
'''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()
lowercase					:							Dict   =      logging.get_logger()







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

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

		from_model.eval()
		_snake_case     		=	LevitForImageClassificationWithTeacher(UpperCamelCase__			).eval()
		_snake_case     		=	OrderedDict()

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

		_snake_case     		=	torch.randn((2, 3, 224, 224)			)
		_snake_case     		=	from_model(UpperCamelCase__			)
		_snake_case     		=	our_model(UpperCamelCase__			).logits

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

	_snake_case     		=	name
	print(UpperCamelCase__			)

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

		print(F'Pushed {checkpoint_name}'			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A = None    ,       __A = True			) ->			Optional[int]:
	_snake_case     		=	"""imagenet-1k-id2label.json"""
	_snake_case     		=	1_000
	_snake_case     		=	(1, num_labels)

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

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

	_snake_case     		=	partial(UpperCamelCase__    ,       num_labels=UpperCamelCase__    ,       idalabel=UpperCamelCase__    ,       labelaid=UpperCamelCase__			)

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

	_snake_case     		=	{
	    """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__":
					lowercase					:							Union[str, 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",
					)

					lowercase					:							List[Any]   =      parser.parse_args()
					lowercase					:							Path   =      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'''







lowercase					:							Tuple   =      {
    "a": "AAAAA",
    "b": "AAAAB",
    "c": "AAABA",
    "d": "AAABB",
    "e": "AABAA",
    "f": "AABAB",
    "g": "AABBA",
    "h": "AABBB",
    "i": "ABAAA",
    "j": "BBBAA",
    "k": "ABAAB",
    "l": "ABABA",
    "m": "ABABB",
    "n": "ABBAA",
    "o": "ABBAB",
    "p": "ABBBA",
    "q": "ABBBB",
    "r": "BAAAA",
    "s": "BAAAB",
    "t": "BAABA",
    "u": "BAABB",
    "v": "BBBAB",
    "w": "BABAA",
    "x": "BABAB",
    "y": "BABBA",
    "z": "BABBB",
    " ": " ",
}


lowercase					:							Union[str, Any]   =      {value: key for key, value in encode_dict.items()}







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			str:
	_snake_case     		=	''
	for letter in word.lower():
		if letter.isalpha() or letter == " ":
			encoded += encode_dict[letter]
		else:
			raise Exception('encode() accepts only letters of the alphabet and spaces'			)
	return encoded







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			str:
	if set(__A			) - {"A", "B", " "} != set():
		raise Exception('decode() accepts only \'A\', \'B\' and spaces'			)
	_snake_case     		=	''
	for word in coded.split():
		while len(__A			) != 0:
			decoded += decode_dict[word[:5]]
			_snake_case     		=	word[5:]
		decoded += " "
	return decoded.strip()


if __name__ == "__main__":
					from doctest import testmod

					testmod()

