


"""simple docstring"""







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()
lowerCamelCase_   :   List[Any]									=  logging.get_logger(__name__)

lowerCamelCase_   :   Tuple									=  {
    """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""",
}
lowerCamelCase_   :   Dict									=  [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]
def _A						(						lowercase       ,    lowercase       ,    lowercase       ,    lowercase       ,    lowercase		):







    """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 _A						(						lowercase       ,    lowercase		):







    """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():
                if key in name or key.split('''w2v_model.'''		)[-1] == name.split('''.'''		)[0]:
                    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 "bias" in name and "relative_attention_bias" not in name:
                        a 		='''bias'''
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        a 		='''weight'''
                    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 _A						(						lowercase       ,    lowercase       ,    lowercase       ,    lowercase       ,    lowercase		):







    """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		)
@torch.no_grad()
def _A						(						lowercase       ,    lowercase       ,    lowercase=None		):







    """simple docstring"""
    # load the pre-trained checkpoints
    a 		=torch.load(lowercase		)
    a 		=WavLMConfigOrig(checkpoint['''cfg''']		)
    a 		=WavLMOrig(lowercase		)
    model.load_state_dict(checkpoint['''model''']		)
    model.eval()

    if config_path is not None:
        a 		=WavLMConfig.from_pretrained(lowercase		)
    else:
        a 		=WavLMConfig()

    a 		=WavLMModel(lowercase		)

    recursively_load_weights(lowercase       ,    lowercase		)

    hf_wavlm.save_pretrained(lowercase		)


if __name__ == "__main__":
       lowerCamelCase_   :   int									=  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""")
       lowerCamelCase_   :   Tuple									=  parser.parse_args()
       convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)


from timeit import timeit
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int      )							->      int:





    '''simple docstring'''






    if number < 0:
        raise ValueError('the value of input must not be negative'      )
    A__          =   0
    while number:
        number &= number - 1
        result += 1
    return result
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int      )							->      int:





    '''simple docstring'''






    if number < 0:
        raise ValueError('the value of input must not be negative'      )
    A__          =   0
    while number:
        if number % 2 == 1:
            result += 1
        number >>= 1
    return result
def 						_snake_case(    )							->      None:





    '''simple docstring'''






    def do_benchmark(SCREAMING_SNAKE_CASE__			:	int      ) -> None:
        A__          =   'import __main__ as z'
        print(f'Benchmark when {number = }:'      )
        print(f'{get_set_bits_count_using_modulo_operator(SCREAMING_SNAKE_CASE__      ) = }'      )
        A__          =   timeit('z.get_set_bits_count_using_modulo_operator(25)'    ,			setup=SCREAMING_SNAKE_CASE__      )
        print(f'timeit() runs in {timing} seconds'      )
        print(f'{get_set_bits_count_using_brian_kernighans_algorithm(SCREAMING_SNAKE_CASE__      ) = }'      )
        A__          =   timeit(
            'z.get_set_bits_count_using_brian_kernighans_algorithm(25)'    ,			setup=SCREAMING_SNAKE_CASE__    ,			)
        print(f'timeit() runs in {timing} seconds'      )

    for number in (25, 37, 58, 0):
        do_benchmark(SCREAMING_SNAKE_CASE__      )
        print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()

