
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        __UpperCamelCase					(    lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    List[str]  ):
 __a			:  Dict		    =  old_name

 if "patch_embed" in old_name:
  __a			:  Union[str, Any]		    =  old_name.split('''.'''  )

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

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

   __a			:  Optional[Any]		    =  '''last_stage.''' + trimmed_name

 elif "network" in old_name and re.search(R'''.\d.'''  ,       lowerCAmelCase__  ):
  __a			:  str		    =  old_name.replace('''network'''  ,       '''intermediate_stages'''  )

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

 return new_name




def        __UpperCamelCase					(    lowerCAmelCase__							:    Any  ,       lowerCAmelCase__							:    int  ):
 for key in checkpoint.copy().keys():
  __a			:  Any		    =  checkpoint.pop(lowerCAmelCase__  )
  __a			:  str		    =  val

 return checkpoint




def        __UpperCamelCase					(    ):
 __a			:  int		    =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 __a			:  str		    =  Image.open(requests.get(lowerCAmelCase__  ,       stream=lowerCAmelCase__  ).raw  )

 return image




def        __UpperCamelCase					(    lowerCAmelCase__							:    Path  ,       lowerCAmelCase__							:    Path  ,       lowerCAmelCase__							:    Path  ,       lowerCAmelCase__							:    bool  ):
 __a			:  Tuple		    =  torch.load(lowerCAmelCase__  ,       map_location='''cpu'''  )['''model''']
 __a			:  Any		    =  EfficientFormerConfig.from_json_file(lowerCAmelCase__  )
 __a			:  int		    =  EfficientFormerForImageClassificationWithTeacher(lowerCAmelCase__  )
 __a			:  Optional[int]		    =  '''_'''.join(checkpoint_path.split('''/'''  )[-1].split('''.'''  )[0].split('''_'''  )[:-1]  )

 __a			:  Optional[Any]		    =  config.depths[-1] - config.num_metaad_blocks + 1
 __a			:  Optional[int]		    =  convert_torch_checkpoint(lowerCAmelCase__  ,       lowerCAmelCase__  )

 model.load_state_dict(lowerCAmelCase__  )
 model.eval()

 __a			:  List[Any]		    =  {
     '''bilinear''': PILImageResampling.BILINEAR,
     '''bicubic''': PILImageResampling.BICUBIC,
     '''nearest''': PILImageResampling.NEAREST,
 }

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

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

 assert torch.allclose(lowerCAmelCase__  ,       lowerCAmelCase__  )

 __a			:  Optional[Any]		    =  model(lowerCAmelCase__  )
 __a			:  str		    =  outputs.logits

 __a			:  Optional[int]		    =  (1, 1_0_0_0)

 if "l1" in model_name:
  __a			:  Any		    =  torch.Tensor(
      [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28]  )
  assert torch.allclose(logits[0, :1_0]  ,       lowerCAmelCase__  ,       atol=1e-3  )
  assert logits.shape == expected_shape
 elif "l3" in model_name:
  __a			:  List[Any]		    =  torch.Tensor(
      [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27]  )
  assert torch.allclose(logits[0, :1_0]  ,       lowerCAmelCase__  ,       atol=1e-3  )
  assert logits.shape == expected_shape
 elif "l7" in model_name:
  __a			:  str		    =  torch.Tensor(
      [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78]  )
  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(lowerCAmelCase__  ).mkdir(exist_ok=lowerCAmelCase__  )
 model.save_pretrained(lowerCAmelCase__  )
 print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"  )
 processor.save_pretrained(lowerCAmelCase__  )
 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=lowerCAmelCase__  ,       )
  processor.push_to_hub(
      repo_id=f"Bearnardd/{pytorch_dump_path}"  ,       commit_message='''Add image processor'''  ,       use_temp_dir=lowerCAmelCase__  ,       )


if __name__ == "__main__":
      lowercase__             =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)

      lowercase__             =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,
      )


def        __UpperCamelCase					(    lowerCAmelCase__							:    int = 1_0_0_0  ):
 __a			:  int		    =  -1
 __a			:  Any		    =  0
 for a in range(1  ,       n // 3  ):
  # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
  __a			:  List[Any]		    =  (n * n - 2 * a * n) // (2 * n - 2 * a)
  __a			:  Union[str, Any]		    =  n - a - b
  if c * c == (a * a + b * b):
   __a			:  Union[str, Any]		    =  a * b * c
   if candidate >= product:
    __a			:  List[Any]		    =  candidate
 return product


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