

import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE          = logging.get_logger()


@dataclass
class     lowerCamelCase_   :
	_lowerCAmelCase       		=  42
	_lowerCAmelCase       		=  field(default_factory=_lowerCAmelCase)
	_lowerCAmelCase       		=  field(default_factory=_lowerCAmelCase)
	_lowerCAmelCase       		=  field(default_factory=_lowerCAmelCase)




	def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
			__UpperCAmelCase  :     Dict		=    len(list(m.modules()       )       ) == 1 or isinstance(_UpperCAmelCase     ,     nn.Convad       ) or isinstance(_UpperCAmelCase     ,     nn.BatchNormad       )
			if has_not_submodules:
					self.traced.append(_UpperCAmelCase       )
					__UpperCAmelCase  :     Union[str, Any]		=    m




	def __call__( self     ,     _UpperCAmelCase       ):
			for name, m in self.module.named_modules():
					self.handles.append(m.register_forward_hook(partial(self._forward_hook     ,     name=_UpperCAmelCase       )       )       )
			self.module(_UpperCAmelCase       )
			[x.remove() for x in self.handles]
			return self




	@property
	def 						a					( self       ):
			# check the len of the state_dict keys to see if we have learnable params
			return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()       )       ) > 0}



class     lowerCamelCase_   ( nn.Module):




	def __init__( self     ,     _UpperCAmelCase       ):
			super().__init__()

			__UpperCAmelCase  :     List[Tuple[str, nn.Module]]		=    []
			# - get the stem
			feature_blocks.append(('conv1', model.stem)       )
			# - get all the feature blocks
			for k, v in model.trunk_output.named_children():
					assert k.startswith('block'       ), F"Unexpected layer name {k}"
					__UpperCAmelCase  :     Union[str, Any]		=    len(_UpperCAmelCase       ) + 1
					feature_blocks.append((F"res{block_index}", v)       )

			__UpperCAmelCase  :     Union[str, Any]		=    nn.ModuleDict(_UpperCAmelCase       )




	def 						a					( self     ,     _UpperCAmelCase       ):
			return get_trunk_forward_outputs(
			    _UpperCAmelCase     ,     out_feat_keys=_UpperCAmelCase     ,     feature_blocks=self._feature_blocks     ,     )



class     lowerCamelCase_   ( _lowerCAmelCase):




	def 						a					( self       ):
			return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]





def    _snake_case		(snake_case_				:    str     )       ->		Dict[str, str]:
		__UpperCAmelCase  :     Dict		=    RegNetConfig(depths=[2, 7, 17, 1]						,					hidden_sizes=[8, 8, 8, 8]						,					groups_width=8     )
		if "in1k" in model_name:
				__UpperCAmelCase  :     Optional[int]		=    RegNetForImageClassification(snake_case_     )
		else:
				__UpperCAmelCase  :     int		=    RegNetModel(snake_case_     )
		# create from model (with small weights)
		__UpperCAmelCase  :     Any		=    FakeRegNetVisslWrapper(
		    RegNet(FakeRegNetParams(depth=27						,					group_width=1010						,					w_a=1744						,					w_a=620.83						,					w_m=2.52     )     )     )

		with torch.no_grad():
				__UpperCAmelCase  :     Union[str, Any]		=    from_model.eval()
				__UpperCAmelCase  :     List[Any]		=    our_model.eval()

				__UpperCAmelCase  :     List[Any]		=    torch.randn((1, 3, 32, 32)     )
				# trace both
				__UpperCAmelCase  :     Dict		=    Tracker(snake_case_     )
				__UpperCAmelCase  :     Dict		=    dest_tracker(snake_case_     ).parametrized

				pprint(dest_tracker.nameamodule     )
				__UpperCAmelCase  :     Optional[Any]		=    Tracker(snake_case_     )
				__UpperCAmelCase  :     List[Any]		=    src_tracker(snake_case_     ).parametrized

		# convert the keys -> module dict to keys -> params
		def to_params_dict(snake_case_				:    List[Any]     ):
				__UpperCAmelCase  :     str		=    OrderedDict()
				for name, module in dict_with_modules.items():
						for param_name, param in module.state_dict().items():
								__UpperCAmelCase  :     List[str]		=    param
				return params_dict

