


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

_SCREAMING_SNAKE_CASE      :			List[Any]      				=   torch.device("cpu")
def       UpperCAmelCase__						() ->    Any:



     """simple docstring"""






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



     """simple docstring"""






     if swiftformer_name == "swiftformer_xs":
          return torch.tensor([-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0, 8.8_6_8_5e-0_1, 2.4_3_6_0e-0_1]    )

     elif swiftformer_name == "swiftformer_s":
          return torch.tensor([3.9_6_3_6e-0_1, 2.3_4_7_8e-0_1, -1.6_9_6_3e0_0, -1.7_3_8_1e0_0, -8.6_3_3_7e-0_1]    )

     elif swiftformer_name == "swiftformer_l1":
          return torch.tensor([-4.2_7_6_8e-0_1, -4.7_4_2_9e-0_1, -1.0_8_9_7e0_0, -1.0_2_4_8e0_0, 3.5_5_2_3e-0_2]    )

     elif swiftformer_name == "swiftformer_l3":
          return torch.tensor([-2.5_3_3_0e-0_1, 2.4_2_1_1e-0_1, -6.0_1_8_5e-0_1, -8.2_7_8_9e-0_1, -6.0_4_4_6e-0_2]    )
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ) ->    int:



     """simple docstring"""






     snake_case					=    dct.pop(snake_case_    )
     snake_case					=    val
def       UpperCAmelCase__						(UpperCamelCase_    ) ->    Optional[int]:



     """simple docstring"""






     snake_case					=    []
     for k in state_dict.keys():
          snake_case					=    k
          if ".pwconv" in k:
               snake_case					=    k_new.replace('''.pwconv'''							,'''.point_wise_conv'''    )
          if ".dwconv" in k:
               snake_case					=    k_new.replace('''.dwconv'''							,'''.depth_wise_conv'''    )
          if ".Proj." in k:
               snake_case					=    k_new.replace('''.Proj.'''							,'''.proj.'''    )
          if "patch_embed" in k_new:
               snake_case					=    k_new.replace('''patch_embed'''							,'''swiftformer.patch_embed.patch_embedding'''    )
          if "network" in k_new:
               snake_case					=    k_new.split('''.'''    )
               if ls[2].isdigit():
                    snake_case					=    '''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]    )
               else:
                    snake_case					=    k_new.replace('''network'''							,'''swiftformer.encoder.network'''    )
          rename_keys.append((k, k_new)    )
     return rename_keys
@torch.no_grad()
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ) ->    Optional[Any]:



     """simple docstring"""






     snake_case					=    SwiftFormerConfig()

     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     snake_case					=    10_00
     snake_case					=    '''huggingface/label-files'''
     snake_case					=    '''imagenet-1k-id2label.json'''
     snake_case					=    json.load(open(hf_hub_download(snake_case_							,snake_case_							,repo_type='''dataset'''    )							,'''r'''    )    )
     snake_case					=    {int(snake_case_    ): v for k, v in idalabel.items()}
     snake_case					=    idalabel
     snake_case					=    {v: k for k, v in idalabel.items()}

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

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

     elif swiftformer_name == "swiftformer_l1":
          snake_case					=    [4, 3, 10, 5]
          snake_case					=    [48, 96, 1_92, 3_84]

     elif swiftformer_name == "swiftformer_l3":
          snake_case					=    [4, 4, 12, 6]
          snake_case					=    [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'''    ):
               snake_case					=    torch.hub.load_state_dict_from_url(snake_case_							,map_location='''cpu'''							,check_hash=snake_case_    )
          else:
               snake_case					=    torch.load(snake_case_							,map_location='''cpu'''    )
     snake_case					=    checkpoint

     snake_case					=    create_rename_keys(snake_case_    )
     for rename_key_src, rename_key_dest in rename_keys:
          rename_key(snake_case_							,snake_case_							,snake_case_    )

     # load HuggingFace model
     snake_case					=    SwiftFormerForImageClassification(snake_case_    ).eval()
     hf_model.load_state_dict(snake_case_    )

     # prepare test inputs
     snake_case					=    prepare_img()
     snake_case					=    ViTImageProcessor.from_pretrained('''preprocessor_config'''    )
     snake_case					=    processor(images=snake_case_							,return_tensors='''pt'''    )

     # compare outputs from both models
     snake_case					=    get_expected_output(snake_case_    )
     snake_case					=    hf_model(inputs['''pixel_values''']    ).logits

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

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


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE      :			List[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      :			str      				=   parser.parse_args()
 convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)








import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


_SCREAMING_SNAKE_CASE      :			List[str]      				=   logging.get_logger(__name__)

class   A__ (   snake_case__     ):







    """simple docstring"""

    def __init__(	self      , *__snake_case      , **__snake_case   ):
         warnings.warn(
             '''The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
             ''' use BeitImageProcessor instead.'''      , __snake_case      , )
         super().__init__(*__snake_case      , **__snake_case   )




