




import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_					    = logging.get_logger()






@dataclass
class  _lowerCAmelCase     :
      A__		     =		42
      A__		     =		field(default_factory=UpperCamelCase_	)
      A__		     =		field(default_factory=UpperCamelCase_	)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Tuple		       =     len(list(m.modules()		)		) == 1 or isinstance(__UpperCAmelCase      ,				nn.Convad		) or isinstance(__UpperCAmelCase      ,				nn.BatchNormad		)
        if has_not_submodules:
          self.traced.append(__UpperCAmelCase		)

      def __call__(   self      ,				__UpperCAmelCase		):
        for m in self.module.modules():
          self.handles.append(m.register_forward_hook(self._forward_hook		)		)
        self.module(__UpperCAmelCase		)
        [x.remove() for x in self.handles]
        return self







      @property
      def 	__magic_name__(   self		):
        # check the len of the state_dict keys to see if we have learnable params
        return list(filter(lambda __UpperCAmelCase		: len(list(x.state_dict().keys()		)		) > 0      ,				self.traced		)		)






@dataclass
class  _lowerCAmelCase     :
      A__		     =		42
      A__		     =		42
      A__		     =		1
      A__		     =		field(default_factory=UpperCamelCase_	)
      A__		     =		field(default_factory=UpperCamelCase_	)
      A__		     =		True

      def __call__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  List[str]		       =     Tracker(self.dest		)(__UpperCAmelCase		).parametrized
        lowerCAmelCase__				:  Any		       =     Tracker(self.src		)(__UpperCAmelCase		).parametrized

        lowerCAmelCase__				:  Any		       =     list(filter(lambda __UpperCAmelCase		: type(__UpperCAmelCase		) not in self.src_skip      ,				__UpperCAmelCase		)		)
        lowerCAmelCase__				:  Tuple		       =     list(filter(lambda __UpperCAmelCase		: type(__UpperCAmelCase		) not in self.dest_skip      ,				__UpperCAmelCase		)		)

        if len(__UpperCAmelCase		) != len(__UpperCAmelCase		) and self.raise_if_mismatch:
          raise Exception(
              f"""Numbers of operations are different. Source module has {len(__UpperCAmelCase		)} operations while"""
              f""" destination module has {len(__UpperCAmelCase		)}."""		)

        for dest_m, src_m in zip(__UpperCAmelCase      ,				__UpperCAmelCase		):
          dest_m.load_state_dict(src_m.state_dict()		)
          if self.verbose == 1:
            print(f"""Transfered from={src_m} to={dest_m}"""		)






class  _lowerCAmelCase     (  nn.Module	):

      def __init__(   self      ,				__UpperCAmelCase		):
        super().__init__()

        lowerCAmelCase__				:  List[Tuple[str, nn.Module]]		       =     []
        # - get the stem
        feature_blocks.append(('''conv1''', model.stem)		)
        # - get all the feature blocks
        for k, v in model.trunk_output.named_children():
          assert k.startswith('''block'''		), f"""Unexpected layer name {k}"""
          lowerCAmelCase__				:  int		       =     len(__UpperCAmelCase		) + 1
          feature_blocks.append((f"""res{block_index}""", v)		)

        lowerCAmelCase__				:  Any		       =     nn.ModuleDict(__UpperCAmelCase		)







      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        return get_trunk_forward_outputs(
            __UpperCAmelCase      ,				out_feat_keys=__UpperCAmelCase      ,				feature_blocks=self._feature_blocks      ,				)






class  _lowerCAmelCase     (  UpperCamelCase_	):

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  List[str]		       =     x.split('''-'''		)
        return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]		)







      def __getitem__(   self      ,				__UpperCAmelCase		):
        # default to timm!
        if x not in self:
          lowerCAmelCase__				:  List[Any]		       =     self.convert_name_to_timm(__UpperCAmelCase		)
          lowerCAmelCase__				:  Optional[Any]		       =     partial(lambda: (timm.create_model(__UpperCAmelCase      ,				pretrained=__UpperCAmelCase		).eval(), None)		)

        else:
          lowerCAmelCase__				:  int		       =     super().__getitem__(__UpperCAmelCase		)

        return val






class  _lowerCAmelCase     (  UpperCamelCase_	):

      def __getitem__(   self      ,				__UpperCAmelCase		):
        if "seer" in x and "in1k" not in x:
          lowerCAmelCase__				:  int		       =     RegNetModel
        else:
          lowerCAmelCase__				:  Optional[Any]		       =     RegNetForImageClassification
        return val



