



'''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  (__A  ,	__A):



     """simple docstring"""



     _a      =  '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
     _a      =  Image.open(requests.get(__A  ,	stream=__A).raw).convert('''RGB''')

     _a      =  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      =  transform(__A).unsqueeze(0).to(__A)
     return image




def lowerCAmelCase  (__A):



     """simple docstring"""



     if "visual_encoder" in key:
          _a      =  re.sub('''visual_encoder*'''  ,	'''vision_model.encoder'''  ,	__A)
     if "blocks" in key:
          _a      =  re.sub(r'''blocks'''  ,	'''layers'''  ,	__A)
     if "attn" in key:
          _a      =  re.sub(r'''attn'''  ,	'''self_attn'''  ,	__A)
     if "norm1" in key:
          _a      =  re.sub(r'''norm1'''  ,	'''layer_norm1'''  ,	__A)
     if "norm2" in key:
          _a      =  re.sub(r'''norm2'''  ,	'''layer_norm2'''  ,	__A)
     if "encoder.norm" in key:
          _a      =  re.sub(r'''encoder.norm'''  ,	'''post_layernorm'''  ,	__A)
     if "encoder.patch_embed.proj" in key:
          _a      =  re.sub(r'''encoder.patch_embed.proj'''  ,	'''embeddings.patch_embedding'''  ,	__A)

     if "encoder.pos_embed" in key:
          _a      =  re.sub(r'''encoder.pos_embed'''  ,	'''embeddings.position_embedding'''  ,	__A)
     if "encoder.cls_token" in key:
          _a      =  re.sub(r'''encoder.cls_token'''  ,	'''embeddings.class_embedding'''  ,	__A)

     if "self_attn" in key:
          _a      =  re.sub(r'''self_attn.proj'''  ,	'''self_attn.projection'''  ,	__A)

     return key




@torch.no_grad()
def lowerCAmelCase  (__A  ,	__A=None):



     """simple docstring"""



     if config_path is not None:
          _a      =  BlipConfig.from_pretrained(__A)
     else:
          _a      =  BlipConfig(projection_dim=512  ,	text_config={}  ,	vision_config={})

     _a      =  BlipForConditionalGeneration(__A).eval()

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

     _a      =  blip_decoder(pretrained=__A  ,	image_size=384  ,	vit='''base''')
     _a      =  pt_model.eval()

     _a      =  pt_model.state_dict()
     for key in modified_state_dict.copy():
          _a      =  modified_state_dict.pop(__A)
          _a      =  rename_key(__A)
          _a      =  value

     hf_model.load_state_dict(__A)

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

     _a      =  hf_model.generate(__A  ,	__A)

     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      =  hf_model.generate(__A)

     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(__A)

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

     _a      =  blip_vqa(pretrained=__A  ,	image_size=__A  ,	vit='''base''')
     vqa_model.eval()

     _a      =  vqa_model.state_dict()
     for key in modified_state_dict.copy():
          _a      =  modified_state_dict.pop(__A)
          _a      =  rename_key(__A)
          _a      =  value

     _a      =  BlipForQuestionAnswering(__A)

     hf_vqa_model.load_state_dict(__A)

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

     _a      =  hf_vqa_model.generate(__A  ,	__A)
     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      =  '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

     _a      =  blip_itm(pretrained=__A  ,	image_size=__A  ,	vit='''base''')
     itm_model.eval()

     _a      =  itm_model.state_dict()
     for key in modified_state_dict.copy():
          _a      =  modified_state_dict.pop(__A)
          _a      =  rename_key(__A)
          _a      =  value

     _a      =  BlipForImageTextRetrieval(__A)

     _a      =  ['''A picture of a woman with a dog sitting in a beach''']
     _a      =  tokenizer(
         __A  ,	return_tensors='''pt'''  ,	padding='''max_length'''  ,	truncation=__A  ,	max_length=35  ,	).input_ids

     hf_itm_model.load_state_dict(__A)
     hf_itm_model.eval()

