


import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


UpperCAmelCase    =					logging.get_logger(__name__)






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=None      )->						Optional[Any]:




      """simple docstring"""

      return field(default_factory=lambda: default      ,      metadata=SCREAMING_SNAKE_CASE      )







@dataclass
class lowerCAmelCase_		:

 '''simple docstring'''

 __snake_case				=						list_field(
     default=[]      ,		metadata={
         "help": (
             "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
             " of all available models"
         )
     }      ,		)

 __snake_case				=						list_field(
     default=[8]      ,		metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )

 __snake_case				=						list_field(
     default=[8, 3_2, 1_2_8, 5_1_2]      ,		metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}      ,		)

 __snake_case				=						field(
     default=lowerCamelCase__      ,		metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}      ,		)
 __snake_case				=						field(
     default=lowerCamelCase__      ,		metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}      ,		)
 __snake_case				=						field(
     default=lowerCamelCase__      ,		metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
 __snake_case				=						field(default=lowerCamelCase__      ,		metadata={"help": "Use FP16 to accelerate inference."} )
 __snake_case				=						field(default=lowerCamelCase__      ,		metadata={"help": "Benchmark training of model"} )
 __snake_case				=						field(default=lowerCamelCase__      ,		metadata={"help": "Verbose memory tracing"} )
 __snake_case				=						field(
     default=lowerCamelCase__      ,		metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}      ,		)
 __snake_case				=						field(
     default=lowerCamelCase__      ,		metadata={
         "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
     }      ,		)
 __snake_case				=						field(default=lowerCamelCase__      ,		metadata={"help": "Trace memory line by line"} )
 __snake_case				=						field(default=lowerCamelCase__      ,		metadata={"help": "Save result to a CSV file"} )
 __snake_case				=						field(default=lowerCamelCase__      ,		metadata={"help": "Save all print statements in a log file"} )
 __snake_case				=						field(default=lowerCamelCase__      ,		metadata={"help": "Whether to print environment information"} )
 __snake_case				=						field(
     default=lowerCamelCase__      ,		metadata={
         "help": (
             "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
             " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
             " for debugging / testing and on TPU."
         )
     }      ,		)
 __snake_case				=						field(
     default=F"inference_time_{round(time() )}.csv"      ,		metadata={"help": "CSV filename used if saving time results to csv."}      ,		)
 __snake_case				=						field(
     default=F"inference_memory_{round(time() )}.csv"      ,		metadata={"help": "CSV filename used if saving memory results to csv."}      ,		)
 __snake_case				=						field(
     default=F"train_time_{round(time() )}.csv"      ,		metadata={"help": "CSV filename used if saving time results to csv for training."}      ,		)
 __snake_case				=						field(
     default=F"train_memory_{round(time() )}.csv"      ,		metadata={"help": "CSV filename used if saving memory results to csv for training."}      ,		)
 __snake_case				=						field(
     default=F"env_info_{round(time() )}.csv"      ,		metadata={"help": "CSV filename used if saving environment information."}      ,		)
 __snake_case				=						field(
     default=F"log_{round(time() )}.csv"      ,		metadata={"help": "Log filename used if print statements are saved in log."}      ,		)
 __snake_case				=						field(default=3      ,		metadata={"help": "Times an experiment will be run."} )
 __snake_case				=						field(
     default=lowerCamelCase__      ,		metadata={
         "help": (
             "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
             " model weights."
         )
     }      ,		)



 def 		UpperCamelCase__  (			self      ):
       warnings.warn(
           F'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
           ''' are deprecated in general and it is advised to use external Benchmarking libraries '''
           ''' to benchmark Transformer models.'''	,   _UpperCAmelCase	,   )



 def 		UpperCamelCase__  (			self      ):
       return json.dumps(dataclasses.asdict(self      )	,   indent=2      )



 @property
 def 		UpperCamelCase__  (			self      ):
       if len(self.models      ) <= 0:
             raise ValueError(
                 '''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
                 ''' bert-base-cased` or `args.models = [\'bert-base-cased\'].'''      )
       return self.models





 @property
 def 		UpperCamelCase__  (			self      ):
       if not self.multi_process:
             return False
       elif self.is_tpu:
             logger.info('''Multiprocessing is currently not possible on TPU.'''      )
             return False
       else:
             return True



import numpy as np

import datasets


UpperCAmelCase    =					"""
Compute the Mahalanobis Distance

Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mahalanobis in 1936
and has been used in various statistical applications ever since
[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]
"""

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

UpperCAmelCase    =					"""
Args:
    X: List of datapoints to be compared with the `reference_distribution`.
    reference_distribution: List of datapoints from the reference distribution we want to compare to.
Returns:
    mahalanobis: The Mahalonobis distance for each datapoint in `X`.
Examples:

    >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")
    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])
    >>> print(results)
    {'mahalanobis': array([0.5])}
"""







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

 '''simple docstring'''

 def 		UpperCamelCase__  (			self      ):
       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 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       # convert to numpy arrays
       snake_case_ =   np.array(_UpperCAmelCase      )
       snake_case_ =   np.array(_UpperCAmelCase      )

       # 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
       snake_case_ =   X - np.mean(_UpperCAmelCase      )
       snake_case_ =   np.cov(reference_distribution.T      )
       try:
             snake_case_ =   np.linalg.inv(_UpperCAmelCase      )
       except np.linalg.LinAlgError:
             snake_case_ =   np.linalg.pinv(_UpperCAmelCase      )
       snake_case_ =   np.dot(_UpperCAmelCase	,   _UpperCAmelCase      )
       snake_case_ =   np.dot(_UpperCAmelCase	,   X_minus_mu.T      ).diagonal()

       return {"mahalanobis": mahal_dist}