





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       :     List[str]   =     logging.get_logger(__name__)

__lowerCamelCase       :     List[Any]   =     {
    """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       :     str   =     [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> Dict:
 for attribute in key.split("."     ):
  UpperCamelCase	:					Dict       	=   getattr(_lowerCAmelCase							,   _lowerCAmelCase     )

 if weight_type is not None:
  UpperCamelCase	:					Union[str, Any]       	=   getattr(_lowerCAmelCase							,   _lowerCAmelCase     ).shape
 else:
  UpperCamelCase	:					Tuple       	=   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	:					int       	=   value
 elif weight_type == "weight_g":
  UpperCamelCase	:					Optional[int]       	=   value
 elif weight_type == "weight_v":
  UpperCamelCase	:					str       	=   value
 elif weight_type == "bias":
  UpperCamelCase	:					Union[str, Any]       	=   value
 else:
  UpperCamelCase	:					List[str]       	=   value

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



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					Optional[int]       	=   []
 UpperCamelCase	:					Optional[int]       	=   fairseq_model.state_dict()

 UpperCamelCase	:					Optional[Any]       	=   hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  UpperCamelCase	:					Dict       	=   False
  if "conv_layers" in name:
   load_conv_layer(
       _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   hf_model.config.feat_extract_norm == "group"							,   )
   UpperCamelCase	:					Any       	=   True
  else:
   for key, mapped_key in MAPPING.items():
    if key in name or key.split("w2v_model."     )[-1] == name.split("."     )[0]:
     UpperCamelCase	:					Dict       	=   True
     if "*" in mapped_key:
      UpperCamelCase	:					Any       	=   name.split(_lowerCAmelCase     )[0].split("."     )[-2]
      UpperCamelCase	:					Optional[int]       	=   mapped_key.replace("*"							,   _lowerCAmelCase     )
     if "weight_g" in name:
      UpperCamelCase	:					List[str]       	=   "weight_g"
     elif "weight_v" in name:
      UpperCamelCase	:					str       	=   "weight_v"
     elif "bias" in name and "relative_attention_bias" not in name:
      UpperCamelCase	:					Optional[Any]       	=   "bias"
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      UpperCamelCase	:					int       	=   "weight"
     else:
      UpperCamelCase	:					str       	=   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							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> Tuple:
 UpperCamelCase	:					str       	=   full_name.split("conv_layers."     )[-1]
 UpperCamelCase	:					List[Any]       	=   name.split("."     )
 UpperCamelCase	:					Union[str, Any]       	=   int(items[0]     )
 UpperCamelCase	:					Tuple       	=   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	:					List[Any]       	=   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	:					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."
   )
   UpperCamelCase	:					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."""
   )
   UpperCamelCase	:					Optional[Any]       	=   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							,   _lowerCAmelCase							,   _lowerCAmelCase=None     )						-> str:
 # load the pre-trained checkpoints
 UpperCamelCase	:					Tuple       	=   torch.load(_lowerCAmelCase     )
 UpperCamelCase	:					List[Any]       	=   WavLMConfigOrig(checkpoint["cfg"]     )
 UpperCamelCase	:					Any       	=   WavLMOrig(_lowerCAmelCase     )
 model.load_state_dict(checkpoint["model"]     )
 model.eval()

 if config_path is not None:
  UpperCamelCase	:					Dict       	=   WavLMConfig.from_pretrained(_lowerCAmelCase     )
 else:
  UpperCamelCase	:					Tuple       	=   WavLMConfig()

 UpperCamelCase	:					Any       	=   WavLMModel(_lowerCAmelCase     )

 recursively_load_weights(_lowerCAmelCase							,   _lowerCAmelCase     )

 hf_wavlm.save_pretrained(_lowerCAmelCase     )


if __name__ == "__main__":
    __lowerCamelCase       :     Tuple   =     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       :     int   =     parser.parse_args()
    convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




UpperCAmelCase_      : Optional[int]  					=       [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_      : str  					=       [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_      : str  					=       {
    0: 'Sunday',
    1: 'Monday',
    2: 'Tuesday',
    3: 'Wednesday',
    4: 'Thursday',
    5: 'Friday',
    6: 'Saturday',
}







def        SCREAMING_SNAKE_CASE_						(			__A :			int  ,    __A :			int  ,    __A :			int				)	->		str:

       """simple docstring"""

       assert len(str(__A				)				) > 2, "year should be in YYYY format"
       assert 1 <= month <= 12, "month should be between 1 to 12"
       assert 1 <= day <= 31, "day should be between 1 to 31"

       # Doomsday algorithm:
       a_					:		List[str]							=  year // 1_00
       a_					:		Optional[int]							=  (5 * (century % 4) + 2) % 7
       a_					:		List[str]							=  year % 1_00
       a_					:		str							=  centurian % 12
       a_					:		List[str]							=  (
           (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
       ) % 7
       a_					:		Any							=  (
           DOOMSDAY_NOT_LEAP[month - 1]
           if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
           else DOOMSDAY_LEAP[month - 1]
       )
       a_					:		Any							=  (dooms_day + day - day_anchor) % 7
       return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
   import doctest

   doctest.testmod()


