






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        _a  (     UpperCamelCase_					:				int    ,   UpperCamelCase_					:				str    ,   UpperCamelCase_					:				LevitConfig    ,   UpperCamelCase_					:				Path    ,   UpperCamelCase_					:				bool = True   ) ->   Dict:
						"""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        _a  (     UpperCamelCase_					:				Path    ,   UpperCamelCase_					:				str = None    ,   UpperCamelCase_					:				bool = True   ) ->   str:
						"""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__":
	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)














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_							=    {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    '''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''SEWForCTC''',
	    '''SEWForSequenceClassification''',
	    '''SEWModel''',
	    '''SEWPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_sew import (
		    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SEWForCTC,
		    SEWForSequenceClassification,
		    SEWModel,
		    SEWPreTrainedModel,
		)


else:
	import sys

	a_							=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






