
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_						:     int	       =							logging.get_logger(__name__)

UpperCamelCase_						:     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""",
}
UpperCamelCase_						:     Any	       =							[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def 		UpperCamelCase     (       _UpperCAmelCase  :   int ,			_UpperCAmelCase  :   List[str] ,			_UpperCAmelCase  :   Optional[Any] ,			_UpperCAmelCase  :   List[str] ,			_UpperCAmelCase  :   Dict  )   ->			int:






						'''simple docstring'''

						for attribute in key.split("."  ):
												_lowercase       :	str           =   getattr(_UpperCAmelCase ,			_UpperCAmelCase  )

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

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



def 		UpperCamelCase     (       _UpperCAmelCase  :   List[str] ,			_UpperCAmelCase  :   Optional[int]  )   ->			Tuple:






						'''simple docstring'''

						_lowercase       :	str           =   []
						_lowercase       :	int           =   fairseq_model.state_dict()

						_lowercase       :	Any           =   hf_model.feature_extractor

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






						'''simple docstring'''

						_lowercase       :	Dict           =   full_name.split("conv_layers."  )[-1]
						_lowercase       :	str           =   name.split("."  )
						_lowercase       :	Any           =   int(items[0]  )
						_lowercase       :	Union[str, 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."""
																		)
																		_lowercase       :	Union[str, 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."""
																		)
																		_lowercase       :	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."
																		)
																		_lowercase       :	Dict           =   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       :	int           =   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  :   Any ,			_UpperCAmelCase  :   Optional[int] ,			_UpperCAmelCase  :   List[str]=None  )   ->			Union[str, Any]:






						'''simple docstring'''

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

						if config_path is not None:
												_lowercase       :	Any           =   WavLMConfig.from_pretrained(_UpperCAmelCase  )
						else:
												_lowercase       :	List[Any]           =   WavLMConfig()

						_lowercase       :	Optional[int]           =   WavLMModel(_UpperCAmelCase  )

						recursively_load_weights(_UpperCAmelCase ,			_UpperCAmelCase  )

						hf_wavlm.save_pretrained(_UpperCAmelCase  )


if __name__ == "__main__":
							UpperCamelCase_						:     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""")
							UpperCamelCase_						:     Union[str, Any]	       =							parser.parse_args()
							convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_						:     Optional[int]	       =							logging.get_logger(__name__)




class   __lowercase						(						__snake_case       ):
	_A       =   "timm_backbone"






	def __init__(self				: Any      ,	snake_case				: List[Any]=None      ,	snake_case				: int=3      ,	snake_case				: Dict=True      ,	snake_case				: Union[str, Any]=True      ,	snake_case				: Any=None      ,	**snake_case				: int      ,	) ->			Optional[int]:
							super().__init__(**snake_case    )
							_lowercase       :	Dict           =   backbone
							_lowercase       :	Optional[Any]           =   num_channels
							_lowercase       :	Union[str, Any]           =   features_only
							_lowercase       :	Tuple           =   use_pretrained_backbone
							_lowercase       :	List[str]           =   True
							_lowercase       :	Tuple           =   out_indices if out_indices is not None else (-1,)




