






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
  import PIL

__a   :							Optional[int]						= logging.get_logger(__name__)



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[str]     )    ->   List[List[ImageInput]]:





    """simple docstring"""

    if isinstance(UpperCamelCase_      ,					(list, tuple)     ) and isinstance(videos[0]      ,					(list, tuple)     ) and is_valid_image(videos[0][0]     ):
        return videos

    elif isinstance(UpperCamelCase_      ,					(list, tuple)     ) and is_valid_image(videos[0]     ):
        return [videos]

    elif is_valid_image(UpperCamelCase_     ):
        return [[videos]]

    raise ValueError(f"Could not make batched video from {videos}"     )






class    __lowercase   (						A__	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					["pixel_values"]




     def __init__(					self							:      Optional[Any]			,						UpperCamelCase_							:      Optional[int] = True			,						UpperCamelCase_							:      str = None			,						UpperCamelCase_							:      int = PILImageResampling.BILINEAR			,						UpperCamelCase_							:      Optional[int] = True			,						UpperCamelCase_							:      List[str] = None			,						UpperCamelCase_							:      Any = True			,						UpperCamelCase_							:      List[Any] = 1 / 255			,						UpperCamelCase_							:      Any = True			,						UpperCamelCase_							:      Tuple = True			,						UpperCamelCase_							:      List[Any] = None			,						UpperCamelCase_							:      Optional[int] = None			,						**UpperCamelCase_							:      Optional[int]			,						):


         """simple docstring"""



         super().__init__(**a__					)
         __A       =						size if size is not None else {"""shortest_edge""": 256}
         __A       =						get_size_dict(a__			,						default_to_square=a__					)
         __A       =						crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
         __A       =						get_size_dict(a__			,						param_name="""crop_size"""					)

         __A       =						do_resize
         __A       =						size
         __A       =						do_center_crop
         __A       =						crop_size
         __A       =						resample
         __A       =						do_rescale
         __A       =						rescale_factor
         __A       =						offset
         __A       =						do_normalize
         __A       =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __A       =						image_std if image_std is not None else IMAGENET_STANDARD_STD




     def 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      Any			,						UpperCamelCase_							:      List[str]			,						UpperCamelCase_							:      Optional[Any] = PILImageResampling.BILINEAR			,						UpperCamelCase_							:      Dict = None			,						**UpperCamelCase_							:      int			,						):


         """simple docstring"""



         __A       =						get_size_dict(a__			,						default_to_square=a__					)
         if "shortest_edge" in size:
             __A       =						get_resize_output_image_size(a__			,						size["""shortest_edge"""]			,						default_to_square=a__					)
         elif "height" in size and "width" in size:
             __A       =						(size["""height"""], size["""width"""])
         else:
             raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"					)
         return resize(a__			,						size=a__			,						resample=a__			,						data_format=a__			,						**a__					)




     def 		lowerCAmelCase_  (					self							:      List[str]			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      Tuple = None			,						**UpperCamelCase_							:      Union[str, Any]			,						):


         """simple docstring"""



         __A       =						get_size_dict(a__					)
         if "height" not in size or "width" not in size:
             raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}"					)
         return center_crop(a__			,						size=(size["""height"""], size["""width"""])			,						data_format=a__			,						**a__					)




     def 		lowerCAmelCase_  (					self							:      Optional[Any]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      str = True			,						UpperCamelCase_							:      str = None			,						**UpperCamelCase_							:      str			,						):


         """simple docstring"""



         __A       =						image.astype(np.floataa					)
         if offset:
             __A       =						image - (scale / 2)
         return rescale(a__			,						scale=a__			,						data_format=a__			,						**a__					)




     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Tuple = None			,						**UpperCamelCase_							:      str			,						):


         """simple docstring"""



         return normalize(a__			,						mean=a__			,						std=a__			,						data_format=a__			,						**a__					)




     def 		lowerCAmelCase_  (					self							:      List[str]			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Optional[Any] = None			,						UpperCamelCase_							:      Optional[int] = None			,						UpperCamelCase_							:      Optional[Any] = None			,						UpperCamelCase_							:      List[str] = None			,						UpperCamelCase_							:      int = None			,						UpperCamelCase_							:      Optional[Any] = None			,						UpperCamelCase_							:      Optional[Any] = None			,						UpperCamelCase_							:      Any = None			,						UpperCamelCase_							:      List[str] = None			,						UpperCamelCase_							:      str = None			,						UpperCamelCase_							:      List[str] = None			,						UpperCamelCase_							:      Optional[Any] = ChannelDimension.FIRST			,						):


         """simple docstring"""



         if do_resize and size is None or resample is None:
             raise ValueError("""Size and resample must be specified if do_resize is True."""					)

         if do_center_crop and crop_size is None:
             raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

         if do_rescale and rescale_factor is None:
             raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

         if offset and not do_rescale:
             raise ValueError("""For offset, do_rescale must also be set to True."""					)

         # All transformations expect numpy arrays.
         __A       =						to_numpy_array(a__					)

         if do_resize:
             __A       =						self.resize(image=a__			,						size=a__			,						resample=a__					)

         if do_center_crop:
             __A       =						self.center_crop(a__			,						size=a__					)

         if do_rescale:
             __A       =						self.rescale(image=a__			,						scale=a__			,						offset=a__					)

         if do_normalize:
             __A       =						self.normalize(image=a__			,						mean=a__			,						std=a__					)

