"""simple docstring"""







import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_    :    int											=						logging.get_logger(__name__)


@dataclass
class      _lowercase						:
				lowerCamelCase__									=    42
				lowerCamelCase__									=    field(default_factory=__lowerCamelCase					)
				lowerCamelCase__									=    field(default_factory=__lowerCamelCase					)

				def     A	(       self    :      str    ,      _lowercase    :      List[Any]    ,      _lowercase    :      Tensor    ,      _lowercase    :      Tensor       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						len(list(m.modules()       )       ) == 1 or isinstance(_lowercase    ,      nn.Convad       ) or isinstance(_lowercase    ,      nn.BatchNormad       )
										if has_not_submodules:
																if not isinstance(_lowercase    ,      _lowercase       ):
																						self.traced.append(_lowercase       )

				def __call__(       self    :      List[Any]    ,      _lowercase    :      Tensor       ):


										'''simple docstring'''
										for m in self.module.modules():
																self.handles.append(m.register_forward_hook(self._forward_hook       )       )
										self.module(_lowercase       )
										[x.remove() for x in self.handles]
										return self

				@property
				def     A	(       self    :      str       ):


										'''simple docstring'''
										# check the len of the state_dict keys to see if we have learnable params
										return list(filter(lambda _lowercase       : len(list(x.state_dict().keys()       )       ) > 0    ,      self.traced       )       )


@dataclass
class      _lowercase						:
				lowerCamelCase__									=    42
				lowerCamelCase__									=    42
				lowerCamelCase__									=    0
				lowerCamelCase__									=    field(default_factory=__lowerCamelCase					)
				lowerCamelCase__									=    field(default_factory=__lowerCamelCase					)

				def __call__(       self    :      Optional[int]    ,      _lowercase    :      Tensor       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						Tracker(self.dest       )(_lowercase       ).parametrized
										UpperCAmelCase__   		=						Tracker(self.src       )(_lowercase       ).parametrized

										UpperCAmelCase__   		=						list(filter(lambda _lowercase       : type(_lowercase       ) not in self.src_skip    ,      _lowercase       )       )
										UpperCAmelCase__   		=						list(filter(lambda _lowercase       : type(_lowercase       ) not in self.dest_skip    ,      _lowercase       )       )

										if len(_lowercase       ) != len(_lowercase       ):
																raise Exception(
																    f'Numbers of operations are different. Source module has {len(_lowercase       )} operations while'
																    f' destination module has {len(_lowercase       )}.'       )

										for dest_m, src_m in zip(_lowercase    ,      _lowercase       ):
																dest_m.load_state_dict(src_m.state_dict()       )
																if self.verbose == 1:
																						print(f'Transfered from={src_m} to={dest_m}'       )


def 					a__ (						UpperCAmelCase__    :     nn.Module						,     UpperCAmelCase__    :     nn.Module     )->		nn.Module:
						# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
						UpperCAmelCase__   		=						from_model.state_dict()
						UpperCAmelCase__   		=						our_model.state_dict()
						UpperCAmelCase__   		=						our_model.config
						UpperCAmelCase__   		=						[]
						for stage_idx in range(len(config.hidden_sizes     )     ):
												for block_id in range(config.depths[stage_idx]     ):
																		UpperCAmelCase__   		=						f'block{stage_idx + 1}.{block_id}.layer_scale_1'
																		UpperCAmelCase__   		=						f'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'

																		all_keys.append((from_key, to_key)     )
																		UpperCAmelCase__   		=						f'block{stage_idx + 1}.{block_id}.layer_scale_2'
																		UpperCAmelCase__   		=						f'van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'

																		all_keys.append((from_key, to_key)     )

						for from_key, to_key in all_keys:
												UpperCAmelCase__   		=						from_state_dict.pop(UpperCAmelCase__     )