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	int:
  for from_key, to_key in keys:
    lowerCAmelCase__				:  Dict		       =     from_state_dict[from_key].clone()
    print(F"""Copied key={from_key} to={to_key}"""      )
  return to_state_dict



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase = True	,      )     ->	Union[str, Any]:
  print(F"""Converting {name}..."""      )
  with torch.no_grad():
    lowerCAmelCase__				:  Union[str, Any]		       =     from_model_func()
    lowerCAmelCase__				:  Any		       =     our_model_func(snake_case_      ).eval()
    lowerCAmelCase__				:  Optional[Any]		       =     ModuleTransfer(src=snake_case_	,      dest=snake_case_	,      raise_if_mismatch=snake_case_      )
    lowerCAmelCase__				:  List[str]		       =     torch.randn((1, 3, 224, 224)      )
    module_transfer(snake_case_      )

  if from_state_dict is not None:
    lowerCAmelCase__				:  int		       =     []
    # for seer - in1k finetuned we have to manually copy the head
    if "seer" in name and "in1k" in name:
      lowerCAmelCase__				:  List[str]		       =     [('''0.clf.0.weight''', '''classifier.1.weight'''), ('''0.clf.0.bias''', '''classifier.1.bias''')]
    lowerCAmelCase__				:  Union[str, Any]		       =     manually_copy_vissl_head(snake_case_	,      our_model.state_dict()	,      snake_case_      )
    our_model.load_state_dict(snake_case_      )

  lowerCAmelCase__				:  Tuple		       =     our_model(snake_case_	,      output_hidden_states=snake_case_      )
  lowerCAmelCase__				:  List[str]		       =     (
      our_outputs.logits if isinstance(snake_case_	,      snake_case_      ) else our_outputs.last_hidden_state
  )

  lowerCAmelCase__				:  str		       =     from_model(snake_case_      )
  lowerCAmelCase__				:  List[str]		       =     from_output[-1] if type(snake_case_      ) is list else from_output

  # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
  if "seer" in name and "in1k" in name:
    lowerCAmelCase__				:  Tuple		       =     our_outputs.hidden_states[-1]

  assert torch.allclose(snake_case_	,      snake_case_      ), "The model logits don't match the original one."

  if push_to_hub:
    our_model.push_to_hub(
        repo_path_or_name=save_directory / name	,      commit_message='''Add model'''	,      use_temp_dir=snake_case_	,      )

    lowerCAmelCase__				:  List[str]		       =     224 if '''seer''' not in name else 384
    # we can use the convnext one
    lowerCAmelCase__				:  Union[str, Any]		       =     AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''	,      size=snake_case_      )
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / name	,      commit_message='''Add image processor'''	,      use_temp_dir=snake_case_	,      )

    print(F"""Pushed {name}"""      )



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase = None	,      UpperCamelCase = True      )     ->	Optional[int]:
  lowerCAmelCase__				:  List[str]		       =     '''imagenet-1k-id2label.json'''
  lowerCAmelCase__				:  int		       =     1000
  lowerCAmelCase__				:  List[Any]		       =     (1, num_labels)

  lowerCAmelCase__				:  Optional[int]		       =     '''huggingface/label-files'''
  lowerCAmelCase__				:  Optional[Any]		       =     num_labels
  lowerCAmelCase__				:  Union[str, Any]		       =     json.load(open(cached_download(hf_hub_url(snake_case_	,      snake_case_	,      repo_type='''dataset'''      )      )	,      '''r'''      )      )
  lowerCAmelCase__				:  Optional[Any]		       =     {int(snake_case_      ): v for k, v in idalabel.items()}

