





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_  )     ->		List[str]:
       UpperCamelCase_          =  "https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
       UpperCamelCase_          =  Image.open(requests.get(lowercase_					,		stream=lowercase_  ).raw  ).convert("RGB"  )

       UpperCamelCase_          =  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)  ),
           ]  )
       UpperCamelCase_          =  transform(lowercase_  ).unsqueeze(0  ).to(lowercase_  )
       return image




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		str:

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

       if "encoder.pos_embed" in key:
              UpperCamelCase_          =  re.sub(r"encoder.pos_embed"					,		"embeddings.position_embedding"					,		lowercase_  )
       if "encoder.cls_token" in key:
              UpperCamelCase_          =  re.sub(r"encoder.cls_token"					,		"embeddings.class_embedding"					,		lowercase_  )

       if "self_attn" in key:
              UpperCamelCase_          =  re.sub(r"self_attn.proj"					,		"self_attn.projection"					,		lowercase_  )

       return key




@torch.no_grad()
def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_=None  )     ->		Tuple:

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

       UpperCamelCase_          =  BlipForConditionalGeneration(lowercase_  ).eval()

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

       UpperCamelCase_          =  blip_decoder(pretrained=lowercase_					,		image_size=384					,		vit="base"  )
       UpperCamelCase_          =  pt_model.eval()

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

       hf_model.load_state_dict(lowercase_  )

       UpperCamelCase_          =  384
       UpperCamelCase_          =  load_demo_image(image_size=lowercase_					,		device="cpu"  )
       UpperCamelCase_          =  BertTokenizer.from_pretrained("bert-base-uncased"  )
       UpperCamelCase_          =  tokenizer(["a picture of"]  ).input_ids

       UpperCamelCase_          =  hf_model.generate(lowercase_					,		lowercase_  )

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

       UpperCamelCase_          =  hf_model.generate(lowercase_  )

       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(lowercase_  )

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

       UpperCamelCase_          =  blip_vqa(pretrained=lowercase_					,		image_size=lowercase_					,		vit="base"  )
       vqa_model.eval()

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

       UpperCamelCase_          =  BlipForQuestionAnswering(lowercase_  )

       hf_vqa_model.load_state_dict(lowercase_  )

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

       UpperCamelCase_          =  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"  )

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

       UpperCamelCase_          =  blip_itm(pretrained=lowercase_					,		image_size=lowercase_					,		vit="base"  )
       itm_model.eval()

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

       UpperCamelCase_          =  BlipForImageTextRetrieval(lowercase_  )

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

       hf_itm_model.load_state_dict(lowercase_  )
       hf_itm_model.eval()

       UpperCamelCase_          =  hf_itm_model(lowercase_					,		lowercase_					,		use_itm_head=lowercase_  )
       UpperCamelCase_          =  hf_itm_model(lowercase_					,		lowercase_					,		use_itm_head=lowercase_  )

       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        =				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)










import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Optional[int]:
       return x + 2







class 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: Optional[int]		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  "x = 3"
              UpperCamelCase_          =  {}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 3
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3}		)

              UpperCamelCase_          =  "x = y"
              UpperCamelCase_          =  {"y": 5}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              # evaluate returns the value of the last assignment.
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 5, "y": 5}		)

       def lowercase (		self: List[str]		)       -> Dict:




              """simple docstring"""


              UpperCamelCase_          =  "y = add_two(x)"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"add_two": add_two}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "y": 5}		)

              # Won't work without the tool
              with CaptureStdout() as out:
                     UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result is None
              assert "tried to execute add_two" in out.out

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




              """simple docstring"""


              UpperCamelCase_          =  "x = 3"
              UpperCamelCase_          =  {}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 3
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3}		)

       def lowercase (		self: Dict		)       -> Any:




              """simple docstring"""


              UpperCamelCase_          =  "test_dict = {'x': x, 'y': add_two(x)}"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"add_two": add_two}					,   state=_SCREAMING_SNAKE_CASE		)
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "y": 5}		)
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "test_dict": {"x": 3, "y": 5}}		)

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




              """simple docstring"""


              UpperCamelCase_          =  "x = 3\ny = 5"
              UpperCamelCase_          =  {}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              # evaluate returns the value of the last assignment.
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "y": 5}		)

       def lowercase (		self: List[Any]		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  "text = f'This is x: {x}.'"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              # evaluate returns the value of the last assignment.
              assert result == "This is x: 3."
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "text": "This is x: 3."}		)

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




              """simple docstring"""


              UpperCamelCase_          =  "if x <= 3:\n    y = 2\nelse:\n    y = 5"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              # evaluate returns the value of the last assignment.
              assert result == 2
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "y": 2}		)

              UpperCamelCase_          =  {"x": 8}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              # evaluate returns the value of the last assignment.
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 8, "y": 5}		)

       def lowercase (		self: str		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  "test_list = [x, add_two(x)]"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"add_two": add_two}					,   state=_SCREAMING_SNAKE_CASE		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   [3, 5]		)
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "test_list": [3, 5]}		)

       def lowercase (		self: List[str]		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  "y = x"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 3
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "y": 3}		)

       def lowercase (		self: str		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  "test_list = [x, add_two(x)]\ntest_list[1]"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"add_two": add_two}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "test_list": [3, 5]}		)

              UpperCamelCase_          =  "test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"add_two": add_two}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "test_dict": {"x": 3, "y": 5}}		)



       def lowercase (		self: Dict		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  "x = 0\nfor i in range(3):\n    x = i"
              UpperCamelCase_          =  {}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"range": range}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 2
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 2, "i": 2}		)

