


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


_lowercase				     = logging.get_logger(__name__)

_lowercase				     = {
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class 	lowerCamelCase__			(      _a      ):
   __lowerCamelCase			      =  """xglm"""
   __lowerCamelCase			      =  ["""past_key_values"""]

   __lowerCamelCase			      =  {
       """num_attention_heads""": """attention_heads""",
       """hidden_size""": """d_model""",
       """num_hidden_layers""": """num_layers""",
   }




   def __init__(   self						:						Dict    ,    __a						:						List[Any]=256008    ,    __a						:						Any=2048    ,    __a						:						Dict=1024    ,    __a						:						Tuple=4096    ,    __a						:						Union[str, Any]=24    ,    __a						:						List[Any]=16    ,    __a						:						List[str]="gelu"    ,    __a						:						int=0.1    ,    __a						:						Tuple=0.1    ,    __a						:						str=0.0    ,    __a						:						str=0.0    ,    __a						:						Union[str, Any]=0.02    ,    __a						:						Any=True    ,    __a						:						List[str]=True    ,    __a						:						List[Any]=2    ,    __a						:						Optional[Any]=1    ,    __a						:						int=0    ,    __a						:						List[str]=2    ,    **__a						:						Dict    ,    ):




         '''simple docstring'''







         lowerCamelCase__:						Optional[int]							   = vocab_size
         lowerCamelCase__:						List[Any]							   = max_position_embeddings
         lowerCamelCase__:						Dict							   = d_model
         lowerCamelCase__:						Union[str, Any]							   = ffn_dim
         lowerCamelCase__:						Dict							   = num_layers
         lowerCamelCase__:						Dict							   = attention_heads
         lowerCamelCase__:						Any							   = activation_function
         lowerCamelCase__:						Optional[int]							   = dropout
         lowerCamelCase__:						Tuple							   = attention_dropout
         lowerCamelCase__:						str							   = activation_dropout
         lowerCamelCase__:						Union[str, Any]							   = layerdrop
         lowerCamelCase__:						Tuple							   = init_std
         lowerCamelCase__:						int							   = scale_embedding  # scale factor will be sqrt(d_model) if True
         lowerCamelCase__:						str							   = use_cache

         super().__init__(
             pad_token_id=_SCREAMING_SNAKE_CASE    ,    bos_token_id=_SCREAMING_SNAKE_CASE    ,    eos_token_id=_SCREAMING_SNAKE_CASE    ,    decoder_start_token_id=_SCREAMING_SNAKE_CASE    ,    **_SCREAMING_SNAKE_CASE    ,    )












import numpy as np

import datasets


__a							     =					'\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n'

__a							     =					'\\n@article{de2000mahalanobis,\n  title={The mahalanobis distance},\n  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n  journal={Chemometrics and intelligent laboratory systems},\n  volume={50},\n  number={1},\n  pages={1--18},\n  year={2000},\n  publisher={Elsevier}\n}\n'

__a							     =					'\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric("mahalanobis")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {\'mahalanobis\': array([0.5])}\n'

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






    """simple docstring"""





    def 				a__ ( self    )					->						Union[str, Any]:
         return datasets.MetricInfo(
             description=_DESCRIPTION  ,citation=_CITATION  ,inputs_description=_KWARGS_DESCRIPTION  ,features=datasets.Features(
                 {
                     '''X''': datasets.Sequence(datasets.Value('''float'''  ,id='''sequence'''    )  ,id='''X'''    ),
                 }    )  ,)





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Any:
         # convert to numpy arrays
         UpperCAmelCase_     :				str  =     np.array(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				List[Any]  =     np.array(_SCREAMING_SNAKE_CASE    )

         # Assert that arrays are 2D
         if len(X.shape    ) != 2:
              raise ValueError('''Expected `X` to be a 2D vector'''    )
         if len(reference_distribution.shape    ) != 2:
              raise ValueError('''Expected `reference_distribution` to be a 2D vector'''    )
         if reference_distribution.shape[0] < 2:
              raise ValueError(
                  '''Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension'''    )

         # Get mahalanobis distance for each prediction
         UpperCAmelCase_     :				List[str]  =     X - np.mean(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Dict  =     np.cov(reference_distribution.T    )
         try:
              UpperCAmelCase_     :				Any  =     np.linalg.inv(_SCREAMING_SNAKE_CASE    )
         except np.linalg.LinAlgError:
              UpperCAmelCase_     :				List[str]  =     np.linalg.pinv(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Union[str, Any]  =     np.dot(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				List[Any]  =     np.dot(_SCREAMING_SNAKE_CASE  ,X_minus_mu.T    ).diagonal()

         return {"mahalanobis": mahal_dist}