


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


snake_case_						,				snake_case_						,				snake_case_						:			Dict													=							False, False, False

@dataclass
class 						A__							:
						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  )							->     Optional[Any]:


										"""simple docstring"""





										return self.pa_type




						def 							__UpperCamelCase				(       self					:      Dict				,    _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
														_SCREAMING_SNAKE_CASE			  =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!)
																						_SCREAMING_SNAKE_CASE			  =np.frombuffer(value['''bytes''']				,    dtype=np.intaa  ).astype(np.floataa  ) / 3_2767
																		else:
																						_SCREAMING_SNAKE_CASE			  =np.memmap(value['''path''']				,    dtype='''h'''				,    mode='''r'''  ).astype(np.floataa  ) / 3_2767

																		_SCREAMING_SNAKE_CASE			  =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 							__UpperCamelCase				(       self					:      List[Any]				,    _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.'''  )

										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =(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

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

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

										else:
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =sf.read(_a  )

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

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




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     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 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      Union[pa.StringArray, pa.StructArray]  )							->     pa.StructArray:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      int				,    _a					:      pa.StructArray  )							->     pa.StructArray:


										"""simple docstring"""





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

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



from datetime import datetime

import matplotlib.pyplot as plt
import torch







def 						lowerCamelCase(   a__):
				for param in module.parameters():
								_SCREAMING_SNAKE_CASE			  =False







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  ='''cuda''' if torch.cuda.is_available() else '''cpu'''
				if torch.backends.mps.is_available() and torch.backends.mps.is_built():
								_SCREAMING_SNAKE_CASE			  ='''mps'''
				if device == "mps":
								print(
								    '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
								    ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
								    ''' with generations.''')
				return device







def 						lowerCamelCase(   a__):
				_SCREAMING_SNAKE_CASE			  =plt.imshow(a__)
				fig.axes.get_xaxis().set_visible(a__)
				fig.axes.get_yaxis().set_visible(a__)
				plt.show()







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  =datetime.now()
				_SCREAMING_SNAKE_CASE			  =current_time.strftime('''%H:%M:%S''')
				return timestamp