


'''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 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Optional[int]    ,_UpperCAmelCase	:	List[Any]  )  ->			Dict:
       _a    : Optional[int]     					="""https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
       _a    : List[str]     					=Image.open(requests.get(_UpperCAmelCase    ,stream=_UpperCAmelCase  ).raw  ).convert("""RGB"""  )

       _a    : Dict     					=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(_UpperCAmelCase  ).unsqueeze(0  ).to(_UpperCAmelCase  )
       return image


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Optional[Any]  )  ->			Optional[int]:
       if "visual_encoder" in key:
              _a    : Optional[int]     					=re.sub("""visual_encoder*"""    ,"""vision_model.encoder"""    ,_UpperCAmelCase  )
       if "blocks" in key:
              _a    : int     					=re.sub(R"""blocks"""    ,"""layers"""    ,_UpperCAmelCase  )
       if "attn" in key:
              _a    : Any     					=re.sub(R"""attn"""    ,"""self_attn"""    ,_UpperCAmelCase  )
       if "norm1" in key:
              _a    : Optional[int]     					=re.sub(R"""norm1"""    ,"""layer_norm1"""    ,_UpperCAmelCase  )
       if "norm2" in key:
              _a    : Optional[Any]     					=re.sub(R"""norm2"""    ,"""layer_norm2"""    ,_UpperCAmelCase  )
       if "encoder.norm" in key:
              _a    : str     					=re.sub(R"""encoder.norm"""    ,"""post_layernorm"""    ,_UpperCAmelCase  )
       if "encoder.patch_embed.proj" in key:
              _a    : List[Any]     					=re.sub(R"""encoder.patch_embed.proj"""    ,"""embeddings.patch_embedding"""    ,_UpperCAmelCase  )

       if "encoder.pos_embed" in key:
              _a    : Optional[int]     					=re.sub(R"""encoder.pos_embed"""    ,"""embeddings.position_embedding"""    ,_UpperCAmelCase  )
       if "encoder.cls_token" in key:
              _a    : Dict     					=re.sub(R"""encoder.cls_token"""    ,"""embeddings.class_embedding"""    ,_UpperCAmelCase  )

       if "self_attn" in key:
              _a    : Optional[Any]     					=re.sub(R"""self_attn.proj"""    ,"""self_attn.projection"""    ,_UpperCAmelCase  )

       return key


@torch.no_grad()
def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Optional[int]    ,_UpperCAmelCase	:	int=None  )  ->			Any:
       if config_path is not None:
              _a    : List[Any]     					=BlipConfig.from_pretrained(_UpperCAmelCase  )
       else:
              _a    : Optional[int]     					=BlipConfig(projection_dim=512    ,text_config={}    ,vision_config={}  )

       _a    : Optional[int]     					=BlipForConditionalGeneration(_UpperCAmelCase  ).eval()

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

       _a    : Any     					=blip_decoder(pretrained=_UpperCAmelCase    ,image_size=384    ,vit="""base"""  )
       _a    : int     					=pt_model.eval()

       _a    : Union[str, Any]     					=pt_model.state_dict()
       for key in modified_state_dict.copy():
              _a    : Union[str, Any]     					=modified_state_dict.pop(_UpperCAmelCase  )
              _a    : Dict     					=rename_key(_UpperCAmelCase  )
              _a    : Optional[int]     					=value

       hf_model.load_state_dict(_UpperCAmelCase  )

       _a    : Union[str, Any]     					=384
       _a    : Optional[Any]     					=load_demo_image(image_size=_UpperCAmelCase    ,device="""cpu"""  )
       _a    : Optional[Any]     					=BertTokenizer.from_pretrained("""bert-base-uncased"""  )
       _a    : List[str]     					=tokenizer(["""a picture of"""]  ).input_ids

       _a    : Any     					=hf_model.generate(_UpperCAmelCase    ,_UpperCAmelCase  )

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

       _a    : Dict     					=hf_model.generate(_UpperCAmelCase  )

       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(_UpperCAmelCase  )

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

       _a    : List[str]     					=blip_vqa(pretrained=_UpperCAmelCase    ,image_size=_UpperCAmelCase    ,vit="""base"""  )
       vqa_model.eval()

       _a    : Optional[Any]     					=vqa_model.state_dict()
       for key in modified_state_dict.copy():
              _a    : Any     					=modified_state_dict.pop(_UpperCAmelCase  )
              _a    : Tuple     					=rename_key(_UpperCAmelCase  )
              _a    : Union[str, Any]     					=value

       _a    : int     					=BlipForQuestionAnswering(_UpperCAmelCase  )

       hf_vqa_model.load_state_dict(_UpperCAmelCase  )

       _a    : Tuple     					=["""How many dogs are in this image?"""]
       _a    : Optional[int]     					=tokenizer(_UpperCAmelCase    ,return_tensors="""pt"""  ).input_ids

