






'''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


lowerCamelCase_   ,     lowerCamelCase_   ,     lowerCamelCase_				   =   False, False, False

@dataclass
class   _UpperCAmelCase     :




   """simple docstring"""

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

   def __call__(						self					:      Optional[Any]						):





         '''simple docstring'''





         return self.pa_type

   def 		lowerCAmelCase					(						self					:      Union[str, Any] , __UpperCAmelCase					:      Union[str, bytes, 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						) / 32767
                     else:
                           _A     	=      np.memmap(value["path"] , dtype="h" , mode="r"						).astype(np.floataa						) / 32767

                     _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 		lowerCAmelCase					(						self					:      Optional[int] , __UpperCAmelCase					:      dict , __UpperCAmelCase					:      Optional[Dict[str, Union[str, bool, None]]] = None						):





         '''simple docstring'''





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

         _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     	=      sf.read(a_						)

         else:
               _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 		lowerCAmelCase					(						self					:      List[str]						):





         '''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					:      int , __UpperCAmelCase					:      Union[pa.StringArray, 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 		lowerCAmelCase					(						self					:      Optional[int] , __UpperCAmelCase					:      pa.StructArray						):





         '''simple docstring'''





         @no_op_if_value_is_null
         def path_to_bytes(__UpperCAmelCase					:      str						):
               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 random import randint
from tempfile import TemporaryFile

import numpy as np







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    int      							=0
			if start < end:
						snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
						snake_case:    Optional[Any]      							=a[end]
						snake_case:    List[Any]      							=a[pivot]
						snake_case:    int      							=temp

						snake_case							,  snake_case:    Optional[int]      							=_in_place_partition(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							__UpperCAmelCase				,							p - 1					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							p + 1				,							__UpperCAmelCase					)
			return count







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Optional[int]:





			"""simple docstring"""







			snake_case:    Tuple      							=0
			snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
			snake_case:    List[Any]      							=a[end]
			snake_case:    List[str]      							=a[pivot]
			snake_case:    Dict      							=temp
			snake_case:    Union[str, Any]      							=start - 1
			for index in range(__UpperCAmelCase				,							__UpperCAmelCase					):
						count += 1
						if a[index] < a[end]:  # check if current val is less than pivot value
									snake_case:    Optional[Any]      							=new_pivot_index + 1
									snake_case:    Dict      							=a[new_pivot_index]
									snake_case:    Union[str, Any]      							=a[index]
									snake_case:    List[Any]      							=temp

			snake_case:    Union[str, Any]      							=a[new_pivot_index + 1]
			snake_case:    Tuple      							=a[end]
			snake_case:    Union[str, Any]      							=temp
			return new_pivot_index + 1, count


a							   =				TemporaryFile()
a							   =				100  # 1000 elements are to be sorted


a   ,      a							   =				0, 1  # mean and standard deviation
a							   =				np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
a							   =				np.load(outfile)
a							   =				len(M) - 1
a							   =				_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)







print(z)




