



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

  a_     :      Optional[int] 						=     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)	),
      ]	)
  a_     :      Tuple 						=     transform(SCREAMING_SNAKE_CASE__	).unsqueeze(0	).to(SCREAMING_SNAKE_CASE__	)
  return image



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      List[str]:
  if "visual_encoder" in key:
    a_     :      int 						=     re.sub("visual_encoder*", "vision_model.encoder", SCREAMING_SNAKE_CASE__	)
  if "blocks" in key:
    a_     :      Union[str, Any] 						=     re.sub(r"blocks", "layers", SCREAMING_SNAKE_CASE__	)
  if "attn" in key:
    a_     :      Tuple 						=     re.sub(r"attn", "self_attn", SCREAMING_SNAKE_CASE__	)
  if "norm1" in key:
    a_     :      Tuple 						=     re.sub(r"norm1", "layer_norm1", SCREAMING_SNAKE_CASE__	)
  if "norm2" in key:
    a_     :      Dict 						=     re.sub(r"norm2", "layer_norm2", SCREAMING_SNAKE_CASE__	)
  if "encoder.norm" in key:
    a_     :      int 						=     re.sub(r"encoder.norm", "post_layernorm", SCREAMING_SNAKE_CASE__	)
  if "encoder.patch_embed.proj" in key:
    a_     :      Union[str, 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_     :      Dict 						=     re.sub(r"encoder.cls_token", "embeddings.class_embedding", SCREAMING_SNAKE_CASE__	)

  if "self_attn" in key:
    a_     :      Optional[Any] 						=     re.sub(r"self_attn.proj", "self_attn.projection", SCREAMING_SNAKE_CASE__	)

  return key



@torch.no_grad()
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=None	)			->      Dict:
  if config_path is not None:
    a_     :      int 						=     BlipConfig.from_pretrained(SCREAMING_SNAKE_CASE__	)
  else:
    a_     :      Dict 						=     BlipConfig(projection_dim=512, text_config={}, vision_config={}	)

  a_     :      int 						=     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_     :      Optional[int] 						=     pt_model.eval()

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

  hf_model.load_state_dict(SCREAMING_SNAKE_CASE__	)

  a_     :      int 						=     384
  a_     :      str 						=     load_demo_image(image_size=SCREAMING_SNAKE_CASE__, device="cpu"	)
  a_     :      Tuple 						=     BertTokenizer.from_pretrained("bert-base-uncased"	)
  a_     :      Dict 						=     tokenizer(["a picture of"]	).input_ids

  a_     :      Optional[Any] 						=     hf_model.generate(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

  assert out[0].tolist() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

  a_     :      Dict 						=     hf_model.generate(SCREAMING_SNAKE_CASE__	)

  assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 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_     :      Optional[int] 						=     (
      "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"
  )

  a_     :      Optional[int] 						=     blip_vqa(pretrained=SCREAMING_SNAKE_CASE__, image_size=SCREAMING_SNAKE_CASE__, vit="base"	)
  vqa_model.eval()

  a_     :      str 						=     vqa_model.state_dict()
  for key in modified_state_dict.copy():
    a_     :      List[Any] 						=     modified_state_dict.pop(SCREAMING_SNAKE_CASE__	)
    a_     :      Union[str, Any] 						=     rename_key(SCREAMING_SNAKE_CASE__	)
    a_     :      Union[str, Any] 						=     value

  a_     :      str 						=     BlipForQuestionAnswering(SCREAMING_SNAKE_CASE__	)

  hf_vqa_model.load_state_dict(SCREAMING_SNAKE_CASE__	)

  a_     :      Union[str, Any] 						=     ["How many dogs are in this image?"]
  a_     :      List[str] 						=     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_     :      str 						=     "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_     :      List[str] 						=     itm_model.state_dict()
  for key in modified_state_dict.copy():
    a_     :      Union[str, Any] 						=     modified_state_dict.pop(SCREAMING_SNAKE_CASE__	)
    a_     :      int 						=     rename_key(SCREAMING_SNAKE_CASE__	)
    a_     :      str 						=     value

  a_     :      Optional[int] 						=     BlipForImageTextRetrieval(SCREAMING_SNAKE_CASE__	)

  a_     :      Union[str, Any] 						=     ["A picture of a woman with a dog sitting in a beach"]
  a_     :      Optional[int] 						=     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_     :      List[str] 						=     hf_itm_model(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, use_itm_head=SCREAMING_SNAKE_CASE__	)
  a_     :      Any 						=     hf_itm_model(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, use_itm_head=SCREAMING_SNAKE_CASE__	)

  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__":
     SCREAMING_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""")
     SCREAMING_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
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


SCREAMING_SNAKE_CASE_				= False




class 			snake_case_						(						unittest.TestCase				):
							pass




@slow
@require_torch_gpu
class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									a_     :      Tuple 						=     VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"      )
									pipe.to(a_      )
									pipe.set_progress_bar_config(disable=a_      )

									a_     :      Optional[Any] 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"      )
									a_     :      Optional[Any] 						=     torch.manual_seed(0      )
									a_     :      Dict 						=     pipe(
									    image=a_			,     generator=a_			,     guidance_scale=7.5			,     num_inference_steps=5_0			,     output_type="numpy"			,     ).images

									a_     :      List[Any] 						=     image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									a_     :      Any 						=     np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2