       _a    : Union[str, Any]     					=hf_vqa_model.generate(_UpperCAmelCase    ,_UpperCAmelCase  )
       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    : Optional[Any]     					="""https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"""

       _a    : Optional[int]     					=blip_itm(pretrained=_UpperCAmelCase    ,image_size=_UpperCAmelCase    ,vit="""base"""  )
       itm_model.eval()

       _a    : Any     					=itm_model.state_dict()
       for key in modified_state_dict.copy():
              _a    : str     					=modified_state_dict.pop(_UpperCAmelCase  )
              _a    : Optional[Any]     					=rename_key(_UpperCAmelCase  )
              _a    : Union[str, Any]     					=value

       _a    : Optional[Any]     					=BlipForImageTextRetrieval(_UpperCAmelCase  )

       _a    : Any     					=["""A picture of a woman with a dog sitting in a beach"""]
       _a    : List[str]     					=tokenizer(
           _UpperCAmelCase    ,return_tensors="""pt"""    ,padding="""max_length"""    ,truncation=_UpperCAmelCase    ,max_length=35    ,).input_ids

       hf_itm_model.load_state_dict(_UpperCAmelCase  )
       hf_itm_model.eval()

       _a    : int     					=hf_itm_model(_UpperCAmelCase    ,_UpperCAmelCase    ,use_itm_head=_UpperCAmelCase  )
       _a    : Optional[int]     					=hf_itm_model(_UpperCAmelCase    ,_UpperCAmelCase    ,use_itm_head=_UpperCAmelCase  )

       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__":
   A__:    List[Any]					    =       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''')
   A__:    Dict					    =       parser.parse_args()

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






'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__:    List[str]					    =       logging.get_logger(__name__)

A__:    Union[str, Any]					    =       {
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  int	       =							"data2vec-text"






      def __init__( self			:str     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=3_0_5_2_2     ,						SCREAMING_SNAKE_CASE			:Any=7_6_8     ,						SCREAMING_SNAKE_CASE			:List[Any]=1_2     ,						SCREAMING_SNAKE_CASE			:List[str]=1_2     ,						SCREAMING_SNAKE_CASE			:Dict=3_0_7_2     ,						SCREAMING_SNAKE_CASE			:List[str]="gelu"     ,						SCREAMING_SNAKE_CASE			:Any=0.1     ,						SCREAMING_SNAKE_CASE			:List[str]=0.1     ,						SCREAMING_SNAKE_CASE			:int=5_1_2     ,						SCREAMING_SNAKE_CASE			:int=2     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=0.02     ,						SCREAMING_SNAKE_CASE			:Dict=1e-12     ,						SCREAMING_SNAKE_CASE			:int=1     ,						SCREAMING_SNAKE_CASE			:Dict=0     ,						SCREAMING_SNAKE_CASE			:List[Any]=2     ,						SCREAMING_SNAKE_CASE			:str="absolute"     ,						SCREAMING_SNAKE_CASE			:Tuple=True     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=None     ,						**SCREAMING_SNAKE_CASE			:Union[str, Any]     ,						)       ->					List[str]:




             '''simple docstring'''
             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE     ,						bos_token_id=SCREAMING_SNAKE_CASE     ,						eos_token_id=SCREAMING_SNAKE_CASE     ,						**SCREAMING_SNAKE_CASE	)

             _a    : Optional[Any]     					=vocab_size
             _a    : Optional[Any]     					=hidden_size
             _a    : Any     					=num_hidden_layers
             _a    : List[str]     					=num_attention_heads
             _a    : Union[str, Any]     					=hidden_act
             _a    : Any     					=intermediate_size
             _a    : str     					=hidden_dropout_prob
             _a    : Optional[Any]     					=attention_probs_dropout_prob
             _a    : Optional[Any]     					=max_position_embeddings
             _a    : Union[str, Any]     					=type_vocab_size
             _a    : Tuple     					=initializer_range
             _a    : Optional[int]     					=layer_norm_eps
             _a    : Tuple     					=position_embedding_type
             _a    : int     					=use_cache
             _a    : List[str]     					=classifier_dropout







class A__       (						UpperCAmelCase__      ):






      @property
      def __UpperCAmelCase       ( self			:int	)       ->					Mapping[str, Mapping[int, str]]:




             '''simple docstring'''
             if self.task == "multiple-choice":
                    _a    : Tuple     					={0: """batch""", 1: """choice""", 2: """sequence"""}
             else:
                    _a    : List[Any]     					={0: """batch""", 1: """sequence"""}
             return OrderedDict(
                 [
                     ("""input_ids""", dynamic_axis),
                     ("""attention_mask""", dynamic_axis),
                 ]	)


