





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 							_lowerCAmelCase (  _a   :					List[Any]					,   _a   :					str			)	->			List[str]:
     lowerCAmelCase_					:       int    =		"https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
     lowerCAmelCase_					:       Any    =		Image.open(requests.get(lowercase_					,   stream=lowercase_			).raw			).convert("""RGB"""			)

     lowerCAmelCase_					:       Dict    =		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)			),
         ]			)
     lowerCAmelCase_					:       int    =		transform(lowercase_			).unsqueeze(0			).to(lowercase_			)
     return image






def 							_lowerCAmelCase (  _a   :					Optional[int]			)	->			str:

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

     if "encoder.pos_embed" in key:
          lowerCAmelCase_					:       List[Any]    =		re.sub(R"""encoder.pos_embed"""					,   """embeddings.position_embedding"""					,   lowercase_			)
     if "encoder.cls_token" in key:
          lowerCAmelCase_					:       int    =		re.sub(R"""encoder.cls_token"""					,   """embeddings.class_embedding"""					,   lowercase_			)

     if "self_attn" in key:
          lowerCAmelCase_					:       Optional[int]    =		re.sub(R"""self_attn.proj"""					,   """self_attn.projection"""					,   lowercase_			)

     return key






@torch.no_grad()
def 							_lowerCAmelCase (  _a   :					Optional[int]					,   _a   :					Tuple=None			)	->			Tuple:

     if config_path is not None:
          lowerCAmelCase_					:       Dict    =		BlipConfig.from_pretrained(lowercase_			)
     else:
          lowerCAmelCase_					:       List[str]    =		BlipConfig(projection_dim=5_12					,   text_config={}					,   vision_config={}			)

     lowerCAmelCase_					:       Optional[int]    =		BlipForConditionalGeneration(lowercase_			).eval()

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

     lowerCAmelCase_					:       Union[str, Any]    =		blip_decoder(pretrained=lowercase_					,   image_size=3_84					,   vit="""base"""			)
     lowerCAmelCase_					:       Union[str, Any]    =		pt_model.eval()

     lowerCAmelCase_					:       Optional[Any]    =		pt_model.state_dict()
     for key in modified_state_dict.copy():
          lowerCAmelCase_					:       List[Any]    =		modified_state_dict.pop(lowercase_			)
          lowerCAmelCase_					:       List[Any]    =		rename_key(lowercase_			)
          lowerCAmelCase_					:       Any    =		value

     hf_model.load_state_dict(lowercase_			)

     lowerCAmelCase_					:       Optional[int]    =		3_84
     lowerCAmelCase_					:       int    =		load_demo_image(image_size=lowercase_					,   device="""cpu"""			)
     lowerCAmelCase_					:       List[Any]    =		BertTokenizer.from_pretrained("""bert-base-uncased"""			)
     lowerCAmelCase_					:       Union[str, Any]    =		tokenizer(["""a picture of"""]			).input_ids

     lowerCAmelCase_					:       Optional[int]    =		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_					:       List[str]    =		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_					:       List[Any]    =		(
         "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"
     )

     lowerCAmelCase_					:       List[str]    =		blip_vqa(pretrained=lowercase_					,   image_size=lowercase_					,   vit="""base"""			)
     vqa_model.eval()

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

     lowerCAmelCase_					:       List[Any]    =		BlipForQuestionAnswering(lowercase_			)

     hf_vqa_model.load_state_dict(lowercase_			)

     lowerCAmelCase_					:       Any    =		["How many dogs are in this image?"]
     lowerCAmelCase_					:       int    =		tokenizer(lowercase_					,   return_tensors="""pt"""			).input_ids

     lowerCAmelCase_					:       Tuple    =		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_					:       List[Any]    =		"https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"

     lowerCAmelCase_					:       Optional[Any]    =		blip_itm(pretrained=lowercase_					,   image_size=lowercase_					,   vit="""base"""			)
     itm_model.eval()

     lowerCAmelCase_					:       str    =		itm_model.state_dict()
     for key in modified_state_dict.copy():
          lowerCAmelCase_					:       Optional[Any]    =		modified_state_dict.pop(lowercase_			)
          lowerCAmelCase_					:       Tuple    =		rename_key(lowercase_			)
          lowerCAmelCase_					:       List[str]    =		value

     lowerCAmelCase_					:       Union[str, Any]    =		BlipForImageTextRetrieval(lowercase_			)

     lowerCAmelCase_					:       Dict    =		["A picture of a woman with a dog sitting in a beach"]
     lowerCAmelCase_					:       List[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()

     lowerCAmelCase_					:       Optional[Any]    =		hf_itm_model(lowercase_					,   lowercase_					,   use_itm_head=lowercase_			)
     lowerCAmelCase_					:       Any    =		hf_itm_model(lowercase_					,   lowercase_					,   use_itm_head=lowercase_			)

     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__":
      UpperCAmelCase_					:   Optional[Any] 							=						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""")
      UpperCAmelCase_					:   int 							=						parser.parse_args()

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







import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  """M-CLIP"""







  def __init__(	self    ,						_lowercase=1_024    ,						_lowercase=768    ,						**_lowercase						):
       lowerCAmelCase_					:       Tuple    =		transformerDimSize
       lowerCAmelCase_					:       Optional[Any]    =		imageDimSize
       super().__init__(**_lowercase						)





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  MCLIPConfig







  def __init__(	self    ,						_lowercase    ,						*_lowercase    ,						**_lowercase						):
       super().__init__(_lowercase    ,						*_lowercase    ,						**_lowercase						)
       lowerCAmelCase_					:       str    =		XLMRobertaModel(_lowercase						)
       lowerCAmelCase_					:       str    =		torch.nn.Linear(
           in_features=config.transformerDimensions    ,						out_features=config.numDims						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       Optional[int]    =		self.transformer(input_ids=_lowercase    ,						attention_mask=_lowercase						)[0]
       lowerCAmelCase_					:       Any    =		(embs * attention_mask.unsqueeze(2						)).sum(dim=1						) / attention_mask.sum(dim=1						)[:, None]
       return self.LinearTransformation(_lowercase						), embs
