






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[int]						= logging.get_logger(__name__)



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Union[str, Any]     )    ->   int:





    """simple docstring"""

    __A       =						"""huggingface/label-files"""
    __A       =						"""imagenet-1k-id2label.json"""
    __A       =						json.load(open(hf_hub_download(__lowercase      ,					__lowercase      ,					repo_type="""dataset"""     )      ,					"""r"""     )     )
    __A       =						{int(__lowercase     ): v for k, v in idalabel.items()}
    __A       =						{v: k for k, v in idalabel.items()}

    __A       =						"""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"
    __A       =						BitConfig(
        conv_layer=__lowercase      ,					num_labels=1_0_0_0      ,					idalabel=__lowercase      ,					labelaid=__lowercase      ,					)

    return config



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[Any]     )    ->   List[str]:





    """simple docstring"""

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

    return name



def       _SCREAMING_SNAKE_CASE    (			)    ->   Any:





    """simple docstring"""

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



@torch.no_grad()
def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[str]      ,					__lowercase :      Tuple      ,					__lowercase :      Optional[Any]=False     )    ->   Optional[Any]:





    """simple docstring"""

    __A       =						get_config(__lowercase     )

    # load original model from timm
    __A       =						create_model(__lowercase      ,					pretrained=__lowercase     )
    timm_model.eval()

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

    # load HuggingFace model
    __A       =						BitForImageClassification(__lowercase     )
    model.eval()
    model.load_state_dict(__lowercase     )

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

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

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

    __A       =						prepare_img()
    __A       =						transform(__lowercase     ).unsqueeze(0     )
    __A       =						processor(__lowercase      ,					return_tensors="""pt"""     ).pixel_values

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

    # verify logits
    with torch.no_grad():
        __A       =						model(__lowercase     )
        __A       =						outputs.logits

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

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

    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   :							Dict						= 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   :							int						= parser.parse_args()
  convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[Any]     )    ->   Any:





    """simple docstring"""

    stooge(__lowercase      ,					0      ,					len(__lowercase     ) - 1     )
    return arr



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      Dict      ,					__lowercase :      Dict     )    ->   int:





    """simple docstring"""

    if i >= h:
        return

    # If first element is smaller than the last then swap them
    if arr[i] > arr[h]:
        __A     ,						__A       =						arr[h], arr[i]

    # If there are more than 2 elements in the array
    if h - i + 1 > 2:
        __A       =						(int)((h - i + 1) / 3     )

        # Recursively sort first 2/3 elements
        stooge(__lowercase      ,					__lowercase      ,					(h - t)     )

        # Recursively sort last 2/3 elements
        stooge(__lowercase      ,					i + t      ,					(__lowercase)     )

        # Recursively sort first 2/3 elements
        stooge(__lowercase      ,					__lowercase      ,					(h - t)     )


if __name__ == "__main__":
  __a   :							List[Any]						= input("Enter numbers separated by a comma:\n").strip()
  __a   :							List[str]						= [int(item) for item in user_input.split(",")]
  print(stooge_sort(unsorted))




