





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()
snake_case_     =	logging.get_logger(__name__)

snake_case_     =	torch.device("""cpu""")
def 							_lowerCamelCase(  )		->  Union[str, Any]:
    A       :       Union[str, Any]							=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A       :       int							=						Image.open(requests.get(UpperCamelCase__    ,       stream=UpperCamelCase__		).raw		)
    return im
def 							_lowerCamelCase(  UpperCamelCase__    : List[str]		)		->  str:
    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 							_lowerCamelCase(  UpperCamelCase__    : Dict    ,       UpperCamelCase__    : List[Any]    ,       UpperCamelCase__    : str		)		->  int:
    A       :       Any							=						dct.pop(UpperCamelCase__		)
    A       :       Optional[int]							=						val
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[int]		)		->  str:
    A       :       Optional[Any]							=						[]
    for k in state_dict.keys():
        A       :       str							=						k
        if ".pwconv" in k:
            A       :       Optional[Any]							=						k_new.replace('''.pwconv'''    ,       '''.point_wise_conv'''		)
        if ".dwconv" in k:
            A       :       List[str]							=						k_new.replace('''.dwconv'''    ,       '''.depth_wise_conv'''		)
        if ".Proj." in k:
            A       :       Dict							=						k_new.replace('''.Proj.'''    ,       '''.proj.'''		)
        if "patch_embed" in k_new:
            A       :       List[str]							=						k_new.replace('''patch_embed'''    ,       '''swiftformer.patch_embed.patch_embedding'''		)
        if "network" in k_new:
            A       :       int							=						k_new.split('''.'''		)
            if ls[2].isdigit():
                A       :       Optional[int]							=						'''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]		)
            else:
                A       :       Optional[int]							=						k_new.replace('''network'''    ,       '''swiftformer.encoder.network'''		)
        rename_keys.append((k, k_new)		)
    return rename_keys
@torch.no_grad()
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[Any]    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : Dict		)		->  Dict:
    A       :       int							=						SwiftFormerConfig()

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

    # size of the architecture
    if swiftformer_name == "swiftformer_xs":
        A       :       Optional[Any]							=						[3, 3, 6, 4]
        A       :       List[str]							=						[48, 56, 112, 220]

    elif swiftformer_name == "swiftformer_s":
        A       :       Optional[int]							=						[3, 3, 9, 6]
        A       :       Any							=						[48, 64, 168, 224]

    elif swiftformer_name == "swiftformer_l1":
        A       :       str							=						[4, 3, 10, 5]
        A       :       Optional[Any]							=						[48, 96, 192, 384]

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

    # load state_dict of original model, remove and rename some keys
    if original_ckpt:
        if original_ckpt.startswith('''https'''		):
            A       :       Tuple							=						torch.hub.load_state_dict_from_url(UpperCamelCase__    ,       map_location='''cpu'''    ,       check_hash=UpperCamelCase__		)
        else:
            A       :       Dict							=						torch.load(UpperCamelCase__    ,       map_location='''cpu'''		)
    A       :       Optional[int]							=						checkpoint

    A       :       str							=						create_rename_keys(UpperCamelCase__		)
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)

    # load HuggingFace model
    A       :       Any							=						SwiftFormerForImageClassification(UpperCamelCase__		).eval()
    hf_model.load_state_dict(UpperCamelCase__		)

    # prepare test inputs
    A       :       str							=						prepare_img()
    A       :       Optional[int]							=						ViTImageProcessor.from_pretrained('''preprocessor_config'''		)
    A       :       Optional[int]							=						processor(images=UpperCamelCase__    ,       return_tensors='''pt'''		)

    # compare outputs from both models
    A       :       Tuple							=						get_expected_output(UpperCamelCase__		)
    A       :       Dict							=						hf_model(inputs['''pixel_values''']		).logits

    assert hf_logits.shape == torch.Size([1, 1_000]		)
    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__":
     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.""")

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







'''simple docstring'''






snake_case_     =	[
    9_99,
    8_00,
    7_99,
    6_00,
    5_99,
    5_00,
    4_00,
    3_99,
    3_77,
    3_55,
    3_33,
    3_11,
    2_88,
    2_66,
    2_44,
    2_22,
    2_00,
    1_99,
    1_77,
    1_55,
    1_33,
    1_11,
    88,
    66,
    44,
    22,
    0,
]

snake_case_     =	[
    9_99,
    9_76,
    9_52,
    9_28,
    9_05,
    8_82,
    8_58,
    8_57,
    8_10,
    7_62,
    7_15,
    7_14,
    5_72,
    4_29,
    4_28,
    2_86,
    2_85,
    2_38,
    1_90,
    1_43,
    1_42,
    1_18,
    95,
    71,
    47,
    24,
    0,
]

snake_case_     =	[
    9_99,
    9_88,
    9_77,
    9_66,
    9_55,
    9_44,
    9_33,
    9_22,
    9_11,
    9_00,
    8_99,
    8_79,
    8_59,
    8_40,
    8_20,
    8_00,
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    7_66,
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    6_50,
    6_00,
    5_99,
    5_00,
    4_99,
    4_00,
    3_99,
    3_50,
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    2_99,
    2_66,
    2_33,
    2_00,
    1_99,
    1_79,
    1_59,
    1_40,
    1_20,
    1_00,
    99,
    88,
    77,
    66,
    55,
    44,
    33,
    22,
    11,
    0,
]

