






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()
__snake_case												=  logging.get_logger(__name__)


def   _A   (				SCREAMING_SNAKE_CASE__  :	Optional[int]       ):
    UpperCamelCase       :Optional[int]								=   'huggingface/label-files'
    UpperCamelCase       :Union[str, Any]								=   'imagenet-1k-id2label.json'
    UpperCamelCase       :Optional[int]								=   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       :Union[str, Any]								=   {v: k for k, v in idalabel.items()}

    UpperCamelCase       :Union[str, 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       :Dict								=   BitConfig(
        conv_layer=lowerCAmelCase__     ,  num_labels=1000     ,  idalabel=lowerCAmelCase__     ,  labelaid=lowerCAmelCase__     ,  )

    return config


def   _A   (				SCREAMING_SNAKE_CASE__  :	List[str]       ):




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

    return name


def   _A   (				):
    UpperCamelCase       :int								=   'http://images.cocodataset.org/val2017/000000039769.jpg'
    UpperCamelCase       :Optional[Any]								=   Image.open(requests.get(lowerCAmelCase__     ,  stream=lowerCAmelCase__       ).raw       )
    return im


@torch.no_grad()
def   _A   (				SCREAMING_SNAKE_CASE__  :	List[Any]     ,  SCREAMING_SNAKE_CASE__  :	Tuple     ,  SCREAMING_SNAKE_CASE__  :	Tuple=False       ):
    UpperCamelCase       :Dict								=   get_config(lowerCAmelCase__       )

    # load original model from timm
    UpperCamelCase       :List[Any]								=   create_model(lowerCAmelCase__     ,  pretrained=lowerCAmelCase__       )
    timm_model.eval()

    # load state_dict of original model
    UpperCamelCase       :Optional[int]								=   timm_model.state_dict()
    for key in state_dict.copy().keys():
        UpperCamelCase       :Tuple								=   state_dict.pop(lowerCAmelCase__       )
        UpperCamelCase       :str								=   val.squeeze() if 'head' in key else val

    # load HuggingFace model
    UpperCamelCase       :Optional[Any]								=   BitForImageClassification(lowerCAmelCase__       )
    model.eval()
    model.load_state_dict(lowerCAmelCase__       )

    # create image processor
    UpperCamelCase       :Union[str, Any]								=   create_transform(**resolve_data_config({}     ,  model=lowerCAmelCase__       )       )
    UpperCamelCase       :Any								=   transform.transforms

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

    UpperCamelCase       :Dict								=   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       :Union[str, Any]								=   prepare_img()
    UpperCamelCase       :Optional[int]								=   transform(lowerCAmelCase__       ).unsqueeze(0       )
    UpperCamelCase       :Union[str, Any]								=   processor(lowerCAmelCase__     ,  return_tensors='''pt'''       ).pixel_values

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

    # verify logits
    with torch.no_grad():
        UpperCamelCase       :str								=   model(lowerCAmelCase__       )
        UpperCamelCase       :Any								=   outputs.logits

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

 __snake_case												=  parser.parse_args()
 convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""







from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate







@dataclass(frozen=lowercase_  )
class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     field(default="""summarization""",					metadata={"""include_in_asdict_even_if_is_default""": True}  )
      _SCREAMING_SNAKE_CASE  				=     Features({"""text""": Value("""string"""  )}  )
      _SCREAMING_SNAKE_CASE  				=     Features({"""summary""": Value("""string"""  )}  )
      _SCREAMING_SNAKE_CASE  				=     "text"
      _SCREAMING_SNAKE_CASE  				=     "summary"



      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        return {self.text_column: "text", self.summary_column: "summary"}





