


"""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


UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				=						False, False, False




@dataclass
class 	lowercase__				:
   __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=A_ ,repr=A_				)





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





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  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(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE):
         return {"bytes": None, "path": value}
      elif isinstance(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE):
         return {"bytes": value, "path": None}
      elif "array" in value:
         # convert the audio array to wav bytes
         _lowerCamelCase	:					str   =		BytesIO()
         sf.write(SCREAMING_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	:					Dict   =		np.frombuffer(value["""bytes"""]      ,   dtype=np.intaa).astype(np.floataa) / 3_2767
            else:
               _lowerCamelCase	:					Optional[Any]   =		np.memmap(value["""path"""]      ,   dtype="""h"""      ,   mode="""r""").astype(np.floataa) / 3_2767

            _lowerCamelCase	:					List[Any]   =		BytesIO(bytes())
            sf.write(SCREAMING_SNAKE_CASE      ,   SCREAMING_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 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None)  ->  dict:
      if not self.decode:
         raise RuntimeError("""Decoding is disabled for this feature. Please use Audio(decode=True) instead.""")

      _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		(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	:					List[Any]   =		xsplitext(SCREAMING_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	:					List[Any]   =		token_per_repo_id or {}
         _lowerCamelCase	:					Dict   =		path.split("""::""")[-1]
         try:
            _lowerCamelCase	:					str   =		string_to_dict(SCREAMING_SNAKE_CASE      ,   config.HUB_DATASETS_URL)["""repo_id"""]
            _lowerCamelCase	:					Tuple   =		token_per_repo_id[repo_id]
         except (ValueError, KeyError):
            _lowerCamelCase	:					Tuple   =		None

         with xopen(SCREAMING_SNAKE_CASE      ,   """rb"""      ,   use_auth_token=SCREAMING_SNAKE_CASE) as f:
            _lowerCamelCase   ,    _lowerCamelCase	:					Dict   =		sf.read(SCREAMING_SNAKE_CASE)

      else:
         _lowerCamelCase   ,    _lowerCamelCase	:					Tuple   =		sf.read(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					str   =		array.T
      if self.mono:
         _lowerCamelCase	:					Optional[int]   =		librosa.to_mono(SCREAMING_SNAKE_CASE)
      if self.sampling_rate and self.sampling_rate != sampling_rate:
         _lowerCamelCase	:					Optional[Any]   =		librosa.resample(SCREAMING_SNAKE_CASE      ,   orig_sr=SCREAMING_SNAKE_CASE      ,   target_sr=self.sampling_rate)
         _lowerCamelCase	:					Dict   =		self.sampling_rate

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





   def 		UpperCamelCase_  (							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 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  pa.StructArray:
      if pa.types.is_string(storage.type):
         _lowerCamelCase	:					Dict   =		pa.array([None] * len(SCREAMING_SNAKE_CASE)      ,   type=pa.binary())
         _lowerCamelCase	:					Optional[Any]   =		pa.StructArray.from_arrays([bytes_array, storage]      ,   ["""bytes""", """path"""]      ,   mask=storage.is_null())
      elif pa.types.is_binary(storage.type):
         _lowerCamelCase	:					List[Any]   =		pa.array([None] * len(SCREAMING_SNAKE_CASE)      ,   type=pa.string())
         _lowerCamelCase	:					List[Any]   =		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	:					List[Any]   =		pa.array([Audio().encode_example(SCREAMING_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	:					Any   =		storage.field("""bytes""")
         else:
            _lowerCamelCase	:					List[Any]   =		pa.array([None] * len(SCREAMING_SNAKE_CASE)      ,   type=pa.binary())
         if storage.type.get_field_index("""path""") >= 0:
            _lowerCamelCase	:					List[Any]   =		storage.field("""path""")
         else:
            _lowerCamelCase	:					List[str]   =		pa.array([None] * len(SCREAMING_SNAKE_CASE)      ,   type=pa.string())
         _lowerCamelCase	:					Tuple   =		pa.StructArray.from_arrays([bytes_array, path_array]      ,   ["""bytes""", """path"""]      ,   mask=storage.is_null())
      return array_cast(SCREAMING_SNAKE_CASE      ,   self.pa_type)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  pa.StructArray:

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

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





"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		HfArgumentParser(__snake_case   )
   _lowerCamelCase	:					int   =		parser.parse_args_into_dataclasses()[0]
   _lowerCamelCase	:					Dict   =		TensorFlowBenchmark(args=__snake_case   )
   try:
      _lowerCamelCase	:					Optional[int]   =		parser.parse_args_into_dataclasses()[0]
   except ValueError as e:
      _lowerCamelCase	:					Union[str, Any]   =		"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
      _lowerCamelCase	:					List[str]   =		""" """.join(str(__snake_case   ).split(""" """   )[:-1]   )
      _lowerCamelCase	:					Dict   =		""""""
      _lowerCamelCase	:					List[Any]   =		eval(str(__snake_case   ).split(""" """   )[-1]   )
      _lowerCamelCase	:					Tuple   =		[]
      for arg in depreciated_args:
         # arg[2:] removes '--'
         if arg[2:] in TensorFlowBenchmark.deprecated_args:
            # arg[5:] removes '--no_'
            full_error_msg += arg_error_msg.format(arg[5:]   )
         else:
            wrong_args.append(__snake_case   )
      if len(__snake_case   ) > 0:
         _lowerCamelCase	:					Tuple   =		full_error_msg + begin_error_msg + str(__snake_case   )
      raise ValueError(__snake_case   )
   benchmark.run()


if __name__ == "__main__":
 main()

