



"""simple docstring"""





import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
SCREAMING_SNAKE_CASE				:     Optional[int]          =	'src/transformers'

# Matches is_xxx_available()
SCREAMING_SNAKE_CASE				:     Optional[int]          =	re.compile(R'''is\_([a-z_]*)_available()''')
# Matches from xxx import bla
SCREAMING_SNAKE_CASE				:     Any          =	re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
SCREAMING_SNAKE_CASE				:     Any          =	re.compile(R'''^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)''')


SCREAMING_SNAKE_CASE				:     List[Any]          =	'\n{0} = None\n'

SCREAMING_SNAKE_CASE				:     str          =	'\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


SCREAMING_SNAKE_CASE				:     Union[str, Any]          =	'\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'






def 	__UpperCAmelCase					(       snake_case_  :  Optional[int]   )							->	List[str]:






   """simple docstring"""


   if _re_test_backend.search(lowercase_   ) is None:
      return None
   _lowerCAmelCase					     =		[b[0] for b in _re_backend.findall(lowercase_   )]
   backends.sort()
   return "_and_".join(lowercase_   )






def 	__UpperCAmelCase					(       )							->	int:






   """simple docstring"""


   with open(os.path.join(lowercase_   ,	"""__init__.py"""   )   ,	"""r"""   ,	encoding="""utf-8"""   ,	newline="""\n"""   ) as f:
      _lowerCAmelCase					     =		f.readlines()

   # Get to the point we do the actual imports for type checking
   _lowerCAmelCase					     =		0
   while not lines[line_index].startswith("""if TYPE_CHECKING"""   ):
      line_index += 1

   _lowerCAmelCase					     =		{}
   # Go through the end of the file
   while line_index < len(lowercase_   ):
      # If the line is an if is_backend_available, we grab all objects associated.
      _lowerCAmelCase					     =		find_backend(lines[line_index]   )
      if backend is not None:
         while not lines[line_index].startswith("""    else:"""   ):
            line_index += 1
         line_index += 1

         _lowerCAmelCase					     =		[]
         # Until we unindent, add backend objects to the list
         while len(lines[line_index]   ) <= 1 or lines[line_index].startswith(""" """ * 8   ):
            _lowerCAmelCase					     =		lines[line_index]
            _lowerCAmelCase					     =		_re_single_line_import.search(lowercase_   )
            if single_line_import_search is not None:
               objects.extend(single_line_import_search.groups()[0].split(""", """   )   )
            elif line.startswith(""" """ * 12   ):
               objects.append(line[12:-2]   )
            line_index += 1

         _lowerCAmelCase					     =		objects
      else:
         line_index += 1

   return backend_specific_objects






def 	__UpperCAmelCase					(       snake_case_  :  str   ,	snake_case_  :  List[Any]   )							->	List[Any]:






   """simple docstring"""


   if name.isupper():
      return DUMMY_CONSTANT.format(lowercase_   )
   elif name.islower():
      return DUMMY_FUNCTION.format(lowercase_   ,	lowercase_   )
   else:
      return DUMMY_CLASS.format(lowercase_   ,	lowercase_   )






def 	__UpperCAmelCase					(       snake_case_  :  int=None   )							->	Tuple:






   """simple docstring"""


   if backend_specific_objects is None:
      _lowerCAmelCase					     =		read_init()
   # For special correspondence backend to module name as used in the function requires_modulename
   _lowerCAmelCase					     =		{}

   for backend, objects in backend_specific_objects.items():
      _lowerCAmelCase					     =		"""[""" + """, """.join(F"""\"{b}\"""" for b in backend.split("""_and_"""   )   ) + """]"""
      _lowerCAmelCase					     =		"""# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
      dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
      dummy_file += "\n".join([create_dummy_object(lowercase_   ,	lowercase_   ) for o in objects]   )
      _lowerCAmelCase					     =		dummy_file

   return dummy_files






def 	__UpperCAmelCase					(       snake_case_  :  Dict=False   )							->	Any:






