




import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: Optional[Any]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					old_name

      if "patch_embed" in old_name:
            lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					old_name.split('.'  )

            if layer == "0":
                  lowerCamelCase_	  =					old_name.replace('0'			,							'convolution1'  )
            elif layer == "1":
                  lowerCamelCase_	  =					old_name.replace('1'			,							'batchnorm_before'  )
            elif layer == "3":
                  lowerCamelCase_	  =					old_name.replace('3'			,							'convolution2'  )
            else:
                  lowerCamelCase_	  =					old_name.replace('4'			,							'batchnorm_after'  )

      if "network" in old_name and re.search(r'\d\.\d'			,							lowercase  ):
            lowerCamelCase_	  =					r'\b\d{2}\b'
            if bool(re.search(lowercase			,							lowercase  )  ):
                  lowerCamelCase_	  =					re.search(r'\d\.\d\d.'			,							lowercase  ).group()
            else:
                  lowerCamelCase_	  =					re.search(r'\d\.\d.'			,							lowercase  ).group()
            if int(match[0]  ) < 6:
                  lowerCamelCase_	  =					old_name.replace(lowercase			,							''  )
                  lowerCamelCase_	  =					trimmed_name.replace('network'			,							match[0] + '.meta4D_layers.blocks.' + match[2:-1]  )
                  lowerCamelCase_	  =					'intermediate_stages.' + trimmed_name
            else:
                  lowerCamelCase_	  =					old_name.replace(lowercase			,							''  )
                  if int(match[2]  ) < num_meta4D_last_stage:
                        lowerCamelCase_	  =					trimmed_name.replace('network'			,							'meta4D_layers.blocks.' + match[2]  )
                  else:
                        lowerCamelCase_	  =					str(int(match[2]  ) - num_meta4D_last_stage  )
                        lowerCamelCase_	  =					trimmed_name.replace('network'			,							'meta3D_layers.blocks.' + layer_index  )
                        if "norm1" in old_name:
                              lowerCamelCase_	  =					trimmed_name.replace('norm1'			,							'layernorm1'  )
                        elif "norm2" in old_name:
                              lowerCamelCase_	  =					trimmed_name.replace('norm2'			,							'layernorm2'  )
                        elif "fc1" in old_name:
                              lowerCamelCase_	  =					trimmed_name.replace('fc1'			,							'linear_in'  )
                        elif "fc2" in old_name:
                              lowerCamelCase_	  =					trimmed_name.replace('fc2'			,							'linear_out'  )

                  lowerCamelCase_	  =					'last_stage.' + trimmed_name

      elif "network" in old_name and re.search(r'.\d.'			,							lowercase  ):
            lowerCamelCase_	  =					old_name.replace('network'			,							'intermediate_stages'  )

      if "fc" in new_name:
            lowerCamelCase_	  =					new_name.replace('fc'			,							'convolution'  )
      elif ("norm1" in new_name) and ("layernorm1" not in new_name):
            lowerCamelCase_	  =					new_name.replace('norm1'			,							'batchnorm_before'  )
      elif ("norm2" in new_name) and ("layernorm2" not in new_name):
            lowerCamelCase_	  =					new_name.replace('norm2'			,							'batchnorm_after'  )
      if "proj" in new_name:
            lowerCamelCase_	  =					new_name.replace('proj'			,							'projection'  )
      if "dist_head" in new_name:
            lowerCamelCase_	  =					new_name.replace('dist_head'			,							'distillation_classifier'  )
      elif "head" in new_name:
            lowerCamelCase_	  =					new_name.replace('head'			,							'classifier'  )
      elif "patch_embed" in new_name:
            lowerCamelCase_	  =					'efficientformer.' + new_name
      elif new_name == "norm.weight" or new_name == "norm.bias":
            lowerCamelCase_	  =					new_name.replace('norm'			,							'layernorm'  )
            lowerCamelCase_	  =					'efficientformer.' + new_name
      else:
            lowerCamelCase_	  =					'efficientformer.encoder.' + new_name

      return new_name

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[int]			,							lowercase			: List[Any]  ):


      '''simple docstring'''



      for key in checkpoint.copy().keys():
            lowerCamelCase_	  =					checkpoint.pop(lowercase  )
            lowerCamelCase_	  =					val

      return checkpoint

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					'http://images.cocodataset.org/val2017/000000039769.jpg'
      lowerCamelCase_	  =					Image.open(requests.get(lowercase			,							stream=lowercase  ).raw  )

      return image

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Path			,							lowercase			: Path			,							lowercase			: Path			,							lowercase			: bool  ):


