






'''simple docstring'''
import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)

def 				a						( _UpperCAmelCase       ,	_UpperCAmelCase			)					->					List[Any]:






					"""simple docstring"""







					a_						= 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
					a_						= Image.open(requests.get(_A       ,	stream=_A			).raw			).convert('RGB'			)

					a_						= transforms.Compose(
					    [
					        transforms.Resize((image_size, image_size)       ,	interpolation=InterpolationMode.BICUBIC			),
					        transforms.ToTensor(),
					        transforms.Normalize((0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3)       ,	(0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1)			),
					    ]			)
					a_						= transform(_A			).unsqueeze(0			).to(_A			)
					return image

def 				a						( _UpperCAmelCase			)					->					Optional[Any]:






					"""simple docstring"""







					if "visual_encoder" in key:
										a_						= re.sub('visual_encoder*'       ,	'vision_model.encoder'       ,	_A			)
					if "blocks" in key:
										a_						= re.sub(R'blocks'       ,	'layers'       ,	_A			)
					if "attn" in key:
										a_						= re.sub(R'attn'       ,	'self_attn'       ,	_A			)
					if "norm1" in key:
										a_						= re.sub(R'norm1'       ,	'layer_norm1'       ,	_A			)
					if "norm2" in key:
										a_						= re.sub(R'norm2'       ,	'layer_norm2'       ,	_A			)
					if "encoder.norm" in key:
										a_						= re.sub(R'encoder.norm'       ,	'post_layernorm'       ,	_A			)
					if "encoder.patch_embed.proj" in key:
										a_						= re.sub(R'encoder.patch_embed.proj'       ,	'embeddings.patch_embedding'       ,	_A			)

					if "encoder.pos_embed" in key:
										a_						= re.sub(R'encoder.pos_embed'       ,	'embeddings.position_embedding'       ,	_A			)
					if "encoder.cls_token" in key:
										a_						= re.sub(R'encoder.cls_token'       ,	'embeddings.class_embedding'       ,	_A			)

					if "self_attn" in key:
										a_						= re.sub(R'self_attn.proj'       ,	'self_attn.projection'       ,	_A			)

					return key

@torch.no_grad()
def 				a						( _UpperCAmelCase       ,	_UpperCAmelCase=None			)					->					Optional[int]:






					"""simple docstring"""







					if config_path is not None:
										a_						= BlipConfig.from_pretrained(_A			)
					else:
										a_						= BlipConfig(projection_dim=5_1_2       ,	text_config={}       ,	vision_config={}			)

					a_						= BlipForConditionalGeneration(_A			).eval()

					a_						= 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'

					a_						= blip_decoder(pretrained=_A       ,	image_size=3_8_4       ,	vit='base'			)
					a_						= pt_model.eval()

					a_						= pt_model.state_dict()
					for key in modified_state_dict.copy():
										a_						= modified_state_dict.pop(_A			)
										a_						= rename_key(_A			)
										a_						= value

					hf_model.load_state_dict(_A			)

					a_						= 3_8_4
					a_						= load_demo_image(image_size=_A       ,	device='cpu'			)
					a_						= BertTokenizer.from_pretrained('bert-base-uncased'			)
					a_						= tokenizer(['a picture of']			).input_ids

					a_						= hf_model.generate(_A       ,	_A			)

					assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 3_8_6_1, 1_9_9_7, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

					a_						= hf_model.generate(_A			)

					assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

					if pytorch_dump_folder_path is not None:
										hf_model.save_pretrained(_A			)

					# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
					a_						= (
					    'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
					)

					a_						= blip_vqa(pretrained=_A       ,	image_size=_A       ,	vit='base'			)
					vqa_model.eval()

					a_						= vqa_model.state_dict()
					for key in modified_state_dict.copy():
										a_						= modified_state_dict.pop(_A			)
										a_						= rename_key(_A			)
										a_						= value

					a_						= BlipForQuestionAnswering(_A			)

					hf_vqa_model.load_state_dict(_A			)

					a_						= ['How many dogs are in this image?']
					a_						= tokenizer(_A       ,	return_tensors='pt'			).input_ids

					a_						= hf_vqa_model.generate(_A       ,	_A			)
					print(tokenizer.decode(answer[0]			)			)

					assert tokenizer.decode(answer[0]			) == "[UNK] 1 [SEP]"
					if pytorch_dump_folder_path is not None:
										hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '_vqa'			)

					a_						= 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'

					a_						= blip_itm(pretrained=_A       ,	image_size=_A       ,	vit='base'			)
					itm_model.eval()

					a_						= itm_model.state_dict()
					for key in modified_state_dict.copy():
										a_						= modified_state_dict.pop(_A			)
										a_						= rename_key(_A			)
										a_						= value

					a_						= BlipForImageTextRetrieval(_A			)

					a_						= ['A picture of a woman with a dog sitting in a beach']
					a_						= tokenizer(
					    _A       ,	return_tensors='pt'       ,	padding='max_length'       ,	truncation=_A       ,	max_length=3_5       ,	).input_ids

					hf_itm_model.load_state_dict(_A			)
					hf_itm_model.eval()

					a_						= hf_itm_model(_A       ,	_A       ,	use_itm_head=_A			)
					a_						= hf_itm_model(_A       ,	_A       ,	use_itm_head=_A			)

					assert out[0].item() == 0.2_1_1_0_6_8_7_4_9_4_2_7_7_9_5_4
					assert torch.nn.functional.softmax(out_itm[0]       ,	dim=1			)[:, 1].item() == 0.4_5_6_9_8_8_4_5_3_8_6_5_0_5_1_2_7

					if pytorch_dump_folder_path is not None:
										hf_itm_model.save_pretrained(pytorch_dump_folder_path + '_itm'			)


if __name__ == "__main__":
						__lowerCAmelCase				=argparse.ArgumentParser()
						parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
						parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
						__lowerCAmelCase				=parser.parse_args()

						convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)


'''simple docstring'''




from __future__ import annotations

import math






def 							snake_case__				(     _A:   int					)       ->      list[int]:

		'''simple docstring'''


		if num <= 0:
				lowerCAmelCase     =    f"{num}: Invalid input, please enter a positive integer."
				raise ValueError(_A					)

		lowerCAmelCase     =    [True] * (num + 1)
		lowerCAmelCase     =    []
		lowerCAmelCase     =    2
		lowerCAmelCase     =    int(math.sqrt(_A					)					)

		while start <= end:
				# If start is a prime
				if sieve[start] is True:
						prime.append(_A					)

						# Set multiples of start be False
						for i in range(start * start ,  num + 1 ,  _A					):
								if sieve[i] is True:
										lowerCAmelCase     =    False

				start += 1

		for j in range(end + 1 ,  num + 1					):
				if sieve[j] is True:
						prime.append(_A					)

		return prime


if __name__ == "__main__":
						print(prime_sieve(int(input('''Enter a positive integer: ''').strip())))




