



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_										=					logging.get_logger(__name__)







def 	lowerCamelCase_   (  _a    :		int  ):



    '''simple docstring'''




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

    UpperCAmelCase_			:    Optional[int]   						= '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_			:    Any   						= BitConfig(
        conv_layer=__SCREAMING_SNAKE_CASE					,						num_labels=1000					,						idalabel=__SCREAMING_SNAKE_CASE					,						labelaid=__SCREAMING_SNAKE_CASE					,						)

    return config







def 	lowerCamelCase_   (  _a    :		List[str]  ):



    '''simple docstring'''




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

    return name







def 	lowerCamelCase_   (  ):



    '''simple docstring'''




    UpperCAmelCase_			:    str   						= 'http://images.cocodataset.org/val2017/000000039769.jpg'
    UpperCAmelCase_			:    int   						= Image.open(requests.get(__SCREAMING_SNAKE_CASE					,						stream=__SCREAMING_SNAKE_CASE  ).raw  )
    return im







@torch.no_grad()
def 	lowerCamelCase_   (  _a    :		Any					,						_a    :		int					,						_a    :		Tuple=False  ):



    '''simple docstring'''




    UpperCAmelCase_			:    Any   						= get_config(__SCREAMING_SNAKE_CASE  )

    # load original model from timm
    UpperCAmelCase_			:    int   						= create_model(__SCREAMING_SNAKE_CASE					,						pretrained=__SCREAMING_SNAKE_CASE  )
    timm_model.eval()

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

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

    # create image processor
    UpperCAmelCase_			:    List[Any]   						= create_transform(**resolve_data_config({}					,						model=__SCREAMING_SNAKE_CASE  )  )
    UpperCAmelCase_			:    List[str]   						= transform.transforms

    UpperCAmelCase_			:    List[Any]   						= {
        'bilinear': PILImageResampling.BILINEAR,
        'bicubic': PILImageResampling.BICUBIC,
        'nearest': PILImageResampling.NEAREST,
    }

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

    UpperCAmelCase_			:    Tuple   						= prepare_img()
    UpperCAmelCase_			:    Tuple   						= transform(__SCREAMING_SNAKE_CASE  ).unsqueeze(0  )
    UpperCAmelCase_			:    List[Any]   						= processor(__SCREAMING_SNAKE_CASE					,						return_tensors="""pt"""  ).pixel_values

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

    # verify logits
    with torch.no_grad():
        UpperCAmelCase_			:    Optional[int]   						= model(__SCREAMING_SNAKE_CASE  )
        UpperCAmelCase_			:    Any   						= outputs.logits

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

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

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

"""simple docstring"""







__SCREAMING_SNAKE_CASE    	=[
    "DownloadConfig",
    "DownloadManager",
    "DownloadMode",
    "StreamingDownloadManager",
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager

