


import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class       lowercase_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =DebertaTokenizer
				snake_case	    =True
				snake_case	    =DebertaTokenizerFast





				def 						__UpperCamelCase    (				self)  -> Tuple:
											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											a__      =[
											    'l',
											    'o',
											    'w',
											    'e',
											    'r',
											    's',
											    't',
											    'i',
											    'd',
											    'n',
											    '\u0120',
											    '\u0120l',
											    '\u0120n',
											    '\u0120lo',
											    '\u0120low',
											    'er',
											    '\u0120lowest',
											    '\u0120newer',
											    '\u0120wider',
											    '[UNK]',
											]
											a__      =dict(zip(lowercase_       ,  range(len(lowercase_))))
											a__      =['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
											a__      ={'unk_token': '[UNK]'}

											a__      =os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES['vocab_file'])
											a__      =os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES['merges_file'])
											with open(self.vocab_file       ,  'w'       ,  encoding='utf-8') as fp:
																		fp.write(json.dumps(lowercase_) + '\n')
											with open(self.merges_file       ,  'w'       ,  encoding='utf-8') as fp:
																		fp.write('\n'.join(lowercase_))





				def 						__UpperCamelCase    (				self       ,  **lowercase_)  -> int:
											kwargs.update(self.special_tokens_map)
											return self.tokenizer_class.from_pretrained(self.tmpdirname       ,  **lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> Optional[int]:
											a__      ='lower newer'
											a__      ='lower newer'
											return input_text, output_text





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =self.get_tokenizer()
											a__      ='lower newer'
											a__      =['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
											a__      =tokenizer.tokenize(lowercase_)
											self.assertListEqual(lowercase_       ,  lowercase_)

											a__      =tokens + [tokenizer.unk_token]
											a__      =[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_)       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =self.get_tokenizer()
											a__      =tokenizer('Hello'       ,  'World')
											a__      =[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
											self.assertListEqual(tokd['token_type_ids']       ,  lowercase_)





				@slow
				def 						__UpperCamelCase    (				self)  -> int:
											a__      =self.tokenizer_class.from_pretrained('microsoft/deberta-base')

											a__      =tokenizer.encode('sequence builders'       ,  add_special_tokens=lowercase_)
											a__      =tokenizer.encode('multi-sequence build'       ,  add_special_tokens=lowercase_)

											a__      =tokenizer.encode(
											    'sequence builders'       ,  add_special_tokens=lowercase_       ,  add_prefix_space=lowercase_)
											a__      =tokenizer.encode(
											    'sequence builders'       ,  'multi-sequence build'       ,  add_special_tokens=lowercase_       ,  add_prefix_space=lowercase_)

											a__      =tokenizer.build_inputs_with_special_tokens(lowercase_)
											a__      =tokenizer.build_inputs_with_special_tokens(lowercase_       ,  lowercase_)

											assert encoded_sentence == encoded_text_from_decode
											assert encoded_pair == encoded_pair_from_decode





				@slow
				def 						__UpperCamelCase    (				self)  -> str:
											a__      =[self.tokenizer_class]
											if self.test_rust_tokenizer:
																		tokenizer_classes.append(self.rust_tokenizer_class)

											for tokenizer_class in tokenizer_classes:
																		a__      =tokenizer_class.from_pretrained('microsoft/deberta-base')

																		a__      =[
																		    'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
																		    'ALBERT incorporates two parameter reduction techniques',
																		    'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
																		    ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
																		    ' vocabulary embedding.',
																		]

																		a__      =tokenizer(lowercase_       ,  padding=lowercase_)
																		a__      =[tokenizer.decode(lowercase_       ,  skip_special_tokens=lowercase_) for seq in encoding['input_ids']]

																		# fmt: off
																		a__      ={
																		    'input_ids': [
																		        [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																		        [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																		        [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
																		    ],
																		    'token_type_ids': [
																		        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																		        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																		        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
																		    ],
																		    'attention_mask': [
																		        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																		        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																		        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
																		    ]
																		}
																		# fmt: on

																		a__      =[
																		    'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
																		    'ALBERT incorporates two parameter reduction techniques',
																		    'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
																		    ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
																		    ' vocabulary embedding.',
																		]

																		self.assertDictEqual(encoding.data       ,  lowercase_)

																		for expected, decoded in zip(lowercase_       ,  lowercase_):
																									self.assertEqual(lowercase_       ,  lowercase_)





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)
