'''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()
a_					:    int          =    logging.get_logger(__name__)






def 	_A				(lowerCAmelCase__  :Optional[Any]		)   ->  str:




     '''simple docstring'''



     _a			      =							'huggingface/label-files'
     _a			      =							'imagenet-1k-id2label.json'
     _a			      =							json.load(open(hf_hub_download(lowerCAmelCase__				,						lowerCAmelCase__				,						repo_type='dataset'		)				,						'r'		)		)
     _a			      =							{int(lowerCAmelCase__		): 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=lowerCAmelCase__				,						num_labels=10_00				,						idalabel=lowerCAmelCase__				,						labelaid=lowerCAmelCase__				,						)

     return config






def 	_A				(lowerCAmelCase__  :Optional[int]		)   ->  Union[str, Any]:




     '''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 	_A				()   ->  Optional[Any]:




     '''simple docstring'''



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






@torch.no_grad()
def 	_A				(lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :List[Any]=False		)   ->  Any:




     '''simple docstring'''



     _a			      =							get_config(lowerCAmelCase__		)

     # load original model from timm
     _a			      =							create_model(lowerCAmelCase__				,						pretrained=lowerCAmelCase__		)
     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(lowerCAmelCase__		)
          _a			      =							val.squeeze() if 'head' in key else val

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

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

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

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

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

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

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

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

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

     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_					:    List[str]          =    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_					:    List[Any]          =    parser.parse_args()
 convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


a_					:    Optional[Any]          =    logging.get_logger(__name__)




class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							None:
      warnings.warn(
          'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use DonutImageProcessor instead.'      ,				__magic_name__      ,				)
      super().__init__(*__magic_name__      ,				**__magic_name__						)

