



"""simple docstring"""







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    =			logging.get_logger()

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = True			) ->							Tuple:
							print(f"""Converting {name}..."""			)

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

														from_model.eval()
														A         =					LevitForImageClassificationWithTeacher(lowerCamelCase__			).eval()
														A         =					OrderedDict()

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

														A         =					torch.randn((2, 3, 224, 224)			)
														A         =					from_model(lowerCamelCase__			)
														A         =					our_model(lowerCamelCase__			).logits

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

							A         =					name
							print(lowerCamelCase__			)

							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   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = True			) ->							Union[str, 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(lowerCamelCase__				,      lowerCamelCase__				,      repo_type='dataset'			)				,      'r'			)			)
							A         =					{int(lowerCamelCase__			): v for k, v in idalabel.items()}

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

							A         =					partial(lowerCamelCase__				,      num_labels=lowerCamelCase__				,      idalabel=lowerCamelCase__				,      labelaid=lowerCamelCase__			)

							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]				,      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    =			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    =			parser.parse_args()
							A    =			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   lowerCAmelCase__			(     lowerCamelCase__			) ->							int:

							if not isinstance(lowerCamelCase__				,      lowerCamelCase__			):
														raise ValueError('multiplicative_persistence() only accepts integral values'			)
							if num < 0:
														raise ValueError('multiplicative_persistence() does not accept negative values'			)

							A         =					0
							A         =					str(lowerCamelCase__			)

							while len(lowerCamelCase__			) != 1:
														A         =					[int(lowerCamelCase__			) for i in num_string]

														A         =					1
														for i in range(0				,      len(lowerCamelCase__			)			):
																					total *= numbers[i]

														A         =					str(lowerCamelCase__			)

														steps += 1
							return steps

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							int:

							if not isinstance(lowerCamelCase__				,      lowerCamelCase__			):
														raise ValueError('additive_persistence() only accepts integral values'			)
							if num < 0:
														raise ValueError('additive_persistence() does not accept negative values'			)

							A         =					0
							A         =					str(lowerCamelCase__			)

							while len(lowerCamelCase__			) != 1:
														A         =					[int(lowerCamelCase__			) for i in num_string]

														A         =					0
														for i in range(0				,      len(lowerCamelCase__			)			):
																					total += numbers[i]

														A         =					str(lowerCamelCase__			)

														steps += 1
							return steps


if __name__ == "__main__":
							import doctest

							doctest.testmod()



