"""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()
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.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__  		=						[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  for attribute in key.split("""."""		):
    _UpperCAmelCase						 =      getattr(snake_case__   ,snake_case__		)

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

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






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      []
  _UpperCAmelCase						 =      fairseq_model.state_dict()

  _UpperCAmelCase						 =      hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    _UpperCAmelCase						 =      False
    if "conv_layers" in name:
      load_conv_layer(
          snake_case__   ,snake_case__   ,snake_case__   ,snake_case__   ,hf_model.config.feat_extract_norm == """group"""   ,)
      _UpperCAmelCase						 =      True
    else:
      for key, mapped_key in MAPPING.items():
        if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
          _UpperCAmelCase						 =      True
          if "*" in mapped_key:
            _UpperCAmelCase						 =      name.split(snake_case__		)[0].split("""."""		)[-2]
            _UpperCAmelCase						 =      mapped_key.replace("""*"""   ,snake_case__		)
          if "weight_g" in name:
            _UpperCAmelCase						 =      """weight_g"""
          elif "weight_v" in name:
            _UpperCAmelCase						 =      """weight_v"""
          elif "bias" in name and "relative_attention_bias" not in name:
            _UpperCAmelCase						 =      """bias"""
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            _UpperCAmelCase						 =      """weight"""
          else:
            _UpperCAmelCase						 =      None

          set_recursively(snake_case__   ,snake_case__   ,snake_case__   ,snake_case__   ,snake_case__		)
        continue
    if not is_used:
      unused_weights.append(snake_case__		)

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






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      full_name.split("""conv_layers."""		)[-1]
  _UpperCAmelCase						 =      name.split("""."""		)
  _UpperCAmelCase						 =      int(items[0]		)
  _UpperCAmelCase						 =      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.'''
      )
      _UpperCAmelCase						 =      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.'''
      )
      _UpperCAmelCase						 =      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."
      )
      _UpperCAmelCase						 =      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.'''
      )
      _UpperCAmelCase						 =      value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
  else:
    unused_weights.append(snake_case__		)






@torch.no_grad()
def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase=None		):







  """simple docstring"""
  # load the pre-trained checkpoints
  _UpperCAmelCase						 =      torch.load(snake_case__		)
  _UpperCAmelCase						 =      WavLMConfigOrig(checkpoint["""cfg"""]		)
  _UpperCAmelCase						 =      WavLMOrig(snake_case__		)
  model.load_state_dict(checkpoint["""model"""]		)
  model.eval()

  if config_path is not None:
    _UpperCAmelCase						 =      WavLMConfig.from_pretrained(snake_case__		)
  else:
    _UpperCAmelCase						 =      WavLMConfig()

  _UpperCAmelCase						 =      WavLMModel(snake_case__		)

  recursively_load_weights(snake_case__   ,snake_case__		)

  hf_wavlm.save_pretrained(snake_case__		)


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("""--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__  		=						parser.parse_args()
 convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

"""simple docstring"""


import unittest

from knapsack import knapsack as k


class        a				(		unittest.TestCase   ):


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase						 =      0
      _UpperCAmelCase						 =      [0]
      _UpperCAmelCase						 =      [0]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				0					)

      _UpperCAmelCase						 =      [60]
      _UpperCAmelCase						 =      [10]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				0					)


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      _UpperCAmelCase						 =      3
      _UpperCAmelCase						 =      [1, 2, 3]
      _UpperCAmelCase						 =      [3, 2, 1]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				5					)


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      50
      _UpperCAmelCase						 =      [60, 100, 120]
      _UpperCAmelCase						 =      [10, 20, 30]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				220					)


if __name__ == "__main__":
    unittest.main()
