






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class 					A__     (     __snake_case ):

     '''simple docstring'''






     snake_case__					=					"""Speech2TextFeatureExtractor"""
     snake_case__					=					"""Speech2TextTokenizer"""







     def __init__( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    Any ):






           """simple docstring"""




           super().__init__(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  self.feature_extractor
           UpperCamelCase					       =  False







     def __call__( self      :    Any   ,						*_SCREAMING_SNAKE_CASE      :    List[Any]   ,						**_SCREAMING_SNAKE_CASE      :    Optional[Any] ):






           """simple docstring"""




           if self._in_target_context_manager:
                 return self.current_processor(*_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )

           if "raw_speech" in kwargs:
                 warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.' )
                 UpperCamelCase					       =  kwargs.pop('raw_speech' )
           else:
                 UpperCamelCase					       =  kwargs.pop('audio'   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  kwargs.pop('sampling_rate'   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  kwargs.pop('text'   ,						_SCREAMING_SNAKE_CASE )
           if len(_SCREAMING_SNAKE_CASE ) > 0:
                 UpperCamelCase					       =  args[0]
                 UpperCamelCase					       =  args[1:]

           if audio is None and text is None:
                 raise ValueError('You need to specify either an `audio` or `text` input to process.' )

           if audio is not None:
                 UpperCamelCase					       =  self.feature_extractor(_SCREAMING_SNAKE_CASE   ,						*_SCREAMING_SNAKE_CASE   ,						sampling_rate=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )
           if text is not None:
                 UpperCamelCase					       =  self.tokenizer(_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )

           if text is None:
                 return inputs
           elif audio is None:
                 return encodings
           else:
                 UpperCamelCase					       =  encodings['input_ids']
                 return inputs







     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any]   ,						*_SCREAMING_SNAKE_CASE      :    int   ,						**_SCREAMING_SNAKE_CASE      :    List[Any] ):






           """simple docstring"""




           return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    int   ,						*_SCREAMING_SNAKE_CASE      :    Tuple   ,						**_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )







     @contextmanager
     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




           warnings.warn(
               '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
               'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
               'your audio inputs, or in a separate call.' )
           UpperCamelCase					       =  True
           UpperCamelCase					       =  self.tokenizer
           yield
           UpperCamelCase					       =  self.feature_extractor
           UpperCamelCase					       =  False











import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__   :  str        =							logging.get_logger(__name__)
def 		lowercase__  (				_UpperCamelCase) ->			int:
      """simple docstring"""

      UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location='cpu')
      if "model" in sd.keys():
            UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location='cpu')['model']

      # pop unnecessary weights
      UpperCamelCase					       =  [
          'decoder.version',
          'decoder.output_projection.weight',
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(_UpperCamelCase)

      UpperCamelCase					       =  {
          'decoder.project_in_dim.weight': 'decoder.project_in.weight',
          'decoder.project_out_dim.weight': 'decoder.project_out.weight',
          'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
          'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
      }
      for old_key, new_key in keys_to_rename.items():
            if old_key in sd:
                  UpperCamelCase					       =  sd.pop(_UpperCamelCase)

      UpperCamelCase					       =  list(sd.keys())
      for key in keys:
            if ".qkv_proj." in key:
                  UpperCamelCase					       =  sd[key]
                  # We split QKV in separate Q,K,V

                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.q_proj.')
                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.k_proj.')
                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.v_proj.')

                  UpperCamelCase					       =  value.shape[0]
                  assert depth % 3 == 0
                  # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                  # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  torch.split(_UpperCamelCase       ,							depth // 3       ,							dim=0)

                  UpperCamelCase					       =  q
                  UpperCamelCase					       =  k
                  UpperCamelCase					       =  v
                  del sd[key]

      return sd
@torch.no_grad()
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=None) ->			Optional[int]:
      """simple docstring"""

      UpperCamelCase					       =  load_checkpoint(_UpperCamelCase)

      if config is not None:
            UpperCamelCase					       =  OPTConfig.from_pretrained(_UpperCamelCase)
      else:
            UpperCamelCase					       =  OPTConfig()

      UpperCamelCase					       =  OPTModel(_UpperCamelCase).half().eval()
      model.load_state_dict(_UpperCamelCase)

      # Check results
      Path(_UpperCamelCase).mkdir(exist_ok=_UpperCamelCase)
      model.save_pretrained(_UpperCamelCase)


if __name__ == "__main__":
       __magic_name__   :  Any        =							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--fairseq_path''',
           type=str,
           help=(
               '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
               ''' https://huggingface.co/models?other=opt_metasq'''
           ),
       )
       parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
       parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
       __magic_name__   :  str        =							parser.parse_args()
       convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



