
'''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()
lowercase					:							int   =      logging.get_logger(__name__)

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







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A    ,       __A			) ->			Any:
	for attribute in key.split('.'			):
		_snake_case     		=	getattr(__A    ,       __A			)

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

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







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			Dict:
	_snake_case     		=	[]
	_snake_case     		=	fairseq_model.state_dict()

	_snake_case     		=	hf_model.feature_extractor

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

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

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







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A    ,       __A			) ->			Union[str, Any]:
	_snake_case     		=	full_name.split('conv_layers.'			)[-1]
	_snake_case     		=	name.split('.'			)
	_snake_case     		=	int(items[0]			)
	_snake_case     		=	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.'
			)
			_snake_case     		=	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.'
			)
			_snake_case     		=	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."
			)
			_snake_case     		=	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.'
			)
			_snake_case     		=	value
			logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'			)
	else:
		unused_weights.append(__A			)







@torch.no_grad()
def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A=None			) ->			Tuple:
	# load the pre-trained checkpoints
	_snake_case     		=	torch.load(__A			)
	_snake_case     		=	WavLMConfigOrig(checkpoint['cfg']			)
	_snake_case     		=	WavLMOrig(__A			)
	model.load_state_dict(checkpoint['model']			)
	model.eval()

	if config_path is not None:
		_snake_case     		=	WavLMConfig.from_pretrained(__A			)
	else:
		_snake_case     		=	WavLMConfig()

	_snake_case     		=	WavLMModel(__A			)

	recursively_load_weights(__A    ,       __A			)

	hf_wavlm.save_pretrained(__A			)


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



'''simple docstring'''







from abc import ABC, abstractmethod
from argparse import ArgumentParser


class 		__UpperCAmelCase   (      _lowerCamelCase		):


							@staticmethod
							@abstractmethod
							def 				lowerCamelCase       ( lowerCAmelCase_						):





								"""simple docstring"""






								raise NotImplementedError()




							@abstractmethod
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								raise NotImplementedError()

