





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()
A__:    Optional[Any]  	=	logging.get_logger(__name__)



def 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	Dict					=     "huggingface/label-files"
    UpperCamelCase__:	List[str]					=     "imagenet-1k-id2label.json"
    UpperCamelCase__:	Union[str, Any]					=     json.load(open(hf_hub_download(A_    ,A_    ,repo_type="dataset")    ,"r"))
    UpperCamelCase__:	Dict					=     {int(A_): v for k, v in idalabel.items()}
    UpperCamelCase__:	Any					=     {v: k for k, v in idalabel.items()}

    UpperCamelCase__:	Dict					=     "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__:	Tuple					=     BitConfig(
        conv_layer=A_    ,num_labels=10_00    ,idalabel=A_    ,labelaid=A_    ,)

    return config



def 				lowerCAmelCase_ (							A_):
    if "stem.conv" in name:
        UpperCamelCase__:	Tuple					=     name.replace("stem.conv"    ,"bit.embedder.convolution")
    if "blocks" in name:
        UpperCamelCase__:	Tuple					=     name.replace("blocks"    ,"layers")
    if "head.fc" in name:
        UpperCamelCase__:	str					=     name.replace("head.fc"    ,"classifier.1")
    if name.startswith("norm"):
        UpperCamelCase__:	List[str]					=     "bit." + name
    if "bit" not in name and "classifier" not in name:
        UpperCamelCase__:	Optional[Any]					=     "bit.encoder." + name

    return name



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



@torch.no_grad()
def 				lowerCAmelCase_ (							A_    ,A_    ,A_=False):
    UpperCamelCase__:	str					=     get_config(A_)

    # load original model from timm
    UpperCamelCase__:	Dict					=     create_model(A_    ,pretrained=A_)
    timm_model.eval()

    # load state_dict of original model
    UpperCamelCase__:	Tuple					=     timm_model.state_dict()
    for key in state_dict.copy().keys():
        UpperCamelCase__:	Optional[int]					=     state_dict.pop(A_)
        UpperCamelCase__:	int					=     val.squeeze() if "head" in key else val

    # load HuggingFace model
    UpperCamelCase__:	List[str]					=     BitForImageClassification(A_)
    model.eval()
    model.load_state_dict(A_)

    # create image processor
    UpperCamelCase__:	List[str]					=     create_transform(**resolve_data_config({}    ,model=A_))
    UpperCamelCase__:	List[str]					=     transform.transforms

    UpperCamelCase__:	Optional[Any]					=     {
        "bilinear": PILImageResampling.BILINEAR,
        "bicubic": PILImageResampling.BICUBIC,
        "nearest": PILImageResampling.NEAREST,
    }

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

    UpperCamelCase__:	Dict					=     prepare_img()
    UpperCamelCase__:	Dict					=     transform(A_).unsqueeze(0)
    UpperCamelCase__:	List[Any]					=     processor(A_    ,return_tensors="pt").pixel_values

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

    # verify logits
    with torch.no_grad():
        UpperCamelCase__:	List[Any]					=     model(A_)
        UpperCamelCase__:	Optional[int]					=     outputs.logits

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

    if pytorch_dump_folder_path is not None:
        Path(A_).mkdir(exist_ok=A_)
        print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}")
        model.save_pretrained(A_)
        processor.save_pretrained(A_)

    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__":
   A__:    int  	=	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.''',
   )

   A__:    Union[str, Any]  	=	parser.parse_args()
   convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








def 				lowerCAmelCase_ (							A_    ,A_):
    UpperCamelCase__:	str					=     [1]
    for i in range(2    ,A_):
        factorials.append(factorials[-1] * i)
    assert 0 <= k < factorials[-1] * n, "k out of bounds"

    UpperCamelCase__:	Optional[int]					=     []
    UpperCamelCase__:	Optional[Any]					=     list(range(A_))

    # Find permutation
    while factorials:
        UpperCamelCase__:	Dict					=     factorials.pop()
        UpperCamelCase__				, UpperCamelCase__:	Optional[int]					=     divmod(A_    ,A_)
        permutation.append(elements[number])
        elements.remove(elements[number])
    permutation.append(elements[0])

    return permutation


if __name__ == "__main__":
   import doctest

   doctest.testmod()

