


def   _UpperCAmelCase       (     UpperCAmelCase      :			str						,	UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			List[str]      ):
      """simple docstring"""




      __lowerCamelCase     :      Tuple       						=						(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
      # formula for sum of series
      return total





def   _UpperCAmelCase       (     ):
      """simple docstring"""




      print(sum_of_series(1						,	1						,	10      )      )


if __name__ == "__main__":
      import doctest

      doctest.testmod()







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()
__UpperCamelCase    :						Any     =							logging.get_logger(__name__)

__UpperCamelCase    :						str     =							{
    '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',
}
__UpperCamelCase    :						Optional[int]     =							[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Tuple      ):
      """simple docstring"""




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

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

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





def   _UpperCAmelCase       (     UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			List[str]      ):
      """simple docstring"""




      __lowerCamelCase     :      int       						=						[]
      __lowerCamelCase     :      int       						=						fairseq_model.state_dict()

      __lowerCamelCase     :      List[str]       						=						hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            __lowerCamelCase     :      str       						=						False
            if "conv_layers" in name:
                  load_conv_layer(
                      UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	hf_model.config.feat_extract_norm == """group"""						,	)
                  __lowerCamelCase     :      Optional[int]       						=						True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split("""w2v_model."""      )[-1] == name.split("""."""      )[0]:
                              __lowerCamelCase     :      int       						=						True
                              if "*" in mapped_key:
                                    __lowerCamelCase     :      Optional[int]       						=						name.split(UpperCAmelCase      )[0].split("""."""      )[-2]
                                    __lowerCamelCase     :      Optional[int]       						=						mapped_key.replace("""*"""						,	UpperCAmelCase      )
                              if "weight_g" in name:
                                    __lowerCamelCase     :      Optional[Any]       						=						"""weight_g"""
                              elif "weight_v" in name:
                                    __lowerCamelCase     :      Dict       						=						"""weight_v"""
                              elif "bias" in name and "relative_attention_bias" not in name:
                                    __lowerCamelCase     :      List[Any]       						=						"""bias"""
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    __lowerCamelCase     :      List[str]       						=						"""weight"""
                              else:
                                    __lowerCamelCase     :      List[str]       						=						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   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			Any						,	UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Optional[int]      ):
      """simple docstring"""




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





@torch.no_grad()
def   _UpperCAmelCase       (     UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Optional[int]=None      ):
      """simple docstring"""




      __lowerCamelCase     :      Any       						=						torch.load(UpperCAmelCase      )
      __lowerCamelCase     :      Optional[int]       						=						WavLMConfigOrig(checkpoint["""cfg"""]      )
      __lowerCamelCase     :      Any       						=						WavLMOrig(UpperCAmelCase      )
      model.load_state_dict(checkpoint["""model"""]      )
      model.eval()

      if config_path is not None:
            __lowerCamelCase     :      Union[str, Any]       						=						WavLMConfig.from_pretrained(UpperCAmelCase      )
      else:
            __lowerCamelCase     :      str       						=						WavLMConfig()

      __lowerCamelCase     :      Dict       						=						WavLMModel(UpperCAmelCase      )

      recursively_load_weights(UpperCAmelCase						,	UpperCAmelCase      )

      hf_wavlm.save_pretrained(UpperCAmelCase      )


if __name__ == "__main__":
    __UpperCamelCase    :						List[str]     =							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')
    __UpperCamelCase    :						Tuple     =							parser.parse_args()
    convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)



