






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,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[Any]   =   old_name

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

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

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

												__magic_name__		:Any   =   '''last_stage.''' + trimmed_name

				elif "network" in old_name and re.search(R'''.\d.''',	snake_case			):
								__magic_name__		:Optional[Any]   =   old_name.replace('''network''',	'''intermediate_stages'''			)

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

				return new_name

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				for key in checkpoint.copy().keys():
								__magic_name__		:Any   =   checkpoint.pop(snake_case			)
								__magic_name__		:List[Any]   =   val

				return checkpoint

def   __lowercase       (			):







				"""simple docstring"""






				__magic_name__		:List[str]   =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__magic_name__		:List[Any]   =   Image.open(requests.get(snake_case,	stream=snake_case			).raw			)

				return image

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[int]   =   torch.load(snake_case,	map_location='''cpu'''			)['''model''']
				__magic_name__		:List[Any]   =   EfficientFormerConfig.from_json_file(snake_case			)
				__magic_name__		:str   =   EfficientFormerForImageClassificationWithTeacher(snake_case			)
				__magic_name__		:int   =   '''_'''.join(checkpoint_path.split('''/'''			)[-1].split('''.'''			)[0].split('''_'''			)[:-1]			)

				__magic_name__		:int   =   config.depths[-1] - config.num_metaad_blocks + 1
				__magic_name__		:Optional[int]   =   convert_torch_checkpoint(snake_case,	snake_case			)

				model.load_state_dict(snake_case			)
				model.eval()

				__magic_name__		:Optional[int]   =   {
				    '''bilinear''': PILImageResampling.BILINEAR,
				    '''bicubic''': PILImageResampling.BICUBIC,
				    '''nearest''': PILImageResampling.NEAREST,
				}

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

				# original processing pipeline
				__magic_name__		:Any   =   Compose(
				    [
				        Resize(snake_case,	interpolation=pillow_resamplings['''bicubic''']			),
				        CenterCrop(snake_case			),
				        ToTensor(),
				        Normalize(snake_case,	snake_case			),
				    ]			)
				__magic_name__		:Optional[int]   =   image_transforms(snake_case			).unsqueeze(0			)

				assert torch.allclose(snake_case,	snake_case			)

				__magic_name__		:Tuple   =   model(snake_case			)
				__magic_name__		:Optional[int]   =   outputs.logits

				__magic_name__		:Union[str, Any]   =   (1, 1_0_0_0)

				if "l1" in model_name:
								__magic_name__		:Dict   =   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:
								__magic_name__		:Union[str, Any]   =   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:
								__magic_name__		:List[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__":
					SCREAMING_SNAKE_CASE__   :      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)

					SCREAMING_SNAKE_CASE__   :      List[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,
					)














import math
from collections.abc import Iterator
from itertools import takewhile

def   __lowercase       (			snake_case			):







				"""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(snake_case			) + 1			),	6			):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True

def   __lowercase       (			):







				"""simple docstring"""






				__magic_name__		:str   =   2
				while True:
								if is_prime(snake_case			):
												yield num
								num += 1

def   __lowercase       (			snake_case = 2_0_0_0_0_0_0			):







				"""simple docstring"""






				return sum(takewhile(lambda snake_case			: x < n,	prime_generator()			)			)


if __name__ == "__main__":
					print(f"{solution() = }")






