






"""simple docstring"""





import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch







def      lowerCamelCase__       (							_lowerCamelCase       :		Union[str, Any] )      ->				Tuple:  # picklable for multiprocessing
  return x.sum()







def      lowerCamelCase__       (							_lowerCamelCase       :		Union[str, Any] )      ->				int:  # picklable for multiprocessing
  return i + 1






@dataclass
class      a						:
    SCREAMING_SNAKE_CASE						:     int
    SCREAMING_SNAKE_CASE						:     str






class      a						(					__snake_case		):






    def    UpperCamelCase       (       self     :							Dict					)     ->		Optional[int]:
      lowerCamelCase_         =						{}
      lowerCamelCase_         =						[]
      lowerCamelCase_         =						1
      lowerCamelCase_         =						[1, 2]
      lowerCamelCase_         =						{'a': 1, 'b': 2}
      lowerCamelCase_         =						{'a': [1, 2], 'b': [3, 4]}
      lowerCamelCase_         =						{'a': {'1': 1}, 'b': 2}
      lowerCamelCase_         =						{'a': 1, 'b': 2, 'c': 3, 'd': 4}
      lowerCamelCase_         =						{}
      lowerCamelCase_         =						[]
      lowerCamelCase_         =						2
      lowerCamelCase_         =						[2, 3]
      lowerCamelCase_         =						{'a': 2, 'b': 3}
      lowerCamelCase_         =						{'a': [2, 3], 'b': [4, 5]}
      lowerCamelCase_         =						{'a': {'1': 2}, 'b': 3}
      lowerCamelCase_         =						{'a': 2, 'b': 3, 'c': 4, 'd': 5}
      self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)
      self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)
      self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)
      self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)
      self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)
      self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)
      self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)
      self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						2
      self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						num_proc=__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)
      self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						num_proc=__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)
      self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						num_proc=__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)
      self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						num_proc=__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)
      self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						num_proc=__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)
      self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						num_proc=__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)
      self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						num_proc=__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)
      self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						num_proc=__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						{'a': np.eye(2					), 'b': np.zeros(3					), 'c': np.ones(2					)}
      lowerCamelCase_         =						{'a': 2, 'b': 0, 'c': 2}
      lowerCamelCase_         =						{
          'a': np.eye(2					).astype(__SCREAMING_SNAKE_CASE					),
          'b': np.zeros(3					).astype(__SCREAMING_SNAKE_CASE					),
          'c': np.ones(2					).astype(__SCREAMING_SNAKE_CASE					),
      }
      self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						map_numpy=__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)
      self.assertEqual(
          {k: v.tolist() for k, v in map_nested(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						map_numpy=__SCREAMING_SNAKE_CASE					).items()}       ,						{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}       ,						)
      self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						map_numpy=__SCREAMING_SNAKE_CASE       ,						num_proc=__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)
      self.assertEqual(
          {k: v.tolist() for k, v in map_nested(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						map_numpy=__SCREAMING_SNAKE_CASE       ,						num_proc=__SCREAMING_SNAKE_CASE					).items()}       ,						{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}       ,						)
      with self.assertRaises(__SCREAMING_SNAKE_CASE					):  # can't pickle a local lambda
        map_nested(lambda __SCREAMING_SNAKE_CASE					: x + 1       ,						__SCREAMING_SNAKE_CASE       ,						num_proc=__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Dict					)     ->		List[Any]:
      lowerCamelCase_         =						{'a': 1, 'b': 2}
      lowerCamelCase_         =						{'a': 3, 'b': 4}
      lowerCamelCase_         =						{'a': 5, 'b': 6}
      lowerCamelCase_         =						sorted([('a', (1, 3, 5)), ('b', (2, 4, 6))]					)
      self.assertEqual(sorted(zip_dict(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)					)       ,						__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							str					)     ->		List[Any]:






      class      a						:
          SCREAMING_SNAKE_CASE						:     Dict						= """bar"""
      lowerCamelCase_         =						Foo()
      self.assertEqual(foo.my_attr       ,						'bar'					)
      with temporary_assignment(__SCREAMING_SNAKE_CASE       ,						'my_attr'       ,						'BAR'					):
        self.assertEqual(foo.my_attr       ,						'BAR'					)
      self.assertEqual(foo.my_attr       ,						'bar'					)







