




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     _SCREAMING_SNAKE_CASE    (							lowercase			: List[Any]			,							lowercase			: List[str]  ):


      '''simple docstring'''



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

      lowerCamelCase_	  =					transforms.Compose(
          [
              transforms.Resize((image_size, image_size)			,							interpolation=InterpolationMode.BICUBIC  ),
              transforms.ToTensor(),
              transforms.Normalize((0.4814_5466, 0.457_8275, 0.4082_1073)			,							(0.2686_2954, 0.2613_0258, 0.2757_7711)  ),
          ]  )
      lowerCamelCase_	  =					transform(lowercase  ).unsqueeze(0  ).to(lowercase  )
      return image

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int  ):


      '''simple docstring'''



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

      if "encoder.pos_embed" in key:
            lowerCamelCase_	  =					re.sub(r'encoder.pos_embed'			,							'embeddings.position_embedding'			,							lowercase  )
      if "encoder.cls_token" in key:
            lowerCamelCase_	  =					re.sub(r'encoder.cls_token'			,							'embeddings.class_embedding'			,							lowercase  )

      if "self_attn" in key:
            lowerCamelCase_	  =					re.sub(r'self_attn.proj'			,							'self_attn.projection'			,							lowercase  )

      return key

@torch.no_grad()
def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: Optional[Any]=None  ):


      '''simple docstring'''



      if config_path is not None:
            lowerCamelCase_	  =					BlipConfig.from_pretrained(lowercase  )
      else:
            lowerCamelCase_	  =					BlipConfig(projection_dim=5_12			,							text_config={}			,							vision_config={}  )

      lowerCamelCase_	  =					BlipForConditionalGeneration(lowercase  ).eval()

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

      lowerCamelCase_	  =					blip_decoder(pretrained=lowercase			,							image_size=3_84			,							vit='base'  )
      lowerCamelCase_	  =					pt_model.eval()

      lowerCamelCase_	  =					pt_model.state_dict()
      for key in modified_state_dict.copy():
            lowerCamelCase_	  =					modified_state_dict.pop(lowercase  )
            lowerCamelCase_	  =					rename_key(lowercase  )
            lowerCamelCase_	  =					value

      hf_model.load_state_dict(lowercase  )

      lowerCamelCase_	  =					3_84
      lowerCamelCase_	  =					load_demo_image(image_size=lowercase			,							device='cpu'  )
      lowerCamelCase_	  =					BertTokenizer.from_pretrained('bert-base-uncased'  )
      lowerCamelCase_	  =					tokenizer(['a picture of']  ).input_ids

      lowerCamelCase_	  =					hf_model.generate(lowercase			,							lowercase  )

      assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

      lowerCamelCase_	  =					hf_model.generate(lowercase  )

      assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

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

      lowerCamelCase_	  =					blip_vqa(pretrained=lowercase			,							image_size=lowercase			,							vit='base'  )
      vqa_model.eval()

      lowerCamelCase_	  =					vqa_model.state_dict()
      for key in modified_state_dict.copy():
            lowerCamelCase_	  =					modified_state_dict.pop(lowercase  )
            lowerCamelCase_	  =					rename_key(lowercase  )
            lowerCamelCase_	  =					value

      lowerCamelCase_	  =					BlipForQuestionAnswering(lowercase  )

      hf_vqa_model.load_state_dict(lowercase  )

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

      lowerCamelCase_	  =					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'  )

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

      lowerCamelCase_	  =					blip_itm(pretrained=lowercase			,							image_size=lowercase			,							vit='base'  )
      itm_model.eval()

      lowerCamelCase_	  =					itm_model.state_dict()
      for key in modified_state_dict.copy():
            lowerCamelCase_	  =					modified_state_dict.pop(lowercase  )
            lowerCamelCase_	  =					rename_key(lowercase  )
            lowerCamelCase_	  =					value

      lowerCamelCase_	  =					BlipForImageTextRetrieval(lowercase  )

      lowerCamelCase_	  =					['A picture of a woman with a dog sitting in a beach']
      lowerCamelCase_	  =					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()

      lowerCamelCase_	  =					hf_itm_model(lowercase			,							lowercase			,							use_itm_head=lowercase  )
      lowerCamelCase_	  =					hf_itm_model(lowercase			,							lowercase			,							use_itm_head=lowercase  )

      assert out[0].item() == 0.2110_6874_9427_7954
      assert torch.nn.functional.softmax(out_itm[0]			,							dim=1  )[:, 1].item() == 0.4_5698_8453_8650_5127

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


if __name__ == "__main__":
 lowerCamelCase       :	str									=     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       :	int									=     parser.parse_args()

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









def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					0
      for i in range(1			,							10_01  ):
            total += i**i
      return str(lowercase  )[-10:]


if __name__ == "__main__":
 print(solution())



