

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()
lowercase_             =     logging.get_logger()





@dataclass
class 				__lowerCAmelCase  :
  _a		    =						42
  _a		    =						field(default_factory=SCREAMING_SNAKE_CASE							)
  _a		    =						field(default_factory=SCREAMING_SNAKE_CASE							)




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				int:
      '''simple docstring'''






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




  def __call__(       self							,     lowerCAmelCase      )						->				Dict:
      '''simple docstring'''






      for m in self.module.modules():
          self.handles.append(m.register_forward_hook(self._forward_hook      )      )
      self.module(lowerCAmelCase      )
      [x.remove() for x in self.handles]
      return self




  @property
  def 						A__  (       self      )						->				List[str]:
      '''simple docstring'''






      return list(filter(lambda lowerCAmelCase      : len(list(x.state_dict().keys()      )      ) > 0							,     self.traced      )      )





@dataclass
class 				__lowerCAmelCase  :
  _a		    =						42
  _a		    =						42
  _a		    =						1
  _a		    =						field(default_factory=SCREAMING_SNAKE_CASE							)
  _a		    =						field(default_factory=SCREAMING_SNAKE_CASE							)
  _a		    =						True




  def __call__(       self							,     lowerCAmelCase      )						->				int:
      '''simple docstring'''






      _lowercase			 =Tracker(self.dest      )(lowerCAmelCase      ).parametrized
      _lowercase			 =Tracker(self.src      )(lowerCAmelCase      ).parametrized

      _lowercase			 =list(filter(lambda lowerCAmelCase      : type(lowerCAmelCase      ) not in self.src_skip							,     lowerCAmelCase      )      )
      _lowercase			 =list(filter(lambda lowerCAmelCase      : type(lowerCAmelCase      ) not in self.dest_skip							,     lowerCAmelCase      )      )

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

      for dest_m, src_m in zip(lowerCAmelCase							,     lowerCAmelCase      ):
          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							,     lowerCAmelCase      )						->				Dict:
      '''simple docstring'''






      super().__init__()

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

      _lowercase			 =nn.ModuleDict(lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				Union[str, Any]:
      '''simple docstring'''






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





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):




  def 						A__  (       self							,     lowerCAmelCase      )						->				str:
      '''simple docstring'''






      _lowercase			 =x.split('-'      )
      return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]      )




  def __getitem__(       self							,     lowerCAmelCase      )						->				Callable[[], Tuple[nn.Module, Dict]]:
      '''simple docstring'''






      if x not in self:
          _lowercase			 =self.convert_name_to_timm(lowerCAmelCase      )
          _lowercase			 =partial(lambda: (timm.create_model(lowerCAmelCase							,     pretrained=lowerCAmelCase      ).eval(), None)      )

      else:
          _lowercase			 =super().__getitem__(lowerCAmelCase      )

      return val





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):




  def __getitem__(       self							,     lowerCAmelCase      )						->				Callable[[], nn.Module]:
      '''simple docstring'''






      if "seer" in x and "in1k" not in x:
          _lowercase			 =RegNetModel
      else:
          _lowercase			 =RegNetForImageClassification
      return val
def   a   (    A__			:     Union[str, Any]   ,     A__			:     Optional[int]   ,     A__			:     List[Tuple[str, str]]  )					->   Union[str, Any]:





    """simple docstring"""



    for from_key, to_key in keys:
        _lowercase			 =from_state_dict[from_key].clone()
        print(F'''Copied key={from_key} to={to_key}'''  )
    return to_state_dict
def   a   (    A__			:     str   ,     A__			:     Callable[[], nn.Module]   ,     A__			:     Callable[[], nn.Module]   ,     A__			:     RegNetConfig   ,     A__			:     Path   ,     A__			:     bool = True   ,     )					->   str:





    """simple docstring"""



    print(F'''Converting {name}...'''  )
    with torch.no_grad():
        _lowercase , _lowercase			 =from_model_func()
        _lowercase			 =our_model_func(A__  ).eval()
        _lowercase			 =ModuleTransfer(src=A__   ,     dest=A__   ,     raise_if_mismatch=A__  )
        _lowercase			 =torch.randn((1, 3, 224, 224)  )
        module_transfer(A__  )

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

    _lowercase			 =our_model(A__   ,     output_hidden_states=A__  )
    _lowercase			 =(
        our_outputs.logits if isinstance(A__   ,     A__  ) else our_outputs.last_hidden_state
    )

    _lowercase			 =from_model(A__  )
    _lowercase			 =from_output[-1] if type(A__  ) 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:
        _lowercase			 =our_outputs.hidden_states[-1]

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

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

        print(F'''Pushed {name}'''  )
def   a   (    A__			:     Path   ,     A__			:     str = None   ,     A__			:     bool = True  )					->   Optional[int]:





    """simple docstring"""



    _lowercase			 ='imagenet-1k-id2label.json'
    _lowercase			 =1000
    _lowercase			 =(1, num_labels)

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

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

    _lowercase			 =partial(A__   ,     num_labels=A__   ,     idalabel=A__   ,     labelaid=A__  )

    _lowercase			 ={
        '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  ),
    }

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

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

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

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

    _lowercase			 =partial(
        A__   ,     '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()  )   ,     )

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

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

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

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


if __name__ == "__main__":
  lowercase_             =     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.',
  )

  lowercase_             =     parser.parse_args()

  lowercase_             =     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)





import unittest

from knapsack import knapsack as k





class 				__lowerCAmelCase  (      unittest.TestCase							):




  def 						A__  (       self      )						->				List[str]:
        '''simple docstring'''






        _lowercase			 =0
        _lowercase			 =[0]
        _lowercase			 =[0]
        _lowercase			 =len(lowerCAmelCase      )
        self.assertEqual(k.knapsack(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )							,     0      )

        _lowercase			 =[60]
        _lowercase			 =[10]
        _lowercase			 =len(lowerCAmelCase      )
        self.assertEqual(k.knapsack(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )							,     0      )




  def 						A__  (       self      )						->				List[Any]:
        '''simple docstring'''






        _lowercase			 =3
        _lowercase			 =[1, 2, 3]
        _lowercase			 =[3, 2, 1]
        _lowercase			 =len(lowerCAmelCase      )
        self.assertEqual(k.knapsack(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )							,     5      )




  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        _lowercase			 =50
        _lowercase			 =[60, 100, 120]
        _lowercase			 =[10, 20, 30]
        _lowercase			 =len(lowerCAmelCase      )
        self.assertEqual(k.knapsack(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )							,     220      )


if __name__ == "__main__":
  unittest.main()