         __A       =						to_channel_dimension_format(a__			,						a__					)
         return image





     def 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      str = None			,						UpperCamelCase_							:      Optional[Any] = None			,						UpperCamelCase_							:      List[str] = None			,						UpperCamelCase_							:      Optional[Any] = None			,						UpperCamelCase_							:      List[Any] = None			,						UpperCamelCase_							:      Optional[Any] = None			,						UpperCamelCase_							:      str = None			,						UpperCamelCase_							:      Tuple = None			,						UpperCamelCase_							:      int = None			,						UpperCamelCase_							:      List[Any] = None			,						UpperCamelCase_							:      Union[str, Any] = None			,						UpperCamelCase_							:      Optional[Any] = None			,						UpperCamelCase_							:      Optional[int] = ChannelDimension.FIRST			,						**UpperCamelCase_							:      Union[str, Any]			,						):


         """simple docstring"""



         __A       =						do_resize if do_resize is not None else self.do_resize
         __A       =						resample if resample is not None else self.resample
         __A       =						do_center_crop if do_center_crop is not None else self.do_center_crop
         __A       =						do_rescale if do_rescale is not None else self.do_rescale
         __A       =						rescale_factor if rescale_factor is not None else self.rescale_factor
         __A       =						offset if offset is not None else self.offset
         __A       =						do_normalize if do_normalize is not None else self.do_normalize
         __A       =						image_mean if image_mean is not None else self.image_mean
         __A       =						image_std if image_std is not None else self.image_std

         __A       =						size if size is not None else self.size
         __A       =						get_size_dict(a__			,						default_to_square=a__					)
         __A       =						crop_size if crop_size is not None else self.crop_size
         __A       =						get_size_dict(a__			,						param_name="""crop_size"""					)

         if not valid_images(a__					):
             raise ValueError(
                 """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                 """torch.Tensor, tf.Tensor or jax.ndarray."""					)

         __A       =						make_batched(a__					)

         __A       =						[
             [
                 self._preprocess_image(
                     image=a__			,						do_resize=a__			,						size=a__			,						resample=a__			,						do_center_crop=a__			,						crop_size=a__			,						do_rescale=a__			,						rescale_factor=a__			,						offset=a__			,						do_normalize=a__			,						image_mean=a__			,						image_std=a__			,						data_format=a__			,						)
                 for img in video
             ]
             for video in videos
         ]

         __A       =						{"""pixel_values""": videos}
         return BatchFeature(data=a__			,						tensor_type=a__					)









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()
_lowercase    	=		logging.get_logger(__name__)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							str						)->	int:
    A__					=       '''huggingface/label-files'''
    A__					=       '''imagenet-1k-id2label.json'''
    A__					=       json.load(open(hf_hub_download(UpperCamelCase_			,							UpperCamelCase_			,							repo_type='''dataset'''						)			,							'''r'''						)						)
    A__					=       {int(UpperCamelCase_						): v for k, v in idalabel.items()}
    A__					=       {v: k for k, v in idalabel.items()}

    A__					=       '''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"
    A__					=       BitConfig(
        conv_layer=UpperCamelCase_			,							num_labels=1_0_0_0			,							idalabel=UpperCamelCase_			,							labelaid=UpperCamelCase_			,							)

    return config



def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[Any]						)->	Optional[int]:
    if "stem.conv" in name:
        A__					=       name.replace('''stem.conv'''			,							'''bit.embedder.convolution'''						)
    if "blocks" in name:
        A__					=       name.replace('''blocks'''			,							'''layers'''						)
    if "head.fc" in name:
        A__					=       name.replace('''head.fc'''			,							'''classifier.1'''						)
    if name.startswith('''norm'''						):
        A__					=       '''bit.''' + name
    if "bit" not in name and "classifier" not in name:
        A__					=       '''bit.encoder.''' + name

    return name



def 		lowerCAmelCase__     (       )->	str:
    A__					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A__					=       Image.open(requests.get(UpperCamelCase_			,							stream=UpperCamelCase_						).raw						)
    return im



@torch.no_grad()
def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							Tuple			,							UpperCamelCase_					:							List[Any]=False						)->	Optional[int]:
    A__					=       get_config(UpperCamelCase_						)

    # load original model from timm
    A__					=       create_model(UpperCamelCase_			,							pretrained=UpperCamelCase_						)
    timm_model.eval()

    # load state_dict of original model
    A__					=       timm_model.state_dict()
    for key in state_dict.copy().keys():
        A__					=       state_dict.pop(UpperCamelCase_						)
        A__					=       val.squeeze() if '''head''' in key else val

    # load HuggingFace model
    A__					=       BitForImageClassification(UpperCamelCase_						)
    model.eval()
    model.load_state_dict(UpperCamelCase_						)

    # create image processor
    A__					=       create_transform(**resolve_data_config({}			,							model=UpperCamelCase_						)						)
    A__					=       transform.transforms

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

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

    A__					=       prepare_img()
    A__					=       transform(UpperCamelCase_						).unsqueeze(0						)
    A__					=       processor(UpperCamelCase_			,							return_tensors='''pt'''						).pixel_values

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

    # verify logits
    with torch.no_grad():
        A__					=       model(UpperCamelCase_						)
        A__					=       outputs.logits

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

    if pytorch_dump_folder_path is not None:
        Path(UpperCamelCase_						).mkdir(exist_ok=UpperCamelCase_						)
        print(f"Saving model {model_name} and processor to {pytorch_dump_folder_path}"						)
        model.save_pretrained(UpperCamelCase_						)
        processor.save_pretrained(UpperCamelCase_						)

    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__":
   _lowercase    	=		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.",
   )

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