
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      ,						_A      ,						_A						      =				False, False, False







@dataclass
class   lowerCamelCase	:
     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=A_							,				repr=A_						)





     def __call__(self   :					List[Any]     )		->		List[str]:
         return self.pa_type





     def 	UpperCAmelCase(self   :					str       ,					_A   :					Union[str, bytes, dict]     )		->		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(_A       ,					_A     ):
             return {"bytes": None, "path": value}
         elif isinstance(_A       ,					_A     ):
             return {"bytes": value, "path": None}
         elif "array" in value:
             # convert the audio array to wav bytes
             snake_case				  =      BytesIO()
             sf.write(_A       ,					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!)
                     snake_case				  =      np.frombuffer(value["bytes"]       ,					dtype=np.intaa     ).astype(np.floataa     ) / 3_2_7_6_7
                 else:
                     snake_case				  =      np.memmap(value["path"]       ,					dtype="h"       ,					mode="r"     ).astype(np.floataa     ) / 3_2_7_6_7

                 snake_case				  =      BytesIO(bytes()     )
                 sf.write(_A       ,					_A       ,					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   :					Any       ,					_A   :					dict       ,					_A   :					Optional[Dict[str, Union[str, bool, None]]] = None     )		->		dict:
         if not self.decode:
             raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead."     )

         snake_case  ,							snake_case				  =      (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

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

             with xopen(_A       ,					"rb"       ,					use_auth_token=_A     ) as f:
                 snake_case  ,							snake_case				  =      sf.read(_A     )

         else:
             snake_case  ,							snake_case				  =      sf.read(_A     )

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

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





     def 	UpperCAmelCase(self   :					Optional[int]     )		->		Union["FeatureType", Dict[str, "FeatureType"]]:
         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   :					str       ,					_A   :					Union[pa.StringArray, pa.StructArray]     )		->		pa.StructArray:
         if pa.types.is_string(storage.type     ):
             snake_case				  =      pa.array([None] * len(_A     )       ,					type=pa.binary()     )
             snake_case				  =      pa.StructArray.from_arrays([bytes_array, storage]       ,					["bytes", "path"]       ,					mask=storage.is_null()     )
         elif pa.types.is_binary(storage.type     ):
             snake_case				  =      pa.array([None] * len(_A     )       ,					type=pa.string()     )
             snake_case				  =      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"     ):
             snake_case				  =      pa.array([Audio().encode_example(_A     ) 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:
                 snake_case				  =      storage.field("bytes"     )
             else:
                 snake_case				  =      pa.array([None] * len(_A     )       ,					type=pa.binary()     )
             if storage.type.get_field_index("path"     ) >= 0:
                 snake_case				  =      storage.field("path"     )
             else:
                 snake_case				  =      pa.array([None] * len(_A     )       ,					type=pa.string()     )
             snake_case				  =      pa.StructArray.from_arrays([bytes_array, path_array]       ,					["bytes", "path"]       ,					mask=storage.is_null()     )
         return array_cast(_A       ,					self.pa_type     )





     def 	UpperCAmelCase(self   :					int       ,					_A   :					pa.StructArray     )		->		pa.StructArray:

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

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



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


_A						      =				logging.get_logger(__name__)

_A						      =				{
    "google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Dict     =	"canine"





     def __init__(self   :					List[Any]       ,					_A   :					Union[str, Any]=7_6_8       ,					_A   :					Any=1_2       ,					_A   :					List[Any]=1_2       ,					_A   :					List[Any]=3_0_7_2       ,					_A   :					Dict="gelu"       ,					_A   :					Optional[Any]=0.1       ,					_A   :					Tuple=0.1       ,					_A   :					str=1_6_3_8_4       ,					_A   :					Union[str, Any]=1_6       ,					_A   :					Any=0.02       ,					_A   :					List[str]=1E-12       ,					_A   :					Union[str, Any]=0       ,					_A   :					Dict=0Xe0_00       ,					_A   :					List[Any]=0Xe0_01       ,					_A   :					int=4       ,					_A   :					str=4       ,					_A   :					Optional[int]=8       ,					_A   :					Optional[Any]=1_6_3_8_4       ,					_A   :					Optional[Any]=1_2_8       ,					**_A   :					Union[str, Any]       ,					)		->		Union[str, Any]:
         super().__init__(pad_token_id=_A       ,					bos_token_id=_A       ,					eos_token_id=_A       ,					**_A     )

         snake_case				  =      max_position_embeddings
         snake_case				  =      hidden_size
         snake_case				  =      num_hidden_layers
         snake_case				  =      num_attention_heads
         snake_case				  =      intermediate_size
         snake_case				  =      hidden_act
         snake_case				  =      hidden_dropout_prob
         snake_case				  =      attention_probs_dropout_prob
         snake_case				  =      initializer_range
         snake_case				  =      type_vocab_size
         snake_case				  =      layer_norm_eps

         # Character config:
         snake_case				  =      downsampling_rate
         snake_case				  =      upsampling_kernel_size
         snake_case				  =      num_hash_functions
         snake_case				  =      num_hash_buckets
         snake_case				  =      local_transformer_stride

