


from math import loga

def 	lowerCAmelCase__			(      a__:		int	)  ->		int:


							'''simple docstring'''





							if a < 0:
														raise ValueError('Input value must be a positive integer'	)
							elif isinstance(a__      ,  a__	):
														raise TypeError('Input value must be a \'int\' type'	)
							return 0 if (a == 0) else int(loga(a & -a	)	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




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__			(      a__:		str      ,  a__:		List[Any]	)  ->		Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase					=      'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
							_UpperCAmelCase					=      Image.open(requests.get(a__      ,  stream=a__	).raw	).convert('RGB'	)

							_UpperCAmelCase					=      transforms.Compose(
							    [
							        transforms.Resize((image_size, image_size)      ,  interpolation=InterpolationMode.BICUBIC	),
							        transforms.ToTensor(),
							        transforms.Normalize((0.48_145_466, 0.4_578_275, 0.40_821_073)      ,  (0.26_862_954, 0.26_130_258, 0.27_577_711)	),
							    ]	)
							_UpperCAmelCase					=      transform(a__	).unsqueeze(0	).to(a__	)
							return image

def 	lowerCAmelCase__			(      a__:		Optional[int]	)  ->		int:


							'''simple docstring'''





							if "visual_encoder" in key:
														_UpperCAmelCase					=      re.sub('visual_encoder*'      ,  'vision_model.encoder'      ,  a__	)
							if "blocks" in key:
														_UpperCAmelCase					=      re.sub(R'blocks'      ,  'layers'      ,  a__	)
							if "attn" in key:
														_UpperCAmelCase					=      re.sub(R'attn'      ,  'self_attn'      ,  a__	)
							if "norm1" in key:
														_UpperCAmelCase					=      re.sub(R'norm1'      ,  'layer_norm1'      ,  a__	)
							if "norm2" in key:
														_UpperCAmelCase					=      re.sub(R'norm2'      ,  'layer_norm2'      ,  a__	)
							if "encoder.norm" in key:
														_UpperCAmelCase					=      re.sub(R'encoder.norm'      ,  'post_layernorm'      ,  a__	)
							if "encoder.patch_embed.proj" in key:
														_UpperCAmelCase					=      re.sub(R'encoder.patch_embed.proj'      ,  'embeddings.patch_embedding'      ,  a__	)

							if "encoder.pos_embed" in key:
														_UpperCAmelCase					=      re.sub(R'encoder.pos_embed'      ,  'embeddings.position_embedding'      ,  a__	)
							if "encoder.cls_token" in key:
														_UpperCAmelCase					=      re.sub(R'encoder.cls_token'      ,  'embeddings.class_embedding'      ,  a__	)

							if "self_attn" in key:
														_UpperCAmelCase					=      re.sub(R'self_attn.proj'      ,  'self_attn.projection'      ,  a__	)

							return key

@torch.no_grad()
def 	lowerCAmelCase__			(      a__:		Optional[Any]      ,  a__:		List[str]=None	)  ->		Optional[Any]:


							'''simple docstring'''





							if config_path is not None:
														_UpperCAmelCase					=      BlipConfig.from_pretrained(a__	)
							else:
														_UpperCAmelCase					=      BlipConfig(projection_dim=5_1_2      ,  text_config={}      ,  vision_config={}	)

							_UpperCAmelCase					=      BlipForConditionalGeneration(a__	).eval()

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

							_UpperCAmelCase					=      blip_decoder(pretrained=a__      ,  image_size=3_8_4      ,  vit='base'	)
							_UpperCAmelCase					=      pt_model.eval()

							_UpperCAmelCase					=      pt_model.state_dict()
							for key in modified_state_dict.copy():
														_UpperCAmelCase					=      modified_state_dict.pop(a__	)
														_UpperCAmelCase					=      rename_key(a__	)
														_UpperCAmelCase					=      value

							hf_model.load_state_dict(a__	)

							_UpperCAmelCase					=      3_8_4
							_UpperCAmelCase					=      load_demo_image(image_size=a__      ,  device='cpu'	)
							_UpperCAmelCase					=      BertTokenizer.from_pretrained('bert-base-uncased'	)
							_UpperCAmelCase					=      tokenizer(['a picture of']	).input_ids

							_UpperCAmelCase					=      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]

							_UpperCAmelCase					=      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'
							_UpperCAmelCase					=      (
							    'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
							)

							_UpperCAmelCase					=      blip_vqa(pretrained=a__      ,  image_size=a__      ,  vit='base'	)
							vqa_model.eval()

							_UpperCAmelCase					=      vqa_model.state_dict()
							for key in modified_state_dict.copy():
														_UpperCAmelCase					=      modified_state_dict.pop(a__	)
														_UpperCAmelCase					=      rename_key(a__	)
														_UpperCAmelCase					=      value

							_UpperCAmelCase					=      BlipForQuestionAnswering(a__	)

							hf_vqa_model.load_state_dict(a__	)

							_UpperCAmelCase					=      ['How many dogs are in this image?']
							_UpperCAmelCase					=      tokenizer(a__      ,  return_tensors='pt'	).input_ids

							_UpperCAmelCase					=      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'	)

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

							_UpperCAmelCase					=      blip_itm(pretrained=a__      ,  image_size=a__      ,  vit='base'	)
							itm_model.eval()

							_UpperCAmelCase					=      itm_model.state_dict()
							for key in modified_state_dict.copy():
														_UpperCAmelCase					=      modified_state_dict.pop(a__	)
														_UpperCAmelCase					=      rename_key(a__	)
														_UpperCAmelCase					=      value

							_UpperCAmelCase					=      BlipForImageTextRetrieval(a__	)

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

							_UpperCAmelCase					=      hf_itm_model(a__      ,  a__      ,  use_itm_head=a__	)
							_UpperCAmelCase					=      hf_itm_model(a__      ,  a__      ,  use_itm_head=a__	)

							assert out[0].item() == 0.2_110_687_494_277_954
							assert torch.nn.functional.softmax(out_itm[0]      ,  dim=1	)[:, 1].item() == 0.45_698_845_386_505_127

							if pytorch_dump_folder_path is not None:
														hf_itm_model.save_pretrained(pytorch_dump_folder_path + '_itm'	)


if __name__ == "__main__":
		lowerCAmelCase__  :int             =							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''')
		lowerCAmelCase__  :List[Any]             =							parser.parse_args()

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