



import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_          =				logging.get_logger(__name__)





@dataclass
class 		UpperCAmelCase    :
   SCREAMING_SNAKE_CASE__					      =  42
   SCREAMING_SNAKE_CASE__					      =  field(default_factory=snake_case_					)
   SCREAMING_SNAKE_CASE__					      =  field(default_factory=snake_case_					)





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase      ):
         _lowerCAmelCase			      =	len(list(m.modules()      )      ) == 1 or isinstance(_lowerCAmelCase							,  nn.Convad      ) or isinstance(_lowerCAmelCase							,  nn.BatchNormad      )
         if has_not_submodules:
               if not isinstance(_lowerCAmelCase							,  _lowerCAmelCase      ):
                     self.traced.append(_lowerCAmelCase      )





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






@dataclass
class 		UpperCAmelCase    :
   SCREAMING_SNAKE_CASE__					      =  42
   SCREAMING_SNAKE_CASE__					      =  42
   SCREAMING_SNAKE_CASE__					      =  0
   SCREAMING_SNAKE_CASE__					      =  field(default_factory=snake_case_					)
   SCREAMING_SNAKE_CASE__					      =  field(default_factory=snake_case_					)





   def __call__(						self							,  _lowerCAmelCase      ):
         _lowerCAmelCase			      =	Tracker(self.dest      )(_lowerCAmelCase      ).parametrized
         _lowerCAmelCase			      =	Tracker(self.src      )(_lowerCAmelCase      ).parametrized

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

         if len(_lowerCAmelCase      ) != len(_lowerCAmelCase      ):
               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}'''      )






def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						nn.Module  ,					_SCREAMING_SNAKE_CASE :						nn.Module					)->nn.Module:
      # nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
      _lowerCAmelCase			      =	from_model.state_dict()
      _lowerCAmelCase			      =	our_model.state_dict()
      _lowerCAmelCase			      =	our_model.config
      _lowerCAmelCase			      =	[]
      for stage_idx in range(len(config.hidden_sizes					)					):
            for block_id in range(config.depths[stage_idx]					):
                  _lowerCAmelCase			      =	f'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
                  _lowerCAmelCase			      =	f'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

                  all_keys.append((from_key, to_key)					)
                  _lowerCAmelCase			      =	f'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
                  _lowerCAmelCase			      =	f'''van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'''

                  all_keys.append((from_key, to_key)					)

      for from_key, to_key in all_keys:
            _lowerCAmelCase			      =	from_state_dict.pop(_SCREAMING_SNAKE_CASE					)

      our_model.load_state_dict(_SCREAMING_SNAKE_CASE					)
      return our_model
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						str  ,					_SCREAMING_SNAKE_CASE :						VanConfig  ,					_SCREAMING_SNAKE_CASE :						str  ,					_SCREAMING_SNAKE_CASE :						nn.Module  ,					_SCREAMING_SNAKE_CASE :						Path  ,					_SCREAMING_SNAKE_CASE :						bool = True  ,					)->List[Any]:
      print(f'''Downloading weights for {name}...'''					)
      _lowerCAmelCase			      =	cached_download(_SCREAMING_SNAKE_CASE					)
      print(f'''Converting {name}...'''					)
      _lowerCAmelCase			      =	torch.load(_SCREAMING_SNAKE_CASE					)['''state_dict''']
      from_model.load_state_dict(_SCREAMING_SNAKE_CASE					)
      from_model.eval()
      with torch.no_grad():
            _lowerCAmelCase			      =	VanForImageClassification(_SCREAMING_SNAKE_CASE					).eval()
            _lowerCAmelCase			      =	ModuleTransfer(src=_SCREAMING_SNAKE_CASE  ,					dest=_SCREAMING_SNAKE_CASE					)
            _lowerCAmelCase			      =	torch.randn((1, 3, 2_2_4, 2_2_4)					)
            module_transfer(_SCREAMING_SNAKE_CASE					)
            _lowerCAmelCase			      =	copy_parameters(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE					)

      if not torch.allclose(from_model(_SCREAMING_SNAKE_CASE					)  ,					our_model(_SCREAMING_SNAKE_CASE					).logits					):
            raise ValueError('''The model logits don\'t match the original one.'''					)

      _lowerCAmelCase			      =	name
      print(_SCREAMING_SNAKE_CASE					)