  lowerCAmelCase__				:  int		       =     idalabel
  lowerCAmelCase__				:  int		       =     {v: k for k, v in idalabel.items()}

  lowerCAmelCase__				:  List[Any]		       =     partial(snake_case_	,      num_labels=snake_case_	,      idalabel=snake_case_	,      labelaid=snake_case_      )

  lowerCAmelCase__				:  Tuple		       =     {
      '''regnet-x-002''': ImageNetPreTrainedConfig(
          depths=[1, 1, 4, 7]	,      hidden_sizes=[24, 56, 152, 368]	,      groups_width=8	,      layer_type='''x'''      ),
      '''regnet-x-004''': ImageNetPreTrainedConfig(
          depths=[1, 2, 7, 12]	,      hidden_sizes=[32, 64, 160, 384]	,      groups_width=16	,      layer_type='''x'''      ),
      '''regnet-x-006''': ImageNetPreTrainedConfig(
          depths=[1, 3, 5, 7]	,      hidden_sizes=[48, 96, 240, 528]	,      groups_width=24	,      layer_type='''x'''      ),
      '''regnet-x-008''': ImageNetPreTrainedConfig(
          depths=[1, 3, 7, 5]	,      hidden_sizes=[64, 128, 288, 672]	,      groups_width=16	,      layer_type='''x'''      ),
      '''regnet-x-016''': ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 2]	,      hidden_sizes=[72, 168, 408, 912]	,      groups_width=24	,      layer_type='''x'''      ),
      '''regnet-x-032''': ImageNetPreTrainedConfig(
          depths=[2, 6, 15, 2]	,      hidden_sizes=[96, 192, 432, 1008]	,      groups_width=48	,      layer_type='''x'''      ),
      '''regnet-x-040''': ImageNetPreTrainedConfig(
          depths=[2, 5, 14, 2]	,      hidden_sizes=[80, 240, 560, 1360]	,      groups_width=40	,      layer_type='''x'''      ),
      '''regnet-x-064''': ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 1]	,      hidden_sizes=[168, 392, 784, 1624]	,      groups_width=56	,      layer_type='''x'''      ),
      '''regnet-x-080''': ImageNetPreTrainedConfig(
          depths=[2, 5, 15, 1]	,      hidden_sizes=[80, 240, 720, 1920]	,      groups_width=120	,      layer_type='''x'''      ),
      '''regnet-x-120''': ImageNetPreTrainedConfig(
          depths=[2, 5, 11, 1]	,      hidden_sizes=[224, 448, 896, 2240]	,      groups_width=112	,      layer_type='''x'''      ),
      '''regnet-x-160''': ImageNetPreTrainedConfig(
          depths=[2, 6, 13, 1]	,      hidden_sizes=[256, 512, 896, 2048]	,      groups_width=128	,      layer_type='''x'''      ),
      '''regnet-x-320''': ImageNetPreTrainedConfig(
          depths=[2, 7, 13, 1]	,      hidden_sizes=[336, 672, 1344, 2520]	,      groups_width=168	,      layer_type='''x'''      ),
      # y variant
      '''regnet-y-002''': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]	,      hidden_sizes=[24, 56, 152, 368]	,      groups_width=8      ),
      '''regnet-y-004''': ImageNetPreTrainedConfig(
          depths=[1, 3, 6, 6]	,      hidden_sizes=[48, 104, 208, 440]	,      groups_width=8      ),
      '''regnet-y-006''': ImageNetPreTrainedConfig(
          depths=[1, 3, 7, 4]	,      hidden_sizes=[48, 112, 256, 608]	,      groups_width=16      ),
      '''regnet-y-008''': ImageNetPreTrainedConfig(
          depths=[1, 3, 8, 2]	,      hidden_sizes=[64, 128, 320, 768]	,      groups_width=16      ),
      '''regnet-y-016''': ImageNetPreTrainedConfig(
          depths=[2, 6, 17, 2]	,      hidden_sizes=[48, 120, 336, 888]	,      groups_width=24      ),
      '''regnet-y-032''': ImageNetPreTrainedConfig(
          depths=[2, 5, 13, 1]	,      hidden_sizes=[72, 216, 576, 1512]	,      groups_width=24      ),
      '''regnet-y-040''': ImageNetPreTrainedConfig(
          depths=[2, 6, 12, 2]	,      hidden_sizes=[128, 192, 512, 1088]	,      groups_width=64      ),
      '''regnet-y-064''': ImageNetPreTrainedConfig(
          depths=[2, 7, 14, 2]	,      hidden_sizes=[144, 288, 576, 1296]	,      groups_width=72      ),
      '''regnet-y-080''': ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 1]	,      hidden_sizes=[168, 448, 896, 2016]	,      groups_width=56      ),
      '''regnet-y-120''': ImageNetPreTrainedConfig(
          depths=[2, 5, 11, 1]	,      hidden_sizes=[224, 448, 896, 2240]	,      groups_width=112      ),
      '''regnet-y-160''': ImageNetPreTrainedConfig(
          depths=[2, 4, 11, 1]	,      hidden_sizes=[224, 448, 1232, 3024]	,      groups_width=112      ),
      '''regnet-y-320''': ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1]	,      hidden_sizes=[232, 696, 1392, 3712]	,      groups_width=232      ),
      # models created by SEER -> https://arxiv.org/abs/2202.08360
      '''regnet-y-320-seer''': RegNetConfig(depths=[2, 5, 12, 1]	,      hidden_sizes=[232, 696, 1392, 3712]	,      groups_width=232      ),
      '''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 12, 1]	,      hidden_sizes=[328, 984, 1968, 4920]	,      groups_width=328      ),
      '''regnet-y-1280-seer''': RegNetConfig(
          depths=[2, 7, 17, 1]	,      hidden_sizes=[528, 1056, 2904, 7392]	,      groups_width=264      ),
      '''regnet-y-2560-seer''': RegNetConfig(
          depths=[3, 7, 16, 1]	,      hidden_sizes=[640, 1696, 2544, 5088]	,      groups_width=640      ),
      '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]	,      hidden_sizes=[2020, 4040, 11110, 28280]	,      groups_width=1010      ),
      # finetuned on imagenet
      '''regnet-y-320-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1]	,      hidden_sizes=[232, 696, 1392, 3712]	,      groups_width=232      ),
      '''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1]	,      hidden_sizes=[328, 984, 1968, 4920]	,      groups_width=328      ),
      '''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]	,      hidden_sizes=[528, 1056, 2904, 7392]	,      groups_width=264      ),
      '''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[3, 7, 16, 1]	,      hidden_sizes=[640, 1696, 2544, 5088]	,      groups_width=640      ),
      '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]	,      hidden_sizes=[2020, 4040, 11110, 28280]	,      groups_width=1010      ),
  }

