






'''simple docstring'''
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        _a(     UpperCamelCase__ :  List[str],		UpperCamelCase__ :  int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__   : int													=old_name

      if "patch_embed" in old_name:
            SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__   : List[str]													=old_name.split('''.'''							)

            if layer == "0":
                  SCREAMING_SNAKE_CASE__   : Any													=old_name.replace('''0''',		'''convolution1'''							)
            elif layer == "1":
                  SCREAMING_SNAKE_CASE__   : Dict													=old_name.replace('''1''',		'''batchnorm_before'''							)
            elif layer == "3":
                  SCREAMING_SNAKE_CASE__   : str													=old_name.replace('''3''',		'''convolution2'''							)
            else:
                  SCREAMING_SNAKE_CASE__   : Optional[Any]													=old_name.replace('''4''',		'''batchnorm_after'''							)

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

                  SCREAMING_SNAKE_CASE__   : List[str]													='''last_stage.''' + trimmed_name

      elif "network" in old_name and re.search(R'''.\d.''',		A__							):
            SCREAMING_SNAKE_CASE__   : Union[str, Any]													=old_name.replace('''network''',		'''intermediate_stages'''							)

      if "fc" in new_name:
            SCREAMING_SNAKE_CASE__   : Tuple													=new_name.replace('''fc''',		'''convolution'''							)
      elif ("norm1" in new_name) and ("layernorm1" not in new_name):
            SCREAMING_SNAKE_CASE__   : Optional[int]													=new_name.replace('''norm1''',		'''batchnorm_before'''							)
      elif ("norm2" in new_name) and ("layernorm2" not in new_name):
            SCREAMING_SNAKE_CASE__   : Dict													=new_name.replace('''norm2''',		'''batchnorm_after'''							)
      if "proj" in new_name:
            SCREAMING_SNAKE_CASE__   : Dict													=new_name.replace('''proj''',		'''projection'''							)
      if "dist_head" in new_name:
            SCREAMING_SNAKE_CASE__   : Tuple													=new_name.replace('''dist_head''',		'''distillation_classifier'''							)
      elif "head" in new_name:
            SCREAMING_SNAKE_CASE__   : int													=new_name.replace('''head''',		'''classifier'''							)
      elif "patch_embed" in new_name:
            SCREAMING_SNAKE_CASE__   : Tuple													='''efficientformer.''' + new_name
      elif new_name == "norm.weight" or new_name == "norm.bias":
            SCREAMING_SNAKE_CASE__   : List[str]													=new_name.replace('''norm''',		'''layernorm'''							)
            SCREAMING_SNAKE_CASE__   : Any													='''efficientformer.''' + new_name
      else:
            SCREAMING_SNAKE_CASE__   : List[str]													='''efficientformer.encoder.''' + new_name

      return new_name





def        _a(     UpperCamelCase__ :  Optional[int],		UpperCamelCase__ :  List[Any]							):




      '''simple docstring'''






      for key in checkpoint.copy().keys():
            SCREAMING_SNAKE_CASE__   : Optional[int]													=checkpoint.pop(A__							)
            SCREAMING_SNAKE_CASE__   : int													=val

      return checkpoint





def        _a(     ):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__   : Tuple													='''http://images.cocodataset.org/val2017/000000039769.jpg'''
      SCREAMING_SNAKE_CASE__   : str													=Image.open(requests.get(A__,		stream=A__							).raw							)

      return image





def        _a(     UpperCamelCase__ :  Path,		UpperCamelCase__ :  Path,		UpperCamelCase__ :  Path,		UpperCamelCase__ :  bool							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__   : Optional[int]													=torch.load(A__,		map_location='''cpu'''							)['''model''']
      SCREAMING_SNAKE_CASE__   : List[Any]													=EfficientFormerConfig.from_json_file(A__							)
      SCREAMING_SNAKE_CASE__   : Optional[int]													=EfficientFormerForImageClassificationWithTeacher(A__							)
      SCREAMING_SNAKE_CASE__   : Tuple													='''_'''.join(checkpoint_path.split('''/'''							)[-1].split('''.'''							)[0].split('''_'''							)[:-1]							)

      SCREAMING_SNAKE_CASE__   : Optional[int]													=config.depths[-1] - config.num_metaad_blocks + 1
      SCREAMING_SNAKE_CASE__   : List[str]													=convert_torch_checkpoint(A__,		A__							)

      model.load_state_dict(A__							)
      model.eval()

