






'''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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


			"""simple docstring"""




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

			lowercase_	:    List[str]           =     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)							),
			    ]							)
			lowercase_	:    Dict           =     transform(__snake_case							).unsqueeze(0							).to(__snake_case							)
			return image




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


			"""simple docstring"""




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

			if "encoder.pos_embed" in key:
						lowercase_	:    List[str]           =     re.sub(R"encoder.pos_embed"						,		"embeddings.position_embedding"						,		__snake_case							)
			if "encoder.cls_token" in key:
						lowercase_	:    Union[str, Any]           =     re.sub(R"encoder.cls_token"						,		"embeddings.class_embedding"						,		__snake_case							)

			if "self_attn" in key:
						lowercase_	:    Union[str, Any]           =     re.sub(R"self_attn.proj"						,		"self_attn.projection"						,		__snake_case							)

			return key




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=None							):


			"""simple docstring"""




			if config_path is not None:
						lowercase_	:    Union[str, Any]           =     BlipConfig.from_pretrained(__snake_case							)
			else:
						lowercase_	:    Optional[int]           =     BlipConfig(projection_dim=512						,		text_config={}						,		vision_config={}							)

			lowercase_	:    Any           =     BlipForConditionalGeneration(__snake_case							).eval()

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

			lowercase_	:    Dict           =     blip_decoder(pretrained=__snake_case						,		image_size=384						,		vit="base"							)
			lowercase_	:    str           =     pt_model.eval()

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

			hf_model.load_state_dict(__snake_case							)

			lowercase_	:    List[str]           =     384
			lowercase_	:    Tuple           =     load_demo_image(image_size=__snake_case						,		device="cpu"							)
			lowercase_	:    str           =     BertTokenizer.from_pretrained("bert-base-uncased"							)
			lowercase_	:    Dict           =     tokenizer(["a picture of"]							).input_ids

			lowercase_	:    Dict           =     hf_model.generate(__snake_case						,		__snake_case							)

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

			lowercase_	:    str           =     hf_model.generate(__snake_case							)

			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(__snake_case							)

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

			lowercase_	:    List[Any]           =     blip_vqa(pretrained=__snake_case						,		image_size=__snake_case						,		vit="base"							)
			vqa_model.eval()

			lowercase_	:    Optional[Any]           =     vqa_model.state_dict()
			for key in modified_state_dict.copy():
						lowercase_	:    Union[str, Any]           =     modified_state_dict.pop(__snake_case							)
						lowercase_	:    Dict           =     rename_key(__snake_case							)
						lowercase_	:    Optional[int]           =     value

			lowercase_	:    Tuple           =     BlipForQuestionAnswering(__snake_case							)

			hf_vqa_model.load_state_dict(__snake_case							)

			lowercase_	:    str           =     ["How many dogs are in this image?"]
			lowercase_	:    Dict           =     tokenizer(__snake_case						,		return_tensors="pt"							).input_ids

			lowercase_	:    int           =     hf_vqa_model.generate(__snake_case						,		__snake_case							)
			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_	:    List[str]           =     "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"

			lowercase_	:    List[str]           =     blip_itm(pretrained=__snake_case						,		image_size=__snake_case						,		vit="base"							)
			itm_model.eval()

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

			lowercase_	:    Optional[int]           =     BlipForImageTextRetrieval(__snake_case							)

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

			hf_itm_model.load_state_dict(__snake_case							)
			hf_itm_model.eval()

			lowercase_	:    Union[str, Any]           =     hf_itm_model(__snake_case						,		__snake_case						,		use_itm_head=__snake_case							)
			lowercase_	:    Tuple           =     hf_itm_model(__snake_case						,		__snake_case						,		use_itm_head=__snake_case							)

			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__ =			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__ =			parser.parse_args()

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












'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator


class        _UpperCAmelCase						:




    def __init__(			self  :       Optional[Any]  ,     a  :       Any			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     str(id_			)
       lowercase_	:    List[str]           =     None
       lowercase_	:    Tuple           =     None
       lowercase_	:    Dict           =     []
       lowercase_	:    Union[str, Any]           =     {}  # {vertex:distance}




    def __lt__(			self  :       Optional[Any]  ,     a  :       int			):






       '''simple docstring'''






       return self.key < other.key




    def __repr__(			self  :       Union[str, Any]			):






       '''simple docstring'''






       return self.id




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Optional[int]			):






       '''simple docstring'''






       self.neighbors.append(a			)





    def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       int  ,     a  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    int           =     weight




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   graph[a - 1].add_neighbor(graph[b - 1]							)
   graph[b - 1].add_neighbor(graph[a - 1]							)
   # add the edges:
   graph[a - 1].add_edge(graph[b - 1]						,		_UpperCamelCase							)
   graph[b - 1].add_edge(graph[a - 1]						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Any           =     []
   for u in graph:
      lowercase_	:    List[Any]           =     math.inf
      lowercase_	:    str           =     None
   lowercase_	:    Tuple           =     0
   lowercase_	:    Tuple           =     graph[:]
   while q:
      lowercase_	:    List[Any]           =     min(_UpperCamelCase							)
      q.remove(_UpperCamelCase							)
      for v in u.neighbors:
         if (v in q) and (u.edges[v.id] < v.key):
            lowercase_	:    Optional[int]           =     u
            lowercase_	:    Union[str, Any]           =     u.edges[v.id]
   for i in range(1						,		len(_UpperCamelCase							)							):
      a.append((int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)							)
   return a




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   for u in graph:
      lowercase_	:    str           =     math.inf
      lowercase_	:    int           =     None
   lowercase_	:    List[Any]           =     0

   lowercase_	:    str           =     list(_UpperCamelCase							)
   hq.heapify(_UpperCamelCase							)

   while h:
      lowercase_	:    List[Any]           =     hq.heappop(_UpperCamelCase							)
      for v in u.neighbors:
         if (v in h) and (u.edges[v.id] < v.key):
            lowercase_	:    str           =     u
            lowercase_	:    Optional[int]           =     u.edges[v.id]
            hq.heapify(_UpperCamelCase							)

   for i in range(1						,		len(_UpperCamelCase							)							):
      yield (int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)




def 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""






if __name__ == "__main__":
 import doctest

 doctest.testmod()




