




"""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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					List[Any]:
     a     =				"https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
     a     =				Image.open(requests.get(__UpperCamelCase		, stream=__UpperCamelCase).raw).convert("RGB")

     a     =				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)),
         ])
     a     =				transform(__UpperCamelCase).unsqueeze(0).to(__UpperCamelCase)
     return image

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Optional[int]:
     if "visual_encoder" in key:
          a     =				re.sub("visual_encoder*"		, "vision_model.encoder"		, __UpperCamelCase)
     if "blocks" in key:
          a     =				re.sub(r"blocks"		, "layers"		, __UpperCamelCase)
     if "attn" in key:
          a     =				re.sub(r"attn"		, "self_attn"		, __UpperCamelCase)
     if "norm1" in key:
          a     =				re.sub(r"norm1"		, "layer_norm1"		, __UpperCamelCase)
     if "norm2" in key:
          a     =				re.sub(r"norm2"		, "layer_norm2"		, __UpperCamelCase)
     if "encoder.norm" in key:
          a     =				re.sub(r"encoder.norm"		, "post_layernorm"		, __UpperCamelCase)
     if "encoder.patch_embed.proj" in key:
          a     =				re.sub(r"encoder.patch_embed.proj"		, "embeddings.patch_embedding"		, __UpperCamelCase)

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

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

     return key

@torch.no_grad()
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase=None)      ->					List[str]:
     if config_path is not None:
          a     =				BlipConfig.from_pretrained(__UpperCamelCase)
     else:
          a     =				BlipConfig(projection_dim=5_12		, text_config={}		, vision_config={})

     a     =				BlipForConditionalGeneration(__UpperCamelCase).eval()

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

     a     =				blip_decoder(pretrained=__UpperCamelCase		, image_size=3_84		, vit="base")
     a     =				pt_model.eval()

     a     =				pt_model.state_dict()
     for key in modified_state_dict.copy():
          a     =				modified_state_dict.pop(__UpperCamelCase)
          a     =				rename_key(__UpperCamelCase)
          a     =				value

     hf_model.load_state_dict(__UpperCamelCase)

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

     a     =				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]

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

     a     =				blip_vqa(pretrained=__UpperCamelCase		, image_size=__UpperCamelCase		, vit="base")
     vqa_model.eval()

     a     =				vqa_model.state_dict()
     for key in modified_state_dict.copy():
          a     =				modified_state_dict.pop(__UpperCamelCase)
          a     =				rename_key(__UpperCamelCase)
          a     =				value

     a     =				BlipForQuestionAnswering(__UpperCamelCase)

     hf_vqa_model.load_state_dict(__UpperCamelCase)

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

     a     =				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")

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

     a     =				blip_itm(pretrained=__UpperCamelCase		, image_size=__UpperCamelCase		, vit="base")
     itm_model.eval()

     a     =				itm_model.state_dict()
     for key in modified_state_dict.copy():
          a     =				modified_state_dict.pop(__UpperCamelCase)
          a     =				rename_key(__UpperCamelCase)
          a     =				value

     a     =				BlipForImageTextRetrieval(__UpperCamelCase)

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

     a     =				hf_itm_model(__UpperCamelCase		, __UpperCamelCase		, use_itm_head=__UpperCamelCase)
     a     =				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__":
   lowercase__				:      Tuple	   = 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")
   lowercase__				:      Union[str, Any]	   = parser.parse_args()

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







from __future__ import annotations

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					list[int]:
     a     =				2
     a     =				[]
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(__UpperCamelCase)
     if n > 1:
          factors.append(__UpperCamelCase)
     return factors


if __name__ == "__main__":
   import doctest

   doctest.testmod()

