





"""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        lowercase__     (       lowercase_		,lowercase_      )       ->		str:




      """simple docstring"""

      _UpperCamelCase   :						Any    =							"https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
      _UpperCamelCase   :						Optional[int]    =							Image.open(requests.get(lowercase_		,stream=lowercase_      ).raw      ).convert("RGB"      )

      _UpperCamelCase   :						str    =							transforms.Compose(
          [
              transforms.Resize((image_size, image_size)		,interpolation=InterpolationMode.BICUBIC      ),
              transforms.ToTensor(),
              transforms.Normalize((0.4814_5466, 0.457_8275, 0.4082_1073)		,(0.2686_2954, 0.2613_0258, 0.2757_7711)      ),
          ]      )
      _UpperCamelCase   :						List[str]    =							transform(lowercase_      ).unsqueeze(0      ).to(lowercase_      )
      return image




def        lowercase__     (       lowercase_      )       ->		int:




      """simple docstring"""

      if "visual_encoder" in key:
            _UpperCamelCase   :						List[Any]    =							re.sub("visual_encoder*"		,"vision_model.encoder"		,lowercase_      )
      if "blocks" in key:
            _UpperCamelCase   :						int    =							re.sub(r"blocks"		,"layers"		,lowercase_      )
      if "attn" in key:
            _UpperCamelCase   :						str    =							re.sub(r"attn"		,"self_attn"		,lowercase_      )
      if "norm1" in key:
            _UpperCamelCase   :						Any    =							re.sub(r"norm1"		,"layer_norm1"		,lowercase_      )
      if "norm2" in key:
            _UpperCamelCase   :						int    =							re.sub(r"norm2"		,"layer_norm2"		,lowercase_      )
      if "encoder.norm" in key:
            _UpperCamelCase   :						List[str]    =							re.sub(r"encoder.norm"		,"post_layernorm"		,lowercase_      )
      if "encoder.patch_embed.proj" in key:
            _UpperCamelCase   :						Optional[Any]    =							re.sub(r"encoder.patch_embed.proj"		,"embeddings.patch_embedding"		,lowercase_      )

      if "encoder.pos_embed" in key:
            _UpperCamelCase   :						List[Any]    =							re.sub(r"encoder.pos_embed"		,"embeddings.position_embedding"		,lowercase_      )
      if "encoder.cls_token" in key:
            _UpperCamelCase   :						List[Any]    =							re.sub(r"encoder.cls_token"		,"embeddings.class_embedding"		,lowercase_      )

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

      return key




@torch.no_grad()
def        lowercase__     (       lowercase_		,lowercase_=None      )       ->		Optional[Any]:




      """simple docstring"""

      if config_path is not None:
            _UpperCamelCase   :						str    =							BlipConfig.from_pretrained(lowercase_      )
      else:
            _UpperCamelCase   :						Union[str, Any]    =							BlipConfig(projection_dim=512		,text_config={}		,vision_config={}      )

      _UpperCamelCase   :						str    =							BlipForConditionalGeneration(lowercase_      ).eval()

      _UpperCamelCase   :						List[str]    =							"https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"

      _UpperCamelCase   :						Optional[int]    =							blip_decoder(pretrained=lowercase_		,image_size=384		,vit="base"      )
      _UpperCamelCase   :						int    =							pt_model.eval()

      _UpperCamelCase   :						int    =							pt_model.state_dict()
      for key in modified_state_dict.copy():
            _UpperCamelCase   :						Any    =							modified_state_dict.pop(lowercase_      )
            _UpperCamelCase   :						Optional[int]    =							rename_key(lowercase_      )
            _UpperCamelCase   :						str    =							value

      hf_model.load_state_dict(lowercase_      )

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

      _UpperCamelCase   :						Tuple    =							hf_model.generate(lowercase_		,lowercase_      )

      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]

      _UpperCamelCase   :						Dict    =							hf_model.generate(lowercase_      )

      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(lowercase_      )

