






"""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_	:    Optional[int]            =      logging.get_logger(__name__)

lowerCAmelCase_	:    str            =      {
    '''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_	:    Optional[int]            =      [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       for attribute in key.split("""."""		):
              UpperCAmelCase	    =			getattr(lowerCAmelCase	,      lowerCAmelCase		)

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

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






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[]
       UpperCAmelCase	    =			fairseq_model.state_dict()

       UpperCAmelCase	    =			hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              UpperCAmelCase	    =			False
              if "conv_layers" in name:
                     load_conv_layer(
                         lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      hf_model.config.feat_extract_norm == """group"""	,      )
                     UpperCAmelCase	    =			True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
                                   UpperCAmelCase	    =			True
                                   if "*" in mapped_key:
                                          UpperCAmelCase	    =			name.split(lowerCAmelCase		)[0].split("""."""		)[-2]
                                          UpperCAmelCase	    =			mapped_key.replace("""*"""	,      lowerCAmelCase		)
                                   if "weight_g" in name:
                                          UpperCAmelCase	    =			'weight_g'
                                   elif "weight_v" in name:
                                          UpperCAmelCase	    =			'weight_v'
                                   elif "bias" in name and "relative_attention_bias" not in name:
                                          UpperCAmelCase	    =			'bias'
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          UpperCAmelCase	    =			'weight'
                                   else:
                                          UpperCAmelCase	    =			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		):







       '''simple docstring'''
       UpperCAmelCase	    =			full_name.split("""conv_layers."""		)[-1]
       UpperCAmelCase	    =			name.split("""."""		)
       UpperCAmelCase	    =			int(items[0]		)
       UpperCAmelCase	    =			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.'''
                     )
                     UpperCAmelCase	    =			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.'''
                     )
                     UpperCAmelCase	    =			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."
                     )
                     UpperCAmelCase	    =			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.'''
                     )
                     UpperCAmelCase	    =			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		):







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

       if config_path is not None:
              UpperCAmelCase	    =			WavLMConfig.from_pretrained(lowerCAmelCase		)
       else:
              UpperCAmelCase	    =			WavLMConfig()

       UpperCAmelCase	    =			WavLMModel(lowerCAmelCase		)

       recursively_load_weights(lowerCAmelCase	,      lowerCAmelCase		)

       hf_wavlm.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
 lowerCAmelCase_	:    Union[str, Any]            =      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_	:    Optional[Any]            =      parser.parse_args()
 convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)








"""simple docstring"""







from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)
