




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 SCREAMING_SNAKE_CASE    (	lowercase_			,      lowercase_						)       ->	int:



       """simple docstring"""

       A__							=   old_name

       if "patch_embed" in old_name:
              A__							=   old_name.split('''.'''						)

              if layer == "0":
                     A__							=   old_name.replace('''0'''			,      '''convolution1'''						)
              elif layer == "1":
                     A__							=   old_name.replace('''1'''			,      '''batchnorm_before'''						)
              elif layer == "3":
                     A__							=   old_name.replace('''3'''			,      '''convolution2'''						)
              else:
                     A__							=   old_name.replace('''4'''			,      '''batchnorm_after'''						)

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

                     A__							=   'last_stage.' + trimmed_name

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

       if "fc" in new_name:
              A__							=   new_name.replace('''fc'''			,      '''convolution'''						)
       elif ("norm1" in new_name) and ("layernorm1" not in new_name):
              A__							=   new_name.replace('''norm1'''			,      '''batchnorm_before'''						)
       elif ("norm2" in new_name) and ("layernorm2" not in new_name):
              A__							=   new_name.replace('''norm2'''			,      '''batchnorm_after'''						)
       if "proj" in new_name:
              A__							=   new_name.replace('''proj'''			,      '''projection'''						)
       if "dist_head" in new_name:
              A__							=   new_name.replace('''dist_head'''			,      '''distillation_classifier'''						)
       elif "head" in new_name:
              A__							=   new_name.replace('''head'''			,      '''classifier'''						)
       elif "patch_embed" in new_name:
              A__							=   'efficientformer.' + new_name
       elif new_name == "norm.weight" or new_name == "norm.bias":
              A__							=   new_name.replace('''norm'''			,      '''layernorm'''						)
              A__							=   'efficientformer.' + new_name
       else:
              A__							=   'efficientformer.encoder.' + new_name

       return new_name







def SCREAMING_SNAKE_CASE    (	lowercase_			,      lowercase_						)       ->	Union[str, Any]:



       """simple docstring"""

       for key in checkpoint.copy().keys():
              A__							=   checkpoint.pop(lowerCAmelCase_						)
              A__							=   val

       return checkpoint







def SCREAMING_SNAKE_CASE    (	)       ->	Any:



       """simple docstring"""

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

       return image







def SCREAMING_SNAKE_CASE    (	lowercase_			,      lowercase_			,      lowercase_			,      lowercase_						)       ->	List[Any]:



       """simple docstring"""

       A__							=   torch.load(lowerCAmelCase_			,      map_location='''cpu'''						)['model']
       A__							=   EfficientFormerConfig.from_json_file(lowerCAmelCase_						)
       A__							=   EfficientFormerForImageClassificationWithTeacher(lowerCAmelCase_						)
       A__							=   '_'.join(checkpoint_path.split('''/'''						)[-1].split('''.'''						)[0].split('''_'''						)[:-1]						)

       A__							=   config.depths[-1] - config.num_metaad_blocks + 1
       A__							=   convert_torch_checkpoint(lowerCAmelCase_			,      lowerCAmelCase_						)

       model.load_state_dict(lowerCAmelCase_						)
       model.eval()

       A__							=   {
           'bilinear': PILImageResampling.BILINEAR,
           'bicubic': PILImageResampling.BICUBIC,
           'nearest': PILImageResampling.NEAREST,
       }

       # prepare image
       A__							=   prepare_img()
       A__							=   256
       A__							=   224
       A__							=   EfficientFormerImageProcessor(
           size={'''shortest_edge''': image_size}			,      crop_size={'''height''': crop_size, '''width''': crop_size}			,      resample=pillow_resamplings['''bicubic''']			,      )
       A__							=   processor(images=lowerCAmelCase_			,      return_tensors='''pt'''						).pixel_values

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

       assert torch.allclose(lowerCAmelCase_			,      lowerCAmelCase_						)

       A__							=   model(lowerCAmelCase_						)
       A__							=   outputs.logits

       A__							=   (1, 1_000)

       if "l1" in model_name:
              A__							=   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, :10]			,      lowerCAmelCase_			,      atol=1E-3						)
              assert logits.shape == expected_shape
       elif "l3" in model_name:
              A__							=   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, :10]			,      lowerCAmelCase_			,      atol=1E-3						)
              assert logits.shape == expected_shape
       elif "l7" in model_name:
              A__							=   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__":
  _lowerCamelCase     :		Union[str, Any]								=       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     :		str								=       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,
  )








class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     List[str] ,			UpperCAmelCase__				:     int)						->Union[str, Any]:





           '''simple docstring'''

           A__							=   n
           A__							=   [None] * self.n
           A__							=   0  # index of the first element
           A__							=   0
           A__							=   0


    def __len__(      self				:     List[Any])						->int:





           '''simple docstring'''

           return self.size


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->bool:





           '''simple docstring'''

           return self.size == 0


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->List[Any]:





           '''simple docstring'''

           return False if self.is_empty() else self.array[self.front]


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     str)						->Any:





           '''simple docstring'''

           if self.size >= self.n:
                  raise Exception('''QUEUE IS FULL''')

           A__							=   data
           A__							=   (self.rear + 1) % self.n
           self.size += 1
           return self





    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Any:





           '''simple docstring'''

           if self.size == 0:
                  raise Exception('''UNDERFLOW''')

           A__							=   self.array[self.front]
           A__							=   None
           A__							=   (self.front + 1) % self.n
           self.size -= 1
           return temp


