





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCAmelCase__    :      str       					=logging.get_logger(__name__)

UpperCAmelCase__    :      List[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_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   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Union[str, Any]:
				for attribute in key.split("""."""					):
								lowerCamelCase									=getattr(_UpperCAmelCase      ,   _UpperCAmelCase					)

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

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





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Union[str, Any]:
				lowerCamelCase									=[]
				lowerCamelCase									=fairseq_model.state_dict()

				lowerCamelCase									=hf_model.feature_extractor

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

																if key in name:
																				lowerCamelCase									=True
																				if "*" in mapped_key:
																								lowerCamelCase									=name.split(_UpperCAmelCase					)[0].split("""."""					)[-2]
																								lowerCamelCase									=mapped_key.replace("""*"""      ,   _UpperCAmelCase					)
																				if "weight_g" in name:
																								lowerCamelCase									="""weight_g"""
																				elif "weight_v" in name:
																								lowerCamelCase									="""weight_v"""
																				elif "weight" in name:
																								lowerCamelCase									="""weight"""
																				elif "bias" in name:
																								lowerCamelCase									="""bias"""
																				else:
																								lowerCamelCase									=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   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Tuple:
				lowerCamelCase									=full_name.split("""conv_layers."""					)[-1]
				lowerCamelCase									=name.split("""."""					)
				lowerCamelCase									=int(items[0]					)
				lowerCamelCase									=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."""
												)
												lowerCamelCase									=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."""
												)
												lowerCamelCase									=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."
												)
												lowerCamelCase									=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."""
												)
												lowerCamelCase									=value
												logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
				else:
								unused_weights.append(_UpperCAmelCase					)





def   _lowercase       (    _UpperCAmelCase					)    ->	Any:
				lowerCamelCase									=HubertConfig()
				lowerCamelCase									=model.config

				lowerCamelCase									=fs_config.activation_dropout
				lowerCamelCase									=False
				lowerCamelCase									=fs_config.attention_dropout
				lowerCamelCase									=False
				lowerCamelCase									=eval(fs_config.extractor_conv_feature_layers					)
				lowerCamelCase									=[x[0] for x in conv_layers]
				lowerCamelCase									=[x[1] for x in conv_layers]
				lowerCamelCase									=[x[2] for x in conv_layers]
				lowerCamelCase									="""gelu"""
				lowerCamelCase									="""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
				lowerCamelCase									=False
				lowerCamelCase									=0.0
				lowerCamelCase									=0.0
				lowerCamelCase									=fs_config.activation_fn
				lowerCamelCase									=fs_config.dropout
				lowerCamelCase									=fs_config.encoder_embed_dim
				lowerCamelCase									=0.0_2
				lowerCamelCase									=fs_config.encoder_ffn_embed_dim
				lowerCamelCase									=1e-5
				lowerCamelCase									=0.0
				lowerCamelCase									=fs_config.encoder_attention_heads
				lowerCamelCase									=fs_config.conv_pos_groups
				lowerCamelCase									=fs_config.conv_pos
				lowerCamelCase									=len(_UpperCAmelCase					)
				lowerCamelCase									=fs_config.encoder_layers

				return config





@torch.no_grad()
def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase=None					)    ->	Dict:
				lowerCamelCase									=distilhubert().model.model

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

				lowerCamelCase									=WavaVecaFeatureExtractor(
				    feature_size=1      ,   sampling_rate=1_60_00      ,   padding_value=0      ,   do_normalize=_UpperCAmelCase      ,   return_attention_mask=_UpperCAmelCase      ,   )
				lowerCamelCase									=HubertModel(_UpperCAmelCase					)

				recursively_load_weights(_UpperCAmelCase      ,   _UpperCAmelCase					)

				feature_extractor.save_pretrained(_UpperCAmelCase					)
				hf_model.save_pretrained(_UpperCAmelCase					)


if __name__ == "__main__":
							UpperCAmelCase__    :      List[Any]       					=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''')
							UpperCAmelCase__    :      List[Any]       					=parser.parse_args()
							convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)












from sklearn.metrics import accuracy_score

import datasets


UpperCAmelCase__    :      Optional[Any]       					='''
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
'''


UpperCAmelCase__    :      Optional[int]       					='''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {\'accuracy\': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {\'accuracy\': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {\'accuracy\': 0.8778625954198473}
'''


UpperCAmelCase__    :      List[str]       					='''
@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    __A  (    datasets.Metric     ):


				def _snake_case							(		self				):
								return datasets.MetricInfo(
								    description=_DESCRIPTION ,			citation=_CITATION ,			inputs_description=_KWARGS_DESCRIPTION ,			features=datasets.Features(
								        {
								            """predictions""": datasets.Sequence(datasets.Value("""int32"""				)				),
								            """references""": datasets.Sequence(datasets.Value("""int32"""				)				),
								        }
								        if self.config_name == """multilabel"""
								        else {
								            """predictions""": datasets.Value("""int32"""				),
								            """references""": datasets.Value("""int32"""				),
								        }				) ,			reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"""] ,			)


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_=True ,			UpperCAmelCase_=None				):
								return {
								    "accuracy": float(
								        accuracy_score(UpperCAmelCase_ ,			UpperCAmelCase_ ,			normalize=UpperCAmelCase_ ,			sample_weight=UpperCAmelCase_				)				)
								}





