






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      lowercase__      (     __snake_case    :							Optional[Any]     ,       __snake_case    :							str     ,       __snake_case    :							Optional[Any]     ,       __snake_case    :							Union[str, Any]     ,       __snake_case    :							Optional[Any]  ):


					'''simple docstring'''


					for attribute in key.split('.'  ):
										UpperCAmelCase_    :      List[Any]            =						getattr(__snake_case     ,       __snake_case  )

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

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




def      lowercase__      (     __snake_case    :							Optional[int]     ,       __snake_case    :							Any  ):


					'''simple docstring'''


					UpperCAmelCase_    :      List[Any]            =						[]
					UpperCAmelCase_    :      List[Any]            =						fairseq_model.state_dict()

					UpperCAmelCase_    :      int            =						hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										UpperCAmelCase_    :      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'     ,       )
															UpperCAmelCase_    :      int            =						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_    :      int            =						name.split(__snake_case  )[0].split('.'  )[-2]
																														UpperCAmelCase_    :      Dict            =						mapped_key.replace('*'     ,       __snake_case  )
																									if "weight_g" in name:
																														UpperCAmelCase_    :      Optional[Any]            =						'weight_g'
																									elif "weight_v" in name:
																														UpperCAmelCase_    :      List[Any]            =						'weight_v'
																									elif "bias" in name and "relative_attention_bias" not in name:
																														UpperCAmelCase_    :      int            =						'bias'
																									elif "weight" in name:
																														# TODO: don't match quantizer.weight_proj
																														UpperCAmelCase_    :      Optional[int]            =						'weight'
																									else:
																														UpperCAmelCase_    :      Union[str, Any]            =						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      lowercase__      (     __snake_case    :							Any     ,       __snake_case    :							Tuple     ,       __snake_case    :							List[str]     ,       __snake_case    :							Union[str, Any]     ,       __snake_case    :							List[Any]  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Optional[Any]            =						full_name.split('conv_layers.'  )[-1]
					UpperCAmelCase_    :      List[Any]            =						name.split('.'  )
					UpperCAmelCase_    :      Dict            =						int(items[0]  )
					UpperCAmelCase_    :      Optional[int]            =						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_    :      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."
															)
															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_    :      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_    :      List[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      lowercase__      (     __snake_case    :							Union[str, Any]     ,       __snake_case    :							Optional[int]     ,       __snake_case    :							Tuple=None  ):


					'''simple docstring'''


					UpperCAmelCase_    :      List[str]            =						torch.load(__snake_case  )
					UpperCAmelCase_    :      List[Any]            =						WavLMConfigOrig(checkpoint['cfg']  )
					UpperCAmelCase_    :      Union[str, Any]            =						WavLMOrig(__snake_case  )
					model.load_state_dict(checkpoint['model']  )
					model.eval()

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

					UpperCAmelCase_    :      Tuple            =						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"""




from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : List[str]	    =   CustomTokenizer
      pass