





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    __UpperCAmelCase(      ):
       _lowerCamelCase							:     Optional[Any]	=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       _lowerCamelCase							:     List[str]	=		Image.open(requests.get(lowercase_ ,    stream=lowercase_      ).raw      )
       return im




def    __UpperCAmelCase(      lowercase_      ):
       if swiftformer_name == "swiftformer_xs":
              return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]      )

       elif swiftformer_name == "swiftformer_s":
              return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]      )

       elif swiftformer_name == "swiftformer_l1":
              return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]      )

       elif swiftformer_name == "swiftformer_l3":
              return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]      )




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     int	=		dct.pop(lowercase_      )
       _lowerCamelCase							:     Tuple	=		val




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




@torch.no_grad()
def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     Any	=		SwiftFormerConfig()

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

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

       elif swiftformer_name == "swiftformer_s":
              _lowerCamelCase							:     List[str]	=		[3, 3, 9, 6]
              _lowerCamelCase							:     List[Any]	=		[48, 64, 1_68, 2_24]

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

       elif swiftformer_name == "swiftformer_l3":
              _lowerCamelCase							:     str	=		[4, 4, 12, 6]
              _lowerCamelCase							:     Dict	=		[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'''      ):
                     _lowerCamelCase							:     Any	=		torch.hub.load_state_dict_from_url(lowercase_ ,    map_location='''cpu''' ,    check_hash=lowercase_      )
              else:
                     _lowerCamelCase							:     Tuple	=		torch.load(lowercase_ ,    map_location='''cpu'''      )
       _lowerCamelCase							:     Dict	=		checkpoint

       _lowerCamelCase							:     int	=		create_rename_keys(lowercase_      )
       for rename_key_src, rename_key_dest in rename_keys:
              rename_key(lowercase_ ,    lowercase_ ,    lowercase_      )

       # load HuggingFace model
       _lowerCamelCase							:     int	=		SwiftFormerForImageClassification(lowercase_      ).eval()
       hf_model.load_state_dict(lowercase_      )

       # prepare test inputs
       _lowerCamelCase							:     int	=		prepare_img()
       _lowerCamelCase							:     Tuple	=		ViTImageProcessor.from_pretrained('''preprocessor_config'''      )
       _lowerCamelCase							:     Optional[Any]	=		processor(images=lowercase_ ,    return_tensors='''pt'''      )

       # compare outputs from both models
       _lowerCamelCase							:     Dict	=		get_expected_output(lowercase_      )
       _lowerCamelCase							:     Optional[Any]	=		hf_model(inputs['''pixel_values''']      ).logits

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

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


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)








import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowerCamelCase			       =      logging.get_logger(__name__)







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      def __init__(		self      ,							*a__      ,							**a__):



             """simple docstring"""





             warnings.warn(
                 '''The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
                 ''' use DonutImageProcessor instead.'''      ,							a__      ,							)
             super().__init__(*a__      ,							**a__)

