






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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__ = True      ):

					"""simple docstring"""






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

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

										from_model.eval()
										__lowerCAmelCase					=							LevitForImageClassificationWithTeacher(UpperCAmelCase__      ).eval()
										__lowerCAmelCase					=							OrderedDict()

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

										__lowerCAmelCase					=							torch.randn((2, 3, 224, 224)      )
										__lowerCAmelCase					=							from_model(UpperCAmelCase__      )
										__lowerCAmelCase					=							our_model(UpperCAmelCase__      ).logits

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

					__lowerCAmelCase					=							name
					print(UpperCAmelCase__      )

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

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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__ = None		,					UpperCAmelCase__ = True      ):

					"""simple docstring"""






					__lowerCAmelCase					=							'imagenet-1k-id2label.json'
					__lowerCAmelCase					=							1_000
					__lowerCAmelCase					=							(1, num_labels)

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

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

					__lowerCAmelCase					=							partial(UpperCAmelCase__		,					num_labels=UpperCAmelCase__		,					idalabel=UpperCAmelCase__		,					labelaid=UpperCAmelCase__      )

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

					__lowerCAmelCase					=							{
					    '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__":
		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)









from graphs.minimum_spanning_tree_kruskal import kruskal


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							9
					__lowerCAmelCase					=							[
					    [0, 1, 4],
					    [0, 7, 8],
					    [1, 2, 8],
					    [7, 8, 7],
					    [7, 6, 1],
					    [2, 8, 2],
					    [8, 6, 6],
					    [2, 3, 7],
					    [2, 5, 4],
					    [6, 5, 2],
					    [3, 5, 14],
					    [3, 4, 9],
					    [5, 4, 10],
					    [1, 7, 11],
					]

					__lowerCAmelCase					=							kruskal(UpperCAmelCase__		,					UpperCAmelCase__      )

					__lowerCAmelCase					=							[
					    [7, 6, 1],
					    [2, 8, 2],
					    [6, 5, 2],
					    [0, 1, 4],
					    [2, 5, 4],
					    [2, 3, 7],
					    [0, 7, 8],
					    [3, 4, 9],
					]

					assert sorted(UpperCAmelCase__      ) == sorted(UpperCAmelCase__      )






