






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__			(     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[str]							=    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
  SCREAMING_SNAKE_CASE       :      List[str]							=    Image.open(requests.get(lowerCamelCase_						,     stream=lowerCamelCase_				).raw				).convert("RGB"				)

  SCREAMING_SNAKE_CASE       :      List[str]							=    transforms.Compose(
      [
          transforms.Resize((image_size, image_size)						,     interpolation=InterpolationMode.BICUBIC				),
          transforms.ToTensor(),
          transforms.Normalize((0.48145466, 0.4578275, 0.40821073)						,     (0.26862954, 0.26130258, 0.27577711)				),
      ]				)
  SCREAMING_SNAKE_CASE       :      List[Any]							=    transform(lowerCamelCase_				).unsqueeze(0				).to(lowerCamelCase_				)
  return image


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  if "visual_encoder" in key:
    SCREAMING_SNAKE_CASE       :      int							=    re.sub("visual_encoder*"						,     "vision_model.encoder"						,     lowerCamelCase_				)
  if "blocks" in key:
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    re.sub(R"blocks"						,     "layers"						,     lowerCamelCase_				)
  if "attn" in key:
    SCREAMING_SNAKE_CASE       :      Tuple							=    re.sub(R"attn"						,     "self_attn"						,     lowerCamelCase_				)
  if "norm1" in key:
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    re.sub(R"norm1"						,     "layer_norm1"						,     lowerCamelCase_				)
  if "norm2" in key:
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    re.sub(R"norm2"						,     "layer_norm2"						,     lowerCamelCase_				)
  if "encoder.norm" in key:
    SCREAMING_SNAKE_CASE       :      Dict							=    re.sub(R"encoder.norm"						,     "post_layernorm"						,     lowerCamelCase_				)
  if "encoder.patch_embed.proj" in key:
    SCREAMING_SNAKE_CASE       :      int							=    re.sub(R"encoder.patch_embed.proj"						,     "embeddings.patch_embedding"						,     lowerCamelCase_				)

  if "encoder.pos_embed" in key:
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    re.sub(R"encoder.pos_embed"						,     "embeddings.position_embedding"						,     lowerCamelCase_				)
  if "encoder.cls_token" in key:
    SCREAMING_SNAKE_CASE       :      List[str]							=    re.sub(R"encoder.cls_token"						,     "embeddings.class_embedding"						,     lowerCamelCase_				)

  if "self_attn" in key:
    SCREAMING_SNAKE_CASE       :      int							=    re.sub(R"self_attn.proj"						,     "self_attn.projection"						,     lowerCamelCase_				)

  return key


@torch.no_grad()
def   lowerCamelCase__			(     lowercase						,     lowercase=None				):

  """simple docstring"""






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

  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    BlipForConditionalGeneration(lowerCamelCase_				).eval()

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

  SCREAMING_SNAKE_CASE       :      int							=    blip_decoder(pretrained=lowerCamelCase_						,     image_size=384						,     vit="base"				)
  SCREAMING_SNAKE_CASE       :      List[str]							=    pt_model.eval()

  SCREAMING_SNAKE_CASE       :      str							=    pt_model.state_dict()
  for key in modified_state_dict.copy():
    SCREAMING_SNAKE_CASE       :      int							=    modified_state_dict.pop(lowerCamelCase_				)
    SCREAMING_SNAKE_CASE       :      List[Any]							=    rename_key(lowerCamelCase_				)
    SCREAMING_SNAKE_CASE       :      Tuple							=    value

  hf_model.load_state_dict(lowerCamelCase_				)

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

  SCREAMING_SNAKE_CASE       :      int							=    hf_model.generate(lowerCamelCase_						,     lowerCamelCase_				)

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

  SCREAMING_SNAKE_CASE       :      Optional[int]							=    hf_model.generate(lowerCamelCase_				)

  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(lowerCamelCase_				)

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

  SCREAMING_SNAKE_CASE       :      str							=    blip_vqa(pretrained=lowerCamelCase_						,     image_size=lowerCamelCase_						,     vit="base"				)
  vqa_model.eval()

  SCREAMING_SNAKE_CASE       :      Any							=    vqa_model.state_dict()
  for key in modified_state_dict.copy():
    SCREAMING_SNAKE_CASE       :      List[str]							=    modified_state_dict.pop(lowerCamelCase_				)
    SCREAMING_SNAKE_CASE       :      Any							=    rename_key(lowerCamelCase_				)
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    value

