

'''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			:				List[Any] 			=					logging.get_logger(__name__)


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[str]    )				->      int:
     """simple docstring"""




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

     _SCREAMING_SNAKE_CASE							   ='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"
     _SCREAMING_SNAKE_CASE							   =BitConfig(
         conv_layer=_UpperCamelCase							,			num_labels=10_00							,			idalabel=_UpperCamelCase							,			labelaid=_UpperCamelCase							,			)

     return config


def 	_lowerCAmelCase (						_UpperCamelCase						:					Dict    )				->      Tuple:
     """simple docstring"""




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

     return name


def 	_lowerCAmelCase (						)				->      List[Any]:
     """simple docstring"""




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


@torch.no_grad()
def 	_lowerCAmelCase (						_UpperCamelCase						:					int							,			_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					str=False    )				->      Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =get_config(_UpperCamelCase    )

     # load original model from timm
     _SCREAMING_SNAKE_CASE							   =create_model(_UpperCamelCase							,			pretrained=_UpperCamelCase    )
     timm_model.eval()

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

     # load HuggingFace model
     _SCREAMING_SNAKE_CASE							   =BitForImageClassification(_UpperCamelCase    )
     model.eval()
     model.load_state_dict(_UpperCamelCase    )

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

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

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

     _SCREAMING_SNAKE_CASE							   =prepare_img()
     _SCREAMING_SNAKE_CASE							   =transform(_UpperCamelCase    ).unsqueeze(0    )
     _SCREAMING_SNAKE_CASE							   =processor(_UpperCamelCase							,			return_tensors='pt'    ).pixel_values

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

     # verify logits
     with torch.no_grad():
          _SCREAMING_SNAKE_CASE							   =model(_UpperCamelCase    )
          _SCREAMING_SNAKE_CASE							   =outputs.logits

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

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

     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			:				List[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.",
     )

     lowerCamelCase			:				Tuple 			=					parser.parse_args()
     convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



'''simple docstring'''
import os


def 	_lowerCAmelCase (						)				->      List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =os.path.dirname(os.path.realpath(_UpperCamelCase    )    )
     _SCREAMING_SNAKE_CASE							   =os.path.join(_UpperCamelCase							,			'triangle.txt'    )

     with open(_UpperCamelCase    ) as f:
          _SCREAMING_SNAKE_CASE							   =f.readlines()

     _SCREAMING_SNAKE_CASE							   =[]
     for line in triangle:
          _SCREAMING_SNAKE_CASE							   =[]
          for number in line.strip().split(' '    ):
               numbers_from_line.append(int(_UpperCamelCase    )    )
          a.append(_UpperCamelCase    )

     for i in range(1							,			len(_UpperCamelCase    )    ):
          for j in range(len(a[i]    )    ):
               _SCREAMING_SNAKE_CASE							   =a[i - 1][j] if j != len(a[i - 1]    ) else 0
               _SCREAMING_SNAKE_CASE							   =a[i - 1][j - 1] if j > 0 else 0
               a[i][j] += max(_UpperCamelCase							,			_UpperCamelCase    )
     return max(a[-1]    )


if __name__ == "__main__":
     print(solution())
