




"""simple docstring"""

import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


a							      =     TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow('''''', '''|''', '''|'''),
    datarow=DataRow('''''', '''|''', '''|'''),
    padding=1,
    with_header_hide=None,
)


a							      =     []
a							      =     []

a							      =     {'''type''': '''section''', '''text''': {'''type''': '''plain_text''', '''text''': '''No failed tests! 🤗''', '''emoji''': True}}

a							      =     [
    {
        '''type''': '''header''',
        '''text''': {
            '''type''': '''plain_text''',
            '''text''': F'''🤗 Accelerate nightly {os.environ.get("TEST_TYPE", "")} test results''',
            '''emoji''': True,
        },
    }
]

a							      =     0
for log in Path().glob('''*.log'''):
       a							      =     0
       with open(log, '''r''') as f:
              for line in f:
                     a							      =     json.loads(line)
                     if line.get('''nodeid''', '''''') != "":
                            a							      =     line['''nodeid''']
                            if line.get('''duration''', None) is not None:
                                   a							      =     F'''{line["duration"]:.4f}'''
                                   if line.get('''outcome''', '''''') == "failed":
                                          section_num_failed += 1
                                          failed.append([test, duration, log.name.split('''_''')[0]])
                                          total_num_failed += 1
       group_info.append([str(log), section_num_failed, failed])
       a							      =     []
       log.unlink()

a							      =     ''''''
a							      =     []
if total_num_failed > 0:
       for name, num_failed, failed_tests in group_info:
              if num_failed > 0:
                     if num_failed == 1:
                            message += F"*{name[1:]}: {num_failed} failed test*\n"
                     else:
                            message += F"*{name[1:]}: {num_failed} failed tests*\n"
                     a							      =     []
                     a							      =     {}
                     for test in failed_tests:
                            a							      =     test[0].split('''::''')
                            a							      =     data[0].split('''/''')[-1]
                            if data[0] not in filesafailed:
                                   a							      =     [data[1:]]
                            else:
                                   filesafailed[data[0]] += [data[1:]]
                            failed_table.append(data)

                     a							      =     [test[0] for test in failed_table]
                     a							      =     list(set(files))
                     # Count number of instances in failed_tests
                     a							      =     []
                     for file in individual_files:
                            table.append([file, len(filesafailed[file])])

                     a							      =     tabulate(
                         table,
                         headers=['''Test Location''', '''Num Failed'''],
                         tablefmt=hf_table_format,
                         stralign='''right''',
                     )
                     message += F"\n```\n{failed_table}\n```"
                     all_filesafailed.append(filesafailed)
       if len(message) > 3_000:
              a							      =     '''Too many failed tests, please see the full report in the Action results.'''
              a							      =     len(err) + 10
              a							      =     message[: 3_000 - offset] + F'''\n...\n```\n{err}'''
       print(F'''### {message}''')
else:
       a							      =     '''No failed tests! 🤗'''
       print(F'''## {message}''')
       payload.append(no_error_payload)

if os.environ.get('''TEST_TYPE''', '''''') != "":
       from slack_sdk import WebClient

       a							      =     WebClient(token=os.environ['''SLACK_API_TOKEN'''])
       if message != "No failed tests! 🤗":
              a							      =     {
                  '''type''': '''section''',
                  '''text''': {
                      '''type''': '''mrkdwn''',
                      '''text''': message,
                  },
              }
              payload.append(md_report)
              a							      =     {
                  '''type''': '''section''',
                  '''text''': {
                      '''type''': '''mrkdwn''',
                      '''text''': '''*For more details:*''',
                  },
                  '''accessory''': {
                      '''type''': '''button''',
                      '''text''': {
                          '''type''': '''plain_text''',
                          '''text''': '''Check Action results''',
                          '''emoji''': True,
                      },
                      '''url''': F'''https://github.com/{os.environ["GITHUB_REPOSITORY"]}/actions/runs/{os.environ["GITHUB_RUN_ID"]}''',
                  },
              }
              payload.append(action_button)
              a							      =     {
                  '''type''': '''context''',
                  '''elements''': [
                      {
                          '''type''': '''plain_text''',
                          '''text''': F'''Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}''',
                      }
                  ],
              }
              payload.append(date_report)
       a							      =     client.chat_postMessage(channel='''#accelerate-ci-daily''', text=message, blocks=payload)
       a							      =     response.data['''ts''']
       for failed_file in all_filesafailed:
              for test_location, test_failures in failed_file.items():
                     # Keep only the first instance of the test name
                     a							      =     ''''''
                     for i, row in enumerate(test_failures):
                            if row[0] != test_class:
                                   a							      =     row[0]
                            else:
                                   a							      =     ''''''

                     a							      =     {
                         '''type''': '''section''',
                         '''text''': {
                             '''type''': '''mrkdwn''',
                             '''text''': F'''Test location: {test_location}\n```\n{tabulate(test_failures, headers=["Class", "Test"], tablefmt=hf_table_format, stralign="right")}\n```''',
                         },
                     }

                     client.chat_postMessage(
                         channel='''#accelerate-ci-daily''',
                         thread_ts=ts,
                         blocks=[payload],
                     )


import numpy as np

import datasets


SCREAMING_SNAKE_CASE		:     Optional[int]         =	"\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"

SCREAMING_SNAKE_CASE		:     Dict         =	"\\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"

SCREAMING_SNAKE_CASE		:     List[str]         =	"\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        A_							(self						)				->				str:
												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			,     __UpperCamelCase			,     __UpperCamelCase						)				->				int:
												# convert to numpy arrays
												UpperCamelCase_		:							int  =		np.array(__UpperCamelCase						)
												UpperCamelCase_		:							Union[str, Any]  =		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
												UpperCamelCase_		:							str  =		X - np.mean(__UpperCamelCase						)
												UpperCamelCase_		:							Dict  =		np.cov(reference_distribution.T						)
												try:
																		UpperCamelCase_		:							str  =		np.linalg.inv(__UpperCamelCase						)
												except np.linalg.LinAlgError:
																		UpperCamelCase_		:							List[Any]  =		np.linalg.pinv(__UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		np.dot(__UpperCamelCase			,     __UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		np.dot(__UpperCamelCase			,     X_minus_mu.T						).diagonal()

												return {"mahalanobis": mahal_dist}






