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__				= False, False, False



@dataclass
class  UpperCamelCase				:

     '''simple docstring'''

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



     def __call__(					self						) ->				Optional[Any]:






           """simple docstring"""
           return self.pa_type



     def 		UpperCamelCase_						(					self				,				A_						) ->				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(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
                 _lowerCamelCase			    =				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!)
                             _lowerCamelCase			    =				np.frombuffer(value['''bytes''']				,				dtype=np.intaa						).astype(np.floataa						) / 3_27_67
                       else:
                             _lowerCamelCase			    =				np.memmap(value['''path''']				,				dtype='''h'''				,				mode='''r'''						).astype(np.floataa						) / 3_27_67

                       _lowerCamelCase			    =				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				,				A_				,				A_ = None						) ->				dict:






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

           _lowerCamelCase    ,					_lowerCamelCase			    =				(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

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

                 with xopen(A__				,				'''rb'''				,				use_auth_token=A__						) as f:
                       _lowerCamelCase    ,					_lowerCamelCase			    =				sf.read(A__						)

           else:
                 _lowerCamelCase    ,					_lowerCamelCase			    =				sf.read(A__						)

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

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



     def 		UpperCamelCase_						(					self						) ->				Union["FeatureType", Dict[str, "FeatureType"]]:






           """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				,				A_						) ->				pa.StructArray:






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





     def 		UpperCamelCase_						(					self				,				A_						) ->				pa.StructArray:






           """simple docstring"""

           @no_op_if_value_is_null
           def path_to_bytes(A_						):
                 with xopen(A__				,				'''rb'''						) as f:
                       _lowerCamelCase			    =				f.read()
                 return bytes_

           _lowerCamelCase			    =				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()				,				)
           _lowerCamelCase			    =				pa.array(
               [os.path.basename(A__						) if path is not None else None for path in storage.field('''path'''						).to_pylist()]				,				type=pa.string()				,				)
           _lowerCamelCase			    =				pa.StructArray.from_arrays([bytes_array, path_array]				,				['''bytes''', '''path''']				,				mask=bytes_array.is_null()						)
           return array_cast(A__				,				self.pa_type						)
from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip