



'''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     snake_case_					(     lowerCAmelCase_		)->		Union[str, Any]:





       '''simple docstring'''


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

       _UpperCAmelCase  :						Any			     = """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  :						List[str]			     = BitConfig(
           conv_layer=lowerCAmelCase_       ,   num_labels=1000       ,   idalabel=lowerCAmelCase_       ,   labelaid=lowerCAmelCase_       ,   )

       return config




def     snake_case_					(     lowerCAmelCase_		)->		str:





       '''simple docstring'''


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

       return name




def     snake_case_					(     )->		Union[str, Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[int]			     = """http://images.cocodataset.org/val2017/000000039769.jpg"""
       _UpperCAmelCase  :						int			     = Image.open(requests.get(lowerCAmelCase_       ,   stream=lowerCAmelCase_		).raw		)
       return im




@torch.no_grad()
def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_=False		)->		str:





       '''simple docstring'''


       _UpperCAmelCase  :						Union[str, Any]			     = get_config(lowerCAmelCase_		)

       # load original model from timm
       _UpperCAmelCase  :						Dict			     = create_model(lowerCAmelCase_       ,   pretrained=lowerCAmelCase_		)
       timm_model.eval()

       # load state_dict of original model
       _UpperCAmelCase  :						List[str]			     = timm_model.state_dict()
       for key in state_dict.copy().keys():
              _UpperCAmelCase  :						str			     = state_dict.pop(lowerCAmelCase_		)
              _UpperCAmelCase  :						Optional[int]			     = val.squeeze() if """head""" in key else val

       # load HuggingFace model
       _UpperCAmelCase  :						List[Any]			     = BitForImageClassification(lowerCAmelCase_		)
       model.eval()
       model.load_state_dict(lowerCAmelCase_		)

       # create image processor
       _UpperCAmelCase  :						Optional[int]			     = create_transform(**resolve_data_config({}       ,   model=lowerCAmelCase_		)		)
       _UpperCAmelCase  :						Tuple			     = transform.transforms

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

       _UpperCAmelCase  :						List[Any]			     = 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()       ,   )

       _UpperCAmelCase  :						List[Any]			     = prepare_img()
       _UpperCAmelCase  :						Any			     = transform(lowerCAmelCase_		).unsqueeze(0		)
       _UpperCAmelCase  :						str			     = processor(lowerCAmelCase_       ,   return_tensors="""pt"""		).pixel_values

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

       # verify logits
       with torch.no_grad():
              _UpperCAmelCase  :						Optional[Any]			     = model(lowerCAmelCase_		)
              _UpperCAmelCase  :						List[Any]			     = outputs.logits

       print("""Logits:"""       ,   logits[0, :3]		)
       print("""Predicted class:"""       ,   model.config.idalabel[logits.argmax(-1		).item()]		)
       _UpperCAmelCase  :						Tuple			     = 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_							: 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.""",
   )

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








'''simple docstring'''






from torch import nn



class 							lowercase       (  nn.Module				):


 """simple docstring"""


 def __init__(							self  ,a_  ,a_							)		->   List[Any]:
        super().__init__()
        _UpperCAmelCase  :						Dict			     = class_size
        _UpperCAmelCase  :						Union[str, Any]			     = embed_size
        # self.mlp1 = nn.Linear(embed_size, embed_size)
        # self.mlp2 = (nn.Linear(embed_size, class_size))
        _UpperCAmelCase  :						List[Any]			     = nn.Linear(a_  ,a_							)







 def _snake_case (							self  ,a_							)		->   Tuple:
        # hidden_state = nn.functional.relu(self.mlp1(hidden_state))
        # hidden_state = self.mlp2(hidden_state)
        _UpperCAmelCase  :						Optional[int]			     = self.mlp(a_							)
        return logits



