


'''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_    :			Dict      				=			False, False, False




@dataclass
class        a   :
 _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  )    ->							str:
      return self.pa_type
 def 							__UpperCAmelCase						(       self					, __magic_name__  )    ->							Tuple:
      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(_UpperCAmelCase					, _UpperCAmelCase  ):
           return {"bytes": None, "path": value}
      elif isinstance(_UpperCAmelCase					, _UpperCAmelCase  ):
           return {"bytes": value, "path": None}
      elif "array" in value:
           # convert the audio array to wav bytes
           _a						=							BytesIO()
           sf.write(_UpperCAmelCase					, 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(_UpperCAmelCase					, _UpperCAmelCase					, 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					, __magic_name__					, __magic_name__ = None  )    ->							Dict:
      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(_UpperCAmelCase  )[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(_UpperCAmelCase					, config.HUB_DATASETS_URL  )['''repo_id''']
                _a						=							token_per_repo_id[repo_id]
           except (ValueError, KeyError):
                _a						=							None

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

      else:
           _a						=							sf.read(_UpperCAmelCase  )

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

      return {"path": path, "array": array, "sampling_rate": sampling_rate}
 def 							__UpperCAmelCase						(       self  )    ->							Optional[Any]:
      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					, __magic_name__  )    ->							Optional[int]:
      if pa.types.is_string(storage.type  ):
           _a						=							pa.array([None] * len(_UpperCAmelCase  )					, 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(_UpperCAmelCase  )					, 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(_UpperCAmelCase  ) 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(_UpperCAmelCase  )					, type=pa.binary()  )
           if storage.type.get_field_index('path'  ) >= 0:
                _a						=							storage.field('path'  )
           else:
                _a						=							pa.array([None] * len(_UpperCAmelCase  )					, type=pa.string()  )
           _a						=							pa.StructArray.from_arrays([bytes_array, path_array]					, ['bytes', 'path']					, mask=storage.is_null()  )
      return array_cast(_UpperCAmelCase					, self.pa_type  )




 def 							__UpperCAmelCase						(       self					, __magic_name__  )    ->							Tuple:

      @no_op_if_value_is_null
      def path_to_bytes(__magic_name__  ):
           with xopen(_UpperCAmelCase					, '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(_UpperCAmelCase  ) 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(_UpperCAmelCase					, self.pa_type  )


"""simple docstring"""






import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[Any]:
   # Initialise PyTorch model
   lowercase__:   Optional[Any]					=      FunnelConfig.from_json_file(__UpperCAmelCase )
   print(F"""Building PyTorch model from configuration: {config}""" )
   lowercase__:   List[Any]					=      FunnelBaseModel(__UpperCAmelCase ) if base_model else FunnelModel(__UpperCAmelCase )

   # Load weights from tf checkpoint
   load_tf_weights_in_funnel(__UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )

   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}""" )
   torch.save(model.state_dict()       ,    __UpperCAmelCase )


if __name__ == "__main__":
       __A			   = argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
       )
       parser.add_argument(
           "--config_file",
           default=None,
           type=str,
           required=True,
           help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
       )
       parser.add_argument(
           "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       parser.add_argument(
           "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
       )
       __A			   = parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
       )
