



"""simple docstring"""


import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, 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""",
    """mask_emb""": """masked_spec_embed""",
}

def 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_						,      lowercase_						,      lowercase_		):
						for attribute in key.split('.'		):
												UpperCAmelCase			 =					getattr(lowercase_						,      lowercase_		)

						if weight_type is not None:
												UpperCAmelCase			 =					getattr(lowercase_						,      lowercase_		).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 (  lowercase_						,      lowercase_		):
						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(
																		    lowercase_						,      lowercase_						,      lowercase_						,      lowercase_						,      hf_model.config.feat_extract_norm == 'group'						,      )
																		UpperCAmelCase			 =					True
												else:
																		for key, mapped_key in MAPPING.items():
																								UpperCAmelCase			 =					mapped_key

																								if key in name:
																														UpperCAmelCase			 =					True
																														if "*" in mapped_key:
																																				UpperCAmelCase			 =					name.split(lowercase_		)[0].split('.'		)[-2]
																																				UpperCAmelCase			 =					mapped_key.replace('*'						,      lowercase_		)
																														if "weight_g" in name:
																																				UpperCAmelCase			 =					'weight_g'
																														elif "weight_v" in name:
																																				UpperCAmelCase			 =					'weight_v'
																														elif "weight" in name:
																																				UpperCAmelCase			 =					'weight'
																														elif "bias" in name:
																																				UpperCAmelCase			 =					'bias'
																														else:
																																				UpperCAmelCase			 =					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 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_						,      lowercase_						,      lowercase_		):
						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(lowercase_		)

def 					_lowerCAmelCase (  lowercase_		):
						UpperCAmelCase			 =					HubertConfig()
						UpperCAmelCase			 =					model.config

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

						return config

@torch.no_grad()
def 					_lowerCAmelCase (  lowercase_						,      lowercase_=None		):
						UpperCAmelCase			 =					distilhubert().model.model

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

						UpperCAmelCase			 =					WavaVecaFeatureExtractor(
						    feature_size=1						,      sampling_rate=16000						,      padding_value=0						,      do_normalize=lowercase_						,      return_attention_mask=lowercase_						,      )
						UpperCAmelCase			 =					HubertModel(lowercase_		)

						recursively_load_weights(lowercase_						,      lowercase_		)

						feature_extractor.save_pretrained(lowercase_		)
						hf_model.save_pretrained(lowercase_		)


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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
				snake_case_								=			parser.parse_args()
				convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)










'''simple docstring'''



from sklearn.metrics import accuracy_score

import datasets


lowerCAmelCase						:		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
'''


lowerCAmelCase						:		str						      = '''
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}
'''


lowerCAmelCase						:		int						      = '''
@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        UpperCAmelCase__						(		datasets.Metric			):



					def 							UpperCAmelCase_			(     self  )  ->      Optional[Any]:
						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 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase=True      ,					UpperCamelCase=None  )  ->      List[str]:
						return {
						    "accuracy": float(
						        accuracy_score(UpperCamelCase      ,					UpperCamelCase      ,					normalize=UpperCamelCase      ,					sample_weight=UpperCamelCase  )  )
						}