


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   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     Any						) ->   List[Any]:





	"""simple docstring"""







	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"""							,					UpperCamelCase__						):
		A								=				r"""\b\d{2}\b"""
		if bool(re.search(UpperCamelCase__							,					UpperCamelCase__						)						):
			A								=				re.search(r"""\d\.\d\d."""							,					UpperCamelCase__						).group()
		else:
			A								=				re.search(r"""\d\.\d."""							,					UpperCamelCase__						).group()
		if int(match[0]						) < 6:
			A								=				old_name.replace(UpperCamelCase__							,					""""""						)
			A								=				trimmed_name.replace("""network"""							,					match[0] + """.meta4D_layers.blocks.""" + match[2:-1]						)
			A								=				"""intermediate_stages.""" + trimmed_name
		else:
			A								=				old_name.replace(UpperCamelCase__							,					""""""						)
			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."""							,					UpperCamelCase__						):
		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   _lowerCAmelCase				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     List[Any]						) ->   Any:





	"""simple docstring"""







	for key in checkpoint.copy().keys():
		A								=				checkpoint.pop(UpperCamelCase__						)
		A								=				val

	return checkpoint





def   _lowerCAmelCase				(   ) ->   Optional[Any]:





	"""simple docstring"""







	A								=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	A								=				Image.open(requests.get(UpperCamelCase__							,					stream=UpperCamelCase__						).raw						)

	return image





def   _lowerCAmelCase				(   UpperCamelCase__:     Path							,					UpperCamelCase__:     Path							,					UpperCamelCase__:     Path							,					UpperCamelCase__:     bool						) ->   Optional[Any]:





	"""simple docstring"""







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

	A								=				config.depths[-1] - config.num_metaad_blocks + 1
	A								=				convert_torch_checkpoint(UpperCamelCase__							,					UpperCamelCase__						)

	model.load_state_dict(UpperCamelCase__						)
	model.eval()

	A								=				{
	    """bilinear""": PILImageResampling.BILINEAR,
	    """bicubic""": PILImageResampling.BICUBIC,
	    """nearest""": PILImageResampling.NEAREST,
	}

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

	# original processing pipeline
	A								=				Compose(
	    [
	        Resize(UpperCamelCase__							,					interpolation=pillow_resamplings["""bicubic"""]						),
	        CenterCrop(UpperCamelCase__						),
	        ToTensor(),
	        Normalize(UpperCamelCase__							,					UpperCamelCase__						),
	    ]						)
	A								=				image_transforms(UpperCamelCase__						).unsqueeze(0						)

	assert torch.allclose(UpperCamelCase__							,					UpperCamelCase__						)

	A								=				model(UpperCamelCase__						)
	A								=				outputs.logits

	A								=				(1, 10_00)

	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]							,					UpperCamelCase__							,					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]							,					UpperCamelCase__							,					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(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__":
				_lowercase       :      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)

				_lowercase       :      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,
				)







import math





def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   bool:





	"""simple docstring"""







	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5							,					int(math.sqrt(UpperCamelCase__						) + 1						)							,					6						):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True





def   _lowerCAmelCase				(   UpperCamelCase__:     float = 0.1						) ->   int:





	"""simple docstring"""







	A								=				3
	A								=				3

	while primes / (2 * j - 1) >= ratio:
		for i in range(j * j + j + 1							,					(j + 2) * (j + 2)							,					j + 1						):
			primes += is_prime(UpperCamelCase__						)
		j += 2
	return j


if __name__ == "__main__":
				import doctest

				doctest.testmod()