  SCREAMING_SNAKE_CASE       :      Any							=    BlipForQuestionAnswering(lowerCamelCase_				)

  hf_vqa_model.load_state_dict(lowerCamelCase_				)

  SCREAMING_SNAKE_CASE       :      List[str]							=    ['''How many dogs are in this image?''']
  SCREAMING_SNAKE_CASE       :      List[str]							=    tokenizer(lowerCamelCase_						,     return_tensors="pt"				).input_ids

  SCREAMING_SNAKE_CASE       :      List[Any]							=    hf_vqa_model.generate(lowerCamelCase_						,     lowerCamelCase_				)
  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"				)

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

  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    blip_itm(pretrained=lowerCamelCase_						,     image_size=lowerCamelCase_						,     vit="base"				)
  itm_model.eval()

  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    itm_model.state_dict()
  for key in modified_state_dict.copy():
    SCREAMING_SNAKE_CASE       :      Any							=    modified_state_dict.pop(lowerCamelCase_				)
    SCREAMING_SNAKE_CASE       :      List[Any]							=    rename_key(lowerCamelCase_				)
    SCREAMING_SNAKE_CASE       :      List[str]							=    value

  SCREAMING_SNAKE_CASE       :      List[Any]							=    BlipForImageTextRetrieval(lowerCamelCase_				)

  SCREAMING_SNAKE_CASE       :      Any							=    ['''A picture of a woman with a dog sitting in a beach''']
  SCREAMING_SNAKE_CASE       :      int							=    tokenizer(
      lowerCamelCase_						,     return_tensors="pt"						,     padding="max_length"						,     truncation=lowerCamelCase_						,     max_length=35						,     ).input_ids

  hf_itm_model.load_state_dict(lowerCamelCase_				)
  hf_itm_model.eval()

  SCREAMING_SNAKE_CASE       :      Dict							=    hf_itm_model(lowerCamelCase_						,     lowerCamelCase_						,     use_itm_head=lowerCamelCase_				)
  SCREAMING_SNAKE_CASE       :      Optional[int]							=    hf_itm_model(lowerCamelCase_						,     lowerCamelCase_						,     use_itm_head=lowerCamelCase_				)

  assert out[0].item() == 0.2110687494277954
  assert torch.nn.functional.softmax(out_itm[0]						,     dim=1				)[:, 1].item() == 0.45698845386505127

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


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

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














from collections import OrderedDict
from typing import Mapping

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


snake_case    				=		logging.get_logger(__name__)

snake_case    				=		{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Dict			=	'''camembert'''


     def __init__(				self       :	str					, UpperCAmelCase_       :	Optional[Any]=3_0522					, UpperCAmelCase_       :	Optional[int]=768					, UpperCAmelCase_       :	List[Any]=12					, UpperCAmelCase_       :	Union[str, Any]=12					, UpperCAmelCase_       :	Optional[Any]=3072					, UpperCAmelCase_       :	Optional[int]="gelu"					, UpperCAmelCase_       :	List[Any]=0.1					, UpperCAmelCase_       :	Dict=0.1					, UpperCAmelCase_       :	List[str]=512					, UpperCAmelCase_       :	List[Any]=2					, UpperCAmelCase_       :	Optional[int]=0.02					, UpperCAmelCase_       :	List[str]=1E-12					, UpperCAmelCase_       :	int=1					, UpperCAmelCase_       :	Union[str, Any]=0					, UpperCAmelCase_       :	List[str]=2					, UpperCAmelCase_       :	int="absolute"					, UpperCAmelCase_       :	Tuple=True					, UpperCAmelCase_       :	Optional[Any]=None					, **UpperCAmelCase_       :	Optional[int]					, ):
       super().__init__(pad_token_id=UpperCAmelCase_					, bos_token_id=UpperCAmelCase_					, eos_token_id=UpperCAmelCase_					, **UpperCAmelCase_      )

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





class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     @property
     def 				_A       (				self       :	Optional[int]      ):
       if self.task == "multiple-choice":
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {0: "batch", 1: "choice", 2: "sequence"}
       else:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]      )