     _a      =  hf_itm_model(__A  ,	__A  ,	use_itm_head=__A)
     _a      =  hf_itm_model(__A  ,	__A  ,	use_itm_head=__A)

     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__":
  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)








'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowercase_		       =  {
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

lowercase_		       =  {
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       List[Any]          =       VOCAB_FILES_NAMES
 __lowerCamelCase    :       Tuple          =       PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase    :       Any          =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase    :       str          =       ['input_ids', 'attention_mask']
 __lowerCamelCase    :       Union[str, Any]          =       RobertaTokenizer



 def __init__(self ,   A=None ,   A=None ,   A=None ,   A="replace" ,   A="<s>" ,   A="</s>" ,   A="</s>" ,   A="<s>" ,   A="<unk>" ,   A="<pad>" ,   A="<mask>" ,   A=False ,   A=True ,   **A ,   )    ->     List[str]:


      """simple docstring"""



      super().__init__(
          A ,   A ,   tokenizer_file=A ,   errors=A ,   bos_token=A ,   eos_token=A ,   sep_token=A ,   cls_token=A ,   unk_token=A ,   pad_token=A ,   mask_token=A ,   add_prefix_space=A ,   trim_offsets=A ,   **A ,   )

      _a      =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
      if pre_tok_state.get('''add_prefix_space''' ,   A       ) != add_prefix_space:
           _a      =  getattr(A ,   pre_tok_state.pop('''type'''       )       )
           _a      =  add_prefix_space
           _a      =  pre_tok_class(**A       )

      _a      =  add_prefix_space

      _a      =  '''post_processor'''
      _a      =  getattr(self.backend_tokenizer ,   A ,   A       )
      if tokenizer_component_instance:
           _a      =  json.loads(tokenizer_component_instance.__getstate__()       )

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
                _a      =  tuple(state['''sep''']       )
           if "cls" in state:
                _a      =  tuple(state['''cls''']       )

           _a      =  False

           if state.get('''add_prefix_space''' ,   A       ) != add_prefix_space:
                _a      =  add_prefix_space
                _a      =  True

           if state.get('''trim_offsets''' ,   A       ) != trim_offsets:
                _a      =  trim_offsets
                _a      =  True

           if changes_to_apply:
                _a      =  getattr(A ,   state.pop('''type'''       )       )
                _a      =  component_class(**A       )
                setattr(self.backend_tokenizer ,   A ,   A       )



 @property
 def 					a__  (self       )    ->     str:


      """simple docstring"""



      if self._mask_token is None:
           if self.verbose:
                logger.error('''Using mask_token, but it is not set yet.'''       )
           return None
      return str(self._mask_token       )



 @mask_token.setter
 def 					a__  (self ,   A       )    ->     Optional[Any]:


      """simple docstring"""



      _a      =  AddedToken(A ,   lstrip=A ,   rstrip=A       ) if isinstance(A ,   A       ) else value
      _a      =  value



 def 					a__  (self ,   *A ,   **A       )    ->     BatchEncoding:


      """simple docstring"""



      _a      =  kwargs.get('''is_split_into_words''' ,   A       )
      assert self.add_prefix_space or not is_split_into_words, (
          f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*A ,   **A       )



 def 					a__  (self ,   *A ,   **A       )    ->     BatchEncoding:


      """simple docstring"""



      _a      =  kwargs.get('''is_split_into_words''' ,   A       )

      assert self.add_prefix_space or not is_split_into_words, (
          f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*A ,   **A       )



 def 					a__  (self ,   A ,   A = None       )    ->     Tuple[str]:


      """simple docstring"""



      _a      =  self._tokenizer.model.save(A ,   name=A       )
      return tuple(A       )



 def 					a__  (self ,   A ,   A=None       )    ->     List[str]:


      """simple docstring"""



      _a      =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
           return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



 def 					a__  (self ,   A ,   A = None       )    ->     List[int]:


      """simple docstring"""



      _a      =  [self.sep_token_id]
      _a      =  [self.cls_token_id]

      if token_ids_a is None:
           return len(cls + token_ids_a + sep       ) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]



