






'''simple docstring'''
import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

UpperCamelCase__	=     {
    '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 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   for attribute in key.split("."							):
      lowercase_		:      Union[str, Any]          =       getattr(_UpperCamelCase  ,						_UpperCamelCase							)

   if weight_type is not None:
      lowercase_		:      Dict          =       getattr(_UpperCamelCase  ,						_UpperCamelCase							).shape
   else:
      lowercase_		:      List[Any]          =       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":
      lowercase_		:      Union[str, Any]          =       value
   elif weight_type == "weight_g":
      lowercase_		:      List[Any]          =       value
   elif weight_type == "weight_v":
      lowercase_		:      Optional[int]          =       value
   elif weight_type == "bias":
      lowercase_		:      Union[str, Any]          =       value
   else:
      lowercase_		:      Optional[Any]          =       value

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




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   lowercase_		:      Optional[int]          =       []
   lowercase_		:      Dict          =       fairseq_model.state_dict()

   lowercase_		:      Tuple          =       hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      lowercase_		:      Optional[int]          =       False
      if "conv_layers" in name:
         load_conv_layer(
             _UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						hf_model.config.feat_extract_norm == "group"  ,						)
         lowercase_		:      List[Any]          =       True
      else:
         for key, mapped_key in MAPPING.items():
            lowercase_		:      List[Any]          =       mapped_key

            if key in name:
               lowercase_		:      Union[str, Any]          =       True
               if "*" in mapped_key:
                  lowercase_		:      List[str]          =       name.split(_UpperCamelCase							)[0].split("."							)[-2]
                  lowercase_		:      Dict          =       mapped_key.replace("*"  ,						_UpperCamelCase							)
               if "weight_g" in name:
                  lowercase_		:      int          =       "weight_g"
               elif "weight_v" in name:
                  lowercase_		:      Any          =       "weight_v"
               elif "weight" in name:
                  lowercase_		:      List[str]          =       "weight"
               elif "bias" in name:
                  lowercase_		:      Optional[Any]          =       "bias"
               else:
                  lowercase_		:      Tuple          =       None
               set_recursively(_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase							)
            continue
      if not is_used:
         unused_weights.append(_UpperCamelCase							)

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




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   lowercase_		:      int          =       full_name.split("conv_layers."							)[-1]
   lowercase_		:      List[str]          =       name.split("."							)
   lowercase_		:      List[Any]          =       int(items[0]							)
   lowercase_		:      List[str]          =       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."""
         )
         lowercase_		:      int          =       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."""
         )
         lowercase_		:      Union[str, Any]          =       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."
         )
         lowercase_		:      Tuple          =       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."""
         )
         lowercase_		:      List[Any]          =       value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
   else:
      unused_weights.append(_UpperCamelCase							)




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




   lowercase_		:      Any          =       HubertConfig()
   lowercase_		:      Dict          =       model.config

   lowercase_		:      List[Any]          =       fs_config.activation_dropout
   lowercase_		:      Any          =       False
   lowercase_		:      Union[str, Any]          =       fs_config.attention_dropout
   lowercase_		:      str          =       False
   lowercase_		:      int          =       eval(fs_config.extractor_conv_feature_layers							)
   lowercase_		:      Union[str, Any]          =       [x[0] for x in conv_layers]
   lowercase_		:      Optional[int]          =       [x[1] for x in conv_layers]
   lowercase_		:      List[str]          =       [x[2] for x in conv_layers]
   lowercase_		:      Optional[Any]          =       "gelu"
   lowercase_		:      Optional[Any]          =       "layer" if fs_config.extractor_mode == "layer_norm" else "group"
   lowercase_		:      int          =       False
   lowercase_		:      Optional[Any]          =       0.0
   lowercase_		:      Optional[Any]          =       0.0
   lowercase_		:      Optional[Any]          =       fs_config.activation_fn
   lowercase_		:      Dict          =       fs_config.dropout
   lowercase_		:      List[Any]          =       fs_config.encoder_embed_dim
   lowercase_		:      Dict          =       0.02
   lowercase_		:      Optional[Any]          =       fs_config.encoder_ffn_embed_dim
   lowercase_		:      Optional[Any]          =       1e-5
   lowercase_		:      List[str]          =       0.0
   lowercase_		:      List[str]          =       fs_config.encoder_attention_heads
   lowercase_		:      List[str]          =       fs_config.conv_pos_groups
   lowercase_		:      str          =       fs_config.conv_pos
   lowercase_		:      Tuple          =       len(_UpperCamelCase							)
   lowercase_		:      str          =       fs_config.encoder_layers

   return config




@torch.no_grad()
def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase=None							):


   """simple docstring"""




   lowercase_		:      Union[str, Any]          =       distilhubert().model.model

   if config_path is not None:
      lowercase_		:      Optional[Any]          =       HubertConfig.from_pretrained(_UpperCamelCase							)
   else:
      lowercase_		:      Optional[Any]          =       convert_config(_UpperCamelCase							)
   lowercase_		:      List[str]          =       model.eval()

   lowercase_		:      Tuple          =       WavaVecaFeatureExtractor(
       feature_size=1  ,						sampling_rate=1_6000  ,						padding_value=0  ,						do_normalize=_UpperCamelCase  ,						return_attention_mask=_UpperCamelCase  ,						)
   lowercase_		:      Union[str, Any]          =       HubertModel(_UpperCamelCase							)

   recursively_load_weights(_UpperCamelCase  ,						_UpperCamelCase							)

   feature_extractor.save_pretrained(_UpperCamelCase							)
   hf_model.save_pretrained(_UpperCamelCase							)


if __name__ == "__main__":
 UpperCamelCase__	=     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')
 UpperCamelCase__	=     parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)












'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCamelCase__	=     {
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__	=     [
     'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'UniSpeechSatForAudioFrameClassification',
     'UniSpeechSatForCTC',
     'UniSpeechSatForPreTraining',
     'UniSpeechSatForSequenceClassification',
     'UniSpeechSatForXVector',
     'UniSpeechSatModel',
     'UniSpeechSatPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_unispeech_sat import (
      UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      UniSpeechSatForAudioFrameClassification,
      UniSpeechSatForCTC,
      UniSpeechSatForPreTraining,
      UniSpeechSatForSequenceClassification,
      UniSpeechSatForXVector,
      UniSpeechSatModel,
      UniSpeechSatPreTrainedModel,
  )

else:
 import sys

 UpperCamelCase__	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




