


'''simple docstring'''



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

     __lowerCamelCase   :	int								=		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(UpperCAmelCase_							).unsqueeze(0							).to(UpperCAmelCase_							)
     return image



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

     if "encoder.pos_embed" in key:
          __lowerCamelCase   :	Dict								=		re.sub(R'encoder.pos_embed'      ,    'embeddings.position_embedding'      ,    UpperCAmelCase_							)
     if "encoder.cls_token" in key:
          __lowerCamelCase   :	List[str]								=		re.sub(R'encoder.cls_token'      ,    'embeddings.class_embedding'      ,    UpperCAmelCase_							)

     if "self_attn" in key:
          __lowerCamelCase   :	Union[str, Any]								=		re.sub(R'self_attn.proj'      ,    'self_attn.projection'      ,    UpperCAmelCase_							)

     return key



@torch.no_grad()
def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				List[Any]=None							)     ->		Optional[Any]:
     if config_path is not None:
          __lowerCamelCase   :	List[str]								=		BlipConfig.from_pretrained(UpperCAmelCase_							)
     else:
          __lowerCamelCase   :	List[str]								=		BlipConfig(projection_dim=5_12      ,    text_config={}      ,    vision_config={}							)

     __lowerCamelCase   :	Optional[int]								=		BlipForConditionalGeneration(UpperCAmelCase_							).eval()

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

     __lowerCamelCase   :	Union[str, Any]								=		blip_decoder(pretrained=UpperCAmelCase_      ,    image_size=3_84      ,    vit='base'							)
     __lowerCamelCase   :	Union[str, Any]								=		pt_model.eval()

     __lowerCamelCase   :	Tuple								=		pt_model.state_dict()
     for key in modified_state_dict.copy():
          __lowerCamelCase   :	List[str]								=		modified_state_dict.pop(UpperCAmelCase_							)
          __lowerCamelCase   :	Optional[int]								=		rename_key(UpperCAmelCase_							)
          __lowerCamelCase   :	int								=		value

     hf_model.load_state_dict(UpperCAmelCase_							)

     __lowerCamelCase   :	Optional[int]								=		3_84
     __lowerCamelCase   :	List[str]								=		load_demo_image(image_size=UpperCAmelCase_      ,    device='cpu'							)
     __lowerCamelCase   :	Any								=		BertTokenizer.from_pretrained('bert-base-uncased'							)
     __lowerCamelCase   :	Tuple								=		tokenizer(['a picture of']							).input_ids

     __lowerCamelCase   :	Optional[Any]								=		hf_model.generate(UpperCAmelCase_      ,    UpperCAmelCase_							)

     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(UpperCAmelCase_							)

     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(UpperCAmelCase_							)

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

     __lowerCamelCase   :	Dict								=		blip_vqa(pretrained=UpperCAmelCase_      ,    image_size=UpperCAmelCase_      ,    vit='base'							)
     vqa_model.eval()

     __lowerCamelCase   :	int								=		vqa_model.state_dict()
     for key in modified_state_dict.copy():
          __lowerCamelCase   :	List[Any]								=		modified_state_dict.pop(UpperCAmelCase_							)
          __lowerCamelCase   :	Optional[Any]								=		rename_key(UpperCAmelCase_							)
          __lowerCamelCase   :	Any								=		value

     __lowerCamelCase   :	Tuple								=		BlipForQuestionAnswering(UpperCAmelCase_							)

     hf_vqa_model.load_state_dict(UpperCAmelCase_							)

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

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

     __lowerCamelCase   :	Optional[Any]								=		blip_itm(pretrained=UpperCAmelCase_      ,    image_size=UpperCAmelCase_      ,    vit='base'							)
     itm_model.eval()

     __lowerCamelCase   :	Optional[int]								=		itm_model.state_dict()
     for key in modified_state_dict.copy():
          __lowerCamelCase   :	List[Any]								=		modified_state_dict.pop(UpperCAmelCase_							)
          __lowerCamelCase   :	Dict								=		rename_key(UpperCAmelCase_							)
          __lowerCamelCase   :	Optional[Any]								=		value

     __lowerCamelCase   :	int								=		BlipForImageTextRetrieval(UpperCAmelCase_							)

     __lowerCamelCase   :	Any								=		['A picture of a woman with a dog sitting in a beach']
     __lowerCamelCase   :	Tuple								=		tokenizer(
         UpperCAmelCase_      ,    return_tensors='pt'      ,    padding='max_length'      ,    truncation=UpperCAmelCase_      ,    max_length=35      ,    ).input_ids

     hf_itm_model.load_state_dict(UpperCAmelCase_							)
     hf_itm_model.eval()

     __lowerCamelCase   :	Tuple								=		hf_itm_model(UpperCAmelCase_      ,    UpperCAmelCase_      ,    use_itm_head=UpperCAmelCase_							)
     __lowerCamelCase   :	int								=		hf_itm_model(UpperCAmelCase_      ,    UpperCAmelCase_      ,    use_itm_head=UpperCAmelCase_							)

