

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   __UpperCAmelCase					(      __a :     Optional[Any]						,__a :     List[str]      )     ->	Union[str, Any]:

					"""simple docstring"""

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

					_a      :       Dict   =						transforms.Compose(
					    [
					        transforms.Resize((image_size, image_size)						,interpolation=InterpolationMode.BICUBIC      ),
					        transforms.ToTensor(),
					        transforms.Normalize((0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73)						,(0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11)      ),
					    ]      )
					_a      :       Dict   =						transform(lowercase__      ).unsqueeze(0      ).to(lowercase__      )
					return image



def   __UpperCAmelCase					(      __a :     List[str]      )     ->	str:

					"""simple docstring"""

					if "visual_encoder" in key:
										_a      :       int   =						re.sub('''visual_encoder*'''						,'''vision_model.encoder'''						,lowercase__      )
					if "blocks" in key:
										_a      :       Tuple   =						re.sub(R'''blocks'''						,'''layers'''						,lowercase__      )
					if "attn" in key:
										_a      :       Union[str, Any]   =						re.sub(R'''attn'''						,'''self_attn'''						,lowercase__      )
					if "norm1" in key:
										_a      :       Dict   =						re.sub(R'''norm1'''						,'''layer_norm1'''						,lowercase__      )
					if "norm2" in key:
										_a      :       Optional[int]   =						re.sub(R'''norm2'''						,'''layer_norm2'''						,lowercase__      )
					if "encoder.norm" in key:
										_a      :       Optional[int]   =						re.sub(R'''encoder.norm'''						,'''post_layernorm'''						,lowercase__      )
					if "encoder.patch_embed.proj" in key:
										_a      :       int   =						re.sub(R'''encoder.patch_embed.proj'''						,'''embeddings.patch_embedding'''						,lowercase__      )

					if "encoder.pos_embed" in key:
										_a      :       Any   =						re.sub(R'''encoder.pos_embed'''						,'''embeddings.position_embedding'''						,lowercase__      )
					if "encoder.cls_token" in key:
										_a      :       Dict   =						re.sub(R'''encoder.cls_token'''						,'''embeddings.class_embedding'''						,lowercase__      )

					if "self_attn" in key:
										_a      :       Tuple   =						re.sub(R'''self_attn.proj'''						,'''self_attn.projection'''						,lowercase__      )

					return key



@torch.no_grad()
def   __UpperCAmelCase					(      __a :     List[str]						,__a :     Dict=None      )     ->	Any:

					"""simple docstring"""

					if config_path is not None:
										_a      :       Optional[Any]   =						BlipConfig.from_pretrained(lowercase__      )
					else:
										_a      :       List[str]   =						BlipConfig(projection_dim=512						,text_config={}						,vision_config={}      )

					_a      :       List[Any]   =						BlipForConditionalGeneration(lowercase__      ).eval()

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

					_a      :       List[Any]   =						blip_decoder(pretrained=lowercase__						,image_size=384						,vit='''base'''      )
					_a      :       Dict   =						pt_model.eval()

					_a      :       Dict   =						pt_model.state_dict()
					for key in modified_state_dict.copy():
										_a      :       Tuple   =						modified_state_dict.pop(lowercase__      )
										_a      :       Union[str, Any]   =						rename_key(lowercase__      )
										_a      :       Union[str, Any]   =						value

					hf_model.load_state_dict(lowercase__      )

					_a      :       Dict   =						384
					_a      :       Union[str, Any]   =						load_demo_image(image_size=lowercase__						,device='''cpu'''      )
					_a      :       Tuple   =						BertTokenizer.from_pretrained('''bert-base-uncased'''      )
					_a      :       List[str]   =						tokenizer(['''a picture of''']      ).input_ids

					_a      :       str   =						hf_model.generate(lowercase__						,lowercase__      )

					assert out[0].tolist() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

					_a      :       Dict   =						hf_model.generate(lowercase__      )

					assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

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

					# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
					_a      :       List[Any]   =						(
					    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
					)

					_a      :       Optional[int]   =						blip_vqa(pretrained=lowercase__						,image_size=lowercase__						,vit='''base'''      )
					vqa_model.eval()

					_a      :       Tuple   =						vqa_model.state_dict()
					for key in modified_state_dict.copy():
										_a      :       Any   =						modified_state_dict.pop(lowercase__      )
										_a      :       Union[str, Any]   =						rename_key(lowercase__      )
										_a      :       List[Any]   =						value

					_a      :       int   =						BlipForQuestionAnswering(lowercase__      )

					hf_vqa_model.load_state_dict(lowercase__      )

					_a      :       List[str]   =						['''How many dogs are in this image?''']
					_a      :       Union[str, Any]   =						tokenizer(lowercase__						,return_tensors='''pt'''      ).input_ids

					_a      :       Optional[Any]   =						hf_vqa_model.generate(lowercase__						,lowercase__      )
					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      :       Tuple   =						'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

					_a      :       Any   =						blip_itm(pretrained=lowercase__						,image_size=lowercase__						,vit='''base'''      )
					itm_model.eval()

					_a      :       Optional[Any]   =						itm_model.state_dict()
					for key in modified_state_dict.copy():
										_a      :       Union[str, Any]   =						modified_state_dict.pop(lowercase__      )
										_a      :       List[str]   =						rename_key(lowercase__      )
										_a      :       Dict   =						value

					_a      :       Union[str, Any]   =						BlipForImageTextRetrieval(lowercase__      )

					_a      :       List[str]   =						['''A picture of a woman with a dog sitting in a beach''']
					_a      :       Union[str, Any]   =						tokenizer(
					    lowercase__						,return_tensors='''pt'''						,padding='''max_length'''						,truncation=lowercase__						,max_length=35						,).input_ids

					hf_itm_model.load_state_dict(lowercase__      )
					hf_itm_model.eval()

					_a      :       Union[str, Any]   =						hf_itm_model(lowercase__						,lowercase__						,use_itm_head=lowercase__      )
					_a      :       Union[str, Any]   =						hf_itm_model(lowercase__						,lowercase__						,use_itm_head=lowercase__      )

					assert out[0].item() == 0.21_10_68_74_94_27_79_54
					assert torch.nn.functional.softmax(out_itm[0]						,dim=1      )[:, 1].item() == 0.4_56_98_84_53_86_50_51_27

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


if __name__ == "__main__":
			a__												=						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''')
			a__												=						parser.parse_args()

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






'''simple docstring'''

import string

def 					__UpperCamelCase				(						lowercase__    :	str			):






     '''simple docstring'''





     for key in range(len(string.ascii_uppercase			)			):
          __lowercase      		=''
          for symbol in message:
               if symbol in string.ascii_uppercase:
                    __lowercase      		=string.ascii_uppercase.find(lowercase__			)
                    __lowercase      		=num - key
                    if num < 0:
                         __lowercase      		=num + len(string.ascii_uppercase			)
                    __lowercase      		=translated + string.ascii_uppercase[num]
               else:
                    __lowercase      		=translated + symbol
          print(F'''Decryption using Key #{key}: {translated}'''			)

def 					__UpperCamelCase				(						):






     '''simple docstring'''





     __lowercase      		=input('Encrypted message: '			)
     __lowercase      		=message.upper()
     decrypt(lowercase__			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()


