

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 (	__a      :			Dict		,__a      :			Optional[int]	)			->     int:

					"""simple docstring"""

					_a	:    Tuple         =		'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
					_a	:    Optional[int]         =		Image.open(requests.get(__a		,stream=__a	).raw	).convert('''RGB'''	)

					_a	:    List[Any]         =		transforms.Compose(
					    [
					        transforms.Resize((image_size, image_size)		,interpolation=InterpolationMode.BICUBIC	),
					        transforms.ToTensor(),
					        transforms.Normalize((0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73)		,(0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11)	),
					    ]	)
					_a	:    Tuple         =		transform(__a	).unsqueeze(0	).to(__a	)
					return image



def    __UpperCAmelCase (	__a      :			Any	)			->     Optional[Any]:

					"""simple docstring"""

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

					if "encoder.pos_embed" in key:
										_a	:    Union[str, Any]         =		re.sub(R'''encoder.pos_embed'''		,'''embeddings.position_embedding'''		,__a	)
					if "encoder.cls_token" in key:
										_a	:    Union[str, Any]         =		re.sub(R'''encoder.cls_token'''		,'''embeddings.class_embedding'''		,__a	)

					if "self_attn" in key:
										_a	:    int         =		re.sub(R'''self_attn.proj'''		,'''self_attn.projection'''		,__a	)

					return key



@torch.no_grad()
def    __UpperCAmelCase (	__a      :			Tuple		,__a      :			Optional[Any]=None	)			->     List[Any]:

					"""simple docstring"""

					if config_path is not None:
										_a	:    List[str]         =		BlipConfig.from_pretrained(__a	)
					else:
										_a	:    Optional[Any]         =		BlipConfig(projection_dim=512		,text_config={}		,vision_config={}	)

					_a	:    Union[str, Any]         =		BlipForConditionalGeneration(__a	).eval()

					_a	:    Optional[int]         =		'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

					_a	:    Union[str, Any]         =		blip_decoder(pretrained=__a		,image_size=384		,vit='''base'''	)
					_a	:    List[Any]         =		pt_model.eval()

					_a	:    List[Any]         =		pt_model.state_dict()
					for key in modified_state_dict.copy():
										_a	:    List[str]         =		modified_state_dict.pop(__a	)
										_a	:    List[Any]         =		rename_key(__a	)
										_a	:    Union[str, Any]         =		value

					hf_model.load_state_dict(__a	)

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

					_a	:    Tuple         =		hf_model.generate(__a		,__a	)

					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]

					_a	:    str         =		hf_model.generate(__a	)

					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(__a	)

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

					_a	:    Optional[int]         =		blip_vqa(pretrained=__a		,image_size=__a		,vit='''base'''	)
					vqa_model.eval()

					_a	:    List[Any]         =		vqa_model.state_dict()
					for key in modified_state_dict.copy():
										_a	:    Optional[Any]         =		modified_state_dict.pop(__a	)
										_a	:    Any         =		rename_key(__a	)
										_a	:    Dict         =		value

					_a	:    Union[str, Any]         =		BlipForQuestionAnswering(__a	)

					hf_vqa_model.load_state_dict(__a	)

					_a	:    Union[str, Any]         =		['''How many dogs are in this image?''']
					_a	:    List[Any]         =		tokenizer(__a		,return_tensors='''pt'''	).input_ids

					_a	:    Union[str, Any]         =		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'''	)

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

					_a	:    str         =		blip_itm(pretrained=__a		,image_size=__a		,vit='''base'''	)
					itm_model.eval()

					_a	:    Dict         =		itm_model.state_dict()
					for key in modified_state_dict.copy():
										_a	:    Optional[Any]         =		modified_state_dict.pop(__a	)
										_a	:    Any         =		rename_key(__a	)
										_a	:    Any         =		value

					_a	:    Tuple         =		BlipForImageTextRetrieval(__a	)

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

					_a	:    Tuple         =		hf_itm_model(__a		,__a		,use_itm_head=__a	)
					_a	:    Optional[Any]         =		hf_itm_model(__a		,__a		,use_itm_head=__a	)

					assert out[0].item() == 0.21_10_68_74_94_27_79_54
					assert torch.nn.functional.softmax(out_itm[0]		,dim=1	)[:, 1].item() == 0.4_56_98_84_53_86_50_51_27

					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)






from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__				=		{'''configuration_mmbt''': ['''MMBTConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__				=		['''MMBTForClassification''', '''MMBTModel''', '''ModalEmbeddings''']


if TYPE_CHECKING:
			from .configuration_mmbt import MMBTConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
			import sys

			a__				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



