






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()
snake_case__   :						Optional[Any]													=   logging.get_logger()


@dataclass
class     _a							:






  """simple docstring"""





  A_						       = 42
  A_						       = field(default_factory=UpperCAmelCase__     )
  A_						       = field(default_factory=UpperCAmelCase__     )




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			str:
     UpperCamelCase_						=  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			)      ->			List[str]:
     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  _UpperCAmelCase  (  self			)      ->			List[str]:
     # 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     _a							:






  """simple docstring"""





  A_						       = 42
  A_						       = 42
  A_						       = 1
  A_						       = field(default_factory=UpperCAmelCase__     )
  A_						       = field(default_factory=UpperCAmelCase__     )
  A_						       = True




  def __call__(  self		,		_UpperCAmelCase			)      ->			Dict:
     UpperCamelCase_						=  Tracker(self.dest			)(_UpperCAmelCase			).parametrized
     UpperCamelCase_						=  Tracker(self.src			)(_UpperCAmelCase			).parametrized

     UpperCamelCase_						=  list(filter(lambda _UpperCAmelCase			: type(_UpperCAmelCase			) not in self.src_skip		,		_UpperCAmelCase			)			)
     UpperCamelCase_						=  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     _a							(	nn.Module     ):






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase			)      ->			Optional[Any]:
     super().__init__()

     UpperCamelCase_						=  []
     # - 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}"""
        UpperCamelCase_						=  len(_UpperCAmelCase			) + 1
        feature_blocks.append((f"""res{block_index}""", v)			)

     UpperCamelCase_						=  nn.ModuleDict(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			List[str]:
     return get_trunk_forward_outputs(
         _UpperCAmelCase		,		out_feat_keys=_UpperCAmelCase		,		feature_blocks=self._feature_blocks		,		)




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			str:
     UpperCamelCase_						=  x.split('-'			)
     return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]			)




  def __getitem__(  self		,		_UpperCAmelCase			)      ->			Callable[[], Tuple[nn.Module, Dict]]:
     # default to timm!
     if x not in self:
        UpperCamelCase_						=  self.convert_name_to_timm(_UpperCAmelCase			)
        UpperCamelCase_						=  partial(lambda: (timm.create_model(_UpperCAmelCase		,		pretrained=_UpperCAmelCase			).eval(), None)			)

     else:
        UpperCamelCase_						=  super().__getitem__(_UpperCAmelCase			)

     return val




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def __getitem__(  self		,		_UpperCAmelCase			)      ->			Callable[[], nn.Module]:
     if "seer" in x and "in1k" not in x:
        UpperCamelCase_						=  RegNetModel
     else:
        UpperCamelCase_						=  RegNetForImageClassification
     return val






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   for from_key, to_key in keys:
      UpperCamelCase_						=  from_state_dict[from_key].clone()
      print(f"""Copied key={from_key} to={to_key}""")
   return to_state_dict






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase = True     ,   ):
   print(f"""Converting {name}...""")
   with torch.no_grad():
      UpperCamelCase_		,   UpperCamelCase_						=  from_model_func()
      UpperCamelCase_						=  our_model_func(__lowercase).eval()
      UpperCamelCase_						=  ModuleTransfer(src=__lowercase     ,   dest=__lowercase     ,   raise_if_mismatch=__lowercase)
      UpperCamelCase_						=  torch.randn((1, 3, 224, 224))
      module_transfer(__lowercase)

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

   UpperCamelCase_						=  our_model(__lowercase     ,   output_hidden_states=__lowercase)
   UpperCamelCase_						=  (
       our_outputs.logits if isinstance(__lowercase     ,   __lowercase) else our_outputs.last_hidden_state
   )

   UpperCamelCase_						=  from_model(__lowercase)
   UpperCamelCase_						=  from_output[-1] if type(__lowercase) 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:
      UpperCamelCase_						=  our_outputs.hidden_states[-1]

   assert torch.allclose(__lowercase     ,   __lowercase), "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=__lowercase     ,   )

      UpperCamelCase_						=  224 if 'seer' not in name else 384
      # we can use the convnext one
      UpperCamelCase_						=  AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'     ,   size=__lowercase)
      image_processor.push_to_hub(
          repo_path_or_name=save_directory / name     ,   commit_message='Add image processor'     ,   use_temp_dir=__lowercase     ,   )

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






def      _snake_case     (__lowercase     ,   __lowercase = None     ,   __lowercase = True):
   UpperCamelCase_						=  'imagenet-1k-id2label.json'
   UpperCamelCase_						=  1000
   UpperCamelCase_						=  (1, num_labels)

   UpperCamelCase_						=  'huggingface/label-files'
   UpperCamelCase_						=  num_labels
   UpperCamelCase_						=  json.load(open(cached_download(hf_hub_url(__lowercase     ,   __lowercase     ,   repo_type='dataset'))     ,   'r'))
   UpperCamelCase_						=  {int(__lowercase): v for k, v in idalabel.items()}

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

   UpperCamelCase_						=  partial(__lowercase     ,   num_labels=__lowercase     ,   idalabel=__lowercase     ,   labelaid=__lowercase)

   UpperCamelCase_						=  {
       '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),
   }

   UpperCamelCase_						=  NameToOurModelFuncMap()
   UpperCamelCase_						=  NameToFromModelFuncMap()
   # add seer weights logic

