



"""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()
lowerCAmelCase__      =    logging.get_logger(__name__)






def 	snake_case_  (				A_ :		Union[str, Any]						):


       '''simple docstring'''





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

       _lowerCamelCase						:    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"
       _lowerCamelCase						:    List[str]         =			BitConfig(
           conv_layer=A_,    num_labels=10_00,    idalabel=A_,    labelaid=A_,    )

       return config






def 	snake_case_  (				A_ :		Any						):


       '''simple docstring'''





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

       return name






def 	snake_case_  (				):


       '''simple docstring'''





       _lowerCamelCase						:    Any         =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       _lowerCamelCase						:    List[Any]         =			Image.open(requests.get(A_,    stream=A_						).raw						)
       return im






@torch.no_grad()
def 	snake_case_  (				A_ :		str,    A_ :		Tuple,    A_ :		Optional[int]=False						):


       '''simple docstring'''





       _lowerCamelCase						:    List[Any]         =			get_config(A_						)

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

       # load state_dict of original model
       _lowerCamelCase						:    Dict         =			timm_model.state_dict()
       for key in state_dict.copy().keys():
              _lowerCamelCase						:    Union[str, Any]         =			state_dict.pop(A_						)
              _lowerCamelCase						:    Tuple         =			val.squeeze() if '''head''' in key else val

       # load HuggingFace model
       _lowerCamelCase						:    Union[str, Any]         =			BitForImageClassification(A_						)
       model.eval()
       model.load_state_dict(A_						)

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

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

       _lowerCamelCase						:    List[str]         =			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(),    )

       _lowerCamelCase						:    List[Any]         =			prepare_img()
       _lowerCamelCase						:    str         =			transform(A_						).unsqueeze(0						)
       _lowerCamelCase						:    str         =			processor(A_,    return_tensors='''pt'''						).pixel_values

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

       # verify logits
       with torch.no_grad():
              _lowerCamelCase						:    Tuple         =			model(A_						)
              _lowerCamelCase						:    Union[str, Any]         =			outputs.logits

       print('''Logits:''',    logits[0, :3]						)
       print('''Predicted class:''',    model.config.idalabel[logits.argmax(-1						).item()]						)
       _lowerCamelCase						:    Optional[int]         =			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__":
   lowerCAmelCase__      =    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.''',
   )

   lowerCAmelCase__      =    parser.parse_args()
   convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""






from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat






def 	snake_case_  (				):


       '''simple docstring'''





       _lowerCamelCase						:    Optional[Any]         =			[randint(-10_00,    10_00						) for i in range(10						)]
       _lowerCamelCase						:    Tuple         =			randint(-50_00,    50_00						)
       return (arr, r)


lowerCAmelCase__      =    make_dataset()






def 	snake_case_  (				A_ :		list[int],    A_ :		int						):


       '''simple docstring'''





       for triplet in permutations(A_,    3						):
              if sum(A_						) == target:
                     return tuple(sorted(A_						)						)
       return (0, 0, 0)






def 	snake_case_  (				A_ :		list[int],    A_ :		int						):


       '''simple docstring'''





       arr.sort()
       _lowerCamelCase						:    Optional[Any]         =			len(A_						)
       for i in range(n - 1						):
              _lowerCamelCase      ,   _lowerCamelCase						:    int         =			i + 1, n - 1
              while left < right:
                     if arr[i] + arr[left] + arr[right] == target:
                            return (arr[i], arr[left], arr[right])
                     elif arr[i] + arr[left] + arr[right] < target:
                            left += 1
                     elif arr[i] + arr[left] + arr[right] > target:
                            right -= 1
       return (0, 0, 0)






def 	snake_case_  (				):


       '''simple docstring'''





       _lowerCamelCase						:    Tuple         =			'''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
       _lowerCamelCase						:    List[Any]         =			'''
triplet_sum1(*dataset)
'''
       _lowerCamelCase						:    str         =			'''
triplet_sum2(*dataset)
'''
       _lowerCamelCase						:    Optional[int]         =			repeat(setup=A_,    stmt=A_,    repeat=5,    number=1_00_00						)
       _lowerCamelCase						:    Union[str, Any]         =			repeat(setup=A_,    stmt=A_,    repeat=5,    number=1_00_00						)
       return (min(A_						), min(A_						))


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   lowerCAmelCase__      =    solution_times()
   print(F"""The time for naive implementation is {times[0]}.""")
   print(F"""The time for optimized implementation is {times[1]}.""")


