






"""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 						lowercase    (				__snake_case				: Tuple		,					__snake_case				: Dict	):
		lowercase_ : int							       =   old_name

		if "patch_embed" in old_name:
				lowercase_       ,					lowercase_       ,					lowercase_ : Optional[int]							       =   old_name.split('''.'''	)

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

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

						lowercase_ : str							       =   '''last_stage.''' + trimmed_name

		elif "network" in old_name and re.search(r'''.\d.'''		,					__snake_case	):
				lowercase_ : Union[str, Any]							       =   old_name.replace('''network'''		,					'''intermediate_stages'''	)

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

		return new_name



def 						lowercase    (				__snake_case				: int		,					__snake_case				: List[Any]	):
		for key in checkpoint.copy().keys():
				lowercase_ : int							       =   checkpoint.pop(__snake_case	)
				lowercase_ : int							       =   val

		return checkpoint



def 						lowercase    (				):
		lowercase_ : Any							       =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowercase_ : List[Any]							       =   Image.open(requests.get(__snake_case		,					stream=__snake_case	).raw	)

		return image



def 						lowercase    (				__snake_case				: Path		,					__snake_case				: Path		,					__snake_case				: Path		,					__snake_case				: bool	):
		lowercase_ : Union[str, Any]							       =   torch.load(__snake_case		,					map_location='''cpu'''	)['''model''']
		lowercase_ : str							       =   EfficientFormerConfig.from_json_file(__snake_case	)
		lowercase_ : Tuple							       =   EfficientFormerForImageClassificationWithTeacher(__snake_case	)
		lowercase_ : Dict							       =   '''_'''.join(checkpoint_path.split('''/'''	)[-1].split('''.'''	)[0].split('''_'''	)[:-1]	)

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

		model.load_state_dict(__snake_case	)
		model.eval()

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

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

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

		assert torch.allclose(__snake_case		,					__snake_case	)

		lowercase_ : str							       =   model(__snake_case	)
		lowercase_ : List[str]							       =   outputs.logits

		lowercase_ : Any							       =   (1, 1_0_0_0)

		if "l1" in model_name:
				lowercase_ : Any							       =   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, :1_0]		,					__snake_case		,					atol=1e-3	)
				assert logits.shape == expected_shape
		elif "l3" in model_name:
				lowercase_ : Dict							       =   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, :1_0]		,					__snake_case		,					atol=1e-3	)
				assert logits.shape == expected_shape
		elif "l7" in model_name:
				lowercase_ : Optional[Any]							       =   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(__snake_case	).mkdir(exist_ok=__snake_case	)
		model.save_pretrained(__snake_case	)
		print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''	)
		processor.save_pretrained(__snake_case	)
		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=__snake_case		,					)
				processor.push_to_hub(
				    repo_id=F'''Bearnardd/{pytorch_dump_path}'''		,					commit_message='''Add image processor'''		,					use_temp_dir=__snake_case		,					)


if __name__ == "__main__":
						__A		:       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)

						__A		:       int  				=		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"""



def 						lowercase    (				__snake_case				: int	):
		if not isinstance(__snake_case		,					__snake_case	):
				raise ValueError('''Input must be an integer'''	)
		if input_num <= 0:
				raise ValueError('''Input must be positive'''	)
		return sum(
		    divisor for divisor in range(1		,					input_num // 2 + 1	) if input_num % divisor == 0	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



