
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 						lowercase_      (       A__			,		A__		)							->							Tuple:
    """simple docstring"""

    snake_case								= "https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
    snake_case								= Image.open(requests.get(A__			,		stream=A__		).raw		).convert("RGB"		)

    snake_case								= transforms.Compose(
        [
            transforms.Resize((image_size, image_size)			,		interpolation=InterpolationMode.BICUBIC		),
            transforms.ToTensor(),
            transforms.Normalize((0.48145466, 0.4578275, 0.40821073)			,		(0.26862954, 0.26130258, 0.27577711)		),
        ]		)
    snake_case								= transform(A__		).unsqueeze(0		).to(A__		)
    return image



def 						lowercase_      (       A__		)							->							str:
    """simple docstring"""

    if "visual_encoder" in key:
        snake_case								= re.sub("visual_encoder*"			,		"vision_model.encoder"			,		A__		)
    if "blocks" in key:
        snake_case								= re.sub(r"blocks"			,		"layers"			,		A__		)
    if "attn" in key:
        snake_case								= re.sub(r"attn"			,		"self_attn"			,		A__		)
    if "norm1" in key:
        snake_case								= re.sub(r"norm1"			,		"layer_norm1"			,		A__		)
    if "norm2" in key:
        snake_case								= re.sub(r"norm2"			,		"layer_norm2"			,		A__		)
    if "encoder.norm" in key:
        snake_case								= re.sub(r"encoder.norm"			,		"post_layernorm"			,		A__		)
    if "encoder.patch_embed.proj" in key:
        snake_case								= re.sub(r"encoder.patch_embed.proj"			,		"embeddings.patch_embedding"			,		A__		)

    if "encoder.pos_embed" in key:
        snake_case								= re.sub(r"encoder.pos_embed"			,		"embeddings.position_embedding"			,		A__		)
    if "encoder.cls_token" in key:
        snake_case								= re.sub(r"encoder.cls_token"			,		"embeddings.class_embedding"			,		A__		)

    if "self_attn" in key:
        snake_case								= re.sub(r"self_attn.proj"			,		"self_attn.projection"			,		A__		)

    return key



@torch.no_grad()
def 						lowercase_      (       A__			,		A__=None		)							->							int:
    """simple docstring"""

    if config_path is not None:
        snake_case								= BlipConfig.from_pretrained(A__		)
    else:
        snake_case								= BlipConfig(projection_dim=512			,		text_config={}			,		vision_config={}		)

    snake_case								= BlipForConditionalGeneration(A__		).eval()

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

    snake_case								= blip_decoder(pretrained=A__			,		image_size=384			,		vit="base"		)
    snake_case								= pt_model.eval()

    snake_case								= pt_model.state_dict()
    for key in modified_state_dict.copy():
        snake_case								= modified_state_dict.pop(A__		)
        snake_case								= rename_key(A__		)
        snake_case								= value

    hf_model.load_state_dict(A__		)

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

    snake_case								= hf_model.generate(A__			,		A__		)

    assert out[0].tolist() == [3_0522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

    snake_case								= hf_model.generate(A__		)

    assert out[0].tolist() == [3_0522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

    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'
    snake_case								= (
        "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"
    )

    snake_case								= blip_vqa(pretrained=A__			,		image_size=A__			,		vit="base"		)
    vqa_model.eval()

    snake_case								= vqa_model.state_dict()
    for key in modified_state_dict.copy():
        snake_case								= modified_state_dict.pop(A__		)
        snake_case								= rename_key(A__		)
        snake_case								= value

    snake_case								= BlipForQuestionAnswering(A__		)

    hf_vqa_model.load_state_dict(A__		)

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

    snake_case								= 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"		)

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

    snake_case								= blip_itm(pretrained=A__			,		image_size=A__			,		vit="base"		)
    itm_model.eval()

    snake_case								= itm_model.state_dict()
    for key in modified_state_dict.copy():
        snake_case								= modified_state_dict.pop(A__		)
        snake_case								= rename_key(A__		)
        snake_case								= value

    snake_case								= BlipForImageTextRetrieval(A__		)

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

    snake_case								= hf_itm_model(A__			,		A__			,		use_itm_head=A__		)
    snake_case								= hf_itm_model(A__			,		A__			,		use_itm_head=A__		)

    assert out[0].item() == 0.2110687494277954
    assert torch.nn.functional.softmax(out_itm[0]			,		dim=1		)[:, 1].item() == 0.45698845386505127

    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)



def 						lowercase_      (       A__ = 1000		)							->							int:
    """simple docstring"""

    return sum(2 * a * ((a - 1) // 2) for a in range(3			,		n + 1		)		)


if __name__ == "__main__":
   print(solution())

