


from ..utils import DummyObject, requires_backends


class 			_a				(     metaclass=UpperCamelCase__     ):
							_lowercase      :     Dict     =					['''flax''', '''transformers''']

							def __init__(				self:      Optional[int]       ,		*UpperCamelCase_:      Any       ,		**UpperCamelCase_:      List[str]				)     ->  Tuple:





									"""simple docstring"""







									requires_backends(self       ,		['''flax''', '''transformers''']				)

							@classmethod
							def  lowerCamelCase_					(				cls:      Optional[Any]       ,		*UpperCamelCase_:      Dict       ,		**UpperCamelCase_:      Tuple				)     ->  str:





									"""simple docstring"""







									requires_backends(cls       ,		['''flax''', '''transformers''']				)


							@classmethod
							def  lowerCamelCase_					(				cls:      int       ,		*UpperCamelCase_:      Optional[int]       ,		**UpperCamelCase_:      Tuple				)     ->  List[Any]:





									"""simple docstring"""







									requires_backends(cls       ,		['''flax''', '''transformers''']				)


class 			_a				(     metaclass=UpperCamelCase__     ):
							_lowercase      :     str     =					['''flax''', '''transformers''']

							def __init__(				self:      Dict       ,		*UpperCamelCase_:      str       ,		**UpperCamelCase_:      Dict				)     ->  Dict:





									"""simple docstring"""







									requires_backends(self       ,		['''flax''', '''transformers''']				)

							@classmethod
							def  lowerCamelCase_					(				cls:      Union[str, Any]       ,		*UpperCamelCase_:      List[Any]       ,		**UpperCamelCase_:      Dict				)     ->  Any:





									"""simple docstring"""







									requires_backends(cls       ,		['''flax''', '''transformers''']				)


							@classmethod
							def  lowerCamelCase_					(				cls:      Tuple       ,		*UpperCamelCase_:      List[Any]       ,		**UpperCamelCase_:      int				)     ->  Any:





									"""simple docstring"""







									requires_backends(cls       ,		['''flax''', '''transformers''']				)


class 			_a				(     metaclass=UpperCamelCase__     ):
							_lowercase      :     Dict     =					['''flax''', '''transformers''']

							def __init__(				self:      List[Any]       ,		*UpperCamelCase_:      List[Any]       ,		**UpperCamelCase_:      Optional[Any]				)     ->  List[Any]:





									"""simple docstring"""







									requires_backends(self       ,		['''flax''', '''transformers''']				)

							@classmethod
							def  lowerCamelCase_					(				cls:      Optional[Any]       ,		*UpperCamelCase_:      Tuple       ,		**UpperCamelCase_:      Any				)     ->  Tuple:





									"""simple docstring"""







									requires_backends(cls       ,		['''flax''', '''transformers''']				)


							@classmethod
							def  lowerCamelCase_					(				cls:      str       ,		*UpperCamelCase_:      Dict       ,		**UpperCamelCase_:      Union[str, Any]				)     ->  Optional[int]:





									"""simple docstring"""







									requires_backends(cls       ,		['''flax''', '''transformers''']				)


class 			_a				(     metaclass=UpperCamelCase__     ):
							_lowercase      :     Union[str, Any]     =					['''flax''', '''transformers''']

							def __init__(				self:      List[str]       ,		*UpperCamelCase_:      List[str]       ,		**UpperCamelCase_:      Dict				)     ->  Any:





									"""simple docstring"""







									requires_backends(self       ,		['''flax''', '''transformers''']				)

							@classmethod
							def  lowerCamelCase_					(				cls:      int       ,		*UpperCamelCase_:      Optional[int]       ,		**UpperCamelCase_:      Any				)     ->  Optional[Any]:





									"""simple docstring"""







									requires_backends(cls       ,		['''flax''', '''transformers''']				)


							@classmethod
							def  lowerCamelCase_					(				cls:      Any       ,		*UpperCamelCase_:      Union[str, Any]       ,		**UpperCamelCase_:      Optional[int]				)     ->  Any:





									"""simple docstring"""







									requires_backends(cls       ,		['''flax''', '''transformers''']				)







"""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()
UpperCamelCase     =							logging.get_logger()

def       _lowerCamelCase				(							UpperCAmelCase_ :       int,	UpperCAmelCase_ :       str,	UpperCAmelCase_ :       LevitConfig,	UpperCAmelCase_ :       Path,	UpperCAmelCase_ :       bool = True     )	-> int:






	"""simple docstring"""





	print(F"""Converting {name}..."""     )

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

		from_model.eval()
		A__											=					LevitForImageClassificationWithTeacher(UpperCAmelCase_     ).eval()
		A__											=					OrderedDict()

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

		A__											=					torch.randn((2, 3, 224, 224)     )
		A__											=					from_model(UpperCAmelCase_     )
		A__											=					our_model(UpperCAmelCase_     ).logits

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

	A__											=					name
	print(UpperCAmelCase_     )

	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				(							UpperCAmelCase_ :       Path,	UpperCAmelCase_ :       str = None,	UpperCAmelCase_ :       bool = True     )	-> Union[str, Any]:






	"""simple docstring"""





	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(UpperCAmelCase_,	UpperCAmelCase_,	repo_type="dataset"     ),	"r"     )     )
	A__											=					{int(UpperCAmelCase_     ): v for k, v in idalabel.items()}

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

	A__											=					partial(UpperCAmelCase_,	num_labels=UpperCAmelCase_,	idalabel=UpperCAmelCase_,	labelaid=UpperCAmelCase_     )

	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],	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__":
			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)




