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




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




				def      __a						(					self				,				_UpperCamelCase     )				->      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(_UpperCamelCase				,				_UpperCamelCase     ):
										return {"bytes": None, "path": value}
							elif isinstance(_UpperCamelCase				,				_UpperCamelCase     ):
										return {"bytes": value, "path": None}
							elif "array" in value:
										# convert the audio array to wav bytes
										lowerCAmelCase_						=	BytesIO()
										sf.write(_UpperCamelCase				,				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     ) / 32_767
													else:
																lowerCAmelCase_						=	np.memmap(value["path"]				,				dtype="h"				,				mode="r"     ).astype(np.floataa     ) / 32_767

													lowerCAmelCase_						=	BytesIO(bytes()     )
													sf.write(_UpperCamelCase				,				_UpperCamelCase				,				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				,				_UpperCamelCase				,				_UpperCamelCase = 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(_UpperCamelCase     )[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(_UpperCamelCase				,				config.HUB_DATASETS_URL     )["repo_id"]
													lowerCAmelCase_						=	token_per_repo_id[repo_id]
										except (ValueError, KeyError):
													lowerCAmelCase_						=	None

										with xopen(_UpperCamelCase				,				"rb"				,				use_auth_token=_UpperCamelCase     ) as f:
													lowerCAmelCase_	,  lowerCAmelCase_						=	sf.read(_UpperCamelCase     )

							else:
										lowerCAmelCase_	,  lowerCAmelCase_						=	sf.read(_UpperCamelCase     )

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

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




				def      __a						(					self     )				->      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				,				_UpperCamelCase     )				->      pa.StructArray:
							if pa.types.is_string(storage.type     ):
										lowerCAmelCase_						=	pa.array([None] * len(_UpperCamelCase     )				,				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(_UpperCamelCase     )				,				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(_UpperCamelCase     ) 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(_UpperCamelCase     )				,				type=pa.binary()     )
										if storage.type.get_field_index("path"     ) >= 0:
													lowerCAmelCase_						=	storage.field("path"     )
										else:
													lowerCAmelCase_						=	pa.array([None] * len(_UpperCamelCase     )				,				type=pa.string()     )
										lowerCAmelCase_						=	pa.StructArray.from_arrays([bytes_array, path_array]				,				["bytes", "path"]				,				mask=storage.is_null()     )
							return array_cast(_UpperCamelCase				,				self.pa_type     )




				def      __a						(					self				,				_UpperCamelCase     )				->      pa.StructArray:

							@no_op_if_value_is_null
							def path_to_bytes(_UpperCamelCase     ):
										with xopen(_UpperCamelCase				,				"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(_UpperCamelCase     ) 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(_UpperCamelCase				,				self.pa_type     )





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_A   =     {}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["NllbTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["NllbTokenizerFast"]


if TYPE_CHECKING:
			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_nllb import NllbTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_nllb_fast import NllbTokenizerFast

else:
			import sys

			_A   =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



