






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

   _lowercase              =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)),
       ])
   _lowercase              =transform(_snake_case).unsqueeze(0).to(_snake_case)
   return image





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

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

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

   return key





@torch.no_grad()
def        _snake_case   (_snake_case				:       Tuple      ,    _snake_case				:       Union[str, Any]=None)					->	Tuple:
   if config_path is not None:
      _lowercase              =BlipConfig.from_pretrained(_snake_case)
   else:
      _lowercase              =BlipConfig(projection_dim=512      ,    text_config={}      ,    vision_config={})

   _lowercase              =BlipForConditionalGeneration(_snake_case).eval()

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

   _lowercase              =blip_decoder(pretrained=_snake_case      ,    image_size=384      ,    vit='base')
   _lowercase              =pt_model.eval()

   _lowercase              =pt_model.state_dict()
   for key in modified_state_dict.copy():
      _lowercase              =modified_state_dict.pop(_snake_case)
      _lowercase              =rename_key(_snake_case)
      _lowercase              =value

   hf_model.load_state_dict(_snake_case)

   _lowercase              =384
   _lowercase              =load_demo_image(image_size=_snake_case      ,    device='cpu')
   _lowercase              =BertTokenizer.from_pretrained('bert-base-uncased')
   _lowercase              =tokenizer(['a picture of']).input_ids

   _lowercase              =hf_model.generate(_snake_case      ,    _snake_case)

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

   _lowercase              =hf_model.generate(_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(_snake_case)

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

   _lowercase              =blip_vqa(pretrained=_snake_case      ,    image_size=_snake_case      ,    vit='base')
   vqa_model.eval()

   _lowercase              =vqa_model.state_dict()
   for key in modified_state_dict.copy():
      _lowercase              =modified_state_dict.pop(_snake_case)
      _lowercase              =rename_key(_snake_case)
      _lowercase              =value

   _lowercase              =BlipForQuestionAnswering(_snake_case)

   hf_vqa_model.load_state_dict(_snake_case)

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

   _lowercase              =hf_vqa_model.generate(_snake_case      ,    _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')

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

   _lowercase              =blip_itm(pretrained=_snake_case      ,    image_size=_snake_case      ,    vit='base')
   itm_model.eval()

   _lowercase              =itm_model.state_dict()
   for key in modified_state_dict.copy():
      _lowercase              =modified_state_dict.pop(_snake_case)
      _lowercase              =rename_key(_snake_case)
      _lowercase              =value

   _lowercase              =BlipForImageTextRetrieval(_snake_case)

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

   hf_itm_model.load_state_dict(_snake_case)
   hf_itm_model.eval()

   _lowercase              =hf_itm_model(_snake_case      ,    _snake_case      ,    use_itm_head=_snake_case)
   _lowercase              =hf_itm_model(_snake_case      ,    _snake_case      ,    use_itm_head=_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   (_snake_case				:       list      ,    _snake_case				:       int      ,    _snake_case				:       int = 0      ,    _snake_case				:       int = 0)					->	int:
   _lowercase              =right or len(_snake_case) - 1
   if left > right:
      return -1
   elif list_data[left] == key:
      return left
   elif list_data[right] == key:
      return right
   else:
      return search(_snake_case      ,    _snake_case      ,    left + 1      ,    right - 1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


