

import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel
def   _lowerCAmelCase(						__A ,							__A ,							__A      ):
  UpperCAmelCase   ,						UpperCAmelCase   ,						UpperCAmelCase          =	pt_weights[F"{prefix}.in_proj_weight"].chunk(3 ,							dim=0      )
  UpperCAmelCase   ,						UpperCAmelCase   ,						UpperCAmelCase          =	pt_weights[F"{prefix}.in_proj_bias"].chunk(3 ,							dim=0      )

  UpperCAmelCase          =	pt_weights[F"{prefix}.out_proj.weight"]
  UpperCAmelCase          =	pt_weights[F"{prefix}.out_proj.bias"]

  UpperCAmelCase          =	q_proj
  UpperCAmelCase          =	q_proj_bias

  UpperCAmelCase          =	k_proj
  UpperCAmelCase          =	k_proj_bias

  UpperCAmelCase          =	v_proj
  UpperCAmelCase          =	v_proj_bias

  UpperCAmelCase          =	out_proj_weights
  UpperCAmelCase          =	out_proj_bias
def   _lowerCAmelCase(						__A ,							__A ,							__A      ):
  copy_linear(hf_mlp.fca ,							__A ,							F"{prefix}.c_fc"      )
  copy_linear(hf_mlp.fca ,							__A ,							F"{prefix}.c_proj"      )
def   _lowerCAmelCase(						__A ,							__A ,							__A      ):
  UpperCAmelCase          =	pt_weights[F"{prefix}.weight"].data
  UpperCAmelCase          =	pt_weights[F"{prefix}.bias"].data
def   _lowerCAmelCase(						__A ,							__A ,							__A      ):
  # copy layer norms
  copy_linear(hf_layer.layer_norma ,							__A ,							F"{prefix}.ln_1"      )
  copy_linear(hf_layer.layer_norma ,							__A ,							F"{prefix}.ln_2"      )

  # copy MLP
  copy_mlp(hf_layer.mlp ,							__A ,							F"{prefix}.mlp"      )

  # copy attn
  copy_attn_layer(hf_layer.self_attn ,							__A ,							F"{prefix}.attn"      )
def   _lowerCAmelCase(						__A ,							__A ,							__A      ):
  for layer_id, hf_layer in enumerate(__A      ):
    copy_layer(__A ,							__A ,							F"{prefix}.{layer_id}"      )
def   _lowerCAmelCase(						__A ,							__A      ):
  # copy projection
  UpperCAmelCase          =	pt_weights["text_projection"].data.T

  # copy text encoder
  for name, param in hf_model.text_model.named_parameters():
    UpperCAmelCase          =	pt_weights[F"bert.{name}"].data
def   _lowerCAmelCase(						__A ,							__A      ):
  # copy projection
  UpperCAmelCase          =	pt_weights["visual.proj"].data.T

  # copy layer norms
  copy_linear(hf_model.vision_model.pre_layrnorm ,							__A ,							"visual.ln_pre"      )
  copy_linear(hf_model.vision_model.post_layernorm ,							__A ,							"visual.ln_post"      )

  # copy embeddings
  UpperCAmelCase          =	pt_weights["visual.conv1.weight"].data
  UpperCAmelCase          =	pt_weights["visual.class_embedding"].data
  UpperCAmelCase          =	pt_weights["visual.positional_embedding"].data

  # copy encoder
  copy_layers(hf_model.vision_model.encoder.layers ,							__A ,							"visual.transformer.resblocks"      )
@torch.no_grad()
def   _lowerCAmelCase(						__A ,							__A ,							__A=None      ):
  assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
  UpperCAmelCase          =	ChineseCLIPConfig.from_pretrained(__A      )

  UpperCAmelCase          =	ChineseCLIPModel(__A      ).eval()

