





"""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_     :      int								=logging.get_logger(__name__)







@dataclass
class 	__a   :
     SCREAMING_SNAKE_CASE__ :      nn.Module
     SCREAMING_SNAKE_CASE__ :      List[nn.Module]       		=			field(default_factory=lowerCAmelCase__		)
     SCREAMING_SNAKE_CASE__ :      list       		=			field(default_factory=lowerCAmelCase__		)



     def 						snake_case_			(       self					,     a__					,     a__					,     a__   ):
            _lowerCamelCase    	=					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__   ):
            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   ):
            # check the len of the state_dict keys to see if we have learnable params
            return list(filter(lambda a__   : len(list(x.state_dict().keys()   )   ) > 0					,     self.traced   )   )







@dataclass
class 	__a   :
     SCREAMING_SNAKE_CASE__ :      nn.Module
     SCREAMING_SNAKE_CASE__ :      nn.Module
     SCREAMING_SNAKE_CASE__ :      int       		=			0
     SCREAMING_SNAKE_CASE__ :      List       		=			field(default_factory=lowerCAmelCase__		)
     SCREAMING_SNAKE_CASE__ :      List       		=			field(default_factory=lowerCAmelCase__		)



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

            _lowerCamelCase    	=					list(filter(lambda a__   : type(a__   ) not in self.src_skip					,     a__   )   )
            _lowerCamelCase    	=					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 SCREAMING_SNAKE_CASE_   (       snake_case   :					nn.Module						,  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(snake_case		)

       our_model.load_state_dict(snake_case		)
       return our_model





def SCREAMING_SNAKE_CASE_   (       snake_case   :					str						,  snake_case   :					VanConfig						,  snake_case   :					str						,  snake_case   :					nn.Module						,  snake_case   :					Path						,  snake_case   :					bool = True						,  )->		Dict:
       print(f'Downloading weights for {name}...'		)
       _lowerCamelCase    	=					cached_download(snake_case		)
       print(f'Converting {name}...'		)
       _lowerCamelCase    	=					torch.load(snake_case		)['state_dict']
       from_model.load_state_dict(snake_case		)
       from_model.eval()
       with torch.no_grad():
              _lowerCamelCase    	=					VanForImageClassification(snake_case		).eval()
              _lowerCamelCase    	=					ModuleTransfer(src=snake_case						,  dest=snake_case		)
              _lowerCamelCase    	=					torch.randn((1, 3, 224, 224)		)
              module_transfer(snake_case		)
              _lowerCamelCase    	=					copy_parameters(snake_case						,  snake_case		)

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

       _lowerCamelCase    	=					name
       print(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=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=snake_case						,  )

              print(f'Pushed {checkpoint_name}'		)





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Path						,  snake_case   :					str = None						,  snake_case   :					bool = True		)->		int:
       _lowerCamelCase    	=					'imagenet-1k-id2label.json'
       _lowerCamelCase    	=					1_000

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

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

       _lowerCamelCase    	=					partial(snake_case						,  num_labels=snake_case						,  idalabel=snake_case						,  labelaid=snake_case		)

       _lowerCamelCase    	=					{
           '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]						,  ),
       }

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


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


import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

A_     :      Any								=1_8_0





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Dict						,  snake_case   :					Any		)->		Tuple:
       _lowerCamelCase    	=					k_size // 2
       _lowerCamelCase	,	_lowerCamelCase    	=					np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       _lowerCamelCase    	=					(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(snake_case		) + np.square(snake_case		)) / (2 * np.square(snake_case		))		)
       )
       return g





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Optional[int]						,  snake_case   :					int						,  snake_case   :					Dict		)->		Optional[Any]:
       _lowerCamelCase    	=					np.zeros(snake_case		)

       for row in range(1						,  image_shape[0] - 1		):
              for col in range(1						,  image_shape[1] - 1		):
                     _lowerCamelCase    	=					gradient_direction[row, col]

                     if (
                         0 <= direction < PI / 8
                         or 15 * PI / 8 <= direction <= 2 * PI
                         or 7 * PI / 8 <= direction <= 9 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row, col - 1]
                            _lowerCamelCase    	=					sobel_grad[row, col + 1]
                            if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

                     elif (
                         PI / 8 <= direction < 3 * PI / 8
                         or 9 * PI / 8 <= direction < 11 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row + 1, col - 1]
                            _lowerCamelCase    	=					sobel_grad[row - 1, col + 1]
                            if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

                     elif (
                         3 * PI / 8 <= direction < 5 * PI / 8
                         or 11 * PI / 8 <= direction < 13 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row - 1, col]
                            _lowerCamelCase    	=					sobel_grad[row + 1, col]
                            if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

                     elif (
                         5 * PI / 8 <= direction < 7 * PI / 8
                         or 13 * PI / 8 <= direction < 15 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row - 1, col - 1]
                            _lowerCamelCase    	=					sobel_grad[row + 1, col + 1]
                            if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

       return destination





def SCREAMING_SNAKE_CASE_   (       snake_case   :					List[Any]						,  snake_case   :					List[Any]						,  snake_case   :					int						,  snake_case   :					int						,  snake_case   :					List[str]						,  snake_case   :					str		)->		Tuple:
       for row in range(1						,  image_shape[0] - 1		):
              for col in range(1						,  image_shape[1] - 1		):
                     if destination[row, col] >= threshold_high:
                            _lowerCamelCase    	=					strong
                     elif destination[row, col] <= threshold_low:
                            _lowerCamelCase    	=					0
                     else:
                            _lowerCamelCase    	=					weak





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Union[str, Any]						,  snake_case   :					Optional[int]						,  snake_case   :					Dict						,  snake_case   :					Tuple		)->		Optional[int]:
       for row in range(1						,  image_shape[0]		):
              for col in range(1						,  image_shape[1]		):
                     if destination[row, col] == weak:
                            if 255 in (
                                destination[row, col + 1],
                                destination[row, col - 1],
                                destination[row - 1, col],
                                destination[row + 1, col],
                                destination[row - 1, col - 1],
                                destination[row + 1, col - 1],
                                destination[row - 1, col + 1],
                                destination[row + 1, col + 1],
                            ):
                                   _lowerCamelCase    	=					strong
                            else:
                                   _lowerCamelCase    	=					0





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Dict						,  snake_case   :					Union[str, Any]=15						,  snake_case   :					Optional[int]=30						,  snake_case   :					int=128						,  snake_case   :					str=255		)->		List[Any]:
       # gaussian_filter
       _lowerCamelCase    	=					img_convolve(snake_case						,  gen_gaussian_kernel(9						,  sigma=1.4		)		)
       # get the gradient and degree by sobel_filter
       _lowerCamelCase	,	_lowerCamelCase    	=					sobel_filter(snake_case		)
       _lowerCamelCase    	=					PI + np.radadeg(snake_case		)

       _lowerCamelCase    	=					suppress_non_maximum(image.shape						,  snake_case						,  snake_case		)

       detect_high_low_threshold(
           image.shape						,  snake_case						,  snake_case						,  snake_case						,  snake_case						,  snake_case		)

       track_edge(image.shape						,  snake_case						,  snake_case						,  snake_case		)

       return destination


if __name__ == "__main__":
      # read original image in gray mode
      A_     :      Optional[Any]								=cva.imread(R"""../image_data/lena.jpg""", 0)
      # canny edge detection
      A_     :      Optional[Any]								=canny(lena)
      cva.imshow("""canny""", canny_destination)
      cva.waitKey(0)






