




"""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


lowercase__		,							lowercase__		,							lowercase__  		=		False, False, False






@dataclass
class lowerCAmelCase__			:






			'''simple docstring'''



			lowerCamelCase__   =    None
			lowerCamelCase__   =    True
			lowerCamelCase__   =    True
			lowerCamelCase__   =    None
			# Automatically constructed
			lowerCamelCase__   =    "dict"
			lowerCamelCase__   =    pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}		)
			lowerCamelCase__   =    field(default="""Audio""",			init=lowercase,			repr=lowercase		)
			def __call__(						self						):
							return self.pa_type
			def       A_					(						self						,   lowercase						):
							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(lowercase						,   lowercase						):
											return {"bytes": None, "path": value}
							elif isinstance(lowercase						,   lowercase						):
											return {"bytes": value, "path": None}
							elif "array" in value:
											# convert the audio array to wav bytes
											_lowerCamelCase  :    int					=					BytesIO()
											sf.write(lowercase						,   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						) / 32767
															else:
																			_lowerCamelCase  :    Any					=					np.memmap(value['path']						,   dtype='h'						,   mode='r'						).astype(np.floataa						) / 32767

															_lowerCamelCase  :    Dict					=					BytesIO(bytes()						)
															sf.write(lowercase						,   lowercase						,   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						,   lowercase						,   lowercase = None						):
							if not self.decode:
											raise RuntimeError('Decoding is disabled for this feature. Please use Audio(decode=True) instead.'						)

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

											with xopen(lowercase						,   'rb'						,   use_auth_token=lowercase						) as f:
															_lowerCamelCase,				_lowerCamelCase  :    Optional[Any]					=					sf.read(lowercase						)

							else:
											_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					sf.read(lowercase						)

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

							return {"path": path, "array": array, "sampling_rate": sampling_rate}
			def       A_					(						self						):
							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						,   lowercase						):
							if pa.types.is_string(storage.type						):
											_lowerCamelCase  :    List[Any]					=					pa.array([None] * len(lowercase						)						,   type=pa.binary()						)
											_lowerCamelCase  :    Optional[Any]					=					pa.StructArray.from_arrays([bytes_array, storage]						,   ['bytes', 'path']						,   mask=storage.is_null()						)
							elif pa.types.is_binary(storage.type						):
											_lowerCamelCase  :    str					=					pa.array([None] * len(lowercase						)						,   type=pa.string()						)
											_lowerCamelCase  :    List[Any]					=					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  :    Tuple					=					pa.array([Audio().encode_example(lowercase						) 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  :    Dict					=					storage.field('bytes'						)
											else:
															_lowerCamelCase  :    Optional[Any]					=					pa.array([None] * len(lowercase						)						,   type=pa.binary()						)
											if storage.type.get_field_index('path'						) >= 0:
															_lowerCamelCase  :    Tuple					=					storage.field('path'						)
											else:
															_lowerCamelCase  :    Tuple					=					pa.array([None] * len(lowercase						)						,   type=pa.string()						)
											_lowerCamelCase  :    int					=					pa.StructArray.from_arrays([bytes_array, path_array]						,   ['bytes', 'path']						,   mask=storage.is_null()						)
							return array_cast(lowercase						,   self.pa_type						)






			def       A_					(						self						,   lowercase						):

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

							_lowerCamelCase  :    str					=					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  :    Union[str, Any]					=					pa.array(
							    [os.path.basename(lowercase						) if path is not None else None for path in storage.field('path'						).to_pylist()]						,   type=pa.string()						,   )
							_lowerCamelCase  :    Tuple					=					pa.StructArray.from_arrays([bytes_array, path_array]						,   ['bytes', 'path']						,   mask=bytes_array.is_null()						)
							return array_cast(lowercase						,   self.pa_type						)





"""simple docstring"""






from __future__ import annotations

lowercase__  		=		10







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[Any]					=					1
				_lowerCamelCase  :    Optional[int]					=					max(lowercase__	)
				while placement <= max_digit:
								# declare and initialize empty buckets
								_lowerCamelCase  :    list[list]					=					[[] for _ in range(lowercase__	)]
								# split list_of_ints between the buckets
								for i in list_of_ints:
												_lowerCamelCase  :    Dict					=					int((i / placement) % RADIX	)
												buckets[tmp].append(lowercase__	)
								# put each buckets' contents into list_of_ints
								_lowerCamelCase  :    str					=					0
								for b in range(lowercase__	):
												for i in buckets[b]:
																_lowerCamelCase  :    str					=					i
																a += 1
        # move to next
								placement *= RADIX
				return list_of_ints


if __name__ == "__main__":
			import doctest

			doctest.testmod()