		__UpperCAmelCase  :     Any		=    {}

		__UpperCAmelCase  :     List[str]		=    to_params_dict(snake_case_     )
		__UpperCAmelCase  :     Dict		=    to_params_dict(snake_case_     )

		for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()						,					dst_state_dict.items()     ):
				__UpperCAmelCase  :     Tuple		=    dest_key
				logger.info(F"{src_key} -> {dest_key}"     )
		# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
		if "in1k" in model_name:
				__UpperCAmelCase  :     Optional[Any]		=    'classifier.1.weight'
				__UpperCAmelCase  :     Any		=    'classifier.1.bias'

		return from_to_ours_keys





def    _snake_case		(snake_case_				:    Path						,					snake_case_				:    str = None						,					snake_case_				:    bool = True     )       ->		Union[str, Any]:
		__UpperCAmelCase  :     Any		=    'imagenet-1k-id2label.json'
		__UpperCAmelCase  :     Any		=    1000

		__UpperCAmelCase  :     Tuple		=    'huggingface/label-files'
		__UpperCAmelCase  :     str		=    num_labels
		__UpperCAmelCase  :     Optional[Any]		=    json.load(open(cached_download(hf_hub_url(snake_case_						,					snake_case_						,					repo_type='dataset'     )     )						,					'r'     )     )
		__UpperCAmelCase  :     Optional[int]		=    {int(snake_case_     ): v for k, v in idalabel.items()}

		__UpperCAmelCase  :     List[str]		=    idalabel
		__UpperCAmelCase  :     List[Any]		=    {v: k for k, v in idalabel.items()}

		__UpperCAmelCase  :     Tuple		=    partial(snake_case_						,					num_labels=snake_case_						,					idalabel=snake_case_						,					labelaid=snake_case_     )

		__UpperCAmelCase  :     List[Any]		=    {
		    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
		        depths=[2, 7, 17, 1]						,					hidden_sizes=[2020, 4040, 1_1110, 2_8280]						,					groups_width=1010     ),
		    # finetuned on imagenet
		    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
		        depths=[2, 7, 17, 1]						,					hidden_sizes=[2020, 4040, 1_1110, 2_8280]						,					groups_width=1010     ),
		}

		# add seer weights logic
		def load_using_classy_vision(snake_case_				:    str     ) -> Tuple[Dict, Dict]:
				__UpperCAmelCase  :     List[str]		=    torch.hub.load_state_dict_from_url(snake_case_						,					model_dir=str(snake_case_     )						,					map_location='cpu'     )
				# check if we have a head, if yes add it
				__UpperCAmelCase  :     int		=    files['classy_state_dict']['base_model']['model']
				return model_state_dict["trunk"], model_state_dict["heads"]

		__UpperCAmelCase  :     Dict		=    {
		    'regnet-y-10b-seer': partial(
		        snake_case_						,					'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'						,					),
		    'regnet-y-10b-seer-in1k': partial(
		        snake_case_						,					'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'						,					),
		}

		__UpperCAmelCase  :     Union[str, Any]		=    get_from_to_our_keys(snake_case_     )

		if not (save_directory / F"{model_name}.pth").exists():
				logger.info('Loading original state_dict.'     )
				__UpperCAmelCase  :     Optional[Any]		=    names_to_from_model[model_name]()
				__UpperCAmelCase  :     int		=    from_state_dict_trunk
				if "in1k" in model_name:
						# add the head
						__UpperCAmelCase  :     Any		=    {**from_state_dict_trunk, **from_state_dict_head}
				logger.info('Done!'     )

				__UpperCAmelCase  :     str		=    {}

				__UpperCAmelCase  :     Dict		=    list(from_state_dict.keys()     )
				__UpperCAmelCase  :     Union[str, Any]		=    R'\.block.-part.'
				# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
				for key in from_state_dict.keys():
						# remove the weird "block[0,1]-part" from the key
						__UpperCAmelCase  :     Optional[int]		=    re.sub(snake_case_						,					''						,					snake_case_     )
						# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
						__UpperCAmelCase  :     Optional[Any]		=    from_to_ours_keys[src_key]
						# store the parameter with our key
						__UpperCAmelCase  :     Optional[Any]		=    from_state_dict[key]
						not_used_keys.remove(snake_case_     )
				# check that all keys have been updated
				assert len(snake_case_     ) == 0, F"Some keys where not used {','.join(snake_case_     )}"

				logger.info(F"The following keys were not used: {','.join(snake_case_     )}"     )

