





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      A__       (    SCREAMING_SNAKE_CASE_   :      Any				,	SCREAMING_SNAKE_CASE_   :      Tuple						)  ->  Dict:






   """simple docstring"""







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

   _UpperCAmelCase           =   transforms.Compose(
       [
           transforms.Resize((image_size, image_size)				,	interpolation=InterpolationMode.BICUBIC						),
           transforms.ToTensor(),
           transforms.Normalize((0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3)				,	(0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1)						),
       ]						)
   _UpperCAmelCase           =   transform(SCREAMING_SNAKE_CASE_						).unsqueeze(0						).to(SCREAMING_SNAKE_CASE_						)
   return image
def      A__       (    SCREAMING_SNAKE_CASE_   :      List[str]						)  ->  Tuple:






   """simple docstring"""







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

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

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

   return key
@torch.no_grad()
def      A__       (    SCREAMING_SNAKE_CASE_   :      Optional[int]				,	SCREAMING_SNAKE_CASE_   :      int=None						)  ->  Dict:






   """simple docstring"""







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

   _UpperCAmelCase           =   BlipForConditionalGeneration(SCREAMING_SNAKE_CASE_						).eval()

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

   _UpperCAmelCase           =   blip_decoder(pretrained=SCREAMING_SNAKE_CASE_				,	image_size=3_84				,	vit='''base'''						)
   _UpperCAmelCase           =   pt_model.eval()

   _UpperCAmelCase           =   pt_model.state_dict()
   for key in modified_state_dict.copy():
      _UpperCAmelCase           =   modified_state_dict.pop(SCREAMING_SNAKE_CASE_						)
      _UpperCAmelCase           =   rename_key(SCREAMING_SNAKE_CASE_						)
      _UpperCAmelCase           =   value

   hf_model.load_state_dict(SCREAMING_SNAKE_CASE_						)

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

   _UpperCAmelCase           =   hf_model.generate(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)

   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]

   _UpperCAmelCase           =   hf_model.generate(SCREAMING_SNAKE_CASE_						)

   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(SCREAMING_SNAKE_CASE_						)

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

   _UpperCAmelCase           =   blip_vqa(pretrained=SCREAMING_SNAKE_CASE_				,	image_size=SCREAMING_SNAKE_CASE_				,	vit='''base'''						)
   vqa_model.eval()

   _UpperCAmelCase           =   vqa_model.state_dict()
   for key in modified_state_dict.copy():
      _UpperCAmelCase           =   modified_state_dict.pop(SCREAMING_SNAKE_CASE_						)
      _UpperCAmelCase           =   rename_key(SCREAMING_SNAKE_CASE_						)
      _UpperCAmelCase           =   value

   _UpperCAmelCase           =   BlipForQuestionAnswering(SCREAMING_SNAKE_CASE_						)

   hf_vqa_model.load_state_dict(SCREAMING_SNAKE_CASE_						)

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

   _UpperCAmelCase           =   hf_vqa_model.generate(SCREAMING_SNAKE_CASE_				,	SCREAMING_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'''						)

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

   _UpperCAmelCase           =   blip_itm(pretrained=SCREAMING_SNAKE_CASE_				,	image_size=SCREAMING_SNAKE_CASE_				,	vit='''base'''						)
   itm_model.eval()

   _UpperCAmelCase           =   itm_model.state_dict()
   for key in modified_state_dict.copy():
      _UpperCAmelCase           =   modified_state_dict.pop(SCREAMING_SNAKE_CASE_						)
      _UpperCAmelCase           =   rename_key(SCREAMING_SNAKE_CASE_						)
      _UpperCAmelCase           =   value

   _UpperCAmelCase           =   BlipForImageTextRetrieval(SCREAMING_SNAKE_CASE_						)

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

   hf_itm_model.load_state_dict(SCREAMING_SNAKE_CASE_						)
   hf_itm_model.eval()

   _UpperCAmelCase           =   hf_itm_model(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_				,	use_itm_head=SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   hf_itm_model(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_				,	use_itm_head=SCREAMING_SNAKE_CASE_						)

   assert out[0].item() == 0.2_1_1_0_6_8_7_4_9_4_2_7_7_9_5_4
   assert torch.nn.functional.softmax(out_itm[0]				,	dim=1						)[:, 1].item() == 0.4_5_6_9_8_8_4_5_3_8_6_5_0_5_1_2_7

   if pytorch_dump_folder_path is not None:
      hf_itm_model.save_pretrained(pytorch_dump_folder_path + '''_itm'''						)


if __name__ == "__main__":
   UpperCAmelCase_           =	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_           =	parser.parse_args()

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






def      A__       (    SCREAMING_SNAKE_CASE_   :      int						)  ->  bool:






   """simple docstring"""







   if not isinstance(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						):
      _UpperCAmelCase           =   F'''Input value of [number={number}] must be an integer'''
      raise TypeError(SCREAMING_SNAKE_CASE_						)
   if number < 0:
      return False
   _UpperCAmelCase           =   number * number
   while number > 0:
      if number % 10 != number_square % 10:
         return False
      number //= 10
      number_square //= 10
   return True


if __name__ == "__main__":
   import doctest

   doctest.testmod()