





'''simple docstring'''






import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

snake_case_     =				{
    """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""",
    """encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
    """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""": """lm_head""",
    """label_embs_concat""": """label_embeddings_concat""",
    """mask_emb""": """masked_spec_embed""",
    """spk_proj""": """speaker_proj""",
}
snake_case_     =				[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]
def 							_lowerCamelCase(  UpperCamelCase__    : Any    ,       UpperCamelCase__    : Tuple    ,       UpperCamelCase__    : Tuple    ,       UpperCamelCase__    : List[Any]    ,       UpperCamelCase__    : Optional[Any]		)		->  List[str]:
    for attribute in key.split('''.'''		):
        A       :       Union[str, Any]							=						getattr(UpperCamelCase__    ,       UpperCamelCase__		)

    if weight_type is not None:
        A       :       Optional[Any]							=						getattr(UpperCamelCase__    ,       UpperCamelCase__		).shape
    else:
        A       :       List[str]							=						hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            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       :       Optional[Any]							=						value
    elif weight_type == "weight_g":
        A       :       Any							=						value
    elif weight_type == "weight_v":
        A       :       int							=						value
    elif weight_type == "bias":
        A       :       Optional[Any]							=						value
    else:
        A       :       List[Any]							=						value

    logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''		)
def 							_lowerCamelCase(  UpperCamelCase__    : str    ,       UpperCamelCase__    : Optional[int]		)		->  Union[str, Any]:
    A       :       Tuple							=						[]
    A       :       Any							=						fairseq_model.state_dict()

    A       :       Tuple							=						hf_model.unispeech_sat.feature_extractor

    for name, value in fairseq_dict.items():
        A       :       Dict							=						False
        if "conv_layers" in name:
            load_conv_layer(
                UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       hf_model.config.feat_extract_norm == '''group'''    ,       )
            A       :       int							=						True
        else:
            for key, mapped_key in MAPPING.items():
                A       :       List[str]							=						'''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                if key in name or key.split('''w2v_model.'''		)[-1] == name.split('''.'''		)[0]:
                    if "layer_norm_for_extract" in name and (".".join(name.split('''.'''		)[:-1]		) != key):
                        # special case since naming is very similar
                        continue
                    A       :       Dict							=						True
                    if "*" in mapped_key:
                        A       :       Optional[Any]							=						name.split(UpperCamelCase__		)[0].split('''.'''		)[-2]
                        A       :       Union[str, Any]							=						mapped_key.replace('''*'''    ,       UpperCamelCase__		)
                    if "weight_g" in name:
                        A       :       Any							=						'''weight_g'''
                    elif "weight_v" in name:
                        A       :       Any							=						'''weight_v'''
                    elif "bias" in name:
                        A       :       Optional[Any]							=						'''bias'''
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        A       :       Tuple							=						'''weight'''
                    else:
                        A       :       Tuple							=						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__    : Dict    ,       UpperCamelCase__    : int    ,       UpperCamelCase__    : str    ,       UpperCamelCase__    : Union[str, Any]    ,       UpperCamelCase__    : int		)		->  Dict:
    A       :       List[str]							=						full_name.split('''conv_layers.'''		)[-1]
    A       :       Dict							=						name.split('''.'''		)
    A       :       int							=						int(items[0]		)
    A       :       Any							=						int(items[1]		)

    if type_id == 0:
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                raise ValueError(
                    F'''{full_name} has size {value.shape}, but'''
                    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''		)
            A       :       Optional[int]							=						value
            logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''		)
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                raise ValueError(
                    F'''{full_name} has size {value.shape}, but'''
                    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''		)
            A       :       Union[str, 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:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                raise ValueError(
                    F'''{full_name} has size {value.shape}, but'''
                    F''' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'''		)
            A       :       List[str]							=						value
            logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                raise ValueError(
                    F'''{full_name} has size {value.shape}, but'''
                    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''		)
            A       :       Optional[int]							=						value
            logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
    else:
        unused_weights.append(UpperCamelCase__		)
@torch.no_grad()
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[int]    ,       UpperCamelCase__    : Tuple    ,       UpperCamelCase__    : Any=None    ,       UpperCamelCase__    : Any=None    ,       UpperCamelCase__    : str=True		)		->  Any:
    if config_path is not None:
        A       :       Optional[int]							=						UniSpeechSatConfig.from_pretrained(UpperCamelCase__		)
    else:
        A       :       Optional[Any]							=						UniSpeechSatConfig()

    A       :       str							=						''''''

    if is_finetuned:
        A       :       Optional[Any]							=						UniSpeechSatForCTC(UpperCamelCase__		)
    else:
        A       :       Dict							=						UniSpeechSatForPreTraining(UpperCamelCase__		)

    A       :       List[Any]							=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]    ,       arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''		)[:-1]		)}		)
    A       :       Union[str, Any]							=						model[0].eval()

    recursively_load_weights(UpperCamelCase__    ,       UpperCamelCase__		)

    hf_wavavec.save_pretrained(UpperCamelCase__		)


if __name__ == "__main__":
     snake_case_     =				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("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     parser.add_argument(
         """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
     )
     snake_case_     =				parser.parse_args()
     convert_unispeech_sat_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )







'''simple docstring'''






def 							_lowerCamelCase(  UpperCamelCase__    : Union[str, Any]    ,       UpperCamelCase__    : str		)		->  Optional[Any]:
    A       :       Tuple							=						0
    while b > 0:
        if b & 1:
            res += a

        a += a
        b >>= 1

    return res
def 							_lowerCamelCase(  UpperCamelCase__    : Any    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : List[str]		)		->  Any:
    A       :       str							=						0
    while b > 0:
        if b & 1:
            A       :       List[Any]							=						((res % c) + (a % c)) % c

        a += a
        b >>= 1

    return res
