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

  a__:					Any      							=     transforms.Compose(
      [
          transforms.Resize((image_size, image_size)		,					interpolation=InterpolationMode.BICUBIC       ),
          transforms.ToTensor(),
          transforms.Normalize((0.48_145_466, 0.4_578_275, 0.40_821_073)		,					(0.26_862_954, 0.26_130_258, 0.27_577_711)       ),
      ]       )
  a__:					int      							=     transform(_SCREAMING_SNAKE_CASE       ).unsqueeze(0       ).to(_SCREAMING_SNAKE_CASE       )
  return image







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Optional[int]:
  if "visual_encoder" in key:
    a__:					Any      							=     re.sub('visual_encoder*'		,					'vision_model.encoder'		,					_SCREAMING_SNAKE_CASE       )
  if "blocks" in key:
    a__:					List[str]      							=     re.sub(r'blocks'		,					'layers'		,					_SCREAMING_SNAKE_CASE       )
  if "attn" in key:
    a__:					List[Any]      							=     re.sub(r'attn'		,					'self_attn'		,					_SCREAMING_SNAKE_CASE       )
  if "norm1" in key:
    a__:					Union[str, Any]      							=     re.sub(r'norm1'		,					'layer_norm1'		,					_SCREAMING_SNAKE_CASE       )
  if "norm2" in key:
    a__:					List[Any]      							=     re.sub(r'norm2'		,					'layer_norm2'		,					_SCREAMING_SNAKE_CASE       )
  if "encoder.norm" in key:
    a__:					Dict      							=     re.sub(r'encoder.norm'		,					'post_layernorm'		,					_SCREAMING_SNAKE_CASE       )
  if "encoder.patch_embed.proj" in key:
    a__:					Optional[Any]      							=     re.sub(r'encoder.patch_embed.proj'		,					'embeddings.patch_embedding'		,					_SCREAMING_SNAKE_CASE       )

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

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

  return key







@torch.no_grad()
def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=None       )     ->int:
  if config_path is not None:
    a__:					Optional[int]      							=     BlipConfig.from_pretrained(_SCREAMING_SNAKE_CASE       )
  else:
    a__:					str      							=     BlipConfig(projection_dim=512		,					text_config={}		,					vision_config={}       )

  a__:					Dict      							=     BlipForConditionalGeneration(_SCREAMING_SNAKE_CASE       ).eval()

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

  a__:					Optional[Any]      							=     blip_decoder(pretrained=_SCREAMING_SNAKE_CASE		,					image_size=384		,					vit='base'       )
  a__:					Tuple      							=     pt_model.eval()

  a__:					Dict      							=     pt_model.state_dict()
  for key in modified_state_dict.copy():
    a__:					str      							=     modified_state_dict.pop(_SCREAMING_SNAKE_CASE       )
    a__:					Optional[Any]      							=     rename_key(_SCREAMING_SNAKE_CASE       )
    a__:					List[Any]      							=     value

  hf_model.load_state_dict(_SCREAMING_SNAKE_CASE       )

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

  a__:					Dict      							=     hf_model.generate(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

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

  a__:					int      							=     hf_model.generate(_SCREAMING_SNAKE_CASE       )

  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(_SCREAMING_SNAKE_CASE       )

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

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

  a__:					Union[str, Any]      							=     vqa_model.state_dict()
  for key in modified_state_dict.copy():
    a__:					Union[str, Any]      							=     modified_state_dict.pop(_SCREAMING_SNAKE_CASE       )
    a__:					Union[str, Any]      							=     rename_key(_SCREAMING_SNAKE_CASE       )
    a__:					Optional[Any]      							=     value

  a__:					Optional[Any]      							=     BlipForQuestionAnswering(_SCREAMING_SNAKE_CASE       )

  hf_vqa_model.load_state_dict(_SCREAMING_SNAKE_CASE       )

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

  a__:					Dict      							=     hf_vqa_model.generate(_SCREAMING_SNAKE_CASE		,					_SCREAMING_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'       )

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

  a__:					Optional[int]      							=     blip_itm(pretrained=_SCREAMING_SNAKE_CASE		,					image_size=_SCREAMING_SNAKE_CASE		,					vit='base'       )
  itm_model.eval()

  a__:					Optional[int]      							=     itm_model.state_dict()
  for key in modified_state_dict.copy():
    a__:					List[str]      							=     modified_state_dict.pop(_SCREAMING_SNAKE_CASE       )
    a__:					Dict      							=     rename_key(_SCREAMING_SNAKE_CASE       )
    a__:					Tuple      							=     value

  a__:					Optional[Any]      							=     BlipForImageTextRetrieval(_SCREAMING_SNAKE_CASE       )

  a__:					Optional[Any]      							=     ['A picture of a woman with a dog sitting in a beach']
  a__:					List[Any]      							=     tokenizer(
      _SCREAMING_SNAKE_CASE		,					return_tensors='pt'		,					padding='max_length'		,					truncation=_SCREAMING_SNAKE_CASE		,					max_length=35		,					).input_ids

  hf_itm_model.load_state_dict(_SCREAMING_SNAKE_CASE       )
  hf_itm_model.eval()

  a__:					Dict      							=     hf_itm_model(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					use_itm_head=_SCREAMING_SNAKE_CASE       )
  a__:					int      							=     hf_itm_model(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					use_itm_head=_SCREAMING_SNAKE_CASE       )

  assert out[0].item() == 0.2_110_687_494_277_954
  assert torch.nn.functional.softmax(out_itm[0]		,					dim=1       )[:, 1].item() == 0.45_698_845_386_505_127

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


if __name__ == "__main__":
 lowercase__ 	=     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')
 lowercase__ 	=     parser.parse_args()

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



"""simple docstring"""
import unittest

from knapsack import knapsack as k




class        __snake_case	(					unittest.TestCase    ):



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					List[Any]      							=     0
        a__:					Dict      							=     [0]
        a__:					int      							=     [0]
        a__:					Optional[Any]      							=     len(lowercase)
        self.assertEqual(k.knapsack(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)   ,      0)

        a__:					str      							=     [60]
        a__:					Dict      							=     [10]
        a__:					List[str]      							=     len(lowercase)
        self.assertEqual(k.knapsack(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)   ,      0)



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        a__:					int      							=     3
        a__:					str      							=     [1, 2, 3]
        a__:					Dict      							=     [3, 2, 1]
        a__:					Optional[int]      							=     len(lowercase)
        self.assertEqual(k.knapsack(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)   ,      5)



      def   lowerCamelCase_       (						self)     ->      Union[str, Any]:





        '''simple docstring'''





        a__:					Any      							=     50
        a__:					Optional[int]      							=     [60, 1_00, 1_20]
        a__:					str      							=     [10, 20, 30]
        a__:					int      							=     len(lowercase)
        self.assertEqual(k.knapsack(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)   ,      2_20)


if __name__ == "__main__":
 unittest.main()


