

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE		:Optional[int]						=     logging.get_logger(__name__)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						"huggingface/label-files"
    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_			  =						"std_conv" if "bit" in model_name else False

    # note that when using BiT as backbone for ViT-hybrid checkpoints,
    # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
    # config.conv_layer = "std_conv_same"
    UpperCamelCase_			  =						BitConfig(
        conv_layer=SCREAMING_SNAKE_CASE_			,						num_labels=1_0_0_0			,						idalabel=SCREAMING_SNAKE_CASE_			,						labelaid=SCREAMING_SNAKE_CASE_			,						)

    return config




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



    """simple docstring"""






    if "stem.conv" in name:
        UpperCamelCase_			  =						name.replace("stem.conv"			,						"bit.embedder.convolution"			)
    if "blocks" in name:
        UpperCamelCase_			  =						name.replace("blocks"			,						"layers"			)
    if "head.fc" in name:
        UpperCamelCase_			  =						name.replace("head.fc"			,						"classifier.1"			)
    if name.startswith("norm"			):
        UpperCamelCase_			  =						"bit." + name
    if "bit" not in name and "classifier" not in name:
        UpperCamelCase_			  =						"bit.encoder." + name

    return name




def 			lowerCAmelCase(      )->						int:



    """simple docstring"""






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




@torch.no_grad()
def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=False			)->						Tuple:



    """simple docstring"""






    UpperCamelCase_			  =						get_config(SCREAMING_SNAKE_CASE_			)

    # load original model from timm
    UpperCamelCase_			  =						create_model(SCREAMING_SNAKE_CASE_			,						pretrained=SCREAMING_SNAKE_CASE_			)
    timm_model.eval()

    # load state_dict of original model
    UpperCamelCase_			  =						timm_model.state_dict()
    for key in state_dict.copy().keys():
        UpperCamelCase_			  =						state_dict.pop(SCREAMING_SNAKE_CASE_			)
        UpperCamelCase_			  =						val.squeeze() if "head" in key else val

    # load HuggingFace model
    UpperCamelCase_			  =						BitForImageClassification(SCREAMING_SNAKE_CASE_			)
    model.eval()
    model.load_state_dict(SCREAMING_SNAKE_CASE_			)

    # create image processor
    UpperCamelCase_			  =						create_transform(**resolve_data_config({}			,						model=SCREAMING_SNAKE_CASE_			)			)
    UpperCamelCase_			  =						transform.transforms

    UpperCamelCase_			  =						{
        "bilinear": PILImageResampling.BILINEAR,
        "bicubic": PILImageResampling.BICUBIC,
        "nearest": PILImageResampling.NEAREST,
    }

    UpperCamelCase_			  =						BitImageProcessor(
        do_resize=SCREAMING_SNAKE_CASE_			,						size={"shortest_edge": timm_transforms[0].size}			,						resample=pillow_resamplings[timm_transforms[0].interpolation.value]			,						do_center_crop=SCREAMING_SNAKE_CASE_			,						crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}			,						do_normalize=SCREAMING_SNAKE_CASE_			,						image_mean=timm_transforms[-1].mean.tolist()			,						image_std=timm_transforms[-1].std.tolist()			,						)

    UpperCamelCase_			  =						prepare_img()
    UpperCamelCase_			  =						transform(SCREAMING_SNAKE_CASE_			).unsqueeze(0			)
    UpperCamelCase_			  =						processor(SCREAMING_SNAKE_CASE_			,						return_tensors="pt"			).pixel_values

    # verify pixel values
    assert torch.allclose(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)

    # verify logits
    with torch.no_grad():
        UpperCamelCase_			  =						model(SCREAMING_SNAKE_CASE_			)
        UpperCamelCase_			  =						outputs.logits

    print("Logits:"			,						logits[0, :3]			)
    print("Predicted class:"			,						model.config.idalabel[logits.argmax(-1			).item()]			)
    UpperCamelCase_			  =						timm_model(SCREAMING_SNAKE_CASE_			)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(SCREAMING_SNAKE_CASE_			,						outputs.logits			,						atol=1E-3			)
    print("Looks ok!"			)

    if pytorch_dump_folder_path is not None:
        Path(SCREAMING_SNAKE_CASE_			).mkdir(exist_ok=SCREAMING_SNAKE_CASE_			)
        print(f"Saving model {model_name} and processor 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 {model_name} and processor to the hub"			)
        model.push_to_hub(f"ybelkada/{model_name}"			)
        processor.push_to_hub(f"ybelkada/{model_name}"			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:Optional[Any]						=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""resnetv2_50x1_bitm""",
     type=str,
     help="""Name of the BiT timm 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 to the hub.""",
 )

 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     parser.parse_args()
 convert_bit_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,
]
