





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     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     for attribute in key.split("."	):
          _SCREAMING_SNAKE_CASE       :	str						    =							getattr(__lowerCamelCase,       __lowerCamelCase	)

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

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






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							[]
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							fairseq_model.state_dict()

     _SCREAMING_SNAKE_CASE       :	Tuple						    =							hf_model.feature_extractor

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

     if config_path is not None:
          _SCREAMING_SNAKE_CASE       :	str						    =							WavLMConfig.from_pretrained(__lowerCamelCase	)
     else:
          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							WavLMConfig()

     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							WavLMModel(__lowerCamelCase	)

     recursively_load_weights(__lowerCamelCase,       __lowerCamelCase	)

     hf_wavlm.save_pretrained(__lowerCamelCase	)


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)






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer





@dataclass
class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    42





class 			lowerCAmelCase__( __lowercase  ,     __lowercase    ):






       '''simple docstring'''


       @register_to_config
       def __init__(      self    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = ("DownEncoderBlock2D",)    ,						__lowerCamelCase = ("UpDecoderBlock2D",)    ,						__lowerCamelCase = (6_4,)    ,						__lowerCamelCase = 1    ,						__lowerCamelCase = "silu"    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = 3_2    ,						__lowerCamelCase = 2_5_6    ,						__lowerCamelCase = 3_2    ,						__lowerCamelCase = None    ,						__lowerCamelCase = 0.1_8215    ,						__lowerCamelCase = "group"    ,						)			->     List[str]:
            super().__init__()

            # pass init params to Encoder
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							Encoder(
                in_channels=__lowerCamelCase    ,						out_channels=__lowerCamelCase    ,						down_block_types=__lowerCamelCase    ,						block_out_channels=__lowerCamelCase    ,						layers_per_block=__lowerCamelCase    ,						act_fn=__lowerCamelCase    ,						norm_num_groups=__lowerCamelCase    ,						double_z=__lowerCamelCase    ,						)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							vq_embed_dim if vq_embed_dim is not None else latent_channels

            _SCREAMING_SNAKE_CASE       :	str						    =							nn.Convad(__lowerCamelCase    ,						__lowerCamelCase    ,						1							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							VectorQuantizer(__lowerCamelCase    ,						__lowerCamelCase    ,						beta=0.25    ,						remap=__lowerCamelCase    ,						sane_index_shape=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	int						    =							nn.Convad(__lowerCamelCase    ,						__lowerCamelCase    ,						1							)

            # pass init params to Decoder
            _SCREAMING_SNAKE_CASE       :	Dict						    =							Decoder(
                in_channels=__lowerCamelCase    ,						out_channels=__lowerCamelCase    ,						up_block_types=__lowerCamelCase    ,						block_out_channels=__lowerCamelCase    ,						layers_per_block=__lowerCamelCase    ,						act_fn=__lowerCamelCase    ,						norm_num_groups=__lowerCamelCase    ,						norm_type=__lowerCamelCase    ,						)




       @apply_forward_hook
       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = True							)			->     VQEncoderOutput:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.encoder(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.quant_conv(__lowerCamelCase							)

            if not return_dict:
                 return (h,)

            return VQEncoderOutput(latents=__lowerCamelCase							)




       @apply_forward_hook
       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = False    ,						__lowerCamelCase = True							)			->     Union[DecoderOutput, torch.FloatTensor]:
            # also go through quantization layer
            if not force_not_quantize:
                 _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.quantize(__lowerCamelCase							)
            else:
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							h
            _SCREAMING_SNAKE_CASE       :	str						    =							self.post_quant_conv(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.decoder(__lowerCamelCase    ,						quant if self.config.norm_type == "spatial" else None							)

            if not return_dict:
                 return (dec,)

            return DecoderOutput(sample=__lowerCamelCase							)







       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = True							)			->     Union[DecoderOutput, torch.FloatTensor]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							sample
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.encode(__lowerCamelCase							).latents
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.decode(__lowerCamelCase							).sample

            if not return_dict:
                 return (dec,)

            return DecoderOutput(sample=__lowerCamelCase							)