
import numpy as np

import datasets


lowercase__       ='\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'

lowercase__       ='\\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'

lowercase__       ='\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 		UpperCamelCase__  (       datasets.Metric    ):
   def       lowerCAmelCase			(self :					Tuple   ):
    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       lowerCAmelCase			(self :					int					,				snake_case_ :					List[str]					,				snake_case_ :					List[Any]   ):
    # convert to numpy arrays
    __a :  str						=  np.array(snake_case_   )
    __a :  Dict						=  np.array(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
    __a :  Tuple						=  X - np.mean(snake_case_   )
    __a :  List[Any]						=  np.cov(reference_distribution.T   )
    try:
     __a :  str						=  np.linalg.inv(snake_case_   )
    except np.linalg.LinAlgError:
     __a :  str						=  np.linalg.pinv(snake_case_   )
    __a :  int						=  np.dot(snake_case_					,				snake_case_   )
    __a :  Any						=  np.dot(snake_case_					,				X_minus_mu.T   ).diagonal()

    return {"mahalanobis": mahal_dist}


from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image


lowercase__       =['text', 'image', 'audio']




def    __UpperCamelCase     (				lowerCAmelCase__							:				List[str]  ):
 __a :  Optional[int]						=  []

 for input_type in input_types:
  if input_type == "text":
   inputs.append('''Text input'''  )
  elif input_type == "image":
   inputs.append(
       Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''  )  ) / '''000000039769.png'''  ).resize((5_1_2, 5_1_2)  )  )
  elif input_type == "audio":
   inputs.append(torch.ones(3_0_0_0  )  )
  elif isinstance(lowerCAmelCase__  ,   lowerCAmelCase__  ):
   inputs.append(create_inputs(lowerCAmelCase__  )  )
  else:
   raise ValueError(f"Invalid type requested: {input_type}"  )

 return inputs




def    __UpperCamelCase     (				lowerCAmelCase__							:				List  ):
 __a :  List[str]						=  []

 for output in outputs:
  if isinstance(lowerCAmelCase__  ,   (str, AgentText)  ):
   output_types.append('''text'''  )
  elif isinstance(lowerCAmelCase__  ,   (Image.Image, AgentImage)  ):
   output_types.append('''image'''  )
  elif isinstance(lowerCAmelCase__  ,   (torch.Tensor, AgentAudio)  ):
   output_types.append('''audio'''  )
  else:
   raise ValueError(f"Invalid output: {output}"  )

 return output_types



@is_tool_test
class 		UpperCamelCase__  :
   def       lowerCAmelCase			(self :					Any   ):
    self.assertTrue(hasattr(self.tool					,				'''inputs'''   )   )
    self.assertTrue(hasattr(self.tool					,				'''outputs'''   )   )

    __a :  Any						=  self.tool.inputs
    for _input in inputs:
     if isinstance(_input					,				snake_case_   ):
      for __input in _input:
       self.assertTrue(__input in authorized_types   )
     else:
      self.assertTrue(_input in authorized_types   )

    __a :  Optional[int]						=  self.tool.outputs
    for _output in outputs:
     self.assertTrue(_output in authorized_types   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    __a :  Union[str, Any]						=  create_inputs(self.tool.inputs   )
    __a :  List[Any]						=  self.tool(*snake_case_   )

    # There is a single output
    if len(self.tool.outputs   ) == 1:
     __a :  Tuple						=  [outputs]

    self.assertListEqual(output_types(snake_case_   )					,				self.tool.outputs   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    self.assertTrue(hasattr(self.tool					,				'''description'''   )   )
    self.assertTrue(hasattr(self.tool					,				'''default_checkpoint'''   )   )
    self.assertTrue(self.tool.description.startswith('''This is a tool that'''   )   )
   def       lowerCAmelCase			(self :					Any   ):
    __a :  Any						=  create_inputs(self.tool.inputs   )
    __a :  Union[str, Any]						=  self.tool(*snake_case_   )

    if not isinstance(snake_case_					,				snake_case_   ):
     __a :  Tuple						=  [outputs]

    self.assertEqual(len(snake_case_   )					,				len(self.tool.outputs   )   )

    for output, output_type in zip(snake_case_					,				self.tool.outputs   ):
     __a :  List[Any]						=  AGENT_TYPE_MAPPING[output_type]
     self.assertTrue(isinstance(snake_case_					,				snake_case_   )   )







   def       lowerCAmelCase			(self :					Optional[int]   ):
    __a :  Any						=  create_inputs(self.tool.inputs   )

    __a :  Dict						=  []

    for _input, input_type in zip(snake_case_					,				self.tool.inputs   ):
     if isinstance(snake_case_					,				snake_case_   ):
      _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input   ) for _input_type in input_type]   )
     else:
      _inputs.append(AGENT_TYPE_MAPPING[input_type](_input   )   )

        # Should not raise an error
    __a :  Optional[Any]						=  self.tool(*snake_case_   )

    if not isinstance(snake_case_					,				snake_case_   ):
     __a :  Dict						=  [outputs]

    self.assertEqual(len(snake_case_   )					,				len(self.tool.outputs   )   )
