





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  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Tuple:
  lowercase__           =  'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
  lowercase__           =  Image.open(requests.get(_SCREAMING_SNAKE_CASE		,						stream=_SCREAMING_SNAKE_CASE		).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(_SCREAMING_SNAKE_CASE		).unsqueeze(0		).to(_SCREAMING_SNAKE_CASE		)
  return image




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Union[str, Any]:
  if "visual_encoder" in key:
    lowercase__           =  re.sub('visual_encoder*'		,						'vision_model.encoder'		,						_SCREAMING_SNAKE_CASE		)
  if "blocks" in key:
    lowercase__           =  re.sub(R'blocks'		,						'layers'		,						_SCREAMING_SNAKE_CASE		)
  if "attn" in key:
    lowercase__           =  re.sub(R'attn'		,						'self_attn'		,						_SCREAMING_SNAKE_CASE		)
  if "norm1" in key:
    lowercase__           =  re.sub(R'norm1'		,						'layer_norm1'		,						_SCREAMING_SNAKE_CASE		)
  if "norm2" in key:
    lowercase__           =  re.sub(R'norm2'		,						'layer_norm2'		,						_SCREAMING_SNAKE_CASE		)
  if "encoder.norm" in key:
    lowercase__           =  re.sub(R'encoder.norm'		,						'post_layernorm'		,						_SCREAMING_SNAKE_CASE		)
  if "encoder.patch_embed.proj" in key:
    lowercase__           =  re.sub(R'encoder.patch_embed.proj'		,						'embeddings.patch_embedding'		,						_SCREAMING_SNAKE_CASE		)

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

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

  return key




@torch.no_grad()
def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=None		)    ->       Optional[Any]:
  if config_path is not None:
    lowercase__           =  BlipConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
  else:
    lowercase__           =  BlipConfig(projection_dim=512		,						text_config={}		,						vision_config={}		)

  lowercase__           =  BlipForConditionalGeneration(_SCREAMING_SNAKE_CASE		).eval()

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

  lowercase__           =  blip_decoder(pretrained=_SCREAMING_SNAKE_CASE		,						image_size=384		,						vit='base'		)
  lowercase__           =  pt_model.eval()

  lowercase__           =  pt_model.state_dict()
  for key in modified_state_dict.copy():
    lowercase__           =  modified_state_dict.pop(_SCREAMING_SNAKE_CASE		)
    lowercase__           =  rename_key(_SCREAMING_SNAKE_CASE		)
    lowercase__           =  value

  hf_model.load_state_dict(_SCREAMING_SNAKE_CASE		)

  lowercase__           =  384
  lowercase__           =  load_demo_image(image_size=_SCREAMING_SNAKE_CASE		,						device='cpu'		)
  lowercase__           =  BertTokenizer.from_pretrained('bert-base-uncased'		)
  lowercase__           =  tokenizer(['a picture of']		).input_ids

  lowercase__           =  hf_model.generate(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)

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

  lowercase__           =  hf_model.generate(_SCREAMING_SNAKE_CASE		)

  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(_SCREAMING_SNAKE_CASE		)

  # 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=_SCREAMING_SNAKE_CASE		,						image_size=_SCREAMING_SNAKE_CASE		,						vit='base'		)
  vqa_model.eval()

  lowercase__           =  vqa_model.state_dict()
  for key in modified_state_dict.copy():
    lowercase__           =  modified_state_dict.pop(_SCREAMING_SNAKE_CASE		)
    lowercase__           =  rename_key(_SCREAMING_SNAKE_CASE		)
    lowercase__           =  value

  lowercase__           =  BlipForQuestionAnswering(_SCREAMING_SNAKE_CASE		)

  hf_vqa_model.load_state_dict(_SCREAMING_SNAKE_CASE		)

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

  lowercase__           =  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'		)

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

  lowercase__           =  blip_itm(pretrained=_SCREAMING_SNAKE_CASE		,						image_size=_SCREAMING_SNAKE_CASE		,						vit='base'		)
  itm_model.eval()

  lowercase__           =  itm_model.state_dict()
  for key in modified_state_dict.copy():
    lowercase__           =  modified_state_dict.pop(_SCREAMING_SNAKE_CASE		)
    lowercase__           =  rename_key(_SCREAMING_SNAKE_CASE		)
    lowercase__           =  value

  lowercase__           =  BlipForImageTextRetrieval(_SCREAMING_SNAKE_CASE		)

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

  lowercase__           =  hf_itm_model(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						use_itm_head=_SCREAMING_SNAKE_CASE		)
  lowercase__           =  hf_itm_model(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						use_itm_head=_SCREAMING_SNAKE_CASE		)

  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__":
      lowercase_          =		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""")
      lowercase_          =		parser.parse_args()

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









from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):


   def __init__(   self      :    List[str]      ,					a      :    Callable      ,					a      :    Optional[Features] = None      ,					a      :    str = None      ,					a      :    bool = False      ,					a      :    bool = False      ,					a      :    Optional[dict] = None      ,					a      :    Optional[int] = None      ,					**a      :    str      ,					)->       Tuple:





     """simple docstring"""







     super().__init__(
         features=a      ,					cache_dir=a      ,					keep_in_memory=a      ,					streaming=a      ,					num_proc=a      ,					**a      ,					)
     lowercase__           =  Generator(
         cache_dir=a      ,					features=a      ,					generator=a      ,					gen_kwargs=a      ,					**a      ,					)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       Dict:





     """simple docstring"""







     if self.streaming:
       lowercase__           =  self.builder.as_streaming_dataset(split='train'	)
     # Build regular (map-style) dataset
     else:
       lowercase__           =  None
       lowercase__           =  None
       lowercase__           =  None
       lowercase__           =  None

       self.builder.download_and_prepare(
           download_config=a      ,					download_mode=a      ,					verification_mode=a      ,					base_path=a      ,					num_proc=self.num_proc      ,					)
       lowercase__           =  self.builder.as_dataset(
           split='train'      ,					verification_mode=a      ,					in_memory=self.keep_in_memory	)
     return dataset


