'''simple docstring'''

import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
lowerCAmelCase_ : Optional[Any]						  =      logging.get_logger(__name__)

lowerCAmelCase_ : Tuple						  =      {
    "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.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
    "self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
    "self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
    "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",
    "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": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
lowerCAmelCase_ : List[str]						  =      [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]


def __a (	__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						Optional[Any]				)	->							Optional[int]:
      '''simple docstring'''


      for attribute in key.split("."				):
            lowercase_     =      getattr(__lowerCamelCase ,		__lowerCamelCase				)

      if weight_type is not None:
            lowercase_     =      getattr(__lowerCamelCase ,		__lowerCamelCase				).shape
      else:
            lowercase_     =      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_     =      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 (	__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						Dict				)	->							Tuple:
      '''simple docstring'''


      lowercase_     =      []
      lowercase_     =      fairseq_model.state_dict()

      lowercase_     =      hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            lowercase_     =      False
            if "conv_layers" in name:
                  load_conv_layer(
                      __lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase ,		hf_model.config.feat_extract_norm == "group" ,		)
                  lowercase_     =      True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split("w2v_model."				)[-1] == name.split("."				)[0]:
                              lowercase_     =      True
                              if "*" in mapped_key:
                                    lowercase_     =      name.split(__lowerCamelCase				)[0].split("."				)[-2]
                                    lowercase_     =      mapped_key.replace("*" ,		__lowerCamelCase				)
                              if "weight_g" in name:
                                    lowercase_     =      "weight_g"
                              elif "weight_v" in name:
                                    lowercase_     =      "weight_v"
                              elif "bias" in name and "relative_attention_bias" not in name:
                                    lowercase_     =      "bias"
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    lowercase_     =      "weight"
                              else:
                                    lowercase_     =      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 __a (	__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						Optional[int]				)	->							Optional[Any]:
      '''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:
                  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_     =      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_     =      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_     =      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_     =      value
                  logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
      else:
            unused_weights.append(__lowerCamelCase				)


@torch.no_grad()
def __a (	__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						Optional[int]=None				)	->							str:
      '''simple docstring'''


      lowercase_     =      torch.load(__lowerCamelCase				)
      lowercase_     =      WavLMConfigOrig(checkpoint["cfg"]				)
      lowercase_     =      WavLMOrig(__lowerCamelCase				)
      model.load_state_dict(checkpoint["model"]				)
      model.eval()

      if config_path is not None:
            lowercase_     =      WavLMConfig.from_pretrained(__lowerCamelCase				)
      else:
            lowercase_     =      WavLMConfig()

      lowercase_     =      WavLMModel(__lowerCamelCase				)

      recursively_load_weights(__lowerCamelCase ,		__lowerCamelCase				)

      hf_wavlm.save_pretrained(__lowerCamelCase				)


if __name__ == "__main__":
       lowerCAmelCase_ : Optional[Any]						  =      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("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       lowerCAmelCase_ : Optional[Any]						  =      parser.parse_args()
       convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)







'''simple docstring'''

def __a (	__lowerCamelCase			:						list[int] ,		__lowerCamelCase			:						int				)	->							bool:
      '''simple docstring'''


      lowercase_     =      len(__lowerCamelCase				)
      lowercase_     =      [[False] * (required_sum + 1) for _ in range(arr_len + 1				)]

      # for each arr value, a sum of zero(0) can be formed by not taking any element
      # hence True/1
      for i in range(arr_len + 1				):
            lowercase_     =      True

      # sum is not zero and set is empty then false
      for i in range(1 ,		required_sum + 1				):
            lowercase_     =      False

      for i in range(1 ,		arr_len + 1				):
            for j in range(1 ,		required_sum + 1				):
                  if arr[i - 1] > j:
                        lowercase_     =      subset[i - 1][j]
                  if arr[i - 1] <= j:
                        lowercase_     =      subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

      return subset[arr_len][required_sum]


if __name__ == "__main__":
       import doctest

       doctest.testmod()






