





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 						=      False, False, False






@dataclass
class 					__A    :

			'''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=UpperCAmelCase__			,				repr=UpperCAmelCase__ )





			def __call__(    self							):



				'''simple docstring'''







				return self.pa_type





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''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(snake_case_       ,  snake_case_							):
					return {"bytes": None, "path": value}
				elif isinstance(snake_case_       ,  snake_case_							):
					return {"bytes": value, "path": None}
				elif "array" in value:
					# convert the audio array to wav bytes
					lowerCamelCase__								=		BytesIO()
					sf.write(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_2_7_6_7
						else:
							lowerCamelCase__								=		np.memmap(value['''path''']       ,  dtype='''h'''       ,  mode='''r'''							).astype(np.floataa							) / 3_2_7_6_7

						lowerCamelCase__								=		BytesIO(bytes()							)
						sf.write(snake_case_       ,  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       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None							):



				'''simple docstring'''







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

				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(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(snake_case_       ,  config.HUB_DATASETS_URL							)['repo_id']
						lowerCamelCase__								=		token_per_repo_id[repo_id]
					except (ValueError, KeyError):
						lowerCamelCase__								=		None

					with xopen(snake_case_       ,  '''rb'''       ,  use_auth_token=snake_case_							) as f:
						lowerCamelCase__								=		sf.read(snake_case_							)

				else:
					lowerCamelCase__								=		sf.read(snake_case_							)

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

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





			def 		__lowerCamelCase				(    self							):



				'''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 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				@no_op_if_value_is_null
				def path_to_bytes(__lowerCAmelCase							):
					with xopen(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(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(snake_case_       ,  self.pa_type							)










"""simple docstring"""




from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin

class  _UpperCAmelCase  (   UpperCAmelCase__			,   UpperCAmelCase__		):



      '''simple docstring'''

      @register_to_config
      def __init__(     self					,      snake_case_ = 7_6_8					,      ):

        """simple docstring"""

        super().__init__()

        A_  :       Optional[int]										=  nn.Parameter(torch.zeros(1					,      snake_case_  )  )
        A_  :       Optional[int]										=  nn.Parameter(torch.ones(1					,      snake_case_  )  )

      def 					lowerCamelCase_			(     self					,      snake_case_ = None					,      snake_case_ = None					,      ):

        """simple docstring"""

        A_  :       str										=  nn.Parameter(self.mean.to(snake_case_  ).to(snake_case_  )  )
        A_  :       Optional[int]										=  nn.Parameter(self.std.to(snake_case_  ).to(snake_case_  )  )
        return self

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       Tuple										=  (embeds - self.mean) * 1.0 / self.std
        return embeds

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       List[str]										=  (embeds * self.std) + self.mean
        return embeds