@pytest.mark.parametrize(
    'iterable_length, num_proc, expected_num_proc'		,				[
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]		,				)
def      lowerCamelCase__       (							_lowerCamelCase       :		List[str]		,				_lowerCamelCase       :		Any		,				_lowerCamelCase       :		Optional[int] )      ->				Tuple:
  with patch('datasets.utils.py_utils._single_map_nested' ) as mock_single_map_nested, patch(
      'datasets.parallel.parallel.Pool' ) as mock_multiprocessing_pool:
    lowerCamelCase_         =						{F'''{i}''': i for i in range(_lowerCamelCase )}
    lowerCamelCase_         =						map_nested(lambda _lowerCamelCase : x + 10		,				_lowerCamelCase		,				num_proc=_lowerCamelCase		,				parallel_min_length=16 )
    if expected_num_proc == 1:
      assert mock_single_map_nested.called
      assert not mock_multiprocessing_pool.called
    else:
      assert not mock_single_map_nested.called
      assert mock_multiprocessing_pool.called
      assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc






class      a						(					__snake_case		):






    @require_tf
    def    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		List[str]:
      import tensorflow as tf
      from tensorflow.keras import layers

      lowerCamelCase_         =						layers.Dense(2					)

      def gen_random_output():
        lowerCamelCase_         =						tf.random.uniform((1, 3)					)
        return model(__SCREAMING_SNAKE_CASE					).numpy()

      with temp_seed(42       ,						set_tensorflow=__SCREAMING_SNAKE_CASE					):
        lowerCamelCase_         =						gen_random_output()
      with temp_seed(42       ,						set_tensorflow=__SCREAMING_SNAKE_CASE					):
        lowerCamelCase_         =						gen_random_output()
      lowerCamelCase_         =						gen_random_output()

      np.testing.assert_equal(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
      self.assertGreater(np.abs(outa - outa					).sum()       ,						0					)






    @require_torch
    def    UpperCamelCase       (       self     :							str					)     ->		Tuple:
      import torch

      def gen_random_output():
        lowerCamelCase_         =						torch.nn.Linear(3       ,						2					)
        lowerCamelCase_         =						torch.rand(1       ,						3					)
        return model(__SCREAMING_SNAKE_CASE					).detach().numpy()

      with temp_seed(42       ,						set_pytorch=__SCREAMING_SNAKE_CASE					):
        lowerCamelCase_         =						gen_random_output()
      with temp_seed(42       ,						set_pytorch=__SCREAMING_SNAKE_CASE					):
        lowerCamelCase_         =						gen_random_output()
      lowerCamelCase_         =						gen_random_output()

      np.testing.assert_equal(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
      self.assertGreater(np.abs(outa - outa					).sum()       ,						0					)






    def    UpperCamelCase       (       self     :							Optional[int]					)     ->		Optional[Any]:
      def gen_random_output():
        return np.random.rand(1       ,						3					)

      with temp_seed(42					):
        lowerCamelCase_         =						gen_random_output()
      with temp_seed(42					):
        lowerCamelCase_         =						gen_random_output()
      lowerCamelCase_         =						gen_random_output()

      np.testing.assert_equal(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
      self.assertGreater(np.abs(outa - outa					).sum()       ,						0					)







@pytest.mark.parametrize('input_data'		,				[{}] )
def      lowerCamelCase__       (							_lowerCamelCase       :		Dict )      ->				List[str]:
  lowerCamelCase_         =						NestedDataStructure(_lowerCamelCase ).data
  assert output_data == input_data







@pytest.mark.parametrize(
    'data, expected_output'		,				[
        ({}, []),
        ([], []),
        ('foo', ['foo']),
        (['foo', 'bar'], ['foo', 'bar']),
        ([['foo', 'bar']], ['foo', 'bar']),
        ([[['foo'], ['bar']]], ['foo', 'bar']),
        ([[['foo'], 'bar']], ['foo', 'bar']),
        ({'a': 1, 'b': 2}, [1, 2]),
        ({'a': [1, 2], 'b': [3, 4]}, [1, 2, 3, 4]),
        ({'a': [[1, 2]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
        ({'a': [[1, 2]], 'b': [3, 4]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [[[3], [4]]]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [3, 4]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [3, [4]]}, [1, 2, 3, 4]),
        ({'a': {'1': 1}, 'b': 2}, [1, 2]),
        ({'a': {'1': [1]}, 'b': 2}, [1, 2]),
        ({'a': {'1': [1]}, 'b': [2]}, [1, 2]),
    ]		,				)
def      lowerCamelCase__       (							_lowerCamelCase       :		Optional[int]		,				_lowerCamelCase       :		str )      ->				Tuple:
  lowerCamelCase_         =						NestedDataStructure(_lowerCamelCase ).flatten()
  assert output == expected_output







