
"""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()
lowerCamelCase__        =		logging.get_logger()
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = True		):
	print(F"Converting {name}..."		)

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

		from_model.eval()
		__lowerCAmelCase      :						List[str]						      =				LevitForImageClassificationWithTeacher(_UpperCamelCase		).eval()
		__lowerCAmelCase      :						Any						      =				OrderedDict()

		__lowerCAmelCase      :						List[str]						      =				from_model.state_dict()
		__lowerCAmelCase      :						Dict						      =				list(from_model.state_dict().keys()		)
		__lowerCAmelCase      :						Union[str, Any]						      =				list(our_model.state_dict().keys()		)
		print(len(_UpperCamelCase		)	,  len(_UpperCamelCase		)		)
		for i in range(len(_UpperCamelCase		)		):
			__lowerCAmelCase      :						Dict						      =				weights[og_keys[i]]
		our_model.load_state_dict(_UpperCamelCase		)

		__lowerCAmelCase      :						Any						      =				torch.randn((2, 3, 224, 224)		)
		__lowerCAmelCase      :						Dict						      =				from_model(_UpperCamelCase		)
		__lowerCAmelCase      :						Tuple						      =				our_model(_UpperCamelCase		).logits

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

	__lowerCAmelCase      :						Any						      =				name
	print(_UpperCamelCase		)

	if push_to_hub:
		our_model.save_pretrained(save_directory / checkpoint_name		)
		__lowerCAmelCase      :						List[str]						      =				LevitImageProcessor()
		image_processor.save_pretrained(save_directory / checkpoint_name		)

		print(F"Pushed {checkpoint_name}"		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase = None	,  _UpperCamelCase = True		):
	__lowerCAmelCase      :						Optional[int]						      =				'imagenet-1k-id2label.json'
	__lowerCAmelCase      :						Optional[Any]						      =				1000
	__lowerCAmelCase      :						Union[str, Any]						      =				(1, num_labels)

	__lowerCAmelCase      :						Tuple						      =				'huggingface/label-files'
	__lowerCAmelCase      :						List[str]						      =				num_labels
	__lowerCAmelCase      :						Union[str, Any]						      =				json.load(open(hf_hub_download(_UpperCamelCase	,  _UpperCamelCase	,  repo_type='dataset'		)	,  'r'		)		)
	__lowerCAmelCase      :						int						      =				{int(_UpperCamelCase		): v for k, v in idalabel.items()}

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

	__lowerCAmelCase      :						str						      =				partial(_UpperCamelCase	,  num_labels=_UpperCamelCase	,  idalabel=_UpperCamelCase	,  labelaid=_UpperCamelCase		)

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

	__lowerCAmelCase      :						Optional[Any]						      =				{
	    '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)

"""simple docstring"""







from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = None		):
	if version.parse(hfh.__version__		).release < version.parse('0.11.0'		).release:
		# old versions of hfh don't url-encode the file path
		__lowerCAmelCase      :						List[str]						      =				quote(_UpperCamelCase		)
	return hfh.hf_hub_url(_UpperCamelCase	,  _UpperCamelCase	,  repo_type='dataset'	,  revision=_UpperCamelCase		)