




'''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()
A							:      Optional[Any] 	=       logging.get_logger(__name__)






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

       def     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )    ->					List[str]:

           """simple docstring"""



           __lowercase							=						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__     )    ->					List[str]:

           """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     snake_case__			(    self     )    ->					int:

           """simple docstring"""



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






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

       def __call__(    self					,		lowerCamelCase__     )    ->					Optional[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__     ):
               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 				snake_case_  (   a__		:	nn.Module    ,a__		:	nn.Module			):



    """simple docstring"""




    __lowercase							=						from_model.state_dict()
    __lowercase							=						our_model.state_dict()
    __lowercase							=						our_model.config
    __lowercase							=						[]
    for stage_idx in range(len(config.hidden_sizes			)			):
        for block_id in range(config.depths[stage_idx]			):
            __lowercase							=						f'block{stage_idx + 1}.{block_id}.layer_scale_1'
            __lowercase							=						f'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'

            all_keys.append((from_key, to_key)			)
            __lowercase							=						f'block{stage_idx + 1}.{block_id}.layer_scale_2'
            __lowercase							=						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:
        __lowercase							=						from_state_dict.pop(__A			)

    our_model.load_state_dict(__A			)
    return our_model
def 				snake_case_  (   a__		:	str    ,a__		:	VanConfig    ,a__		:	str    ,a__		:	nn.Module    ,a__		:	Path    ,a__		:	bool = True    ,):



    """simple docstring"""




    print(f'Downloading weights for {name}...'			)
    __lowercase							=						cached_download(__A			)
    print(f'Converting {name}...'			)
    __lowercase							=						torch.load(__A			)["""state_dict"""]
    from_model.load_state_dict(__A			)
    from_model.eval()
    with torch.no_grad():
        __lowercase							=						VanForImageClassification(__A			).eval()
        __lowercase							=						ModuleTransfer(src=__A    ,dest=__A			)
        __lowercase							=						torch.randn((1, 3, 2_24, 2_24)			)
        module_transfer(__A			)
        __lowercase							=						copy_parameters(__A    ,__A			)

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

    __lowercase							=						name
    print(__A			)

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

        # we can use the convnext one
        __lowercase							=						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=__A    ,)

        print(f'Pushed {checkpoint_name}'			)
def 				snake_case_  (   a__		:	Path    ,a__		:	str = None    ,a__		:	bool = True			):



    """simple docstring"""




    __lowercase							=						"""imagenet-1k-id2label.json"""
    __lowercase							=						10_00

    __lowercase							=						"""huggingface/label-files"""
    __lowercase							=						num_labels
    __lowercase							=						json.load(open(hf_hub_download(__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							=						{
        """van-tiny""": ImageNetPreTrainedConfig(
            hidden_sizes=[32, 64, 1_60, 2_56]    ,depths=[3, 3, 5, 2]    ,mlp_ratios=[8, 8, 4, 4]    ,),
        """van-small""": ImageNetPreTrainedConfig(
            hidden_sizes=[64, 1_28, 3_20, 5_12]    ,depths=[2, 2, 4, 2]    ,mlp_ratios=[8, 8, 4, 4]    ,),
        """van-base""": ImageNetPreTrainedConfig(
            hidden_sizes=[64, 1_28, 3_20, 5_12]    ,depths=[3, 3, 12, 3]    ,mlp_ratios=[8, 8, 4, 4]    ,),
        """van-large""": ImageNetPreTrainedConfig(
            hidden_sizes=[64, 1_28, 3_20, 5_12]    ,depths=[3, 5, 27, 3]    ,mlp_ratios=[8, 8, 4, 4]    ,),
    }

    __lowercase							=						{
        """van-tiny""": van_tiny,
        """van-small""": van_small,
        """van-base""": van_base,
        """van-large""": van_large,
    }

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


if __name__ == "__main__":
  A							:      Optional[Any] 	=       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.""",
  )

  A							:      List[str] 	=       parser.parse_args()
  A							:      int 	=       args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  A							:      Tuple 	=       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)







from math import sqrt





def 	snake_case_      (__A     :						int     )			->	list[int]:
   __lowerCAmelCase  :			List[str]					    =						[1]
   for i in range(2					,   int(sqrt(__A     )     ) + 1					,   1     ):
      if number % i == 0:
         values.append(__A     )
         if int(number // i     ) != i:
            values.append(int(number // i     )     )
   return sorted(__A     )





def 	snake_case_      (__A     :						int     )			->	bool:
   return sum(factors(__A     )     ) > n





def 	snake_case_      (__A     :						int     )			->	bool:
   __lowerCAmelCase  :			Any					    =						factors(__A     )
   __lowerCAmelCase  :			Union[str, Any]					    =						len(__A     )
   __lowerCAmelCase  :			str					    =						[[0 for i in range(number + 1     )] for j in range(r + 1     )]
   for i in range(r + 1     ):
      __lowerCAmelCase  :			Tuple					    =						True

   for i in range(1					,   number + 1     ):
      __lowerCAmelCase  :			List[str]					    =						False

   for i in range(1					,   r + 1     ):
      for j in range(1					,   number + 1     ):
         if j < values[i - 1]:
            __lowerCAmelCase  :			Dict					    =						subset[i - 1][j]
         else:
            __lowerCAmelCase  :			Dict					    =						subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

   return subset[r][number] != 0





def 	snake_case_      (__A     :						int     )			->	bool:
   return abundant(__A     ) and not semi_perfect(__A     )


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   for number in (69, 70, 71):
      print(F'{number} is {"" if weird(number) else "not "}weird.')


