"""simple docstring"""







from __future__ import annotations

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

__SCREAMING_SNAKE_CASE             =TypeVar("T")
__SCREAMING_SNAKE_CASE             =TypeVar("U")







class 			UpperCamelCase						(     Generic[T, U]    ):
   def __init__(     self	,__UpperCamelCase	,__UpperCamelCase     )      -> List[Any]:


        '''simple docstring'''


        lowercase_      :							Optional[Any]						=      key
        lowercase_      :							Optional[int]						=      val
        lowercase_      :							DoubleLinkedListNode[T, U] | None						=      None
        lowercase_      :							DoubleLinkedListNode[T, U] | None						=      None







   def __repr__(     self     )      -> str:


        '''simple docstring'''


        return (
            f'''Node: key: {self.key}, val: {self.val}, '''
            f'''has next: {bool(self.next     )}, has prev: {bool(self.prev     )}'''
        )







class 			UpperCamelCase						(     Generic[T, U]    ):
   def __init__(     self     )      -> None:


        '''simple docstring'''


        lowercase_      :							DoubleLinkedListNode[T, U]						=      DoubleLinkedListNode(lowerCAmelCase_	,lowerCAmelCase_     )
        lowercase_      :							DoubleLinkedListNode[T, U]						=      DoubleLinkedListNode(lowerCAmelCase_	,lowerCAmelCase_     )
        lowercase_      :							Union[str, Any]						=      self.rear, self.head
   def __repr__(     self     )      -> str:


        '''simple docstring'''


        lowercase_      :							Tuple						=      ["DoubleLinkedList"]
        lowercase_      :							List[Any]						=      self.head
        while node.next is not None:
             rep.append(str(lowerCAmelCase_     )     )
             lowercase_      :							Tuple						=      node.next
        rep.append(str(self.rear     )     )
        return ",\n    ".join(lowerCAmelCase_     )
   def      _UpperCAmelCase  (     self	,__UpperCamelCase     )      -> None:


        '''simple docstring'''


        lowercase_      :							Dict						=      self.rear.prev

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

        lowercase_      :							List[Any]						=      node
        lowercase_      :							Any						=      previous
        lowercase_      :							List[Any]						=      node
        lowercase_      :							Dict						=      self.rear







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


        '''simple docstring'''


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

        lowercase_      :							List[Any]						=      node.next
        lowercase_      :							Optional[Any]						=      node.prev
        lowercase_      :							List[str]						=      None
        lowercase_      :							str						=      None
        return node







class 			UpperCamelCase						(     Generic[T, U]    ):
   lowercase				=    {}
   def __init__(     self	,__UpperCamelCase     )      -> List[Any]:


        '''simple docstring'''


        lowercase_      :							DoubleLinkedList[T, U]						=      DoubleLinkedList()
        lowercase_      :							List[str]						=      capacity
        lowercase_      :							Tuple						=      0
        lowercase_      :							List[str]						=      0
        lowercase_      :							List[str]						=      0
        lowercase_      :							dict[T, DoubleLinkedListNode[T, U]]						=      {}
   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	,__UpperCamelCase     )      -> bool:


        '''simple docstring'''


        return key in self.cache
   def      _UpperCAmelCase  (     self	,__UpperCamelCase     )      -> U | None:


        '''simple docstring'''


        if key in self.cache:
             self.hits += 1
             lowercase_      :							DoubleLinkedListNode[T, U]						=      self.cache[key]
             lowercase_      :							int						=      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(lowerCAmelCase_     )
             return node.val
        self.miss += 1
        return None
   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase     )      -> None:


        '''simple docstring'''


        if key not in self.cache:
             if self.num_keys >= self.capacity:
                  # delete first node (oldest) when over capacity
                  lowercase_      :							str						=      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(lowerCAmelCase_     ) 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
             lowercase_      :							List[Any]						=      DoubleLinkedListNode(lowerCAmelCase_	,lowerCAmelCase_     )
             self.list.add(self.cache[key]     )
             self.num_keys += 1

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







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


        '''simple docstring'''


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

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

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

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

             return cache_decorator_wrapper

        return cache_decorator_inner


if __name__ == "__main__":
       import doctest

       doctest.testmod()









"""simple docstring"""
import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = torch.device('''cpu''')

def 							snake_case (  ):
			UpperCAmelCase_	:					str     	=   "http://images.cocodataset.org/val2017/000000039769.jpg"
			UpperCAmelCase_	:					str     	=   Image.open(requests.get(A__      ,stream=A__		).raw		)
			return im

def 							snake_case (  A__		):
			if swiftformer_name == "swiftformer_xs":
						return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01]		)

			elif swiftformer_name == "swiftformer_s":
						return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01]		)

			elif swiftformer_name == "swiftformer_l1":
						return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02]		)

			elif swiftformer_name == "swiftformer_l3":
						return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02]		)

def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Tuple     	=   dct.pop(A__		)
			UpperCAmelCase_	:					Optional[Any]     	=   val

