






'''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    (  a		:	Any						,			a		:	Optional[int]							)			->			Optional[int]:





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

						lowercase_      :   int        =			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)							),
						    ]							)
						lowercase_      :   List[str]        =			transform(a							).unsqueeze(0							).to(a							)
						return image






def 	_UpperCAmelCase    (  a		:	Tuple							)			->			int:





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

						if "encoder.pos_embed" in key:
												lowercase_      :   Tuple        =			re.sub(R'encoder.pos_embed'						,			'embeddings.position_embedding'						,			a							)
						if "encoder.cls_token" in key:
												lowercase_      :   List[str]        =			re.sub(R'encoder.cls_token'						,			'embeddings.class_embedding'						,			a							)

						if "self_attn" in key:
												lowercase_      :   List[Any]        =			re.sub(R'self_attn.proj'						,			'self_attn.projection'						,			a							)

						return key






@torch.no_grad()
def 	_UpperCAmelCase    (  a		:	Union[str, Any]						,			a		:	str=None							)			->			str:





						"""simple docstring"""
						if config_path is not None:
												lowercase_      :   List[Any]        =			BlipConfig.from_pretrained(a							)
						else:
												lowercase_      :   Tuple        =			BlipConfig(projection_dim=5_1_2						,			text_config={}						,			vision_config={}							)

						lowercase_      :   Optional[int]        =			BlipForConditionalGeneration(a							).eval()

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

						lowercase_      :   int        =			blip_decoder(pretrained=a						,			image_size=3_8_4						,			vit='base'							)
						lowercase_      :   Optional[Any]        =			pt_model.eval()

						lowercase_      :   List[str]        =			pt_model.state_dict()
						for key in modified_state_dict.copy():
												lowercase_      :   int        =			modified_state_dict.pop(a							)
												lowercase_      :   Dict        =			rename_key(a							)
												lowercase_      :   Dict        =			value

						hf_model.load_state_dict(a							)

						lowercase_      :   Dict        =			3_8_4
						lowercase_      :   List[Any]        =			load_demo_image(image_size=a						,			device='cpu'							)
						lowercase_      :   List[Any]        =			BertTokenizer.from_pretrained('bert-base-uncased'							)
						lowercase_      :   Dict        =			tokenizer(['a picture of']							).input_ids

						lowercase_      :   List[str]        =			hf_model.generate(a						,			a							)

						assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 3_8_6_1, 1_9_9_7, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

						lowercase_      :   Any        =			hf_model.generate(a							)

						assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

						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'
						lowercase_      :   List[Any]        =			(
						    'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
						)

						lowercase_      :   Any        =			blip_vqa(pretrained=a						,			image_size=a						,			vit='base'							)
						vqa_model.eval()

						lowercase_      :   str        =			vqa_model.state_dict()
						for key in modified_state_dict.copy():
												lowercase_      :   Any        =			modified_state_dict.pop(a							)
												lowercase_      :   Tuple        =			rename_key(a							)
												lowercase_      :   List[str]        =			value

						lowercase_      :   Tuple        =			BlipForQuestionAnswering(a							)

						hf_vqa_model.load_state_dict(a							)

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

						lowercase_      :   Optional[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'							)

						lowercase_      :   List[str]        =			'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'

						lowercase_      :   Any        =			blip_itm(pretrained=a						,			image_size=a						,			vit='base'							)
						itm_model.eval()

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

						lowercase_      :   List[Any]        =			BlipForImageTextRetrieval(a							)

						lowercase_      :   Union[str, Any]        =			['A picture of a woman with a dog sitting in a beach']
						lowercase_      :   Any        =			tokenizer(
						    a						,			return_tensors='pt'						,			padding='max_length'						,			truncation=a						,			max_length=3_5						,			).input_ids

						hf_itm_model.load_state_dict(a							)
						hf_itm_model.eval()

						lowercase_      :   int        =			hf_itm_model(a						,			a						,			use_itm_head=a							)
						lowercase_      :   str        =			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:      str  				=				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:      Union[str, Any]  				=				parser.parse_args()

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








'''simple docstring'''

from ..utils import DummyObject, requires_backends






class 							__magic_name__     (	metaclass=UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      str        =  ['transformers', 'torch', 'note_seq']
	def __init__(							self  ,      *_lowercase  ,      **_lowercase       )						->  Dict:
							requires_backends(self  ,      ['transformers', 'torch', 'note_seq']       )
	@classmethod
	def        lowerCamelCase__		(							cls  ,      *_lowercase  ,      **_lowercase       )						->  List[str]:
							requires_backends(cls  ,      ['transformers', 'torch', 'note_seq']       )







	@classmethod
	def        lowerCamelCase__		(							cls  ,      *_lowercase  ,      **_lowercase       )						->  Dict:
							requires_backends(cls  ,      ['transformers', 'torch', 'note_seq']       )
