




import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__			:       List[Any]         =							logging.get_logger(__name__)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]      ,      __UpperCAmelCase						:     Optional[int]      ,      __UpperCAmelCase						:     Dict      ,      __UpperCAmelCase						:     str							) ->			Union[str, Any]:
						SCREAMING_SNAKE_CASE_     						=      original_name.split('.'							)[0]
						SCREAMING_SNAKE_CASE_     						=      key.split('.'							)
						SCREAMING_SNAKE_CASE_     						=      int(key_list[key_list.index(lowercase__							) - 2]							)
						SCREAMING_SNAKE_CASE_     						=      int(key_list[key_list.index(lowercase__							) - 1]							)
						SCREAMING_SNAKE_CASE_     						=      orig_block_num - offset

						SCREAMING_SNAKE_CASE_     						=      key.replace(f"{orig_block_num}.{layer_num}.{original_name}"      ,      f"block.{new_block_num}.{layer_num}.{new_name}"							)
						return key

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Tuple							) ->			int:
						SCREAMING_SNAKE_CASE_     						=      OrderedDict()
						SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      0, 0
						for key, value in state_dict.items():
												if key.startswith('network'							):
																		SCREAMING_SNAKE_CASE_     						=      key.replace('network'      ,      'poolformer.encoder'							)
												if "proj" in key:
																		# Works for the first embedding as well as the internal embedding layers
																		if key.endswith('bias'							) and "patch_embed" not in key:
																								patch_emb_offset += 1
																		SCREAMING_SNAKE_CASE_     						=      key[: key.find('proj'							)]
																		SCREAMING_SNAKE_CASE_     						=      key.replace(lowercase__      ,      f"patch_embeddings.{total_embed_found}."							)
																		SCREAMING_SNAKE_CASE_     						=      key.replace('proj'      ,      'projection'							)
																		if key.endswith('bias'							):
																								total_embed_found += 1
												if "patch_embeddings" in key:
																		SCREAMING_SNAKE_CASE_     						=      'poolformer.encoder.' + key
												if "mlp.fc1" in key:
																		SCREAMING_SNAKE_CASE_     						=      replace_key_with_offset(lowercase__      ,      lowercase__      ,      'mlp.fc1'      ,      'output.conv1'							)
												if "mlp.fc2" in key:
																		SCREAMING_SNAKE_CASE_     						=      replace_key_with_offset(lowercase__      ,      lowercase__      ,      'mlp.fc2'      ,      'output.conv2'							)
												if "norm1" in key:
																		SCREAMING_SNAKE_CASE_     						=      replace_key_with_offset(lowercase__      ,      lowercase__      ,      'norm1'      ,      'before_norm'							)
												if "norm2" in key:
																		SCREAMING_SNAKE_CASE_     						=      replace_key_with_offset(lowercase__      ,      lowercase__      ,      'norm2'      ,      'after_norm'							)
												if "layer_scale_1" in key:
																		SCREAMING_SNAKE_CASE_     						=      replace_key_with_offset(lowercase__      ,      lowercase__      ,      'layer_scale_1'      ,      'layer_scale_1'							)
												if "layer_scale_2" in key:
																		SCREAMING_SNAKE_CASE_     						=      replace_key_with_offset(lowercase__      ,      lowercase__      ,      'layer_scale_2'      ,      'layer_scale_2'							)
												if "head" in key:
																		SCREAMING_SNAKE_CASE_     						=      key.replace('head'      ,      'classifier'							)
												SCREAMING_SNAKE_CASE_     						=      value
						return new_state_dict

def  UpperCAmelCase_							(				) ->			str:
						SCREAMING_SNAKE_CASE_     						=      'http://images.cocodataset.org/val2017/000000039769.jpg'
						SCREAMING_SNAKE_CASE_     						=      Image.open(requests.get(lowercase__      ,      stream=lowercase__							).raw							)

						return image

@torch.no_grad()
def  UpperCAmelCase_							(				__UpperCAmelCase						:     Any      ,      __UpperCAmelCase						:     Union[str, Any]      ,      __UpperCAmelCase						:     str							) ->			Union[str, Any]:
						SCREAMING_SNAKE_CASE_     						=      PoolFormerConfig()

