



import argparse
import json
import subprocess





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    []

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

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

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

    if len(_UpperCAmelCase    ) > 0:
        __a							=    '''\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     (      _UpperCAmelCase    ):
     return values.split(''','''    )

 __snake_case						:str						=					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.'''
 )
 __snake_case						:Optional[Any]						=					parser.parse_args()
 get_runner_status(args.target_runners, args.token)










from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case						:Dict						=					logging.get_logger(__name__)

__snake_case						:List[Any]						=					{
    '''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
        '''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'''
    ),
}

class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   List[str]						 =						'''gptsan-japanese'''
       UpperCamelCase__  :   Dict						 =						[
           '''past_key_values''',
       ]
       UpperCamelCase__  :   Dict						 =						{
           '''hidden_size''': '''d_model''',
           '''num_attention_heads''': '''num_heads''',
           '''num_hidden_layers''': '''num_layers''',
       }



       def __init__(		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=36_000					,		__SCREAMING_SNAKE_CASE							:					Tuple=1_280					,		__SCREAMING_SNAKE_CASE							:					List[Any]=1_024					,		__SCREAMING_SNAKE_CASE							:					List[Any]=8_192					,		__SCREAMING_SNAKE_CASE							:					str=4_096					,		__SCREAMING_SNAKE_CASE							:					Any=128					,		__SCREAMING_SNAKE_CASE							:					int=10					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=0					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=16					,		__SCREAMING_SNAKE_CASE							:					List[Any]=16					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=128					,		__SCREAMING_SNAKE_CASE							:					Tuple=0.0					,		__SCREAMING_SNAKE_CASE							:					List[Any]=1E-5					,		__SCREAMING_SNAKE_CASE							:					str=False					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=0.0					,		__SCREAMING_SNAKE_CASE							:					List[str]="float32"					,		__SCREAMING_SNAKE_CASE							:					List[str]=False					,		__SCREAMING_SNAKE_CASE							:					Any=False					,		__SCREAMING_SNAKE_CASE							:					Any=False					,		__SCREAMING_SNAKE_CASE							:					int=0.0_02					,		__SCREAMING_SNAKE_CASE							:					Any=False					,		__SCREAMING_SNAKE_CASE							:					Tuple=True					,		__SCREAMING_SNAKE_CASE							:					int=35_998					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=35_995					,		__SCREAMING_SNAKE_CASE							:					List[str]=35_999					,		**__SCREAMING_SNAKE_CASE							:					List[str]					,		):


           '''simple docstring'''

           __a							=    vocab_size
           __a							=    max_position_embeddings
           __a							=    d_model
           __a							=    d_ff
           __a							=    d_ext
           __a							=    d_spout
           __a							=    num_switch_layers
           __a							=    num_ext_layers
           __a							=    num_switch_layers + num_ext_layers
           __a							=    num_heads
           __a							=    num_experts
           __a							=    expert_capacity
           __a							=    dropout_rate
           __a							=    layer_norm_epsilon
           __a							=    router_bias
           __a							=    router_jitter_noise
           __a							=    router_dtype
           __a							=    router_ignore_padding_tokens
           __a							=    output_hidden_states
           __a							=    output_attentions
           __a							=    initializer_factor
           __a							=    output_router_logits
           __a							=    use_cache

           super().__init__(
               separator_token_id=__SCREAMING_SNAKE_CASE					,		pad_token_id=__SCREAMING_SNAKE_CASE					,		eos_token_id=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)