  lowerCAmelCase__				:  List[str]		       =     NameToOurModelFuncMap()
  lowerCAmelCase__				:  Tuple		       =     NameToFromModelFuncMap()
  # add seer weights logic

  def load_using_classy_vision(UpperCamelCase	,      UpperCamelCase      ) -> Tuple[nn.Module, Dict]:
    lowerCAmelCase__				:  List[str]		       =     torch.hub.load_state_dict_from_url(snake_case_	,      model_dir=str(snake_case_      )	,      map_location='''cpu'''      )
    lowerCAmelCase__				:  Optional[int]		       =     model_func()
    # check if we have a head, if yes add it
    lowerCAmelCase__				:  Optional[int]		       =     files['''classy_state_dict''']['''base_model''']['''model''']
    lowerCAmelCase__				:  Dict		       =     model_state_dict['''trunk''']
    model.load_state_dict(snake_case_      )
    return model.eval(), model_state_dict["heads"]

  # pretrained
  lowerCAmelCase__				:  Optional[Any]		       =     partial(
      snake_case_	,      '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''	,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )	,      )

  lowerCAmelCase__				:  Dict		       =     partial(
      snake_case_	,      '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'''	,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )	,      )

  lowerCAmelCase__				:  Tuple		       =     partial(
      snake_case_	,      '''https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'''	,      lambda: FakeRegNetVisslWrapper(RegNetYaaagf()      )	,      )

  lowerCAmelCase__				:  Any		       =     partial(
      snake_case_	,      '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''	,      lambda: FakeRegNetVisslWrapper(
          RegNet(RegNetParams(depth=27	,      group_width=1010	,      w_a=1744	,      w_a=620.83	,      w_m=2.52      )      )      )	,      )

