






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()
A__			:    List[Any]         =				logging.get_logger()






def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_ = True       ):






				'''simple docstring'''
				print(F"""Converting {name}..."""       )

				with torch.no_grad():
								if hidden_sizes == 128:
												if name[-1] == "S":
																lowercase__     				=    timm.create_model('''levit_128s'''	,     pretrained=__A       )
												else:
																lowercase__     				=    timm.create_model('''levit_128'''	,     pretrained=__A       )
								if hidden_sizes == 192:
												lowercase__     				=    timm.create_model('''levit_192'''	,     pretrained=__A       )
								if hidden_sizes == 256:
												lowercase__     				=    timm.create_model('''levit_256'''	,     pretrained=__A       )
								if hidden_sizes == 384:
												lowercase__     				=    timm.create_model('''levit_384'''	,     pretrained=__A       )

								from_model.eval()
								lowercase__     				=    LevitForImageClassificationWithTeacher(__A       ).eval()
								lowercase__     				=    OrderedDict()

								lowercase__     				=    from_model.state_dict()
								lowercase__     				=    list(from_model.state_dict().keys()       )
								lowercase__     				=    list(our_model.state_dict().keys()       )
								print(len(__A       )	,     len(__A       )       )
								for i in range(len(__A       )       ):
												lowercase__     				=    weights[og_keys[i]]
								our_model.load_state_dict(__A       )

								lowercase__     				=    torch.randn((2, 3, 224, 224)       )
								lowercase__     				=    from_model(__A       )
								lowercase__     				=    our_model(__A       ).logits

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

				lowercase__     				=    name
				print(__A       )

				if push_to_hub:
								our_model.save_pretrained(save_directory / checkpoint_name       )
								lowercase__     				=    LevitImageProcessor()
								image_processor.save_pretrained(save_directory / checkpoint_name       )

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






def 						a    (		lowerCamelCase_	,     lowerCamelCase_ = None	,     lowerCamelCase_ = True       ):






				'''simple docstring'''
				lowercase__     				=    '''imagenet-1k-id2label.json'''
				lowercase__     				=    1000
				lowercase__     				=    (1, num_labels)

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

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

				lowercase__     				=    partial(__A	,     num_labels=__A	,     idalabel=__A	,     labelaid=__A       )

				lowercase__     				=    {
				    '''levit-128S''': 128,
				    '''levit-128''': 128,
				    '''levit-192''': 192,
				    '''levit-256''': 256,
				    '''levit-384''': 384,
				}

				lowercase__     				=    {
				    '''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]	,     __A	,     names_to_config[model_name]	,     __A	,     __A       )
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(names_to_hidden_sizes[model_name]	,     __A	,     __A	,     __A	,     __A       )
				return config, expected_shape


if __name__ == "__main__":
							A__			:    List[str]         =				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',
							)

							A__			:    int         =				parser.parse_args()
							A__			:    Optional[int]         =				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)







class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								# we need a list not a string, so do something to change the type
								lowercase__     				=    arr.split(''','''						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    [int(self.array[0]						)] * len(self.array						)
								lowercase__     				=    [int(self.array[0]						)] * len(self.array						)
								for i in range(1,						len(self.array						)						):
												lowercase__     				=    max(
												    int(self.array[i]						) + sum_value[i - 1],						int(self.array[i]						)						)
												lowercase__     				=    max(sum_value[i],						rear[i - 1]						)
								return rear[len(self.array						) - 1]


if __name__ == "__main__":
							A__			:    Dict         =				input('please input some numbers:')
							A__			:    Union[str, Any]         =				SubArray(whole_array)
							A__			:    int         =				array.solve_sub_array()
							print(('the results is:', re))




