

"""simple docstring"""




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     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






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

							snake_case_          =   transforms.Compose(
							    [
							        transforms.Resize((image_size, image_size)       ,							interpolation=InterpolationMode.BICUBIC				),
							        transforms.ToTensor(),
							        transforms.Normalize((0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73)       ,							(0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11)				),
							    ]				)
							snake_case_          =   transform(lowerCAmelCase__				).unsqueeze(0				).to(lowerCAmelCase__				)
							return image





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






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

							if "encoder.pos_embed" in key:
														snake_case_          =   re.sub(r'encoder.pos_embed'       ,							'embeddings.position_embedding'       ,							lowerCAmelCase__				)
							if "encoder.cls_token" in key:
														snake_case_          =   re.sub(r'encoder.cls_token'       ,							'embeddings.class_embedding'       ,							lowerCAmelCase__				)

							if "self_attn" in key:
														snake_case_          =   re.sub(r'self_attn.proj'       ,							'self_attn.projection'       ,							lowerCAmelCase__				)

							return key





@torch.no_grad()
def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__=None				):







							'''simple docstring'''






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

							snake_case_          =   BlipForConditionalGeneration(lowerCAmelCase__				).eval()

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

							snake_case_          =   blip_decoder(pretrained=lowerCAmelCase__       ,							image_size=384       ,							vit='base'				)
							snake_case_          =   pt_model.eval()

							snake_case_          =   pt_model.state_dict()
							for key in modified_state_dict.copy():
														snake_case_          =   modified_state_dict.pop(lowerCAmelCase__				)
														snake_case_          =   rename_key(lowerCAmelCase__				)
														snake_case_          =   value

							hf_model.load_state_dict(lowerCAmelCase__				)

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

							snake_case_          =   hf_model.generate(lowerCAmelCase__       ,							lowerCAmelCase__				)

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

							snake_case_          =   hf_model.generate(lowerCAmelCase__				)

							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(lowerCAmelCase__				)

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

							snake_case_          =   blip_vqa(pretrained=lowerCAmelCase__       ,							image_size=lowerCAmelCase__       ,							vit='base'				)
							vqa_model.eval()

							snake_case_          =   vqa_model.state_dict()
							for key in modified_state_dict.copy():
														snake_case_          =   modified_state_dict.pop(lowerCAmelCase__				)
														snake_case_          =   rename_key(lowerCAmelCase__				)
														snake_case_          =   value

							snake_case_          =   BlipForQuestionAnswering(lowerCAmelCase__				)

							hf_vqa_model.load_state_dict(lowerCAmelCase__				)

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

							snake_case_          =   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'				)

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

							snake_case_          =   blip_itm(pretrained=lowerCAmelCase__       ,							image_size=lowerCAmelCase__       ,							vit='base'				)
							itm_model.eval()

							snake_case_          =   itm_model.state_dict()
							for key in modified_state_dict.copy():
														snake_case_          =   modified_state_dict.pop(lowerCAmelCase__				)
														snake_case_          =   rename_key(lowerCAmelCase__				)
														snake_case_          =   value

							snake_case_          =   BlipForImageTextRetrieval(lowerCAmelCase__				)

							snake_case_          =   ["""A picture of a woman with a dog sitting in a beach"""]
							snake_case_          =   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()

							snake_case_          =   hf_itm_model(lowerCAmelCase__       ,							lowerCAmelCase__       ,							use_itm_head=lowerCAmelCase__				)
							snake_case_          =   hf_itm_model(lowerCAmelCase__       ,							lowerCAmelCase__       ,							use_itm_head=lowerCAmelCase__				)

							assert out[0].item() == 0.21_10_68_74_94_27_79_54
							assert torch.nn.functional.softmax(out_itm[0]       ,							dim=1				)[:, 1].item() == 0.4_56_98_84_53_86_50_51_27

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


if __name__ == "__main__":
					_UpperCAmelCase		:       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""")
					_UpperCAmelCase		:       List[Any]						    =	parser.parse_args()

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







from __future__ import annotations





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__ = None       ,							UpperCamelCase__ = None				):







							'''simple docstring'''






							if start is None:
														snake_case_          =   0

							if end is None:
														snake_case_          =   len(UpperCamelCase__				) - 1

							if start >= end:
														return

							snake_case_          =   (start + end) // 2

							slowsort(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)
							slowsort(UpperCamelCase__       ,							mid + 1       ,							UpperCamelCase__				)

							if sequence[end] < sequence[mid]:
														snake_case_    ,   snake_case_          =   sequence[mid], sequence[end]

							slowsort(UpperCamelCase__       ,							UpperCamelCase__       ,							end - 1				)


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

					testmod()




