
import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel






def     lowerCamelCase__				(   a						,						a ) ->  Optional[Any]:
   _A:    Optional[int]						     =			UNetaDModel(**a )
   _A:    Any						     =			checkpoint['''all_modules.0.W'''].data
   _A:    Any						     =			checkpoint['''all_modules.0.W'''].data
   _A:    Optional[Any]						     =			checkpoint['''all_modules.1.weight'''].data
   _A:    Any						     =			checkpoint['''all_modules.1.bias'''].data

   _A:    Optional[Any]						     =			checkpoint['''all_modules.2.weight'''].data
   _A:    Optional[int]						     =			checkpoint['''all_modules.2.bias'''].data

   _A:    Optional[int]						     =			checkpoint['''all_modules.3.weight'''].data
   _A:    Optional[int]						     =			checkpoint['''all_modules.3.bias'''].data

   _A:    Union[str, Any]						     =			checkpoint[list(checkpoint.keys() )[-4]].data
   _A:    Union[str, Any]						     =			checkpoint[list(checkpoint.keys() )[-3]].data
   _A:    Any						     =			checkpoint[list(checkpoint.keys() )[-2]].data
   _A:    Any						     =			checkpoint[list(checkpoint.keys() )[-1]].data

   _A:    Union[str, Any]						     =			4

   def set_attention_weights(a						,						a						,						a ):
      _A:    List[str]						     =			old_checkpoint[f"""all_modules.{index}.NIN_0.W"""].data.T
      _A:    List[Any]						     =			old_checkpoint[f"""all_modules.{index}.NIN_1.W"""].data.T
      _A:    Union[str, Any]						     =			old_checkpoint[f"""all_modules.{index}.NIN_2.W"""].data.T

      _A:    List[Any]						     =			old_checkpoint[f"""all_modules.{index}.NIN_0.b"""].data
      _A:    Union[str, Any]						     =			old_checkpoint[f"""all_modules.{index}.NIN_1.b"""].data
      _A:    Any						     =			old_checkpoint[f"""all_modules.{index}.NIN_2.b"""].data

      _A:    Optional[Any]						     =			old_checkpoint[f"""all_modules.{index}.NIN_3.W"""].data.T
      _A:    int						     =			old_checkpoint[f"""all_modules.{index}.NIN_3.b"""].data

      _A:    List[Any]						     =			old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
      _A:    Optional[Any]						     =			old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

   def set_resnet_weights(a						,						a						,						a ):
      _A:    List[Any]						     =			old_checkpoint[f"""all_modules.{index}.Conv_0.weight"""].data
      _A:    str						     =			old_checkpoint[f"""all_modules.{index}.Conv_0.bias"""].data
      _A:    Dict						     =			old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
      _A:    Optional[Any]						     =			old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

      _A:    List[str]						     =			old_checkpoint[f"""all_modules.{index}.Conv_1.weight"""].data
      _A:    Optional[int]						     =			old_checkpoint[f"""all_modules.{index}.Conv_1.bias"""].data
      _A:    Dict						     =			old_checkpoint[f"""all_modules.{index}.GroupNorm_1.weight"""].data
      _A:    List[Any]						     =			old_checkpoint[f"""all_modules.{index}.GroupNorm_1.bias"""].data

      _A:    Any						     =			old_checkpoint[f"""all_modules.{index}.Dense_0.weight"""].data
      _A:    Tuple						     =			old_checkpoint[f"""all_modules.{index}.Dense_0.bias"""].data

      if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
         _A:    str						     =			old_checkpoint[f"""all_modules.{index}.Conv_2.weight"""].data
         _A:    Optional[Any]						     =			old_checkpoint[f"""all_modules.{index}.Conv_2.bias"""].data

   for i, block in enumerate(new_model_architecture.downsample_blocks ):
      _A:    Optional[int]						     =			hasattr(a						,						'''attentions''' )
      for j in range(len(block.resnets ) ):
         set_resnet_weights(block.resnets[j]						,						a						,						a )
         module_index += 1
         if has_attentions:
            set_attention_weights(block.attentions[j]						,						a						,						a )
            module_index += 1

      if hasattr(a						,						'''downsamplers''' ) and block.downsamplers is not None:
         set_resnet_weights(block.resnet_down						,						a						,						a )
         module_index += 1
         _A:    List[str]						     =			checkpoint[f"""all_modules.{module_index}.Conv_0.weight"""].data
         _A:    List[str]						     =			checkpoint[f"""all_modules.{module_index}.Conv_0.bias"""].data
         module_index += 1

   set_resnet_weights(new_model_architecture.mid_block.resnets[0]						,						a						,						a )
   module_index += 1
   set_attention_weights(new_model_architecture.mid_block.attentions[0]						,						a						,						a )
   module_index += 1
   set_resnet_weights(new_model_architecture.mid_block.resnets[1]						,						a						,						a )
   module_index += 1

