






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


SCREAMING_SNAKE_CASE							,			SCREAMING_SNAKE_CASE							,			SCREAMING_SNAKE_CASE 							=						False, False, False
@dataclass
class 							A_    :






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Any =					None
 _SCREAMING_SNAKE_CASE							:			str =					True
 _SCREAMING_SNAKE_CASE							:			Tuple =					True
 _SCREAMING_SNAKE_CASE							:			Optional[Any] =					None
 # Automatically constructed
 _SCREAMING_SNAKE_CASE							:			int =					"dict"
 _SCREAMING_SNAKE_CASE							:			int =					pa.struct({"bytes": pa.binary(), "path": pa.string()}			)
 _SCREAMING_SNAKE_CASE							:			Optional[Any] =					field(default="Audio" ,   init=_snake_case ,   repr=_snake_case			)


 def __call__(	self) ->		List[Any]:

     """simple docstring"""



     return self.pa_type


 def  snake_case__				(	self							,  _A) ->		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(lowerCAmelCase__							,  lowerCAmelCase__):
         return {"bytes": None, "path": value}
     elif isinstance(lowerCAmelCase__							,  lowerCAmelCase__):
         return {"bytes": value, "path": None}
     elif "array" in value:
         # convert the audio array to wav bytes
         _UpperCAmelCase				:      List[Any]			  =				BytesIO()
         sf.write(lowerCAmelCase__							,  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!)
                 _UpperCAmelCase				:      Tuple			  =				np.frombuffer(value['''bytes''']							,  dtype=np.intaa).astype(np.floataa) / 32767
             else:
                 _UpperCAmelCase				:      List[str]			  =				np.memmap(value['''path''']							,  dtype='''h'''							,  mode='''r''').astype(np.floataa) / 32767

             _UpperCAmelCase				:      int			  =				BytesIO(bytes())
             sf.write(lowerCAmelCase__							,  lowerCAmelCase__							,  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  snake_case__				(	self							,  _A							,  _A = None) ->		dict:

     """simple docstring"""



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

     _UpperCAmelCase  , _UpperCAmelCase				:      str			  =				(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

     _UpperCAmelCase				:      Any			  =				xsplitext(lowerCAmelCase__)[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:
         _UpperCAmelCase				:      Optional[Any]			  =				token_per_repo_id or {}
         _UpperCAmelCase				:      Union[str, Any]			  =				path.split('''::''')[-1]
         try:
             _UpperCAmelCase				:      Dict			  =				string_to_dict(lowerCAmelCase__							,  config.HUB_DATASETS_URL)['''repo_id''']
             _UpperCAmelCase				:      Any			  =				token_per_repo_id[repo_id]
         except (ValueError, KeyError):
             _UpperCAmelCase				:      Optional[int]			  =				None

         with xopen(lowerCAmelCase__							,  '''rb'''							,  use_auth_token=lowerCAmelCase__) as f:
             _UpperCAmelCase  , _UpperCAmelCase				:      List[str]			  =				sf.read(lowerCAmelCase__)

     else:
         _UpperCAmelCase  , _UpperCAmelCase				:      int			  =				sf.read(lowerCAmelCase__)

     _UpperCAmelCase				:      List[Any]			  =				array.T
     if self.mono:
         _UpperCAmelCase				:      int			  =				librosa.to_mono(lowerCAmelCase__)
     if self.sampling_rate and self.sampling_rate != sampling_rate:
         _UpperCAmelCase				:      Optional[Any]			  =				librosa.resample(lowerCAmelCase__							,  orig_sr=lowerCAmelCase__							,  target_sr=self.sampling_rate)
         _UpperCAmelCase				:      Optional[int]			  =				self.sampling_rate

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


 def  snake_case__				(	self) ->		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  snake_case__				(	self							,  _A) ->		pa.StructArray:

     """simple docstring"""



     if pa.types.is_string(storage.type):
         _UpperCAmelCase				:      Union[str, Any]			  =				pa.array([None] * len(lowerCAmelCase__)							,  type=pa.binary())
         _UpperCAmelCase				:      Optional[int]			  =				pa.StructArray.from_arrays([bytes_array, storage]							,  ['''bytes''', '''path''']							,  mask=storage.is_null())
     elif pa.types.is_binary(storage.type):
         _UpperCAmelCase				:      List[str]			  =				pa.array([None] * len(lowerCAmelCase__)							,  type=pa.string())
         _UpperCAmelCase				:      List[str]			  =				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'''):
         _UpperCAmelCase				:      Dict			  =				pa.array([Audio().encode_example(lowerCAmelCase__) 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:
             _UpperCAmelCase				:      Optional[Any]			  =				storage.field('''bytes''')
         else:
             _UpperCAmelCase				:      Dict			  =				pa.array([None] * len(lowerCAmelCase__)							,  type=pa.binary())
         if storage.type.get_field_index('''path''') >= 0:
             _UpperCAmelCase				:      Tuple			  =				storage.field('''path''')
         else:
             _UpperCAmelCase				:      List[Any]			  =				pa.array([None] * len(lowerCAmelCase__)							,  type=pa.string())
         _UpperCAmelCase				:      List[Any]			  =				pa.StructArray.from_arrays([bytes_array, path_array]							,  ['''bytes''', '''path''']							,  mask=storage.is_null())
     return array_cast(lowerCAmelCase__							,  self.pa_type)


 def  snake_case__				(	self							,  _A) ->		pa.StructArray:

     """simple docstring"""



     @no_op_if_value_is_null
     def path_to_bytes(_A):
         with xopen(lowerCAmelCase__							,  '''rb''') as f:
             _UpperCAmelCase				:      Dict			  =				f.read()
         return bytes_

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










import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()

def      _lowerCamelCase	(						__A    :							int		,		__A    :							Optional[Any]		,		__A    :							int )      ->					int:
    # Initialise PyTorch model
    _UpperCAmelCase				:      Dict			  =				RemBertConfig.from_json_file(__A )
    print('''Building PyTorch model from configuration: {}'''.format(str(__A ) ) )
    _UpperCAmelCase				:      int			  =				RemBertModel(__A )

    # Load weights from tf checkpoint
    load_tf_weights_in_rembert(__A		,		__A		,		__A )

    # Save pytorch-model
    print('''Save PyTorch model to {}'''.format(__A ) )
    torch.save(model.state_dict()		,		__A )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						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(
      '--rembert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained RemBERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)




