


import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase      :						Union[str, Any]    =		logging.get_logger(__name__)






def   __lowerCamelCase	(       lowerCamelCase__		:	List[Any]     ,       lowerCamelCase__		:	Optional[Any]=False     ,       lowerCamelCase__		:	str=False			):





			'''simple docstring'''




			lowerCamelCase								=     """backbone.""" if is_semantic else """"""

			lowerCamelCase								=     []
			for i in range(config.num_hidden_layers			):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append((f'{prefix}blocks.{i}.norm1.weight', f'beit.encoder.layer.{i}.layernorm_before.weight')			)
						rename_keys.append((f'{prefix}blocks.{i}.norm1.bias', f'beit.encoder.layer.{i}.layernorm_before.bias')			)
						rename_keys.append(
						    (f'{prefix}blocks.{i}.attn.proj.weight', f'beit.encoder.layer.{i}.attention.output.dense.weight')			)
						rename_keys.append(
						    (f'{prefix}blocks.{i}.attn.proj.bias', f'beit.encoder.layer.{i}.attention.output.dense.bias')			)
						rename_keys.append((f'{prefix}blocks.{i}.norm2.weight', f'beit.encoder.layer.{i}.layernorm_after.weight')			)
						rename_keys.append((f'{prefix}blocks.{i}.norm2.bias', f'beit.encoder.layer.{i}.layernorm_after.bias')			)
						rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.weight', f'beit.encoder.layer.{i}.intermediate.dense.weight')			)
						rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.bias', f'beit.encoder.layer.{i}.intermediate.dense.bias')			)
						rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.weight', f'beit.encoder.layer.{i}.output.dense.weight')			)
						rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.bias', f'beit.encoder.layer.{i}.output.dense.bias')			)

			# projection layer + position embeddings
			rename_keys.extend(
			    [
			        (f'{prefix}cls_token', """beit.embeddings.cls_token"""),
			        (f'{prefix}patch_embed.proj.weight', """beit.embeddings.patch_embeddings.projection.weight"""),
			        (f'{prefix}patch_embed.proj.bias', """beit.embeddings.patch_embeddings.projection.bias"""),
			    ]			)

			if has_lm_head:
						# mask token + shared relative position bias + layernorm
						rename_keys.extend(
						    [
						        ("""mask_token""", """beit.embeddings.mask_token"""),
						        (
						            """rel_pos_bias.relative_position_bias_table""",
						            """beit.encoder.relative_position_bias.relative_position_bias_table""",
						        ),
						        (
						            """rel_pos_bias.relative_position_index""",
						            """beit.encoder.relative_position_bias.relative_position_index""",
						        ),
						        ("""norm.weight""", """layernorm.weight"""),
						        ("""norm.bias""", """layernorm.bias"""),
						    ]			)
			elif is_semantic:
						# semantic segmentation classification heads
						rename_keys.extend(
						    [
						        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
						        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
						        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
						        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
						    ]			)
			else:
						# layernorm + classification head
						rename_keys.extend(
						    [
						        ("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
						        ("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
						        ("""head.weight""", """classifier.weight"""),
						        ("""head.bias""", """classifier.bias"""),
						    ]			)

			return rename_keys






def   __lowerCamelCase	(       lowerCamelCase__		:	int     ,       lowerCamelCase__		:	int     ,       lowerCamelCase__		:	List[str]=False     ,       lowerCamelCase__		:	List[Any]=False			):





			'''simple docstring'''




			for i in range(config.num_hidden_layers			):
						lowerCamelCase								=     """backbone.""" if is_semantic else """"""
						# queries, keys and values
						lowerCamelCase								=     state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'			)
						lowerCamelCase								=     state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'			)
						lowerCamelCase								=     state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'			)

						lowerCamelCase								=     in_proj_weight[
						    : config.hidden_size, :
						]
						lowerCamelCase								=     q_bias
						lowerCamelCase								=     in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						lowerCamelCase								=     in_proj_weight[
						    -config.hidden_size :, :
						]
						lowerCamelCase								=     v_bias

