



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 snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Union[str, Any]:
			UpperCAmelCase_						=				"https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
			UpperCAmelCase_						=				Image.open(requests.get(__SCREAMING_SNAKE_CASE			,     stream=__SCREAMING_SNAKE_CASE ).raw ).convert("RGB" )

			UpperCAmelCase_						=				transforms.Compose(
			    [
			        transforms.Resize((image_size, image_size)			,     interpolation=InterpolationMode.BICUBIC ),
			        transforms.ToTensor(),
			        transforms.Normalize((0.48_145_466, 0.4_578_275, 0.40_821_073)			,     (0.26_862_954, 0.26_130_258, 0.27_577_711) ),
			    ] )
			UpperCAmelCase_						=				transform(__SCREAMING_SNAKE_CASE ).unsqueeze(0 ).to(__SCREAMING_SNAKE_CASE )
			return image







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							int:
			if "visual_encoder" in key:
						UpperCAmelCase_						=				re.sub("visual_encoder*"			,     "vision_model.encoder"			,     __SCREAMING_SNAKE_CASE )
			if "blocks" in key:
						UpperCAmelCase_						=				re.sub(R"blocks"			,     "layers"			,     __SCREAMING_SNAKE_CASE )
			if "attn" in key:
						UpperCAmelCase_						=				re.sub(R"attn"			,     "self_attn"			,     __SCREAMING_SNAKE_CASE )
			if "norm1" in key:
						UpperCAmelCase_						=				re.sub(R"norm1"			,     "layer_norm1"			,     __SCREAMING_SNAKE_CASE )
			if "norm2" in key:
						UpperCAmelCase_						=				re.sub(R"norm2"			,     "layer_norm2"			,     __SCREAMING_SNAKE_CASE )
			if "encoder.norm" in key:
						UpperCAmelCase_						=				re.sub(R"encoder.norm"			,     "post_layernorm"			,     __SCREAMING_SNAKE_CASE )
			if "encoder.patch_embed.proj" in key:
						UpperCAmelCase_						=				re.sub(R"encoder.patch_embed.proj"			,     "embeddings.patch_embedding"			,     __SCREAMING_SNAKE_CASE )

			if "encoder.pos_embed" in key:
						UpperCAmelCase_						=				re.sub(R"encoder.pos_embed"			,     "embeddings.position_embedding"			,     __SCREAMING_SNAKE_CASE )
			if "encoder.cls_token" in key:
						UpperCAmelCase_						=				re.sub(R"encoder.cls_token"			,     "embeddings.class_embedding"			,     __SCREAMING_SNAKE_CASE )

			if "self_attn" in key:
						UpperCAmelCase_						=				re.sub(R"self_attn.proj"			,     "self_attn.projection"			,     __SCREAMING_SNAKE_CASE )

			return key







@torch.no_grad()
def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=None )		->							Optional[Any]:
			if config_path is not None:
						UpperCAmelCase_						=				BlipConfig.from_pretrained(__SCREAMING_SNAKE_CASE )
			else:
						UpperCAmelCase_						=				BlipConfig(projection_dim=512			,     text_config={}			,     vision_config={} )

			UpperCAmelCase_						=				BlipForConditionalGeneration(__SCREAMING_SNAKE_CASE ).eval()

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

			UpperCAmelCase_						=				blip_decoder(pretrained=__SCREAMING_SNAKE_CASE			,     image_size=384			,     vit="base" )
			UpperCAmelCase_						=				pt_model.eval()

			UpperCAmelCase_						=				pt_model.state_dict()
			for key in modified_state_dict.copy():
						UpperCAmelCase_						=				modified_state_dict.pop(__SCREAMING_SNAKE_CASE )
						UpperCAmelCase_						=				rename_key(__SCREAMING_SNAKE_CASE )
						UpperCAmelCase_						=				value

			hf_model.load_state_dict(__SCREAMING_SNAKE_CASE )

