


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__):

				'''simple docstring'''





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

				_SCREAMING_SNAKE_CASE			  =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			  =transform(a__).unsqueeze(0).to(a__)
				return image







def 						lowerCamelCase(   a__):

				'''simple docstring'''





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

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

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

				return key







@torch.no_grad()
def 						lowerCamelCase(   a__	,a__=None):

				'''simple docstring'''





				if config_path is not None:
								_SCREAMING_SNAKE_CASE			  =BlipConfig.from_pretrained(a__)
				else:
								_SCREAMING_SNAKE_CASE			  =BlipConfig(projection_dim=512	,text_config={}	,vision_config={})

				_SCREAMING_SNAKE_CASE			  =BlipForConditionalGeneration(a__).eval()

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

				_SCREAMING_SNAKE_CASE			  =blip_decoder(pretrained=a__	,image_size=384	,vit='''base''')
				_SCREAMING_SNAKE_CASE			  =pt_model.eval()

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

				hf_model.load_state_dict(a__)

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

				_SCREAMING_SNAKE_CASE			  =hf_model.generate(a__	,a__)

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

				_SCREAMING_SNAKE_CASE			  =hf_model.generate(a__)

				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(a__)

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

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

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

				_SCREAMING_SNAKE_CASE			  =BlipForQuestionAnswering(a__)

				hf_vqa_model.load_state_dict(a__)

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

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

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

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

				_SCREAMING_SNAKE_CASE			  =BlipForImageTextRetrieval(a__)

				_SCREAMING_SNAKE_CASE			  =['A picture of a woman with a dog sitting in a beach']
				_SCREAMING_SNAKE_CASE			  =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			  =hf_itm_model(a__	,a__	,use_itm_head=a__)
				_SCREAMING_SNAKE_CASE			  =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__":
							snake_case_						:			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''')
							snake_case_						:			Union[str, Any]													=							parser.parse_args()

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



from datetime import datetime

import matplotlib.pyplot as plt
import torch







def 						lowerCamelCase(   a__):
				for param in module.parameters():
								_SCREAMING_SNAKE_CASE			  =False







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  ='''cuda''' if torch.cuda.is_available() else '''cpu'''
				if torch.backends.mps.is_available() and torch.backends.mps.is_built():
								_SCREAMING_SNAKE_CASE			  ='''mps'''
				if device == "mps":
								print(
								    '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
								    ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
								    ''' with generations.''')
				return device







def 						lowerCamelCase(   a__):
				_SCREAMING_SNAKE_CASE			  =plt.imshow(a__)
				fig.axes.get_xaxis().set_visible(a__)
				fig.axes.get_yaxis().set_visible(a__)
				plt.show()







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  =datetime.now()
				_SCREAMING_SNAKE_CASE			  =current_time.strftime('''%H:%M:%S''')
				return timestamp