						# gamma_1 and gamma_2
						# we call them lambda because otherwise they are renamed when using .from_pretrained
						lowerCamelCase								=     state_dict.pop(f'{prefix}blocks.{i}.gamma_1'			)
						lowerCamelCase								=     state_dict.pop(f'{prefix}blocks.{i}.gamma_2'			)

						lowerCamelCase								=     gamma_a
						lowerCamelCase								=     gamma_a

						# relative_position bias table + index
						if not has_lm_head:
									# each layer has its own relative position bias
									lowerCamelCase								=     state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_bias_table'			)
									lowerCamelCase								=     state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_index'			)

									lowerCamelCase								=     table
									lowerCamelCase								=     index






def   __lowerCamelCase	(       lowerCamelCase__		:	Optional[Any]     ,       lowerCamelCase__		:	Union[str, Any]     ,       lowerCamelCase__		:	Union[str, Any]			):





			'''simple docstring'''




			lowerCamelCase								=     dct.pop(lowerCamelCase__			)
			lowerCamelCase								=     val






def   __lowerCamelCase	(       ):





			'''simple docstring'''




			lowerCamelCase								=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			lowerCamelCase								=     Image.open(requests.get(lowerCamelCase__     ,       stream=lowerCamelCase__			).raw			)
			return im






@torch.no_grad()
def   __lowerCamelCase	(       lowerCamelCase__		:	int     ,       lowerCamelCase__		:	Tuple			):





			'''simple docstring'''




			lowerCamelCase								=     BeitConfig()
			lowerCamelCase								=     False
			lowerCamelCase								=     False
			lowerCamelCase								=     """huggingface/label-files"""
			# set config parameters based on URL
			if checkpoint_url[-9:-4] == "pt22k":
						# masked image modeling
						lowerCamelCase								=     True
						lowerCamelCase								=     True
						lowerCamelCase								=     True
			elif checkpoint_url[-9:-4] == "ft22k":
						# intermediate fine-tuning on ImageNet-22k
						lowerCamelCase								=     True
						lowerCamelCase								=     21841
						lowerCamelCase								=     """imagenet-22k-id2label.json"""
						lowerCamelCase								=     json.load(open(hf_hub_download(lowerCamelCase__     ,       lowerCamelCase__     ,       repo_type="""dataset"""			)     ,       """r"""			)			)
						lowerCamelCase								=     {int(lowerCamelCase__			): v for k, v in idalabel.items()}
						# this dataset contains 21843 labels but the model only has 21841
						# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
						del idalabel[9205]
						del idalabel[15027]
						lowerCamelCase								=     idalabel
						lowerCamelCase								=     {v: k for k, v in idalabel.items()}
			elif checkpoint_url[-8:-4] == "to1k":
						# fine-tuning on ImageNet-1k
						lowerCamelCase								=     True
						lowerCamelCase								=     1000
						lowerCamelCase								=     """imagenet-1k-id2label.json"""
						lowerCamelCase								=     json.load(open(hf_hub_download(lowerCamelCase__     ,       lowerCamelCase__     ,       repo_type="""dataset"""			)     ,       """r"""			)			)
						lowerCamelCase								=     {int(lowerCamelCase__			): v for k, v in idalabel.items()}
						lowerCamelCase								=     idalabel
						lowerCamelCase								=     {v: k for k, v in idalabel.items()}
						if "384" in checkpoint_url:
									lowerCamelCase								=     384
						if "512" in checkpoint_url:
									lowerCamelCase								=     512
			elif "ade20k" in checkpoint_url:
						# fine-tuning
						lowerCamelCase								=     True
						lowerCamelCase								=     150
						lowerCamelCase								=     """ade20k-id2label.json"""
						lowerCamelCase								=     json.load(open(hf_hub_download(lowerCamelCase__     ,       lowerCamelCase__     ,       repo_type="""dataset"""			)     ,       """r"""			)			)
						lowerCamelCase								=     {int(lowerCamelCase__			): v for k, v in idalabel.items()}
						lowerCamelCase								=     idalabel
						lowerCamelCase								=     {v: k for k, v in idalabel.items()}
						lowerCamelCase								=     640
						lowerCamelCase								=     True
			else:
						raise ValueError("""Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'"""			)

