


"""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							(		lowerCAmelCase__ :     Any    ,					lowerCAmelCase__ :     Any			) ->      str:
		__a           =			old_name

		if "patch_embed" in old_name:
				__a      ,     __a      ,     __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        lowercase							(		lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     Tuple			) ->      Tuple:
		for key in checkpoint.copy().keys():
				__a           =			checkpoint.pop(lowerCAmelCase__			)
				__a           =			val

		return checkpoint

def        lowercase							(		) ->      Any:
		__a           =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		__a           =			Image.open(requests.get(lowerCAmelCase__    ,					stream=lowerCAmelCase__			).raw			)

		return image

def        lowercase							(		lowerCAmelCase__ :     Path    ,					lowerCAmelCase__ :     Path    ,					lowerCAmelCase__ :     Path    ,					lowerCAmelCase__ :     bool			) ->      Any:
		__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, 1000)

		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__":
				lowercase_			     =  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)

				lowercase_			     =  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							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     list[str]			) ->      str:
		__a           =			''''''
		for word_or_phrase in separated:
				if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
						raise Exception('''join() accepts only strings to be joined'''			)
				joined += word_or_phrase + separator
		return joined.strip(lowerCAmelCase__			)


if __name__ == "__main__":
				from doctest import testmod

				testmod()






