





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_ (							A_    ,A_):
    UpperCamelCase__:	Any					=     "https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
    UpperCamelCase__:	Union[str, Any]					=     Image.open(requests.get(A_    ,stream=A_).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__:	Any					=     transform(A_).unsqueeze(0).to(A_)
    return image



def 				lowerCAmelCase_ (							A_):
    if "visual_encoder" in key:
        UpperCamelCase__:	Tuple					=     re.sub("visual_encoder*"    ,"vision_model.encoder"    ,A_)
    if "blocks" in key:
        UpperCamelCase__:	Union[str, Any]					=     re.sub(R"blocks"    ,"layers"    ,A_)
    if "attn" in key:
        UpperCamelCase__:	Tuple					=     re.sub(R"attn"    ,"self_attn"    ,A_)
    if "norm1" in key:
        UpperCamelCase__:	Any					=     re.sub(R"norm1"    ,"layer_norm1"    ,A_)
    if "norm2" in key:
        UpperCamelCase__:	Dict					=     re.sub(R"norm2"    ,"layer_norm2"    ,A_)
    if "encoder.norm" in key:
        UpperCamelCase__:	Dict					=     re.sub(R"encoder.norm"    ,"post_layernorm"    ,A_)
    if "encoder.patch_embed.proj" in key:
        UpperCamelCase__:	Any					=     re.sub(R"encoder.patch_embed.proj"    ,"embeddings.patch_embedding"    ,A_)

    if "encoder.pos_embed" in key:
        UpperCamelCase__:	str					=     re.sub(R"encoder.pos_embed"    ,"embeddings.position_embedding"    ,A_)
    if "encoder.cls_token" in key:
        UpperCamelCase__:	Any					=     re.sub(R"encoder.cls_token"    ,"embeddings.class_embedding"    ,A_)

    if "self_attn" in key:
        UpperCamelCase__:	List[str]					=     re.sub(R"self_attn.proj"    ,"self_attn.projection"    ,A_)

    return key



@torch.no_grad()
def 				lowerCAmelCase_ (							A_    ,A_=None):
    if config_path is not None:
        UpperCamelCase__:	Optional[int]					=     BlipConfig.from_pretrained(A_)
    else:
        UpperCamelCase__:	List[str]					=     BlipConfig(projection_dim=5_12    ,text_config={}    ,vision_config={})

    UpperCamelCase__:	Optional[int]					=     BlipForConditionalGeneration(A_).eval()

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

    UpperCamelCase__:	str					=     blip_decoder(pretrained=A_    ,image_size=3_84    ,vit="base")
    UpperCamelCase__:	Optional[Any]					=     pt_model.eval()

    UpperCamelCase__:	Any					=     pt_model.state_dict()
    for key in modified_state_dict.copy():
        UpperCamelCase__:	Optional[Any]					=     modified_state_dict.pop(A_)
        UpperCamelCase__:	Any					=     rename_key(A_)
        UpperCamelCase__:	List[str]					=     value

    hf_model.load_state_dict(A_)

    UpperCamelCase__:	str					=     3_84
    UpperCamelCase__:	Any					=     load_demo_image(image_size=A_    ,device="cpu")
    UpperCamelCase__:	str					=     BertTokenizer.from_pretrained("bert-base-uncased")
    UpperCamelCase__:	List[str]					=     tokenizer(["a picture of"]).input_ids

    UpperCamelCase__:	Dict					=     hf_model.generate(A_    ,A_)

    assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

    UpperCamelCase__:	Optional[Any]					=     hf_model.generate(A_)

    assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

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

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

    UpperCamelCase__:	int					=     blip_vqa(pretrained=A_    ,image_size=A_    ,vit="base")
    vqa_model.eval()

    UpperCamelCase__:	int					=     vqa_model.state_dict()
    for key in modified_state_dict.copy():
        UpperCamelCase__:	Dict					=     modified_state_dict.pop(A_)
        UpperCamelCase__:	Optional[Any]					=     rename_key(A_)
        UpperCamelCase__:	List[str]					=     value

    UpperCamelCase__:	Any					=     BlipForQuestionAnswering(A_)

    hf_vqa_model.load_state_dict(A_)

    UpperCamelCase__:	Optional[int]					=     ["How many dogs are in this image?"]
    UpperCamelCase__:	int					=     tokenizer(A_    ,return_tensors="pt").input_ids

    UpperCamelCase__:	Optional[int]					=     hf_vqa_model.generate(A_    ,A_)
    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__:	Optional[Any]					=     "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"

    UpperCamelCase__:	Union[str, Any]					=     blip_itm(pretrained=A_    ,image_size=A_    ,vit="base")
    itm_model.eval()

    UpperCamelCase__:	Optional[Any]					=     itm_model.state_dict()
    for key in modified_state_dict.copy():
        UpperCamelCase__:	Any					=     modified_state_dict.pop(A_)
        UpperCamelCase__:	int					=     rename_key(A_)
        UpperCamelCase__:	int					=     value

    UpperCamelCase__:	str					=     BlipForImageTextRetrieval(A_)

    UpperCamelCase__:	Optional[int]					=     ["A picture of a woman with a dog sitting in a beach"]
    UpperCamelCase__:	Dict					=     tokenizer(
        A_    ,return_tensors="pt"    ,padding="max_length"    ,truncation=A_    ,max_length=35    ,).input_ids

    hf_itm_model.load_state_dict(A_)
    hf_itm_model.eval()

    UpperCamelCase__:	Optional[int]					=     hf_itm_model(A_    ,A_    ,use_itm_head=A_)
    UpperCamelCase__:	List[str]					=     hf_itm_model(A_    ,A_    ,use_itm_head=A_)

    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__:    List[Any]  	=	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__:    Dict  	=	parser.parse_args()

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








from __future__ import annotations

from collections import namedtuple



def 				lowerCAmelCase_ (							A_    ,A_    ,A_):
    UpperCamelCase__:	List[str]					=     namedtuple("result"    ,"name value")
    if (voltage, current, power).count(0) != 1:
        raise ValueError("Only one argument must be 0")
    elif power < 0:
        raise ValueError(
            "Power cannot be negative in any electrical/electronics system")
    elif voltage == 0:
        return result("voltage"    ,power / current)
    elif current == 0:
        return result("current"    ,power / voltage)
    elif power == 0:
        return result("power"    ,float(round(abs(voltage * current)    ,2)))
    else:
        raise ValueError("Exactly one argument must be 0")


if __name__ == "__main__":
   import doctest

   doctest.testmod()