      '''simple docstring'''



      lowerCamelCase_	  =					torch.load(lowercase			,							map_location='cpu'  )['model']
      lowerCamelCase_	  =					EfficientFormerConfig.from_json_file(lowercase  )
      lowerCamelCase_	  =					EfficientFormerForImageClassificationWithTeacher(lowercase  )
      lowerCamelCase_	  =					'_'.join(checkpoint_path.split('/'  )[-1].split('.'  )[0].split('_'  )[:-1]  )

      lowerCamelCase_	  =					config.depths[-1] - config.num_metaad_blocks + 1
      lowerCamelCase_	  =					convert_torch_checkpoint(lowercase			,							lowercase  )

      model.load_state_dict(lowercase  )
      model.eval()

      lowerCamelCase_	  =					{
          'bilinear': PILImageResampling.BILINEAR,
          'bicubic': PILImageResampling.BICUBIC,
          'nearest': PILImageResampling.NEAREST,
      }

      # prepare image
      lowerCamelCase_	  =					prepare_img()
      lowerCamelCase_	  =					2_56
      lowerCamelCase_	  =					2_24
      lowerCamelCase_	  =					EfficientFormerImageProcessor(
          size={'shortest_edge': image_size}			,							crop_size={'height': crop_size, 'width': crop_size}			,							resample=pillow_resamplings['bicubic']			,							)
      lowerCamelCase_	  =					processor(images=lowercase			,							return_tensors='pt'  ).pixel_values

      # original processing pipeline
      lowerCamelCase_	  =					Compose(
          [
              Resize(lowercase			,							interpolation=pillow_resamplings['bicubic']  ),
              CenterCrop(lowercase  ),
              ToTensor(),
              Normalize(lowercase			,							lowercase  ),
          ]  )
      lowerCamelCase_	  =					image_transforms(lowercase  ).unsqueeze(0  )

      assert torch.allclose(lowercase			,							lowercase  )

      lowerCamelCase_	  =					model(lowercase  )
      lowerCamelCase_	  =					outputs.logits

      lowerCamelCase_	  =					(1, 10_00)

      if "l1" in model_name:
            lowerCamelCase_	  =					torch.Tensor(
                [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]  )
            assert torch.allclose(logits[0, :10]			,							lowercase			,							atol=1e-3  )
            assert logits.shape == expected_shape
      elif "l3" in model_name:
            lowerCamelCase_	  =					torch.Tensor(
                [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]  )
            assert torch.allclose(logits[0, :10]			,							lowercase			,							atol=1e-3  )
            assert logits.shape == expected_shape
      elif "l7" in model_name:
            lowerCamelCase_	  =					torch.Tensor(
                [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]  )
            assert logits.shape == expected_shape
      else:
            raise ValueError(
                f"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""  )

      # Save Checkpoints
      Path(lowercase  ).mkdir(exist_ok=lowercase  )
      model.save_pretrained(lowercase  )
      print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""  )
      processor.save_pretrained(lowercase  )
      print(f"""Processor successfuly saved at {pytorch_dump_path}"""  )

      if push_to_hub:
            print('Pushing model to the hub...'  )

            model.push_to_hub(
                repo_id=f"""Bearnardd/{pytorch_dump_path}"""			,							commit_message='Add model'			,							use_temp_dir=lowercase			,							)
            processor.push_to_hub(
                repo_id=f"""Bearnardd/{pytorch_dump_path}"""			,							commit_message='Add image processor'			,							use_temp_dir=lowercase			,							)


if __name__ == "__main__":
 lowerCamelCase       :	int									=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--pytorch_model_path",
     default=None,
     type=str,
     required=True,
     help="Path to EfficientFormer pytorch checkpoint.",
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The json file for EfficientFormer model config.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )

 parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
 parser.add_argument(
     "--no-push_to_hub",
     dest="push_to_hub",
     action="store_false",
     help="Do not push model and image processor to the hub",
 )
 parser.set_defaults(push_to_hub=True)

 lowerCamelCase       :	str									=     parser.parse_args()
 convert_efficientformer_checkpoint(
     checkpoint_path=args.pytorch_model_path,
     efficientformer_config_file=args.config_file,
     pytorch_dump_path=args.pytorch_dump_path,
     push_to_hub=args.push_to_hub,
 )









import cva
import numpy as np







class 						A:



 '''simple docstring'''


 def __init__(       self      :						int		,							A_      :						float		,							A_      :						int						)     ->			List[Any]:

       """simple docstring"""






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

 def __str__(       self      :						str						)     ->			str:

       """simple docstring"""






       return str(self.k						)





 def        a__				(       self      :						Any		,							A_      :						str						)     ->			tuple[cva.Mat, list[list[int]]]:

       """simple docstring"""






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

                   lowerCamelCase_	  =					(wxx * wyy) - (wxy**2)
                   lowerCamelCase_	  =					wxx + wyy
                   lowerCamelCase_	  =					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       :	Optional[int]									=     edge_detect.detect("path_to_image")
 cva.imwrite("detect.png", color_img)



