




"""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 		_snake_case	(   _snake_case				:   Union[str, Any]							,    _snake_case				:   List[str]				)       ->   str:






      '''simple docstring'''


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

      _A     =  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)				),
          ]				)
      _A     =  transform(_snake_case				).unsqueeze(0				).to(_snake_case				)
      return image






def 		_snake_case	(   _snake_case				:   List[str]				)       ->   Dict:






      '''simple docstring'''


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

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

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

      return key






@torch.no_grad()
def 		_snake_case	(   _snake_case				:   Optional[int]							,    _snake_case				:   List[str]=None				)       ->   Any:






      '''simple docstring'''


      if config_path is not None:
            _A     =  BlipConfig.from_pretrained(_snake_case				)
      else:
            _A     =  BlipConfig(projection_dim=5_12							,    text_config={}							,    vision_config={}				)

      _A     =  BlipForConditionalGeneration(_snake_case				).eval()

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

      _A     =  blip_decoder(pretrained=_snake_case							,    image_size=3_84							,    vit='base'				)
      _A     =  pt_model.eval()

      _A     =  pt_model.state_dict()
      for key in modified_state_dict.copy():
            _A     =  modified_state_dict.pop(_snake_case				)
            _A     =  rename_key(_snake_case				)
            _A     =  value

      hf_model.load_state_dict(_snake_case				)

      _A     =  3_84
      _A     =  load_demo_image(image_size=_snake_case							,    device='cpu'				)
      _A     =  BertTokenizer.from_pretrained('bert-base-uncased'				)
      _A     =  tokenizer(['a picture of']				).input_ids

      _A     =  hf_model.generate(_snake_case							,    _snake_case				)

      assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

      _A     =  hf_model.generate(_snake_case				)

      assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

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

      _A     =  blip_vqa(pretrained=_snake_case							,    image_size=_snake_case							,    vit='base'				)
      vqa_model.eval()

      _A     =  vqa_model.state_dict()
      for key in modified_state_dict.copy():
            _A     =  modified_state_dict.pop(_snake_case				)
            _A     =  rename_key(_snake_case				)
            _A     =  value

      _A     =  BlipForQuestionAnswering(_snake_case				)

      hf_vqa_model.load_state_dict(_snake_case				)

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

      _A     =  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'				)

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

      _A     =  blip_itm(pretrained=_snake_case							,    image_size=_snake_case							,    vit='base'				)
      itm_model.eval()

      _A     =  itm_model.state_dict()
      for key in modified_state_dict.copy():
            _A     =  modified_state_dict.pop(_snake_case				)
            _A     =  rename_key(_snake_case				)
            _A     =  value

      _A     =  BlipForImageTextRetrieval(_snake_case				)

      _A     =  ['A picture of a woman with a dog sitting in a beach']
      _A     =  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()

      _A     =  hf_itm_model(_snake_case							,    _snake_case							,    use_itm_head=_snake_case				)
      _A     =  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__":
       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)









import requests






def      _snake_case     (__lowercase     ,   __lowercase):
   UpperCamelCase_						=  {'Content-Type': 'application/json'}
   UpperCamelCase_						=  requests.post(__lowercase     ,   json={'text': message_body}     ,   headers=__lowercase)
   if response.status_code != 200:
      UpperCamelCase_						=  (
          'Request to slack returned an error '
          f"""{response.status_code}, the response is:\n{response.text}"""
      )
      raise ValueError(__lowercase)


if __name__ == "__main__":
 # Set the slack url to the one provided by Slack when you create the webhook at
 # https://my.slack.com/services/new/incoming-webhook/
 send_slack_message("""<YOUR MESSAGE BODY>""", """<SLACK CHANNEL URL>""")





