




"""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 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			List[Any]:


			"""simple docstring"""
			__UpperCAmelCase	: Tuple		=					"https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
			__UpperCAmelCase	: Any		=					Image.open(requests.get(UpperCamelCase				,       stream=UpperCamelCase							).raw							).convert("RGB"							)

			__UpperCAmelCase	: 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)							),
			    ]							)
			__UpperCAmelCase	: Any		=					transform(UpperCamelCase							).unsqueeze(0							).to(UpperCamelCase							)
			return image

def 					_UpperCamelCase    (			UpperCamelCase							)			->			Optional[Any]:


			"""simple docstring"""
			if "visual_encoder" in key:
						__UpperCAmelCase	: Dict		=					re.sub("visual_encoder*"				,       "vision_model.encoder"				,       UpperCamelCase							)
			if "blocks" in key:
						__UpperCAmelCase	: Dict		=					re.sub(R"blocks"				,       "layers"				,       UpperCamelCase							)
			if "attn" in key:
						__UpperCAmelCase	: Optional[int]		=					re.sub(R"attn"				,       "self_attn"				,       UpperCamelCase							)
			if "norm1" in key:
						__UpperCAmelCase	: Any		=					re.sub(R"norm1"				,       "layer_norm1"				,       UpperCamelCase							)
			if "norm2" in key:
						__UpperCAmelCase	: Union[str, Any]		=					re.sub(R"norm2"				,       "layer_norm2"				,       UpperCamelCase							)
			if "encoder.norm" in key:
						__UpperCAmelCase	: Dict		=					re.sub(R"encoder.norm"				,       "post_layernorm"				,       UpperCamelCase							)
			if "encoder.patch_embed.proj" in key:
						__UpperCAmelCase	: Any		=					re.sub(R"encoder.patch_embed.proj"				,       "embeddings.patch_embedding"				,       UpperCamelCase							)

			if "encoder.pos_embed" in key:
						__UpperCAmelCase	: List[str]		=					re.sub(R"encoder.pos_embed"				,       "embeddings.position_embedding"				,       UpperCamelCase							)
			if "encoder.cls_token" in key:
						__UpperCAmelCase	: Optional[Any]		=					re.sub(R"encoder.cls_token"				,       "embeddings.class_embedding"				,       UpperCamelCase							)

			if "self_attn" in key:
						__UpperCAmelCase	: Optional[int]		=					re.sub(R"self_attn.proj"				,       "self_attn.projection"				,       UpperCamelCase							)

			return key

@torch.no_grad()
def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase=None							)			->			Optional[Any]:


			"""simple docstring"""
			if config_path is not None:
						__UpperCAmelCase	: Tuple		=					BlipConfig.from_pretrained(UpperCamelCase							)
			else:
						__UpperCAmelCase	: List[str]		=					BlipConfig(projection_dim=512				,       text_config={}				,       vision_config={}							)

			__UpperCAmelCase	: List[Any]		=					BlipForConditionalGeneration(UpperCamelCase							).eval()

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

			__UpperCAmelCase	: Union[str, Any]		=					blip_decoder(pretrained=UpperCamelCase				,       image_size=384				,       vit="base"							)
			__UpperCAmelCase	: List[Any]		=					pt_model.eval()

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

			hf_model.load_state_dict(UpperCamelCase							)

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

			__UpperCAmelCase	: Optional[int]		=					hf_model.generate(UpperCamelCase				,       UpperCamelCase							)

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

			__UpperCAmelCase	: Any		=					hf_model.generate(UpperCamelCase							)

			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(UpperCamelCase							)

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

			__UpperCAmelCase	: List[str]		=					blip_vqa(pretrained=UpperCamelCase				,       image_size=UpperCamelCase				,       vit="base"							)
			vqa_model.eval()

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

			__UpperCAmelCase	: Tuple		=					BlipForQuestionAnswering(UpperCamelCase							)

			hf_vqa_model.load_state_dict(UpperCamelCase							)

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

			__UpperCAmelCase	: Union[str, Any]		=					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"							)

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

			__UpperCAmelCase	: List[Any]		=					blip_itm(pretrained=UpperCamelCase				,       image_size=UpperCamelCase				,       vit="base"							)
			itm_model.eval()

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

			__UpperCAmelCase	: List[str]		=					BlipForImageTextRetrieval(UpperCamelCase							)

			__UpperCAmelCase	: List[str]		=					["A picture of a woman with a dog sitting in a beach"]
			__UpperCAmelCase	: int		=					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()

			__UpperCAmelCase	: Tuple		=					hf_itm_model(UpperCamelCase				,       UpperCamelCase				,       use_itm_head=UpperCamelCase							)
			__UpperCAmelCase	: Dict		=					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   =       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)












"""simple docstring"""





from ...processing_utils import ProcessorMixin



class      a__				(	__magic_name__   ):
			lowercase_    =	["image_processor", "feature_extractor"]
			lowercase_    =	"TvltImageProcessor"
			lowercase_    =	"TvltFeatureExtractor"


			def __init__(      self      :    Tuple      ,  UpperCamelCase_      :    Optional[int]      ,  UpperCamelCase_      :    Dict):


						"""simple docstring"""




						super().__init__(image_processor=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						__UpperCAmelCase	: Optional[int]		=					image_processor
						__UpperCAmelCase	: Dict		=					feature_extractor


			def __call__(      self      :    Tuple      ,  UpperCamelCase_      :    str=None      ,  UpperCamelCase_      :    Optional[int]=None      ,  UpperCamelCase_      :    Optional[int]=None      ,  UpperCamelCase_      :    str=None      ,  UpperCamelCase_      :    List[str]=False      ,  UpperCamelCase_      :    Optional[int]=False      ,  *UpperCamelCase_      :    List[str]      ,  **UpperCamelCase_      :    str      ,  ):


						"""simple docstring"""




						if images is None and audio is None:
									raise ValueError("You need to specify either an `images` or `audio` input to process.")

						__UpperCAmelCase	: Optional[Any]		=					None
						if images is not None:
									__UpperCAmelCase	: int		=					self.image_processor(UpperCamelCase_      ,  mask_pixel=UpperCamelCase_      ,  *UpperCamelCase_      ,  **UpperCamelCase_)
						if images_mixed is not None:
									__UpperCAmelCase	: int		=					self.image_processor(UpperCamelCase_      ,  is_mixed=UpperCamelCase_      ,  *UpperCamelCase_      ,  **UpperCamelCase_)
						if audio is not None:
									__UpperCAmelCase	: List[Any]		=					self.feature_extractor(
									    UpperCamelCase_      ,  *UpperCamelCase_      ,  sampling_rate=UpperCamelCase_      ,  mask_audio=UpperCamelCase_      ,  **UpperCamelCase_)

						__UpperCAmelCase	: List[str]		=					{}
						if audio is not None:
									output_dict.update(UpperCamelCase_)
						if images is not None:
									output_dict.update(UpperCamelCase_)
						if images_mixed_dict is not None:
									output_dict.update(UpperCamelCase_)
						return output_dict


			@property
			def       a_     (      self      :    str):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					self.image_processor.model_input_names
						__UpperCAmelCase	: List[Any]		=					self.feature_extractor.model_input_names
						return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names))


