




import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD



def 						lowerCAmelCase		(		):




 """simple docstring"""







 UpperCAmelCase__							 =						"https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
 UpperCAmelCase__							 =						Image.open(requests.get(_lowerCAmelCase						,							stream=_lowerCAmelCase	).raw	).convert("RGB"	)

 return image



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Union[str, Any]	):




 """simple docstring"""







 UpperCAmelCase__							 =						[]
 # fmt: off

 # vision encoder
 rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding")	)
 rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding")	)
 rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight")	)
 rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias")	)
 rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight")	)
 rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias")	)

 for i in range(config.vision_config.num_hidden_layers	):
  rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''')	)
  rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''')	)
  rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''')	)
  rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''')	)
  rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')	)
  rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)	)
  rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')	)
  rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')	)
  rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')	)
  rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')	)
  rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')	)

 # QFormer
 rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.layernorm.weight")	)
 rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias")	)

 # fmt: on
 return rename_keys



def 						lowerCAmelCase		(		_lowerCAmelCase							:   List[Any]						,							_lowerCAmelCase							:   Dict						,							_lowerCAmelCase							:   Optional[int]	):




 """simple docstring"""







 UpperCAmelCase__							 =						dct.pop(_lowerCAmelCase	)
 UpperCAmelCase__							 =						val



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[int]						,							_lowerCAmelCase							:   Tuple	):




 """simple docstring"""







 for i in range(config.vision_config.num_hidden_layers	):
  # read in original q and v biases
  UpperCAmelCase__							 =						state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''	)
  UpperCAmelCase__							 =						state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''	)

  # next, set bias in the state dict
  UpperCAmelCase__							 =						torch.cat((q_bias, torch.zeros_like(_lowerCAmelCase						,							requires_grad=_lowerCAmelCase	), v_bias)	)
  UpperCAmelCase__							 =						qkv_bias



def 						lowerCAmelCase		(		_lowerCAmelCase							:   int						,							_lowerCAmelCase							:   Union[str, Any]	):




 """simple docstring"""







 UpperCAmelCase__							 =						364 if "coco" in model_name else 224
 UpperCAmelCase__							 =						BlipaVisionConfig(image_size=_lowerCAmelCase	).to_dict()

 # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
 # seems like flan-T5 models don't have bos_token_id properly set?
 if "opt-2.7b" in model_name:
  UpperCAmelCase__							 =						OPTConfig.from_pretrained("facebook/opt-2.7b"						,							eos_token_id=_lowerCAmelCase	).to_dict()
 elif "opt-6.7b" in model_name:
  UpperCAmelCase__							 =						OPTConfig.from_pretrained("facebook/opt-6.7b"						,							eos_token_id=_lowerCAmelCase	).to_dict()
 elif "t5-xl" in model_name:
  UpperCAmelCase__							 =						TaConfig.from_pretrained("google/flan-t5-xl"						,							dense_act_fn="gelu"						,							bos_token_id=1	).to_dict()
 elif "t5-xxl" in model_name:
  UpperCAmelCase__							 =						TaConfig.from_pretrained("google/flan-t5-xxl"						,							dense_act_fn="gelu"						,							bos_token_id=1	).to_dict()

 UpperCAmelCase__							 =						BlipaConfig(vision_config=_lowerCAmelCase						,							text_config=_lowerCAmelCase	)

 return config, image_size



@torch.no_grad()
def 						lowerCAmelCase		(		_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   int=None						,							_lowerCAmelCase							:   str=False	):




 """simple docstring"""







 UpperCAmelCase__							 =						(
     AutoTokenizer.from_pretrained("facebook/opt-2.7b"	)
     if "opt" in model_name
     else AutoTokenizer.from_pretrained("google/flan-t5-xl"	)
 )
 UpperCAmelCase__							 =						tokenizer("\n"						,							add_special_tokens=_lowerCAmelCase	).input_ids[0]
 UpperCAmelCase__ , UpperCAmelCase__							 =						get_blipa_config(_lowerCAmelCase						,							eos_token_id=_lowerCAmelCase	)

 UpperCAmelCase__							 =						BlipaForConditionalGeneration(_lowerCAmelCase	).eval()

 UpperCAmelCase__							 =						{
     "blip2-opt-2.7b": ("blip2_opt", "pretrain_opt2.7b"),
     "blip2-opt-6.7b": ("blip2_opt", "pretrain_opt6.7b"),
     "blip2-opt-2.7b-coco": ("blip2_opt", "caption_coco_opt2.7b"),
     "blip2-opt-6.7b-coco": ("blip2_opt", "caption_coco_opt6.7b"),
     "blip2-flan-t5-xl": ("blip2_t5", "pretrain_flant5xl"),
     "blip2-flan-t5-xl-coco": ("blip2_t5", "caption_coco_flant5xl"),
     "blip2-flan-t5-xxl": ("blip2_t5", "pretrain_flant5xxl"),
 }