snake_case_     =	[
    9_99,
    9_95,
    9_92,
    9_89,
    9_85,
    9_81,
    9_78,
    9_75,
    9_71,
    9_67,
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    9_37,
    9_33,
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    9_14,
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    8_81,
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    8_71,
    8_70,
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    8_20,
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    7_70,
    7_63,
    7_56,
    7_49,
    7_42,
    7_41,
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    6_88,
    6_77,
    6_66,
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    6_55,
    6_45,
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    6_23,
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    5_55,
    5_41,
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    4_39,
    3_96,
    3_95,
    3_52,
    3_51,
    3_08,
    3_07,
    2_64,
    2_63,
    2_20,
    2_19,
    1_76,
    1_32,
    88,
    44,
    0,
]

snake_case_     =	[
    9_99,
    9_97,
    9_95,
    9_92,
    9_90,
    9_88,
    9_86,
    9_84,
    9_81,
    9_79,
    9_77,
    9_75,
    9_72,
    9_70,
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    9_66,
    9_64,
    9_61,
    9_59,
    9_57,
    9_56,
    9_54,
    9_51,
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    9_44,
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    9_39,
    9_36,
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    9_31,
    9_29,
    9_26,
    9_24,
    9_21,
    9_19,
    9_16,
    9_14,
    9_13,
    9_10,
    9_07,
    9_05,
    9_02,
    8_99,
    8_96,
    8_93,
    8_91,
    8_88,
    8_85,
    8_82,
    8_79,
    8_77,
    8_74,
    8_71,
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    8_67,
    8_64,
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    8_58,
    8_55,
    8_52,
    8_49,
    8_46,
    8_43,
    8_40,
    8_37,
    8_34,
    8_31,
    8_28,
    8_27,
    8_24,
    8_21,
    8_17,
    8_14,
    8_11,
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    8_01,
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    7_91,
    7_88,
    7_85,
    7_84,
    7_80,
    7_77,
    7_74,
    7_70,
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    7_63,
    7_60,
    7_56,
    7_52,
    7_49,
    7_46,
    7_42,
    7_41,
    7_37,
    7_33,
    7_30,
    7_26,
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    7_07,
    7_03,
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    6_98,
    6_94,
    6_90,
    6_85,
    6_81,
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    6_73,
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    6_64,
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    6_56,
    6_55,
    6_50,
    6_46,
    6_41,
    6_36,
    6_32,
    6_27,
    6_22,
    6_18,
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    6_02,
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    5_91,
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    5_80,
    5_75,
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    5_33,
    5_27,
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    5_12,
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    4_49,
    4_40,
    4_39,
    4_28,
    4_18,
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    3_96,
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    3_81,
    3_66,
    3_52,
    3_51,
    3_30,
    3_08,
    3_07,
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    2_64,
    2_63,
    2_42,
    2_20,
    2_19,
    1_76,
    1_75,
    1_32,
    1_31,
    88,
    44,
    0,
]

snake_case_     =	[
    9_99,
    9_91,
    9_82,
    9_74,
    9_66,
    9_58,
    9_50,
    9_41,
    9_33,
    9_25,
    9_16,
    9_08,
    9_00,
    8_99,
    8_74,
    8_50,
    8_25,
    8_00,
    7_99,
    7_00,
    6_00,
    5_00,
    4_00,
    3_00,
    2_00,
    1_00,
    0,
]

snake_case_     =	[
    9_99,
    9_92,
    9_85,
    9_78,
    9_71,
    9_64,
    9_57,
    9_49,
    9_42,
    9_35,
    9_28,
    9_21,
    9_14,
    9_07,
    9_00,
    8_99,
    8_79,
    8_59,
    8_40,
    8_20,
    8_00,
    7_99,
    7_66,
    7_33,
    7_00,
    6_99,
    6_50,
    6_00,
    5_99,
    5_00,
    4_99,
    4_00,
    3_99,
    3_00,
    2_99,
    2_00,
    1_99,
    1_00,
    99,
    0,
]

snake_case_     =	[
    9_99,
    9_96,
    9_92,
    9_89,
    9_85,
    9_82,
    9_79,
    9_75,
    9_72,
    9_68,
    9_65,
    9_61,
    9_58,
    9_55,
    9_51,
    9_48,
    9_44,
    9_41,
    9_38,
    9_34,
    9_31,
    9_27,
    9_24,
    9_20,
    9_17,
    9_14,
    9_10,
    9_07,
    9_03,
    9_00,
    8_99,
    8_91,
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    8_61,
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    8_38,
    8_30,
    8_23,
    8_15,
    8_08,
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    7_88,
    7_77,
    7_66,
    7_55,
    7_44,
    7_33,
    7_22,
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    3_20,
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    2_20,
    2_00,
    1_99,
    1_66,
    1_33,
    1_00,
    99,
    66,
    33,
    0,
]
