






import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer


class  _lowerCAmelCase      (		lowercase_   ):



				"""simple docstring"""



				def        __magic_name__				(   self     :     List[Any]):






											'''simple docstring'''







											snake_case__							=							tempfile.mkdtemp()
											snake_case__							=							5

											# Realm tok
											snake_case__							=							[
											    """[UNK]""",
											    """[CLS]""",
											    """[SEP]""",
											    """[PAD]""",
											    """[MASK]""",
											    """test""",
											    """question""",
											    """this""",
											    """is""",
											    """the""",
											    """first""",
											    """second""",
											    """third""",
											    """fourth""",
											    """fifth""",
											    """record""",
											    """want""",
											    """##want""",
											    """##ed""",
											    """wa""",
											    """un""",
											    """runn""",
											    """##ing""",
											    """,""",
											    """low""",
											    """lowest""",
											]
											snake_case__							=							os.path.join(self.tmpdirname      ,		"""realm_tokenizer""")
											os.makedirs(_SCREAMING_SNAKE_CASE      ,		exist_ok=_SCREAMING_SNAKE_CASE)
											snake_case__							=							os.path.join(_SCREAMING_SNAKE_CASE      ,		VOCAB_FILES_NAMES["""vocab_file"""])
											with open(self.vocab_file      ,		"""w"""      ,		encoding="""utf-8""") as vocab_writer:
																		vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]))

											snake_case__							=							os.path.join(self.tmpdirname      ,		"""realm_block_records""")
											os.makedirs(_SCREAMING_SNAKE_CASE      ,		exist_ok=_SCREAMING_SNAKE_CASE)



				def        __magic_name__				(   self     :     Optional[Any]):






											'''simple docstring'''







											return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname      ,		"""realm_tokenizer"""))



				def        __magic_name__				(   self     :     int):






											'''simple docstring'''







											shutil.rmtree(self.tmpdirname)



				def        __magic_name__				(   self     :     Optional[int]):






											'''simple docstring'''







											snake_case__							=							RealmConfig(num_block_records=self.num_block_records)
											return config



				def        __magic_name__				(   self     :     Any):






											'''simple docstring'''







											snake_case__							=							Dataset.from_dict(
											    {
											        """id""": ["""0""", """1"""],
											        """question""": ["""foo""", """bar"""],
											        """answers""": [["""Foo""", """Bar"""], ["""Bar"""]],
											    })
											return dataset



				def        __magic_name__				(   self     :     List[str]):






											'''simple docstring'''







											snake_case__							=							np.array(
											    [
											        b"""This is the first record""",
											        b"""This is the second record""",
											        b"""This is the third record""",
											        b"""This is the fourth record""",
											        b"""This is the fifth record""",
											        b"""This is a longer longer longer record""",
											    ]      ,		dtype=_SCREAMING_SNAKE_CASE      ,		)
											return block_records



				def        __magic_name__				(   self     :     int):






											'''simple docstring'''







											snake_case__							=							RealmRetriever(
											    block_records=self.get_dummy_block_records()      ,		tokenizer=self.get_tokenizer()      ,		)
											return retriever



				def        __magic_name__				(   self     :     Optional[Any]):






											'''simple docstring'''







											snake_case__							=							self.get_config()
											snake_case__							=							self.get_dummy_retriever()
											snake_case__							=							retriever.tokenizer

											snake_case__							=							np.array([0, 3]      ,		dtype="""long""")
											snake_case__							=							tokenizer(["""Test question"""]).input_ids
											snake_case__							=							tokenizer(
											    ["""the fourth"""]      ,		add_special_tokens=_SCREAMING_SNAKE_CASE      ,		return_token_type_ids=_SCREAMING_SNAKE_CASE      ,		return_attention_mask=_SCREAMING_SNAKE_CASE      ,		).input_ids
											snake_case__							=							config.reader_seq_len

											snake_case__	,			snake_case__	,			snake_case__	,			snake_case__							=							retriever(
											    _SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		answer_ids=_SCREAMING_SNAKE_CASE      ,		max_length=_SCREAMING_SNAKE_CASE      ,		return_tensors="""np""")