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

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










'''simple docstring'''



from importlib import import_module

from .logging import get_logger


A__		:					str												=  get_logger(__name__)





class    UpperCAmelCase_  :





    """simple docstring"""






    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=None	)				->     List[Any]:
         __lowerCamelCase   :	List[str]								=		attrs or []
         if module is not None:
              for key in module.__dict__:
                   if key in attrs or not key.startswith('__'	):
                        setattr(self			,     SCREAMING_SNAKE_CASE_			,     getattr(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)	)
         __lowerCamelCase   :	Optional[int]								=		module._original_module if isinstance(SCREAMING_SNAKE_CASE_			,     _PatchedModuleObj	) else module







class    UpperCAmelCase_  :





    """simple docstring"""






    lowerCamelCase  :					Tuple							     =			[]







    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=None	)				->     Union[str, Any]:
         __lowerCamelCase   :	Optional[int]								=		obj
         __lowerCamelCase   :	List[Any]								=		target
         __lowerCamelCase   :	Union[str, Any]								=		new
         __lowerCamelCase   :	Union[str, Any]								=		target.split('.'	)[0]
         __lowerCamelCase   :	Dict								=		{}
         __lowerCamelCase   :	Dict								=		attrs or []







    def __enter__(							self	)				->     Optional[int]:
         *__lowerCamelCase				,       __lowerCamelCase   :	int								=		self.target.split('.'	)

         # Patch modules:
         # it's used to patch attributes of submodules like "os.path.join";
         # in this case we need to patch "os" and "os.path"

         for i in range(len(SCREAMING_SNAKE_CASE_	)	):
              try:
                   __lowerCamelCase   :	Optional[int]								=		import_module('.'.join(submodules[: i + 1]	)	)
              except ModuleNotFoundError:
                   continue
              # We iterate over all the globals in self.obj in case we find "os" or "os.path"
              for attr in self.obj.__dir__():
                   __lowerCamelCase   :	List[Any]								=		getattr(self.obj			,     SCREAMING_SNAKE_CASE_	)
                   # We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
                   # This allows to patch renamed modules like "from os import path as ospath".
                   if obj_attr is submodule or (
                       (isinstance(SCREAMING_SNAKE_CASE_			,     _PatchedModuleObj	) and obj_attr._original_module is submodule)
                   ):
                        __lowerCamelCase   :	Optional[Any]								=		obj_attr
                        # patch at top level
                        setattr(self.obj			,     SCREAMING_SNAKE_CASE_			,     _PatchedModuleObj(SCREAMING_SNAKE_CASE_			,     attrs=self.attrs	)	)
                        __lowerCamelCase   :	str								=		getattr(self.obj			,     SCREAMING_SNAKE_CASE_	)
                        # construct lower levels patches
                        for key in submodules[i + 1 :]:
                             setattr(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     _PatchedModuleObj(getattr(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)			,     attrs=self.attrs	)	)
                             __lowerCamelCase   :	List[Any]								=		getattr(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
                        # finally set the target attribute
                        setattr(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     self.new	)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

         if submodules:  # if it's an attribute of a submodule like "os.path.join"
              try:
                   __lowerCamelCase   :	Union[str, Any]								=		getattr(import_module('.'.join(SCREAMING_SNAKE_CASE_	)	)			,     SCREAMING_SNAKE_CASE_	)
              except (AttributeError, ModuleNotFoundError):
                   return
              # We iterate over all the globals in self.obj in case we find "os.path.join"
              for attr in self.obj.__dir__():
                   # We don't check for the name of the global, but rather if its value *is* "os.path.join".
                   # This allows to patch renamed attributes like "from os.path import join as pjoin".
                   if getattr(self.obj			,     SCREAMING_SNAKE_CASE_	) is attr_value:
                        __lowerCamelCase   :	Optional[int]								=		getattr(self.obj			,     SCREAMING_SNAKE_CASE_	)
                        setattr(self.obj			,     SCREAMING_SNAKE_CASE_			,     self.new	)
         elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
              __lowerCamelCase   :	List[Any]								=		globals()['__builtins__'][target_attr]
              setattr(self.obj			,     SCREAMING_SNAKE_CASE_			,     self.new	)
         else:
              raise RuntimeError(f'Tried to patch attribute {target_attr} instead of a submodule.'	)







    def __exit__(							self			,     *SCREAMING_SNAKE_CASE_	)				->     Optional[Any]:
         for attr in list(self.original	):
              setattr(self.obj			,     SCREAMING_SNAKE_CASE_			,     self.original.pop(SCREAMING_SNAKE_CASE_	)	)







    def       lowercase_				(							self	)				->     Optional[int]:
         self.__enter__()
         self._active_patches.append(self	)







    def       lowercase_				(							self	)				->     str:
         try:
              self._active_patches.remove(self	)
         except ValueError:
              # If the patch hasn't been started this will fail
              return None

         return self.__exit__()






