





from sklearn.metrics import matthews_corrcoef

import datasets


lowerCAmelCase_        =							'''
Compute the Matthews correlation coefficient (MCC)

The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction.  The statistic
is also known as the phi coefficient. [source: Wikipedia]
'''

lowerCAmelCase_        =							'''
Args:
    predictions (list of int): Predicted labels, as returned by a model.
    references (list of int): Ground truth labels.
    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
    matthews_correlation (dict containing float): Matthews correlation.
Examples:
    Example 1, a basic example with only predictions and references as inputs:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3])
        >>> print(round(results[\'matthews_correlation\'], 2))
        0.54

    Example 2, the same example as above, but also including sample weights:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])
        >>> print(round(results[\'matthews_correlation\'], 2))
        0.1

    Example 3, the same example as above, but with sample weights that cause a negative correlation:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])
        >>> print(round(results[\'matthews_correlation\'], 2))
        -0.25
'''

lowerCAmelCase_        =							'''\
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
'''



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,	_KWARGS_DESCRIPTION	)
class    __lowerCAmelCase			(     datasets.Metric	):






			def      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



							return datasets.MetricInfo(
							    description=_DESCRIPTION   ,				citation=_CITATION   ,				inputs_description=_KWARGS_DESCRIPTION   ,				features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''int32'''       ),
							            '''references''': datasets.Value('''int32'''       ),
							        }       )   ,				reference_urls=[
							        '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'''
							    ]   ,				)






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__=None       )	->  int:


							'''simple docstring'''



							return {
							    "matthews_correlation": float(matthews_corrcoef(__magic_name__   ,				__magic_name__   ,				sample_weight=__magic_name__       )       ),
							}







'''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()
lowercase__       : Union[str, Any]         =       logging.get_logger(__name__)

lowercase__       : 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.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''',
}
lowercase__       : Dict         =       [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 			_lowerCAmelCase     (    __snake_case    :					int					,				__snake_case    :					Union[str, Any]					,				__snake_case    :					Tuple					,				__snake_case    :					Any					,				__snake_case    :					List[str]     )	->	Union[str, Any]:
     for attribute in key.split('.'     ):
          __A		:					int						=			getattr(__snake_case					,				__snake_case     )

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

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






def 			_lowerCAmelCase     (    __snake_case    :					Any					,				__snake_case    :					List[str]     )	->	List[Any]:
     __A		:					Optional[Any]						=			[]
     __A		:					Any						=			fairseq_model.state_dict()

     __A		:					Union[str, Any]						=			hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          __A		:					Union[str, Any]						=			False
          if "conv_layers" in name:
               load_conv_layer(
                   __snake_case					,				__snake_case					,				__snake_case					,				__snake_case					,				hf_model.config.feat_extract_norm == 'group'					,				)
               __A		:					Optional[Any]						=			True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split('w2v_model.'     )[-1] == name.split('.'     )[0]:
                         __A		:					int						=			True
                         if "*" in mapped_key:
                              __A		:					Any						=			name.split(__snake_case     )[0].split('.'     )[-2]
                              __A		:					List[Any]						=			mapped_key.replace('*'					,				__snake_case     )
                         if "weight_g" in name:
                              __A		:					Optional[Any]						=			'weight_g'
                         elif "weight_v" in name:
                              __A		:					Union[str, Any]						=			'weight_v'
                         elif "bias" in name and "relative_attention_bias" not in name:
                              __A		:					Optional[Any]						=			'bias'
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              __A		:					Tuple						=			'weight'
                         else:
                              __A		:					Dict						=			None

                         set_recursively(__snake_case					,				__snake_case					,				__snake_case					,				__snake_case					,				__snake_case     )
                    continue
          if not is_used:
               unused_weights.append(__snake_case     )

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






def 			_lowerCAmelCase     (    __snake_case    :					List[str]					,				__snake_case    :					List[Any]					,				__snake_case    :					Union[str, Any]					,				__snake_case    :					List[str]					,				__snake_case    :					Optional[int]     )	->	int:
     __A		:					int						=			full_name.split('conv_layers.'     )[-1]
     __A		:					List[str]						=			name.split('.'     )
     __A		:					Optional[int]						=			int(items[0]     )
     __A		:					str						=			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		:					Optional[int]						=			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		:					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:
               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		:					Dict						=			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		:					Any						=			value
               logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'     )
     else:
          unused_weights.append(__snake_case     )






@torch.no_grad()
def 			_lowerCAmelCase     (    __snake_case    :					Union[str, Any]					,				__snake_case    :					List[str]					,				__snake_case    :					Tuple=None     )	->	Any:
     # load the pre-trained checkpoints
     __A		:					List[str]						=			torch.load(__snake_case     )
     __A		:					Dict						=			WavLMConfigOrig(checkpoint['cfg']     )
     __A		:					Optional[int]						=			WavLMOrig(__snake_case     )
     model.load_state_dict(checkpoint['model']     )
     model.eval()

     if config_path is not None:
          __A		:					List[Any]						=			WavLMConfig.from_pretrained(__snake_case     )
     else:
          __A		:					Dict						=			WavLMConfig()

     __A		:					Optional[Any]						=			WavLMModel(__snake_case     )

     recursively_load_weights(__snake_case					,				__snake_case     )

     hf_wavlm.save_pretrained(__snake_case     )


if __name__ == "__main__":
  lowercase__       : Tuple         =       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''')
  lowercase__       : Any         =       parser.parse_args()
  convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)