

'''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 	UpperCamelCase   (       lowercase_						:    Dict     ,					lowercase_						:    Union[str, Any]	)		->	Dict:




  '''simple docstring'''



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

  lowercase									=transforms.Compose(
      [
          transforms.Resize((image_size, image_size)     ,					interpolation=InterpolationMode.BICUBIC	),
          transforms.ToTensor(),
          transforms.Normalize((0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3)     ,					(0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1)	),
      ]	)
  lowercase									=transform(lowercase_	).unsqueeze(0	).to(lowercase_	)
  return image




def 	UpperCamelCase   (       lowercase_						:    Any	)		->	int:




  '''simple docstring'''



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

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

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

  return key




@torch.no_grad()
def 	UpperCamelCase   (       lowercase_						:    Dict     ,					lowercase_						:    Tuple=None	)		->	str:




  '''simple docstring'''



  if config_path is not None:
    lowercase									=BlipConfig.from_pretrained(lowercase_	)
  else:
    lowercase									=BlipConfig(projection_dim=5_1_2     ,					text_config={}     ,					vision_config={}	)

  lowercase									=BlipForConditionalGeneration(lowercase_	).eval()

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

  lowercase									=blip_decoder(pretrained=lowercase_     ,					image_size=3_8_4     ,					vit='''base'''	)
  lowercase									=pt_model.eval()

  lowercase									=pt_model.state_dict()
  for key in modified_state_dict.copy():
    lowercase									=modified_state_dict.pop(lowercase_	)
    lowercase									=rename_key(lowercase_	)
    lowercase									=value

  hf_model.load_state_dict(lowercase_	)

  lowercase									=3_8_4
  lowercase									=load_demo_image(image_size=lowercase_     ,					device='''cpu'''	)
  lowercase									=BertTokenizer.from_pretrained('''bert-base-uncased'''	)
  lowercase									=tokenizer(['''a picture of''']	).input_ids

  lowercase									=hf_model.generate(lowercase_     ,					lowercase_	)

  assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 3_8_6_1, 1_9_9_7, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

  lowercase									=hf_model.generate(lowercase_	)

  assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

  if pytorch_dump_folder_path is not None:
    hf_model.save_pretrained(lowercase_	)

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

  lowercase									=blip_vqa(pretrained=lowercase_     ,					image_size=lowercase_     ,					vit='''base'''	)
  vqa_model.eval()

  lowercase									=vqa_model.state_dict()
  for key in modified_state_dict.copy():
    lowercase									=modified_state_dict.pop(lowercase_	)
    lowercase									=rename_key(lowercase_	)
    lowercase									=value

  lowercase									=BlipForQuestionAnswering(lowercase_	)

  hf_vqa_model.load_state_dict(lowercase_	)

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

  lowercase									=hf_vqa_model.generate(lowercase_     ,					lowercase_	)
  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									='''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

  lowercase									=blip_itm(pretrained=lowercase_     ,					image_size=lowercase_     ,					vit='''base'''	)
  itm_model.eval()

  lowercase									=itm_model.state_dict()
  for key in modified_state_dict.copy():
    lowercase									=modified_state_dict.pop(lowercase_	)
    lowercase									=rename_key(lowercase_	)
    lowercase									=value

  lowercase									=BlipForImageTextRetrieval(lowercase_	)

  lowercase									=['''A picture of a woman with a dog sitting in a beach''']
  lowercase									=tokenizer(
      lowercase_     ,					return_tensors='''pt'''     ,					padding='''max_length'''     ,					truncation=lowercase_     ,					max_length=3_5     ,					).input_ids

  hf_itm_model.load_state_dict(lowercase_	)
  hf_itm_model.eval()

  lowercase									=hf_itm_model(lowercase_     ,					lowercase_     ,					use_itm_head=lowercase_	)
  lowercase									=hf_itm_model(lowercase_     ,					lowercase_     ,					use_itm_head=lowercase_	)

  assert out[0].item() == 0.2_1_1_0_6_8_7_4_9_4_2_7_7_9_5_4
  assert torch.nn.functional.softmax(out_itm[0]     ,					dim=1	)[:, 1].item() == 0.4_5_6_9_8_8_4_5_3_8_6_5_0_5_1_2_7

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


if __name__ == "__main__":
     _UpperCAmelCase  :		Union[str, Any]  =		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  :		Tuple  =		parser.parse_args()

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







'''simple docstring'''

from ..utils import DummyObject, requires_backends
class 							__lowerCAmelCase	(       metaclass=__magic_name__					):






       """simple docstring"""
       _snake_case      :	Dict									=       ['transformers', 'torch', 'note_seq']





       def __init__(				self  :						Tuple			,					*lowerCAmelCase__  :						List[str]			,					**lowerCAmelCase__  :						Optional[int]	)       ->	int:

            '''simple docstring'''




            requires_backends(self			,					['''transformers''', '''torch''', '''note_seq''']	)





       @classmethod
       def snake_case__		(				cls  :						Dict			,					*lowerCAmelCase__  :						Optional[Any]			,					**lowerCAmelCase__  :						Optional[int]	)       ->	Any:

            '''simple docstring'''




            requires_backends(cls			,					['''transformers''', '''torch''', '''note_seq''']	)







       @classmethod
       def snake_case__		(				cls  :						str			,					*lowerCAmelCase__  :						Any			,					**lowerCAmelCase__  :						List[Any]	)       ->	Tuple:

            '''simple docstring'''




            requires_backends(cls			,					['''transformers''', '''torch''', '''note_seq''']	)



