






'''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__				:						Dict,	UpperCamelCase__				:						str							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Tuple									=old_name

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

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

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

                  SCREAMING_SNAKE_CASE__     :				Union[str, Any]									='''last_stage.''' + trimmed_name

      elif "network" in old_name and re.search(R'''.\d.''',	UpperCamelCase__							):
            SCREAMING_SNAKE_CASE__     :				Any									=old_name.replace('''network''',	'''intermediate_stages'''							)

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

      return new_name





def  _a(  UpperCamelCase__				:						List[Any],	UpperCamelCase__				:						Dict							):




      '''simple docstring'''






      for key in checkpoint.copy().keys():
            SCREAMING_SNAKE_CASE__     :				str									=checkpoint.pop(UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__     :				Any									=val

      return checkpoint





def  _a(  ):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[Any]									='''http://images.cocodataset.org/val2017/000000039769.jpg'''
      SCREAMING_SNAKE_CASE__     :				Dict									=Image.open(requests.get(UpperCamelCase__,	stream=UpperCamelCase__							).raw							)

      return image





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




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				int									=torch.load(UpperCamelCase__,	map_location='''cpu'''							)['''model''']
      SCREAMING_SNAKE_CASE__     :				str									=EfficientFormerConfig.from_json_file(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=EfficientFormerForImageClassificationWithTeacher(UpperCamelCase__							)
      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__     :				Optional[Any]									=convert_torch_checkpoint(UpperCamelCase__,	UpperCamelCase__							)

      model.load_state_dict(UpperCamelCase__							)
      model.eval()

      SCREAMING_SNAKE_CASE__     :				Optional[int]									={
          '''bilinear''': PILImageResampling.BILINEAR,
          '''bicubic''': PILImageResampling.BICUBIC,
          '''nearest''': PILImageResampling.NEAREST,
      }

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

      # original processing pipeline
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=Compose(
          [
              Resize(UpperCamelCase__,	interpolation=pillow_resamplings['''bicubic''']							),
              CenterCrop(UpperCamelCase__							),
              ToTensor(),
              Normalize(UpperCamelCase__,	UpperCamelCase__							),
          ]							)
      SCREAMING_SNAKE_CASE__     :				Dict									=image_transforms(UpperCamelCase__							).unsqueeze(0							)

      assert torch.allclose(UpperCamelCase__,	UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__     :				str									=model(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				str									=outputs.logits

      SCREAMING_SNAKE_CASE__     :				Tuple									=(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],	UpperCamelCase__,	atol=1e-3							)
            assert logits.shape == expected_shape
      elif "l3" in model_name:
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=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],	UpperCamelCase__,	atol=1e-3							)
            assert logits.shape == expected_shape
      elif "l7" in model_name:
            SCREAMING_SNAKE_CASE__     :				int									=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(UpperCamelCase__							).mkdir(exist_ok=UpperCamelCase__							)
      model.save_pretrained(UpperCamelCase__							)
      print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"							)
      processor.save_pretrained(UpperCamelCase__							)
      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=UpperCamelCase__,	)
            processor.push_to_hub(
                repo_id=f"Bearnardd/{pytorch_dump_path}",	commit_message='''Add image processor''',	use_temp_dir=UpperCamelCase__,	)


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,
  )







'''simple docstring'''
from math import isqrt





def  _a(  UpperCamelCase__				:						int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				int									=[True] * max_number
      for i in range(2,	isqrt(max_number - 1							) + 1							):
            if is_prime[i]:
                  for j in range(i**2,	UpperCamelCase__,	UpperCamelCase__							):
                        SCREAMING_SNAKE_CASE__     :				Any									=False

      return [i for i in range(2,	UpperCamelCase__							) if is_prime[i]]





def  _a(  UpperCamelCase__				:						int = 1_0**8							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Tuple									=calculate_prime_numbers(max_number // 2							)

      SCREAMING_SNAKE_CASE__     :				int									=0
      SCREAMING_SNAKE_CASE__     :				int									=0
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=len(UpperCamelCase__							) - 1
      while left <= right:
            while prime_numbers[left] * prime_numbers[right] >= max_number:
                  right -= 1
            semiprimes_count += right - left + 1
            left += 1

      return semiprimes_count


if __name__ == "__main__":
  print(F'''{solution() = }''')