

import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						List[Any]:



    """simple docstring"""






    UpperCamelCase_			  =						[2, 2, 6, 2] if "tiny" in model_name else [2, 2, 1_8, 2]
    UpperCamelCase_			  =						True if "large" in model_name or "huge" in model_name else False
    UpperCamelCase_			  =						True if "large" in model_name or "huge" in model_name else False
    UpperCamelCase_			  =						True if "large" in model_name or "huge" in model_name else False

    if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
        if "fl3" in model_name:
            UpperCamelCase_			  =						[3, 3, 3, 3]
            UpperCamelCase_			  =						[5, 5, 5, 5]
        elif "fl4" in model_name:
            UpperCamelCase_			  =						[4, 4, 4, 4]
            UpperCamelCase_			  =						[3, 3, 3, 3]

    if "tiny" in model_name or "small" in model_name or "base" in model_name:
        UpperCamelCase_			  =						[3, 3, 3, 3]
        if "lrf" in model_name:
            UpperCamelCase_			  =						[3, 3, 3, 3]
        else:
            UpperCamelCase_			  =						[2, 2, 2, 2]

    if "tiny" in model_name:
        UpperCamelCase_			  =						9_6
    elif "small" in model_name:
        UpperCamelCase_			  =						9_6
    elif "base" in model_name:
        UpperCamelCase_			  =						1_2_8
    elif "large" in model_name:
        UpperCamelCase_			  =						1_9_2
    elif "xlarge" in model_name:
        UpperCamelCase_			  =						2_5_6
    elif "huge" in model_name:
        UpperCamelCase_			  =						3_5_2

    # set label information
    UpperCamelCase_			  =						"huggingface/label-files"
    if "large" in model_name or "huge" in model_name:
        UpperCamelCase_			  =						"imagenet-22k-id2label.json"
    else:
        UpperCamelCase_			  =						"imagenet-1k-id2label.json"

    UpperCamelCase_			  =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						repo_type="dataset"			)			,						"r"			)			)
    UpperCamelCase_			  =						{int(SCREAMING_SNAKE_CASE_			): v for k, v in idalabel.items()}
    UpperCamelCase_			  =						{v: k for k, v in idalabel.items()}

    UpperCamelCase_			  =						FocalNetConfig(
        embed_dim=SCREAMING_SNAKE_CASE_			,						depths=SCREAMING_SNAKE_CASE_			,						focal_levels=SCREAMING_SNAKE_CASE_			,						focal_windows=SCREAMING_SNAKE_CASE_			,						use_conv_embed=SCREAMING_SNAKE_CASE_			,						idalabel=SCREAMING_SNAKE_CASE_			,						labelaid=SCREAMING_SNAKE_CASE_			,						use_post_layernorm=SCREAMING_SNAKE_CASE_			,						use_layerscale=SCREAMING_SNAKE_CASE_			,						)

    return config




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						str:



    """simple docstring"""






    if "patch_embed.proj" in name:
        UpperCamelCase_			  =						name.replace("patch_embed.proj"			,						"embeddings.patch_embeddings.projection"			)
    if "patch_embed.norm" in name:
        UpperCamelCase_			  =						name.replace("patch_embed.norm"			,						"embeddings.norm"			)
    if "layers" in name:
        UpperCamelCase_			  =						"encoder." + name
    if "encoder.layers" in name:
        UpperCamelCase_			  =						name.replace("encoder.layers"			,						"encoder.stages"			)
    if "downsample.proj" in name:
        UpperCamelCase_			  =						name.replace("downsample.proj"			,						"downsample.projection"			)
    if "blocks" in name:
        UpperCamelCase_			  =						name.replace("blocks"			,						"layers"			)
    if "modulation.f.weight" in name or "modulation.f.bias" in name:
        UpperCamelCase_			  =						name.replace("modulation.f"			,						"modulation.projection_in"			)
    if "modulation.h.weight" in name or "modulation.h.bias" in name:
        UpperCamelCase_			  =						name.replace("modulation.h"			,						"modulation.projection_context"			)
    if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
        UpperCamelCase_			  =						name.replace("modulation.proj"			,						"modulation.projection_out"			)

    if name == "norm.weight":
        UpperCamelCase_			  =						"layernorm.weight"
    if name == "norm.bias":
        UpperCamelCase_			  =						"layernorm.bias"

    if "head" in name:
        UpperCamelCase_			  =						name.replace("head"			,						"classifier"			)
    else:
        UpperCamelCase_			  =						"focalnet." + name

    return name




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=False			)->						str:



    """simple docstring"""






    UpperCamelCase_			  =						{
        "focalnet-tiny": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth",
        "focalnet-tiny-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth",
        "focalnet-small": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth",
        "focalnet-small-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth",
        "focalnet-base": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth",
        "focalnet-base-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth",
        "focalnet-large-lrf-fl3": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth",
        "focalnet-large-lrf-fl4": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth",
        "focalnet-xlarge-lrf-fl3": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth",
        "focalnet-xlarge-lrf-fl4": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth",
    }
    # fmt: on