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

   # pretrained
   UpperCamelCase_						=  partial(
       __lowercase     ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'     ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf())     ,   )

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

   UpperCamelCase_						=  partial(
       __lowercase     ,   '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())     ,   )

   UpperCamelCase_						=  partial(
       __lowercase     ,   '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
   UpperCamelCase_						=  partial(
       __lowercase     ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'     ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf())     ,   )

   UpperCamelCase_						=  partial(
       __lowercase     ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'     ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf())     ,   )

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

   UpperCamelCase_						=  partial(
       __lowercase     ,   '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(
          __lowercase     ,   names_to_from_model_map[model_name]     ,   names_to_ours_model_map[model_name]     ,   names_to_config[model_name]     ,   __lowercase     ,   __lowercase     ,   )
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(
             __lowercase     ,   names_to_from_model_map[model_name]     ,   names_to_ours_model_map[model_name]     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   )
   return config, expected_shape


if __name__ == "__main__":
 snake_case__   :						List[Any]													=   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.""",
 )

 snake_case__   :						Dict													=   parser.parse_args()

 snake_case__   :						Path													=   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)







"""simple docstring"""






from collections.abc import Callable





class 				a__					:



     def __init__(							self :Tuple      ,  _lowerCamelCase :Callable | None = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					list   	=[]
      # Stores indexes of each item for supporting updates and deletion.
      UpperCamelCase_     :					dict   	={}
      # Stores current size of heap.
      UpperCamelCase_     :					Any   	=0
      # Stores function used to evaluate the score of an item on which basis ordering
      # will be done.
      UpperCamelCase_     :					List[str]   	=key or (lambda _lowerCamelCase   : x)



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      return int((i - 1) / 2   ) if i > 0 else None



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[str]   	=int(2 * i + 1   )
      return left if 0 < left < self.size else None



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=int(2 * i + 2   )
      return right if 0 < right < self.size else None



     def  lowerCamelCase_						(							self :Dict      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_	,     UpperCamelCase_     :					Optional[int]   	=(
          self.pos_map[self.arr[j][0]],
          self.pos_map[self.arr[i][0]],
      )
      # Then swap the items in the list.
      UpperCamelCase_	,     UpperCamelCase_     :					Union[str, Any]   	=self.arr[j], self.arr[i]



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      return self.arr[i][1] < self.arr[j][1]



     def  lowerCamelCase_						(							self :Any      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=self._left(_lowerCamelCase   )
      UpperCamelCase_     :					List[Any]   	=self._right(_lowerCamelCase   )
      UpperCamelCase_     :					Optional[Any]   	=i

      if left is not None and not self._cmp(_lowerCamelCase      ,  _lowerCamelCase   ):
       UpperCamelCase_     :					Optional[int]   	=left
      if right is not None and not self._cmp(_lowerCamelCase      ,  _lowerCamelCase   ):
       UpperCamelCase_     :					List[Any]   	=right

      return valid_parent



     def  lowerCamelCase_						(							self :Any      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=self._parent(_lowerCamelCase   )
      while parent is not None and not self._cmp(_lowerCamelCase      ,  _lowerCamelCase   ):
       self._swap(_lowerCamelCase      ,  _lowerCamelCase   )
       UpperCamelCase_	,     UpperCamelCase_     :					Dict   	=parent, self._parent(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :List[str]      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=self._get_valid_parent(_lowerCamelCase   )
      while valid_parent != index:
       self._swap(_lowerCamelCase      ,  _lowerCamelCase   )
       UpperCamelCase_	,     UpperCamelCase_     :					int   	=valid_parent, self._get_valid_parent(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      if item not in self.pos_map:
       return
      UpperCamelCase_     :					List[Any]   	=self.pos_map[item]
      UpperCamelCase_     :					int   	=[item, self.key(_lowerCamelCase   )]
      # Make sure heap is right in both up and down direction.
      # Ideally only one of them will make any change.
      self._heapify_up(_lowerCamelCase   )
      self._heapify_down(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      if item not in self.pos_map:
       return
      UpperCamelCase_     :					Any   	=self.pos_map[item]
      del self.pos_map[item]
      UpperCamelCase_     :					Dict   	=self.arr[self.size - 1]
      UpperCamelCase_     :					Optional[int]   	=index
      self.size -= 1
      # Make sure heap is right in both up and down direction. Ideally only one
      # of them will make any change- so no performance loss in calling both.
      if self.size > index:
       self._heapify_up(_lowerCamelCase   )
       self._heapify_down(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Optional[int]      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[int]   	=len(self.arr   )
      if arr_len == self.size:
       self.arr.append([item, self.key(_lowerCamelCase   )]   )
      else:
       UpperCamelCase_     :					str   	=[item, self.key(_lowerCamelCase   )]
      UpperCamelCase_     :					Optional[int]   	=self.size
      self.size += 1
      self._heapify_up(self.size - 1   )



     def  lowerCamelCase_						(							self :List[Any]   ):







      '''simple docstring'''



      return self.arr[0] if self.size else None




     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=self.get_top()
      if top_item_tuple:
       self.delete_item(top_item_tuple[0]   )
      return top_item_tuple




def       A_  (				):
 pass


if __name__ == "__main__":
 import doctest

 doctest.testmod()