  # IN1K finetuned
  lowerCAmelCase__				:  Tuple		       =     partial(
      snake_case_	,      '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'''	,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )	,      )

  lowerCAmelCase__				:  Optional[Any]		       =     partial(
      snake_case_	,      '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'''	,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )	,      )

  lowerCAmelCase__				:  Any		       =     partial(
      snake_case_	,      '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'''	,      lambda: FakeRegNetVisslWrapper(RegNetYaaagf()      )	,      )

  lowerCAmelCase__				:  int		       =     partial(
      snake_case_	,      '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''	,      lambda: FakeRegNetVisslWrapper(
          RegNet(RegNetParams(depth=27	,      group_width=1010	,      w_a=1744	,      w_a=620.83	,      w_m=2.52      )      )      )	,      )

  if model_name:
    convert_weight_and_push(
        snake_case_	,      names_to_from_model_map[model_name]	,      names_to_ours_model_map[model_name]	,      names_to_config[model_name]	,      snake_case_	,      snake_case_	,      )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(
          snake_case_	,      names_to_from_model_map[model_name]	,      names_to_ours_model_map[model_name]	,      snake_case_	,      snake_case_	,      snake_case_	,      )
  return config, expected_shape


if __name__ == "__main__":
     lowerCAmelCase_					    = argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--model_name""",
         default=None,
         type=str,
         help=(
             """The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
             """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default=None,
         type=Path,
         required=True,
         help="""Path to the output PyTorch model directory.""",
     )
     parser.add_argument(
         """--push_to_hub""",
         default=True,
         type=bool,
         required=False,
         help="""If True, push model and image processor to the hub.""",
     )

     lowerCAmelCase_					    = parser.parse_args()

     lowerCAmelCase_					    = args.pytorch_dump_folder_path
     pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
     convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)







lowerCAmelCase_					    = [
    [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],
]



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	int:
  # Return True if there is node that has not iterated.
  lowerCAmelCase__				:  List[str]		       =     [False] * len(UpperCamelCase      )
  lowerCAmelCase__				:  int		       =     [s]
  lowerCAmelCase__				:  Dict		       =     True

  while queue:
    lowerCAmelCase__				:  Union[str, Any]		       =     queue.pop(0      )
    for ind in range(len(graph[u]      )      ):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(UpperCamelCase      )
        lowerCAmelCase__				:  List[str]		       =     True
        lowerCAmelCase__				:  int		       =     u

  return visited[t]



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Optional[Any]:
  lowerCAmelCase__				:  Dict		       =     [-1] * (len(UpperCamelCase      ))
  lowerCAmelCase__				:  Dict		       =     0
  lowerCAmelCase__				:  List[str]		       =     []
  lowerCAmelCase__				:  Tuple		       =     [i[:] for i in graph]  # Record original cut, copy.
  while bfs(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      ):
    lowerCAmelCase__				:  Optional[Any]		       =     float('''Inf'''      )
    lowerCAmelCase__				:  Optional[Any]		       =     sink

    while s != source:
      # Find the minimum value in select path
      lowerCAmelCase__				:  Optional[Any]		       =     min(UpperCamelCase	,      graph[parent[s]][s]      )
      lowerCAmelCase__				:  Union[str, Any]		       =     parent[s]

    max_flow += path_flow
    lowerCAmelCase__				:  List[str]		       =     sink

    while v != source:
      lowerCAmelCase__				:  Union[str, Any]		       =     parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      lowerCAmelCase__				:  Dict		       =     parent[v]

  for i in range(len(UpperCamelCase      )      ):
    for j in range(len(graph[0]      )      ):
      if graph[i][j] == 0 and temp[i][j] > 0:
        res.append((i, j)      )

  return res


if __name__ == "__main__":
     print(mincut(test_graph, source=0, sink=5))