						our_model.load_state_dict(UpperCAmelCase__     )
						return our_model


def 					a__ (						UpperCAmelCase__    :     str						,     UpperCAmelCase__    :     VanConfig						,     UpperCAmelCase__    :     str						,     UpperCAmelCase__    :     nn.Module						,     UpperCAmelCase__    :     Path						,     UpperCAmelCase__    :     bool = True						,     )->		Any:
						print(f'Downloading weights for {name}...'     )
						UpperCAmelCase__   		=						cached_download(UpperCAmelCase__     )
						print(f'Converting {name}...'     )
						UpperCAmelCase__   		=						torch.load(UpperCAmelCase__     )["""state_dict"""]
						from_model.load_state_dict(UpperCAmelCase__     )
						from_model.eval()
						with torch.no_grad():
												UpperCAmelCase__   		=						VanForImageClassification(UpperCAmelCase__     ).eval()
												UpperCAmelCase__   		=						ModuleTransfer(src=UpperCAmelCase__						,     dest=UpperCAmelCase__     )
												UpperCAmelCase__   		=						torch.randn((1, 3, 2_2_4, 2_2_4)     )
												module_transfer(UpperCAmelCase__     )
												UpperCAmelCase__   		=						copy_parameters(UpperCAmelCase__						,     UpperCAmelCase__     )

						if not torch.allclose(from_model(UpperCAmelCase__     )						,     our_model(UpperCAmelCase__     ).logits     ):
												raise ValueError("""The model logits don't match the original one."""     )

						UpperCAmelCase__   		=						name
						print(UpperCAmelCase__     )

						if push_to_hub:
												our_model.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name						,     commit_message="""Add model"""						,     use_temp_dir=UpperCAmelCase__						,     )

												# we can use the convnext one
												UpperCAmelCase__   		=						AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""     )
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name						,     commit_message="""Add image processor"""						,     use_temp_dir=UpperCAmelCase__						,     )

												print(f'Pushed {checkpoint_name}'     )


def 					a__ (						UpperCAmelCase__    :     Path						,     UpperCAmelCase__    :     str = None						,     UpperCAmelCase__    :     bool = True     )->		List[Any]:
						UpperCAmelCase__   		=						"""imagenet-1k-id2label.json"""
						UpperCAmelCase__   		=						1_0_0_0

						UpperCAmelCase__   		=						"""huggingface/label-files"""
						UpperCAmelCase__   		=						num_labels
						UpperCAmelCase__   		=						json.load(open(hf_hub_download(UpperCAmelCase__						,     UpperCAmelCase__						,     repo_type="""dataset"""     )						,     """r"""     )     )
						UpperCAmelCase__   		=						{int(UpperCAmelCase__     ): v for k, v in idalabel.items()}

						UpperCAmelCase__   		=						idalabel
						UpperCAmelCase__   		=						{v: k for k, v in idalabel.items()}

						UpperCAmelCase__   		=						partial(UpperCAmelCase__						,     num_labels=UpperCAmelCase__						,     idalabel=UpperCAmelCase__						,     labelaid=UpperCAmelCase__     )

						UpperCAmelCase__   		=						{
						    """van-tiny""": ImageNetPreTrainedConfig(
						        hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6]						,     depths=[3, 3, 5, 2]						,     mlp_ratios=[8, 8, 4, 4]						,     ),
						    """van-small""": ImageNetPreTrainedConfig(
						        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]						,     depths=[2, 2, 4, 2]						,     mlp_ratios=[8, 8, 4, 4]						,     ),
						    """van-base""": ImageNetPreTrainedConfig(
						        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]						,     depths=[3, 3, 1_2, 3]						,     mlp_ratios=[8, 8, 4, 4]						,     ),
						    """van-large""": ImageNetPreTrainedConfig(
						        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]						,     depths=[3, 5, 2_7, 3]						,     mlp_ratios=[8, 8, 4, 4]						,     ),
						}

						UpperCAmelCase__   		=						{
						    """van-tiny""": van_tiny,
						    """van-small""": van_small,
						    """van-base""": van_base,
						    """van-large""": van_large,
						}

