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()
__UpperCamelCase						:			Optional[int]     		=	logging.get_logger()
def  A      (       _lowercase						,    _lowercase						,    _lowercase						,    _lowercase						,    _lowercase = True		):
						print(f"""Converting {name}..."""		)

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

												from_model.eval()
												SCREAMING_SNAKE_CASE				:       Tuple     							=				LevitForImageClassificationWithTeacher(__snake_case		).eval()
												SCREAMING_SNAKE_CASE				:       List[Any]     							=				OrderedDict()

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

												SCREAMING_SNAKE_CASE				:       Tuple     							=				torch.randn((2, 3, 224, 224)		)
												SCREAMING_SNAKE_CASE				:       Optional[int]     							=				from_model(__snake_case		)
												SCREAMING_SNAKE_CASE				:       List[str]     							=				our_model(__snake_case		).logits

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

						SCREAMING_SNAKE_CASE				:       List[Any]     							=				name
						print(__snake_case		)

						if push_to_hub:
												our_model.save_pretrained(save_directory / checkpoint_name		)
												SCREAMING_SNAKE_CASE				:       Optional[int]     							=				LevitImageProcessor()
												image_processor.save_pretrained(save_directory / checkpoint_name		)

												print(f"""Pushed {checkpoint_name}"""		)
def  A      (       _lowercase						,    _lowercase = None						,    _lowercase = True		):
						SCREAMING_SNAKE_CASE				:       Tuple     							=				'imagenet-1k-id2label.json'
						SCREAMING_SNAKE_CASE				:       List[str]     							=				1_000
						SCREAMING_SNAKE_CASE				:       Any     							=				(1, num_labels)

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

						SCREAMING_SNAKE_CASE				:       Tuple     							=				idalabel
						SCREAMING_SNAKE_CASE				:       Dict     							=				{v: k for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE				:       List[Any]     							=				partial(__snake_case						,    num_labels=__snake_case						,    idalabel=__snake_case						,    labelaid=__snake_case		)

						SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				{
						    'levit-128S': 128,
						    'levit-128': 128,
						    'levit-192': 192,
						    'levit-256': 256,
						    'levit-384': 384,
						}

						SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				{
						    '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]						,    __snake_case						,    names_to_config[model_name]						,    __snake_case						,    __snake_case		)
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(names_to_hidden_sizes[model_name]						,    __snake_case						,    __snake_case						,    __snake_case						,    __snake_case		)
						return config, expected_shape


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 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',
					)

					__UpperCamelCase						:			Optional[Any]     		=	parser.parse_args()
					__UpperCamelCase						:			Path     		=	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 math
import sys






def 			_lowerCAmelCase     (    __snake_case    :					int     )	->	int:
     if number != int(__snake_case     ):
          raise ValueError('the value of input must be a natural number'     )
     if number < 0:
          raise ValueError('the value of input must not be a negative number'     )
     if number == 0:
          return 1
     __A		:					str						=			[-1] * (number + 1)
     __A		:					Dict						=			0
     for i in range(1					,				number + 1     ):
          __A		:					int						=			sys.maxsize
          __A		:					int						=			int(math.sqrt(__snake_case     )     )
          for j in range(1					,				root + 1     ):
               __A		:					str						=			1 + answers[i - (j**2)]
               __A		:					Dict						=			min(__snake_case					,				__snake_case     )
          __A		:					Union[str, Any]						=			answer
     return answers[number]


if __name__ == "__main__":
  import doctest

  doctest.testmod()