from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer



@dataclass
class  UpperCAmelCase__				( __lowercase ):

     '''simple docstring'''

     A_										=  42





class  UpperCAmelCase__				( __lowercase   ,  __lowercase ):

     '''simple docstring'''

     @register_to_config
     def __init__(					self				,				A_ = 3				,				A_ = 3				,				A_ = ("DownEncoderBlock2D",)				,				A_ = ("UpDecoderBlock2D",)				,				A_ = (64,)				,				A_ = 1				,				A_ = "silu"				,				A_ = 3				,				A_ = 32				,				A_ = 2_56				,				A_ = 32				,				A_ = None				,				A_ = 0.18215				,				A_ = "group"				,				) ->				Any:






           """simple docstring"""
           super().__init__()

           # pass init params to Encoder
           _lowerCamelCase			    =				Encoder(
               in_channels=A_				,				out_channels=A_				,				down_block_types=A_				,				block_out_channels=A_				,				layers_per_block=A_				,				act_fn=A_				,				norm_num_groups=A_				,				double_z=A_				,				)

           _lowerCamelCase			    =				vq_embed_dim if vq_embed_dim is not None else latent_channels

           _lowerCamelCase			    =				nn.Convad(A_				,				A_				,				1						)
           _lowerCamelCase			    =				VectorQuantizer(A_				,				A_				,				beta=0.25				,				remap=A_				,				sane_index_shape=A_						)
           _lowerCamelCase			    =				nn.Convad(A_				,				A_				,				1						)

           # pass init params to Decoder
           _lowerCamelCase			    =				Decoder(
               in_channels=A_				,				out_channels=A_				,				up_block_types=A_				,				block_out_channels=A_				,				layers_per_block=A_				,				act_fn=A_				,				norm_num_groups=A_				,				norm_type=A_				,				)



     @apply_forward_hook
     def 		UpperCamelCase_						(					self				,				A_				,				A_ = True						) ->				VQEncoderOutput:






           """simple docstring"""
           _lowerCamelCase			    =				self.encoder(A_						)
           _lowerCamelCase			    =				self.quant_conv(A_						)

           if not return_dict:
                 return (h,)

           return VQEncoderOutput(latents=A_						)



     @apply_forward_hook
     def 		UpperCamelCase_						(					self				,				A_				,				A_ = False				,				A_ = True						) ->				Union[DecoderOutput, torch.FloatTensor]:






           """simple docstring"""
           # also go through quantization layer
           if not force_not_quantize:
                 _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				self.quantize(A_						)
           else:
                 _lowerCamelCase			    =				h
           _lowerCamelCase			    =				self.post_quant_conv(A_						)
           _lowerCamelCase			    =				self.decoder(A_				,				quant if self.config.norm_type == '''spatial''' else None						)

           if not return_dict:
                 return (dec,)

           return DecoderOutput(sample=A_						)





     def 		UpperCamelCase_						(					self				,				A_				,				A_ = True						) ->				Union[DecoderOutput, torch.FloatTensor]:






           """simple docstring"""
           _lowerCamelCase			    =				sample
           _lowerCamelCase			    =				self.encode(A_						).latents
           _lowerCamelCase			    =				self.decode(A_						).sample

           if not return_dict:
                 return (dec,)

           return DecoderOutput(sample=A_						)
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)