





'''simple docstring'''






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

    A       :       List[str]							=						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)		),
        ]		)
    A       :       List[Any]							=						transform(lowerCamelCase_		).unsqueeze(0		).to(lowerCamelCase_		)
    return image
def 							_lowerCamelCase(  UpperCamelCase__    : str		)		->  List[Any]:

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

    if "encoder.pos_embed" in key:
        A       :       Optional[int]							=						re.sub(R'''encoder.pos_embed'''    ,       '''embeddings.position_embedding'''    ,       lowerCamelCase_		)
    if "encoder.cls_token" in key:
        A       :       List[str]							=						re.sub(R'''encoder.cls_token'''    ,       '''embeddings.class_embedding'''    ,       lowerCamelCase_		)

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

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

    if config_path is not None:
        A       :       Union[str, Any]							=						BlipConfig.from_pretrained(lowerCamelCase_		)
    else:
        A       :       Dict							=						BlipConfig(projection_dim=512    ,       text_config={}    ,       vision_config={}		)

    A       :       Optional[Any]							=						BlipForConditionalGeneration(lowerCamelCase_		).eval()

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

    A       :       int							=						blip_decoder(pretrained=lowerCamelCase_    ,       image_size=384    ,       vit='''base'''		)
    A       :       List[str]							=						pt_model.eval()

    A       :       str							=						pt_model.state_dict()
    for key in modified_state_dict.copy():
        A       :       int							=						modified_state_dict.pop(lowerCamelCase_		)
        A       :       List[Any]							=						rename_key(lowerCamelCase_		)
        A       :       Tuple							=						value

    hf_model.load_state_dict(lowerCamelCase_		)

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

    A       :       int							=						hf_model.generate(lowerCamelCase_    ,       lowerCamelCase_		)

    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       :       Optional[int]							=						hf_model.generate(lowerCamelCase_		)

    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(lowerCamelCase_		)

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

    A       :       str							=						blip_vqa(pretrained=lowerCamelCase_    ,       image_size=lowerCamelCase_    ,       vit='''base'''		)
    vqa_model.eval()

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

    A       :       Any							=						BlipForQuestionAnswering(lowerCamelCase_		)

    hf_vqa_model.load_state_dict(lowerCamelCase_		)

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

    A       :       List[Any]							=						hf_vqa_model.generate(lowerCamelCase_    ,       lowerCamelCase_		)
    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       :       Any							=						'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

    A       :       Union[str, Any]							=						blip_itm(pretrained=lowerCamelCase_    ,       image_size=lowerCamelCase_    ,       vit='''base'''		)
    itm_model.eval()

    A       :       Union[str, Any]							=						itm_model.state_dict()
    for key in modified_state_dict.copy():
        A       :       Any							=						modified_state_dict.pop(lowerCamelCase_		)
        A       :       List[Any]							=						rename_key(lowerCamelCase_		)
        A       :       List[str]							=						value

    A       :       List[Any]							=						BlipForImageTextRetrieval(lowerCamelCase_		)

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

    hf_itm_model.load_state_dict(lowerCamelCase_		)
    hf_itm_model.eval()

    A       :       Dict							=						hf_itm_model(lowerCamelCase_    ,       lowerCamelCase_    ,       use_itm_head=lowerCamelCase_		)
    A       :       Optional[int]							=						hf_itm_model(lowerCamelCase_    ,       lowerCamelCase_    ,       use_itm_head=lowerCamelCase_		)

    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__":
     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)







'''simple docstring'''






def 							_lowerCamelCase(  UpperCamelCase__    : Union[str, Any]    ,       UpperCamelCase__    : str		)		->  Optional[Any]:
    A       :       Tuple							=						0
    while b > 0:
        if b & 1:
            res += a

        a += a
        b >>= 1

    return res
def 							_lowerCamelCase(  UpperCamelCase__    : Any    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : List[str]		)		->  Any:
    A       :       str							=						0
    while b > 0:
        if b & 1:
            A       :       List[Any]							=						((res % c) + (a % c)) % c

        a += a
        b >>= 1

    return res