 UpperCAmelCase__ , UpperCAmelCase__							 =						model_name_to_original[model_name]

 # load original model
 print("Loading original model..."	)
 UpperCAmelCase__							 =						"cuda" if torch.cuda.is_available() else "cpu"
 UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__							 =						load_model_and_preprocess(
     name=_lowerCAmelCase						,							model_type=_lowerCAmelCase						,							is_eval=_lowerCAmelCase						,							device=_lowerCAmelCase	)
 original_model.eval()
 print("Done!"	)

 # update state dict keys
 UpperCAmelCase__							 =						original_model.state_dict()
 UpperCAmelCase__							 =						create_rename_keys(_lowerCAmelCase	)
 for src, dest in rename_keys:
  rename_key(_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase	)

 # some keys can be renamed efficiently
 for key, val in state_dict.copy().items():
  UpperCAmelCase__							 =						state_dict.pop(_lowerCAmelCase	)
  if key.startswith("Qformer.bert"	):
   UpperCAmelCase__							 =						key.replace("Qformer.bert"						,							"qformer"	)
  if "attention.self" in key:
   UpperCAmelCase__							 =						key.replace("self"						,							"attention"	)
  if "opt_proj" in key:
   UpperCAmelCase__							 =						key.replace("opt_proj"						,							"language_projection"	)
  if "t5_proj" in key:
   UpperCAmelCase__							 =						key.replace("t5_proj"						,							"language_projection"	)
  if key.startswith("opt"	):
   UpperCAmelCase__							 =						key.replace("opt"						,							"language"	)
  if key.startswith("t5"	):
   UpperCAmelCase__							 =						key.replace("t5"						,							"language"	)
  UpperCAmelCase__							 =						val

 # read in qv biases
 read_in_q_v_bias(_lowerCAmelCase						,							_lowerCAmelCase	)

 UpperCAmelCase__ , UpperCAmelCase__							 =						hf_model.load_state_dict(_lowerCAmelCase						,							strict=_lowerCAmelCase	)
 assert len(_lowerCAmelCase	) == 0
 assert unexpected_keys == ["qformer.embeddings.position_ids"]

 UpperCAmelCase__							 =						load_demo_image()
 UpperCAmelCase__							 =						vis_processors["eval"](_lowerCAmelCase	).unsqueeze(0	).to(_lowerCAmelCase	)
 UpperCAmelCase__							 =						tokenizer(["\n"]						,							return_tensors="pt"	).input_ids.to(_lowerCAmelCase	)

 # create processor
 UpperCAmelCase__							 =						BlipImageProcessor(
     size={"height": image_size, "width": image_size}						,							image_mean=_lowerCAmelCase						,							image_std=_lowerCAmelCase	)
 UpperCAmelCase__							 =						BlipaProcessor(image_processor=_lowerCAmelCase						,							tokenizer=_lowerCAmelCase	)
 UpperCAmelCase__							 =						processor(images=_lowerCAmelCase						,							return_tensors="pt"	).pixel_values.to(_lowerCAmelCase	)

 # make sure processor creates exact same pixel values
 assert torch.allclose(_lowerCAmelCase						,							_lowerCAmelCase	)

 original_model.to(_lowerCAmelCase	)
 hf_model.to(_lowerCAmelCase	)
 with torch.no_grad():
  if "opt" in model_name:
   UpperCAmelCase__							 =						original_model({"image": original_pixel_values, "text_input": [""]}	).logits
   UpperCAmelCase__							 =						hf_model(_lowerCAmelCase						,							_lowerCAmelCase	).logits
  else:
   UpperCAmelCase__							 =						original_model(
       {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}	).logits
   UpperCAmelCase__							 =						input_ids.masked_fill(input_ids == tokenizer.pad_token_id						,							-100	)
   UpperCAmelCase__							 =						hf_model(_lowerCAmelCase						,							_lowerCAmelCase						,							labels=_lowerCAmelCase	).logits

 assert original_logits.shape == logits.shape
 print("First values of original logits:"						,							original_logits[0, :3, :3]	)
 print("First values of HF logits:"						,							logits[0, :3, :3]	)

 # assert values
 if model_name == "blip2-flan-t5-xl":
  UpperCAmelCase__							 =						torch.tensor(
      [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]]						,							device=_lowerCAmelCase	)
  assert torch.allclose(logits[0, :3, :3]						,							_lowerCAmelCase						,							atol=1E-4	)
 elif model_name == "blip2-flan-t5-xl-coco":
  UpperCAmelCase__							 =						torch.tensor(
      [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]]						,							device=_lowerCAmelCase	)
 else:
  # cast to same type
  UpperCAmelCase__							 =						logits.dtype
  assert torch.allclose(original_logits.to(_lowerCAmelCase	)						,							_lowerCAmelCase						,							atol=1E-2	)
 print("Looks ok!"	)

 print("Generating a caption..."	)
 UpperCAmelCase__							 =						""
 UpperCAmelCase__							 =						tokenizer(_lowerCAmelCase						,							return_tensors="pt"	).input_ids.to(_lowerCAmelCase	)

