import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

_SCREAMING_SNAKE_CASE     =     {
    """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 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->Tuple:

  '''simple docstring'''




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

  if weight_type is not None:
    a__  		=  getattr(lowerCamelCase__			,					lowerCamelCase__       ).shape
  else:
    a__  		=  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":
    a__  		=  value
  elif weight_type == "weight_g":
    a__  		=  value
  elif weight_type == "weight_v":
    a__  		=  value
  elif weight_type == "bias":
    a__  		=  value
  else:
    a__  		=  value

  logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."       )
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__       )					->int:

  '''simple docstring'''




  a__  		=  []
  a__  		=  fairseq_model.state_dict()

  a__  		=  hf_model.feature_extractor

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

        if key in name:
          a__  		=  True
          if "*" in mapped_key:
            a__  		=  name.split(lowerCamelCase__       )[0].split("."       )[-2]
            a__  		=  mapped_key.replace("*"			,					lowerCamelCase__       )
          if "weight_g" in name:
            a__  		=  "weight_g"
          elif "weight_v" in name:
            a__  		=  "weight_v"
          elif "weight" in name:
            a__  		=  "weight"
          elif "bias" in name:
            a__  		=  "bias"
          else:
            a__  		=  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 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->Union[str, Any]:

  '''simple docstring'''




  a__  		=  full_name.split("conv_layers."       )[-1]
  a__  		=  name.split("."       )
  a__  		=  int(items[0]       )
  a__  		=  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."
      )
      a__  		=  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."
      )
      a__  		=  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."
      )
      a__  		=  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."
      )
      a__  		=  value
      logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."       )
  else:
    unused_weights.append(lowerCamelCase__       )
def 					__lowercase						(    lowerCamelCase__       )					->int:

  '''simple docstring'''




  a__  		=  HubertConfig()
  a__  		=  model.config

  a__  		=  fs_config.activation_dropout
  a__  		=  False
  a__  		=  fs_config.attention_dropout
  a__  		=  False
  a__  		=  eval(fs_config.extractor_conv_feature_layers       )
  a__  		=  [x[0] for x in conv_layers]
  a__  		=  [x[1] for x in conv_layers]
  a__  		=  [x[2] for x in conv_layers]
  a__  		=  "gelu"
  a__  		=  "layer" if fs_config.extractor_mode == "layer_norm" else "group"
  a__  		=  False
  a__  		=  0.0
  a__  		=  0.0
  a__  		=  fs_config.activation_fn
  a__  		=  fs_config.dropout
  a__  		=  fs_config.encoder_embed_dim
  a__  		=  0.02
  a__  		=  fs_config.encoder_ffn_embed_dim
  a__  		=  1E-5
  a__  		=  0.0
  a__  		=  fs_config.encoder_attention_heads
  a__  		=  fs_config.conv_pos_groups
  a__  		=  fs_config.conv_pos
  a__  		=  len(lowerCamelCase__       )
  a__  		=  fs_config.encoder_layers

  return config
@torch.no_grad()
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__=None       )					->List[Any]:

  '''simple docstring'''




  a__  		=  distilhubert().model.model

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

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

  recursively_load_weights(lowerCamelCase__			,					lowerCamelCase__       )

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


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE     =     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     =     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)