  UpperCAmelCase          =	torch.load(__A ,							map_location="cpu"      )["state_dict"]
  UpperCAmelCase          =	{(name[7:] if name.startswith("module."      ) else name): value for name, value in pt_weights.items()}

  copy_text_model_and_projection(__A ,							__A      )
  copy_vision_model_and_projection(__A ,							__A      )
  UpperCAmelCase          =	pt_weights["logit_scale"].data

  hf_model.save_pretrained(__A      )


if __name__ == "__main__":
      lowerCAmelCase__							=			argparse.ArgumentParser()
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default=None,
          type=str,
          help="Path to the output folder storing converted hf PyTorch model.",
      )
      parser.add_argument(
          "--checkpoint_path", default=None, type=str, help="Path to original github format ChineseCLIP checkpoint."
      )
      parser.add_argument(
          "--config_path", default=None, required=True, type=str, help="Path to hf config.json of model to convert."
      )
      lowerCAmelCase__							=			parser.parse_args()

      convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
      print("The conversion is finished!")






lowerCAmelCase__							=			float("inf")






class     __magic_name__     :


 def __init__(    self		:			List[str]      ,    lowerCAmelCase__		:			Dict     )     ->   List[Any]:
   UpperCAmelCase          =	[0] * n
   UpperCAmelCase          =	[0] * n
   UpperCAmelCase          =	[0] * n
   UpperCAmelCase          =	[[] for _ in range(lowerCAmelCase__     )]


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			Dict      ,    lowerCAmelCase__		:			str      ,    lowerCAmelCase__		:			Optional[Any]=0     )     ->   Any:
   self.adj[a].append([b, len(self.adj[b]     ), c, 0]     )
   self.adj[b].append([a, len(self.adj[a]     ) - 1, rcap, 0]     )


 def _UpperCamelCase						(    self		:			List[str]      ,    lowerCAmelCase__		:			Union[str, Any]      ,    lowerCAmelCase__		:			List[Any]      ,    lowerCAmelCase__		:			Any     )     ->   List[str]:
   if vertex == sink or not flow:
     return flow

   for i in range(self.ptr[vertex]      ,    len(self.adj[vertex]     )     ):
     UpperCAmelCase          =	self.adj[vertex][i]
     if self.lvl[e[0]] == self.lvl[vertex] + 1:
       UpperCAmelCase          =	self.depth_first_search(e[0]      ,    lowerCAmelCase__      ,    min(lowerCAmelCase__      ,    e[2] - e[3]     )     )
       if p:
         self.adj[vertex][i][3] += p
         self.adj[e[0]][e[1]][3] -= p
         return p
     UpperCAmelCase          =	self.ptr[vertex] + 1
   return 0


 def _UpperCamelCase						(    self		:			str      ,    lowerCAmelCase__		:			str      ,    lowerCAmelCase__		:			Tuple     )     ->   int:
   UpperCAmelCase   ,						UpperCAmelCase          =	0, source
   for l in range(3_1     ):  # noqa: E741  l = 30 maybe faster for random data
     while True:
       UpperCAmelCase   ,						UpperCAmelCase          =	[0] * len(self.q     ), [0] * len(self.q     )
       UpperCAmelCase   ,						UpperCAmelCase   ,						UpperCAmelCase          =	0, 1, 1
       while qi < qe and not self.lvl[sink]:
         UpperCAmelCase          =	self.q[qi]
         qi += 1
         for e in self.adj[v]:
           if not self.lvl[e[0]] and (e[2] - e[3]) >> (3_0 - l):
             UpperCAmelCase          =	e[0]
             qe += 1
             UpperCAmelCase          =	self.lvl[v] + 1

       UpperCAmelCase          =	self.depth_first_search(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__     )
       while p:
         flow += p
         UpperCAmelCase          =	self.depth_first_search(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__     )

       if not self.lvl[sink]:
         break

   return flow
lowerCAmelCase__							=			Dinic(10)
lowerCAmelCase__							=			0
lowerCAmelCase__							=			9
for vertex in range(1, 5):
      graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
      graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
      graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))