   """simple docstring"""


   _lowerCAmelCase					     =		create_dummy_files()
   # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
   _lowerCAmelCase					     =		{"""torch""": """pt"""}

   # Locate actual dummy modules and read their content.
   _lowerCAmelCase					     =		os.path.join(lowercase_   ,	"""utils"""   )
   _lowerCAmelCase					     =		{
       backend: os.path.join(lowercase_   ,	F"""dummy_{short_names.get(lowercase_   ,	lowercase_   )}_objects.py"""   )
       for backend in dummy_files.keys()
   }

   _lowerCAmelCase					     =		{}
   for backend, file_path in dummy_file_paths.items():
      if os.path.isfile(lowercase_   ):
         with open(lowercase_   ,	"""r"""   ,	encoding="""utf-8"""   ,	newline="""\n"""   ) as f:
            _lowerCAmelCase					     =		f.read()
      else:
         _lowerCAmelCase					     =		""""""

   for backend in dummy_files.keys():
      if dummy_files[backend] != actual_dummies[backend]:
         if overwrite:
            print(
                F"""Updating transformers.utils.dummy_{short_names.get(lowercase_   ,	lowercase_   )}_objects.py as the main """
                """__init__ has new objects."""   )
            with open(dummy_file_paths[backend]   ,	"""w"""   ,	encoding="""utf-8"""   ,	newline="""\n"""   ) as f:
               f.write(dummy_files[backend]   )
         else:
            raise ValueError(
                """The main __init__ has objects that are not present in """
                F"""transformers.utils.dummy_{short_names.get(lowercase_   ,	lowercase_   )}_objects.py. Run `make fix-copies` """
                """to fix this."""   )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE				:     Any          =	argparse.ArgumentParser()
  parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
  SCREAMING_SNAKE_CASE				:     Dict          =	parser.parse_args()

  check_dummies(args.fix_and_overwrite)




"""simple docstring"""





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE				:     str          =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:     List[str]          =	{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''mask_emb''': '''masked_spec_embed''',
}






def 	__UpperCAmelCase					(       snake_case_  :  List[str]   ,	snake_case_  :  Dict   ,	snake_case_  :  Any   ,	snake_case_  :  str   ,	snake_case_  :  Tuple   )							->	str:






   """simple docstring"""


   for attribute in key.split("""."""   ):
      _lowerCAmelCase					     =		getattr(snake_case_   ,	snake_case_   )

   if weight_type is not None:
      _lowerCAmelCase					     =		getattr(snake_case_   ,	snake_case_   ).shape
   else:
      _lowerCAmelCase					     =		hf_pointer.shape

   assert hf_shape == value.shape, (
       F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
       F""" {value.shape} for {full_name}"""
   )

   if weight_type == "weight":
      _lowerCAmelCase					     =		value
   elif weight_type == "weight_g":
      _lowerCAmelCase					     =		value
   elif weight_type == "weight_v":
      _lowerCAmelCase					     =		value
   elif weight_type == "bias":
      _lowerCAmelCase					     =		value
   else:
      _lowerCAmelCase					     =		value

   logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""   )






def 	__UpperCAmelCase					(       snake_case_  :  str   ,	snake_case_  :  Any   )							->	List[Any]:






   """simple docstring"""


   _lowerCAmelCase					     =		[]
   _lowerCAmelCase					     =		fairseq_model.state_dict()