						# set attributes based on model_name
						SCREAMING_SNAKE_CASE_     						=      'huggingface/label-files'
						SCREAMING_SNAKE_CASE_     						=      model_name[-3:]
						SCREAMING_SNAKE_CASE_     						=      10_00
						SCREAMING_SNAKE_CASE_     						=      'imagenet-1k-id2label.json'
						SCREAMING_SNAKE_CASE_     						=      (1, 10_00)

						# set config attributes
						SCREAMING_SNAKE_CASE_     						=      json.load(open(hf_hub_download(lowercase__      ,      lowercase__      ,      repo_type='dataset'							)      ,      'r'							)							)
						SCREAMING_SNAKE_CASE_     						=      {int(lowercase__							): v for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE_     						=      idalabel
						SCREAMING_SNAKE_CASE_     						=      {v: k for k, v in idalabel.items()}
						if size == "s12":
												SCREAMING_SNAKE_CASE_     						=      [2, 2, 6, 2]
												SCREAMING_SNAKE_CASE_     						=      [64, 1_28, 3_20, 5_12]
												SCREAMING_SNAKE_CASE_     						=      4.0
												SCREAMING_SNAKE_CASE_     						=      0.9
						elif size == "s24":
												SCREAMING_SNAKE_CASE_     						=      [4, 4, 12, 4]
												SCREAMING_SNAKE_CASE_     						=      [64, 1_28, 3_20, 5_12]
												SCREAMING_SNAKE_CASE_     						=      4.0
												SCREAMING_SNAKE_CASE_     						=      0.9
						elif size == "s36":
												SCREAMING_SNAKE_CASE_     						=      [6, 6, 18, 6]
												SCREAMING_SNAKE_CASE_     						=      [64, 1_28, 3_20, 5_12]
												SCREAMING_SNAKE_CASE_     						=      4.0
												SCREAMING_SNAKE_CASE_     						=      1E-6
												SCREAMING_SNAKE_CASE_     						=      0.9
						elif size == "m36":
												SCREAMING_SNAKE_CASE_     						=      [6, 6, 18, 6]
												SCREAMING_SNAKE_CASE_     						=      [96, 1_92, 3_84, 7_68]
												SCREAMING_SNAKE_CASE_     						=      4.0
												SCREAMING_SNAKE_CASE_     						=      1E-6
												SCREAMING_SNAKE_CASE_     						=      0.9_5
						elif size == "m48":
												SCREAMING_SNAKE_CASE_     						=      [8, 8, 24, 8]
												SCREAMING_SNAKE_CASE_     						=      [96, 1_92, 3_84, 7_68]
												SCREAMING_SNAKE_CASE_     						=      4.0
												SCREAMING_SNAKE_CASE_     						=      1E-6
												SCREAMING_SNAKE_CASE_     						=      0.9_5
						else:
												raise ValueError(f"Size {size} not supported"							)

						# load image processor
						SCREAMING_SNAKE_CASE_     						=      PoolFormerImageProcessor(crop_pct=lowercase__							)

						# Prepare image
						SCREAMING_SNAKE_CASE_     						=      prepare_img()
						SCREAMING_SNAKE_CASE_     						=      image_processor(images=lowercase__      ,      return_tensors='pt'							).pixel_values

						logger.info(f"Converting model {model_name}..."							)

						# load original state dict
						SCREAMING_SNAKE_CASE_     						=      torch.load(lowercase__      ,      map_location=torch.device('cpu'							)							)

						# rename keys
						SCREAMING_SNAKE_CASE_     						=      rename_keys(lowercase__							)

						# create HuggingFace model and load state dict
						SCREAMING_SNAKE_CASE_     						=      PoolFormerForImageClassification(lowercase__							)
						model.load_state_dict(lowercase__							)
						model.eval()

						# Define image processor
						SCREAMING_SNAKE_CASE_     						=      PoolFormerImageProcessor(crop_pct=lowercase__							)
						SCREAMING_SNAKE_CASE_     						=      image_processor(images=prepare_img()      ,      return_tensors='pt'							).pixel_values

