


'''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()
a_   :      Any				    =      logging.get_logger(__name__)






def 		_A			(lowerCAmelCase__					:Optional[Any]						)				->					Optional[Any]:




     '''simple docstring'''



     _a      =							'huggingface/label-files'
     _a      =							'imagenet-1k-id2label.json'
     _a      =							json.load(open(hf_hub_download(lowerCAmelCase__			,  lowerCAmelCase__			,  repo_type='dataset'						)			,  'r'						)						)
     _a      =							{int(lowerCAmelCase__						): 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=lowerCAmelCase__			,  num_labels=10_00			,  idalabel=lowerCAmelCase__			,  labelaid=lowerCAmelCase__			,  )

     return config






def 		_A			(lowerCAmelCase__					:str						)				->					Tuple:




     '''simple docstring'''



     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 		_A			()				->					str:




     '''simple docstring'''



     _a      =							'http://images.cocodataset.org/val2017/000000039769.jpg'
     _a      =							Image.open(requests.get(lowerCAmelCase__			,  stream=lowerCAmelCase__						).raw						)
     return im






@torch.no_grad()
def 		_A			(lowerCAmelCase__					:int			,  lowerCAmelCase__					:List[str]			,  lowerCAmelCase__					:Union[str, Any]=False						)				->					Tuple:




     '''simple docstring'''



     _a      =							get_config(lowerCAmelCase__						)

     # load original model from timm
     _a      =							create_model(lowerCAmelCase__			,  pretrained=lowerCAmelCase__						)
     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(lowerCAmelCase__						)
          _a      =							val.squeeze() if 'head' in key else val

     # load HuggingFace model
     _a      =							BitForImageClassification(lowerCAmelCase__						)
     model.eval()
     model.load_state_dict(lowerCAmelCase__						)

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

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

     _a      =							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()			,  )

     _a      =							prepare_img()
     _a      =							transform(lowerCAmelCase__						).unsqueeze(0						)
     _a      =							processor(lowerCAmelCase__			,  return_tensors='pt'						).pixel_values

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

     # verify logits
     with torch.no_grad():
          _a      =							model(lowerCAmelCase__						)
          _a      =							outputs.logits

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

 a_   :      Any				    =      parser.parse_args()
 convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''






import colorsys

from PIL import Image  # type: ignore






def 		_A			(lowerCAmelCase__					:float			,  lowerCAmelCase__					:float			,  lowerCAmelCase__					:int						)				->					float:




     '''simple docstring'''



     _a      =							x
     _a      =							y
     for step in range(lowerCAmelCase__						):  # noqa: B007
          _a      =							a * a - b * b + x
          _a      =							2 * a * b + y
          _a      =							a_new

          # divergence happens for all complex number with an absolute value
          # greater than 4
          if a * a + b * b > 4:
               break
     return step / (max_step - 1)






def 		_A			(lowerCAmelCase__					:float						)				->					tuple:




     '''simple docstring'''



     if distance == 1:
          return (0, 0, 0)
     else:
          return (2_55, 2_55, 2_55)






def 		_A			(lowerCAmelCase__					:float						)				->					tuple:




     '''simple docstring'''



     if distance == 1:
          return (0, 0, 0)
     else:
          return tuple(round(i * 2_55						) for i in colorsys.hsv_to_rgb(lowerCAmelCase__			,  1			,  1						)						)






def 		_A			(lowerCAmelCase__					:int = 8_00			,  lowerCAmelCase__					:int = 6_00			,  lowerCAmelCase__					:float = -0.6			,  lowerCAmelCase__					:float = 0			,  lowerCAmelCase__					:float = 3.2			,  lowerCAmelCase__					:int = 50			,  lowerCAmelCase__					:bool = True			,  )				->					Image.Image:




     '''simple docstring'''



     _a      =							Image.new('RGB'			,  (image_width, image_height)						)
     _a      =							img.load()

     # loop through the image-coordinates
     for image_x in range(lowerCAmelCase__						):
          for image_y in range(lowerCAmelCase__						):
               # determine the figure-coordinates based on the image-coordinates
               _a      =							figure_width / image_width * image_height
               _a      =							figure_center_x + (image_x / image_width - 0.5) * figure_width
               _a      =							figure_center_y + (image_y / image_height - 0.5) * figure_height

               _a      =							get_distance(lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__						)

               # color the corresponding pixel based on the selected coloring-function
               if use_distance_color_coding:
                    _a      =							get_color_coded_rgb(lowerCAmelCase__						)
               else:
                    _a      =							get_black_and_white_rgb(lowerCAmelCase__						)

     return img


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # colored version, full figure
 a_   :      Optional[Any]				    =      get_image()

 # uncomment for colored version, different section, zoomed in
 # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
 # figure_width = 0.8)

 # uncomment for black and white version, full figure
 # img = get_image(use_distance_color_coding = False)

 # uncomment to save the image
 # img.save("mandelbrot.png")

 img.show()

