'''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[Any]    	=   logging.get_logger(__name__)





@dataclass
class 				lowercase    :
   lowerCamelCase_            =42
   lowerCamelCase_            =field(default_factory=__lowerCamelCase			)
   lowerCamelCase_            =field(default_factory=__lowerCamelCase			)


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						Any ,		__lowerCAmelCase					:						Tensor ,		__lowerCAmelCase					:						Tensor)   ->       List[str]:
         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					:						int ,		__lowerCAmelCase					:						Tensor)   ->       str:
         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 __UpperCAmelCase							( self					:						Dict)   ->       Union[str, Any]:
         # 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 				lowercase    :
   lowerCamelCase_            =42
   lowerCamelCase_            =42
   lowerCamelCase_            =0
   lowerCamelCase_            =field(default_factory=__lowerCamelCase			)
   lowerCamelCase_            =field(default_factory=__lowerCamelCase			)


   def __call__( self					:						Any ,		__lowerCAmelCase					:						Tensor)   ->       str:
         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 __a			(						__lowerCamelCase      :    nn.Module			,		__lowerCamelCase      :    nn.Module					)						->				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(__lowerCamelCase					)

      our_model.load_state_dict(__lowerCamelCase					)
      return our_model


def __a			(						__lowerCamelCase      :    str			,		__lowerCamelCase      :    VanConfig			,		__lowerCamelCase      :    str			,		__lowerCamelCase      :    nn.Module			,		__lowerCamelCase      :    Path			,		__lowerCamelCase      :    bool = True			,		)						->				Any:
      '''simple docstring'''


      print(f'Downloading weights for {name}...'					)
      lowercase_     			=   cached_download(__lowerCamelCase					)
      print(f'Converting {name}...'					)
      lowercase_     			=   torch.load(__lowerCamelCase					)["state_dict"]
      from_model.load_state_dict(__lowerCamelCase					)
      from_model.eval()
      with torch.no_grad():
            lowercase_     			=   VanForImageClassification(__lowerCamelCase					).eval()
            lowercase_     			=   ModuleTransfer(src=__lowerCamelCase			,		dest=__lowerCamelCase					)
            lowercase_     			=   torch.randn((1, 3, 224, 224)					)
            module_transfer(__lowerCamelCase					)
            lowercase_     			=   copy_parameters(__lowerCamelCase			,		__lowerCamelCase					)

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

      lowercase_     			=   name
      print(__lowerCamelCase					)

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

            # 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=__lowerCamelCase			,		)

            print(f'Pushed {checkpoint_name}'					)


def __a			(						__lowerCamelCase      :    Path			,		__lowerCamelCase      :    str = None			,		__lowerCamelCase      :    bool = True					)						->				str:
      '''simple docstring'''


      lowercase_     			=   "imagenet-1k-id2label.json"
      lowercase_     			=   1_000

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

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

      lowercase_     			=   partial(__lowerCamelCase			,		num_labels=__lowerCamelCase			,		idalabel=__lowerCamelCase			,		labelaid=__lowerCamelCase					)

      lowercase_     			=   {
          "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]			,		),
      }

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


if __name__ == "__main__":
       lowerCAmelCase_			:			Optional[int]    	=   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_			:			Dict    	=   parser.parse_args()
       lowerCAmelCase_			:			Path    	=   args.pytorch_dump_folder_path
       pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
       lowerCAmelCase_			:			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)







'''simple docstring'''

from hashlib import mda, shaaaa

lowerCAmelCase_			:			Union[str, Any]    	=   (shaaaa, mda)





class 				lowercase    :


   def __init__( self					:						Optional[Any] ,		__lowerCAmelCase					:						int = 8)   ->       None:
         lowercase_     			=   0B0
         lowercase_     			=   size


   def __UpperCAmelCase							( self					:						Dict ,		__lowerCAmelCase					:						str)   ->       None:
         lowercase_     			=   self.hash_(__lowerCAmelCase)
         self.bitarray |= h


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						str)   ->       bool:
         lowercase_     			=   self.hash_(__lowerCAmelCase)
         return (h & self.bitarray) == h


   def __contains__( self					:						str ,		__lowerCAmelCase					:						str)   ->       bool:
         return self.exists(__lowerCAmelCase)


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						int)   ->       str:
         lowercase_     			=   bin(__lowerCAmelCase)[2:]
         return res.zfill(self.size)


   @property
   def __UpperCAmelCase							( self					:						Dict)   ->       str:
         return self.format_bin(self.bitarray)


   def __UpperCAmelCase							( self					:						Any ,		__lowerCAmelCase					:						str)   ->       int:
         lowercase_     			=   0B0
         for func in HASH_FUNCTIONS:
               lowercase_     			=   (
                   int.from_bytes(func(value.encode()).digest() ,		"little") % self.size
               )
               res |= 2**position
         return res


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						str)   ->       str:
         return self.format_bin(self.hash_(__lowerCAmelCase))


   @property
   def __UpperCAmelCase							( self					:						str)   ->       float:
         lowercase_     			=   bin(self.bitarray).count("1")
         return (n_ones / self.size) ** len(__lowerCAmelCase)






