

import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     Optional[int]:
	if "cls_token" in name:
		__lowerCamelCase				:				Dict					       = name.replace('cls_token'	, 'vit.embeddings.cls_token'			)
	if "mask_token" in name:
		__lowerCamelCase				:				Any					       = name.replace('mask_token'	, 'decoder.mask_token'			)
	if "decoder_pos_embed" in name:
		__lowerCamelCase				:				Optional[Any]					       = name.replace('decoder_pos_embed'	, 'decoder.decoder_pos_embed'			)
	if "pos_embed" in name and "decoder" not in name:
		__lowerCamelCase				:				Dict					       = name.replace('pos_embed'	, 'vit.embeddings.position_embeddings'			)
	if "patch_embed.proj" in name:
		__lowerCamelCase				:				Tuple					       = name.replace('patch_embed.proj'	, 'vit.embeddings.patch_embeddings.projection'			)
	if "patch_embed.norm" in name:
		__lowerCamelCase				:				Optional[int]					       = name.replace('patch_embed.norm'	, 'vit.embeddings.norm'			)
	if "decoder_blocks" in name:
		__lowerCamelCase				:				Optional[Any]					       = name.replace('decoder_blocks'	, 'decoder.decoder_layers'			)
	if "blocks" in name:
		__lowerCamelCase				:				int					       = name.replace('blocks'	, 'vit.encoder.layer'			)
	if "attn.proj" in name:
		__lowerCamelCase				:				str					       = name.replace('attn.proj'	, 'attention.output.dense'			)
	if "attn" in name:
		__lowerCamelCase				:				Any					       = name.replace('attn'	, 'attention.self'			)
	if "norm1" in name:
		__lowerCamelCase				:				Tuple					       = name.replace('norm1'	, 'layernorm_before'			)
	if "norm2" in name:
		__lowerCamelCase				:				Dict					       = name.replace('norm2'	, 'layernorm_after'			)
	if "mlp.fc1" in name:
		__lowerCamelCase				:				Tuple					       = name.replace('mlp.fc1'	, 'intermediate.dense'			)
	if "mlp.fc2" in name:
		__lowerCamelCase				:				Any					       = name.replace('mlp.fc2'	, 'output.dense'			)
	if "decoder_embed" in name:
		__lowerCamelCase				:				List[Any]					       = name.replace('decoder_embed'	, 'decoder.decoder_embed'			)
	if "decoder_norm" in name:
		__lowerCamelCase				:				Dict					       = name.replace('decoder_norm'	, 'decoder.decoder_norm'			)
	if "decoder_pred" in name:
		__lowerCamelCase				:				List[Any]					       = name.replace('decoder_pred'	, 'decoder.decoder_pred'			)
	if "norm.weight" in name and "decoder" not in name:
		__lowerCamelCase				:				Optional[Any]					       = name.replace('norm.weight'	, 'vit.layernorm.weight'			)
	if "norm.bias" in name and "decoder" not in name:
		__lowerCamelCase				:				str					       = name.replace('norm.bias'	, 'vit.layernorm.bias'			)

	return name






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     List[Any]:
	for key in orig_state_dict.copy().keys():
		__lowerCamelCase				:				List[Any]					       = orig_state_dict.pop(lowerCamelCase__			)

		if "qkv" in key:
			__lowerCamelCase				:				Tuple					       = key.split('.'			)
			__lowerCamelCase				:				Tuple					       = int(key_split[1]			)
			if "decoder_blocks" in key:
				__lowerCamelCase				:				int					       = config.decoder_hidden_size
				__lowerCamelCase				:				Dict					       = 'decoder.decoder_layers.'
				if "weight" in key:
					__lowerCamelCase				:				int					       = val[:dim, :]
					__lowerCamelCase				:				str					       = val[dim : dim * 2, :]
					__lowerCamelCase				:				Optional[Any]					       = val[-dim:, :]
				elif "bias" in key:
					__lowerCamelCase				:				Union[str, Any]					       = val[:dim]
					__lowerCamelCase				:				Optional[int]					       = val[dim : dim * 2]
					__lowerCamelCase				:				Dict					       = val[-dim:]
			else:
				__lowerCamelCase				:				List[Any]					       = config.hidden_size
				__lowerCamelCase				:				Tuple					       = 'vit.encoder.layer.'
				if "weight" in key:
					__lowerCamelCase				:				Dict					       = val[:dim, :]
					__lowerCamelCase				:				Tuple					       = val[dim : dim * 2, :]
					__lowerCamelCase				:				Union[str, Any]					       = val[-dim:, :]
				elif "bias" in key:
					__lowerCamelCase				:				Optional[Any]					       = val[:dim]
					__lowerCamelCase				:				Dict					       = val[dim : dim * 2]
					__lowerCamelCase				:				List[Any]					       = val[-dim:]

