





from sklearn.metrics import mean_squared_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 Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\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\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric(\"mse\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {'mse': 0.6123724356957945}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mse': array([0.41666667, 1. ])}\n"

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






							def 				_lowerCamelCase		(						self  ):
														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 				_lowerCamelCase		(						self  ):
														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 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase=None  , __lowerCAmelCase="uniform_average"  , __lowerCAmelCase=True  ):
														UpperCamelCase__				     =			mean_squared_error(
														    __lowerCAmelCase  , __lowerCAmelCase  , sample_weight=__lowerCAmelCase  , multioutput=__lowerCAmelCase  , squared=__lowerCAmelCase  )

														return {"mse": mse}











import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__											= logging.get_logger(__name__)

UpperCamelCase__											= {
    "facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}

class        __SCREAMING_SNAKE_CASE					(     _a       ):
							snake_case     :      Optional[Any]						     =       """data2vec-audio"""






							def __init__(						self  , __lowerCAmelCase=32  , __lowerCAmelCase=768  , __lowerCAmelCase=12  , __lowerCAmelCase=12  , __lowerCAmelCase=3072  , __lowerCAmelCase="gelu"  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.0  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.02  , __lowerCAmelCase=1E-5  , __lowerCAmelCase="gelu"  , __lowerCAmelCase=(512, 512, 512, 512, 512, 512, 512)  , __lowerCAmelCase=(5, 2, 2, 2, 2, 2, 2)  , __lowerCAmelCase=(10, 3, 3, 3, 3, 2, 2)  , __lowerCAmelCase=False  , __lowerCAmelCase=16  , __lowerCAmelCase=19  , __lowerCAmelCase=5  , __lowerCAmelCase=0.05  , __lowerCAmelCase=10  , __lowerCAmelCase=2  , __lowerCAmelCase=0.0  , __lowerCAmelCase=10  , __lowerCAmelCase=0  , __lowerCAmelCase="sum"  , __lowerCAmelCase=False  , __lowerCAmelCase=False  , __lowerCAmelCase=256  , __lowerCAmelCase=(512, 512, 512, 512, 1500)  , __lowerCAmelCase=(5, 3, 3, 1, 1)  , __lowerCAmelCase=(1, 2, 3, 1, 1)  , __lowerCAmelCase=512  , __lowerCAmelCase=0  , __lowerCAmelCase=1  , __lowerCAmelCase=2  , __lowerCAmelCase=False  , __lowerCAmelCase=3  , __lowerCAmelCase=2  , __lowerCAmelCase=3  , __lowerCAmelCase=None  , **__lowerCAmelCase  , ):
														super().__init__(**__lowerCAmelCase  , pad_token_id=__lowerCAmelCase  , bos_token_id=__lowerCAmelCase  , eos_token_id=__lowerCAmelCase  )
														UpperCamelCase__				     =			hidden_size
														UpperCamelCase__				     =			feat_extract_activation
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			conv_bias
														UpperCamelCase__				     =			num_conv_pos_embeddings
														UpperCamelCase__				     =			num_conv_pos_embedding_groups
														UpperCamelCase__				     =			conv_pos_kernel_size
														UpperCamelCase__				     =			len(self.conv_dim  )
														UpperCamelCase__				     =			num_hidden_layers
														UpperCamelCase__				     =			intermediate_size
														UpperCamelCase__				     =			hidden_act
														UpperCamelCase__				     =			num_attention_heads
														UpperCamelCase__				     =			hidden_dropout
														UpperCamelCase__				     =			attention_dropout
														UpperCamelCase__				     =			activation_dropout
														UpperCamelCase__				     =			feat_proj_dropout
														UpperCamelCase__				     =			final_dropout
														UpperCamelCase__				     =			layerdrop
														UpperCamelCase__				     =			layer_norm_eps
														UpperCamelCase__				     =			initializer_range
														UpperCamelCase__				     =			vocab_size
														UpperCamelCase__				     =			use_weighted_layer_sum

														if (
														    (len(self.conv_stride  ) != self.num_feat_extract_layers)
														    or (len(self.conv_kernel  ) != self.num_feat_extract_layers)
														    or (len(self.conv_dim  ) != self.num_feat_extract_layers)
														):
																					raise ValueError(
																					    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
																					    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
																					    f""" {len(self.conv_dim  )}`, `len(config.conv_stride) = {len(self.conv_stride  )}`,"""
																					    f""" `len(config.conv_kernel) = {len(self.conv_kernel  )}`."""  )

														# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
														UpperCamelCase__				     =			mask_time_prob
														UpperCamelCase__				     =			mask_time_length
														UpperCamelCase__				     =			mask_time_min_masks
														UpperCamelCase__				     =			mask_feature_prob
														UpperCamelCase__				     =			mask_feature_length
														UpperCamelCase__				     =			mask_feature_min_masks

														# ctc loss
														UpperCamelCase__				     =			ctc_loss_reduction
														UpperCamelCase__				     =			ctc_zero_infinity

														# adapter
														UpperCamelCase__				     =			add_adapter
														UpperCamelCase__				     =			adapter_kernel_size
														UpperCamelCase__				     =			adapter_stride
														UpperCamelCase__				     =			num_adapter_layers
														UpperCamelCase__				     =			output_hidden_size or hidden_size

														# SequenceClassification-specific parameter. Feel free to ignore for other classes.
														UpperCamelCase__				     =			classifier_proj_size

														# XVector-specific parameters. Feel free to ignore for other classes.
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			list(__lowerCAmelCase  )
														UpperCamelCase__				     =			xvector_output_dim







							@property
							def 				_lowerCamelCase		(						self  ):
														return math.prod(self.conv_stride  )




