


from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

_lowerCAmelCase:						Optional[Any]   				=       TypeVar('T')
_lowerCAmelCase:						Dict   				=       TypeVar('U')




class       lowercase_					(Generic[T, U]   ):





				def __init__(				self       ,  lowercase_       ,  lowercase_)  -> Optional[Any]:
											a__      =key
											a__      =val
											a__      =None
											a__      =None





				def __repr__(				self)  -> str:
											return (
											    F"""Node: key: {self.key}, val: {self.val}, """
											    F"""has next: {bool(self.next)}, has prev: {bool(self.prev)}"""
											)







class       lowercase_					(Generic[T, U]   ):





				def __init__(				self)  -> None:
											a__      =DoubleLinkedListNode(lowercase_       ,  lowercase_)
											a__      =DoubleLinkedListNode(lowercase_       ,  lowercase_)
											a__    ,     a__      =self.rear, self.head





				def __repr__(				self)  -> str:
											a__      =['DoubleLinkedList']
											a__      =self.head
											while node.next is not None:
																		rep.append(str(lowercase_))
																		a__      =node.next
											rep.append(str(self.rear))
											return ",\n    ".join(lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> None:
											a__      =self.rear.prev

											# All nodes other than self.head are guaranteed to have non-None previous
											assert previous is not None

											a__      =node
											a__      =previous
											a__      =node
											a__      =self.rear





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> DoubleLinkedListNode[T, U] | None:

											if node.prev is None or node.next is None:
																		return None

											a__      =node.next
											a__      =node.prev
											a__      =None
											a__      =None
											return node







class       lowercase_					(Generic[T, U]   ):
				snake_case	    ={}





				def __init__(				self       ,  lowercase_)  -> List[Any]:
											a__      =DoubleLinkedList()
											a__      =capacity
											a__      =0
											a__      =0
											a__      =0
											a__      ={}





				def __repr__(				self)  -> str:
											return (
											    F"""CacheInfo(hits={self.hits}, misses={self.miss}, """
											    F"""capacity={self.capacity}, current size={self.num_keys})"""
											)





				def __contains__(				self       ,  lowercase_)  -> bool:
											return key in self.cache





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> U | None:
											# Note: pythonic interface would throw KeyError rather than return None

											if key in self.cache:
																		self.hits += 1
																		a__      =self.cache[key]
																		a__      =self.list.remove(self.cache[key])
																		assert node == value_node

																		# node is guaranteed not None because it is in self.cache
																		assert node is not None
																		self.list.add(lowercase_)
																		return node.val
											self.miss += 1
											return None





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> None:

											if key not in self.cache:
																		if self.num_keys >= self.capacity:
																									# delete first node (oldest) when over capacity
																									a__      =self.list.head.next

																									# guaranteed to have a non-None first node when num_keys > 0
																									# explain to type checker via assertions
																									assert first_node is not None
																									assert first_node.key is not None
																									assert (
																									    self.list.remove(lowercase_) is not None
																									)  # node guaranteed to be in list assert node.key is not None

																									del self.cache[first_node.key]
																									self.num_keys -= 1
																		a__      =DoubleLinkedListNode(lowercase_       ,  lowercase_)
																		self.list.add(self.cache[key])
																		self.num_keys += 1

											else:
																		# bump node to the end of the list, update value
																		a__      =self.list.remove(self.cache[key])
																		assert node is not None  # node guaranteed to be in list
																		a__      =value
																		self.list.add(lowercase_)





				@classmethod
				def 						__UpperCamelCase    (				cls       ,  lowercase_ = 128)  -> Callable[[Callable[[T], U]], Callable[..., U]]:

											def cache_decorator_inner(lowercase_) -> Callable[..., U]:
																		def cache_decorator_wrapper(*lowercase_) -> U:
																									if func not in cls.decorator_function_to_instance_map:
																																a__      =LRUCache(lowercase_)

																									a__      =cls.decorator_function_to_instance_map[func].get(args[0])
																									if result is None:
																																a__      =func(*lowercase_)
																																cls.decorator_function_to_instance_map[func].put(args[0]       ,  lowercase_)
																									return result

																		def cache_info() -> LRUCache[T, U]:
																									return cls.decorator_function_to_instance_map[func]

																		setattr(lowercase_       ,  'cache_info'       ,  lowercase_)  # noqa: B010

																		return cache_decorator_wrapper

											return cache_decorator_inner


if __name__ == "__main__":
	import doctest

	doctest.testmod()





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:						List[str]   				=       logging.get_logger()






def   _lowercase(					__a				:						int    ,     __a				:						str    ,     __a				:						LevitConfig    ,     __a				:						Path    ,     __a				:						bool = True     ):
							print(f"""Converting {name}..."""     )

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

														from_model.eval()
														a__      =LevitForImageClassificationWithTeacher(__a     ).eval()
														a__      =OrderedDict()

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

														a__      =torch.randn((2, 3, 224, 224)     )
														a__      =from_model(__a     )
														a__      =our_model(__a     ).logits

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

							a__      =name
							print(__a     )

							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   _lowercase(					__a				:						Path    ,     __a				:						str = None    ,     __a				:						bool = True     ):
							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(__a    ,     __a    ,     repo_type='dataset'     )    ,     'r'     )     )
							a__      ={int(__a     ): v for k, v in idalabel.items()}

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

							a__      =partial(__a    ,     num_labels=__a    ,     idalabel=__a    ,     labelaid=__a     )

							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]    ,     __a    ,     names_to_config[model_name]    ,     __a    ,     __a     )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(names_to_hidden_sizes[model_name]    ,     __a    ,     __a    ,     __a    ,     __a     )
							return config, expected_shape


if __name__ == "__main__":
	_lowerCAmelCase:						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',
	)

	_lowerCAmelCase:						Union[str, Any]   				=       parser.parse_args()
	_lowerCAmelCase:						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)