						UpperCAmelCase__   		=						{
						    """van-tiny""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"""
						    ),
						    """van-small""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"""
						    ),
						    """van-base""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"""
						    ),
						    """van-large""": (
						        """https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"""
						    ),
						}

						if model_name:
												convert_weight_and_push(
												    UpperCAmelCase__						,     names_to_config[model_name]						,     checkpoint=names_to_original_checkpoints[model_name]						,     from_model=names_to_original_models[model_name]()						,     save_directory=UpperCAmelCase__						,     push_to_hub=UpperCAmelCase__						,     )
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(
																		    UpperCAmelCase__						,     UpperCAmelCase__						,     checkpoint=names_to_original_checkpoints[model_name]						,     from_model=names_to_original_models[model_name]()						,     save_directory=UpperCAmelCase__						,     push_to_hub=UpperCAmelCase__						,     )


if __name__ == "__main__":
						UpperCamelCase_    :    int											=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--model-name""",
						    default=None,
						    type=str,
						    help=(
						        """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
						        """ currently: van-tiny/small/base/large. If `None`, all of them will the converted."""
						    ),
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""",
						    default=None,
						    type=Path,
						    required=True,
						    help="""Path to the output PyTorch model directory.""",
						)
						parser.add_argument(
						    """--van_dir""",
						    required=True,
						    type=Path,
						    help=(
						        """A path to VAN's original implementation directory. You can download from here:"""
						        """ https://github.com/Visual-Attention-Network/VAN-Classification"""
						    ),
						)
						parser.add_argument(
						    """--push_to_hub""",
						    default=True,
						    type=bool,
						    required=False,
						    help="""If True, push model and image processor to the hub.""",
						)

						UpperCamelCase_    :    Dict											=						parser.parse_args()
						UpperCamelCase_    :    Path											=						args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						UpperCamelCase_    :    List[Any]											=						args.van_dir
						# append the path to the parents to maskformer dir
						sys.path.append(str(van_dir.parent))
						from van.models.van import van_base, van_large, van_small, van_tiny

						convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)


"""simple docstring"""







from __future__ import annotations

from collections import Counter


def 					a__ (						UpperCAmelCase__    :     int     )->		list[int]:
						UpperCAmelCase__   		=						[True] * n
						UpperCAmelCase__   		=						False
						UpperCAmelCase__   		=						False
						UpperCAmelCase__   		=						True

						for i in range(3						,     int(n**0.5 + 1     )						,     2     ):
												UpperCAmelCase__   		=						i * 2
												while index < n:
																		UpperCAmelCase__   		=						False
																		UpperCAmelCase__   		=						index + i

						UpperCAmelCase__   		=						[2]

						for i in range(3						,     UpperCAmelCase__						,     2     ):
												if is_prime[i]:
																		primes.append(UpperCAmelCase__     )

						return primes


def 					a__ (						UpperCAmelCase__    :     int     )->		list[list[int]]:
						UpperCAmelCase__   		=						str(UpperCAmelCase__     )
						UpperCAmelCase__   		=						[]
						UpperCAmelCase__   		=						["""0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9"""]

						for duplicate in Counter(UpperCAmelCase__     ) - Counter(set(UpperCAmelCase__     )     ):
												UpperCAmelCase__   		=						[int(number_str.replace(UpperCAmelCase__						,     UpperCAmelCase__     )     ) for digit in digits]
												replacements.append(UpperCAmelCase__     )

						return replacements


def 					a__ (						UpperCAmelCase__    :     int = 8     )->		int:
						UpperCAmelCase__   		=						set()

						# Filter primes with less than 3 replaceable digits
						UpperCAmelCase__   		=						{
						    x for x in set(prime_sieve(1_0_0_0_0_0_0     )     ) if len(str(UpperCAmelCase__     )     ) - len(set(str(UpperCAmelCase__     )     )     ) >= 3
						}

						for prime in primes:
												if prime in numbers_checked:
																		continue

												UpperCAmelCase__   		=						digit_replacements(UpperCAmelCase__     )

												for family in replacements:
																		numbers_checked.update(UpperCAmelCase__     )
																		UpperCAmelCase__   		=						primes.intersection(UpperCAmelCase__     )

																		if len(UpperCAmelCase__     ) != family_length:
																								continue

																		return min(UpperCAmelCase__     )

						return -1


if __name__ == "__main__":
						print(solution())

