





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  __magic_name__  (				lowercase				)				-> Any:

							"""simple docstring"""



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

							lowercase_	:   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"
							lowercase_	:   Union[str, Any]  				=     BitConfig(
							    conv_layer=lowercase				,       num_labels=1000				,       idalabel=lowercase				,       labelaid=lowercase				,       )

							return config





def  __magic_name__  (				lowercase				)				-> List[str]:

							"""simple docstring"""



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

							return name





def  __magic_name__  (				)				-> List[str]:

							"""simple docstring"""



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





@torch.no_grad()
def  __magic_name__  (				lowercase				,       lowercase				,       lowercase=False				)				-> Tuple:

							"""simple docstring"""



							lowercase_	:   Optional[int]  				=     get_config(lowercase				)

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

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

							# load HuggingFace model
							lowercase_	:   str  				=     BitForImageClassification(lowercase				)
							model.eval()
							model.load_state_dict(lowercase				)

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

							lowercase_	:   Tuple  				=     {
							    """bilinear""": PILImageResampling.BILINEAR,
							    """bicubic""": PILImageResampling.BICUBIC,
							    """nearest""": PILImageResampling.NEAREST,
							}

							lowercase_	:   List[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_	:   Optional[Any]  				=     prepare_img()
							lowercase_	:   Dict  				=     transform(lowercase				).unsqueeze(0				)
							lowercase_	:   Tuple  				=     processor(lowercase				,       return_tensors="""pt"""				).pixel_values

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

							# verify logits
							with torch.no_grad():
														lowercase_	:   Dict  				=     model(lowercase				)
														lowercase_	:   Tuple  				=     outputs.logits

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

from math import sqrt

def _SCREAMING_SNAKE_CASE (A = 1_000_000			)			->			int:






       """simple docstring"""







       lowercase__      =					0
       lowercase__      =					0
       lowercase__      =					42

       while num_cuboids <= limit:
              max_cuboid_size += 1
              for sum_shortest_sides in range(2    ,					2 * max_cuboid_size + 1			):
                     if sqrt(sum_shortest_sides**2 + max_cuboid_size**2			).is_integer():
                            num_cuboids += (
                                min(A    ,					sum_shortest_sides // 2			)
                                - max(1    ,					sum_shortest_sides - max_cuboid_size			)
                                + 1
                            )

       return max_cuboid_size


if __name__ == "__main__":
 print(f"""{solution() = }""")




