




'''simple docstring'''


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

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase  =			logging.get_logger()
@dataclass
class 						a_				:
	lowercase_       :     int			  =      42
	lowercase_       :     Tuple			  =      field(default_factory=_lowerCAmelCase						)
	lowercase_       :     Tuple			  =      field(default_factory=_lowerCAmelCase						)




	def   lowercase__   (       self     :  Any       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  List[str]      ):
							__snake_case						 =   len(list(m.modules()      )      ) == 1 or isinstance(__lowerCAmelCase       ,		nn.Convad      ) or isinstance(__lowerCAmelCase       ,		nn.BatchNormad      )
							if has_not_submodules:
													self.traced.append(__lowerCAmelCase      )




	def __call__(       self     :  Dict       ,		__lowerCAmelCase     :  Tuple      ):
							for m in self.module.modules():
													self.handles.append(m.register_forward_hook(self._forward_hook      )      )
							self.module(__lowerCAmelCase      )
							[x.remove() for x in self.handles]
							return self




	@property
	def   lowercase__   (       self     :  Union[str, Any]      ):
							return list(filter(lambda __lowerCAmelCase      : len(list(x.state_dict().keys()      )      ) > 0       ,		self.traced      )      )





@dataclass
class 						a_				:
	lowercase_       :     Optional[int]			  =      42
	lowercase_       :     Union[str, Any]			  =      42
	lowercase_       :     int			  =      0
	lowercase_       :     Optional[Any]			  =      field(default_factory=_lowerCAmelCase						)
	lowercase_       :     str			  =      field(default_factory=_lowerCAmelCase						)




	def __call__(       self     :  List[str]       ,		__lowerCAmelCase     :  List[str]      ):
							__snake_case						 =   Tracker(self.dest      )(__lowerCAmelCase      ).parametrized
							__snake_case						 =   Tracker(self.src      )(__lowerCAmelCase      ).parametrized

							__snake_case						 =   list(filter(lambda __lowerCAmelCase      : type(__lowerCAmelCase      ) not in self.src_skip       ,		__lowerCAmelCase      )      )
							__snake_case						 =   list(filter(lambda __lowerCAmelCase      : type(__lowerCAmelCase      ) not in self.dest_skip       ,		__lowerCAmelCase      )      )

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

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





def 						lowerCamelCase__   (				a    ,	a    ,	a    ,	a = True							):
						print(f'Converting {name}...'							)
						with torch.no_grad():
												__snake_case						 =   timm.create_model(SCREAMING_SNAKE_CASE__    ,	pretrained=SCREAMING_SNAKE_CASE__							).eval()
												__snake_case						 =   ResNetForImageClassification(SCREAMING_SNAKE_CASE__							).eval()
												__snake_case						 =   ModuleTransfer(src=SCREAMING_SNAKE_CASE__    ,	dest=SCREAMING_SNAKE_CASE__							)
												__snake_case						 =   torch.randn((1, 3, 224, 224)							)
												module_transfer(SCREAMING_SNAKE_CASE__							)

						assert torch.allclose(from_model(SCREAMING_SNAKE_CASE__							)    ,	our_model(SCREAMING_SNAKE_CASE__							).logits							), "The model logits don't match the original one."

						__snake_case						 =   f'resnet{"-".join(name.split("resnet"							)							)}'
						print(SCREAMING_SNAKE_CASE__							)

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

												# we can use the convnext one
												__snake_case						 =   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=SCREAMING_SNAKE_CASE__    ,	)

												print(f'Pushed {checkpoint_name}'							)
def 						lowerCamelCase__   (				a    ,	a = None    ,	a = True							):
						__snake_case						 =   'imagenet-1k-id2label.json'
						__snake_case						 =   1000
						__snake_case						 =   (1, num_labels)

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

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

						__snake_case						 =   partial(SCREAMING_SNAKE_CASE__    ,	num_labels=SCREAMING_SNAKE_CASE__    ,	idalabel=SCREAMING_SNAKE_CASE__    ,	labelaid=SCREAMING_SNAKE_CASE__							)

						__snake_case						 =   {
						    'resnet18': ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2]    ,	hidden_sizes=[64, 128, 256, 512]    ,	layer_type='basic'							),
						    'resnet26': ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2]    ,	hidden_sizes=[256, 512, 1024, 2048]    ,	layer_type='bottleneck'							),
						    'resnet34': ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3]    ,	hidden_sizes=[64, 128, 256, 512]    ,	layer_type='basic'							),
						    'resnet50': ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3]    ,	hidden_sizes=[256, 512, 1024, 2048]    ,	layer_type='bottleneck'							),
						    'resnet101': ImageNetPreTrainedConfig(
						        depths=[3, 4, 23, 3]    ,	hidden_sizes=[256, 512, 1024, 2048]    ,	layer_type='bottleneck'							),
						    'resnet152': ImageNetPreTrainedConfig(
						        depths=[3, 8, 36, 3]    ,	hidden_sizes=[256, 512, 1024, 2048]    ,	layer_type='bottleneck'							),
						}

						if model_name:
												convert_weight_and_push(SCREAMING_SNAKE_CASE__    ,	names_to_config[model_name]    ,	SCREAMING_SNAKE_CASE__    ,	SCREAMING_SNAKE_CASE__							)
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(SCREAMING_SNAKE_CASE__    ,	SCREAMING_SNAKE_CASE__    ,	SCREAMING_SNAKE_CASE__    ,	SCREAMING_SNAKE_CASE__							)
						return config, expected_shape


if __name__ == "__main__":
	_lowercase  =			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: resnet18,26,34,50,101,152. 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.""",
	)

	_lowercase  =			parser.parse_args()
	_lowercase  =			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)









'''simple docstring'''



import sys

__UpperCamelCase   :							List[str]         =   (
    """73167176531330624919225119674426574742355349194934"""
    """96983520312774506326239578318016984801869478851843"""
    """85861560789112949495459501737958331952853208805511"""
    """12540698747158523863050715693290963295227443043557"""
    """66896648950445244523161731856403098711121722383113"""
    """62229893423380308135336276614282806444486645238749"""
    """30358907296290491560440772390713810515859307960866"""
    """70172427121883998797908792274921901699720888093776"""
    """65727333001053367881220235421809751254540594752243"""
    """52584907711670556013604839586446706324415722155397"""
    """53697817977846174064955149290862569321978468622482"""
    """83972241375657056057490261407972968652414535100474"""
    """82166370484403199890008895243450658541227588666881"""
    """16427171479924442928230863465674813919123162824586"""
    """17866458359124566529476545682848912883142607690042"""
    """24219022671055626321111109370544217506941658960408"""
    """07198403850962455444362981230987879927244284909188"""
    """84580156166097919133875499200524063689912560717606"""
    """05886116467109405077541002256983155200055935729725"""
    """71636269561882670428252483600823257530420752963450"""
)



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str = N     )	->      int:

   """simple docstring"""
   __a					  =     -sys.maxsize - 1
   for i in range(len(SCREAMING_SNAKE_CASE__     ) - 12     ):
      __a					  =     1
      for j in range(13     ):
         product *= int(n[i + j]     )
      if product > largest_product:
         __a					  =     product
   return largest_product


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