
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


SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__   :			int												=    False, False, False

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






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






		def  __A       (		self		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							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(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
										__lowerCamelCase    					=	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!)
																		__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(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  __A       (		self		:							int							,			SCREAMING_SNAKE_CASE__		:							dict							,			SCREAMING_SNAKE_CASE__		:							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.'''  )

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

										with xopen(SCREAMING_SNAKE_CASE__							,			'''rb'''							,			use_auth_token=SCREAMING_SNAKE_CASE__  ) as f:
														__lowerCamelCase					,    __lowerCamelCase    					=	sf.read(SCREAMING_SNAKE_CASE__  )

						else:
										__lowerCamelCase					,    __lowerCamelCase    					=	sf.read(SCREAMING_SNAKE_CASE__  )

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

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






		def  __A       (		self		:							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  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							Union[pa.StringArray, pa.StructArray]  )     ->							pa.StructArray:
						if pa.types.is_string(storage.type  ):
										__lowerCamelCase    					=	pa.array([None] * len(SCREAMING_SNAKE_CASE__  )							,			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(SCREAMING_SNAKE_CASE__  )							,			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(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:
														__lowerCamelCase    					=	storage.field('''bytes'''  )
										else:
														__lowerCamelCase    					=	pa.array([None] * len(SCREAMING_SNAKE_CASE__  )							,			type=pa.binary()  )
										if storage.type.get_field_index('''path'''  ) >= 0:
														__lowerCamelCase    					=	storage.field('''path'''  )
										else:
														__lowerCamelCase    					=	pa.array([None] * len(SCREAMING_SNAKE_CASE__  )							,			type=pa.string()  )
										__lowerCamelCase    					=	pa.StructArray.from_arrays([bytes_array, path_array]							,			['''bytes''', '''path''']							,			mask=storage.is_null()  )
						return array_cast(SCREAMING_SNAKE_CASE__							,			self.pa_type  )







		def  __A       (		self		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							pa.StructArray  )     ->							pa.StructArray:

						@no_op_if_value_is_null
						def path_to_bytes(SCREAMING_SNAKE_CASE__		:							List[Any]  ):
										with xopen(SCREAMING_SNAKE_CASE__							,			'''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(SCREAMING_SNAKE_CASE__  ) 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(SCREAMING_SNAKE_CASE__							,			self.pa_type  )





from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							NestedDataStructureLike[PathLike]							,			SCREAMING_SNAKE_CASE__		:							Optional[NamedSplit] = None							,			SCREAMING_SNAKE_CASE__		:							Optional[Features] = None							,			SCREAMING_SNAKE_CASE__		:							str = None							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							Optional[int] = None							,			**SCREAMING_SNAKE_CASE__		:							str							,			)     ->							Union[str, Any]:
						super().__init__(
						    SCREAMING_SNAKE_CASE__							,			split=SCREAMING_SNAKE_CASE__							,			features=SCREAMING_SNAKE_CASE__							,			cache_dir=SCREAMING_SNAKE_CASE__							,			keep_in_memory=SCREAMING_SNAKE_CASE__							,			streaming=SCREAMING_SNAKE_CASE__							,			num_proc=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__							,			)
						__lowerCamelCase    					=	path_or_paths if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) else {self.split: path_or_paths}
						__lowerCamelCase    					=	Text(
						    cache_dir=SCREAMING_SNAKE_CASE__							,			data_files=SCREAMING_SNAKE_CASE__							,			features=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__							,			)







		def  __A       (		self		:							int  )     ->							Dict:
						# Build iterable dataset
						if self.streaming:
										__lowerCamelCase    					=	self.builder.as_streaming_dataset(split=self.split  )
						# Build regular (map-style) dataset
						else:
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None

										self.builder.download_and_prepare(
										    download_config=SCREAMING_SNAKE_CASE__							,			download_mode=SCREAMING_SNAKE_CASE__							,			verification_mode=SCREAMING_SNAKE_CASE__							,			base_path=SCREAMING_SNAKE_CASE__							,			num_proc=self.num_proc							,			)
										__lowerCamelCase    					=	self.builder.as_dataset(
										    split=self.split							,			verification_mode=SCREAMING_SNAKE_CASE__							,			in_memory=self.keep_in_memory  )
						return dataset



