


'''simple docstring'''



from sklearn.metrics import mean_squared_error

import datasets


_lowercase    :    str			     =  "\\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"

_lowercase    :    Dict			     =  "\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


_lowercase    :    Union[str, Any]			     =  "\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  UpperCamelCase__(     datasets.Metric ):




    def   a__(	self    :				int   )->  Dict:

          """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_squared_error.html'''
              ]   ,					)




    def   a__(	self    :				Optional[Any]   )->  Tuple:

          """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   a__(	self    :				Any   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				Dict   ,					lowerCAmelCase    :				int=None   ,					lowerCAmelCase    :				List[Any]="uniform_average"   ,					lowerCAmelCase    :				Tuple=True   )->  Any:

          """simple docstring"""




          UpperCAmelCase         =			mean_squared_error(
              _a   ,					_a   ,					sample_weight=_a   ,					multioutput=_a   ,					squared=_a   )

          return {"mse": mse}









'''simple docstring'''



import math
from typing import Optional

import numpy as np

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


_lowercase    :    Union[str, Any]			     =  logging.get_logger(__name__)

_lowercase    :    List[str]			     =  {
    """facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
    """facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   List[str]							= "encodec"




				def __init__(	self    :				List[str]   ,					lowerCAmelCase    :				int=[1.5, 3.0, 6.0, 12.0, 24.0]   ,					lowerCAmelCase    :				Tuple=24000   ,					lowerCAmelCase    :				List[Any]=1   ,					lowerCAmelCase    :				Union[str, Any]=False   ,					lowerCAmelCase    :				str=None   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				str=128   ,					lowerCAmelCase    :				Any=32   ,					lowerCAmelCase    :				Any=1   ,					lowerCAmelCase    :				List[Any]=[8, 5, 4, 2]   ,					lowerCAmelCase    :				Union[str, Any]="weight_norm"   ,					lowerCAmelCase    :				str=7   ,					lowerCAmelCase    :				Optional[int]=7   ,					lowerCAmelCase    :				Any=3   ,					lowerCAmelCase    :				Tuple=2   ,					lowerCAmelCase    :				Optional[Any]=True   ,					lowerCAmelCase    :				List[str]="reflect"   ,					lowerCAmelCase    :				Optional[int]=2   ,					lowerCAmelCase    :				List[Any]=2   ,					lowerCAmelCase    :				Union[str, Any]=1.0   ,					lowerCAmelCase    :				Optional[Any]=1024   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				str=True   ,					**lowerCAmelCase    :				str   ,					)->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			target_bandwidths
										UpperCAmelCase         =			sampling_rate
										UpperCAmelCase         =			audio_channels
										UpperCAmelCase         =			normalize
										UpperCAmelCase         =			chunk_length_s
										UpperCAmelCase         =			overlap
										UpperCAmelCase         =			hidden_size
										UpperCAmelCase         =			num_filters
										UpperCAmelCase         =			num_residual_layers
										UpperCAmelCase         =			upsampling_ratios
										UpperCAmelCase         =			norm_type
										UpperCAmelCase         =			kernel_size
										UpperCAmelCase         =			last_kernel_size
										UpperCAmelCase         =			residual_kernel_size
										UpperCAmelCase         =			dilation_growth_rate
										UpperCAmelCase         =			use_causal_conv
										UpperCAmelCase         =			pad_mode
										UpperCAmelCase         =			compress
										UpperCAmelCase         =			num_lstm_layers
										UpperCAmelCase         =			trim_right_ratio
										UpperCAmelCase         =			codebook_size
										UpperCAmelCase         =			codebook_dim if codebook_dim is not None else hidden_size
										UpperCAmelCase         =			use_conv_shortcut

										if self.norm_type not in ["weight_norm", "time_group_norm"]:
																raise ValueError(
																    F"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"""   )

										super().__init__(**lowerCAmelCase   )




				@property
				def   a__(	self    :				str   )->  Optional[int]:

										"""simple docstring"""




										if self.chunk_length_s is None:
																return None
										else:
																return int(self.chunk_length_s * self.sampling_rate   )




				@property
				def   a__(	self    :				List[str]   )->  Optional[int]:

										"""simple docstring"""




										if self.chunk_length_s is None or self.overlap is None:
																return None
										else:
																return max(1   ,					int((1.0 - self.overlap) * self.chunk_length   )   )




				@property
				def   a__(	self    :				List[Any]   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			np.prod(self.upsampling_ratios   )
										return math.ceil(self.sampling_rate / hop_length   )





				@property
				def   a__(	self    :				int   )->  int:

										"""simple docstring"""




										return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)   )





