






import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__a :    Optional[int]								=				logging.get_logger(__name__)

__a :    Optional[int]								=				{
    "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      _SCREAMING_SNAKE_CASE  (	__lowercase				:     str     ,			__lowercase				:     Optional[Any]     ,			__lowercase				:     Dict     ,			__lowercase				:     int     ,			__lowercase				:     Dict      )  -> Optional[int]:





    """simple docstring"""

    for attribute in key.split("""."""      ):
        __A             =		getattr(__lowercase     ,			__lowercase      )

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

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



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     str     ,			__lowercase				:     Any      )  -> int:





    """simple docstring"""

    __A             =		[]
    __A             =		fairseq_model.state_dict()

    __A             =		hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        __A             =		False
        if "conv_layers" in name:
            load_conv_layer(
                __lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			hf_model.config.feat_extract_norm == """group"""     ,			)
            __A             =		True
        else:
            for key, mapped_key in MAPPING.items():
                __A             =		mapped_key

                if key in name:
                    __A             =		True
                    if "*" in mapped_key:
                        __A             =		name.split(__lowercase      )[0].split("""."""      )[-2]
                        __A             =		mapped_key.replace("""*"""     ,			__lowercase      )
                    if "weight_g" in name:
                        __A             =		"""weight_g"""
                    elif "weight_v" in name:
                        __A             =		"""weight_v"""
                    elif "weight" in name:
                        __A             =		"""weight"""
                    elif "bias" in name:
                        __A             =		"""bias"""
                    else:
                        __A             =		None
                    set_recursively(__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase      )
                continue
        if not is_used:
            unused_weights.append(__lowercase      )

    logger.warning(f"Unused weights: {unused_weights}"      )



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Union[str, Any]     ,			__lowercase				:     Optional[int]     ,			__lowercase				:     Optional[int]     ,			__lowercase				:     Tuple     ,			__lowercase				:     str      )  -> Tuple:





    """simple docstring"""

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



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     List[str]      )  -> Optional[int]:





    """simple docstring"""

    __A             =		HubertConfig()
    __A             =		model.config

    __A             =		fs_config.activation_dropout
    __A             =		False
    __A             =		fs_config.attention_dropout
    __A             =		False
    __A             =		eval(fs_config.extractor_conv_feature_layers      )
    __A             =		[x[0] for x in conv_layers]
    __A             =		[x[1] for x in conv_layers]
    __A             =		[x[2] for x in conv_layers]
    __A             =		"""gelu"""
    __A             =		"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
    __A             =		False
    __A             =		0.0
    __A             =		0.0
    __A             =		fs_config.activation_fn
    __A             =		fs_config.dropout
    __A             =		fs_config.encoder_embed_dim
    __A             =		0.02
    __A             =		fs_config.encoder_ffn_embed_dim
    __A             =		1E-5
    __A             =		0.0
    __A             =		fs_config.encoder_attention_heads
    __A             =		fs_config.conv_pos_groups
    __A             =		fs_config.conv_pos
    __A             =		len(__lowercase      )
    __A             =		fs_config.encoder_layers

    return config



@torch.no_grad()
def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Any     ,			__lowercase				:     List[Any]=None      )  -> Optional[int]:





    """simple docstring"""

    __A             =		distilhubert().model.model

    if config_path is not None:
        __A             =		HubertConfig.from_pretrained(__lowercase      )
    else:
        __A             =		convert_config(__lowercase      )
    __A             =		model.eval()

    __A             =		WavaVecaFeatureExtractor(
        feature_size=1     ,			sampling_rate=1_6_0_0_0     ,			padding_value=0     ,			do_normalize=__lowercase     ,			return_attention_mask=__lowercase     ,			)
    __A             =		HubertModel(__lowercase      )

    recursively_load_weights(__lowercase     ,			__lowercase      )

    feature_extractor.save_pretrained(__lowercase      )
    hf_model.save_pretrained(__lowercase      )


if __name__ == "__main__":
  __a :    Dict								=				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")
  __a :    Optional[Any]								=				parser.parse_args()
  convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)












from ..utils import DummyObject, requires_backends


__a :    int								=				None



def      _SCREAMING_SNAKE_CASE  (	*__lowercase				:     Any     ,			**__lowercase				:     Optional[int]      )  -> Tuple:





    """simple docstring"""

    requires_backends(__lowercase     ,			["""sentencepiece""", """tokenizers"""]      )