def 							snake_case (  A__		):
			UpperCAmelCase_	:					List[str]     	=   []
			for k in state_dict.keys():
						UpperCAmelCase_	:					Union[str, Any]     	=   k
						if ".pwconv" in k:
									UpperCAmelCase_	:					Dict     	=   k_new.replace(".pwconv"      ,".point_wise_conv"		)
						if ".dwconv" in k:
									UpperCAmelCase_	:					Any     	=   k_new.replace(".dwconv"      ,".depth_wise_conv"		)
						if ".Proj." in k:
									UpperCAmelCase_	:					Dict     	=   k_new.replace(".Proj."      ,".proj."		)
						if "patch_embed" in k_new:
									UpperCAmelCase_	:					Tuple     	=   k_new.replace("patch_embed"      ,"swiftformer.patch_embed.patch_embedding"		)
						if "network" in k_new:
									UpperCAmelCase_	:					List[Any]     	=   k_new.split("."		)
									if ls[2].isdigit():
												UpperCAmelCase_	:					Tuple     	=   "swiftformer.encoder.network." + ls[1] + ".blocks." + ls[2] + "." + ".".join(ls[3:]		)
									else:
												UpperCAmelCase_	:					Optional[Any]     	=   k_new.replace("network"      ,"swiftformer.encoder.network"		)
						rename_keys.append((k, k_new)		)
			return rename_keys

@torch.no_grad()
def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Optional[int]     	=   SwiftFormerConfig()

			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			UpperCAmelCase_	:					Optional[Any]     	=   10_00
			UpperCAmelCase_	:					str     	=   "huggingface/label-files"
			UpperCAmelCase_	:					str     	=   "imagenet-1k-id2label.json"
			UpperCAmelCase_	:					List[str]     	=   json.load(open(hf_hub_download(A__      ,A__      ,repo_type="dataset"		)      ,"r"		)		)
			UpperCAmelCase_	:					Tuple     	=   {int(A__		): v for k, v in idalabel.items()}
			UpperCAmelCase_	:					List[Any]     	=   idalabel
			UpperCAmelCase_	:					Optional[Any]     	=   {v: k for k, v in idalabel.items()}

			# size of the architecture
			if swiftformer_name == "swiftformer_xs":
						UpperCAmelCase_	:					Tuple     	=   [3, 3, 6, 4]
						UpperCAmelCase_	:					str     	=   [48, 56, 1_12, 2_20]

			elif swiftformer_name == "swiftformer_s":
						UpperCAmelCase_	:					Optional[Any]     	=   [3, 3, 9, 6]
						UpperCAmelCase_	:					Optional[Any]     	=   [48, 64, 1_68, 2_24]

			elif swiftformer_name == "swiftformer_l1":
						UpperCAmelCase_	:					int     	=   [4, 3, 10, 5]
						UpperCAmelCase_	:					Union[str, Any]     	=   [48, 96, 1_92, 3_84]

			elif swiftformer_name == "swiftformer_l3":
						UpperCAmelCase_	:					Dict     	=   [4, 4, 12, 6]
						UpperCAmelCase_	:					Optional[int]     	=   [64, 1_28, 3_20, 5_12]

			# load state_dict of original model, remove and rename some keys
			if original_ckpt:
						if original_ckpt.startswith("https"		):
									UpperCAmelCase_	:					List[Any]     	=   torch.hub.load_state_dict_from_url(A__      ,map_location="cpu"      ,check_hash=A__		)
						else:
									UpperCAmelCase_	:					Any     	=   torch.load(A__      ,map_location="cpu"		)
			UpperCAmelCase_	:					List[str]     	=   checkpoint

			UpperCAmelCase_	:					Dict     	=   create_rename_keys(A__		)
			for rename_key_src, rename_key_dest in rename_keys:
						rename_key(A__      ,A__      ,A__		)

			# load HuggingFace model
			UpperCAmelCase_	:					Optional[int]     	=   SwiftFormerForImageClassification(A__		).eval()
			hf_model.load_state_dict(A__		)

			# prepare test inputs
			UpperCAmelCase_	:					Tuple     	=   prepare_img()
			UpperCAmelCase_	:					int     	=   ViTImageProcessor.from_pretrained("preprocessor_config"		)
			UpperCAmelCase_	:					int     	=   processor(images=A__      ,return_tensors="pt"		)

			# compare outputs from both models
			UpperCAmelCase_	:					List[Any]     	=   get_expected_output(A__		)
			UpperCAmelCase_	:					int     	=   hf_model(inputs["pixel_values"]		).logits

			assert hf_logits.shape == torch.Size([1, 10_00]		)
			assert torch.allclose(hf_logits[0, 0:5]      ,A__      ,atol=1e-3		)

			Path(A__		).mkdir(exist_ok=A__		)
			print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""		)
			hf_model.save_pretrained(A__		)


if __name__ == "__main__":
			lowerCamelCase_							      = argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--swiftformer_name''',
			    default='''swiftformer_xs''',
			    choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
			    type=str,
			    help='''Name of the SwiftFormer model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default='''./converted_outputs/''',
			    type=str,
			    help='''Path to the output PyTorch model directory.''',
			)
			parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

			lowerCamelCase_							      = parser.parse_args()
			convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)



