





import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
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.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
a_    :				List[Any]					=			logging.get_logger()



@dataclass
class  SCREAMING_SNAKE_CASE_  :


  """simple docstring"""





  _a								=							42
  _a								=							field(default_factory=SCREAMING_SNAKE_CASE__			)
  _a								=							field(default_factory=SCREAMING_SNAKE_CASE__			)
  _a								=							field(default_factory=SCREAMING_SNAKE_CASE__			)





  def 			__A      (  self	,  A	,  A	,  A	,  A						)		->   Any:





       '''simple docstring'''







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





  def __call__(  self	,  A						)		->   str:





       '''simple docstring'''







       for name, m in self.module.named_modules():
            self.handles.append(m.register_forward_hook(partial(self._forward_hook	,  name=A						)						)						)
       self.module(A						)
       [x.remove() for x in self.handles]
       return self







  @property
  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()						)						) > 0}



class  SCREAMING_SNAKE_CASE_  ( nn.Module			):


  """simple docstring"""





  def __init__(  self	,  A						)		->   Optional[int]:





       '''simple docstring'''







       super().__init__()

       __magic_name__				=       []
       # - 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}'
            __magic_name__				=       len(A						) + 1
            feature_blocks.append((F'res{block_index}', v)						)

       __magic_name__				=       nn.ModuleDict(A						)







  def 			__A      (  self	,  A						)		->   Optional[int]:





       '''simple docstring'''







       return get_trunk_forward_outputs(
           A	,  out_feat_keys=A	,  feature_blocks=self._feature_blocks	,  )



class  SCREAMING_SNAKE_CASE_  ( SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  def 			__A      (  self						)		->   Optional[int]:





       '''simple docstring'''







       return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						str						):
     __magic_name__				=       RegNetConfig(depths=[2, 7, 17, 1] ,     hidden_sizes=[8, 8, 8, 8] ,     groups_width=8						)
     if "in1k" in model_name:
          __magic_name__				=       RegNetForImageClassification(snake_case_						)
     else:
          __magic_name__				=       RegNetModel(snake_case_						)
     # create from model (with small weights)
     __magic_name__				=       FakeRegNetVisslWrapper(
         RegNet(FakeRegNetParams(depth=27 ,     group_width=1010 ,     w_a=1744 ,     w_a=620.83 ,     w_m=2.52						)						)						)

     with torch.no_grad():
          __magic_name__				=       from_model.eval()
          __magic_name__				=       our_model.eval()

          __magic_name__				=       torch.randn((1, 3, 32, 32)						)
          # trace both
          __magic_name__				=       Tracker(snake_case_						)
          __magic_name__				=       dest_tracker(snake_case_						).parametrized

          pprint(dest_tracker.nameamodule						)
          __magic_name__				=       Tracker(snake_case_						)
          __magic_name__				=       src_tracker(snake_case_						).parametrized

     # convert the keys -> module dict to keys -> params
     def to_params_dict(snake_case_		:						List[str]						):
          __magic_name__				=       OrderedDict()
          for name, module in dict_with_modules.items():
               for param_name, param in module.state_dict().items():
                    __magic_name__				=       param
          return params_dict

     __magic_name__				=       {}

     __magic_name__				=       to_params_dict(snake_case_						)
     __magic_name__				=       to_params_dict(snake_case_						)

     for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items() ,     dst_state_dict.items()						):
          __magic_name__				=       dest_key
          logger.info(f'{src_key} -> {dest_key}'						)
     # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
     if "in1k" in model_name:
          __magic_name__				=       '''classifier.1.weight'''
          __magic_name__				=       '''classifier.1.bias'''

     return from_to_ours_keys




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						Path ,     snake_case_		:						str = None ,     snake_case_		:						bool = True						):
     __magic_name__				=       '''imagenet-1k-id2label.json'''
     __magic_name__				=       1000

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

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

     __magic_name__				=       partial(snake_case_ ,     num_labels=snake_case_ ,     idalabel=snake_case_ ,     labelaid=snake_case_						)

     __magic_name__				=       {
         '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1] ,     hidden_sizes=[2020, 4040, 1_1110, 2_8280] ,     groups_width=1010						),
         # finetuned on imagenet
         '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1] ,     hidden_sizes=[2020, 4040, 1_1110, 2_8280] ,     groups_width=1010						),
     }