    UpperCamelCase_			  =						model_name_to_url[model_name]
    print("Checkpoint URL: "			,						SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_			,						map_location="cpu"			)["model"]

    # rename keys
    for key in state_dict.copy().keys():
        UpperCamelCase_			  =						state_dict.pop(SCREAMING_SNAKE_CASE_			)
        UpperCamelCase_			  =						val

    UpperCamelCase_			  =						get_focalnet_config(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						FocalNetForImageClassification(SCREAMING_SNAKE_CASE_			)
    model.eval()

    # load state dict
    model.load_state_dict(SCREAMING_SNAKE_CASE_			)

    # verify conversion
    UpperCamelCase_			  =						"http://images.cocodataset.org/val2017/000000039769.jpg"

    UpperCamelCase_			  =						BitImageProcessor(
        do_resize=SCREAMING_SNAKE_CASE_			,						size={"shortest_edge": 2_5_6}			,						resample=PILImageResampling.BILINEAR			,						do_center_crop=SCREAMING_SNAKE_CASE_			,						crop_size=2_2_4			,						do_normalize=SCREAMING_SNAKE_CASE_			,						image_mean=SCREAMING_SNAKE_CASE_			,						image_std=SCREAMING_SNAKE_CASE_			,						)
    UpperCamelCase_			  =						Image.open(requests.get(SCREAMING_SNAKE_CASE_			,						stream=SCREAMING_SNAKE_CASE_			).raw			)
    UpperCamelCase_			  =						processor(images=SCREAMING_SNAKE_CASE_			,						return_tensors="pt"			)

    UpperCamelCase_			  =						transforms.Compose(
        [
            transforms.Resize(2_5_6			),
            transforms.CenterCrop(2_2_4			),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406]			,						std=[0.229, 0.224, 0.225]			),
        ]			)

    UpperCamelCase_			  =						image_transforms(SCREAMING_SNAKE_CASE_			).unsqueeze(0			)

    # verify pixel_values
    assert torch.allclose(inputs.pixel_values			,						SCREAMING_SNAKE_CASE_			,						atol=1E-4			)

    UpperCamelCase_			  =						model(**SCREAMING_SNAKE_CASE_			)

    UpperCamelCase_			  =						outputs.logits.argmax(-1			).item()
    print("Predicted class:"			,						model.config.idalabel[predicted_class_idx]			)

    print("First values of logits:"			,						outputs.logits[0, :3]			)

    if model_name == "focalnet-tiny":
        UpperCamelCase_			  =						torch.tensor([0.2166, -0.4368, 0.2191]			)
    elif model_name == "focalnet-tiny-lrf":
        UpperCamelCase_			  =						torch.tensor([1.1669, 0.0125, -0.1695]			)
    elif model_name == "focalnet-small":
        UpperCamelCase_			  =						torch.tensor([0.4917, -0.0430, 0.1341]			)
    elif model_name == "focalnet-small-lrf":
        UpperCamelCase_			  =						torch.tensor([-0.2588, -0.5342, -0.2331]			)
    elif model_name == "focalnet-base":
        UpperCamelCase_			  =						torch.tensor([-0.1655, -0.4090, -0.1730]			)
    elif model_name == "focalnet-base-lrf":
        UpperCamelCase_			  =						torch.tensor([0.5306, -0.0483, -0.3928]			)
    assert torch.allclose(outputs.logits[0, :3]			,						SCREAMING_SNAKE_CASE_			,						atol=1E-4			)
    print("Looks ok!"			)

    if pytorch_dump_folder_path is not None:
        print(f"Saving model and processor of {model_name} to {pytorch_dump_folder_path}"			)
        model.save_pretrained(SCREAMING_SNAKE_CASE_			)
        processor.save_pretrained(SCREAMING_SNAKE_CASE_			)

    if push_to_hub:
        print(f"Pushing model and processor of {model_name} to the hub..."			)
        model.push_to_hub(f"{model_name}"			)
        processor.push_to_hub(f"{model_name}"			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:List[str]						=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""focalnet-tiny""",
     type=str,
     help="""Name of the FocalNet model you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
 )
 parser.add_argument(
     """--push_to_hub""",
     action="""store_true""",
     help="""Whether to push the model and processor to the hub.""",
 )

 SCREAMING_SNAKE_CASE		:List[str]						=     parser.parse_args()
 convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



SCREAMING_SNAKE_CASE		:Dict						=     [
    999,
    800,
    799,
    600,
    599,
    500,
    400,
    399,
    377,
    355,
    333,
    311,
    288,
    266,
    244,
    222,
    200,
    199,
    177,
    155,
    133,
    111,
    88,
    66,
    44,
    22,
    0,
]

SCREAMING_SNAKE_CASE		:Dict						=     [
    999,
    976,
    952,
    928,
    905,
    882,
    858,
    857,
    810,
    762,
    715,
    714,
    572,
    429,
    428,
    286,
    285,
    238,
    190,
    143,
    142,
    118,
    95,
    71,
    47,
    24,
    0,
]

