


from sklearn.metrics import mean_absolute_error

import datasets


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

__UpperCamelCase      						=    '\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n'


__UpperCamelCase      						=    '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 "raw_values" : Returns a full set of errors in case of multioutput input.\n\n                 "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric("mae")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.5}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric("mae", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results)\n    {\'mae\': array([0.5, 1. ])}\n'







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






			"""simple docstring"""






			def 			snake_case__			(							self       ):

						'''simple docstring'''


						return datasets.MetricInfo(
						    description=_DESCRIPTION ,  citation=_CITATION ,  inputs_description=_KWARGS_DESCRIPTION ,  features=datasets.Features(self._get_feature_types()       ) ,  reference_urls=[
						        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"
						    ] ,  )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						if self.config_name == "multilist":
									return {
									    "predictions": datasets.Sequence(datasets.Value("float"       )       ),
									    "references": datasets.Sequence(datasets.Value("float"       )       ),
									}
						else:
									return {
									    "predictions": datasets.Value("float"       ),
									    "references": datasets.Value("float"       ),
									}







			def 			snake_case__			(							self ,  snake_case ,  snake_case ,  snake_case=None ,  snake_case="uniform_average"       ):

						'''simple docstring'''


						UpperCamelCase__   =    mean_absolute_error(snake_case ,  snake_case ,  sample_weight=snake_case ,  multioutput=snake_case       )

						return {"mae": mae_score}






import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__UpperCamelCase      						=    logging.get_logger(__name__)

__UpperCamelCase      						=    {
    '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     UpperCamelCase_(			_A				:int			,       _A				:Dict			,       _A				:int			,       _A				:List[str]			,       _A				:Optional[int]					)->						Any:
			for attribute in key.split("."					):
						UpperCamelCase__   =    getattr(_A			,       _A					)

			if weight_type is not None:
						UpperCamelCase__   =    getattr(_A			,       _A					).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     UpperCamelCase_(			_A				:Optional[Any]			,       _A				:Dict					)->						List[Any]:
			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(
									    _A			,       _A			,       _A			,       _A			,       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(_A					)[0].split("."					)[-2]
																		UpperCamelCase__   =    mapped_key.replace("*"			,       _A					)
															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(_A			,       _A			,       _A			,       _A			,       _A					)
												continue
						if not is_used:
									unused_weights.append(_A					)

			logger.warning(F'''Unused weights: {unused_weights}'''					)
def     UpperCamelCase_(			_A				:Any			,       _A				:List[Any]			,       _A				:Union[str, Any]			,       _A				:Dict			,       _A				:Union[str, Any]					)->						Optional[Any]:
			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(_A					)
def     UpperCamelCase_(			_A				:Dict					)->						Union[str, Any]:
			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(_A					)
			UpperCamelCase__   =    fs_config.encoder_layers

			return config
@torch.no_grad()
def     UpperCamelCase_(			_A				:str			,       _A				:Union[str, Any]=None					)->						str:
			UpperCamelCase__   =    distilhubert().model.model

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

			UpperCamelCase__   =    WavaVecaFeatureExtractor(
			    feature_size=1			,       sampling_rate=1_60_00			,       padding_value=0			,       do_normalize=_A			,       return_attention_mask=_A			,       )
			UpperCamelCase__   =    HubertModel(_A					)

			recursively_load_weights(_A			,       _A					)

			feature_extractor.save_pretrained(_A					)
			hf_model.save_pretrained(_A					)


if __name__ == "__main__":
						__UpperCamelCase      						=    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      						=    parser.parse_args()
						convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)