		else:
			__lowerCamelCase				:				Optional[Any]					       = val

	return orig_state_dict






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     Any:
	__lowerCamelCase				:				List[Any]					       = ViTMAEConfig()
	if "large" in checkpoint_url:
		__lowerCamelCase				:				Tuple					       = 1_0_2_4
		__lowerCamelCase				:				Optional[Any]					       = 4_0_9_6
		__lowerCamelCase				:				List[Any]					       = 2_4
		__lowerCamelCase				:				Tuple					       = 1_6
	elif "huge" in checkpoint_url:
		__lowerCamelCase				:				int					       = 1_4
		__lowerCamelCase				:				Any					       = 1_2_8_0
		__lowerCamelCase				:				Dict					       = 5_1_2_0
		__lowerCamelCase				:				Tuple					       = 3_2
		__lowerCamelCase				:				Tuple					       = 1_6

	__lowerCamelCase				:				Tuple					       = ViTMAEForPreTraining(lowerCamelCase__			)

	__lowerCamelCase				:				Tuple					       = torch.hub.load_state_dict_from_url(lowerCamelCase__	, map_location='cpu'			)['model']

	__lowerCamelCase				:				str					       = ViTMAEImageProcessor(size=config.image_size			)

	__lowerCamelCase				:				List[Any]					       = convert_state_dict(lowerCamelCase__	, lowerCamelCase__			)

	model.load_state_dict(lowerCamelCase__			)
	model.eval()

	__lowerCamelCase				:				Dict					       = 'https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'

	__lowerCamelCase				:				Optional[Any]					       = Image.open(requests.get(lowerCamelCase__	, stream=lowerCamelCase__			).raw			)
	__lowerCamelCase				:				str					       = ViTMAEImageProcessor(size=config.image_size			)
	__lowerCamelCase				:				Union[str, Any]					       = image_processor(images=lowerCamelCase__	, return_tensors='pt'			)

	# forward pass
	torch.manual_seed(2			)
	__lowerCamelCase				:				Tuple					       = model(**lowerCamelCase__			)
	__lowerCamelCase				:				Optional[int]					       = outputs.logits

	if "large" in checkpoint_url:
		__lowerCamelCase				:				Optional[Any]					       = torch.tensor(
		    [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]			)
	elif "huge" in checkpoint_url:
		__lowerCamelCase				:				Optional[int]					       = torch.tensor(
		    [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]			)
	else:
		__lowerCamelCase				:				List[Any]					       = torch.tensor(
		    [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]			)

	# verify logits
	assert torch.allclose(logits[0, :3, :3]	, lowerCamelCase__	, atol=1e-4			)

	print(F"Saving model to {pytorch_dump_folder_path}"			)
	model.save_pretrained(lowerCamelCase__			)

	print(F"Saving image processor to {pytorch_dump_folder_path}"			)
	image_processor.save_pretrained(lowerCamelCase__			)


if __name__ == "__main__":
					a		=argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--checkpoint_url""",
					    default="""https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth""",
					    type=str,
					    help="""URL of the checkpoint you'd like to convert.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
					)

					a		=parser.parse_args()
					convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)











import itertools
import math





def      lowerCAmelCase_ (       __a		)     ->		bool:







  """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(math.sqrt(__a		) + 1		)	,    6		):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True





def      lowerCAmelCase_ (       )     ->		str:







  """simple docstring"""





  lowerCamelCase__:      Optional[int]			  =2
  while True:
    if is_prime(__a		):
      yield num
    num += 1





def      lowerCAmelCase_ (       __a = 10001		)     ->		int:







  """simple docstring"""





  return next(itertools.islice(prime_generator()	,    nth - 1	,    __a		)		)


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

