





"""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 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Union[str, Any]										=  old_name

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

    if layer == "0":
      A_  :       int										=  old_name.replace('0' ,					'convolution1'					)
    elif layer == "1":
      A_  :       Optional[Any]										=  old_name.replace('1' ,					'batchnorm_before'					)
    elif layer == "3":
      A_  :       Union[str, Any]										=  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' ,					_UpperCAmelCase					):
    A_  :       Tuple										=  R'\b\d{2}\b'
    if bool(re.search(_UpperCAmelCase ,					_UpperCAmelCase					)					):
      A_  :       Tuple										=  re.search(R'\d\.\d\d.' ,					_UpperCAmelCase					).group()
    else:
      A_  :       Any										=  re.search(R'\d\.\d.' ,					_UpperCAmelCase					).group()
    if int(match[0]					) < 6:
      A_  :       List[str]										=  old_name.replace(_UpperCAmelCase ,					''					)
      A_  :       Optional[int]										=  trimmed_name.replace('network' ,					match[0] + '.meta4D_layers.blocks.' + match[2:-1]					)
      A_  :       Tuple										=  'intermediate_stages.' + trimmed_name
    else:
      A_  :       Any										=  old_name.replace(_UpperCAmelCase ,					''					)
      if int(match[2]					) < num_meta4D_last_stage:
        A_  :       Optional[int]										=  trimmed_name.replace('network' ,					'meta4D_layers.blocks.' + match[2]					)
      else:
        A_  :       List[str]										=  str(int(match[2]					) - num_meta4D_last_stage					)
        A_  :       Optional[Any]										=  trimmed_name.replace('network' ,					'meta3D_layers.blocks.' + layer_index					)
        if "norm1" in old_name:
          A_  :       Optional[Any]										=  trimmed_name.replace('norm1' ,					'layernorm1'					)
        elif "norm2" in old_name:
          A_  :       Dict										=  trimmed_name.replace('norm2' ,					'layernorm2'					)
        elif "fc1" in old_name:
          A_  :       Optional[Any]										=  trimmed_name.replace('fc1' ,					'linear_in'					)
        elif "fc2" in old_name:
          A_  :       List[Any]										=  trimmed_name.replace('fc2' ,					'linear_out'					)

      A_  :       Tuple										=  'last_stage.' + trimmed_name

  elif "network" in old_name and re.search(R'.\d.' ,					_UpperCAmelCase					):
    A_  :       Optional[Any]										=  old_name.replace('network' ,					'intermediate_stages'					)

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

  return new_name





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  for key in checkpoint.copy().keys():
    A_  :       List[str]										=  checkpoint.pop(_UpperCAmelCase					)
    A_  :       Dict										=  val

  return checkpoint





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





  A_  :       List[Any]										=  'http://images.cocodataset.org/val2017/000000039769.jpg'
  A_  :       Optional[int]										=  Image.open(requests.get(_UpperCAmelCase ,					stream=_UpperCAmelCase					).raw					)

  return image





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Union[str, Any]										=  torch.load(_UpperCAmelCase ,					map_location='cpu'					)['model']
  A_  :       List[str]										=  EfficientFormerConfig.from_json_file(_UpperCAmelCase					)
  A_  :       Optional[int]										=  EfficientFormerForImageClassificationWithTeacher(_UpperCAmelCase					)
  A_  :       Dict										=  '_'.join(checkpoint_path.split('/'					)[-1].split('.'					)[0].split('_'					)[:-1]					)

  A_  :       List[Any]										=  config.depths[-1] - config.num_metaad_blocks + 1
  A_  :       Optional[int]										=  convert_torch_checkpoint(_UpperCAmelCase ,					_UpperCAmelCase					)

  model.load_state_dict(_UpperCAmelCase					)
  model.eval()

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

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

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

  assert torch.allclose(_UpperCAmelCase ,					_UpperCAmelCase					)

  A_  :       Optional[int]										=  model(_UpperCAmelCase					)
  A_  :       Any										=  outputs.logits

  A_  :       Union[str, Any]										=  (1, 1000)

  if "l1" in model_name:
    A_  :       str										=  torch.Tensor(
        [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]					)
    assert torch.allclose(logits[0, :10] ,					_UpperCAmelCase ,					atol=1E-3					)
    assert logits.shape == expected_shape
  elif "l3" in model_name:
    A_  :       Tuple										=  torch.Tensor(
        [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]					)
    assert torch.allclose(logits[0, :10] ,					_UpperCAmelCase ,					atol=1E-3					)
    assert logits.shape == expected_shape
  elif "l7" in model_name:
    A_  :       Any										=  torch.Tensor(
        [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]					)
    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_     :						Tuple						=					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_     :						Union[str, Any]						=					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 typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin

class  _UpperCAmelCase  (   UpperCAmelCase__			,   UpperCAmelCase__		):



      '''simple docstring'''

      @register_to_config
      def __init__(     self					,      snake_case_ = 7_6_8					,      ):

        """simple docstring"""

        super().__init__()

        A_  :       Optional[int]										=  nn.Parameter(torch.zeros(1					,      snake_case_  )  )
        A_  :       Optional[int]										=  nn.Parameter(torch.ones(1					,      snake_case_  )  )

      def 					lowerCamelCase_			(     self					,      snake_case_ = None					,      snake_case_ = None					,      ):

        """simple docstring"""

        A_  :       str										=  nn.Parameter(self.mean.to(snake_case_  ).to(snake_case_  )  )
        A_  :       Optional[int]										=  nn.Parameter(self.std.to(snake_case_  ).to(snake_case_  )  )
        return self

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       Tuple										=  (embeds - self.mean) * 1.0 / self.std
        return embeds

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       List[str]										=  (embeds * self.std) + self.mean
        return embeds