


'''simple docstring'''
import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowerCAmelCase__  =     logging.get_logger(__name__)

lowerCAmelCase__  =     {
    '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_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',
    'mask_emb': 'masked_spec_embed',
}



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       for attribute in key.split("""."""     ):
              snake_case__					:	Dict          =					getattr(UpperCAmelCase    ,       UpperCAmelCase     )

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

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



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	int          =					[]
       snake_case__					:	Union[str, Any]          =					fairseq_model.state_dict()

       snake_case__					:	Optional[Any]          =					hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              snake_case__					:	Optional[int]          =					False
              if "conv_layers" in name:
                     load_conv_layer(
                         UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       hf_model.config.feat_extract_norm == """group"""    ,       )
                     snake_case__					:	Optional[Any]          =					True
              else:
                     for key, mapped_key in MAPPING.items():
                            snake_case__					:	List[Any]          =					mapped_key

                            if key in name:
                                   snake_case__					:	Any          =					True
                                   if "*" in mapped_key:
                                          snake_case__					:	int          =					name.split(UpperCAmelCase     )[0].split("""."""     )[-2]
                                          snake_case__					:	str          =					mapped_key.replace("""*"""    ,       UpperCAmelCase     )
                                   if "weight_g" in name:
                                          snake_case__					:	List[str]          =					'weight_g'
                                   elif "weight_v" in name:
                                          snake_case__					:	List[str]          =					'weight_v'
                                   elif "weight" in name:
                                          snake_case__					:	int          =					'weight'
                                   elif "bias" in name:
                                          snake_case__					:	List[str]          =					'bias'
                                   else:
                                          snake_case__					:	Dict          =					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 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Any          =					full_name.split("""conv_layers."""     )[-1]
       snake_case__					:	Optional[Any]          =					name.split("""."""     )
       snake_case__					:	int          =					int(items[0]     )
       snake_case__					:	Tuple          =					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__					:	Tuple          =					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__					:	Optional[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."
                     )
                     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."""
                     )
                     snake_case__					:	List[Any]          =					value
                     logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
       else:
              unused_weights.append(UpperCAmelCase     )



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	List[Any]          =					HubertConfig()
       snake_case__					:	str          =					model.config

       snake_case__					:	Any          =					fs_config.activation_dropout
       snake_case__					:	Tuple          =					False
       snake_case__					:	List[str]          =					fs_config.attention_dropout
       snake_case__					:	List[Any]          =					False
       snake_case__					:	Optional[Any]          =					eval(fs_config.extractor_conv_feature_layers     )
       snake_case__					:	Dict          =					[x[0] for x in conv_layers]
       snake_case__					:	List[Any]          =					[x[1] for x in conv_layers]
       snake_case__					:	Optional[int]          =					[x[2] for x in conv_layers]
       snake_case__					:	str          =					'gelu'
       snake_case__					:	int          =					'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
       snake_case__					:	List[str]          =					False
       snake_case__					:	Dict          =					0.0
       snake_case__					:	Tuple          =					0.0
       snake_case__					:	Tuple          =					fs_config.activation_fn
       snake_case__					:	str          =					fs_config.dropout
       snake_case__					:	Optional[int]          =					fs_config.encoder_embed_dim
       snake_case__					:	int          =					0.0_2
       snake_case__					:	Dict          =					fs_config.encoder_ffn_embed_dim
       snake_case__					:	str          =					1E-5
       snake_case__					:	List[str]          =					0.0
       snake_case__					:	List[Any]          =					fs_config.encoder_attention_heads
       snake_case__					:	Any          =					fs_config.conv_pos_groups
       snake_case__					:	Any          =					fs_config.conv_pos
       snake_case__					:	Tuple          =					len(UpperCAmelCase     )
       snake_case__					:	Dict          =					fs_config.encoder_layers

       return config



@torch.no_grad()
def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase=None     ):






       """simple docstring"""





       snake_case__					:	List[str]          =					distilhubert().model.model

       if config_path is not None:
              snake_case__					:	Tuple          =					HubertConfig.from_pretrained(UpperCAmelCase     )
       else:
              snake_case__					:	Optional[int]          =					convert_config(UpperCAmelCase     )
       snake_case__					:	Union[str, Any]          =					model.eval()

       snake_case__					:	Any          =					WavaVecaFeatureExtractor(
           feature_size=1    ,       sampling_rate=1_6000    ,       padding_value=0    ,       do_normalize=UpperCAmelCase    ,       return_attention_mask=UpperCAmelCase    ,       )
       snake_case__					:	int          =					HubertModel(UpperCAmelCase     )

       recursively_load_weights(UpperCAmelCase    ,       UpperCAmelCase     )

       feature_extractor.save_pretrained(UpperCAmelCase     )
       hf_model.save_pretrained(UpperCAmelCase     )


if __name__ == "__main__":
     lowerCAmelCase__  =     argparse.ArgumentParser()
     parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
     lowerCAmelCase__  =     parser.parse_args()
     convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)








from typing import TYPE_CHECKING

from ...utils import _LazyModule


__lowerCAmelCase									=				{'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
 from .tokenization_phobert import PhobertTokenizer

else:
 import sys

 __lowerCAmelCase									=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