      SCREAMING_SNAKE_CASE__   : Union[str, Any]													={
          '''bilinear''': PILImageResampling.BILINEAR,
          '''bicubic''': PILImageResampling.BICUBIC,
          '''nearest''': PILImageResampling.NEAREST,
      }

      # prepare image
      SCREAMING_SNAKE_CASE__   : Tuple													=prepare_img()
      SCREAMING_SNAKE_CASE__   : str													=2_5_6
      SCREAMING_SNAKE_CASE__   : Optional[Any]													=2_2_4
      SCREAMING_SNAKE_CASE__   : List[str]													=EfficientFormerImageProcessor(
          size={'''shortest_edge''': image_size},		crop_size={'''height''': crop_size, '''width''': crop_size},		resample=pillow_resamplings['''bicubic'''],		)
      SCREAMING_SNAKE_CASE__   : str													=processor(images=A__,		return_tensors='''pt'''							).pixel_values

      # original processing pipeline
      SCREAMING_SNAKE_CASE__   : Optional[int]													=Compose(
          [
              Resize(A__,		interpolation=pillow_resamplings['''bicubic''']							),
              CenterCrop(A__							),
              ToTensor(),
              Normalize(A__,		A__							),
          ]							)
      SCREAMING_SNAKE_CASE__   : Optional[int]													=image_transforms(A__							).unsqueeze(0							)

      assert torch.allclose(A__,		A__							)

      SCREAMING_SNAKE_CASE__   : Optional[int]													=model(A__							)
      SCREAMING_SNAKE_CASE__   : str													=outputs.logits

      SCREAMING_SNAKE_CASE__   : List[str]													=(1, 1_0_0_0)

      if "l1" in model_name:
            SCREAMING_SNAKE_CASE__   : Optional[int]													=torch.Tensor(
                [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]							)
            assert torch.allclose(logits[0, :1_0],		A__,		atol=1e-3							)
            assert logits.shape == expected_shape
      elif "l3" in model_name:
            SCREAMING_SNAKE_CASE__   : str													=torch.Tensor(
                [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]							)
            assert torch.allclose(logits[0, :1_0],		A__,		atol=1e-3							)
            assert logits.shape == expected_shape
      elif "l7" in model_name:
            SCREAMING_SNAKE_CASE__   : List[str]													=torch.Tensor(
                [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]							)
            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(A__							).mkdir(exist_ok=A__							)
      model.save_pretrained(A__							)
      print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"							)
      processor.save_pretrained(A__							)
      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=A__,		)
            processor.push_to_hub(
                repo_id=f"Bearnardd/{pytorch_dump_path}",		commit_message='''Add image processor''',		use_temp_dir=A__,		)


if __name__ == "__main__":
  a_ 		=       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)

  a_ 		=       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 numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def      a      (			A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int     )    ->      np.ndarray:





      """simple docstring"""



      if (ksize % 2) == 0:
            _lowercase	       =ksize + 1
      _lowercase	       =np.zeros((ksize, ksize)      ,   dtype=np.floataa     )

      # each value
      for y in range(A__     ):
            for x in range(A__     ):
                  # distance from center
                  _lowercase	       =x - ksize // 2
                  _lowercase	       =y - ksize // 2

                  # degree to radiant
                  _lowercase	       =theta / 180 * np.pi
                  _lowercase	       =np.cos(_theta     )
                  _lowercase	       =np.sin(_theta     )

                  # get kernel x
                  _lowercase	       =cos_theta * px + sin_theta * py

                  # get kernel y
                  _lowercase	       =-sin_theta * px + cos_theta * py

                  # fill kernel
                  _lowercase	       =np.exp(
                      -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)     ) * np.cos(2 * np.pi * _x / lambd + psi     )

      return gabor


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  # read original image
  lowercase_									=   imread('../image_data/lena.jpg')
  # turn image in gray scale value
  lowercase_									=   cvtColor(img, COLOR_BGR2GRAY)

  # Apply multiple Kernel to detect edges
  lowercase_									=   np.zeros(gray.shape[:2])
  for theta in [0, 3_0, 6_0, 9_0, 1_2_0, 1_5_0]:
    lowercase_									=   gabor_filter_kernel(1_0, 8, theta, 1_0, 0, 0)
    out += filteraD(gray, CV_8UC3, kernel_aa)
  lowercase_									=   out / out.max() * 2_5_5
  lowercase_									=   out.astype(np.uinta)

  imshow('Original', gray)
  imshow('Gabor filter with 20x20 mask and 6 directions', out)

  waitKey(0)


