



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__ =						logging.get_logger()






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__						,       lowercase__						,       lowercase__ = True   ) ->      Any:
						print(F"Converting {name}..."   )

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

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

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

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

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

						__lowercase					=				name
						print(lowercase__   )

						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  UpperCAmelCase__    (    lowercase__						,       lowercase__ = None						,       lowercase__ = True   ) ->      Optional[int]:
						__lowercase					=				"""imagenet-1k-id2label.json"""
						__lowercase					=				1_000
						__lowercase					=				(1, num_labels)

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

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

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

						__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]						,       lowercase__						,       names_to_config[model_name]						,       lowercase__						,       lowercase__   )
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(names_to_hidden_sizes[model_name]						,       lowercase__						,       lowercase__						,       lowercase__						,       lowercase__   )
						return config, expected_shape


if __name__ == "__main__":
			UpperCamelCase__ =						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__ =						parser.parse_args()
			UpperCamelCase__ =						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)










UpperCamelCase__ =						{
    "joule": 1.0,
    "kilojoule": 10_00,
    "megajoule": 1_00_00_00,
    "gigajoule": 10_00_00_00_00,
    "wattsecond": 1.0,
    "watthour": 36_00,
    "kilowatthour": 3_60_00_00,
    "newtonmeter": 1.0,
    "calorie_nutr": 41_86.8,
    "kilocalorie_nutr": 4_18_68_00.00,
    "electronvolt": 1.6_0217_6634e-19,
    "britishthermalunit_it": 10_55.0_55_85,
    "footpound": 1.355_818,
}






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__   ) ->      float:
						if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
												__lowercase					=				(
												    F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
												    F"Valid values are: {', '.join(lowercase__   )}"
												)
												raise ValueError(lowercase__   )
						return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





