




'''simple docstring'''






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
		import torch

		from transformers import XLMRobertaModel




@require_sentencepiece
@require_tokenizers
@require_torch
class 		a__    (							unittest.TestCase  ):





							'''simple docstring'''






							@slow
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     XLMRobertaModel.from_pretrained('''xlm-roberta-base'''       )
														lowerCAmelCase__	       =     torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]       )
														# The dog is cute and lives in the garden house

														lowerCAmelCase__	       =     torch.Size((1, 12, 7_68)       )  # batch_size, sequence_length, embedding_vector_dim
														lowerCAmelCase__	       =     torch.tensor(
														    [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]       )
														#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
														#  xlmr.eval()
														#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
														with torch.no_grad():
																					lowerCAmelCase__	       =     model(lowerCamelCase_       )['''last_hidden_state'''].detach()
														self.assertEqual(output.shape ,   lowerCamelCase_       )
														# compare the actual values for a slice of last dim
														self.assertTrue(torch.allclose(output[:, :, -1] ,   lowerCamelCase_ ,   atol=1e-3       )       )







							@slow
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														lowerCAmelCase__	       =     XLMRobertaModel.from_pretrained('''xlm-roberta-large'''       )
														lowerCAmelCase__	       =     torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]       )
														# The dog is cute and lives in the garden house

														lowerCAmelCase__	       =     torch.Size((1, 12, 10_24)       )  # batch_size, sequence_length, embedding_vector_dim
														lowerCAmelCase__	       =     torch.tensor(
														    [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]]       )
														#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
														#  xlmr.eval()
														#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
														with torch.no_grad():
																					lowerCAmelCase__	       =     model(lowerCamelCase_       )['''last_hidden_state'''].detach()
														self.assertEqual(output.shape ,   lowerCamelCase_       )
														# compare the actual values for a slice of last dim
														self.assertTrue(torch.allclose(output[:, :, -1] ,   lowerCamelCase_ ,   atol=1e-3       )       )



'''simple docstring'''






import argparse
import json
import subprocess


def  _snake_case       (					A			,   A	)						->   Tuple:
							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(A			,   shell=A			,   stdout=subprocess.PIPE	)
							lowerCAmelCase__	       =     output.stdout.decode('''utf-8'''	)
							lowerCAmelCase__	       =     json.loads(A	)

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

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

							if len(A	) > 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  _snake_case       (					A	)						->   Optional[Any]:
									return values.split(''','''	)

		__UpperCAmelCase       =							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.'''
		)
		__UpperCAmelCase       =							parser.parse_args()






		get_runner_status(args.target_runners, args.token)