



"""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 			_lowercase				(  __snake_case					,__snake_case	)						->   List[str]:
			__lowerCAmelCase      :  int												=       "https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
			__lowerCAmelCase      :  List[str]												=       Image.open(requests.get(__snake_case					,stream=__snake_case	).raw	).convert("RGB"	)

			__lowerCAmelCase      :  Optional[int]												=       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      :  Union[str, Any]												=       transform(__snake_case	).unsqueeze(0	).to(__snake_case	)
			return image


def 			_lowercase				(  __snake_case	)						->   int:
			if "visual_encoder" in key:
						__lowerCAmelCase      :  List[str]												=       re.sub("visual_encoder*"					,"vision_model.encoder"					,__snake_case	)
			if "blocks" in key:
						__lowerCAmelCase      :  Union[str, Any]												=       re.sub(r"blocks"					,"layers"					,__snake_case	)
			if "attn" in key:
						__lowerCAmelCase      :  Tuple												=       re.sub(r"attn"					,"self_attn"					,__snake_case	)
			if "norm1" in key:
						__lowerCAmelCase      :  Optional[int]												=       re.sub(r"norm1"					,"layer_norm1"					,__snake_case	)
			if "norm2" in key:
						__lowerCAmelCase      :  Dict												=       re.sub(r"norm2"					,"layer_norm2"					,__snake_case	)
			if "encoder.norm" in key:
						__lowerCAmelCase      :  List[Any]												=       re.sub(r"encoder.norm"					,"post_layernorm"					,__snake_case	)
			if "encoder.patch_embed.proj" in key:
						__lowerCAmelCase      :  Any												=       re.sub(r"encoder.patch_embed.proj"					,"embeddings.patch_embedding"					,__snake_case	)

			if "encoder.pos_embed" in key:
						__lowerCAmelCase      :  Dict												=       re.sub(r"encoder.pos_embed"					,"embeddings.position_embedding"					,__snake_case	)
			if "encoder.cls_token" in key:
						__lowerCAmelCase      :  int												=       re.sub(r"encoder.cls_token"					,"embeddings.class_embedding"					,__snake_case	)

			if "self_attn" in key:
						__lowerCAmelCase      :  List[Any]												=       re.sub(r"self_attn.proj"					,"self_attn.projection"					,__snake_case	)

			return key


@torch.no_grad()
def 			_lowercase				(  __snake_case					,__snake_case=None	)						->   str:
			if config_path is not None:
						__lowerCAmelCase      :  Union[str, Any]												=       BlipConfig.from_pretrained(__snake_case	)
			else:
						__lowerCAmelCase      :  Optional[Any]												=       BlipConfig(projection_dim=512					,text_config={}					,vision_config={}	)

			__lowerCAmelCase      :  Tuple												=       BlipForConditionalGeneration(__snake_case	).eval()

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

			__lowerCAmelCase      :  Tuple												=       blip_decoder(pretrained=__snake_case					,image_size=384					,vit="base"	)
			__lowerCAmelCase      :  str												=       pt_model.eval()

			__lowerCAmelCase      :  List[Any]												=       pt_model.state_dict()
			for key in modified_state_dict.copy():
						__lowerCAmelCase      :  List[Any]												=       modified_state_dict.pop(__snake_case	)
						__lowerCAmelCase      :  int												=       rename_key(__snake_case	)
						__lowerCAmelCase      :  int												=       value

			hf_model.load_state_dict(__snake_case	)

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

			__lowerCAmelCase      :  List[str]												=       hf_model.generate(__snake_case					,__snake_case	)

			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]

			__lowerCAmelCase      :  str												=       hf_model.generate(__snake_case	)

			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(__snake_case	)

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

			__lowerCAmelCase      :  List[Any]												=       blip_vqa(pretrained=__snake_case					,image_size=__snake_case					,vit="base"	)
			vqa_model.eval()

			__lowerCAmelCase      :  Union[str, Any]												=       vqa_model.state_dict()
			for key in modified_state_dict.copy():
						__lowerCAmelCase      :  Union[str, Any]												=       modified_state_dict.pop(__snake_case	)
						__lowerCAmelCase      :  Optional[Any]												=       rename_key(__snake_case	)
						__lowerCAmelCase      :  str												=       value

			__lowerCAmelCase      :  List[str]												=       BlipForQuestionAnswering(__snake_case	)

			hf_vqa_model.load_state_dict(__snake_case	)

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

			__lowerCAmelCase      :  int												=       hf_vqa_model.generate(__snake_case					,__snake_case	)
			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      :  str												=       "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"

			__lowerCAmelCase      :  Any												=       blip_itm(pretrained=__snake_case					,image_size=__snake_case					,vit="base"	)
			itm_model.eval()

			__lowerCAmelCase      :  Dict												=       itm_model.state_dict()
			for key in modified_state_dict.copy():
						__lowerCAmelCase      :  Optional[Any]												=       modified_state_dict.pop(__snake_case	)
						__lowerCAmelCase      :  List[str]												=       rename_key(__snake_case	)
						__lowerCAmelCase      :  List[Any]												=       value

			__lowerCAmelCase      :  Dict												=       BlipForImageTextRetrieval(__snake_case	)

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

			hf_itm_model.load_state_dict(__snake_case	)
			hf_itm_model.eval()

			__lowerCAmelCase      :  Tuple												=       hf_itm_model(__snake_case					,__snake_case					,use_itm_head=__snake_case	)
			__lowerCAmelCase      :  int												=       hf_itm_model(__snake_case					,__snake_case					,use_itm_head=__snake_case	)

			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__":
	__snake_case							:   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')
	__snake_case							:   List[str]      =    parser.parse_args()

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




"""simple docstring"""






def 			_lowercase				(  __snake_case					,__snake_case	)						->   float:
			if digit_amount > 0:
						return round(number - int(__snake_case	)					,__snake_case	)
			return number - int(__snake_case	)


if __name__ == "__main__":
	print(decimal_isolate(1.53, 0))
	print(decimal_isolate(35.345, 1))
	print(decimal_isolate(35.345, 2))
	print(decimal_isolate(35.345, 3))
	print(decimal_isolate(-14.789, 3))
	print(decimal_isolate(0, 2))
	print(decimal_isolate(-14.123, 1))
	print(decimal_isolate(-14.123, 2))
	print(decimal_isolate(-14.123, 3))