


"""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


a_							,     a_							,     a_       							=    False, False, False
@dataclass
class snake_case      :
		__UpperCamelCase           =			None
		__UpperCamelCase           =			True
		__UpperCamelCase           =			True
		__UpperCamelCase           =			None
		# Automatically constructed
		__UpperCamelCase           =			'dict'
		__UpperCamelCase           =			pa.struct({'bytes': pa.binary(), 'path': pa.string()})
		__UpperCamelCase           =			field(default='Audio'     ,				init=_UpperCamelCase     ,				repr=_UpperCamelCase)







		def __call__(					self      :       int		)       ->						Any:





						'''simple docstring'''







						return self.pa_type







		def  a_ (					self      :       str    ,   a__      :       Union[str, bytes, dict]		)       ->						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
										_A					  =			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!)
																		_A					  =			np.frombuffer(value["bytes"]    ,   dtype=np.intaa		).astype(np.floataa		) / 3_27_67
														else:
																		_A					  =			np.memmap(value["path"]    ,   dtype="h"    ,   mode="r"		).astype(np.floataa		) / 3_27_67

														_A					  =			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  a_ (					self      :       Optional[int]    ,   a__      :       dict    ,   a__      :       Optional[Dict[str, Union[str, bool, None]]] = None		)       ->						dict:





						'''simple docstring'''







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

						_A  ,		_A					  =			(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

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

										with xopen(a__    ,   "rb"    ,   use_auth_token=a__		) as f:
														_A  ,		_A					  =			sf.read(a__		)

						else:
										_A  ,		_A					  =			sf.read(a__		)

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

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







		def  a_ (					self      :       Dict		)       ->						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  a_ (					self      :       Union[str, Any]    ,   a__      :       Union[pa.StringArray, pa.StructArray]		)       ->						pa.StructArray:





						'''simple docstring'''







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







		def  a_ (					self      :       int    ,   a__      :       pa.StructArray		)       ->						pa.StructArray:





						'''simple docstring'''







						@no_op_if_value_is_null
						def path_to_bytes(a__      :       int		):
										with xopen(a__    ,   "rb"		) as f:
														_A					  =			f.read()
										return bytes_

						_A					  =			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()    ,   )
						_A					  =			pa.array(
						    [os.path.basename(a__		) if path is not None else None for path in storage.field("path"		).to_pylist()]    ,   type=pa.string()    ,   )
						_A					  =			pa.StructArray.from_arrays([bytes_array, path_array]    ,   ["bytes", "path"]    ,   mask=bytes_array.is_null()		)
						return array_cast(a__    ,   self.pa_type		)



"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_       							=    {
    "configuration_upernet": ["UperNetConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "UperNetForSemanticSegmentation",
		    "UperNetPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_upernet import UperNetConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
		import sys

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