



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 	UpperCAmelCase__      (      UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :str					):






 '''simple docstring'''

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

 a						=		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)					),
     ]					)
 a						=		transform(UpperCAmelCase__					).unsqueeze(0					).to(UpperCAmelCase__					)
 return image


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Dict					):






 '''simple docstring'''

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

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

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

 return key


@torch.no_grad()
def 	UpperCAmelCase__      (      UpperCAmelCase__ :int ,					UpperCAmelCase__ :List[str]=None					):






 '''simple docstring'''

 if config_path is not None:
  a						=		BlipConfig.from_pretrained(UpperCAmelCase__					)
 else:
  a						=		BlipConfig(projection_dim=5_12 ,					text_config={} ,					vision_config={}					)

 a						=		BlipForConditionalGeneration(UpperCAmelCase__					).eval()

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

 a						=		blip_decoder(pretrained=UpperCAmelCase__ ,					image_size=3_84 ,					vit="base"					)
 a						=		pt_model.eval()

 a						=		pt_model.state_dict()
 for key in modified_state_dict.copy():
  a						=		modified_state_dict.pop(UpperCAmelCase__					)
  a						=		rename_key(UpperCAmelCase__					)
  a						=		value

 hf_model.load_state_dict(UpperCAmelCase__					)

 a						=		3_84
 a						=		load_demo_image(image_size=UpperCAmelCase__ ,					device="cpu"					)
 a						=		BertTokenizer.from_pretrained("bert-base-uncased"					)
 a						=		tokenizer(["a picture of"]					).input_ids

 a						=		hf_model.generate(UpperCAmelCase__ ,					UpperCAmelCase__					)

 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]

 a						=		hf_model.generate(UpperCAmelCase__					)

 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(UpperCAmelCase__					)

 # 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=UpperCAmelCase__ ,					image_size=UpperCAmelCase__ ,					vit="base"					)
 vqa_model.eval()

 a						=		vqa_model.state_dict()
 for key in modified_state_dict.copy():
  a						=		modified_state_dict.pop(UpperCAmelCase__					)
  a						=		rename_key(UpperCAmelCase__					)
  a						=		value

 a						=		BlipForQuestionAnswering(UpperCAmelCase__					)

 hf_vqa_model.load_state_dict(UpperCAmelCase__					)

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

 a						=		hf_vqa_model.generate(UpperCAmelCase__ ,					UpperCAmelCase__					)
 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=UpperCAmelCase__ ,					image_size=UpperCAmelCase__ ,					vit="base"					)
 itm_model.eval()

 a						=		itm_model.state_dict()
 for key in modified_state_dict.copy():
  a						=		modified_state_dict.pop(UpperCAmelCase__					)
  a						=		rename_key(UpperCAmelCase__					)
  a						=		value

 a						=		BlipForImageTextRetrieval(UpperCAmelCase__					)

 a						=		["A picture of a woman with a dog sitting in a beach"]
 a						=		tokenizer(
     UpperCAmelCase__ ,					return_tensors="pt" ,					padding="max_length" ,					truncation=UpperCAmelCase__ ,					max_length=35 ,					).input_ids

 hf_itm_model.load_state_dict(UpperCAmelCase__					)
 hf_itm_model.eval()

 a						=		hf_itm_model(UpperCAmelCase__ ,					UpperCAmelCase__ ,					use_itm_head=UpperCAmelCase__					)
 a						=		hf_itm_model(UpperCAmelCase__ ,					UpperCAmelCase__ ,					use_itm_head=UpperCAmelCase__					)

 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_		:     Optional[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_		:     Any        =     parser.parse_args()

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






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
     if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
     from .pipeline_unclip import UnCLIPPipeline
     from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
     from .text_proj import UnCLIPTextProjModel