   _lowerCAmelCase					     =		hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      _lowerCAmelCase					     =		False
      if "conv_layers" in name:
         load_conv_layer(
             snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   ,	hf_model.config.feat_extract_norm == """group"""   ,	)
         _lowerCAmelCase					     =		True
      else:
         for key, mapped_key in MAPPING.items():
            _lowerCAmelCase					     =		mapped_key

            if key in name:
               _lowerCAmelCase					     =		True
               if "*" in mapped_key:
                  _lowerCAmelCase					     =		name.split(snake_case_   )[0].split("""."""   )[-2]
                  _lowerCAmelCase					     =		mapped_key.replace("""*"""   ,	snake_case_   )
               if "weight_g" in name:
                  _lowerCAmelCase					     =		"""weight_g"""
               elif "weight_v" in name:
                  _lowerCAmelCase					     =		"""weight_v"""
               elif "weight" in name:
                  _lowerCAmelCase					     =		"""weight"""
               elif "bias" in name:
                  _lowerCAmelCase					     =		"""bias"""
               else:
                  _lowerCAmelCase					     =		None
               set_recursively(snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   )
            continue
      if not is_used:
         unused_weights.append(snake_case_   )

   logger.warning(F"""Unused weights: {unused_weights}"""   )






def 	__UpperCAmelCase					(       snake_case_  :  Any   ,	snake_case_  :  List[Any]   ,	snake_case_  :  Dict   ,	snake_case_  :  Union[str, Any]   ,	snake_case_  :  Optional[Any]   )							->	List[Any]:






   """simple docstring"""


   _lowerCAmelCase					     =		full_name.split("""conv_layers."""   )[-1]
   _lowerCAmelCase					     =		name.split("""."""   )
   _lowerCAmelCase					     =		int(items[0]   )
   _lowerCAmelCase					     =		int(items[1]   )

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
             " found."
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
   else:
      unused_weights.append(snake_case_   )






def 	__UpperCAmelCase					(       snake_case_  :  Optional[int]   )							->	Any:






   """simple docstring"""


   _lowerCAmelCase					     =		HubertConfig()
   _lowerCAmelCase					     =		model.config

   _lowerCAmelCase					     =		fs_config.activation_dropout
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		fs_config.attention_dropout
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		eval(fs_config.extractor_conv_feature_layers   )
   _lowerCAmelCase					     =		[x[0] for x in conv_layers]
   _lowerCAmelCase					     =		[x[1] for x in conv_layers]
   _lowerCAmelCase					     =		[x[2] for x in conv_layers]
   _lowerCAmelCase					     =		"""gelu"""
   _lowerCAmelCase					     =		"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		fs_config.activation_fn
   _lowerCAmelCase					     =		fs_config.dropout
   _lowerCAmelCase					     =		fs_config.encoder_embed_dim
   _lowerCAmelCase					     =		0.0_2
   _lowerCAmelCase					     =		fs_config.encoder_ffn_embed_dim
   _lowerCAmelCase					     =		1e-5
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		fs_config.encoder_attention_heads
   _lowerCAmelCase					     =		fs_config.conv_pos_groups
   _lowerCAmelCase					     =		fs_config.conv_pos
   _lowerCAmelCase					     =		len(snake_case_   )
   _lowerCAmelCase					     =		fs_config.encoder_layers

   return config






@torch.no_grad()
def 	__UpperCAmelCase					(       snake_case_  :  Optional[Any]   ,	snake_case_  :  Union[str, Any]=None   )							->	List[str]:






   """simple docstring"""


   _lowerCAmelCase					     =		distilhubert().model.model

   if config_path is not None:
      _lowerCAmelCase					     =		HubertConfig.from_pretrained(snake_case_   )
   else:
      _lowerCAmelCase					     =		convert_config(snake_case_   )
   _lowerCAmelCase					     =		model.eval()

   _lowerCAmelCase					     =		WavaVecaFeatureExtractor(
       feature_size=1   ,	sampling_rate=16000   ,	padding_value=0   ,	do_normalize=snake_case_   ,	return_attention_mask=snake_case_   ,	)
   _lowerCAmelCase					     =		HubertModel(snake_case_   )

   recursively_load_weights(snake_case_   ,	snake_case_   )

   feature_extractor.save_pretrained(snake_case_   )
   hf_model.save_pretrained(snake_case_   )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE				:     List[str]          =	argparse.ArgumentParser()
  parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
  SCREAMING_SNAKE_CASE				:     Optional[int]          =	parser.parse_args()
  convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)