			UpperCAmelCase_						=				384
			UpperCAmelCase_						=				load_demo_image(image_size=__SCREAMING_SNAKE_CASE			,     device="cpu" )
			UpperCAmelCase_						=				BertTokenizer.from_pretrained("bert-base-uncased" )
			UpperCAmelCase_						=				tokenizer(["a picture of"] ).input_ids

			UpperCAmelCase_						=				hf_model.generate(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

			assert out[0].tolist() == [3_0522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

			UpperCAmelCase_						=				hf_model.generate(__SCREAMING_SNAKE_CASE )

			assert out[0].tolist() == [3_0522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

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

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

			UpperCAmelCase_						=				blip_vqa(pretrained=__SCREAMING_SNAKE_CASE			,     image_size=__SCREAMING_SNAKE_CASE			,     vit="base" )
			vqa_model.eval()

			UpperCAmelCase_						=				vqa_model.state_dict()
			for key in modified_state_dict.copy():
						UpperCAmelCase_						=				modified_state_dict.pop(__SCREAMING_SNAKE_CASE )
						UpperCAmelCase_						=				rename_key(__SCREAMING_SNAKE_CASE )
						UpperCAmelCase_						=				value

			UpperCAmelCase_						=				BlipForQuestionAnswering(__SCREAMING_SNAKE_CASE )

			hf_vqa_model.load_state_dict(__SCREAMING_SNAKE_CASE )

			UpperCAmelCase_						=				["How many dogs are in this image?"]
			UpperCAmelCase_						=				tokenizer(__SCREAMING_SNAKE_CASE			,     return_tensors="pt" ).input_ids

			UpperCAmelCase_						=				hf_vqa_model.generate(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
			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" )

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

			UpperCAmelCase_						=				blip_itm(pretrained=__SCREAMING_SNAKE_CASE			,     image_size=__SCREAMING_SNAKE_CASE			,     vit="base" )
			itm_model.eval()

			UpperCAmelCase_						=				itm_model.state_dict()
			for key in modified_state_dict.copy():
						UpperCAmelCase_						=				modified_state_dict.pop(__SCREAMING_SNAKE_CASE )
						UpperCAmelCase_						=				rename_key(__SCREAMING_SNAKE_CASE )
						UpperCAmelCase_						=				value

			UpperCAmelCase_						=				BlipForImageTextRetrieval(__SCREAMING_SNAKE_CASE )

			UpperCAmelCase_						=				["A picture of a woman with a dog sitting in a beach"]
			UpperCAmelCase_						=				tokenizer(
			    __SCREAMING_SNAKE_CASE			,     return_tensors="pt"			,     padding="max_length"			,     truncation=__SCREAMING_SNAKE_CASE			,     max_length=35			,     ).input_ids

			hf_itm_model.load_state_dict(__SCREAMING_SNAKE_CASE )
			hf_itm_model.eval()

			UpperCAmelCase_						=				hf_itm_model(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     use_itm_head=__SCREAMING_SNAKE_CASE )
			UpperCAmelCase_						=				hf_itm_model(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     use_itm_head=__SCREAMING_SNAKE_CASE )

			assert out[0].item() == 0.2_110_687_494_277_954
			assert torch.nn.functional.softmax(out_itm[0]			,     dim=1 )[:, 1].item() == 0.45_698_845_386_505_127

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


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE     				=						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")
			SCREAMING_SNAKE_CASE     				=						parser.parse_args()

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






def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							int:
		UpperCAmelCase_						=				1
		for i in range(1			,     num + 1 ):
				fact *= i
		return fact







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							int:
		UpperCAmelCase_						=				0
		while number > 0:
				UpperCAmelCase_						=				number % 10
				sum_of_digits += last_digit
				UpperCAmelCase_						=				number // 10  # Removing the last_digit from the given number
		return sum_of_digits







def snake_case__  (			__SCREAMING_SNAKE_CASE = 100 )		->							int:
		UpperCAmelCase_						=				factorial(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				split_and_add(__SCREAMING_SNAKE_CASE )
		return result


if __name__ == "__main__":
			print(solution(int(input("Enter the Number: ").strip())))

