




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 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			Optional[int]:
	a									=				"""https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
	a									=				Image.open(requests.get(__lowerCamelCase				,     stream=__lowerCamelCase       ).raw       ).convert("""RGB"""       )

	a									=				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)       ),
	    ]       )
	a									=				transform(__lowerCamelCase       ).unsqueeze(0       ).to(__lowerCamelCase       )
	return image





def 						__A						(      __lowerCamelCase       ) ->			Optional[int]:
	if "visual_encoder" in key:
		a									=				re.sub("""visual_encoder*"""				,     """vision_model.encoder"""				,     __lowerCamelCase       )
	if "blocks" in key:
		a									=				re.sub(R"""blocks"""				,     """layers"""				,     __lowerCamelCase       )
	if "attn" in key:
		a									=				re.sub(R"""attn"""				,     """self_attn"""				,     __lowerCamelCase       )
	if "norm1" in key:
		a									=				re.sub(R"""norm1"""				,     """layer_norm1"""				,     __lowerCamelCase       )
	if "norm2" in key:
		a									=				re.sub(R"""norm2"""				,     """layer_norm2"""				,     __lowerCamelCase       )
	if "encoder.norm" in key:
		a									=				re.sub(R"""encoder.norm"""				,     """post_layernorm"""				,     __lowerCamelCase       )
	if "encoder.patch_embed.proj" in key:
		a									=				re.sub(R"""encoder.patch_embed.proj"""				,     """embeddings.patch_embedding"""				,     __lowerCamelCase       )

	if "encoder.pos_embed" in key:
		a									=				re.sub(R"""encoder.pos_embed"""				,     """embeddings.position_embedding"""				,     __lowerCamelCase       )
	if "encoder.cls_token" in key:
		a									=				re.sub(R"""encoder.cls_token"""				,     """embeddings.class_embedding"""				,     __lowerCamelCase       )

	if "self_attn" in key:
		a									=				re.sub(R"""self_attn.proj"""				,     """self_attn.projection"""				,     __lowerCamelCase       )

	return key





@torch.no_grad()
def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			List[str]:
	if config_path is not None:
		a									=				BlipConfig.from_pretrained(__lowerCamelCase       )
	else:
		a									=				BlipConfig(projection_dim=512				,     text_config={}				,     vision_config={}       )

	a									=				BlipForConditionalGeneration(__lowerCamelCase       ).eval()

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

	a									=				blip_decoder(pretrained=__lowerCamelCase				,     image_size=384				,     vit="""base"""       )
	a									=				pt_model.eval()

	a									=				pt_model.state_dict()
	for key in modified_state_dict.copy():
		a									=				modified_state_dict.pop(__lowerCamelCase       )
		a									=				rename_key(__lowerCamelCase       )
		a									=				value

	hf_model.load_state_dict(__lowerCamelCase       )

	a									=				384
	a									=				load_demo_image(image_size=__lowerCamelCase				,     device="""cpu"""       )
	a									=				BertTokenizer.from_pretrained("""bert-base-uncased"""       )
	a									=				tokenizer(["""a picture of"""]       ).input_ids

	a									=				hf_model.generate(__lowerCamelCase				,     __lowerCamelCase       )

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

	a									=				hf_model.generate(__lowerCamelCase       )

	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(__lowerCamelCase       )

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

	a									=				blip_vqa(pretrained=__lowerCamelCase				,     image_size=__lowerCamelCase				,     vit="""base"""       )
	vqa_model.eval()

	a									=				vqa_model.state_dict()
	for key in modified_state_dict.copy():
		a									=				modified_state_dict.pop(__lowerCamelCase       )
		a									=				rename_key(__lowerCamelCase       )
		a									=				value

	a									=				BlipForQuestionAnswering(__lowerCamelCase       )

	hf_vqa_model.load_state_dict(__lowerCamelCase       )

	a									=				["""How many dogs are in this image?"""]
	a									=				tokenizer(__lowerCamelCase				,     return_tensors="""pt"""       ).input_ids

	a									=				hf_vqa_model.generate(__lowerCamelCase				,     __lowerCamelCase       )
	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									=				"""https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"""

	a									=				blip_itm(pretrained=__lowerCamelCase				,     image_size=__lowerCamelCase				,     vit="""base"""       )
	itm_model.eval()

	a									=				itm_model.state_dict()
	for key in modified_state_dict.copy():
		a									=				modified_state_dict.pop(__lowerCamelCase       )
		a									=				rename_key(__lowerCamelCase       )
		a									=				value

	a									=				BlipForImageTextRetrieval(__lowerCamelCase       )

	a									=				["""A picture of a woman with a dog sitting in a beach"""]
	a									=				tokenizer(
	    __lowerCamelCase				,     return_tensors="""pt"""				,     padding="""max_length"""				,     truncation=__lowerCamelCase				,     max_length=35				,     ).input_ids

	hf_itm_model.load_state_dict(__lowerCamelCase       )
	hf_itm_model.eval()

	a									=				hf_itm_model(__lowerCamelCase				,     __lowerCamelCase				,     use_itm_head=__lowerCamelCase       )
	a									=				hf_itm_model(__lowerCamelCase				,     __lowerCamelCase				,     use_itm_head=__lowerCamelCase       )

	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__":
		__UpperCamelCase   :			Any          = 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")
		__UpperCamelCase   :			List[str]          = parser.parse_args()

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







import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version





def 						__A						(      *__lowerCamelCase				,     __lowerCamelCase = None				,     __lowerCamelCase=True				,     __lowerCamelCase=2       ) ->			Dict:
	from .. import __version__

	a									=				take_from
	a									=				()
	if not isinstance(args[0]				,     __lowerCamelCase       ):
		a									=				(args,)

	for attribute, version_name, message in args:
		if version.parse(version.parse(__lowerCamelCase       ).base_version       ) >= version.parse(__lowerCamelCase       ):
			raise ValueError(
			    f'The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''
			    f' version {__version__} is >= {version_name}'       )

		a									=				None
		if isinstance(__lowerCamelCase				,     __lowerCamelCase       ) and attribute in deprecated_kwargs:
			values += (deprecated_kwargs.pop(__lowerCamelCase       ),)
			a									=				f'The `{attribute}` argument is deprecated and will be removed in version {version_name}.'
		elif hasattr(__lowerCamelCase				,     __lowerCamelCase       ):
			values += (getattr(__lowerCamelCase				,     __lowerCamelCase       ),)
			a									=				f'The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'
		elif deprecated_kwargs is None:
			a									=				f'`{attribute}` is deprecated and will be removed in version {version_name}.'

		if warning is not None:
			a									=				warning + """ """ if standard_warn else """"""
			warnings.warn(warning + message				,     __lowerCamelCase				,     stacklevel=__lowerCamelCase       )

	if isinstance(__lowerCamelCase				,     __lowerCamelCase       ) and len(__lowerCamelCase       ) > 0:
		a									=				inspect.getouterframes(inspect.currentframe()       )[1]
		a									=				call_frame.filename
		a									=				call_frame.lineno
		a									=				call_frame.function
		a							,					a									=				next(iter(deprecated_kwargs.items()       )       )
		raise TypeError(f'{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'       )

	if len(__lowerCamelCase       ) == 0:
		return
	elif len(__lowerCamelCase       ) == 1:
		return values[0]
	return values

