





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

_SCREAMING_SNAKE_CASE	 =   torch.device("cpu")


def 	__a():



    '''simple docstring'''





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


def 	__a(SCREAMING_SNAKE_CASE_  :					str    ):



    '''simple docstring'''





    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 	__a(SCREAMING_SNAKE_CASE_  :					Optional[int]		,       SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					Union[str, Any]    ):



    '''simple docstring'''





    _lowerCAmelCase				=      dct.pop(__UpperCAmelCase    )
    _lowerCAmelCase				=      val


def 	__a(SCREAMING_SNAKE_CASE_  :					str    ):



    '''simple docstring'''





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


@torch.no_grad()
def 	__a(SCREAMING_SNAKE_CASE_  :					Tuple		,       SCREAMING_SNAKE_CASE_  :					Dict		,       SCREAMING_SNAKE_CASE_  :					Optional[int]    ):



    '''simple docstring'''





    _lowerCAmelCase				=      SwiftFormerConfig()

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

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

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

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

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

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

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

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

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

    # compare outputs from both models
    _lowerCAmelCase				=      get_expected_output(__UpperCAmelCase    )
    _lowerCAmelCase				=      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__":
       _SCREAMING_SNAKE_CASE	 =   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	 =   parser.parse_args()
       convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)









'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					list[int]		,       SCREAMING_SNAKE_CASE_  :					list[int]		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    return not any(
        neighbour == 1 and colored_vertices[i] == color
        for i, neighbour in enumerate(SCREAMING_SNAKE_CASE_    )    )


def 	__a(SCREAMING_SNAKE_CASE_  :					list[list[int]]		,       SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					list[int]		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    if index == len(SCREAMING_SNAKE_CASE_    ):
        return True

    # Recursive Step
    for i in range(SCREAMING_SNAKE_CASE_    ):
        if valid_coloring(graph[index]		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
            # Color current vertex
            _lowerCAmelCase				=      i
            # Validate coloring
            if util_color(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       index + 1    ):
                return True
            # Backtrack
            _lowerCAmelCase				=      -1
    return False





def 	__a(SCREAMING_SNAKE_CASE_  :					list[list[int]]		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      [-1] * len(SCREAMING_SNAKE_CASE_    )

    if util_color(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       0    ):
        return colored_vertices

    return []


