






from sklearn.metrics import mean_squared_error

import datasets


UpperCAmelCase_ :						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}
}
'''

UpperCAmelCase_ :						str      =      '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


UpperCAmelCase_ :						Optional[int]      =      '''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        "raw_values" : Returns a full set of errors in case of multioutput input.

        "uniform_average" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric("mse")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {\'mse\': 0.6123724356957945}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric("mse", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {\'mse\': array([0.41666667, 1. ])}
'''

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





	def 						_A (		self				:					Dict	):
		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_squared_error.html"""
		    ] ,  )





	def 						_A (		self				:					str	):
		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 						_A (		self				:					Union[str, Any] ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					List[Any]=None ,  __lowerCamelCase				:					List[Any]="uniform_average" ,  __lowerCamelCase				:					int=True	):
		UpperCamelCase     :Optional[int]          =	mean_squared_error(
		    __lowerCamelCase ,  __lowerCamelCase ,  sample_weight=__lowerCamelCase ,  multioutput=__lowerCamelCase ,  squared=__lowerCamelCase	)

		return {"mse": mse}








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()
UpperCAmelCase_ :						Optional[Any]      =      logging.get_logger(__name__)

UpperCAmelCase_ :						Optional[Any]      =      {
    '''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''',
}
UpperCAmelCase_ :						int      =      [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Dict	,			__magic_name__					:  Union[str, Any]	,			__magic_name__					:  Tuple	,			__magic_name__					:  Optional[int]					)		->			Dict:






	"""simple docstring"""



	for attribute in key.split("""."""					):
		UpperCamelCase     :Dict          =	getattr(__magic_name__	,			__magic_name__					)

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

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[Any]	,			__magic_name__					:  List[str]					)		->			Optional[Any]:






	"""simple docstring"""



	UpperCamelCase     :Union[str, Any]          =	[]
	UpperCamelCase     :Dict          =	fairseq_model.state_dict()

	UpperCamelCase     :int          =	hf_model.feature_extractor

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

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

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Any	,			__magic_name__					:  str	,			__magic_name__					:  int	,			__magic_name__					:  int	,			__magic_name__					:  List[str]					)		->			Dict:






	"""simple docstring"""



	UpperCamelCase     :Dict          =	full_name.split("""conv_layers."""					)[-1]
	UpperCamelCase     :int          =	name.split("""."""					)
	UpperCamelCase     :str          =	int(items[0]					)
	UpperCamelCase     :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."""
			)
			UpperCamelCase     :Tuple          =	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     :Dict          =	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     :Tuple          =	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     :Union[str, Any]          =	value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
	else:
		unused_weights.append(__magic_name__					)

@torch.no_grad()
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[str]	,			__magic_name__					:  List[Any]	,			__magic_name__					:  str=None					)		->			int:






	"""simple docstring"""



	UpperCamelCase     :List[Any]          =	torch.load(__magic_name__					)
	UpperCamelCase     :List[Any]          =	WavLMConfigOrig(checkpoint["""cfg"""]					)
	UpperCamelCase     :int          =	WavLMOrig(__magic_name__					)
	model.load_state_dict(checkpoint["""model"""]					)
	model.eval()

	if config_path is not None:
		UpperCamelCase     :List[Any]          =	WavLMConfig.from_pretrained(__magic_name__					)
	else:
		UpperCamelCase     :Any          =	WavLMConfig()

	UpperCamelCase     :Dict          =	WavLMModel(__magic_name__					)

	recursively_load_weights(__magic_name__	,			__magic_name__					)

	hf_wavlm.save_pretrained(__magic_name__					)


if __name__ == "__main__":
	UpperCAmelCase_ :						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''')
	UpperCAmelCase_ :						Optional[int]      =      parser.parse_args()
	convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
