

'''simple docstring'''






from importlib import import_module

from .logging import get_logger


lowerCAmelCase__											=	get_logger(__name__)






class 			lowercase_	:




  """simple docstring"""




  def __init__(      self   :     Union[str, Any]					,lowercase__   :     Dict					,lowercase__   :     Dict=None			):
   __lowercase							  = attrs or []
   if module is not None:
    for key in module.__dict__:
     if key in attrs or not key.startswith('''__'''			):
      setattr(self					,lowercase__					,getattr(lowercase__					,lowercase__			)			)
   __lowercase							  = module._original_module if isinstance(lowercase__					,_PatchedModuleObj			) else module






class 			lowercase_	:




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  str							=			[]






  def __init__(      self   :     Optional[int]					,lowercase__   :     Optional[int]					,lowercase__   :     str					,lowercase__   :     Dict					,lowercase__   :     Optional[int]=None			):
   __lowercase							  = obj
   __lowercase							  = target
   __lowercase							  = new
   __lowercase							  = target.split('''.'''			)[0]
   __lowercase							  = {}
   __lowercase							  = attrs or []






  def __enter__(      self   :     Tuple			):
   *__lowercase  ,	__lowercase							  = self.target.split('''.'''			)

   # Patch modules:
   # it's used to patch attributes of submodules like "os.path.join";
   # in this case we need to patch "os" and "os.path"

   for i in range(len(lowercase__			)			):
    try:
     __lowercase							  = import_module('''.'''.join(submodules[: i + 1]			)			)
    except ModuleNotFoundError:
     continue
    # We iterate over all the globals in self.obj in case we find "os" or "os.path"
    for attr in self.obj.__dir__():
     __lowercase							  = getattr(self.obj					,lowercase__			)
     # We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
     # This allows to patch renamed modules like "from os import path as ospath".
     if obj_attr is submodule or (
         (isinstance(lowercase__					,_PatchedModuleObj			) and obj_attr._original_module is submodule)
     ):
      __lowercase							  = obj_attr
      # patch at top level
      setattr(self.obj					,lowercase__					,_PatchedModuleObj(lowercase__					,attrs=self.attrs			)			)
      __lowercase							  = getattr(self.obj					,lowercase__			)
      # construct lower levels patches
      for key in submodules[i + 1 :]:
       setattr(lowercase__					,lowercase__					,_PatchedModuleObj(getattr(lowercase__					,lowercase__					,lowercase__			)					,attrs=self.attrs			)			)
       __lowercase							  = getattr(lowercase__					,lowercase__			)
      # finally set the target attribute
      setattr(lowercase__					,lowercase__					,self.new			)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

   if submodules:  # if it's an attribute of a submodule like "os.path.join"
    try:
     __lowercase							  = getattr(import_module('''.'''.join(lowercase__			)			)					,lowercase__			)
    except (AttributeError, ModuleNotFoundError):
     return
    # We iterate over all the globals in self.obj in case we find "os.path.join"
    for attr in self.obj.__dir__():
     # We don't check for the name of the global, but rather if its value *is* "os.path.join".
     # This allows to patch renamed attributes like "from os.path import join as pjoin".
     if getattr(self.obj					,lowercase__			) is attr_value:
      __lowercase							  = getattr(self.obj					,lowercase__			)
      setattr(self.obj					,lowercase__					,self.new			)
   elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
    __lowercase							  = globals()['''__builtins__'''][target_attr]
    setattr(self.obj					,lowercase__					,self.new			)
   else:
    raise RuntimeError(F"Tried to patch attribute {target_attr} instead of a submodule."			)






  def __exit__(      self   :     Optional[Any]					,*lowercase__   :     int			):
   for attr in list(self.original			):
    setattr(self.obj					,lowercase__					,self.original.pop(lowercase__			)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   self.__enter__()
   self._active_patches.append(self			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   try:
    self._active_patches.remove(self			)
   except ValueError:
    # If the patch hasn't been started this will fail
    return None

   return self.__exit__()




