




import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE     : List[Any]      =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     : Optional[int]      ={
    """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       lowerCAmelCase_      (	lowerCamelCase:							Dict  ,					lowerCamelCase:							List[Any]  ,					lowerCamelCase:							Tuple  ,					lowerCamelCase:							Optional[Any]  ,					lowerCamelCase:							List[Any]    )       ->      Tuple:


      '''simple docstring'''




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

      if weight_type is not None:
            lowerCamelCase__   	=					getattr(lowerCamelCase__  ,					lowerCamelCase__    ).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       lowerCAmelCase_      (	lowerCamelCase:							List[Any]  ,					lowerCamelCase:							Dict    )       ->      int:


      '''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(
                      lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__  ,					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(lowerCamelCase__    )[0].split('.'    )[-2]
                                    lowerCamelCase__   	=					mapped_key.replace('*'  ,					lowerCamelCase__    )
                              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(lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__    )
                        continue
            if not is_used:
                  unused_weights.append(lowerCamelCase__    )

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

def       lowerCAmelCase_      (	lowerCamelCase:							str  ,					lowerCamelCase:							Optional[int]  ,					lowerCamelCase:							Union[str, Any]  ,					lowerCamelCase:							int  ,					lowerCamelCase:							str    )       ->      Union[str, 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(lowerCamelCase__    )

def       lowerCAmelCase_      (	lowerCamelCase:							Any    )       ->      int:


      '''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__   	=					0.02
      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(lowerCamelCase__    )
      lowerCamelCase__   	=					fs_config.encoder_layers

      return config

@torch.no_grad()
def       lowerCAmelCase_      (	lowerCamelCase:							str  ,					lowerCamelCase:							List[Any]=None    )       ->      List[Any]:


      '''simple docstring'''




      lowerCamelCase__   	=					distilhubert().model.model

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

      lowerCamelCase__   	=					WavaVecaFeatureExtractor(
          feature_size=1  ,					sampling_rate=1_60_00  ,					padding_value=0  ,					do_normalize=lowerCamelCase__  ,					return_attention_mask=lowerCamelCase__  ,					)
      lowerCamelCase__   	=					HubertModel(lowerCamelCase__    )

      recursively_load_weights(lowerCamelCase__  ,					lowerCamelCase__    )

      feature_extractor.save_pretrained(lowerCamelCase__    )
      hf_model.save_pretrained(lowerCamelCase__    )


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE     : Union[str, Any]      =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     : Dict      =parser.parse_args()
      convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)
import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_SCREAMING_SNAKE_CASE     =     ["""UtteranceLevel"""]
@torch.no_grad()
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->int:

  '''simple docstring'''




  a__  		=  torch.load(lowerCamelCase__			,					map_location="cpu"       )
  if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
    raise NotImplementedError(f"The supported s3prl models are {SUPPORTED_MODELS}"       )

  a__  		=  checkpoint["Downstream"]

  a__  		=  HubertConfig.from_pretrained(lowerCamelCase__       )
  a__  		=  HubertForSequenceClassification.from_pretrained(lowerCamelCase__			,					config=lowerCamelCase__       )
  a__  		=  WavaVecaFeatureExtractor.from_pretrained(
      lowerCamelCase__			,					return_attention_mask=lowerCamelCase__			,					do_normalize=lowerCamelCase__       )

  if hf_congfig.use_weighted_layer_sum:
    a__  		=  checkpoint["Featurizer"]["weights"]

  a__  		=  downstream_dict["projector.weight"]
  a__  		=  downstream_dict["projector.bias"]
  a__  		=  downstream_dict["model.post_net.linear.weight"]
  a__  		=  downstream_dict["model.post_net.linear.bias"]

  hf_feature_extractor.save_pretrained(lowerCamelCase__       )
  hf_model.save_pretrained(lowerCamelCase__       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE     =     argparse.ArgumentParser()
     parser.add_argument(
         """--base_model_name""", default=None, type=str, help="""Name of the huggingface pretrained base model."""
     )
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to the huggingface classifier config.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to the s3prl checkpoint.""")
     parser.add_argument("""--model_dump_path""", default=None, type=str, help="""Path to the final converted model.""")
     _SCREAMING_SNAKE_CASE     =     parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