SCREAMING_SNAKE_CASE		:int						=     [
    999,
    988,
    977,
    966,
    955,
    944,
    933,
    922,
    911,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    350,
    300,
    299,
    266,
    233,
    200,
    199,
    179,
    159,
    140,
    120,
    100,
    99,
    88,
    77,
    66,
    55,
    44,
    33,
    22,
    11,
    0,
]

SCREAMING_SNAKE_CASE		:Optional[Any]						=     [
    999,
    995,
    992,
    989,
    985,
    981,
    978,
    975,
    971,
    967,
    964,
    961,
    957,
    956,
    951,
    947,
    942,
    937,
    933,
    928,
    923,
    919,
    914,
    913,
    908,
    903,
    897,
    892,
    887,
    881,
    876,
    871,
    870,
    864,
    858,
    852,
    846,
    840,
    834,
    828,
    827,
    820,
    813,
    806,
    799,
    792,
    785,
    784,
    777,
    770,
    763,
    756,
    749,
    742,
    741,
    733,
    724,
    716,
    707,
    699,
    698,
    688,
    677,
    666,
    656,
    655,
    645,
    634,
    623,
    613,
    612,
    598,
    584,
    570,
    569,
    555,
    541,
    527,
    526,
    505,
    484,
    483,
    462,
    440,
    439,
    396,
    395,
    352,
    351,
    308,
    307,
    264,
    263,
    220,
    219,
    176,
    132,
    88,
    44,
    0,
]

SCREAMING_SNAKE_CASE		:int						=     [
    999,
    997,
    995,
    992,
    990,
    988,
    986,
    984,
    981,
    979,
    977,
    975,
    972,
    970,
    968,
    966,
    964,
    961,
    959,
    957,
    956,
    954,
    951,
    949,
    946,
    944,
    941,
    939,
    936,
    934,
    931,
    929,
    926,
    924,
    921,
    919,
    916,
    914,
    913,
    910,
    907,
    905,
    902,
    899,
    896,
    893,
    891,
    888,
    885,
    882,
    879,
    877,
    874,
    871,
    870,
    867,
    864,
    861,
    858,
    855,
    852,
    849,
    846,
    843,
    840,
    837,
    834,
    831,
    828,
    827,
    824,
    821,
    817,
    814,
    811,
    808,
    804,
    801,
    798,
    795,
    791,
    788,
    785,
    784,
    780,
    777,
    774,
    770,
    766,
    763,
    760,
    756,
    752,
    749,
    746,
    742,
    741,
    737,
    733,
    730,
    726,
    722,
    718,
    714,
    710,
    707,
    703,
    699,
    698,
    694,
    690,
    685,
    681,
    677,
    673,
    669,
    664,
    660,
    656,
    655,
    650,
    646,
    641,
    636,
    632,
    627,
    622,
    618,
    613,
    612,
    607,
    602,
    596,
    591,
    586,
    580,
    575,
    570,
    569,
    563,
    557,
    551,
    545,
    539,
    533,
    527,
    526,
    519,
    512,
    505,
    498,
    491,
    484,
    483,
    474,
    466,
    457,
    449,
    440,
    439,
    428,
    418,
    407,
    396,
    395,
    381,
    366,
    352,
    351,
    330,
    308,
    307,
    286,
    264,
    263,
    242,
    220,
    219,
    176,
    175,
    132,
    131,
    88,
    44,
    0,
]

SCREAMING_SNAKE_CASE		:Optional[int]						=     [
    999,
    991,
    982,
    974,
    966,
    958,
    950,
    941,
    933,
    925,
    916,
    908,
    900,
    899,
    874,
    850,
    825,
    800,
    799,
    700,
    600,
    500,
    400,
    300,
    200,
    100,
    0,
]

SCREAMING_SNAKE_CASE		:Dict						=     [
    999,
    992,
    985,
    978,
    971,
    964,
    957,
    949,
    942,
    935,
    928,
    921,
    914,
    907,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    300,
    299,
    200,
    199,
    100,
    99,
    0,
]

SCREAMING_SNAKE_CASE		:int						=     [
    999,
    996,
    992,
    989,
    985,
    982,
    979,
    975,
    972,
    968,
    965,
    961,
    958,
    955,
    951,
    948,
    944,
    941,
    938,
    934,
    931,
    927,
    924,
    920,
    917,
    914,
    910,
    907,
    903,
    900,
    899,
    891,
    884,
    876,
    869,
    861,
    853,
    846,
    838,
    830,
    823,
    815,
    808,
    800,
    799,
    788,
    777,
    766,
    755,
    744,
    733,
    722,
    711,
    700,
    699,
    688,
    677,
    666,
    655,
    644,
    633,
    622,
    611,
    600,
    599,
    585,
    571,
    557,
    542,
    528,
    514,
    500,
    499,
    485,
    471,
    457,
    442,
    428,
    414,
    400,
    399,
    379,
    359,
    340,
    320,
    300,
    299,
    279,
    259,
    240,
    220,
    200,
    199,
    166,
    133,
    100,
    99,
    66,
    33,
    0,
]
