
import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
							import torch

if is_soundfile_availble():
							import soundfile as sf

if is_vision_available():
							from PIL import Image



def 	UpperCamelCase   (							lowerCAmelCase__=""		):
					'''simple docstring'''
					lowercase						     =						tempfile.mkdtemp()
					return os.path.join(lowerCAmelCase__			,      str(uuid.uuida()		) + suffix		)







@require_soundfile
@require_torch
class     lowercase		( unittest.TestCase		):




							def 			A__				(    self):
												lowercase						     =						torch.rand(1_2       ,dtype=torch.floataa) - 0.5
												lowercase						     =						AgentAudio(A__)
												lowercase						     =						str(agent_type.to_string())

												# Ensure that the tensor and the agent_type's tensor are the same
												self.assertTrue(torch.allclose(A__       ,agent_type.to_raw()       ,atol=1E-4))

												del agent_type

												# Ensure the path remains even after the object deletion
												self.assertTrue(os.path.exists(A__))

												# Ensure that the file contains the same value as the original tensor
												lowercase     ,						lowercase						     =						sf.read(A__)
												self.assertTrue(torch.allclose(A__       ,torch.tensor(A__)       ,atol=1E-4))





							def 			A__				(    self):
												lowercase						     =						torch.rand(1_2       ,dtype=torch.floataa) - 0.5
												lowercase						     =						get_new_path(suffix='''.wav''')
												sf.write(A__       ,A__       ,1_6_0_0_0)

												lowercase						     =						AgentAudio(A__)

												self.assertTrue(torch.allclose(A__       ,agent_type.to_raw()       ,atol=1E-4))
												self.assertEqual(agent_type.to_string()       ,A__)







@require_vision
@require_torch
class     lowercase		( unittest.TestCase		):




							def 			A__				(    self):
												lowercase						     =						torch.randint(0       ,2_5_6       ,(6_4, 6_4, 3))
												lowercase						     =						AgentImage(A__)
												lowercase						     =						str(agent_type.to_string())

												# Ensure that the tensor and the agent_type's tensor are the same
												self.assertTrue(torch.allclose(A__       ,agent_type._tensor       ,atol=1E-4))

												self.assertIsInstance(agent_type.to_raw()       ,Image.Image)

												# Ensure the path remains even after the object deletion
												del agent_type
												self.assertTrue(os.path.exists(A__))




							def 			A__				(    self):
												lowercase						     =						Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png'''
												lowercase						     =						Image.open(A__)
												lowercase						     =						AgentImage(A__)

												self.assertTrue(path.samefile(agent_type.to_string()))
												self.assertTrue(image == agent_type.to_raw())

												# Ensure the path remains even after the object deletion
												del agent_type
												self.assertTrue(os.path.exists(A__))





							def 			A__				(    self):
												lowercase						     =						Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png'''
												lowercase						     =						Image.open(A__)
												lowercase						     =						AgentImage(A__)

												self.assertFalse(path.samefile(agent_type.to_string()))
												self.assertTrue(image == agent_type.to_raw())

												# Ensure the path remains even after the object deletion
												del agent_type
												self.assertTrue(os.path.exists(A__))







class     lowercase		( unittest.TestCase		):




							def 			A__				(    self):
												lowercase						     =						'''Hey!'''
												lowercase						     =						AgentText(A__)

												self.assertEqual(A__       ,agent_type.to_string())
												self.assertEqual(A__       ,agent_type.to_raw())
												self.assertEqual(A__       ,A__)






import numpy as np

import datasets


lowercase__		:Dict			  =   "\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__		:List[Any]			  =   "\\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__		:Dict			  =   "\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     lowercase		( datasets.Metric		):




							def 			A__				(    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 			A__				(    self       ,A__       ,A__):
												# convert to numpy arrays
												lowercase						     =						np.array(A__)
												lowercase						     =						np.array(A__)

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

												return {"mahalanobis": mahal_dist}




