


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()
SCREAMING_SNAKE_CASE       :      Tuple												= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :      Union[str, Any]												= torch.device("cpu")



def 				UpperCamelCase_(      )    ->						int:
    _lowercase       :							List[Any]      		=		'http://images.cocodataset.org/val2017/000000039769.jpg'
    _lowercase       :							List[Any]      		=		Image.open(requests.get(lowerCamelCase_							, stream=lowerCamelCase_ ).raw )
    return im



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						str:
    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 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						Tuple:
    _lowercase       :							List[Any]      		=		dct.pop(lowerCamelCase_ )
    _lowercase       :							List[Any]      		=		val



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



@torch.no_grad()
def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						Dict:
    _lowercase       :							Optional[Any]      		=		SwiftFormerConfig()

    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    _lowercase       :							Any      		=		1000
    _lowercase       :							Any      		=		'huggingface/label-files'
    _lowercase       :							int      		=		'imagenet-1k-id2label.json'
    _lowercase       :							Optional[int]      		=		json.load(open(hf_hub_download(lowerCamelCase_							, lowerCamelCase_							, repo_type='dataset' )							, 'r' ) )
    _lowercase       :							Dict      		=		{int(lowerCamelCase_ ): v for k, v in idalabel.items()}
    _lowercase       :							Dict      		=		idalabel
    _lowercase       :							Tuple      		=		{v: k for k, v in idalabel.items()}

    # size of the architecture
    if swiftformer_name == "swiftformer_xs":
        _lowercase       :							Optional[Any]      		=		[3, 3, 6, 4]
        _lowercase       :							List[Any]      		=		[48, 56, 112, 220]

    elif swiftformer_name == "swiftformer_s":
        _lowercase       :							Dict      		=		[3, 3, 9, 6]
        _lowercase       :							Optional[Any]      		=		[48, 64, 168, 224]

    elif swiftformer_name == "swiftformer_l1":
        _lowercase       :							Union[str, Any]      		=		[4, 3, 10, 5]
        _lowercase       :							List[str]      		=		[48, 96, 192, 384]

    elif swiftformer_name == "swiftformer_l3":
        _lowercase       :							Any      		=		[4, 4, 12, 6]
        _lowercase       :							Tuple      		=		[64, 128, 320, 512]

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

    _lowercase       :							str      		=		create_rename_keys(lowerCamelCase_ )
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )

    # load HuggingFace model
    _lowercase       :							Tuple      		=		SwiftFormerForImageClassification(lowerCamelCase_ ).eval()
    hf_model.load_state_dict(lowerCamelCase_ )

    # prepare test inputs
    _lowercase       :							List[str]      		=		prepare_img()
    _lowercase       :							Any      		=		ViTImageProcessor.from_pretrained('preprocessor_config' )
    _lowercase       :							Union[str, Any]      		=		processor(images=lowerCamelCase_							, return_tensors='pt' )

    # compare outputs from both models
    _lowercase       :							List[Any]      		=		get_expected_output(lowerCamelCase_ )
    _lowercase       :							Any      		=		hf_model(inputs['pixel_values'] ).logits

    assert hf_logits.shape == torch.Size([1, 1000] )
    assert torch.allclose(hf_logits[0, 0:5]							, lowerCamelCase_							, atol=1e-3 )

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE       :      Any												= 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.")

 SCREAMING_SNAKE_CASE       :      int												= parser.parse_args()
 convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)






import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
 import ctypes
 import msvcrt  # noqa




 class A  (   ctypes.Structure     ):
       # _fields is a specific attr expected by ctypes
       UpperCamelCase__							:  List[Any]      =[('size', ctypes.c_int), ('visible', ctypes.c_byte)]




def 	a_      (    ):
       '''simple docstring'''





       if os.name == "nt":
              _lowerCamelCase       :		Optional[Any]		=CursorInfo()
              _lowerCamelCase       :		Dict		=ctypes.windll.kernelaa.GetStdHandle(-11     )
              ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE__		,			ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
              _lowerCamelCase       :		Any		=False
              ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE__		,			ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
       elif os.name == "posix":
              sys.stdout.write('\033[?25l'     )
              sys.stdout.flush()




def 	a_      (    ):
       '''simple docstring'''





       if os.name == "nt":
              _lowerCamelCase       :		Any		=CursorInfo()
              _lowerCamelCase       :		Optional[Any]		=ctypes.windll.kernelaa.GetStdHandle(-11     )
              ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE__		,			ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
              _lowerCamelCase       :		Union[str, Any]		=True
              ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE__		,			ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
       elif os.name == "posix":
              sys.stdout.write('\033[?25h'     )
              sys.stdout.flush()




@contextmanager
def 	a_      (    ):
       '''simple docstring'''





       try:
              hide_cursor()
              yield
       finally:
              show_cursor()


