






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 					snake_case_						(   lowerCAmelCase_				:  List[str]				,				lowerCAmelCase_				:  Any					):
							__lowercase					:							Any			=   '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
							__lowercase					:							Union[str, Any]			=   Image.open(requests.get(_lowercase				,				stream=_lowercase					).raw					).convert("""RGB"""					)

							__lowercase					:							Union[str, Any]			=   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)					),
							    ]					)
							__lowercase					:							Tuple			=   transform(_lowercase					).unsqueeze(0					).to(_lowercase					)
							return image
def 					snake_case_						(   lowerCAmelCase_				:  Optional[int]					):
							if "visual_encoder" in key:
														__lowercase					:							Optional[int]			=   re.sub("""visual_encoder*"""				,				"""vision_model.encoder"""				,				_lowercase					)
							if "blocks" in key:
														__lowercase					:							str			=   re.sub(r"""blocks"""				,				"""layers"""				,				_lowercase					)
							if "attn" in key:
														__lowercase					:							List[Any]			=   re.sub(r"""attn"""				,				"""self_attn"""				,				_lowercase					)
							if "norm1" in key:
														__lowercase					:							List[str]			=   re.sub(r"""norm1"""				,				"""layer_norm1"""				,				_lowercase					)
							if "norm2" in key:
														__lowercase					:							str			=   re.sub(r"""norm2"""				,				"""layer_norm2"""				,				_lowercase					)
							if "encoder.norm" in key:
														__lowercase					:							Optional[int]			=   re.sub(r"""encoder.norm"""				,				"""post_layernorm"""				,				_lowercase					)
							if "encoder.patch_embed.proj" in key:
														__lowercase					:							Dict			=   re.sub(r"""encoder.patch_embed.proj"""				,				"""embeddings.patch_embedding"""				,				_lowercase					)

							if "encoder.pos_embed" in key:
														__lowercase					:							int			=   re.sub(r"""encoder.pos_embed"""				,				"""embeddings.position_embedding"""				,				_lowercase					)
							if "encoder.cls_token" in key:
														__lowercase					:							List[str]			=   re.sub(r"""encoder.cls_token"""				,				"""embeddings.class_embedding"""				,				_lowercase					)

							if "self_attn" in key:
														__lowercase					:							int			=   re.sub(r"""self_attn.proj"""				,				"""self_attn.projection"""				,				_lowercase					)

							return key
@torch.no_grad()
def 					snake_case_						(   lowerCAmelCase_				:  int				,				lowerCAmelCase_				:  List[str]=None					):
							if config_path is not None:
														__lowercase					:							Any			=   BlipConfig.from_pretrained(_lowercase					)
							else:
														__lowercase					:							Union[str, Any]			=   BlipConfig(projection_dim=512				,				text_config={}				,				vision_config={}					)

							__lowercase					:							str			=   BlipForConditionalGeneration(_lowercase					).eval()

							__lowercase					:							List[Any]			=   '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

							__lowercase					:							Tuple			=   blip_decoder(pretrained=_lowercase				,				image_size=384				,				vit="""base"""					)
							__lowercase					:							Any			=   pt_model.eval()

							__lowercase					:							Dict			=   pt_model.state_dict()
							for key in modified_state_dict.copy():
														__lowercase					:							Tuple			=   modified_state_dict.pop(_lowercase					)
														__lowercase					:							Optional[int]			=   rename_key(_lowercase					)
														__lowercase					:							Any			=   value

							hf_model.load_state_dict(_lowercase					)

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

							__lowercase					:							Optional[int]			=   hf_model.generate(_lowercase				,				_lowercase					)

							assert out[0].tolist() == [30522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

							__lowercase					:							Union[str, Any]			=   hf_model.generate(_lowercase					)

							assert out[0].tolist() == [30522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

							if pytorch_dump_folder_path is not None:
														hf_model.save_pretrained(_lowercase					)

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

							__lowercase					:							Tuple			=   blip_vqa(pretrained=_lowercase				,				image_size=_lowercase				,				vit="""base"""					)
							vqa_model.eval()

							__lowercase					:							List[str]			=   vqa_model.state_dict()
							for key in modified_state_dict.copy():
														__lowercase					:							Optional[int]			=   modified_state_dict.pop(_lowercase					)
														__lowercase					:							Union[str, Any]			=   rename_key(_lowercase					)
														__lowercase					:							str			=   value

							__lowercase					:							Dict			=   BlipForQuestionAnswering(_lowercase					)

							hf_vqa_model.load_state_dict(_lowercase					)

							__lowercase					:							Dict			=   ['''How many dogs are in this image?''']
							__lowercase					:							Tuple			=   tokenizer(_lowercase				,				return_tensors="""pt"""					).input_ids

							__lowercase					:							Dict			=   hf_vqa_model.generate(_lowercase				,				_lowercase					)
							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					:							Any			=   '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

							__lowercase					:							int			=   blip_itm(pretrained=_lowercase				,				image_size=_lowercase				,				vit="""base"""					)
							itm_model.eval()

							__lowercase					:							List[Any]			=   itm_model.state_dict()
							for key in modified_state_dict.copy():
														__lowercase					:							int			=   modified_state_dict.pop(_lowercase					)
														__lowercase					:							Dict			=   rename_key(_lowercase					)
														__lowercase					:							List[Any]			=   value

							__lowercase					:							int			=   BlipForImageTextRetrieval(_lowercase					)

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

							hf_itm_model.load_state_dict(_lowercase					)
							hf_itm_model.eval()

							__lowercase					:							int			=   hf_itm_model(_lowercase				,				_lowercase				,				use_itm_head=_lowercase					)
							__lowercase					:							Union[str, Any]			=   hf_itm_model(_lowercase				,				_lowercase				,				use_itm_head=_lowercase					)

							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       :       Optional[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       :       int        =				parser.parse_args()

	convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
					import accelerate
def       A	(    _lowercase						):
						if not is_accelerate_available():
												return method
						SCREAMING_SNAKE_CASE							:    int							=							version.parse(accelerate.__version__						).base_version
						if version.parse(_lowercase						) < version.parse('''0.17.0'''						):
												return method

						def wrapper(self		,  *_lowercase		,  **_lowercase						):
												if hasattr(self		,  '''_hf_hook'''						) and hasattr(self._hf_hook		,  '''pre_forward'''						):
																		self._hf_hook.pre_forward(self						)
												return method(self		,  *_lowercase		,  **_lowercase						)

						return wrapper