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

      _UpperCamelCase   :						Dict    =							blip_vqa(pretrained=lowercase_		,image_size=lowercase_		,vit="base"      )
      vqa_model.eval()

      _UpperCamelCase   :						List[Any]    =							vqa_model.state_dict()
      for key in modified_state_dict.copy():
            _UpperCamelCase   :						Any    =							modified_state_dict.pop(lowercase_      )
            _UpperCamelCase   :						Optional[int]    =							rename_key(lowercase_      )
            _UpperCamelCase   :						Dict    =							value

      _UpperCamelCase   :						Tuple    =							BlipForQuestionAnswering(lowercase_      )

      hf_vqa_model.load_state_dict(lowercase_      )

      _UpperCamelCase   :						Union[str, Any]    =							["How many dogs are in this image?"]
      _UpperCamelCase   :						List[str]    =							tokenizer(lowercase_		,return_tensors="pt"      ).input_ids

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

      _UpperCamelCase   :						str    =							blip_itm(pretrained=lowercase_		,image_size=lowercase_		,vit="base"      )
      itm_model.eval()

      _UpperCamelCase   :						List[Any]    =							itm_model.state_dict()
      for key in modified_state_dict.copy():
            _UpperCamelCase   :						Optional[Any]    =							modified_state_dict.pop(lowercase_      )
            _UpperCamelCase   :						List[Any]    =							rename_key(lowercase_      )
            _UpperCamelCase   :						Optional[Any]    =							value

      _UpperCamelCase   :						Dict    =							BlipForImageTextRetrieval(lowercase_      )

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

      assert out[0].item() == 0.2110_6874_9427_7954
      assert torch.nn.functional.softmax(out_itm[0]		,dim=1      )[:, 1].item() == 0.4_5698_8453_8650_5127

      if pytorch_dump_folder_path is not None:
            hf_itm_model.save_pretrained(pytorch_dump_folder_path + "_itm"      )


if __name__ == "__main__":
  lowerCamelCase__						= 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")
  lowerCamelCase__						= parser.parse_args()

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







"""simple docstring"""


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_=5      )       ->		List[Any]:




      """simple docstring"""

      assert masked_input.count("<mask>"      ) == 1
      _UpperCamelCase   :						str    =							torch.tensor(tokenizer.encode(lowercase_		,add_special_tokens=lowercase_      )      ).unsqueeze(0      )  # Batch size 1
      _UpperCamelCase   :						Union[str, Any]    =							model(lowercase_      )[0]  # The last hidden-state is the first element of the output tuple
      _UpperCamelCase   :						List[Any]    =							(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
      _UpperCamelCase   :						Tuple    =							logits[0, masked_index, :]
      _UpperCamelCase   :						List[str]    =							logits.softmax(dim=0      )
      _UpperCamelCase,	_UpperCamelCase   :						str    =							prob.topk(k=lowercase_		,dim=0      )
      _UpperCamelCase   :						str    =							" ".join(
          [tokenizer.convert_ids_to_tokens(indices[i].item()      ) for i in range(len(lowercase_      )      )]      )
      _UpperCamelCase   :						Any    =							tokenizer.mask_token
      _UpperCamelCase   :						List[Any]    =							[]
      for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "      )      ):
            _UpperCamelCase   :						Optional[int]    =							predicted_token_bpe.replace("\u2581"		," "      )
            if " {0}".format(lowercase_      ) in masked_input:
                  topk_filled_outputs.append(
                      (
                          masked_input.replace(" {0}".format(lowercase_      )		,lowercase_      ),
                          values[index].item(),
                          predicted_token,
                      )      )
            else:
                  topk_filled_outputs.append(
                      (
                          masked_input.replace(lowercase_		,lowercase_      ),
                          values[index].item(),
                          predicted_token,
                      )      )
      return topk_filled_outputs


lowerCamelCase__						= CamembertTokenizer.from_pretrained("camembert-base")
lowerCamelCase__						= CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

lowerCamelCase__						= "Le camembert est <mask> :)"

print(fill_mask(masked_input, model, tokenizer, topk=3))
