


"""simple docstring"""

import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig


def lowerCAmelCase_      (				UpperCamelCase__	: Dict				,   UpperCamelCase__	: int				,   UpperCamelCase__	: List[Any]				,   UpperCamelCase__	: List[Any] ):






 """simple docstring"""







 __lowercase									=	s.rsplit(UpperCamelCase__				,   UpperCamelCase__ )
 return new.join(UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any] ):






 """simple docstring"""







 return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items() )


def lowerCAmelCase_      (				UpperCamelCase__	: Tuple ):






 """simple docstring"""







 __lowercase									=	{}

 __lowercase									=	["""group_1""", """group_2""", """group_3""", """group_4"""]
 for key, value in state_dict.items():
  for group_key in group_keys:
   if group_key in key:
    __lowercase									=	key.replace(f'''{group_key}.'''				,   f'''{group_key}.group.''' )

  if "res_path" in key:
   __lowercase									=	key.replace("""res_path."""				,   """res_path.path.""" )

  if key.endswith(""".w""" ):
   __lowercase									=	rreplace(UpperCamelCase__				,   """.w"""				,   """.weight"""				,   1 )
  if key.endswith(""".b""" ):
   __lowercase									=	rreplace(UpperCamelCase__				,   """.b"""				,   """.bias"""				,   1 )

  __lowercase									=	value.float()

 return upgrade


@torch.no_grad()
def lowerCAmelCase_      (				UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: List[str]				,   UpperCamelCase__	: Any=None				,   UpperCamelCase__	: str=True ):






 """simple docstring"""







 from dall_e import Encoder

 __lowercase									=	Encoder()
 if os.path.exists(UpperCamelCase__ ):
  __lowercase									=	torch.load(UpperCamelCase__ )
 else:
  __lowercase									=	torch.hub.load_state_dict_from_url(UpperCamelCase__ )

 if isinstance(UpperCamelCase__				,   UpperCamelCase__ ):
  __lowercase									=	ckpt.state_dict()
 encoder.load_state_dict(UpperCamelCase__ )

 if config_path is not None:
  __lowercase									=	FlavaImageCodebookConfig.from_pretrained(UpperCamelCase__ )
 else:
  __lowercase									=	FlavaImageCodebookConfig()

 __lowercase									=	FlavaImageCodebook(UpperCamelCase__ ).eval()
 __lowercase									=	encoder.state_dict()

 __lowercase									=	upgrade_state_dict(UpperCamelCase__ )
 hf_model.load_state_dict(UpperCamelCase__ )
 __lowercase									=	hf_model.state_dict()
 __lowercase									=	count_parameters(UpperCamelCase__ )
 __lowercase									=	count_parameters(UpperCamelCase__ )

 assert torch.allclose(UpperCamelCase__				,   UpperCamelCase__				,   atol=1E-3 )

 if save_checkpoint:
  hf_model.save_pretrained(UpperCamelCase__ )
 else:
  return hf_state_dict


if __name__ == "__main__":
       UpperCAmelCase__  							=argparse.ArgumentParser()
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       UpperCAmelCase__  							=parser.parse_args()

       convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




"""simple docstring"""

def lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: int				,   UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: str ):






 """simple docstring"""







 __lowercase									=	[False] * len(UpperCamelCase__ )
 __lowercase									=	[]
 queue.append(UpperCamelCase__ )
 __lowercase									=	True

 while queue:
  __lowercase									=	queue.pop(0 )
  for ind in range(len(graph[u] ) ):
   if visited[ind] is False and graph[u][ind] > 0:
    queue.append(UpperCamelCase__ )
    __lowercase									=	True
    __lowercase									=	u

 return visited[t]


def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: Any				,   UpperCamelCase__	: Optional[int] ):






 """simple docstring"""







 __lowercase									=	[-1] * (len(UpperCamelCase__ ))
 __lowercase									=	0
 while bfs(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ ):
  __lowercase									=	float("""Inf""" )
  __lowercase									=	sink

  while s != source:
   # Find the minimum value in select path
   __lowercase									=	min(UpperCamelCase__				,   graph[parent[s]][s] )
   __lowercase									=	parent[s]

  max_flow += path_flow
  __lowercase									=	sink

  while v != source:
   __lowercase									=	parent[v]
   graph[u][v] -= path_flow
   graph[v][u] += path_flow
   __lowercase									=	parent[v]
 return max_flow


UpperCAmelCase__  							=[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

UpperCAmelCase__							,      UpperCAmelCase__  							=0, 5

print(ford_fulkerson(graph, source, sink))