						# forward pass
						SCREAMING_SNAKE_CASE_     						=      model(lowercase__							)
						SCREAMING_SNAKE_CASE_     						=      outputs.logits

						# define expected logit slices for different models
						if size == "s12":
												SCREAMING_SNAKE_CASE_     						=      torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]							)
						elif size == "s24":
												SCREAMING_SNAKE_CASE_     						=      torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]							)
						elif size == "s36":
												SCREAMING_SNAKE_CASE_     						=      torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]							)
						elif size == "m36":
												SCREAMING_SNAKE_CASE_     						=      torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]							)
						elif size == "m48":
												SCREAMING_SNAKE_CASE_     						=      torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3]							)
						else:
												raise ValueError(f"Size {size} not supported"							)

						# verify logits
						assert logits.shape == expected_shape
						assert torch.allclose(logits[0, :3]      ,      lowercase__      ,      atol=1E-2							)

						# finally, save model and image processor
						logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."							)
						Path(lowercase__							).mkdir(exist_ok=lowercase__							)
						model.save_pretrained(lowercase__							)
						print(f"Saving image processor to {pytorch_dump_folder_path}"							)
						image_processor.save_pretrained(lowercase__							)


if __name__ == "__main__":
			lowerCamelCase__			:       Union[str, Any]         =							argparse.ArgumentParser()

			parser.add_argument(
			    '--model_name',
			    default='poolformer_s12',
			    type=str,
			    help='Name of the model you\'d like to convert.',
			)
			parser.add_argument(
			    '--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
			)
			lowerCamelCase__			:       List[Any]         =							parser.parse_args()
			convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





import cva
import numpy as np





class 	lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Optional[Any]    ,    _lowerCAmelCase  :		float    ,    _lowerCAmelCase  :		int						):

       if k in (0.04, 0.06):
             SCREAMING_SNAKE_CASE_     						=      k
             SCREAMING_SNAKE_CASE_     						=      window_size
       else:
             raise ValueError('invalid k value'						)



 def __str__(							self  :		Tuple						):
       return str(self.k						)




 def 				lowerCAmelCase_  (							self  :		int    ,    _lowerCAmelCase  :		str						):
       SCREAMING_SNAKE_CASE_     						=      cva.imread(_lowerCAmelCase    ,    0						)
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      img.shape
       SCREAMING_SNAKE_CASE_     						=      []
       SCREAMING_SNAKE_CASE_     						=      img.copy()
       SCREAMING_SNAKE_CASE_     						=      cva.cvtColor(_lowerCAmelCase    ,    cva.COLOR_GRAY2RGB						)
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      np.gradient(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      dx**2
       SCREAMING_SNAKE_CASE_     						=      dy**2
       SCREAMING_SNAKE_CASE_     						=      dx * dy
       SCREAMING_SNAKE_CASE_     						=      0.04
       SCREAMING_SNAKE_CASE_     						=      self.window_size // 2
       for y in range(_lowerCAmelCase    ,    h - offset						):
             for x in range(_lowerCAmelCase    ,    w - offset						):
                   SCREAMING_SNAKE_CASE_     						=      ixx[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   SCREAMING_SNAKE_CASE_     						=      iyy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   SCREAMING_SNAKE_CASE_     						=      ixy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()

                   SCREAMING_SNAKE_CASE_     						=      (wxx * wyy) - (wxy**2)
                   SCREAMING_SNAKE_CASE_     						=      wxx + wyy
                   SCREAMING_SNAKE_CASE_     						=      det - k * (trace**2)
                   # Can change the value
                   if r > 0.5:
                         corner_list.append([x, y, r]						)
                         color_img.itemset((y, x, 0)    ,    0						)
                         color_img.itemset((y, x, 1)    ,    0						)
                         color_img.itemset((y, x, 2)    ,    255						)
       return color_img, corner_list


if __name__ == "__main__":
   lowerCamelCase__			:       Optional[int]         =							HarrisCorner(0.04, 3)
   lowerCamelCase__    ,							lowerCamelCase__			:       str         =							edge_detect.detect('path_to_image')
   cva.imwrite('detect.png', color_img)