

'''simple docstring'''

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()
A_       						=							logging.get_logger()





@dataclass
class 							_snake_case :
   _A       :     Any       =					4_2
   _A       :     Optional[Any]       =					field(default_factory=_a						)
   _A       :     List[str]       =					field(default_factory=_a						)
   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Dict     ,SCREAMING_SNAKE_CASE__			:      Tensor     ,SCREAMING_SNAKE_CASE__			:      Tensor							):
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					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			:      int     ,SCREAMING_SNAKE_CASE__			:      Tensor							):
    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			:      str							):
    # check the len of the state_dict keys to see if we have learnable params
    return list(filter(lambda SCREAMING_SNAKE_CASE__							: len(list(x.state_dict().keys()							)							) > 0     ,self.traced							)							)





@dataclass
class 							_snake_case :
   _A       :     str       =					4_2
   _A       :     str       =					4_2
   _A       :     Union[str, Any]       =					1
   _A       :     List[Any]       =					field(default_factory=_a						)
   _A       :     List[str]       =					field(default_factory=_a						)
   _A       :     Any       =					True
   def __call__( self			:      str     ,SCREAMING_SNAKE_CASE__			:      Tensor							):
    SCREAMING_SNAKE_CASE:List[Any]						      =					Tracker(self.dest							)(UpperCAmelCase__							).parametrized
    SCREAMING_SNAKE_CASE:List[str]						      =					Tracker(self.src							)(UpperCAmelCase__							).parametrized

    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					list(filter(lambda SCREAMING_SNAKE_CASE__							: type(UpperCAmelCase__							) not in self.src_skip     ,UpperCAmelCase__							)							)
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					list(filter(lambda SCREAMING_SNAKE_CASE__							: 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 							_snake_case (       nn.Module						):
   def __init__( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      nn.Module							):
    super().__init__()

    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[]
    # - 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}'''
     SCREAMING_SNAKE_CASE:List[str]						      =					len(UpperCAmelCase__							) + 1
     feature_blocks.append((F'''res{block_index}''', v)							)

    SCREAMING_SNAKE_CASE:Tuple						      =					nn.ModuleDict(UpperCAmelCase__							)







   def     __UpperCamelCase						( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      Tensor							):
    return get_trunk_forward_outputs(
        UpperCAmelCase__     ,out_feat_keys=UpperCAmelCase__     ,feature_blocks=self._feature_blocks     ,)





class 							_snake_case (       _a						):
   def     __UpperCamelCase						( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      str							):
    SCREAMING_SNAKE_CASE:Tuple						      =					x.split("-"							)
    return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]							)







   def __getitem__( self			:      int     ,SCREAMING_SNAKE_CASE__			:      str							):
    # default to timm!
    if x not in self:
     SCREAMING_SNAKE_CASE:str						      =					self.convert_name_to_timm(UpperCAmelCase__							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					partial(lambda: (timm.create_model(UpperCAmelCase__     ,pretrained=UpperCAmelCase__							).eval(), None)							)

    else:
     SCREAMING_SNAKE_CASE:List[Any]						      =					super().__getitem__(UpperCAmelCase__							)

    return val





class 							_snake_case (       _a						):
   def __getitem__( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      str							):
    if "seer" in x and "in1k" not in x:
     SCREAMING_SNAKE_CASE:Any						      =					RegNetModel
    else:
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					RegNetForImageClassification
    return val





def    A_  ( snake_case      ,       snake_case      ,       snake_case ):


 for from_key, to_key in keys:
  SCREAMING_SNAKE_CASE:int						      =					from_state_dict[from_key].clone()
  print(F'''Copied key={from_key} to={to_key}''' )
 return to_state_dict





def    A_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case = True      ,       ):
 print(F'''Converting {name}...''' )
 with torch.no_grad():
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					from_model_func()
  SCREAMING_SNAKE_CASE:str						      =					our_model_func(_UpperCamelCase ).eval()
  SCREAMING_SNAKE_CASE:List[Any]						      =					ModuleTransfer(src=_UpperCamelCase      ,       dest=_UpperCamelCase      ,       raise_if_mismatch=_UpperCamelCase )
  SCREAMING_SNAKE_CASE:List[str]						      =					torch.randn((1, 3, 224, 224) )
  module_transfer(_UpperCamelCase )

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

 SCREAMING_SNAKE_CASE:str						      =					our_model(_UpperCamelCase      ,       output_hidden_states=_UpperCamelCase )
 SCREAMING_SNAKE_CASE:Optional[Any]						      =					(
     our_outputs.logits if isinstance(_UpperCamelCase      ,       _UpperCamelCase ) else our_outputs.last_hidden_state
 )

