




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 lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


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

     UpperCAmelCase_      :							str       =      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_      :							Tuple       =      transform(_lowercase				).unsqueeze(0				).to(_lowercase				)
     return image







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


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

     if "encoder.pos_embed" in key:
          UpperCAmelCase_      :							Dict       =      re.sub(r'''encoder.pos_embed'''			,						'''embeddings.position_embedding'''			,						_lowercase				)
     if "encoder.cls_token" in key:
          UpperCAmelCase_      :							Optional[int]       =      re.sub(r'''encoder.cls_token'''			,						'''embeddings.class_embedding'''			,						_lowercase				)

     if "self_attn" in key:
          UpperCAmelCase_      :							Tuple       =      re.sub(r'''self_attn.proj'''			,						'''self_attn.projection'''			,						_lowercase				)

     return key







@torch.no_grad()
def lowerCamelCase__       (      _lowercase			,						_lowercase=None				):

     '''simple docstring'''


     if config_path is not None:
          UpperCAmelCase_      :							str       =      BlipConfig.from_pretrained(_lowercase				)
     else:
          UpperCAmelCase_      :							Tuple       =      BlipConfig(projection_dim=512			,						text_config={}			,						vision_config={}				)

     UpperCAmelCase_      :							Optional[Any]       =      BlipForConditionalGeneration(_lowercase				).eval()

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

     UpperCAmelCase_      :							Any       =      blip_decoder(pretrained=_lowercase			,						image_size=384			,						vit='''base'''				)
     UpperCAmelCase_      :							List[str]       =      pt_model.eval()

     UpperCAmelCase_      :							int       =      pt_model.state_dict()
     for key in modified_state_dict.copy():
          UpperCAmelCase_      :							Union[str, Any]       =      modified_state_dict.pop(_lowercase				)
          UpperCAmelCase_      :							Any       =      rename_key(_lowercase				)
          UpperCAmelCase_      :							Any       =      value

     hf_model.load_state_dict(_lowercase				)

     UpperCAmelCase_      :							Optional[int]       =      384
     UpperCAmelCase_      :							str       =      load_demo_image(image_size=_lowercase			,						device='''cpu'''				)
     UpperCAmelCase_      :							Any       =      BertTokenizer.from_pretrained('''bert-base-uncased'''				)
     UpperCAmelCase_      :							Optional[Any]       =      tokenizer(['''a picture of''']				).input_ids

     UpperCAmelCase_      :							Optional[int]       =      hf_model.generate(_lowercase			,						_lowercase				)

     assert out[0].tolist() == [30522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

     UpperCAmelCase_      :							Optional[int]       =      hf_model.generate(_lowercase				)

     assert out[0].tolist() == [30522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

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

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

     UpperCAmelCase_      :							Optional[Any]       =      blip_vqa(pretrained=_lowercase			,						image_size=_lowercase			,						vit='''base'''				)
     vqa_model.eval()

     UpperCAmelCase_      :							Union[str, Any]       =      vqa_model.state_dict()
     for key in modified_state_dict.copy():
          UpperCAmelCase_      :							int       =      modified_state_dict.pop(_lowercase				)
          UpperCAmelCase_      :							Any       =      rename_key(_lowercase				)
          UpperCAmelCase_      :							List[Any]       =      value

     UpperCAmelCase_      :							int       =      BlipForQuestionAnswering(_lowercase				)

     hf_vqa_model.load_state_dict(_lowercase				)

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

     UpperCAmelCase_      :							str       =      hf_vqa_model.generate(_lowercase			,						_lowercase				)
     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_      :							List[Any]       =      blip_itm(pretrained=_lowercase			,						image_size=_lowercase			,						vit='''base'''				)
     itm_model.eval()

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

     UpperCAmelCase_      :							Any       =      BlipForImageTextRetrieval(_lowercase				)

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

     hf_itm_model.load_state_dict(_lowercase				)
     hf_itm_model.eval()

     UpperCAmelCase_      :							List[str]       =      hf_itm_model(_lowercase			,						_lowercase			,						use_itm_head=_lowercase				)
     UpperCAmelCase_      :							Union[str, Any]       =      hf_itm_model(_lowercase			,						_lowercase			,						use_itm_head=_lowercase				)

     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__":
     __a          =			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          =			parser.parse_args()

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





from math import ceil







def lowerCamelCase__       (      _lowercase = 1001				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[Any]       =      1

     for i in range(1			,						int(ceil(n / 2.0				)				)				):
          UpperCAmelCase_      :							List[Any]       =      2 * i + 1
          UpperCAmelCase_      :							Optional[int]       =      2 * i
          UpperCAmelCase_      :							Any       =      total + 4 * odd**2 - 6 * even

     return total


if __name__ == "__main__":
     import sys

     if len(sys.argv) == 1:
          print(solution())
     else:
          try:
               __a          =			int(sys.argv[1])
               print(solution(n))
          except ValueError:
               print('Invalid entry - please enter a number')