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

       _A        =  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)),
           ])
       _A        =  transform(snake_case__).unsqueeze(0).to(snake_case__)
       return image






def  snake_case  (       snake_case__			:int)						->   str:
       if "visual_encoder" in key:
              _A        =  re.sub("""visual_encoder*"""   ,	"""vision_model.encoder"""   ,	snake_case__)
       if "blocks" in key:
              _A        =  re.sub(R"""blocks"""   ,	"""layers"""   ,	snake_case__)
       if "attn" in key:
              _A        =  re.sub(R"""attn"""   ,	"""self_attn"""   ,	snake_case__)
       if "norm1" in key:
              _A        =  re.sub(R"""norm1"""   ,	"""layer_norm1"""   ,	snake_case__)
       if "norm2" in key:
              _A        =  re.sub(R"""norm2"""   ,	"""layer_norm2"""   ,	snake_case__)
       if "encoder.norm" in key:
              _A        =  re.sub(R"""encoder.norm"""   ,	"""post_layernorm"""   ,	snake_case__)
       if "encoder.patch_embed.proj" in key:
              _A        =  re.sub(R"""encoder.patch_embed.proj"""   ,	"""embeddings.patch_embedding"""   ,	snake_case__)

       if "encoder.pos_embed" in key:
              _A        =  re.sub(R"""encoder.pos_embed"""   ,	"""embeddings.position_embedding"""   ,	snake_case__)
       if "encoder.cls_token" in key:
              _A        =  re.sub(R"""encoder.cls_token"""   ,	"""embeddings.class_embedding"""   ,	snake_case__)

       if "self_attn" in key:
              _A        =  re.sub(R"""self_attn.proj"""   ,	"""self_attn.projection"""   ,	snake_case__)

       return key






@torch.no_grad()
def  snake_case  (       snake_case__			:List[Any]   ,	snake_case__			:Dict=None)						->   List[Any]:
       if config_path is not None:
              _A        =  BlipConfig.from_pretrained(snake_case__)
       else:
              _A        =  BlipConfig(projection_dim=512   ,	text_config={}   ,	vision_config={})

       _A        =  BlipForConditionalGeneration(snake_case__).eval()

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

       _A        =  blip_decoder(pretrained=snake_case__   ,	image_size=384   ,	vit="""base""")
       _A        =  pt_model.eval()

       _A        =  pt_model.state_dict()
       for key in modified_state_dict.copy():
              _A        =  modified_state_dict.pop(snake_case__)
              _A        =  rename_key(snake_case__)
              _A        =  value

       hf_model.load_state_dict(snake_case__)

       _A        =  384
       _A        =  load_demo_image(image_size=snake_case__   ,	device="""cpu""")
       _A        =  BertTokenizer.from_pretrained("""bert-base-uncased""")
       _A        =  tokenizer(["""a picture of"""]).input_ids

       _A        =  hf_model.generate(snake_case__   ,	snake_case__)

       assert out[0].tolist() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

       _A        =  hf_model.generate(snake_case__)

       assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 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'
       _A        =  (
           """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"""
       )

       _A        =  blip_vqa(pretrained=snake_case__   ,	image_size=snake_case__   ,	vit="""base""")
       vqa_model.eval()

       _A        =  vqa_model.state_dict()
       for key in modified_state_dict.copy():
              _A        =  modified_state_dict.pop(snake_case__)
              _A        =  rename_key(snake_case__)
              _A        =  value

       _A        =  BlipForQuestionAnswering(snake_case__)

       hf_vqa_model.load_state_dict(snake_case__)

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

       _A        =  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""")

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

       _A        =  blip_itm(pretrained=snake_case__   ,	image_size=snake_case__   ,	vit="""base""")
       itm_model.eval()

       _A        =  itm_model.state_dict()
       for key in modified_state_dict.copy():
              _A        =  modified_state_dict.pop(snake_case__)
              _A        =  rename_key(snake_case__)
              _A        =  value

       _A        =  BlipForImageTextRetrieval(snake_case__)

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

       _A        =  hf_itm_model(snake_case__   ,	snake_case__   ,	use_itm_head=snake_case__)
       _A        =  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										=						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)





import math
import sys






def  snake_case  (       snake_case__			:int)						->   int:
       if number != int(snake_case__):
              raise ValueError("""the value of input must be a natural number""")
       if number < 0:
              raise ValueError("""the value of input must not be a negative number""")
       if number == 0:
              return 1
       _A        =  [-1] * (number + 1)
       _A        =  0
       for i in range(1   ,	number + 1):
              _A        =  sys.maxsize
              _A        =  int(math.sqrt(snake_case__))
              for j in range(1   ,	root + 1):
                     _A        =  1 + answers[i - (j**2)]
                     _A        =  min(snake_case__   ,	snake_case__)
              _A        =  answer
       return answers[number]


if __name__ == "__main__":
    import doctest

    doctest.testmod()