 SCREAMING_SNAKE_CASE:List[Any]						      =					from_model(_UpperCamelCase )
 SCREAMING_SNAKE_CASE:Union[str, Any]						      =					from_output[-1] if type(_UpperCamelCase ) 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:
  SCREAMING_SNAKE_CASE:List[Any]						      =					our_outputs.hidden_states[-1]

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

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

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





def    A_  ( snake_case      ,       snake_case = None      ,       snake_case = True ):
 SCREAMING_SNAKE_CASE:List[str]						      =					"imagenet-1k-id2label.json"
 SCREAMING_SNAKE_CASE:Tuple						      =					1000
 SCREAMING_SNAKE_CASE:Any						      =					(1, num_labels)

 SCREAMING_SNAKE_CASE:Any						      =					"huggingface/label-files"
 SCREAMING_SNAKE_CASE:Tuple						      =					num_labels
 SCREAMING_SNAKE_CASE:str						      =					json.load(open(cached_download(hf_hub_url(_UpperCamelCase      ,       _UpperCamelCase      ,       repo_type="dataset" ) )      ,       "r" ) )
 SCREAMING_SNAKE_CASE:Tuple						      =					{int(_UpperCamelCase ): v for k, v in idalabel.items()}

 SCREAMING_SNAKE_CASE:str						      =					idalabel
 SCREAMING_SNAKE_CASE:Optional[int]						      =					{v: k for k, v in idalabel.items()}

 SCREAMING_SNAKE_CASE:Dict						      =					partial(_UpperCamelCase      ,       num_labels=_UpperCamelCase      ,       idalabel=_UpperCamelCase      ,       labelaid=_UpperCamelCase )

 SCREAMING_SNAKE_CASE: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 ),
 }

 SCREAMING_SNAKE_CASE:Optional[Any]						      =					NameToOurModelFuncMap()
 SCREAMING_SNAKE_CASE:Union[str, Any]						      =					NameToFromModelFuncMap()
 # add seer weights logic

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

 # pretrained
 SCREAMING_SNAKE_CASE:Any						      =					partial(
     _UpperCamelCase      ,       "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"      ,       lambda: FakeRegNetVisslWrapper(RegNetYaagf() )      ,       )

 SCREAMING_SNAKE_CASE:Optional[int]						      =					partial(
     _UpperCamelCase      ,       "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"      ,       lambda: FakeRegNetVisslWrapper(RegNetYaagf() )      ,       )

 SCREAMING_SNAKE_CASE:Union[str, Any]						      =					partial(
     _UpperCamelCase      ,       "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() )      ,       )

 SCREAMING_SNAKE_CASE:Optional[int]						      =					partial(
     _UpperCamelCase      ,       "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
 SCREAMING_SNAKE_CASE:List[str]						      =					partial(
     _UpperCamelCase      ,       "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"      ,       lambda: FakeRegNetVisslWrapper(RegNetYaagf() )      ,       )

 SCREAMING_SNAKE_CASE:Union[str, Any]						      =					partial(
     _UpperCamelCase      ,       "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"      ,       lambda: FakeRegNetVisslWrapper(RegNetYaagf() )      ,       )

 SCREAMING_SNAKE_CASE:Union[str, Any]						      =					partial(
     _UpperCamelCase      ,       "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"      ,       lambda: FakeRegNetVisslWrapper(RegNetYaaagf() )      ,       )

 SCREAMING_SNAKE_CASE:int						      =					partial(
     _UpperCamelCase      ,       "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(
      _UpperCamelCase      ,       names_to_from_model_map[model_name]      ,       names_to_ours_model_map[model_name]      ,       names_to_config[model_name]      ,       _UpperCamelCase      ,       _UpperCamelCase      ,       )
 else:
  for model_name, config in names_to_config.items():
   convert_weight_and_push(
       _UpperCamelCase      ,       names_to_from_model_map[model_name]      ,       names_to_ours_model_map[model_name]      ,       _UpperCamelCase      ,       _UpperCamelCase      ,       _UpperCamelCase      ,       )
 return config, expected_shape


if __name__ == "__main__":
    A_       						=							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.",
    )

    A_       						=							parser.parse_args()

    A_       						=							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'''

def    A_  ( snake_case = 100 ):
  SCREAMING_SNAKE_CASE:Dict						      =					0
  SCREAMING_SNAKE_CASE:Optional[int]						      =					0
  for i in range(1      ,       n + 1 ):
    sum_of_squares += i**2
    sum_of_ints += i
  return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
    print(f'''{solution() = }''')






