


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




def 			_a			(		_lowercase      :			Tuple						):





     '''simple docstring'''






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

     __UpperCAmelCase			:				Optional[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			:				int													=    BitConfig(
         conv_layer=_lowercase    ,      num_labels=1000    ,      idalabel=_lowercase    ,      labelaid=_lowercase    ,      )

     return config




def 			_a			(		_lowercase      :			Tuple						):





     '''simple docstring'''






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

     return name




def 			_a			(		):





     '''simple docstring'''






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




@torch.no_grad()
def 			_a			(		_lowercase      :			Tuple    ,      _lowercase      :			Dict    ,      _lowercase      :			Tuple=False						):





     '''simple docstring'''






     __UpperCAmelCase			:				Any													=    get_config(_lowercase						)

     # load original model from timm
     __UpperCAmelCase			:				Tuple													=    create_model(_lowercase    ,      pretrained=_lowercase						)
     timm_model.eval()

     # load state_dict of original model
     __UpperCAmelCase			:				List[Any]													=    timm_model.state_dict()
     for key in state_dict.copy().keys():
          __UpperCAmelCase			:				Optional[Any]													=    state_dict.pop(_lowercase						)
          __UpperCAmelCase			:				int													=    val.squeeze() if '''head''' in key else val

     # load HuggingFace model
     __UpperCAmelCase			:				Any													=    BitForImageClassification(_lowercase						)
     model.eval()
     model.load_state_dict(_lowercase						)

     # create image processor
     __UpperCAmelCase			:				Optional[int]													=    create_transform(**resolve_data_config({}    ,      model=_lowercase						)						)
     __UpperCAmelCase			:				Union[str, Any]													=    transform.transforms

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

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

     __UpperCAmelCase			:				Optional[Any]													=    prepare_img()
     __UpperCAmelCase			:				Any													=    transform(_lowercase						).unsqueeze(0						)
     __UpperCAmelCase			:				List[Any]													=    processor(_lowercase    ,      return_tensors='''pt'''						).pixel_values

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

     # verify logits
     with torch.no_grad():
          __UpperCAmelCase			:				Union[str, Any]													=    model(_lowercase						)
          __UpperCAmelCase			:				Union[str, Any]													=    outputs.logits

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

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

     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    :Optional[int]				       =     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    :str				       =     parser.parse_args()
      convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



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




def 			_a			(		_lowercase      :			Tuple						):





     '''simple docstring'''






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

     __UpperCAmelCase			:				Optional[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			:				int													=    BitConfig(
         conv_layer=_lowercase    ,      num_labels=1000    ,      idalabel=_lowercase    ,      labelaid=_lowercase    ,      )

     return config




def 			_a			(		_lowercase      :			Tuple						):





     '''simple docstring'''






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

     return name




def 			_a			(		):





     '''simple docstring'''






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




@torch.no_grad()
def 			_a			(		_lowercase      :			Tuple    ,      _lowercase      :			Dict    ,      _lowercase      :			Tuple=False						):





     '''simple docstring'''






     __UpperCAmelCase			:				Any													=    get_config(_lowercase						)

     # load original model from timm
     __UpperCAmelCase			:				Tuple													=    create_model(_lowercase    ,      pretrained=_lowercase						)
     timm_model.eval()

     # load state_dict of original model
     __UpperCAmelCase			:				List[Any]													=    timm_model.state_dict()
     for key in state_dict.copy().keys():
          __UpperCAmelCase			:				Optional[Any]													=    state_dict.pop(_lowercase						)
          __UpperCAmelCase			:				int													=    val.squeeze() if '''head''' in key else val

     # load HuggingFace model
     __UpperCAmelCase			:				Any													=    BitForImageClassification(_lowercase						)
     model.eval()
     model.load_state_dict(_lowercase						)

     # create image processor
     __UpperCAmelCase			:				Optional[int]													=    create_transform(**resolve_data_config({}    ,      model=_lowercase						)						)
     __UpperCAmelCase			:				Union[str, Any]													=    transform.transforms

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

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

     __UpperCAmelCase			:				Optional[Any]													=    prepare_img()
     __UpperCAmelCase			:				Any													=    transform(_lowercase						).unsqueeze(0						)
     __UpperCAmelCase			:				List[Any]													=    processor(_lowercase    ,      return_tensors='''pt'''						).pixel_values

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

     # verify logits
     with torch.no_grad():
          __UpperCAmelCase			:				Union[str, Any]													=    model(_lowercase						)
          __UpperCAmelCase			:				Union[str, Any]													=    outputs.logits

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

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

     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    :Optional[int]				       =     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    :str				       =     parser.parse_args()
      convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)