

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							,	_a):
	SCREAMING_SNAKE_CASE      :					List[Any]    =				"https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				Image.open(requests.get(_a							,	stream=_a).raw).convert("RGB")

	SCREAMING_SNAKE_CASE      :					List[str]    =				transforms.Compose(
	    [
	        transforms.Resize((image_size, image_size)							,	interpolation=InterpolationMode.BICUBIC),
	        transforms.ToTensor(),
	        transforms.Normalize((0.4814_5466, 0.457_8275, 0.4082_1073)							,	(0.2686_2954, 0.2613_0258, 0.2757_7711)),
	    ])
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				transform(_a).unsqueeze(0).to(_a)
	return image

def        lowerCamelCase__       (    _a):
	if "visual_encoder" in key:
		SCREAMING_SNAKE_CASE      :					Optional[Any]    =				re.sub("visual_encoder*"							,	"vision_model.encoder"							,	_a)
	if "blocks" in key:
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				re.sub(r"blocks"							,	"layers"							,	_a)
	if "attn" in key:
		SCREAMING_SNAKE_CASE      :					Optional[int]    =				re.sub(r"attn"							,	"self_attn"							,	_a)
	if "norm1" in key:
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				re.sub(r"norm1"							,	"layer_norm1"							,	_a)
	if "norm2" in key:
		SCREAMING_SNAKE_CASE      :					int    =				re.sub(r"norm2"							,	"layer_norm2"							,	_a)
	if "encoder.norm" in key:
		SCREAMING_SNAKE_CASE      :					Tuple    =				re.sub(r"encoder.norm"							,	"post_layernorm"							,	_a)
	if "encoder.patch_embed.proj" in key:
		SCREAMING_SNAKE_CASE      :					Dict    =				re.sub(r"encoder.patch_embed.proj"							,	"embeddings.patch_embedding"							,	_a)

	if "encoder.pos_embed" in key:
		SCREAMING_SNAKE_CASE      :					int    =				re.sub(r"encoder.pos_embed"							,	"embeddings.position_embedding"							,	_a)
	if "encoder.cls_token" in key:
		SCREAMING_SNAKE_CASE      :					Any    =				re.sub(r"encoder.cls_token"							,	"embeddings.class_embedding"							,	_a)

	if "self_attn" in key:
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				re.sub(r"self_attn.proj"							,	"self_attn.projection"							,	_a)

	return key

@torch.no_grad()
def        lowerCamelCase__       (    _a							,	_a=None):
	if config_path is not None:
		SCREAMING_SNAKE_CASE      :					Any    =				BlipConfig.from_pretrained(_a)
	else:
		SCREAMING_SNAKE_CASE      :					int    =				BlipConfig(projection_dim=512							,	text_config={}							,	vision_config={})

	SCREAMING_SNAKE_CASE      :					str    =				BlipForConditionalGeneration(_a).eval()

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

	SCREAMING_SNAKE_CASE      :					List[Any]    =				blip_decoder(pretrained=_a							,	image_size=384							,	vit="base")
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				pt_model.eval()

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

	hf_model.load_state_dict(_a)

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

	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				hf_model.generate(_a							,	_a)

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

	SCREAMING_SNAKE_CASE      :					Tuple    =				hf_model.generate(_a)

	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(_a)

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

	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				blip_vqa(pretrained=_a							,	image_size=_a							,	vit="base")
	vqa_model.eval()

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

	SCREAMING_SNAKE_CASE      :					Optional[int]    =				BlipForQuestionAnswering(_a)

	hf_vqa_model.load_state_dict(_a)

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

	SCREAMING_SNAKE_CASE      :					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")

	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				"https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"

	SCREAMING_SNAKE_CASE      :					int    =				blip_itm(pretrained=_a							,	image_size=_a							,	vit="base")
	itm_model.eval()

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

	SCREAMING_SNAKE_CASE      :					str    =				BlipForImageTextRetrieval(_a)

	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				["A picture of a woman with a dog sitting in a beach"]
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				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()

	SCREAMING_SNAKE_CASE      :					List[str]    =				hf_itm_model(_a							,	_a							,	use_itm_head=_a)
	SCREAMING_SNAKE_CASE      :					Dict    =				hf_itm_model(_a							,	_a							,	use_itm_head=_a)

	assert out[0].item() == 0.2110_6874_9427_7954
	assert torch.nn.functional.softmax(out_itm[0]							,	dim=1)[:, 1].item() == 0.4_5698_8453_8650_5127

	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)



UpperCAmelCase__   	=							8.31_44_62  # Unit - J mol-1 K-1
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			float:







 """simple docstring"""


 if moles < 0 or kelvin < 0 or volume < 0:
  raise ValueError('''Invalid inputs. Enter positive value.'''						)
 return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			float:







 """simple docstring"""


 if moles < 0 or kelvin < 0 or pressure < 0:
  raise ValueError('''Invalid inputs. Enter positive value.'''						)
 return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
  from doctest import testmod

  testmod()

