






"""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 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Any             =	"""https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
  _a  :       Optional[Any]             =	Image.open(requests.get(UpperCamelCase__     ,   stream=UpperCamelCase__				).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  :       Any             =	transform(UpperCamelCase__				).unsqueeze(0				).to(UpperCamelCase__				)
  return image

def 			lowerCAmelCase__     (     UpperCamelCase__				):



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

  if "encoder.pos_embed" in key:
    _a  :       str             =	re.sub(R"""encoder.pos_embed"""     ,   """embeddings.position_embedding"""     ,   UpperCamelCase__				)
  if "encoder.cls_token" in key:
    _a  :       Optional[int]             =	re.sub(R"""encoder.cls_token"""     ,   """embeddings.class_embedding"""     ,   UpperCamelCase__				)

  if "self_attn" in key:
    _a  :       str             =	re.sub(R"""self_attn.proj"""     ,   """self_attn.projection"""     ,   UpperCamelCase__				)

  return key

@torch.no_grad()
def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__=None				):



  '''simple docstring'''
  if config_path is not None:
    _a  :       Tuple             =	BlipConfig.from_pretrained(UpperCamelCase__				)
  else:
    _a  :       Any             =	BlipConfig(projection_dim=5_1_2     ,   text_config={}     ,   vision_config={}				)

  _a  :       Tuple             =	BlipForConditionalGeneration(UpperCamelCase__				).eval()

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

  _a  :       int             =	blip_decoder(pretrained=UpperCamelCase__     ,   image_size=3_8_4     ,   vit="""base"""				)
  _a  :       Any             =	pt_model.eval()

  _a  :       List[Any]             =	pt_model.state_dict()
  for key in modified_state_dict.copy():
    _a  :       Any             =	modified_state_dict.pop(UpperCamelCase__				)
    _a  :       Optional[int]             =	rename_key(UpperCamelCase__				)
    _a  :       Dict             =	value

  hf_model.load_state_dict(UpperCamelCase__				)

  _a  :       Union[str, Any]             =	3_8_4
  _a  :       Tuple             =	load_demo_image(image_size=UpperCamelCase__     ,   device="""cpu"""				)
  _a  :       Tuple             =	BertTokenizer.from_pretrained("""bert-base-uncased"""				)
  _a  :       Union[str, Any]             =	tokenizer(["""a picture of"""]				).input_ids

  _a  :       Dict             =	hf_model.generate(UpperCamelCase__     ,   UpperCamelCase__				)

  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]

  _a  :       List[Any]             =	hf_model.generate(UpperCamelCase__				)

  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(UpperCamelCase__				)

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

  _a  :       str             =	blip_vqa(pretrained=UpperCamelCase__     ,   image_size=UpperCamelCase__     ,   vit="""base"""				)
  vqa_model.eval()

  _a  :       Any             =	vqa_model.state_dict()
  for key in modified_state_dict.copy():
    _a  :       Optional[Any]             =	modified_state_dict.pop(UpperCamelCase__				)
    _a  :       List[str]             =	rename_key(UpperCamelCase__				)
    _a  :       int             =	value

  _a  :       Union[str, Any]             =	BlipForQuestionAnswering(UpperCamelCase__				)

  hf_vqa_model.load_state_dict(UpperCamelCase__				)

  _a  :       int             =	["""How many dogs are in this image?"""]
  _a  :       Dict             =	tokenizer(UpperCamelCase__     ,   return_tensors="""pt"""				).input_ids

  _a  :       Union[str, Any]             =	hf_vqa_model.generate(UpperCamelCase__     ,   UpperCamelCase__				)
  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  :       Optional[int]             =	"""https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"""

  _a  :       Tuple             =	blip_itm(pretrained=UpperCamelCase__     ,   image_size=UpperCamelCase__     ,   vit="""base"""				)
  itm_model.eval()