				# save our state dict to disk
				torch.save(snake_case_						,					save_directory / F"{model_name}.pth"     )

				del converted_state_dict
		else:
				logger.info('The state_dict was already stored on disk.'     )
		if push_to_hub:
				logger.info(F"Token is {os.environ['HF_TOKEN']}"     )
				logger.info('Loading our model.'     )
				# create our model
				__UpperCAmelCase  :     Dict		=    names_to_config[model_name]
				__UpperCAmelCase  :     str		=    RegNetModel
				if "in1k" in model_name:
						__UpperCAmelCase  :     List[Any]		=    RegNetForImageClassification
				__UpperCAmelCase  :     Optional[Any]		=    our_model_func(snake_case_     )
				# place our model to the meta device (so remove all the weights)
				our_model.to(torch.device('meta'     )     )
				logger.info('Loading state_dict in our model.'     )
				# load state dict
				__UpperCAmelCase  :     Union[str, Any]		=    our_model.state_dict().keys()
				PreTrainedModel._load_pretrained_model_low_mem(
				    snake_case_						,					snake_case_						,					[save_directory / F"{model_name}.pth"]     )
				logger.info('Finally, pushing!'     )
				# push it to hub
				our_model.push_to_hub(
				    repo_path_or_name=save_directory / model_name						,					commit_message='Add model'						,					output_dir=save_directory / model_name						,					)
				__UpperCAmelCase  :     List[Any]		=    384
				# we can use the convnext one
				__UpperCAmelCase  :     Union[str, Any]		=    AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'						,					size=snake_case_     )
				image_processor.push_to_hub(
				    repo_path_or_name=save_directory / model_name						,					commit_message='Add image processor'						,					output_dir=save_directory / model_name						,					)


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE          = 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 regnet* architecture,'''
						        ''' currently: regnetx-*, regnety-*. 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(
						    '''--push_to_hub''',
						    default=True,
						    type=bool,
						    required=False,
						    help='''If True, push model and image processor to the hub.''',
						)

						_SCREAMING_SNAKE_CASE          = parser.parse_args()

						_SCREAMING_SNAKE_CASE          = args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)








import math
from itertools import permutations





def    _snake_case		(snake_case_				:    int     )       ->		bool:

  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    _snake_case		(snake_case_				:    int						,					snake_case_				:    list     )       ->		bool:
  __UpperCAmelCase, __UpperCAmelCase  :     Any		=    0, len(snake_case_     ) - 1
  while left <= right:
    __UpperCAmelCase  :     List[str]		=    (left + right) // 2
    if prime_list[middle] == target:
      return True
    elif prime_list[middle] < target:
      __UpperCAmelCase  :     Tuple		=    middle + 1
    else:
      __UpperCAmelCase  :     Tuple		=    middle - 1

  return False





def    _snake_case		()       ->		str:
  __UpperCAmelCase  :     List[Any]		=    [n for n in range(1001						,					1_0000						,					2     ) if is_prime(snake_case_     )]
  __UpperCAmelCase  :     Tuple		=    []

  for number in prime_list:
    __UpperCAmelCase  :     List[str]		=    []

    for prime_member in permutations(list(str(snake_case_     )     )     ):
      __UpperCAmelCase  :     Dict		=    int(''.join(snake_case_     )     )

      if prime % 2 == 0:
        continue

      if search(snake_case_						,					snake_case_     ):
        tmp_numbers.append(snake_case_     )

    tmp_numbers.sort()
    if len(snake_case_     ) >= 3:
      candidates.append(snake_case_     )

  __UpperCAmelCase  :     Optional[int]		=    []
  for candidate in candidates:
    __UpperCAmelCase  :     int		=    len(snake_case_     )
    __UpperCAmelCase  :     List[Any]		=    False

    for i in range(snake_case_     ):
      for j in range(i + 1						,					snake_case_     ):
        for k in range(j + 1						,					snake_case_     ):
          if (
              abs(candidate[i] - candidate[j]     )
              == abs(candidate[j] - candidate[k]     )
              and len({candidate[i], candidate[j], candidate[k]}     ) == 3
          ):
            passed.append(
                sorted([candidate[i], candidate[j], candidate[k]]     )     )
            __UpperCAmelCase  :     Any		=    True

          if found:
            break
        if found:
          break
      if found:
        break

  __UpperCAmelCase  :     Dict		=    set()
  for seq in passed:
    answer.add(''.join([str(snake_case_     ) for i in seq]     )     )

  return max(int(snake_case_     ) for x in answer     )


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





