


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__    (		snake_case__					:							Optional[int]						,   snake_case__					:							str     ):
  _UpperCAmelCase		:							List[Any]							=     """https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
  _UpperCAmelCase		:							List[Any]							=     Image.open(requests.get(snake_case__						,   stream=snake_case__     ).raw     ).convert("""RGB"""     )

  _UpperCAmelCase		:							Optional[Any]							=     transforms.Compose(
      [
          transforms.Resize((image_size, image_size)						,   interpolation=InterpolationMode.BICUBIC     ),
          transforms.ToTensor(),
          transforms.Normalize((0.4814_5466, 0.457_8275, 0.4082_1073)						,   (0.2686_2954, 0.2613_0258, 0.2757_7711)     ),
      ]     )
  _UpperCAmelCase		:							str							=     transform(snake_case__     ).unsqueeze(0     ).to(snake_case__     )
  return image




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

  if "encoder.pos_embed" in key:
    _UpperCAmelCase		:							Dict							=     re.sub(r"""encoder.pos_embed"""						,   """embeddings.position_embedding"""						,   snake_case__     )
  if "encoder.cls_token" in key:
    _UpperCAmelCase		:							List[str]							=     re.sub(r"""encoder.cls_token"""						,   """embeddings.class_embedding"""						,   snake_case__     )

  if "self_attn" in key:
    _UpperCAmelCase		:							Optional[Any]							=     re.sub(r"""self_attn.proj"""						,   """self_attn.projection"""						,   snake_case__     )

  return key




@torch.no_grad()
def    a__    (		snake_case__					:							Optional[int]						,   snake_case__					:							List[str]=None     ):
  if config_path is not None:
    _UpperCAmelCase		:							str							=     BlipConfig.from_pretrained(snake_case__     )
  else:
    _UpperCAmelCase		:							Optional[Any]							=     BlipConfig(projection_dim=512						,   text_config={}						,   vision_config={}     )

  _UpperCAmelCase		:							List[Any]							=     BlipForConditionalGeneration(snake_case__     ).eval()

  _UpperCAmelCase		:							List[Any]							=     """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"""

  _UpperCAmelCase		:							Union[str, Any]							=     blip_decoder(pretrained=snake_case__						,   image_size=384						,   vit="""base"""     )
  _UpperCAmelCase		:							List[Any]							=     pt_model.eval()

  _UpperCAmelCase		:							Dict							=     pt_model.state_dict()
  for key in modified_state_dict.copy():
    _UpperCAmelCase		:							Any							=     modified_state_dict.pop(snake_case__     )
    _UpperCAmelCase		:							Union[str, Any]							=     rename_key(snake_case__     )
    _UpperCAmelCase		:							Dict							=     value

  hf_model.load_state_dict(snake_case__     )

  _UpperCAmelCase		:							Optional[int]							=     384
  _UpperCAmelCase		:							List[Any]							=     load_demo_image(image_size=snake_case__						,   device="""cpu"""     )
  _UpperCAmelCase		:							Optional[int]							=     BertTokenizer.from_pretrained("""bert-base-uncased"""     )
  _UpperCAmelCase		:							Union[str, Any]							=     tokenizer(["""a picture of"""]     ).input_ids

  _UpperCAmelCase		:							Optional[int]							=     hf_model.generate(snake_case__						,   snake_case__     )

  assert out[0].tolist() == [3_0522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

  _UpperCAmelCase		:							List[Any]							=     hf_model.generate(snake_case__     )

  assert out[0].tolist() == [3_0522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

  if pytorch_dump_folder_path is not None:
    hf_model.save_pretrained(snake_case__     )

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

  _UpperCAmelCase		:							Optional[Any]							=     blip_vqa(pretrained=snake_case__						,   image_size=snake_case__						,   vit="""base"""     )
  vqa_model.eval()

  _UpperCAmelCase		:							Tuple							=     vqa_model.state_dict()
  for key in modified_state_dict.copy():
    _UpperCAmelCase		:							Optional[Any]							=     modified_state_dict.pop(snake_case__     )
    _UpperCAmelCase		:							Tuple							=     rename_key(snake_case__     )
    _UpperCAmelCase		:							str							=     value

  _UpperCAmelCase		:							List[Any]							=     BlipForQuestionAnswering(snake_case__     )

  hf_vqa_model.load_state_dict(snake_case__     )

  _UpperCAmelCase		:							Tuple							=     ["""How many dogs are in this image?"""]
  _UpperCAmelCase		:							Tuple							=     tokenizer(snake_case__						,   return_tensors="""pt"""     ).input_ids

  _UpperCAmelCase		:							int							=     hf_vqa_model.generate(snake_case__						,   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		:							List[Any]							=     """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"""

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

  _UpperCAmelCase		:							Optional[Any]							=     itm_model.state_dict()
  for key in modified_state_dict.copy():
    _UpperCAmelCase		:							Optional[int]							=     modified_state_dict.pop(snake_case__     )
    _UpperCAmelCase		:							str							=     rename_key(snake_case__     )
    _UpperCAmelCase		:							List[str]							=     value

  _UpperCAmelCase		:							str							=     BlipForImageTextRetrieval(snake_case__     )

  _UpperCAmelCase		:							List[Any]							=     ["""A picture of a woman with a dog sitting in a beach"""]
  _UpperCAmelCase		:							Any							=     tokenizer(
      snake_case__						,   return_tensors="""pt"""						,   padding="""max_length"""						,   truncation=snake_case__						,   max_length=35						,   ).input_ids

  hf_itm_model.load_state_dict(snake_case__     )
  hf_itm_model.eval()

  _UpperCAmelCase		:							Dict							=     hf_itm_model(snake_case__						,   snake_case__						,   use_itm_head=snake_case__     )
  _UpperCAmelCase		:							Optional[int]							=     hf_itm_model(snake_case__						,   snake_case__						,   use_itm_head=snake_case__     )

  assert out[0].item() == 0.2110_6874_9427_7954
  assert torch.nn.functional.softmax(out_itm[0]						,   dim=1     )[:, 1].item() == 0.4_5698_8453_8650_5127

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__					:     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')
 SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     parser.parse_args()

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









def    a__    (		snake_case__					:							int						,   snake_case__					:							int     ):
  return x if y == 0 else greatest_common_divisor(snake_case__						,   x % y     )




def    a__    (		snake_case__					:							int						,   snake_case__					:							int     ):
  return (x * y) // greatest_common_divisor(snake_case__						,   snake_case__     )




def    a__    (		snake_case__					:							int = 20     ):
  _UpperCAmelCase		:							Union[str, Any]							=     1
  for i in range(1						,   n + 1     ):
    _UpperCAmelCase		:							Dict							=     lcm(snake_case__						,   snake_case__     )
  return g


if __name__ == "__main__":
 print(F'{solution() = }')