'''simple docstring'''






import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
lowerCAmelCase__											=	logging.get_logger(__name__)

lowerCAmelCase__											=	{
    '''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''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
lowerCAmelCase__											=	[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]


def 		_A     (    A__     ,				A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 for attribute in key.split('''.'''      ):
  __lowercase							  = getattr(A__     ,				A__      )

 if weight_type is not None:
  __lowercase							  = getattr(A__     ,				A__      ).shape
 else:
  __lowercase							  = hf_pointer.shape

 if hf_shape != value.shape:
  raise ValueError(
      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":
  __lowercase							  = value
 elif weight_type == "weight_g":
  __lowercase							  = value
 elif weight_type == "weight_v":
  __lowercase							  = value
 elif weight_type == "bias":
  __lowercase							  = value
 else:
  __lowercase							  = value

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


def 		_A     (    A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = []
 __lowercase							  = fairseq_model.state_dict()

 __lowercase							  = hf_model.unispeech_sat.feature_extractor

 for name, value in fairseq_dict.items():
  __lowercase							  = False
  if "conv_layers" in name:
   load_conv_layer(
       A__     ,				A__     ,				A__     ,				A__     ,				hf_model.config.feat_extract_norm == '''group'''     ,				)
   __lowercase							  = True
  else:
   for key, mapped_key in MAPPING.items():
    __lowercase							  = '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
    if key in name or key.split('''w2v_model.'''      )[-1] == name.split('''.'''      )[0]:
     if "layer_norm_for_extract" in name and (".".join(name.split('''.'''      )[:-1]      ) != key):
      # special case since naming is very similar
      continue
     __lowercase							  = True
     if "*" in mapped_key:
      __lowercase							  = name.split(A__      )[0].split('''.'''      )[-2]
      __lowercase							  = mapped_key.replace('''*'''     ,				A__      )
     if "weight_g" in name:
      __lowercase							  = '''weight_g'''
     elif "weight_v" in name:
      __lowercase							  = '''weight_v'''
     elif "bias" in name:
      __lowercase							  = '''bias'''
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      __lowercase							  = '''weight'''
     else:
      __lowercase							  = None
     set_recursively(A__     ,				A__     ,				A__     ,				A__     ,				A__      )
    continue
  if not is_used:
   unused_weights.append(A__      )

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


def 		_A     (    A__     ,				A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = full_name.split('''conv_layers.'''      )[-1]
 __lowercase							  = name.split('''.'''      )
 __lowercase							  = int(items[0]      )
 __lowercase							  = int(items[1]      )

 if type_id == 0:
  if "bias" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
    raise ValueError(
        F"{full_name} has size {value.shape}, but"
        F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."      )
   __lowercase							  = value
   logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."      )
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
    raise ValueError(
        F"{full_name} has size {value.shape}, but"
        F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."      )
   __lowercase							  = 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:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
    raise ValueError(
        F"{full_name} has size {value.shape}, but"
        F" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."      )
   __lowercase							  = value
   logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
    raise ValueError(
        F"{full_name} has size {value.shape}, but"
        F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."      )
   __lowercase							  = value
   logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
 else:
  unused_weights.append(A__      )


@torch.no_grad()
def 		_A     (    A__     ,				A__     ,				A__=None     ,				A__=None     ,				A__=True      ):






 """simple docstring"""



 if config_path is not None:
  __lowercase							  = UniSpeechSatConfig.from_pretrained(A__      )
 else:
  __lowercase							  = UniSpeechSatConfig()

 __lowercase							  = ''''''

 if is_finetuned:
  __lowercase							  = UniSpeechSatForCTC(A__      )
 else:
  __lowercase							  = UniSpeechSatForPreTraining(A__      )

 __lowercase  ,	__lowercase  ,	__lowercase							  = fairseq.checkpoint_utils.load_model_ensemble_and_task(
     [checkpoint_path]     ,				arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''      )[:-1]      )}      )
 __lowercase							  = model[0].eval()

 recursively_load_weights(A__     ,				A__      )

 hf_wavavec.save_pretrained(A__      )


if __name__ == "__main__":
 lowerCAmelCase__											=	argparse.ArgumentParser()
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
 parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
 parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
 parser.add_argument(
     '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
 )
 lowerCAmelCase__											=	parser.parse_args()
 convert_unispeech_sat_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )

