




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      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	List[Any]:
  lowerCAmelCase__				:  Optional[Any]		       =     old_name

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

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

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

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

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

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

  return new_name



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	Union[str, Any]:
  for key in checkpoint.copy().keys():
    lowerCAmelCase__				:  Any		       =     checkpoint.pop(UpperCamelCase      )
    lowerCAmelCase__				:  Union[str, Any]		       =     val

  return checkpoint



def      __lowerCAmelCase			(					)     ->	Optional[int]:
  lowerCAmelCase__				:  int		       =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  lowerCAmelCase__				:  Dict		       =     Image.open(requests.get(UpperCamelCase	,      stream=UpperCamelCase      ).raw      )

  return image



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	List[str]:
  lowerCAmelCase__				:  List[str]		       =     torch.load(UpperCamelCase	,      map_location='''cpu'''      )['''model''']
  lowerCAmelCase__				:  Dict		       =     EfficientFormerConfig.from_json_file(UpperCamelCase      )
  lowerCAmelCase__				:  Any		       =     EfficientFormerForImageClassificationWithTeacher(UpperCamelCase      )
  lowerCAmelCase__				:  List[str]		       =     '''_'''.join(checkpoint_path.split('''/'''      )[-1].split('''.'''      )[0].split('''_'''      )[:-1]      )

  lowerCAmelCase__				:  Any		       =     config.depths[-1] - config.num_metaad_blocks + 1
  lowerCAmelCase__				:  Tuple		       =     convert_torch_checkpoint(UpperCamelCase	,      UpperCamelCase      )

  model.load_state_dict(UpperCamelCase      )
  model.eval()

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

  # prepare image
  lowerCAmelCase__				:  Any		       =     prepare_img()
  lowerCAmelCase__				:  Any		       =     256
  lowerCAmelCase__				:  Dict		       =     224
  lowerCAmelCase__				:  Tuple		       =     EfficientFormerImageProcessor(
      size={'''shortest_edge''': image_size}	,      crop_size={'''height''': crop_size, '''width''': crop_size}	,      resample=pillow_resamplings['''bicubic''']	,      )
  lowerCAmelCase__				:  List[str]		       =     processor(images=UpperCamelCase	,      return_tensors='''pt'''      ).pixel_values

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

  assert torch.allclose(UpperCamelCase	,      UpperCamelCase      )

  lowerCAmelCase__				:  str		       =     model(UpperCamelCase      )
  lowerCAmelCase__				:  List[Any]		       =     outputs.logits

  lowerCAmelCase__				:  List[str]		       =     (1, 1000)

  if "l1" in model_name:
    lowerCAmelCase__				:  str		       =     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]	,      UpperCamelCase	,      atol=1E-3      )
    assert logits.shape == expected_shape
  elif "l3" in model_name:
    lowerCAmelCase__				:  Union[str, Any]		       =     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]	,      UpperCamelCase	,      atol=1E-3      )
    assert logits.shape == expected_shape
  elif "l7" in model_name:
    lowerCAmelCase__				:  List[str]		       =     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(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__":
     lowerCAmelCase_					    = 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_					    = 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 math



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	float:
  if (
      not isinstance(UpperCamelCase	,      (int, float)      )
      or power_factor < -1
      or power_factor > 1
  ):
    raise ValueError('''power_factor must be a valid float value between -1 and 1.'''      )
  return apparent_power * power_factor



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	float:
  if (
      not isinstance(UpperCamelCase	,      (int, float)      )
      or power_factor < -1
      or power_factor > 1
  ):
    raise ValueError('''power_factor must be a valid float value between -1 and 1.'''      )
  return apparent_power * math.sqrt(1 - power_factor**2      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

