






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      :      str     						=						logging.get_logger(__name__)





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  """huggingface/label-files"""
	__lowercase             =  """imagenet-1k-id2label.json"""
	__lowercase             =  json.load(open(hf_hub_download(lowerCamelCase				,  lowerCamelCase				,  repo_type="""dataset"""					)				,  """r"""					)					)
	__lowercase             =  {int(lowerCamelCase					): 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=lowerCamelCase				,  num_labels=1_000				,  idalabel=lowerCamelCase				,  labelaid=lowerCamelCase				,  )

	return config





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	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 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
	__lowercase             =  Image.open(requests.get(lowerCamelCase				,  stream=lowerCamelCase					).raw					)
	return im





@torch.no_grad()
def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase=False					):




	'''simple docstring'''







	__lowercase             =  get_config(lowerCamelCase					)

	# load original model from timm
	__lowercase             =  create_model(lowerCamelCase				,  pretrained=lowerCamelCase					)
	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(lowerCamelCase					)
		__lowercase             =  val.squeeze() if """head""" in key else val

	# load HuggingFace model
	__lowercase             =  BitForImageClassification(lowerCamelCase					)
	model.eval()
	model.load_state_dict(lowerCamelCase					)

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

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

	__lowercase             =  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()				,  )

	__lowercase             =  prepare_img()
	__lowercase             =  transform(lowerCamelCase					).unsqueeze(0					)
	__lowercase             =  processor(lowerCamelCase				,  return_tensors="""pt"""					).pixel_values

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

	# verify logits
	with torch.no_grad():
		__lowercase             =  model(lowerCamelCase					)
		__lowercase             =  outputs.logits

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

						__UpperCamelCase      :      str     						=						parser.parse_args()
						convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









from math import sqrt





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5				,  int(sqrt(lowerCamelCase					) + 1					)				,  6					):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True





def 				snake_case  (     lowerCamelCase = 10_001					):




	'''simple docstring'''







	__lowercase             =  0
	__lowercase             =  1
	while count != nth and number < 3:
		number += 1
		if is_prime(lowerCamelCase					):
			count += 1
	while count != nth:
		number += 2
		if is_prime(lowerCamelCase					):
			count += 1
	return number


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

