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

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


  '''simple docstring'''






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

  lowercase__	: Tuple     		=   """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"
  lowercase__	: int     		=   BitConfig(
      conv_layer=lowercase_			,			num_labels=10_00			,			idalabel=lowercase_			,			labelaid=lowercase_			,			)

  return config

def 		UpperCamelCase						(   lowercase_ )		->						Tuple:


  '''simple docstring'''






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

  return name

def 		UpperCamelCase						(   )		->						Dict:


  '''simple docstring'''






  lowercase__	: Tuple     		=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowercase__	: Union[str, Any]     		=   Image.open(requests.get(lowercase_			,			stream=lowercase_ ).raw )
  return im

@torch.no_grad()
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_=False )		->						Union[str, Any]:


  '''simple docstring'''






  lowercase__	: Tuple     		=   get_config(lowercase_ )

  # load original model from timm
  lowercase__	: str     		=   create_model(lowercase_			,			pretrained=lowercase_ )
  timm_model.eval()

  # load state_dict of original model
  lowercase__	: Any     		=   timm_model.state_dict()
  for key in state_dict.copy().keys():
    lowercase__	: Optional[Any]     		=   state_dict.pop(lowercase_ )
    lowercase__	: Tuple     		=   val.squeeze() if """head""" in key else val

  # load HuggingFace model
  lowercase__	: List[str]     		=   BitForImageClassification(lowercase_ )
  model.eval()
  model.load_state_dict(lowercase_ )

  # create image processor
  lowercase__	: Tuple     		=   create_transform(**resolve_data_config({}			,			model=lowercase_ ) )
  lowercase__	: Any     		=   transform.transforms

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

  lowercase__	: 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()			,			)

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

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

  # verify logits
  with torch.no_grad():
    lowercase__	: Any     		=   model(lowercase_ )
    lowercase__	: Dict     		=   outputs.logits

  print("""Logits:"""			,			logits[0, :3] )
  print("""Predicted class:"""			,			model.config.idalabel[logits.argmax(-1 ).item()] )
  lowercase__	: List[str]     		=   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__":
     lowerCamelCase__     :		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.""",
     )

     lowerCamelCase__     :		Dict        = parser.parse_args()
     convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""




# flake8: noqa
# Lint as: python3
A_				:      List[str]           =			[
    "VerificationMode",
    "Version",
    "disable_progress_bar",
    "enable_progress_bar",
    "is_progress_bar_enabled",
    "experimental",
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental


