



'''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        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )      ->      List[Any]:





  for attribute in key.split('.'       ):
    __lowerCamelCase       :       Dict           = getattr(__snake_case       ,__snake_case       )

  if weight_type is not None:
    __lowerCamelCase       :       Tuple           = getattr(__snake_case       ,__snake_case       ).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       :       Dict           = value
  elif weight_type == "weight_g":
    __lowerCamelCase       :       Any           = value
  elif weight_type == "weight_v":
    __lowerCamelCase       :       Optional[int]           = value
  elif weight_type == "bias":
    __lowerCamelCase       :       Optional[Any]           = value
  else:
    __lowerCamelCase       :       Optional[Any]           = value

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

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      List[Any]:
  __lowerCamelCase       :       int           = []
  __lowerCamelCase       :       Optional[int]           = fairseq_model.state_dict()

  __lowerCamelCase       :       Optional[Any]           = hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    __lowerCamelCase       :       List[Any]           = False
    if "conv_layers" in name:
      load_conv_layer(
          __snake_case       ,__snake_case       ,__snake_case       ,__snake_case       ,hf_model.config.feat_extract_norm == 'group'       ,)
      __lowerCamelCase       :       Optional[Any]           = 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       :       List[Any]           = name.split(__snake_case       )[0].split('.'       )[-2]
            __lowerCamelCase       :       Any           = mapped_key.replace('*'       ,__snake_case       )
          if "weight_g" in name:
            __lowerCamelCase       :       List[str]           = 'weight_g'
          elif "weight_v" in name:
            __lowerCamelCase       :       Optional[int]           = 'weight_v'
          elif "bias" in name and "relative_attention_bias" not in name:
            __lowerCamelCase       :       Dict           = 'bias'
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            __lowerCamelCase       :       Dict           = 'weight'
          else:
            __lowerCamelCase       :       str           = 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        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )      ->      List[Any]:
  __lowerCamelCase       :       Optional[int]           = full_name.split('conv_layers.'       )[-1]
  __lowerCamelCase       :       Union[str, Any]           = name.split('.'       )
  __lowerCamelCase       :       Optional[Any]           = int(items[0]       )
  __lowerCamelCase       :       Optional[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       :       Optional[int]           = 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       :       List[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       :       Tuple           = 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       :       Any           = 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        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase=None       )      ->      int:
  __lowerCamelCase       :       Dict           = torch.load(__snake_case       )
  __lowerCamelCase       :       Tuple           = WavLMConfigOrig(checkpoint['cfg']       )
  __lowerCamelCase       :       Any           = WavLMOrig(__snake_case       )
  model.load_state_dict(checkpoint['model']       )
  model.eval()

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

  __lowerCamelCase       :       Union[str, Any]           = 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"""



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]		,					__snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       ''
  for i in table:
    res += inp[i - 1]
  return res
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str    ):





  '''simple docstring'''





  return data[1:] + data[0]
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]		,					__snake_case			:  Optional[int]    ):





  '''simple docstring'''





  lowercase							    =       ''
  for i in range(len(__snake_case    )    ):
    if a[i] == b[i]:
      res += "0"
    else:
      res += "1"
  return res
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Dict		,					__snake_case			:  Optional[Any]    ):





  '''simple docstring'''





  lowercase							    =       int('0b' + data[0] + data[-1]		,					2    )
  lowercase							    =       int('0b' + data[1:3]		,					2    )
  return bin(s[row][col]    )[2:]
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]		,					__snake_case			:  Union[str, Any]		,					__snake_case			:  Dict		,					__snake_case			:  Union[str, Any]		,					__snake_case			:  Optional[int]    ):





  '''simple docstring'''





  lowercase							    =       message[:4]
  lowercase							    =       message[4:]
  lowercase							    =       apply_table(__snake_case		,					__snake_case    )
  lowercase							    =       xor(__snake_case		,					__snake_case    )
  lowercase							    =       apply_sbox(__snake_case		,					temp[:4]    )  # noqa: E741
  lowercase							    =       apply_sbox(__snake_case		,					temp[4:]    )
  lowercase							    =       '0' * (2 - len(__snake_case    )) + l  # noqa: E741
  lowercase							    =       '0' * (2 - len(__snake_case    )) + r
  lowercase							    =       apply_table(l + r		,					__snake_case    )
  lowercase							    =       xor(__snake_case		,					__snake_case    )
  return temp + right


if __name__ == "__main__":
    _UpperCamelCase		:  Dict						=		input('Enter 10 bit key: ')
    _UpperCamelCase		:  int						=		input('Enter 8 bit message: ')

    _UpperCamelCase		:  Optional[int]						=		[6, 3, 7, 4, 8, 5, 1_0, 9]
    _UpperCamelCase		:  Optional[Any]						=		[3, 5, 2, 7, 4, 1_0, 1, 9, 8, 6]
    _UpperCamelCase		:  str						=		[2, 4, 3, 1]
    _UpperCamelCase		:  List[Any]						=		[2, 6, 3, 1, 4, 8, 5, 7]
    _UpperCamelCase		:  List[str]						=		[4, 1, 3, 5, 7, 2, 8, 6]
    _UpperCamelCase		:  int						=		[4, 1, 2, 3, 2, 3, 4, 1]
    _UpperCamelCase		:  Optional[Any]						=		[[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
    _UpperCamelCase		:  Optional[int]						=		[[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

    # key generation
    _UpperCamelCase		:  str						=		apply_table(key, paa_table)
    _UpperCamelCase		:  Union[str, Any]						=		temp[:5]
    _UpperCamelCase		:  Dict						=		temp[5:]
    _UpperCamelCase		:  List[Any]						=		left_shift(left)
    _UpperCamelCase		:  str						=		left_shift(right)
    _UpperCamelCase		:  int						=		apply_table(left + right, pa_table)
    _UpperCamelCase		:  Optional[int]						=		left_shift(left)
    _UpperCamelCase		:  Any						=		left_shift(right)
    _UpperCamelCase		:  str						=		left_shift(left)
    _UpperCamelCase		:  Tuple						=		left_shift(right)
    _UpperCamelCase		:  Union[str, Any]						=		apply_table(left + right, pa_table)

    # encryption
    _UpperCamelCase		:  Tuple						=		apply_table(message, IP)
    _UpperCamelCase		:  Any						=		function(expansion, sa, sa, keya, temp)
    _UpperCamelCase		:  List[Any]						=		temp[4:] + temp[:4]
    _UpperCamelCase		:  List[str]						=		function(expansion, sa, sa, keya, temp)
    _UpperCamelCase		:  int						=		apply_table(temp, IP_inv)
    print('Cipher text is:', CT)

    # decryption
    _UpperCamelCase		:  List[Any]						=		apply_table(CT, IP)
    _UpperCamelCase		:  Optional[Any]						=		function(expansion, sa, sa, keya, temp)
    _UpperCamelCase		:  int						=		temp[4:] + temp[:4]
    _UpperCamelCase		:  str						=		function(expansion, sa, sa, keya, temp)
    _UpperCamelCase		:  List[Any]						=		apply_table(temp, IP_inv)
    print('Plain text after decypting is:', PT)


