





class     UpperCamelCase__					:






		'''simple docstring'''






		def __init__(							self,   snake_case__  )       -> str:






									"""simple docstring"""
									lowercase_	:   Tuple  				=     n
									lowercase_	:   Optional[Any]  				=     [None] * self.n
									lowercase_	:   Any  				=     0  # index of the first element
									lowercase_	:   List[str]  				=     0
									lowercase_	:   Tuple  				=     0






		def __len__(							self  )       -> int:






									"""simple docstring"""
									return self.size






		def 						snake_case__				(							self  )       -> bool:






									"""simple docstring"""
									return self.size == 0






		def 						snake_case__				(							self  )       -> Optional[Any]:






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






		def 						snake_case__				(							self,   snake_case__  )       -> Tuple:






									"""simple docstring"""




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

									lowercase_	:   Any  				=     data
									lowercase_	:   int  				=     (self.rear + 1) % self.n
									self.size += 1
									return self






		def 						snake_case__				(							self  )       -> List[str]:






									"""simple docstring"""




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

									lowercase_	:   Union[str, Any]  				=     self.array[self.front]
									lowercase_	:   Optional[Any]  				=     None
									lowercase_	:   Optional[Any]  				=     (self.front + 1) % self.n
									self.size -= 1
									return temp





'''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			(_lowerCamelCase				:  int ,						_lowerCamelCase				:  str						)-> List[str]:




     '''simple docstring'''





     __snake_case            =	old_name

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

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

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

               __snake_case            =	'''last_stage.''' + trimmed_name

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

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

     return new_name







def       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  Tuple						)-> List[str]:




     '''simple docstring'''





     for key in checkpoint.copy().keys():
          __snake_case            =	checkpoint.pop(_lowerCamelCase						)
          __snake_case            =	val

     return checkpoint







def       _UpperCamelCase			()-> Tuple:




     '''simple docstring'''





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

     return image







def       _UpperCamelCase			(_lowerCamelCase				:  Path ,						_lowerCamelCase				:  Path ,						_lowerCamelCase				:  Path ,						_lowerCamelCase				:  bool						)-> Optional[Any]:




     '''simple docstring'''





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

     __snake_case            =	config.depths[-1] - config.num_metaad_blocks + 1
     __snake_case            =	convert_torch_checkpoint(_lowerCamelCase ,						_lowerCamelCase						)

     model.load_state_dict(_lowerCamelCase						)
     model.eval()

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

     # prepare image
     __snake_case            =	prepare_img()
     __snake_case            =	2_56
     __snake_case            =	2_24
     __snake_case            =	EfficientFormerImageProcessor(
         size={'''shortest_edge''': image_size} ,						crop_size={'''height''': crop_size, '''width''': crop_size} ,						resample=pillow_resamplings['''bicubic'''] ,						)
     __snake_case            =	processor(images=_lowerCamelCase ,						return_tensors='''pt'''						).pixel_values

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

     assert torch.allclose(_lowerCamelCase ,						_lowerCamelCase						)

     __snake_case            =	model(_lowerCamelCase						)
     __snake_case            =	outputs.logits

     __snake_case            =	(1, 10_00)

     if "l1" in model_name:
          __snake_case            =	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] ,						_lowerCamelCase ,						atol=1E-3						)
          assert logits.shape == expected_shape
     elif "l3" in model_name:
          __snake_case            =	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] ,						_lowerCamelCase ,						atol=1E-3						)
          assert logits.shape == expected_shape
     elif "l7" in model_name:
          __snake_case            =	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(_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__":
     UpperCAmelCase_							:	Optional[int]	     =		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)

     UpperCAmelCase_							:	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,
     )