  _a  :       Tuple             =	itm_model.state_dict()
  for key in modified_state_dict.copy():
    _a  :       Optional[Any]             =	modified_state_dict.pop(UpperCamelCase__				)
    _a  :       Dict             =	rename_key(UpperCamelCase__				)
    _a  :       Union[str, Any]             =	value

  _a  :       Tuple             =	BlipForImageTextRetrieval(UpperCamelCase__				)

  _a  :       Any             =	["""A picture of a woman with a dog sitting in a beach"""]
  _a  :       Optional[int]             =	tokenizer(
      UpperCamelCase__     ,   return_tensors="""pt"""     ,   padding="""max_length"""     ,   truncation=UpperCamelCase__     ,   max_length=3_5     ,   ).input_ids

  hf_itm_model.load_state_dict(UpperCamelCase__				)
  hf_itm_model.eval()

  _a  :       Dict             =	hf_itm_model(UpperCamelCase__     ,   UpperCamelCase__     ,   use_itm_head=UpperCamelCase__				)
  _a  :       Tuple             =	hf_itm_model(UpperCamelCase__     ,   UpperCamelCase__     ,   use_itm_head=UpperCamelCase__				)

  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__":
 _snake_case          =   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')
 _snake_case          =   parser.parse_args()

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









"""simple docstring"""




import unittest

import numpy as np

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__ = None     ,   ):



  '''simple docstring'''
  _a  :       List[Any]             =	np.shape(UpperCamelCase__				)
  _a  :       Any             =	np.shape(UpperCamelCase__				)
  _a  :       Union[str, Any]             =	np.shape(UpperCamelCase__				)

  if shape_a[0] != shape_b[0]:
    _a  :       int             =	(
        """Expected the same number of rows for A and B. """
        F"""Instead found A of size {shape_a} and B of size {shape_b}"""
    )
    raise ValueError(UpperCamelCase__				)

  if shape_b[1] != shape_c[1]:
    _a  :       Tuple             =	(
        """Expected the same number of columns for B and C. """
        F"""Instead found B of size {shape_b} and C of size {shape_c}"""
    )
    raise ValueError(UpperCamelCase__				)

  _a  :       int             =	pseudo_inv
  if a_inv is None:
    try:
      _a  :       Optional[int]             =	np.linalg.inv(UpperCamelCase__				)
    except np.linalg.LinAlgError:
      raise ValueError(
          """Input matrix A is not invertible. Cannot compute Schur complement."""				)

  return mat_c - mat_b.T @ a_inv @ mat_b






class        UpperCamelCase			(	unittest.TestCase      ):


 def _lowercase	(     self		:						int							)					->       None:
   _a  :       str             =	np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
   _a  :       Tuple             =	np.array([[0, 3], [3, 0], [2, 3]]							)
   _a  :       Optional[int]             =	np.array([[2, 1], [6, 3]]							)

   _a  :       Optional[Any]             =	schur_complement(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)

   _a  :       Union[str, Any]             =	np.block([[a, b], [b.T, c]]							)

   _a  :       int             =	np.linalg.det(UpperCAmelCase__							)
   _a  :       Union[str, Any]             =	np.linalg.det(UpperCAmelCase__							)
   _a  :       List[Any]             =	np.linalg.det(UpperCAmelCase__							)

   self.assertAlmostEqual(UpperCAmelCase__							,						det_a * det_s							)


 def _lowercase	(     self		:						int							)					->       None:
   _a  :       Optional[int]             =	np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
   _a  :       Optional[int]             =	np.array([[0, 3], [3, 0], [2, 3]]							)
   _a  :       Union[str, Any]             =	np.array([[2, 1], [6, 3]]							)

   with self.assertRaises(UpperCAmelCase__							):
     schur_complement(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)







 def _lowercase	(     self		:						List[Any]							)					->       None:
   _a  :       Any             =	np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
   _a  :       Dict             =	np.array([[0, 3], [3, 0], [2, 3]]							)
   _a  :       List[Any]             =	np.array([[2, 1, 3], [6, 3, 5]]							)

   with self.assertRaises(UpperCAmelCase__							):
     schur_complement(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 unittest.main()

