
"""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  A__       :
		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=_lowerCamelCase       ,     repr=_lowerCamelCase)

		def __call__(      self ):
			return self.pa_type

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			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			:						Optional[int]									=      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			:						List[str]									=      np.frombuffer(value['bytes']       ,		dtype=np.intaa ).astype(np.floataa ) / 3_27_67
					else:
						__lowerCAmelCase			:						Optional[int]									=      np.memmap(value['path']       ,		dtype='h'       ,		mode='r' ).astype(np.floataa ) / 3_27_67

					__lowerCAmelCase			:						Union[str, Any]									=      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       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = None ):
			if not self.decode:
				raise RuntimeError('Decoding is disabled for this feature. Please use Audio(decode=True) instead.' )

			__lowerCAmelCase			,   __lowerCAmelCase			:						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

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

				with xopen(_SCREAMING_SNAKE_CASE       ,		'rb'       ,		use_auth_token=_SCREAMING_SNAKE_CASE ) as f:
					__lowerCAmelCase			,   __lowerCAmelCase			:						str									=      sf.read(_SCREAMING_SNAKE_CASE )

			else:
				__lowerCAmelCase			,   __lowerCAmelCase			:						Optional[Any]									=      sf.read(_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Dict									=      array.T
			if self.mono:
				__lowerCAmelCase			:						List[Any]									=      librosa.to_mono(_SCREAMING_SNAKE_CASE )
			if self.sampling_rate and self.sampling_rate != sampling_rate:
				__lowerCAmelCase			:						Tuple									=      librosa.resample(_SCREAMING_SNAKE_CASE       ,		orig_sr=_SCREAMING_SNAKE_CASE       ,		target_sr=self.sampling_rate )
				__lowerCAmelCase			:						List[Any]									=      self.sampling_rate

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

		def 							__lowerCamelCase							(      self ):
			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       ,		_SCREAMING_SNAKE_CASE ):
			if pa.types.is_string(storage.type ):
				__lowerCAmelCase			:						List[Any]									=      pa.array([None] * len(_SCREAMING_SNAKE_CASE )       ,		type=pa.binary() )
				__lowerCAmelCase			:						Dict									=      pa.StructArray.from_arrays([bytes_array, storage]       ,		['bytes', 'path']       ,		mask=storage.is_null() )
			elif pa.types.is_binary(storage.type ):
				__lowerCAmelCase			:						List[str]									=      pa.array([None] * len(_SCREAMING_SNAKE_CASE )       ,		type=pa.string() )
				__lowerCAmelCase			:						List[str]									=      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			:						Union[str, Any]									=      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			:						Any									=      storage.field('bytes' )
				else:
					__lowerCAmelCase			:						Optional[Any]									=      pa.array([None] * len(_SCREAMING_SNAKE_CASE )       ,		type=pa.binary() )
				if storage.type.get_field_index('path' ) >= 0:
					__lowerCAmelCase			:						Union[str, Any]									=      storage.field('path' )
				else:
					__lowerCAmelCase			:						List[str]									=      pa.array([None] * len(_SCREAMING_SNAKE_CASE )       ,		type=pa.string() )
				__lowerCAmelCase			:						str									=      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       ,		_SCREAMING_SNAKE_CASE ):

			@no_op_if_value_is_null
			def path_to_bytes(_SCREAMING_SNAKE_CASE ):
				with xopen(_SCREAMING_SNAKE_CASE       ,		'rb' ) as f:
					__lowerCAmelCase			:						List[Any]									=      f.read()
				return bytes_

			__lowerCAmelCase			:						Dict									=      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			:						List[Any]									=      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			:						Dict									=      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

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				def __init__(     self						:    Dict,  lowerCamelCase						:    pyspark.sql.DataFrame,  lowerCamelCase						:    Optional[NamedSplit] = None,  lowerCamelCase						:    Optional[Features] = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    str = None,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    str = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    str = "arrow",  **lowerCamelCase						:    str,  ):

								'''simple docstring'''
								super().__init__(
								    split=lowerCamelCase,  features=lowerCamelCase,  cache_dir=lowerCamelCase,  keep_in_memory=lowerCamelCase,  streaming=lowerCamelCase,  **lowerCamelCase,  )
								lowercase__    =			load_from_cache_file
								lowercase__    =			file_format
								lowercase__    =			Spark(
								    df=lowerCamelCase,  features=lowerCamelCase,  cache_dir=lowerCamelCase,  working_dir=lowerCamelCase,  **lowerCamelCase,  )




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								if self.streaming:
												return self.builder.as_streaming_dataset(split=self.split  )
								lowercase__    =			None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
								self.builder.download_and_prepare(
								    download_mode=lowerCamelCase,  file_format=self._file_format,  )
								return self.builder.as_dataset(split=self.split  )




