






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()
SCREAMING_SNAKE_CASE								=  logging.get_logger(__name__)
@dataclass
class 							A_      :






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     nn.Module
 _SCREAMING_SNAKE_CASE							:     List[nn.Module]	=   field(default_factory=__lowercase     )
 _SCREAMING_SNAKE_CASE							:     list	=   field(default_factory=__lowercase     )


 def 	snake_case__			(		self							,					_A							,					_A							,					_A)	->						Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase			: List[Any]     					=			len(list(m.modules())) == 1 or isinstance(_A							,					nn.Convad) or isinstance(_A							,					nn.BatchNormad)
     if has_not_submodules:
         if not isinstance(_A							,					_A):
             self.traced.append(_A)


 def __call__(		self							,					_A)	->						Any:

     """simple docstring"""



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


 @property
 def 	snake_case__			(		self)	->						Optional[int]:

     """simple docstring"""



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



@dataclass
class 							A_      :






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     nn.Module
 _SCREAMING_SNAKE_CASE							:     nn.Module
 _SCREAMING_SNAKE_CASE							:     int	=   0
 _SCREAMING_SNAKE_CASE							:     List	=   field(default_factory=__lowercase     )
 _SCREAMING_SNAKE_CASE							:     List	=   field(default_factory=__lowercase     )


 def __call__(		self							,					_A)	->						List[Any]:

     """simple docstring"""



     _UpperCAmelCase			: Any     					=			Tracker(self.dest)(_A).parametrized
     _UpperCAmelCase			: Optional[Any]     					=			Tracker(self.src)(_A).parametrized

     _UpperCAmelCase			: Tuple     					=			list(filter(lambda _A: type(_A) not in self.src_skip							,					_A))
     _UpperCAmelCase			: List[str]     					=			list(filter(lambda _A: type(_A) not in self.dest_skip							,					_A))

     if len(_A) != len(_A):
         raise Exception(
             f'''Numbers of operations are different. Source module has {len(_A)} operations while'''
             f''' destination module has {len(_A)}.''')

     for dest_m, src_m in zip(_A							,					_A):
         dest_m.load_state_dict(src_m.state_dict())
         if self.verbose == 1:
             print(f'''Transfered from={src_m} to={dest_m}''')



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

            all_keys.append((from_key, to_key)	)
            _UpperCAmelCase			: List[Any]     					=			f'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
            _UpperCAmelCase			: List[str]     					=			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:
        _UpperCAmelCase			: List[str]     					=			from_state_dict.pop(__A	)

    our_model.load_state_dict(__A	)
    return our_model

def        _lowerCamelCase		(  __A      :							str						,						__A      :							VanConfig						,						__A      :							str						,						__A      :							nn.Module						,						__A      :							Path						,						__A      :							bool = True						,						) ->   List[str]:
    print(f'''Downloading weights for {name}...'''	)
    _UpperCAmelCase			: Optional[Any]     					=			cached_download(__A	)
    print(f'''Converting {name}...'''	)
    _UpperCAmelCase			: Optional[Any]     					=			torch.load(__A	)['''state_dict''']
    from_model.load_state_dict(__A	)
    from_model.eval()
    with torch.no_grad():
        _UpperCAmelCase			: Tuple     					=			VanForImageClassification(__A	).eval()
        _UpperCAmelCase			: Optional[int]     					=			ModuleTransfer(src=__A						,						dest=__A	)
        _UpperCAmelCase			: Union[str, Any]     					=			torch.randn((1, 3, 224, 224)	)
        module_transfer(__A	)
        _UpperCAmelCase			: Optional[Any]     					=			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.'''	)

    _UpperCAmelCase			: int     					=			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
        _UpperCAmelCase			: 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=__A						,						)

        print(f'''Pushed {checkpoint_name}'''	)

def        _lowerCamelCase		(  __A      :							Path						,						__A      :							str = None						,						__A      :							bool = True	) ->   Tuple:
    _UpperCAmelCase			: List[Any]     					=			'''imagenet-1k-id2label.json'''
    _UpperCAmelCase			: Union[str, Any]     					=			1_000

    _UpperCAmelCase			: Optional[int]     					=			'''huggingface/label-files'''
    _UpperCAmelCase			: List[str]     					=			num_labels
    _UpperCAmelCase			: Tuple     					=			json.load(open(hf_hub_download(__A						,						__A						,						repo_type='''dataset'''	)						,						'''r'''	)	)
    _UpperCAmelCase			: Tuple     					=			{int(__A	): v for k, v in idalabel.items()}

    _UpperCAmelCase			: Any     					=			idalabel
    _UpperCAmelCase			: Dict     					=			{v: k for k, v in idalabel.items()}

    _UpperCAmelCase			: Union[str, Any]     					=			partial(__A						,						num_labels=__A						,						idalabel=__A						,						labelaid=__A	)

    _UpperCAmelCase			: 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]						,						),
    }

    _UpperCAmelCase			: Optional[int]     					=			{
        '''van-tiny''': van_tiny,
        '''van-small''': van_small,
        '''van-base''': van_base,
        '''van-large''': van_large,
    }

    _UpperCAmelCase			: Optional[Any]     					=			{
        '''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__":
  SCREAMING_SNAKE_CASE								=  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.',
  )

  SCREAMING_SNAKE_CASE								=  parser.parse_args()
  SCREAMING_SNAKE_CASE								=  args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  SCREAMING_SNAKE_CASE								=  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 cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like

def        _lowerCamelCase		(  __A      :							str						,						__A      :							List[str]=3	) ->   List[Any]:
    _UpperCAmelCase			: Tuple     					=			int(mask / 2	)
    # copy image size
    _UpperCAmelCase			: Any     					=			zeros_like(__A	)
    for i in range(__A						,						gray_img.shape[0] - bd	):
        for j in range(__A						,						gray_img.shape[1] - bd	):
            # get mask according with mask
            _UpperCAmelCase			: Any     					=			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]	)
            # calculate mask median
            _UpperCAmelCase			: List[str]     					=			sort(__A	)[inta(divide((multiply(__A						,						__A	))						,						2	) + 1	)]
            _UpperCAmelCase			: int     					=			median
    return median_img


if __name__ == "__main__":
  # read original image
  SCREAMING_SNAKE_CASE								=  imread('../image_data/lena.jpg')
  # turn image in gray scale value
  SCREAMING_SNAKE_CASE								=  cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  SCREAMING_SNAKE_CASE								=  median_filter(gray, 3)
  SCREAMING_SNAKE_CASE								=  median_filter(gray, 5)

  # show result images
  imshow('median filter with 3x3 mask', medianaxa)
  imshow('median filter with 5x5 mask', medianaxa)
  waitKey(0)




