




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   :			Union[str, Any]          = logging.get_logger()





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase = True       ) ->			Dict:
	print(f'Converting {name}...'       )

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

		from_model.eval()
		a									=				LevitForImageClassificationWithTeacher(__UpperCAmelCase       ).eval()
		a									=				OrderedDict()

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

		a									=				torch.randn((2, 3, 224, 224)       )
		a									=				from_model(__UpperCAmelCase       )
		a									=				our_model(__UpperCAmelCase       ).logits

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

	a									=				name
	print(__UpperCAmelCase       )

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

		print(f'Pushed {checkpoint_name}'       )





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase = None				,     __lowerCamelCase = True       ) ->			List[Any]:
	a									=				"""imagenet-1k-id2label.json"""
	a									=				1000
	a									=				(1, num_labels)

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

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

	a									=				partial(__UpperCAmelCase				,     num_labels=__UpperCAmelCase				,     idalabel=__UpperCAmelCase				,     labelaid=__UpperCAmelCase       )

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

	a									=				{
	    """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]				,     __UpperCAmelCase				,     names_to_config[model_name]				,     __UpperCAmelCase				,     __UpperCAmelCase       )
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(names_to_hidden_sizes[model_name]				,     __UpperCAmelCase				,     __UpperCAmelCase				,     __UpperCAmelCase				,     __UpperCAmelCase       )
	return config, expected_shape


if __name__ == "__main__":
		__UpperCamelCase   :			Any          = 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   :			str          = parser.parse_args()
		__UpperCamelCase   :			Dict          = 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   a    (     __UpperCAmelCase      :    List[Any]  )    ->							str:
						__magic_name__:	Optional[int]							=       [0] * len(__UpperCAmelCase  )
						__magic_name__:	str							=       []
						__magic_name__:	Any							=       []
						__magic_name__:	Union[str, Any]							=       0

						for values in graph.values():
												for i in values:
																		indegree[i] += 1

						for i in range(len(__UpperCAmelCase  )  ):
												if indegree[i] == 0:
																		queue.append(__UpperCAmelCase  )

						while queue:
												__magic_name__:	Optional[Any]							=       queue.pop(0  )
												cnt += 1
												topo.append(__UpperCAmelCase  )
												for x in graph[vertex]:
																		indegree[x] -= 1
																		if indegree[x] == 0:
																								queue.append(__UpperCAmelCase  )

						if cnt != len(__UpperCAmelCase  ):
												print("""Cycle exists"""  )
						else:
												print(__UpperCAmelCase  )


# Adjacency List of Graph
__lowerCamelCase							=  {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}

topological_sort(graph)



