

'''simple docstring'''
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_lowerCamelCase      					=     logging.get_logger(__name__)

_lowerCamelCase      					=     {
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}
class 					_snake_case		(__SCREAMING_SNAKE_CASE):
			__A  :	List[Any]        ="t5"
			__A  :	List[Any]        =["past_key_values"]
			__A  :	int        ={"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}






			def __init__(  self			,_snake_case=3_21_28			,_snake_case=5_12			,_snake_case=64			,_snake_case=20_48			,_snake_case=6			,_snake_case=None			,_snake_case=8			,_snake_case=32			,_snake_case=1_28			,_snake_case=0.1			,_snake_case=1E-6			,_snake_case=1.0			,_snake_case="relu"			,_snake_case=True			,_snake_case=True			,_snake_case=0			,_snake_case=1			,**_snake_case			,):
				UpperCAmelCase_     :      int						=  vocab_size
				UpperCAmelCase_     :      str						=  d_model
				UpperCAmelCase_     :      Any						=  d_kv
				UpperCAmelCase_     :      Any						=  d_ff
				UpperCAmelCase_     :      str						=  num_layers
				UpperCAmelCase_     :      List[Any]						=  (
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				UpperCAmelCase_     :      Optional[int]						=  num_heads
				UpperCAmelCase_     :      List[str]						=  relative_attention_num_buckets
				UpperCAmelCase_     :      int						=  relative_attention_max_distance
				UpperCAmelCase_     :      Union[str, Any]						=  dropout_rate
				UpperCAmelCase_     :      Optional[Any]						=  layer_norm_epsilon
				UpperCAmelCase_     :      Any						=  initializer_factor
				UpperCAmelCase_     :      List[Any]						=  feed_forward_proj
				UpperCAmelCase_     :      List[str]						=  use_cache

				UpperCAmelCase_     :      Any						=  self.feed_forward_proj.split("-"     )
				UpperCAmelCase_     :      Union[str, Any]						=  act_info[-1]
				UpperCAmelCase_     :      str						=  act_info[0] == "gated"

				if len(_snake_case     ) > 1 and act_info[0] != "gated" or len(_snake_case     ) > 2:
					raise ValueError(
					    f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
					    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
					    "'gated-gelu' or 'relu'"     )

				# for backwards compatibility
				if feed_forward_proj == "gated-gelu":
					UpperCAmelCase_     :      Any						=  "gelu_new"

				super().__init__(
				    pad_token_id=_snake_case			,eos_token_id=_snake_case			,is_encoder_decoder=_snake_case			,**_snake_case			,)




class 					_snake_case		(__SCREAMING_SNAKE_CASE):






			@property
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Tuple						=  {
				    "input_ids": {0: "batch", 1: "encoder_sequence"},
				    "attention_mask": {0: "batch", 1: "encoder_sequence"},
				}
				if self.use_past:
					UpperCAmelCase_     :      Union[str, Any]						=  "past_encoder_sequence + sequence"
					UpperCAmelCase_     :      Any						=  {0: "batch"}
					UpperCAmelCase_     :      Dict						=  {0: "batch", 1: "past_decoder_sequence + sequence"}
				else:
					UpperCAmelCase_     :      List[Any]						=  {0: "batch", 1: "decoder_sequence"}
					UpperCAmelCase_     :      List[Any]						=  {0: "batch", 1: "decoder_sequence"}

				if self.use_past:
					self.fill_with_past_key_values_(_snake_case			,direction="inputs"     )

				return common_inputs






			@property
			def 		UpperCamelCase__							(  self     ):
				return 13










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}