



'''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__:   str								=			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__:   Dict								=			[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]


def  snake_case_     (					_lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Dict  )      ->   Union[str, Any]:
							for attribute in key.split('''.'''  ):
														UpperCAmelCase :       Optional[Any] 							=		getattr(__lowerCamelCase   ,      __lowerCamelCase  )

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

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


def  snake_case_     (					_lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						Optional[Any]  )      ->   Union[str, Any]:
							UpperCAmelCase :       List[str] 							=		[]
							UpperCAmelCase :       List[str] 							=		fairseq_model.state_dict()

							UpperCAmelCase :       Any 							=		hf_model.feature_extractor

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

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

							UpperCAmelCase :       str 							=		WavLMModel(__lowerCamelCase  )

							recursively_load_weights(__lowerCamelCase   ,      __lowerCamelCase  )

							hf_wavlm.save_pretrained(__lowerCamelCase  )


if __name__ == "__main__":
						UpperCamelCase__:   Dict								=			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)










"""simple docstring"""






class        __SCREAMING_SNAKE_CASE	:


					'''simple docstring'''





					def __init__(       self							:  Dict,  lowerCamelCase							:  list     )->					None:
								lowerCamelCase__		:		Tuple			=set_counts
								lowerCamelCase__		:		Dict			=max(lowerCamelCase     )
								lowerCamelCase__		:		Union[str, Any]			=len(lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=[1] * num_sets
								lowerCamelCase__		:		str			=list(range(lowerCamelCase     )     )



					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  int,  lowerCamelCase							:  int     )->					bool:
								lowerCamelCase__		:		List[Any]			=self.get_parent(lowerCamelCase     )
								lowerCamelCase__		:		str			=self.get_parent(lowerCamelCase     )

								if src_parent == dst_parent:
											return False

								if self.ranks[dst_parent] >= self.ranks[src_parent]:
											self.set_counts[dst_parent] += self.set_counts[src_parent]
											lowerCamelCase__		:		Tuple			=0
											lowerCamelCase__		:		int			=dst_parent
											if self.ranks[dst_parent] == self.ranks[src_parent]:
														self.ranks[dst_parent] += 1
											lowerCamelCase__		:		str			=self.set_counts[dst_parent]
								else:
											self.set_counts[src_parent] += self.set_counts[dst_parent]
											lowerCamelCase__		:		List[Any]			=0
											lowerCamelCase__		:		List[str]			=src_parent
											lowerCamelCase__		:		Dict			=self.set_counts[src_parent]

								lowerCamelCase__		:		str			=max(self.max_set,  lowerCamelCase     )
								return True




					def 		snake_case       (       self							:  Union[str, Any],  lowerCamelCase							:  int     )->					int:
								if self.parents[disj_set] == disj_set:
											return disj_set
								lowerCamelCase__		:		Tuple			=self.get_parent(self.parents[disj_set]     )
								return self.parents[disj_set]