			# size of the architecture
			if "base" in checkpoint_url:
						pass
			elif "large" in checkpoint_url:
						lowerCamelCase								=     1024
						lowerCamelCase								=     4096
						lowerCamelCase								=     24
						lowerCamelCase								=     16
						if "ade20k" in checkpoint_url:
									lowerCamelCase								=     640
									lowerCamelCase								=     [7, 11, 15, 23]
			else:
						raise ValueError("""Should either find 'base' or 'large' in checkpoint URL"""			)

			# load state_dict of original model, remove and rename some keys
			lowerCamelCase								=     torch.hub.load_state_dict_from_url(lowerCamelCase__     ,       map_location="""cpu"""     ,       check_hash=lowerCamelCase__			)
			lowerCamelCase								=     state_dict["""model"""] if """ade20k""" not in checkpoint_url else state_dict["""state_dict"""]

			lowerCamelCase								=     create_rename_keys(lowerCamelCase__     ,       has_lm_head=lowerCamelCase__     ,       is_semantic=lowerCamelCase__			)
			for src, dest in rename_keys:
						rename_key(lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__			)
			read_in_q_k_v(lowerCamelCase__     ,       lowerCamelCase__     ,       has_lm_head=lowerCamelCase__     ,       is_semantic=lowerCamelCase__			)
			if is_semantic:
						# add prefix to decoder keys
						for key, val in state_dict.copy().items():
									lowerCamelCase								=     state_dict.pop(lowerCamelCase__			)
									if key.startswith("""backbone.fpn"""			):
												lowerCamelCase								=     key.replace("""backbone.fpn"""     ,       """fpn"""			)
									lowerCamelCase								=     val

    # load HuggingFace model
			if checkpoint_url[-9:-4] == "pt22k":
						lowerCamelCase								=     BeitForMaskedImageModeling(lowerCamelCase__			)
			elif "ade20k" in checkpoint_url:
						lowerCamelCase								=     BeitForSemanticSegmentation(lowerCamelCase__			)
			else:
						lowerCamelCase								=     BeitForImageClassification(lowerCamelCase__			)
			model.eval()
			model.load_state_dict(lowerCamelCase__			)

			# Check outputs on an image
			if is_semantic:
						lowerCamelCase								=     BeitImageProcessor(size=config.image_size     ,       do_center_crop=lowerCamelCase__			)
						lowerCamelCase								=     load_dataset("""hf-internal-testing/fixtures_ade20k"""     ,       split="""test"""			)
						lowerCamelCase								=     Image.open(ds[0]["""file"""]			)
			else:
						lowerCamelCase								=     BeitImageProcessor(
						    size=config.image_size     ,       resample=PILImageResampling.BILINEAR     ,       do_center_crop=lowerCamelCase__			)
						lowerCamelCase								=     prepare_img()

			lowerCamelCase								=     image_processor(images=lowerCamelCase__     ,       return_tensors="""pt"""			)
			lowerCamelCase								=     encoding["""pixel_values"""]

			lowerCamelCase								=     model(lowerCamelCase__			)
			lowerCamelCase								=     outputs.logits