     # add seer weights logic
     def load_using_classy_vision(snake_case_		:						str						) -> Tuple[Dict, Dict]:
          __magic_name__				=       torch.hub.load_state_dict_from_url(snake_case_ ,     model_dir=str(snake_case_						) ,     map_location='''cpu'''						)
          # check if we have a head, if yes add it
          __magic_name__				=       files['''classy_state_dict''']['''base_model''']['''model''']
          return model_state_dict["trunk"], model_state_dict["heads"]

     __magic_name__				=       {
         '''regnet-y-10b-seer''': partial(
             snake_case_ ,     '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch''' ,     ),
         '''regnet-y-10b-seer-in1k''': partial(
             snake_case_ ,     '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch''' ,     ),
     }

     __magic_name__				=       get_from_to_our_keys(snake_case_						)

     if not (save_directory / f'{model_name}.pth').exists():
          logger.info('''Loading original state_dict.'''						)
          __magic_name__		,  __magic_name__				=       names_to_from_model[model_name]()
          __magic_name__				=       from_state_dict_trunk
          if "in1k" in model_name:
               # add the head
               __magic_name__				=       {**from_state_dict_trunk, **from_state_dict_head}
          logger.info('''Done!'''						)

          __magic_name__				=       {}

          __magic_name__				=       list(from_state_dict.keys()						)
          __magic_name__				=       r'''\.block.-part.'''
          # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
          for key in from_state_dict.keys():
               # remove the weird "block[0,1]-part" from the key
               __magic_name__				=       re.sub(snake_case_ ,     '''''' ,     snake_case_						)
               # now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
               __magic_name__				=       from_to_ours_keys[src_key]
               # store the parameter with our key
               __magic_name__				=       from_state_dict[key]
               not_used_keys.remove(snake_case_						)
          # check that all keys have been updated
          assert len(snake_case_						) == 0, f'Some keys where not used {",".join(snake_case_						)}'

          logger.info(f'The following keys were not used: {",".join(snake_case_						)}'						)

          # save our state dict to disk
          torch.save(snake_case_ ,     save_directory / f'{model_name}.pth'						)

          del converted_state_dict
     else:
          logger.info('''The state_dict was already stored on disk.'''						)
     if push_to_hub:
          logger.info(f'Token is {os.environ["HF_TOKEN"]}'						)
          logger.info('''Loading our model.'''						)
          # create our model
          __magic_name__				=       names_to_config[model_name]
          __magic_name__				=       RegNetModel
          if "in1k" in model_name:
               __magic_name__				=       RegNetForImageClassification
          __magic_name__				=       our_model_func(snake_case_						)
          # place our model to the meta device (so remove all the weights)
          our_model.to(torch.device('''meta'''						)						)
          logger.info('''Loading state_dict in our model.'''						)
          # load state dict
          __magic_name__				=       our_model.state_dict().keys()
          PreTrainedModel._load_pretrained_model_low_mem(
              snake_case_ ,     snake_case_ ,     [save_directory / f'{model_name}.pth']						)
          logger.info('''Finally, pushing!'''						)
          # push it to hub
          our_model.push_to_hub(
              repo_path_or_name=save_directory / model_name ,     commit_message='''Add model''' ,     output_dir=save_directory / model_name ,     )
          __magic_name__				=       384
          # we can use the convnext one
          __magic_name__				=       AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' ,     size=snake_case_						)
          image_processor.push_to_hub(
              repo_path_or_name=save_directory / model_name ,     commit_message='''Add image processor''' ,     output_dir=save_directory / model_name ,     )


if __name__ == "__main__":
    a_    :				Tuple					=			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_    :				Tuple					=			parser.parse_args()

    a_    :				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)






from ..utils import DummyObject, requires_backends



class  SCREAMING_SNAKE_CASE_  ( metaclass=SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  _a								=							["""torch""", """transformers""", """k_diffusion"""]





  def __init__(  self	,  *A	,  **A						)		->   Optional[Any]:





       '''simple docstring'''







       requires_backends(self	,  ['''torch''', '''transformers''', '''k_diffusion''']						)





  @classmethod
  def 			__A      (  cls	,  *A	,  **A						)		->   Dict:





       '''simple docstring'''







       requires_backends(cls	,  ['''torch''', '''transformers''', '''k_diffusion''']						)







  @classmethod
  def 			__A      (  cls	,  *A	,  **A						)		->   Optional[Any]:





       '''simple docstring'''







       requires_backends(cls	,  ['''torch''', '''transformers''', '''k_diffusion''']						)