




'''simple docstring'''


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()
lowerCAmelCase_			:     Optional[int]							    =     logging.get_logger(__name__)







@dataclass
class     SCREAMING_SNAKE_CASE						:
      __magic_name__     :					nn.Module
      __magic_name__     :					List[nn.Module]             =				field(default_factory=lowercase_							)
      __magic_name__     :					list             =				field(default_factory=lowercase_							)






      def        lowercase_	(   self      :     Optional[int]	,	lowercase__      :     Optional[Any]	,	lowercase__      :     Tensor	,	lowercase__      :     Tensor					):


           '''simple docstring'''

           a_    :       str    	=     len(list(m.modules()					)					) == 1 or isinstance(lowercase__	,	nn.Convad					) or isinstance(lowercase__	,	nn.BatchNormad					)
           if has_not_submodules:
                if not isinstance(lowercase__	,	lowercase__					):
                     self.traced.append(lowercase__					)






      def __call__(   self      :     Optional[int]	,	lowercase__      :     Tensor					):


           '''simple docstring'''

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






      @property
      def        lowercase_	(   self      :     Dict					):


           '''simple docstring'''

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







@dataclass
class     SCREAMING_SNAKE_CASE						:
      __magic_name__     :					nn.Module
      __magic_name__     :					nn.Module
      __magic_name__     :					int             =				0
      __magic_name__     :					List             =				field(default_factory=lowercase_							)
      __magic_name__     :					List             =				field(default_factory=lowercase_							)






      def __call__(   self      :     int	,	lowercase__      :     Tensor					):


           '''simple docstring'''

           a_    :       Any    	=     Tracker(self.dest					)(lowercase__					).parametrized
           a_    :       List[str]    	=     Tracker(self.src					)(lowercase__					).parametrized

           a_    :       Dict    	=     list(filter(lambda lowercase__					: type(lowercase__					) not in self.src_skip	,	lowercase__					)					)
           a_    :       Union[str, Any]    	=     list(filter(lambda lowercase__					: type(lowercase__					) not in self.dest_skip	,	lowercase__					)					)

           if len(lowercase__					) != len(lowercase__					):
                raise Exception(
                    F"Numbers of operations are different. Source module has {len(lowercase__					)} operations while"
                    F" destination module has {len(lowercase__					)}."					)

           for dest_m, src_m in zip(lowercase__	,	lowercase__					):
                dest_m.load_state_dict(src_m.state_dict()					)
                if self.verbose == 1:
                     print(F"Transfered from={src_m} to={dest_m}"					)



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Dict   ,						UpperCamelCase__						:				Dict							):




     """simple docstring"""


     a_    :       Dict    	=     from_model.state_dict()
     a_    :       Union[str, Any]    	=     our_model.state_dict()
     a_    :       Optional[Any]    	=     our_model.config
     a_    :       int    	=     []
     for stage_idx in range(len(config.hidden_sizes							)							):
          for block_id in range(config.depths[stage_idx]							):
               a_    :       Optional[Any]    	=     F"block{stage_idx + 1}.{block_id}.layer_scale_1"
               a_    :       Any    	=     F"van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"

               all_keys.append((from_key, to_key)							)
               a_    :       Any    	=     F"block{stage_idx + 1}.{block_id}.layer_scale_2"
               a_    :       Optional[int]    	=     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:
          a_    :       str    	=     from_state_dict.pop(UpperCamelCase__							)

     our_model.load_state_dict(UpperCamelCase__							)
     return our_model



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str   ,						UpperCamelCase__						:				Optional[int]   ,						UpperCamelCase__						:				Dict   ,						UpperCamelCase__						:				List[str]   ,						UpperCamelCase__						:				List[str]   ,						UpperCamelCase__						:				int = True   ,						):




