

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__      (   SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]      ):



     '''simple docstring'''





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

     lowercase__   :       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)      ),
         ]      )
     lowercase__   :       str				=	transform(SCREAMING_SNAKE_CASE_      ).unsqueeze(0      ).to(SCREAMING_SNAKE_CASE_      )
     return image

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Union[str, Any]      ):



     '''simple docstring'''





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

     if "encoder.pos_embed" in key:
          lowercase__   :       List[Any]				=	re.sub(R'encoder.pos_embed'     ,					'embeddings.position_embedding'     ,					SCREAMING_SNAKE_CASE_      )
     if "encoder.cls_token" in key:
          lowercase__   :       List[Any]				=	re.sub(R'encoder.cls_token'     ,					'embeddings.class_embedding'     ,					SCREAMING_SNAKE_CASE_      )

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

     return key

@torch.no_grad()
def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   Dict=None      ):



     '''simple docstring'''





     if config_path is not None:
          lowercase__   :       List[str]				=	BlipConfig.from_pretrained(SCREAMING_SNAKE_CASE_      )
     else:
          lowercase__   :       str				=	BlipConfig(projection_dim=512     ,					text_config={}     ,					vision_config={}      )

     lowercase__   :       int				=	BlipForConditionalGeneration(SCREAMING_SNAKE_CASE_      ).eval()

     lowercase__   :       Optional[int]				=	'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'

     lowercase__   :       Union[str, Any]				=	blip_decoder(pretrained=SCREAMING_SNAKE_CASE_     ,					image_size=384     ,					vit='base'      )
     lowercase__   :       Union[str, Any]				=	pt_model.eval()

     lowercase__   :       Dict				=	pt_model.state_dict()
     for key in modified_state_dict.copy():
          lowercase__   :       Dict				=	modified_state_dict.pop(SCREAMING_SNAKE_CASE_      )
          lowercase__   :       str				=	rename_key(SCREAMING_SNAKE_CASE_      )
          lowercase__   :       int				=	value

     hf_model.load_state_dict(SCREAMING_SNAKE_CASE_      )

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

     lowercase__   :       List[str]				=	hf_model.generate(SCREAMING_SNAKE_CASE_     ,					SCREAMING_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]

     lowercase__   :       Optional[Any]				=	hf_model.generate(SCREAMING_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(SCREAMING_SNAKE_CASE_      )

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

     lowercase__   :       List[Any]				=	blip_vqa(pretrained=SCREAMING_SNAKE_CASE_     ,					image_size=SCREAMING_SNAKE_CASE_     ,					vit='base'      )
     vqa_model.eval()

     lowercase__   :       List[Any]				=	vqa_model.state_dict()
     for key in modified_state_dict.copy():
          lowercase__   :       int				=	modified_state_dict.pop(SCREAMING_SNAKE_CASE_      )
          lowercase__   :       List[Any]				=	rename_key(SCREAMING_SNAKE_CASE_      )
          lowercase__   :       int				=	value

     lowercase__   :       List[str]				=	BlipForQuestionAnswering(SCREAMING_SNAKE_CASE_      )

     hf_vqa_model.load_state_dict(SCREAMING_SNAKE_CASE_      )

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

     lowercase__   :       Union[str, Any]				=	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'      )

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

     lowercase__   :       List[Any]				=	blip_itm(pretrained=SCREAMING_SNAKE_CASE_     ,					image_size=SCREAMING_SNAKE_CASE_     ,					vit='base'      )
     itm_model.eval()

     lowercase__   :       Tuple				=	itm_model.state_dict()
     for key in modified_state_dict.copy():
          lowercase__   :       int				=	modified_state_dict.pop(SCREAMING_SNAKE_CASE_      )
          lowercase__   :       Optional[int]				=	rename_key(SCREAMING_SNAKE_CASE_      )
          lowercase__   :       int				=	value

     lowercase__   :       Optional[Any]				=	BlipForImageTextRetrieval(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       Dict				=	['A picture of a woman with a dog sitting in a beach']
     lowercase__   :       List[Any]				=	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()

     lowercase__   :       str				=	hf_itm_model(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					use_itm_head=SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Tuple				=	hf_itm_model(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					use_itm_head=SCREAMING_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__":
       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''')
       snake_case_        =			parser.parse_args()

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




import numpy as np

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   List[Any]      ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	int(np.ceil((x_end - xa) / h      )      )
     lowercase__   :       Dict				=	np.zeros((n + 1,)      )
     lowercase__   :       Dict				=	ya
     lowercase__   :       Union[str, Any]				=	xa

     for k in range(SCREAMING_SNAKE_CASE_      ):
          lowercase__   :       Optional[int]				=	f(SCREAMING_SNAKE_CASE_     ,					y[k]      )
          lowercase__   :       Dict				=	f(x + 0.5 * h     ,					y[k] + 0.5 * h * ka      )
          lowercase__   :       Any				=	f(x + 0.5 * h     ,					y[k] + 0.5 * h * ka      )
          lowercase__   :       List[Any]				=	f(x + h     ,					y[k] + h * ka      )
          lowercase__   :       Tuple				=	y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
          x += h

     return y


if __name__ == "__main__":
       import doctest

       doctest.testmod()

