional[Any]:
       # Message copied from Transformer-XL documentation
       raise NotImplementedError(
           f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )





'''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


__lowerCamelCase					,		__lowerCamelCase					,		__lowerCamelCase												=						False, False, False




@dataclass
class 				UpperCAmelCase			:
 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=_snake_case						, repr=_snake_case						)


 def __call__(      self							:	Any ):
    return self.pa_type


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	Union[str, bytes, dict] ):
    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__	:int      =      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__	:List[Any]      =      np.frombuffer(value['''bytes''']			,       dtype=np.intaa ).astype(np.floataa ) / 3_2_7_6_7
          else:
             UpperCAmelCase__	:Optional[Any]      =      np.memmap(value['''path''']			,       dtype='''h'''			,       mode='''r''' ).astype(np.floataa ) / 3_2_7_6_7

          UpperCAmelCase__	:Optional[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        __SCREAMING_SNAKE_CASE					(      self							:	List[Any]			,       __lowerCamelCase							:	dict			,       __lowerCamelCase							:	Optional[Dict[str, Union[str, bool, None]]] = None ):
    if not self.decode:
       raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.''' )

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

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

    else:
       UpperCAmelCase__   ,						UpperCAmelCase__	:List[Any]      =      sf.read(__lowerCamelCase )

    UpperCAmelCase__	:Optional[int]      =      array.T
    if self.mono:
       UpperCAmelCase__	:Any      =      librosa.to_mono(__lowerCamelCase )
    if self.sampling_rate and self.sampling_rate != sampling_rate:
       UpperCAmelCase__	:Union[str, Any]      =      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        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    from .features import Value

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any			,       __lowerCamelCase							:	Union[pa.StringArray, pa.StructArray] ):
    if pa.types.is_string(storage.type ):
       UpperCAmelCase__	:List[str]      =      pa.array([None] * len(__lowerCamelCase )			,       type=pa.binary() )
       UpperCAmelCase__	:Tuple      =      pa.StructArray.from_arrays([bytes_array, storage]			,       ['''bytes''', '''path''']			,       mask=storage.is_null() )
    elif pa.types.is_binary(storage.type ):
       UpperCAmelCase__	:str      =      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__	:str      =      storage.field('''bytes''' )
       else:
          UpperCAmelCase__	:List[str]      =      pa.array([None] * len(__lowerCamelCase )			,       type=pa.binary() )
       if storage.type.get_field_index('''path''' ) >= 0:
          UpperCAmelCase__	:Optional[int]      =      storage.field('''path''' )
       else:
          UpperCAmelCase__	:Optional[int]      =      pa.array([None] * len(__lowerCamelCase )			,       type=pa.string() )
       UpperCAmelCase__	:List[str]      =      pa.StructArray.from_arrays([bytes_array, path_array]			,       ['''bytes''', '''path''']			,       mask=storage.is_null() )
    return array_cast(__lowerCamelCase			,       self.pa_type )





 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       __lowerCamelCase							:	pa.StructArray ):

    @no_op_if_value_is_null
    def path_to_bytes(__lowerCamelCase							:	Dict ):
       with xopen(__lowerCamelCase			,       '''rb''' ) as f:
          UpperCAmelCase__	:Any      =      f.read()
       return bytes_

    UpperCAmelCase__	:Union[str, Any]      =      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__	:Optional[int]      =      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[int]      =      pa.StructArray.from_arrays([bytes_array, path_array]			,       ['''bytes''', '''path''']			,       mask=bytes_array.is_null() )
    return array_cast(__lowerCamelCase			,       self.pa_type )



