
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 						__magic_name__			(   ):



     '''simple docstring'''




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






def 						__magic_name__			(   __a      :	Tuple						):



     '''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__			(   __a      :	Tuple	,		__a      :	int	,		__a      :	Optional[int]						):



     '''simple docstring'''




     UpperCamelCase__				=    dct.pop(snake_case_						)
     UpperCamelCase__				=    val






def 						__magic_name__			(   __a      :	Union[str, Any]						):



     '''simple docstring'''




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






@torch.no_grad()
def 						__magic_name__			(   __a      :	int	,		__a      :	List[str]	,		__a      :	Any						):



     '''simple docstring'''




     UpperCamelCase__				=    SwiftFormerConfig()

     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     UpperCamelCase__				=    1_000
     UpperCamelCase__				=    """huggingface/label-files"""
     UpperCamelCase__				=    """imagenet-1k-id2label.json"""
     UpperCamelCase__				=    json.load(open(hf_hub_download(snake_case_	,		snake_case_	,		repo_type="""dataset"""						)	,		"""r"""						)						)
     UpperCamelCase__				=    {int(snake_case_						): v for k, v in idalabel.items()}
     UpperCamelCase__				=    idalabel
     UpperCamelCase__				=    {v: k for k, v in idalabel.items()}

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

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

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

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

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

     UpperCamelCase__				=    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
     UpperCamelCase__				=    SwiftFormerForImageClassification(snake_case_						).eval()
     hf_model.load_state_dict(snake_case_						)

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

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

     assert hf_logits.shape == torch.Size([1, 1_000]						)
     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__":
    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 numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor






def 						__magic_name__			(   __a      :	dict						):



     '''simple docstring'''




     return (data["data"], data["target"])






def 						__magic_name__			(   __a      :	np.ndarray	,		__a      :	np.ndarray	,		__a      :	np.ndarray						):



     '''simple docstring'''




     UpperCamelCase__				=    XGBRegressor(verbosity=0	,		random_state=42						)
     xgb.fit(__a	,		__a						)
     # Predict target for test data
     UpperCamelCase__				=    xgb.predict(__a						)
     UpperCamelCase__				=    predictions.reshape(len(__a						)	,		1						)
     return predictions






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    fetch_california_housing()
     UpperCamelCase__		,	UpperCamelCase__				=    data_handling(__a						)
     UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    train_test_split(
         __a	,		__a	,		test_size=0.25	,		random_state=1						)
     UpperCamelCase__				=    xgboost(__a	,		__a	,		__a						)
     # Error printing
     print(f"Mean Absolute Error : {mean_absolute_error(__a	,		__a						)}"						)
     print(f"Mean Square Error  : {mean_squared_error(__a	,		__a						)}"						)


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    main()





