
"""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()
UpperCamelCase     :    Union[str, Any]       =			logging.get_logger(__name__)






def 							A						(    snake_case				:List[Any]		)				->     int:






 """simple docstring"""




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

 __UpperCamelCase      =	"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      =	BitConfig(
     conv_layer=_a    , num_labels=1_0_0_0    , idalabel=_a    , labelaid=_a    , )

 return config






def 							A						(    snake_case				:str		)				->     Tuple:






 """simple docstring"""




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

 return name






def 							A						(    )				->     Optional[int]:






 """simple docstring"""




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






@torch.no_grad()
def 							A						(    snake_case				:Dict    , snake_case				:Tuple    , snake_case				:Dict=False		)				->     Optional[Any]:






 """simple docstring"""




 __UpperCamelCase      =	get_config(_a		)

 # load original model from timm
 __UpperCamelCase      =	create_model(_a    , pretrained=_a		)
 timm_model.eval()

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

 # load HuggingFace model
 __UpperCamelCase      =	BitForImageClassification(_a		)
 model.eval()
 model.load_state_dict(_a		)

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

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

 __UpperCamelCase      =	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      =	prepare_img()
 __UpperCamelCase      =	transform(_a		).unsqueeze(0		)
 __UpperCamelCase      =	processor(_a    , return_tensors='pt'		).pixel_values

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

 # verify logits
 with torch.no_grad():
  __UpperCamelCase      =	model(_a		)
  __UpperCamelCase      =	outputs.logits

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

      UpperCamelCase     :    List[Any]       =			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 typing import Any






def 							A						(    snake_case				:list		)				->     int:
 if not postfix_notation:
  return 0

 __UpperCamelCase      =	{'+', '-', '*', '/'}
 __UpperCamelCase      =	[]

 for token in postfix_notation:
  if token in operations:
   __UpperCamelCase  ,     __UpperCamelCase      =	stack.pop(), stack.pop()
   if token == "+":
    stack.append(a + b		)
   elif token == "-":
    stack.append(a - b		)
   elif token == "*":
    stack.append(a * b		)
   else:
    if a * b < 0 and a % b != 0:
     stack.append(a // b + 1		)
    else:
     stack.append(a // b		)
  else:
   stack.append(int(snake_case		)		)

 return stack.pop()


if __name__ == "__main__":
      import doctest

      doctest.testmod()