			# verify logits
			lowerCamelCase								=     torch.Size([1, 1000]			)
			if checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k"""			):
						lowerCamelCase								=     torch.Size([1, 196, 8192]			)
			elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k"""			):
						lowerCamelCase								=     torch.Size([1, 196, 8192]			)
			elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22k"""			):
						lowerCamelCase								=     torch.Size([1, 21841]			)
						lowerCamelCase								=     torch.tensor([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5]			)
						lowerCamelCase								=     2397
			elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22k"""			):
						lowerCamelCase								=     torch.Size([1, 21841]			)
						lowerCamelCase								=     torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]			)
						lowerCamelCase								=     2396
			elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft1k"""			):
						lowerCamelCase								=     torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9]			)
						lowerCamelCase								=     285
			elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22kto1k"""			):
						lowerCamelCase								=     torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]			)
						lowerCamelCase								=     281
			elif checkpoint_url[:-4].endswith("""beit_base_patch16_384_pt22k_ft22kto1k"""			):
						lowerCamelCase								=     torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7]			)
						lowerCamelCase								=     761
			elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft1k"""			):
						lowerCamelCase								=     torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6]			)
						lowerCamelCase								=     761
			elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22kto1k"""			):
						lowerCamelCase								=     torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7]			)
						lowerCamelCase								=     761
			elif checkpoint_url[:-4].endswith("""beit_large_patch16_384_pt22k_ft22kto1k"""			):
						lowerCamelCase								=     torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]]			)
						lowerCamelCase								=     761
			elif checkpoint_url[:-4].endswith("""beit_large_patch16_512_pt22k_ft22kto1k"""			):
						lowerCamelCase								=     torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2]			)
						lowerCamelCase								=     761
			elif checkpoint_url[:-4].endswith("""beit_base_patch16_640_pt22k_ft22ktoade20k"""			):
						lowerCamelCase								=     (1, 150, 160, 160)
						lowerCamelCase								=     torch.tensor(
						    [
						        [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
						        [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
						        [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
						    ]			)
			elif checkpoint_url[:-4].endswith("""beit_large_patch16_640_pt22k_ft22ktoade20k"""			):
						lowerCamelCase								=     (1, 150, 160, 160)
						lowerCamelCase								=     torch.tensor(
						    [
						        [[-4.3_3_0_5, -2.3_0_4_9, -3.0_1_6_1], [-2.9_5_9_1, -1.5_3_0_5, -2.2_2_5_1], [-3.4_1_9_8, -1.8_0_0_4, -2.9_0_6_2]],
						        [[-5.8_9_2_2, -3.7_4_3_5, -4.3_9_7_8], [-4.2_0_6_3, -2.7_8_7_2, -3.4_7_5_5], [-4.2_7_9_1, -3.1_8_7_4, -4.1_6_8_1]],
						        [[0.9_8_9_5, 4.3_4_6_7, 4.7_6_6_3], [4.2_4_7_6, 5.6_8_3_0, 6.1_5_1_8], [4.5_5_5_0, 6.2_4_9_5, 6.5_1_5_4]],
						    ]			)
			else:
						raise ValueError("""Can't verify logits as model is not supported"""			)

			assert logits.shape == expected_shape, "Shape of logits not as expected"
			if not has_lm_head:
						if is_semantic:
									assert torch.allclose(
									    logits[0, :3, :3, :3]     ,       lowerCamelCase__     ,       atol=1E-3			), "First elements of logits not as expected"
						else:
									print("""Predicted class idx:"""     ,       logits.argmax(-1			).item()			)
									assert torch.allclose(
									    logits[0, :3]     ,       lowerCamelCase__     ,       atol=1E-3			), "First elements of logits not as expected"
									assert logits.argmax(-1			).item() == expected_class_idx, "Predicted class index not as expected"

			Path(lowerCamelCase__			).mkdir(exist_ok=lowerCamelCase__			)
			print(f'Saving model to {pytorch_dump_folder_path}'			)
			model.save_pretrained(lowerCamelCase__			)
			print(f'Saving image processor to {pytorch_dump_folder_path}'			)
			image_processor.save_pretrained(lowerCamelCase__			)


if __name__ == "__main__":
		UpperCAmelCase      :						Any    =		argparse.ArgumentParser()

		parser.add_argument(
		    "--checkpoint_url",
		    default="https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth",
		    type=str,
		    help="URL to the original PyTorch checkpoint (.pth file).",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
		)
		UpperCAmelCase      :						Any    =		parser.parse_args()
		convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





from __future__ import annotations

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

UpperCAmelCase      :						List[str]    =		TypeVar("T")
UpperCAmelCase      :						Dict    =		TypeVar("U")







class 					__lowercase					(       Generic[T, U]     ):



		"""simple docstring"""

		def __init__(   self		,			A		,			A  )     ->   Any:







					'''simple docstring'''







					lowerCamelCase								=     key
					lowerCamelCase								=     val
					lowerCamelCase								=     0
					lowerCamelCase								=     None
					lowerCamelCase								=     None







		def __repr__(   self  )     ->   str:







					'''simple docstring'''







					return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
					    self.key		,			self.val		,			self.freq		,			self.next is not None		,			self.prev is not None  )







class 					__lowercase					(       Generic[T, U]     ):



		"""simple docstring"""

		def __init__(   self  )     ->   None:







					'''simple docstring'''







					lowerCamelCase								=     DoubleLinkedListNode(A		,			A  )
					lowerCamelCase								=     DoubleLinkedListNode(A		,			A  )
					lowerCamelCase		,		lowerCamelCase								=     self.rear, self.head







		def __repr__(   self  )     ->   str:







					'''simple docstring'''







					lowerCamelCase								=     ["""DoubleLinkedList"""]
					lowerCamelCase								=     self.head
					while node.next is not None:
								rep.append(str(A  )  )
								lowerCamelCase								=     node.next
					rep.append(str(self.rear  )  )
					return ",\n    ".join(A  )







		def 				__A      (   self		,			A  )     ->   None:







					'''simple docstring'''







					lowerCamelCase								=     self.rear.prev

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

					lowerCamelCase								=     node
					lowerCamelCase								=     previous
					lowerCamelCase								=     node
					lowerCamelCase								=     self.rear
					node.freq += 1
					self._position_node(A  )







		def 				__A      (   self		,			A  )     ->   None:







					'''simple docstring'''







					while node.prev is not None and node.prev.freq > node.freq:
								# swap node with previous node
								lowerCamelCase								=     node.prev

								lowerCamelCase								=     previous_node.prev
								lowerCamelCase								=     node.prev
								lowerCamelCase								=     previous_node
								lowerCamelCase								=     node







		def 				__A      (   self		,			A  )     ->   DoubleLinkedListNode[T, U] | None:







					'''simple docstring'''







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

					lowerCamelCase								=     node.next
					lowerCamelCase								=     node.prev
					lowerCamelCase								=     None
					lowerCamelCase								=     None
					return node







class 					__lowercase					(       Generic[T, U]     ):



		"""simple docstring"""

		UpperCamelCase    :				dict[Callable[[T], U], LFUCache[T, U]] 					=		{}







		def __init__(   self		,			A  )     ->   Union[str, Any]:







					'''simple docstring'''







					lowerCamelCase								=     DoubleLinkedList()
					lowerCamelCase								=     capacity
					lowerCamelCase								=     0
					lowerCamelCase								=     0
					lowerCamelCase								=     0
					lowerCamelCase								=     {}







		def __repr__(   self  )     ->   str:







					'''simple docstring'''







					return (
					    F'CacheInfo(hits={self.hits}, misses={self.miss}, '
					    F'capacity={self.capacity}, current_size={self.num_keys})'
					)







		def __contains__(   self		,			A  )     ->   bool:







					'''simple docstring'''







					return key in self.cache







		def 				__A      (   self		,			A  )     ->   U | None:







					'''simple docstring'''







					if key in self.cache:
								self.hits += 1
								lowerCamelCase								=     self.cache[key]
								lowerCamelCase								=     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(A  )
								return node.val
					self.miss += 1
					return None







		def 				__A      (   self		,			A		,			A  )     ->   None:







					'''simple docstring'''







					if key not in self.cache:
								if self.num_keys >= self.capacity:
											# delete first node when over capacity
											lowerCamelCase								=     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(A  ) is not None
											# first_node guaranteed to be in list

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

					else:
								lowerCamelCase								=     self.list.remove(self.cache[key]  )
								assert node is not None  # node guaranteed to be in list
								lowerCamelCase								=     value
								self.list.add(A  )







		@classmethod
		def 				__A      (   cls		,			A = 1_28  )     ->   Callable[[Callable[[T], U]], Callable[..., U]]:







					'''simple docstring'''







					def cache_decorator_inner(A  ) -> Callable[..., U]:
								def cache_decorator_wrapper(*A  ) -> U:
											if func not in cls.decorator_function_to_instance_map:
														lowerCamelCase								=     LFUCache(A  )

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

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

								setattr(A		,			"""cache_info"""		,			A  )  # noqa: B010

								return cache_decorator_wrapper

					return cache_decorator_inner


if __name__ == "__main__":
		import doctest

		doctest.testmod()