											self.assertEqual(len(_SCREAMING_SNAKE_CASE)      ,		2)
											self.assertEqual(len(_SCREAMING_SNAKE_CASE)      ,		2)
											self.assertEqual(len(_SCREAMING_SNAKE_CASE)      ,		2)
											self.assertEqual(concat_inputs.input_ids.shape      ,		(2, 1_0))
											self.assertEqual(concat_inputs.attention_mask.shape      ,		(2, 1_0))
											self.assertEqual(concat_inputs.token_type_ids.shape      ,		(2, 1_0))
											self.assertEqual(concat_inputs.special_tokens_mask.shape      ,		(2, 1_0))
											self.assertEqual(
											    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0])      ,		["""[CLS]""", """test""", """question""", """[SEP]""", """this""", """is""", """the""", """first""", """record""", """[SEP]"""]      ,		)
											self.assertEqual(
											    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1])      ,		["""[CLS]""", """test""", """question""", """[SEP]""", """this""", """is""", """the""", """fourth""", """record""", """[SEP]"""]      ,		)



				def        __magic_name__				(   self     :     Tuple):






											'''simple docstring'''







											snake_case__							=							self.get_config()
											snake_case__							=							self.get_dummy_retriever()
											snake_case__							=							retriever.tokenizer

											snake_case__							=							np.array([0, 3, 5]      ,		dtype="""long""")
											snake_case__							=							tokenizer(["""Test question"""]).input_ids
											snake_case__							=							tokenizer(
											    ["""the fourth""", """longer longer"""]      ,		add_special_tokens=_SCREAMING_SNAKE_CASE      ,		return_token_type_ids=_SCREAMING_SNAKE_CASE      ,		return_attention_mask=_SCREAMING_SNAKE_CASE      ,		).input_ids
											snake_case__							=							config.reader_seq_len

											snake_case__	,			snake_case__	,			snake_case__	,			snake_case__							=							retriever(
											    _SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		answer_ids=_SCREAMING_SNAKE_CASE      ,		max_length=_SCREAMING_SNAKE_CASE      ,		return_tensors="""np""")

											self.assertEqual([False, True, True]      ,		_SCREAMING_SNAKE_CASE)
											self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]      ,		_SCREAMING_SNAKE_CASE)
											self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]      ,		_SCREAMING_SNAKE_CASE)



				def        __magic_name__				(   self     :     Optional[Any]):






											'''simple docstring'''







											snake_case__							=							self.get_dummy_retriever()
											retriever.save_pretrained(os.path.join(self.tmpdirname      ,		"""realm_block_records"""))

											# Test local path
											snake_case__							=							retriever.from_pretrained(os.path.join(self.tmpdirname      ,		"""realm_block_records"""))
											self.assertEqual(retriever.block_records[0]      ,		b"""This is the first record""")

											# Test mocked remote path
											with patch("""transformers.models.realm.retrieval_realm.hf_hub_download""") as mock_hf_hub_download:
																		snake_case__							=							os.path.join(
																		    os.path.join(self.tmpdirname      ,		"""realm_block_records""")      ,		_REALM_BLOCK_RECORDS_FILENAME)
																		snake_case__							=							RealmRetriever.from_pretrained("""google/realm-cc-news-pretrained-openqa""")

											self.assertEqual(retriever.block_records[0]      ,		b"""This is the first record""")




import argparse
import json
import subprocess

def 			lowerCAmelCase__					(     a__:						List[str]     ,    a__:						Optional[int]			)    ->						Union[str, Any]:


							'''simple docstring'''





							_UpperCAmelCase											=     []

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

							_UpperCAmelCase											=     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:
														_UpperCAmelCase											=     '\n'.join([x['name'] for x in offline_runners]			)
														raise ValueError(F'''The following runners are offline:\n{failed}'''			)


if __name__ == "__main__":

		def 			lowerCAmelCase__					(     a__:						int			)    ->						List[Any]:


									'''simple docstring'''





									return values.split(','			)

		lowerCAmelCase__    :Optional[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.'''
		)
		lowerCAmelCase__    :Tuple       =				parser.parse_args()







		get_runner_status(args.target_runners, args.token)