     """simple docstring"""


     print(F"Downloading weights for {name}..."							)
     a_    :       str    	=     cached_download(UpperCamelCase__							)
     print(F"Converting {name}..."							)
     a_    :       Dict    	=     torch.load(UpperCamelCase__							)["""state_dict"""]
     from_model.load_state_dict(UpperCamelCase__							)
     from_model.eval()
     with torch.no_grad():
          a_    :       str    	=     VanForImageClassification(UpperCamelCase__							).eval()
          a_    :       Optional[int]    	=     ModuleTransfer(src=UpperCamelCase__   ,						dest=UpperCamelCase__							)
          a_    :       Union[str, Any]    	=     torch.randn((1, 3, 224, 224)							)
          module_transfer(UpperCamelCase__							)
          a_    :       List[str]    	=     copy_parameters(UpperCamelCase__   ,						UpperCamelCase__							)

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

     a_    :       Dict    	=     name
     print(UpperCamelCase__							)

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

          # we can use the convnext one
          a_    :       List[Any]    	=     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=UpperCamelCase__   ,						)

          print(F"Pushed {checkpoint_name}"							)



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				List[Any]   ,						UpperCamelCase__						:				int = None   ,						UpperCamelCase__						:				Union[str, Any] = True							):




     """simple docstring"""


     a_    :       Tuple    	=     """imagenet-1k-id2label.json"""
     a_    :       List[str]    	=     1000

     a_    :       Dict    	=     """huggingface/label-files"""
     a_    :       Optional[int]    	=     num_labels
     a_    :       Union[str, Any]    	=     json.load(open(hf_hub_download(UpperCamelCase__   ,						UpperCamelCase__   ,						repo_type="""dataset"""							)   ,						"""r"""							)							)
     a_    :       List[Any]    	=     {int(UpperCamelCase__							): v for k, v in idalabel.items()}

     a_    :       List[Any]    	=     idalabel
     a_    :       List[str]    	=     {v: k for k, v in idalabel.items()}

     a_    :       Tuple    	=     partial(UpperCamelCase__   ,						num_labels=UpperCamelCase__   ,						idalabel=UpperCamelCase__   ,						labelaid=UpperCamelCase__							)

     a_    :       List[str]    	=     {
         """van-tiny""": ImageNetPreTrainedConfig(
             hidden_sizes=[32, 64, 160, 256]   ,						depths=[3, 3, 5, 2]   ,						mlp_ratios=[8, 8, 4, 4]   ,						),
         """van-small""": ImageNetPreTrainedConfig(
             hidden_sizes=[64, 128, 320, 512]   ,						depths=[2, 2, 4, 2]   ,						mlp_ratios=[8, 8, 4, 4]   ,						),
         """van-base""": ImageNetPreTrainedConfig(
             hidden_sizes=[64, 128, 320, 512]   ,						depths=[3, 3, 12, 3]   ,						mlp_ratios=[8, 8, 4, 4]   ,						),
         """van-large""": ImageNetPreTrainedConfig(
             hidden_sizes=[64, 128, 320, 512]   ,						depths=[3, 5, 27, 3]   ,						mlp_ratios=[8, 8, 4, 4]   ,						),
     }

     a_    :       List[Any]    	=     {
         """van-tiny""": van_tiny,
         """van-small""": van_small,
         """van-base""": van_base,
         """van-large""": van_large,
     }

     a_    :       str    	=     {
         """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(
              UpperCamelCase__   ,						names_to_config[model_name]   ,						checkpoint=names_to_original_checkpoints[model_name]   ,						from_model=names_to_original_models[model_name]()   ,						save_directory=UpperCamelCase__   ,						push_to_hub=UpperCamelCase__   ,						)
     else:
          for model_name, config in names_to_config.items():
               convert_weight_and_push(
                   UpperCamelCase__   ,						UpperCamelCase__   ,						checkpoint=names_to_original_checkpoints[model_name]   ,						from_model=names_to_original_models[model_name]()   ,						save_directory=UpperCamelCase__   ,						push_to_hub=UpperCamelCase__   ,						)


if __name__ == "__main__":
 lowerCAmelCase_			:     str							    =     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.",
 )

 lowerCAmelCase_			:     Union[str, Any]							    =     parser.parse_args()
 lowerCAmelCase_			:     Path							    =     args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 lowerCAmelCase_			:     List[str]							    =     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)






'''simple docstring'''


def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				int = 1000							):




     """simple docstring"""


     return sum(i for i in range(UpperCamelCase__							) if i % 3 == 0 or i % 5 == 0							)


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