





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      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     Dict:


      '''simple docstring'''


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

      SCREAMING_SNAKE_CASE	=		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)			),
          ]			)
      SCREAMING_SNAKE_CASE	=		transform(lowerCamelCase_			).unsqueeze(0			).to(lowerCamelCase_			)
      return image



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Any:


      '''simple docstring'''


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

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

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

      return key



@torch.no_grad()
def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE=None			)				->     Union[str, Any]:


      '''simple docstring'''


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

      SCREAMING_SNAKE_CASE	=		BlipForConditionalGeneration(lowerCamelCase_			).eval()

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

      SCREAMING_SNAKE_CASE	=		blip_decoder(pretrained=lowerCamelCase_				,		image_size=3_84				,		vit="""base"""			)
      SCREAMING_SNAKE_CASE	=		pt_model.eval()

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

      hf_model.load_state_dict(lowerCamelCase_			)

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

      SCREAMING_SNAKE_CASE	=		hf_model.generate(lowerCamelCase_				,		lowerCamelCase_			)

      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]

      SCREAMING_SNAKE_CASE	=		hf_model.generate(lowerCamelCase_			)

      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(lowerCamelCase_			)

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

      SCREAMING_SNAKE_CASE	=		blip_vqa(pretrained=lowerCamelCase_				,		image_size=lowerCamelCase_				,		vit="""base"""			)
      vqa_model.eval()

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

      SCREAMING_SNAKE_CASE	=		BlipForQuestionAnswering(lowerCamelCase_			)

      hf_vqa_model.load_state_dict(lowerCamelCase_			)

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

      SCREAMING_SNAKE_CASE	=		hf_vqa_model.generate(lowerCamelCase_				,		lowerCamelCase_			)
      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"""			)

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

      SCREAMING_SNAKE_CASE	=		blip_itm(pretrained=lowerCamelCase_				,		image_size=lowerCamelCase_				,		vit="""base"""			)
      itm_model.eval()

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

      SCREAMING_SNAKE_CASE	=		BlipForImageTextRetrieval(lowerCamelCase_			)

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

      hf_itm_model.load_state_dict(lowerCamelCase_			)
      hf_itm_model.eval()

      SCREAMING_SNAKE_CASE	=		hf_itm_model(lowerCamelCase_				,		lowerCamelCase_				,		use_itm_head=lowerCamelCase_			)
      SCREAMING_SNAKE_CASE	=		hf_itm_model(lowerCamelCase_				,		lowerCamelCase_				,		use_itm_head=lowerCamelCase_			)

      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      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      return price * (1 + tax_rate)


if __name__ == "__main__":
      print(F'''{price_plus_tax(1_0_0, 0.25) = }''')
      print(F'''{price_plus_tax(125.50, 0.05) = }''')