def      lowerCamelCase__       (							)      ->				Dict:
  lowerCamelCase_         =						A(x=1		,				y='foobar' )
  lowerCamelCase_         =						{'x': 1, 'y': 'foobar'}
  assert asdict(_lowerCamelCase ) == expected_output

  lowerCamelCase_         =						{'a': {'b': A(x=10		,				y='foo' )}, 'c': [A(x=20		,				y='bar' )]}
  lowerCamelCase_         =						{'a': {'b': {'x': 10, 'y': 'foo'}}, 'c': [{'x': 20, 'y': 'bar'}]}
  assert asdict(_lowerCamelCase ) == expected_output

  with pytest.raises(_lowerCamelCase ):
    asdict([1, A(x=10		,				y='foo' )] )







def      lowerCamelCase__       (							_lowerCamelCase       :		str )      ->				Dict:
  return text.split()







def      lowerCamelCase__       (							_lowerCamelCase       :		Tuple )      ->				Optional[int]:
  yield (time.time(), content)
  time.sleep(2 )
  yield (time.time(), content)







def      lowerCamelCase__       (							)      ->				str:
  with Pool(2 ) as pool:
    lowerCamelCase_         =						list(iflatmap_unordered(_lowerCamelCase		,				_split_text		,				kwargs_iterable=[{'text': 'hello there'}] * 10 ) )
    assert out.count('hello' ) == 10
    assert out.count('there' ) == 10
    assert len(_lowerCamelCase ) == 20

  # check multiprocess from pathos (uses dill for pickling)
  with multiprocess.Pool(2 ) as pool:
    lowerCamelCase_         =						list(iflatmap_unordered(_lowerCamelCase		,				_split_text		,				kwargs_iterable=[{'text': 'hello there'}] * 10 ) )
    assert out.count('hello' ) == 10
    assert out.count('there' ) == 10
    assert len(_lowerCamelCase ) == 20

  # check that we get items as fast as possible
  with Pool(2 ) as pool:
    lowerCamelCase_         =						[]
    for yield_time, content in iflatmap_unordered(
        _lowerCamelCase		,				_aseconds_generator_of_aitems_with_timing		,				kwargs_iterable=[{'content': 'a'}, {'content': 'b'}] ):
      assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
      out.append(_lowerCamelCase )
    assert out.count('a' ) == 2
    assert out.count('b' ) == 2
    assert len(_lowerCamelCase ) == 4












"""simple docstring"""





import argparse
import json
import subprocess







def      lowerCamelCase__       (							_lowerCamelCase       :		Tuple		,				_lowerCamelCase       :		str )      ->				List[Any]:
  lowerCamelCase_         =						[]

  lowerCamelCase_         =						(
      F'''curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'''
      ' https://api.github.com/repos/huggingface/transformers/actions/runners'
  )
  lowerCamelCase_         =						subprocess.run(_lowerCamelCase		,				shell=_lowerCamelCase		,				stdout=subprocess.PIPE )
  lowerCamelCase_         =						output.stdout.decode('utf-8' )
  lowerCamelCase_         =						json.loads(_lowerCamelCase )

  lowerCamelCase_         =						status['runners']
  for runner in runners:
    if runner["name"] in target_runners:
      if runner["status"] == "offline":
        offline_runners.append(_lowerCamelCase )

    # save the result so we can report them on Slack
  with open('offline_runners.txt'		,				'w' ) as fp:
    fp.write(json.dumps(_lowerCamelCase ) )

  if len(_lowerCamelCase ) > 0:
    lowerCamelCase_         =						'\n'.join([x['name'] for x in offline_runners] )
    raise ValueError(F'''The following runners are offline:\n{failed}''' )


if __name__ == "__main__":







  def      lowerCamelCase__       (							_lowerCamelCase       :		Dict )      ->				Tuple:
    return values.split(',' )

  _SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--target_runners''',
      default=None,
      type=list_str,
      required=True,
      help='''Comma-separated list of runners to check status.''',
  )

  parser.add_argument(
      '''--token''', default=None, type=str, required=True, help='''A token that has actions:read permission.'''
  )
  _SCREAMING_SNAKE_CASE					:				Any					    =						parser.parse_args()






  get_runner_status(args.target_runners, args.token)




