



'''simple docstring'''

import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase									=   logging.get_logger()



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = True  )			->      str:
		print(f"""Converting {name}..."""  )

		with torch.no_grad():
				if hidden_sizes == 128:
						if name[-1] == "S":
								_a     :      Optional[Any]        =							timm.create_model('levit_128s'						,			pretrained=lowerCAmelCase_  )
						else:
								_a     :      List[str]        =							timm.create_model('levit_128'						,			pretrained=lowerCAmelCase_  )
				if hidden_sizes == 192:
						_a     :      Optional[int]        =							timm.create_model('levit_192'						,			pretrained=lowerCAmelCase_  )
				if hidden_sizes == 256:
						_a     :      Any        =							timm.create_model('levit_256'						,			pretrained=lowerCAmelCase_  )
				if hidden_sizes == 384:
						_a     :      Optional[int]        =							timm.create_model('levit_384'						,			pretrained=lowerCAmelCase_  )

				from_model.eval()
				_a     :      Optional[Any]        =							LevitForImageClassificationWithTeacher(lowerCAmelCase_  ).eval()
				_a     :      List[str]        =							OrderedDict()

				_a     :      Optional[int]        =							from_model.state_dict()
				_a     :      int        =							list(from_model.state_dict().keys()  )
				_a     :      int        =							list(our_model.state_dict().keys()  )
				print(len(lowerCAmelCase_  )						,			len(lowerCAmelCase_  )  )
				for i in range(len(lowerCAmelCase_  )  ):
						_a     :      Any        =							weights[og_keys[i]]
				our_model.load_state_dict(lowerCAmelCase_  )

				_a     :      Optional[Any]        =							torch.randn((2, 3, 224, 224)  )
				_a     :      Optional[Any]        =							from_model(lowerCAmelCase_  )
				_a     :      Optional[Any]        =							our_model(lowerCAmelCase_  ).logits

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

		_a     :      Dict        =							name
		print(lowerCAmelCase_  )

		if push_to_hub:
				our_model.save_pretrained(save_directory / checkpoint_name  )
				_a     :      Union[str, Any]        =							LevitImageProcessor()
				image_processor.save_pretrained(save_directory / checkpoint_name  )

				print(f"""Pushed {checkpoint_name}"""  )



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = True  )			->      Any:
		_a     :      Optional[int]        =							'imagenet-1k-id2label.json'
		_a     :      Dict        =							1000
		_a     :      Union[str, Any]        =							(1, num_labels)

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

		_a     :      str        =							idalabel
		_a     :      Tuple        =							{v: k for k, v in idalabel.items()}

		_a     :      Tuple        =							partial(lowerCAmelCase_						,			num_labels=lowerCAmelCase_						,			idalabel=lowerCAmelCase_						,			labelaid=lowerCAmelCase_  )

		_a     :      List[str]        =							{
		    'levit-128S': 128,
		    'levit-128': 128,
		    'levit-192': 192,
		    'levit-256': 256,
		    'levit-384': 384,
		}

		_a     :      Optional[int]        =							{
		    'levit-128S': ImageNetPreTrainedConfig(
		        hidden_sizes=[128, 256, 384]						,			num_attention_heads=[4, 6, 8]						,			depths=[2, 3, 4]						,			key_dim=[16, 16, 16]						,			drop_path_rate=0						,			),
		    'levit-128': ImageNetPreTrainedConfig(
		        hidden_sizes=[128, 256, 384]						,			num_attention_heads=[4, 8, 12]						,			depths=[4, 4, 4]						,			key_dim=[16, 16, 16]						,			drop_path_rate=0						,			),
		    'levit-192': ImageNetPreTrainedConfig(
		        hidden_sizes=[192, 288, 384]						,			num_attention_heads=[3, 5, 6]						,			depths=[4, 4, 4]						,			key_dim=[32, 32, 32]						,			drop_path_rate=0						,			),
		    'levit-256': ImageNetPreTrainedConfig(
		        hidden_sizes=[256, 384, 512]						,			num_attention_heads=[4, 6, 8]						,			depths=[4, 4, 4]						,			key_dim=[32, 32, 32]						,			drop_path_rate=0						,			),
		    'levit-384': ImageNetPreTrainedConfig(
		        hidden_sizes=[384, 512, 768]						,			num_attention_heads=[6, 9, 12]						,			depths=[4, 4, 4]						,			key_dim=[32, 32, 32]						,			drop_path_rate=0.1						,			),
		}

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


if __name__ == "__main__":
							__lowerCAmelCase									=   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 Levit* architecture,''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default='''levit-dump-folder/''',
							    type=Path,
							    required=False,
							    help='''Path to the output PyTorch model directory.''',
							)
							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''',
							)

							__lowerCAmelCase									=   parser.parse_args()
							__lowerCAmelCase									=   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'''

def      __lowerCamelCase     (      )			->      Tuple:
		for n in range(1						,			1000000  ):
				yield n * (n + 1) // 2



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      List[Any]:
		_a     :      Any        =							1
		_a     :      Tuple        =							2
		while i * i <= n:
				_a     :      Tuple        =							0
				while n % i == 0:
						n //= i
						multiplicity += 1
				divisors_count *= multiplicity + 1
				i += 1
		if n > 1:
				divisors_count *= 2
		return divisors_count



def      __lowerCamelCase     (      )			->      str:
		return next(i for i in triangle_number_generator() if count_divisors(lowerCAmelCase_  ) > 500  )


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




