





'''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()
a        =						logging.get_logger(__name__)

a        =						torch.device("cpu")

def 						__magic_name__       (							)						->					str:

   '''simple docstring'''







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

def 						__magic_name__       (							__UpperCAmelCase      )						->					List[str]:

   '''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 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					List[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       dct.pop(__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       val

def 						__magic_name__       (							__UpperCAmelCase      )						->					Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       []
   for k in state_dict.keys():
      __SCREAMING_SNAKE_CASE      							=       k
      if ".pwconv" in k:
         __SCREAMING_SNAKE_CASE      							=       k_new.replace(""".pwconv"""							,	""".point_wise_conv"""      )
      if ".dwconv" in k:
         __SCREAMING_SNAKE_CASE      							=       k_new.replace(""".dwconv"""							,	""".depth_wise_conv"""      )
      if ".Proj." in k:
         __SCREAMING_SNAKE_CASE      							=       k_new.replace(""".Proj."""							,	""".proj."""      )
      if "patch_embed" in k_new:
         __SCREAMING_SNAKE_CASE      							=       k_new.replace("""patch_embed"""							,	"""swiftformer.patch_embed.patch_embedding"""      )
      if "network" in k_new:
         __SCREAMING_SNAKE_CASE      							=       k_new.split("""."""      )
         if ls[2].isdigit():
            __SCREAMING_SNAKE_CASE      							=       """swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]      )
         else:
            __SCREAMING_SNAKE_CASE      							=       k_new.replace("""network"""							,	"""swiftformer.encoder.network"""      )
      rename_keys.append((k, k_new)      )
   return rename_keys

@torch.no_grad()
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					List[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       SwiftFormerConfig()

   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   __SCREAMING_SNAKE_CASE      							=       1000
   __SCREAMING_SNAKE_CASE      							=       """huggingface/label-files"""
   __SCREAMING_SNAKE_CASE      							=       """imagenet-1k-id2label.json"""
   __SCREAMING_SNAKE_CASE      							=       json.load(open(hf_hub_download(__UpperCAmelCase							,	__UpperCAmelCase							,	repo_type="""dataset"""      )							,	"""r"""      )      )
   __SCREAMING_SNAKE_CASE      							=       {int(__UpperCAmelCase      ): v for k, v in idalabel.items()}
   __SCREAMING_SNAKE_CASE      							=       idalabel
   __SCREAMING_SNAKE_CASE      							=       {v: k for k, v in idalabel.items()}

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

   elif swiftformer_name == "swiftformer_s":
      __SCREAMING_SNAKE_CASE      							=       [3, 3, 9, 6]
      __SCREAMING_SNAKE_CASE      							=       [48, 64, 168, 224]

   elif swiftformer_name == "swiftformer_l1":
      __SCREAMING_SNAKE_CASE      							=       [4, 3, 10, 5]
      __SCREAMING_SNAKE_CASE      							=       [48, 96, 192, 384]

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

   # load state_dict of original model, remove and rename some keys
   if original_ckpt:
      if original_ckpt.startswith("""https"""      ):
         __SCREAMING_SNAKE_CASE      							=       torch.hub.load_state_dict_from_url(__UpperCAmelCase							,	map_location="""cpu"""							,	check_hash=__UpperCAmelCase      )
      else:
         __SCREAMING_SNAKE_CASE      							=       torch.load(__UpperCAmelCase							,	map_location="""cpu"""      )
   __SCREAMING_SNAKE_CASE      							=       checkpoint

   __SCREAMING_SNAKE_CASE      							=       create_rename_keys(__UpperCAmelCase      )
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )

   # load HuggingFace model
   __SCREAMING_SNAKE_CASE      							=       SwiftFormerForImageClassification(__UpperCAmelCase      ).eval()
   hf_model.load_state_dict(__UpperCAmelCase      )

   # prepare test inputs
   __SCREAMING_SNAKE_CASE      							=       prepare_img()
   __SCREAMING_SNAKE_CASE      							=       ViTImageProcessor.from_pretrained("""preprocessor_config"""      )
   __SCREAMING_SNAKE_CASE      							=       processor(images=__UpperCAmelCase							,	return_tensors="""pt"""      )

   # compare outputs from both models
   __SCREAMING_SNAKE_CASE      							=       get_expected_output(__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       hf_model(inputs["""pixel_values"""]      ).logits

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

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


if __name__ == "__main__":
    a        =						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.")

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












'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a        =						{"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a        =						[
        "SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
        "SEWForCTC",
        "SEWForSequenceClassification",
        "SEWModel",
        "SEWPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_sew import (
            SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
            SEWForCTC,
            SEWForSequenceClassification,
            SEWModel,
            SEWPreTrainedModel,
        )


else:
    import sys

    a        =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