   for i, block in enumerate(new_model_architecture.up_blocks ):
      _A:    Dict						     =			hasattr(a						,						'''attentions''' )
      for j in range(len(block.resnets ) ):
         set_resnet_weights(block.resnets[j]						,						a						,						a )
         module_index += 1
      if has_attentions:
         set_attention_weights(
             block.attentions[0]						,						a						,						a )  # why can there only be a single attention layer for up?
         module_index += 1

      if hasattr(a						,						'''resnet_up''' ) and block.resnet_up is not None:
         _A:    Optional[int]						     =			checkpoint[f"""all_modules.{module_index}.weight"""].data
         _A:    Optional[int]						     =			checkpoint[f"""all_modules.{module_index}.bias"""].data
         module_index += 1
         _A:    List[str]						     =			checkpoint[f"""all_modules.{module_index}.weight"""].data
         _A:    Optional[int]						     =			checkpoint[f"""all_modules.{module_index}.bias"""].data
         module_index += 1
         set_resnet_weights(block.resnet_up						,						a						,						a )
         module_index += 1

   _A:    Optional[Any]						     =			checkpoint[f"""all_modules.{module_index}.weight"""].data
   _A:    Optional[int]						     =			checkpoint[f"""all_modules.{module_index}.bias"""].data
   module_index += 1
   _A:    List[str]						     =			checkpoint[f"""all_modules.{module_index}.weight"""].data
   _A:    Optional[int]						     =			checkpoint[f"""all_modules.{module_index}.bias"""].data

   return new_model_architecture.state_dict()


if __name__ == "__main__":
   UpperCAmelCase__						:	Dict	     = argparse.ArgumentParser()

   parser.add_argument(
       '--checkpoint_path',
       default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin',
       type=str,
       required=False,
       help='Path to the checkpoint to convert.',
   )

   parser.add_argument(
       '--config_file',
       default='/Users/arthurzucker/Work/diffusers/ArthurZ/config.json',
       type=str,
       required=False,
       help='The config json file corresponding to the architecture.',
   )

   parser.add_argument(
       '--dump_path',
       default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt',
       type=str,
       required=False,
       help='Path to the output model.',
   )

   UpperCAmelCase__						:	int	     = parser.parse_args()

   UpperCAmelCase__						:	str	     = torch.load(args.checkpoint_path, map_location='cpu')

   with open(args.config_file) as f:
      UpperCAmelCase__						:	Optional[Any]	     = json.loads(f.read())

   UpperCAmelCase__						:	str	     = convert_ncsnpp_checkpoint(
       checkpoint,
       config,
   )

   if "sde" in config:
      del config["sde"]

   UpperCAmelCase__						:	Dict	     = UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

   try:
      UpperCAmelCase__						:	Optional[int]	     = ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

      UpperCAmelCase__						:	str	     = ScoreSdeVePipeline(unet=model, scheduler=scheduler)
      pipe.save_pretrained(args.dump_path)
   except:  # noqa: E722
      model.save_pretrained(args.dump_path)





from __future__ import annotations






def     lowerCamelCase__				(   a ) ->  Union[str, Any]:
   global graph, reversed_graph, scc, component, visit, stack
   if visit[u]:
      return
   _A:    Optional[Any]						     =			True
   for v in graph[u]:
      dfs(a )
   stack.append(a )






def     lowerCamelCase__				(   a ) ->  List[Any]:
   global graph, reversed_graph, scc, component, visit, stack
   if visit[u]:
      return
   _A:    str						     =			True
   component.append(a )
   for v in reversed_graph[u]:
      dfsa(a )






def     lowerCamelCase__				(   ) ->  str:
   global graph, reversed_graph, scc, component, visit, stack
   for i in range(a ):
      dfs(a )
   _A:    Optional[Any]						     =			[False] * n
   for i in stack[::-1]:
      if visit[i]:
         continue
      _A:    Optional[Any]						     =			[]
      dfsa(a )
      scc.append(a )
   return scc


if __name__ == "__main__":
   # n - no of nodes, m - no of edges
   UpperCAmelCase__     ,UpperCAmelCase__						:	Any	     = list(map(int, input().strip().split()))

   UpperCAmelCase__						:	list[list[int]]	     = [[] for _ in range(n)]  # graph
   UpperCAmelCase__						:	list[list[int]]	     = [[] for i in range(n)]  # reversed graph
   # input graph data (edges)
   for _ in range(m):
      UpperCAmelCase__     ,UpperCAmelCase__						:	str	     = list(map(int, input().strip().split()))
      graph[u].append(v)
      reversed_graph[v].append(u)

   UpperCAmelCase__						:	list[int]	     = []
   UpperCAmelCase__						:	list[bool]	     = [False] * n
   UpperCAmelCase__						:	list[int]	     = []
   UpperCAmelCase__						:	list[int]	     = []
   print(kosaraju())



