



from __future__ import annotations

import numpy as np



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			return np.maximum(0						,  SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
							print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]









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 (     SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				"huggingface/label-files"
			lowercase__					  =				"imagenet-1k-id2label.json"
			lowercase__					  =				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  repo_type="dataset"  )						,  "r"  )  )
			lowercase__					  =				{int(SCREAMING_SNAKE_CASE_  ): v for k, v in idalabel.items()}
			lowercase__					  =				{v: k for k, v in idalabel.items()}

			lowercase__					  =				"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__					  =				BitConfig(
			    conv_layer=SCREAMING_SNAKE_CASE_						,  num_labels=1000						,  idalabel=SCREAMING_SNAKE_CASE_						,  labelaid=SCREAMING_SNAKE_CASE_						,  )

			return config



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			if "stem.conv" in name:
						lowercase__					  =				name.replace("stem.conv"						,  "bit.embedder.convolution"  )
			if "blocks" in name:
						lowercase__					  =				name.replace("blocks"						,  "layers"  )
			if "head.fc" in name:
						lowercase__					  =				name.replace("head.fc"						,  "classifier.1"  )
			if name.startswith("norm"  ):
						lowercase__					  =				"bit." + name
			if "bit" not in name and "classifier" not in name:
						lowercase__					  =				"bit.encoder." + name

			return name



def __lowerCAmelCase (     ):
			lowercase__					  =				"http://images.cocodataset.org/val2017/000000039769.jpg"
			lowercase__					  =				Image.open(requests.get(SCREAMING_SNAKE_CASE_						,  stream=SCREAMING_SNAKE_CASE_  ).raw  )
			return im



@torch.no_grad()
def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=False  ):
			lowercase__					  =				get_config(SCREAMING_SNAKE_CASE_  )

			# load original model from timm
			lowercase__					  =				create_model(SCREAMING_SNAKE_CASE_						,  pretrained=SCREAMING_SNAKE_CASE_  )
			timm_model.eval()

			# load state_dict of original model
			lowercase__					  =				timm_model.state_dict()
			for key in state_dict.copy().keys():
						lowercase__					  =				state_dict.pop(SCREAMING_SNAKE_CASE_  )
						lowercase__					  =				val.squeeze() if "head" in key else val

			# load HuggingFace model
			lowercase__					  =				BitForImageClassification(SCREAMING_SNAKE_CASE_  )
			model.eval()
			model.load_state_dict(SCREAMING_SNAKE_CASE_  )

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

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

			lowercase__					  =				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()						,  )

			lowercase__					  =				prepare_img()
			lowercase__					  =				transform(SCREAMING_SNAKE_CASE_  ).unsqueeze(0  )
			lowercase__					  =				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():
						lowercase__					  =				model(SCREAMING_SNAKE_CASE_  )
						lowercase__					  =				outputs.logits

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




