


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				:       Optional[Any]      =  logging.get_logger()





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = True			)    ->						List[Any]:


						'''simple docstring'''






						print(F'''Converting {name}...'''			)

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

												from_model.eval()
												__snake_case				=		LevitForImageClassificationWithTeacher(_lowerCAmelCase			).eval()
												__snake_case				=		OrderedDict()

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

												__snake_case				=		torch.randn((2, 3, 224, 224)			)
												__snake_case				=		from_model(_lowerCAmelCase			)
												__snake_case				=		our_model(_lowerCAmelCase			).logits

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

						__snake_case				=		name
						print(_lowerCAmelCase			)

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

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





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase = None   ,						_lowerCAmelCase = True			)    ->						str:


						'''simple docstring'''






						__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(_lowerCAmelCase   ,						_lowerCAmelCase   ,						repo_type="dataset"			)   ,						"r"			)			)
						__snake_case				=		{int(_lowerCAmelCase			): v for k, v in idalabel.items()}

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

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

						__snake_case				=		{
						    "levit-128S": 128,
						    "levit-128": 128,
						    "levit-192": 192,
						    "levit-256": 256,
						    "levit-384": 384,
						}

						__snake_case				=		{
						    "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__":
	A				:       Optional[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',
	)

	A				:       Tuple      =  parser.parse_args()
	A				:       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)









def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						int:


						'''simple docstring'''






						assert column_title.isupper()
						__snake_case				=		0
						__snake_case				=		len(_lowerCAmelCase			) - 1
						__snake_case				=		0

						while index >= 0:
												__snake_case				=		(ord(column_title[index]			) - 64) * pow(26   ,						_lowerCAmelCase			)
												answer += value
												power += 1
												index -= 1

						return answer


if __name__ == "__main__":
	from doctest import testmod

	testmod()





