import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch



@require_torch
class  UpperCAmelCase__				:

     '''simple docstring'''

     @property
     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           return self.get_dummy_input()



     @property
     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           if self.block_type == "down":
                 return (4, 32, 16, 16)
           elif self.block_type == "mid":
                 return (4, 32, 32, 32)
           elif self.block_type == "up":
                 return (4, 32, 64, 64)

           raise ValueError(F'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'						)



     def 		UpperCamelCase_						(					self				,				A_=True				,				A_=False				,				A_=False				,				A_=False				,				) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				4
           _lowerCamelCase			    =				32
           _lowerCamelCase			    =				(32, 32)

           _lowerCamelCase			    =				torch.manual_seed(0						)
           _lowerCamelCase			    =				torch.device(_a						)
           _lowerCamelCase			    =				(batch_size, num_channels) + sizes
           _lowerCamelCase			    =				randn_tensor(_a				,				generator=_a				,				device=_a						)
           _lowerCamelCase			    =				{'''hidden_states''': hidden_states}

           if include_temb:
                 _lowerCamelCase			    =				1_28
                 _lowerCamelCase			    =				randn_tensor((batch_size, temb_channels)				,				generator=_a				,				device=_a						)

           if include_res_hidden_states_tuple:
                 _lowerCamelCase			    =				torch.manual_seed(1						)
                 _lowerCamelCase			    =				(randn_tensor(_a				,				generator=_a				,				device=_a						),)

           if include_encoder_hidden_states:
                 _lowerCamelCase			    =				floats_tensor((batch_size, 32, 32)						).to(_a						)

           if include_skip_sample:
                 _lowerCamelCase			    =				randn_tensor(((batch_size, 3) + sizes)				,				generator=_a				,				device=_a						)

           return dummy_input



     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				{
               '''in_channels''': 32,
               '''out_channels''': 32,
               '''temb_channels''': 1_28,
           }
           if self.block_type == "up":
                 _lowerCamelCase			    =				32

           if self.block_type == "mid":
                 init_dict.pop('''out_channels'''						)

           _lowerCamelCase			    =				self.dummy_input
           return init_dict, inputs_dict



     def 		UpperCamelCase_						(					self				,				A_						) ->				str:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.prepare_init_args_and_inputs_for_common()
           _lowerCamelCase			    =				self.block_class(**_a						)
           unet_block.to(_a						)
           unet_block.eval()

           with torch.no_grad():
                 _lowerCamelCase			    =				unet_block(**_a						)

           if isinstance(_a				,				_a						):
                 _lowerCamelCase			    =				output[0]

           self.assertEqual(output.shape				,				self.output_shape						)

           _lowerCamelCase			    =				output[0, -1, -3:, -3:]
           _lowerCamelCase			    =				torch.tensor(_a						).to(_a						)
           assert torch_all_close(output_slice.flatten()				,				_a				,				atol=5E-3						)





     @unittest.skipIf(torch_device == '''mps'''				,				'''Training is not supported in mps'''						)
     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.prepare_init_args_and_inputs_for_common()
           _lowerCamelCase			    =				self.block_class(**_a						)
           model.to(_a						)
           model.train()
           _lowerCamelCase			    =				model(**_a						)

           if isinstance(_a				,				_a						):
                 _lowerCamelCase			    =				output[0]

           _lowerCamelCase			    =				torch.device(_a						)
           _lowerCamelCase			    =				randn_tensor(output.shape				,				device=_a						)
           _lowerCamelCase			    =				torch.nn.functional.mse_loss(_a				,				_a						)
           loss.backward()
import argparse
import json
import subprocess
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    int:



      '''simple docstring'''

      _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(__UpperCAmelCase ,						shell=__UpperCAmelCase ,						stdout=subprocess.PIPE		)
      _lowerCamelCase			    =				output.stdout.decode('''utf-8'''		)
      _lowerCamelCase			    =				json.loads(__UpperCAmelCase		)

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


if __name__ == "__main__":
     def       __magic_name__(  __UpperCAmelCase		)						->    str:



           '''simple docstring'''

           return values.split(''','''		)

     snake_case__				= 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__				= parser.parse_args()




     get_runner_status(args.target_runners, args.token)