





'''simple docstring'''


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	 =							logging.get_logger(__name__)


def 	__a(SCREAMING_SNAKE_CASE_  :					Dict    ):



    '''simple docstring'''





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

    _lowerCAmelCase				=      '''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"
    _lowerCAmelCase				=      BitConfig(
        conv_layer=_lowercase		,       num_labels=1000		,       idalabel=_lowercase		,       labelaid=_lowercase		,       )

    return config


def 	__a(SCREAMING_SNAKE_CASE_  :					Union[str, Any]    ):



    '''simple docstring'''





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

    return name


def 	__a():



    '''simple docstring'''





    _lowerCAmelCase				=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    _lowerCAmelCase				=      Image.open(requests.get(_lowercase		,       stream=_lowercase    ).raw    )
    return im


@torch.no_grad()
def 	__a(SCREAMING_SNAKE_CASE_  :					Any		,       SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					Union[str, Any]=False    ):



    '''simple docstring'''





    _lowerCAmelCase				=      get_config(_lowercase    )

    # load original model from timm
    _lowerCAmelCase				=      create_model(_lowercase		,       pretrained=_lowercase    )
    timm_model.eval()

    # load state_dict of original model
    _lowerCAmelCase				=      timm_model.state_dict()
    for key in state_dict.copy().keys():
        _lowerCAmelCase				=      state_dict.pop(_lowercase    )
        _lowerCAmelCase				=      val.squeeze() if '''head''' in key else val

    # load HuggingFace model
    _lowerCAmelCase				=      BitForImageClassification(_lowercase    )
    model.eval()
    model.load_state_dict(_lowercase    )

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

    _lowerCAmelCase				=      {
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

    _lowerCAmelCase				=      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()		,       )

    _lowerCAmelCase				=      prepare_img()
    _lowerCAmelCase				=      transform(_lowercase    ).unsqueeze(0    )
    _lowerCAmelCase				=      processor(_lowercase		,       return_tensors="pt"    ).pixel_values

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

    # verify logits
    with torch.no_grad():
        _lowerCAmelCase				=      model(_lowercase    )
        _lowerCAmelCase				=      outputs.logits

    print("Logits:"		,       logits[0, :3]    )
    print("Predicted class:"		,       model.config.idalabel[logits.argmax(-1    ).item()]    )
    _lowerCAmelCase				=      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__":
       _SCREAMING_SNAKE_CASE	 =							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	 =							parser.parse_args()
       convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''


from __future__ import annotations


def 	__a(SCREAMING_SNAKE_CASE_  :					list[float]		,       SCREAMING_SNAKE_CASE_  :					list[float]    ):



    '''simple docstring'''





    _lowerCAmelCase				=      sorted(numsa + numsa    )
    _lowerCAmelCase			,			_lowerCAmelCase				=      divmod(len(SCREAMING_SNAKE_CASE_    )		,       2    )
    if mod == 1:
        return all_numbers[div]
    else:
        return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       _SCREAMING_SNAKE_CASE	 =							[float(x) for x in input("Enter the elements of first array: ").split()]
       _SCREAMING_SNAKE_CASE	 =							[float(x) for x in input("Enter the elements of second array: ").split()]
       print(f'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')


