



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__	(					lowerCamelCase_	:	Any ,lowerCamelCase_	:	List[Any]):





				'''simple docstring'''

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

				lowerCAmelCase__  :       List[str]				=  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)),
				    ])
				lowerCAmelCase__  :       List[Any]				=  transform(lowerCamelCase_).unsqueeze(0).to(lowerCamelCase_)
				return image






def 			lowerCAmelCase__	(					lowerCamelCase_	:	str):





				'''simple docstring'''

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

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

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

				return key






@torch.no_grad()
def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	Dict=None):





				'''simple docstring'''

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

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

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

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

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

				hf_model.load_state_dict(lowerCamelCase_)

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

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

				assert out[0].tolist() == [30522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

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

				assert out[0].tolist() == [30522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 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'
				lowerCAmelCase__  :       Any				=  (
				    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
				)

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

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

				lowerCAmelCase__  :       Any				=  BlipForQuestionAnswering(lowerCamelCase_)

				hf_vqa_model.load_state_dict(lowerCamelCase_)

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

				lowerCAmelCase__  :       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''')

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

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

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

				lowerCAmelCase__  :       List[Any]				=  BlipForImageTextRetrieval(lowerCamelCase_)

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

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

				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__":
							_UpperCAmelCase       :	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')
							_UpperCAmelCase       :	str            =parser.parse_args()

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int):





				'''simple docstring'''

				if not isinstance(lowerCamelCase_ ,lowerCamelCase_):
								lowerCAmelCase__  :       Dict				=  f"""Input value of [number={number}] must be an integer"""
								raise TypeError(lowerCamelCase_)

				if number < 1:
								lowerCAmelCase__  :       Any				=  f"""Input value of [number={number}] must be > 0"""
								raise ValueError(lowerCamelCase_)

				lowerCAmelCase__  :       List[str]				=  1

				for i in range(1 ,lowerCamelCase_):
								current_number *= 4 * i - 2
								current_number //= i + 1

				return current_number


if __name__ == "__main__":
							import doctest

							doctest.testmod()