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

            # we can use the convnext one
            _lowerCAmelCase			      =	AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''					)
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name  ,					commit_message='''Add image processor'''  ,					use_temp_dir=_SCREAMING_SNAKE_CASE  ,					)

            print(f'''Pushed {checkpoint_name}'''					)
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						Path  ,					_SCREAMING_SNAKE_CASE :						str = None  ,					_SCREAMING_SNAKE_CASE :						bool = True					)->List[Any]:
      _lowerCAmelCase			      =	'''imagenet-1k-id2label.json'''
      _lowerCAmelCase			      =	1_0_0_0

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

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

      _lowerCAmelCase			      =	partial(_SCREAMING_SNAKE_CASE  ,					num_labels=_SCREAMING_SNAKE_CASE  ,					idalabel=_SCREAMING_SNAKE_CASE  ,					labelaid=_SCREAMING_SNAKE_CASE					)

      _lowerCAmelCase			      =	{
          '''van-tiny''': ImageNetPreTrainedConfig(
              hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6]  ,					depths=[3, 3, 5, 2]  ,					mlp_ratios=[8, 8, 4, 4]  ,					),
          '''van-small''': ImageNetPreTrainedConfig(
              hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]  ,					depths=[2, 2, 4, 2]  ,					mlp_ratios=[8, 8, 4, 4]  ,					),
          '''van-base''': ImageNetPreTrainedConfig(
              hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]  ,					depths=[3, 3, 1_2, 3]  ,					mlp_ratios=[8, 8, 4, 4]  ,					),
          '''van-large''': ImageNetPreTrainedConfig(
              hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]  ,					depths=[3, 5, 2_7, 3]  ,					mlp_ratios=[8, 8, 4, 4]  ,					),
      }

      _lowerCAmelCase			      =	{
          '''van-tiny''': van_tiny,
          '''van-small''': van_small,
          '''van-base''': van_base,
          '''van-large''': van_large,
      }

      _lowerCAmelCase			      =	{
          '''van-tiny''': (
              '''https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'''
          ),
          '''van-small''': (
              '''https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'''
          ),
          '''van-base''': (
              '''https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'''
          ),
          '''van-large''': (
              '''https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'''
          ),
      }

      if model_name:
            convert_weight_and_push(
                _SCREAMING_SNAKE_CASE  ,					names_to_config[model_name]  ,					checkpoint=names_to_original_checkpoints[model_name]  ,					from_model=names_to_original_models[model_name]()  ,					save_directory=_SCREAMING_SNAKE_CASE  ,					push_to_hub=_SCREAMING_SNAKE_CASE  ,					)
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(
                      _SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					checkpoint=names_to_original_checkpoints[model_name]  ,					from_model=names_to_original_models[model_name]()  ,					save_directory=_SCREAMING_SNAKE_CASE  ,					push_to_hub=_SCREAMING_SNAKE_CASE  ,					)


if __name__ == "__main__":
    UpperCAmelCase_          =				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 resnet* architecture,"
            " currently: van-tiny/small/base/large. 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(
        "--van_dir",
        required=True,
        type=Path,
        help=(
            "A path to VAN's original implementation directory. You can download from here:"
            " https://github.com/Visual-Attention-Network/VAN-Classification"
        ),
    )
    parser.add_argument(
        "--push_to_hub",
        default=True,
        type=bool,
        required=False,
        help="If True, push model and image processor to the hub.",
    )

    UpperCAmelCase_          =				parser.parse_args()
    UpperCAmelCase_          =				args.pytorch_dump_folder_path
    pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
    UpperCAmelCase_          =				args.van_dir
    # append the path to the parents to maskformer dir
    sys.path.append(str(van_dir.parent))
    from van.models.van import van_base, van_large, van_small, van_tiny

    convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						float  ,					_SCREAMING_SNAKE_CASE :						float					)->float:
      _lowerCAmelCase			      =	float()
      try:
            _lowerCAmelCase			      =	mass * acceleration
      except Exception:
            return -0.0
      return force


if __name__ == "__main__":
    import doctest

    # run doctest
    doctest.testmod()

    # demo
    UpperCAmelCase_          =				12.5
    UpperCAmelCase_          =				1_0
    UpperCAmelCase_          =				newtons_second_law_of_motion(mass, acceleration)
    print("The force is ", force, "N")