


'''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				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       Union[str, Any]       ):
     __a      :	Optional[int]    =  'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
     __a      :	str    =  Image.open(requests.get(_SCREAMING_SNAKE_CASE						,				stream=_SCREAMING_SNAKE_CASE       ).raw       ).convert('RGB'       )

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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Tuple       ):
     if "visual_encoder" in key:
          __a      :	List[Any]    =  re.sub('visual_encoder*'						,				'vision_model.encoder'						,				_SCREAMING_SNAKE_CASE       )
     if "blocks" in key:
          __a      :	List[Any]    =  re.sub(r'blocks'						,				'layers'						,				_SCREAMING_SNAKE_CASE       )
     if "attn" in key:
          __a      :	List[str]    =  re.sub(r'attn'						,				'self_attn'						,				_SCREAMING_SNAKE_CASE       )
     if "norm1" in key:
          __a      :	List[Any]    =  re.sub(r'norm1'						,				'layer_norm1'						,				_SCREAMING_SNAKE_CASE       )
     if "norm2" in key:
          __a      :	str    =  re.sub(r'norm2'						,				'layer_norm2'						,				_SCREAMING_SNAKE_CASE       )
     if "encoder.norm" in key:
          __a      :	List[str]    =  re.sub(r'encoder.norm'						,				'post_layernorm'						,				_SCREAMING_SNAKE_CASE       )
     if "encoder.patch_embed.proj" in key:
          __a      :	Dict    =  re.sub(r'encoder.patch_embed.proj'						,				'embeddings.patch_embedding'						,				_SCREAMING_SNAKE_CASE       )

     if "encoder.pos_embed" in key:
          __a      :	Optional[int]    =  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      :	List[str]    =  re.sub(r'self_attn.proj'						,				'self_attn.projection'						,				_SCREAMING_SNAKE_CASE       )

     return key






@torch.no_grad()
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Tuple						,				_SCREAMING_SNAKE_CASE				:       List[Any]=None       ):
     if config_path is not None:
          __a      :	List[Any]    =  BlipConfig.from_pretrained(_SCREAMING_SNAKE_CASE       )
     else:
          __a      :	Optional[int]    =  BlipConfig(projection_dim=512						,				text_config={}						,				vision_config={}       )

     __a      :	str    =  BlipForConditionalGeneration(_SCREAMING_SNAKE_CASE       ).eval()

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

     __a      :	List[Any]    =  blip_decoder(pretrained=_SCREAMING_SNAKE_CASE						,				image_size=384						,				vit='base'       )
     __a      :	Optional[int]    =  pt_model.eval()

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

     hf_model.load_state_dict(_SCREAMING_SNAKE_CASE       )

     __a      :	Optional[int]    =  384
     __a      :	List[Any]    =  load_demo_image(image_size=_SCREAMING_SNAKE_CASE						,				device='cpu'       )
     __a      :	int    =  BertTokenizer.from_pretrained('bert-base-uncased'       )
     __a      :	Dict    =  tokenizer(['a picture of']       ).input_ids

     __a      :	str    =  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      :	List[str]    =  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[Any]    =  (
         'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
     )

     __a      :	Tuple    =  blip_vqa(pretrained=_SCREAMING_SNAKE_CASE						,				image_size=_SCREAMING_SNAKE_CASE						,				vit='base'       )
     vqa_model.eval()

     __a      :	Optional[Any]    =  vqa_model.state_dict()
     for key in modified_state_dict.copy():
          __a      :	Optional[Any]    =  modified_state_dict.pop(_SCREAMING_SNAKE_CASE       )
          __a      :	Optional[Any]    =  rename_key(_SCREAMING_SNAKE_CASE       )
          __a      :	str    =  value

     __a      :	Dict    =  BlipForQuestionAnswering(_SCREAMING_SNAKE_CASE       )

     hf_vqa_model.load_state_dict(_SCREAMING_SNAKE_CASE       )

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

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

     __a      :	Dict    =  blip_itm(pretrained=_SCREAMING_SNAKE_CASE						,				image_size=_SCREAMING_SNAKE_CASE						,				vit='base'       )
     itm_model.eval()

     __a      :	Dict    =  itm_model.state_dict()
     for key in modified_state_dict.copy():
          __a      :	Dict    =  modified_state_dict.pop(_SCREAMING_SNAKE_CASE       )
          __a      :	List[Any]    =  rename_key(_SCREAMING_SNAKE_CASE       )
          __a      :	str    =  value

     __a      :	Dict    =  BlipForImageTextRetrieval(_SCREAMING_SNAKE_CASE       )

     __a      :	Tuple    =  ['A picture of a woman with a dog sitting in a beach']
     __a      :	Dict    =  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      :	Optional[int]    =  hf_itm_model(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				use_itm_head=_SCREAMING_SNAKE_CASE       )
     __a      :	int    =  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     :						Dict	=     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     :						str	=     parser.parse_args()

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







'''simple docstring'''



import re

from filelock import FileLock


try:
	import nltk

	__lowercase     :						Optional[Any]	=     True
except (ImportError, ModuleNotFoundError):
	__lowercase     :						Dict	=     False

if NLTK_AVAILABLE:
	with FileLock('.lock') as lock:
		nltk.download('punkt', quiet=True)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str       ):
					re.sub('<n>'						,				''						,				_SCREAMING_SNAKE_CASE       )  # remove pegasus newline char
					assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
					return "\n".join(nltk.sent_tokenize(_SCREAMING_SNAKE_CASE       )       )