 UpperCAmelCase__							 =						original_model.generate({"image": original_pixel_values}	)
 UpperCAmelCase__							 =						hf_model.generate(
     _lowerCAmelCase						,							_lowerCAmelCase						,							do_sample=_lowerCAmelCase						,							num_beams=5						,							max_length=30						,							min_length=1						,							top_p=0.9						,							repetition_penalty=1.0						,							length_penalty=1.0						,							temperature=1						,							)
 print("Original generation:"						,							_lowerCAmelCase	)
 UpperCAmelCase__							 =						input_ids.shape[1]
 UpperCAmelCase__							 =						processor.batch_decode(outputs[:, prompt_length:]						,							skip_special_tokens=_lowerCAmelCase	)
 UpperCAmelCase__							 =						[text.strip() for text in output_text]
 print("HF generation:"						,							_lowerCAmelCase	)

 if pytorch_dump_folder_path is not None:
  processor.save_pretrained(_lowerCAmelCase	)
  hf_model.save_pretrained(_lowerCAmelCase	)

 if push_to_hub:
  processor.push_to_hub(F'''nielsr/{model_name}'''	)
  hf_model.push_to_hub(F'''nielsr/{model_name}'''	)


if __name__ == "__main__":
  _lowerCAmelCase						:   Optional[int]		=   argparse.ArgumentParser()
  _lowerCAmelCase						:   int		=   [
      "blip2-opt-2.7b",
      "blip2-opt-6.7b",
      "blip2-opt-2.7b-coco",
      "blip2-opt-6.7b-coco",
      "blip2-flan-t5-xl",
      "blip2-flan-t5-xl-coco",
      "blip2-flan-t5-xxl",
  ]
  parser.add_argument(
      "--model_name",
      default="blip2-opt-2.7b",
      choices=choices,
      type=str,
      help="Path to hf config.json of model to convert",
  )
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument(
      "--push_to_hub",
      action="store_true",
      help="Whether to push the model and processor to the hub after converting",
  )

  _lowerCAmelCase						:   Optional[int]		=   parser.parse_args()

  convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











import torch

from transformers import AutoModel
class 			_UpperCamelCase      (				torch.nn.Module		):
      def __init__(			self   :str ,      lowerCamelCase   :Tuple="sayef/fsner-bert-base-uncased"      )     ->			int:
       super(lowerCamelCase ,      self      ).__init__()

       UpperCAmelCase__							 =						AutoModel.from_pretrained(lowerCamelCase ,      return_dict=lowerCamelCase      )
       UpperCAmelCase__							 =						torch.nn.CosineSimilarity(3 ,      1e-08      )
       UpperCAmelCase__							 =						torch.nn.Softmax(dim=1      )
      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      **lowerCamelCase   :Tuple      )     ->			Dict:
       return self.bert(**lowerCamelCase      ).last_hidden_state
      def 	UpperCAmelCase_						(			self   :Any ,      lowerCamelCase   :Union[str, Any]      )     ->			Union[str, Any]:
       return token_embeddings.sum(2 ,      keepdim=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Dict ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :int ,      lowerCamelCase   :Union[str, Any]=1      )     ->			Dict:
       return self.softmax(T * self.cos(lowerCamelCase ,      lowerCamelCase      )      )







      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :Any ,      lowerCamelCase   :Any      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						W_supports["sizes"].tolist()
       UpperCAmelCase__							 =						W_supports["start_token_id"].item()
       UpperCAmelCase__							 =						W_supports["end_token_id"].item()

       del W_supports["sizes"]
       del W_supports["start_token_id"]
       del W_supports["end_token_id"]

       UpperCAmelCase__							 =						self.BERT(**lowerCamelCase      )
       UpperCAmelCase__							 =						self.BERT(**lowerCamelCase      )

       UpperCAmelCase__							 =						None
       UpperCAmelCase__							 =						None

       UpperCAmelCase__							 =						W_supports["input_ids"] == start_token_id
       UpperCAmelCase__							 =						W_supports["input_ids"] == end_token_id

       for i, size in enumerate(lowerCamelCase      ):
        if i == 0:
         UpperCAmelCase__							 =						0
        else:
         UpperCAmelCase__							 =						support_sizes[i - 1]

        UpperCAmelCase__							 =						S[s : s + size][start_token_masks[s : s + size]]
        UpperCAmelCase__							 =						S[s : s + size][end_token_masks[s : s + size]]

        UpperCAmelCase__							 =						torch.matmul(q[i] ,      s_start.T      ).sum(1      ).softmax(0      )
        UpperCAmelCase__							 =						torch.matmul(q[i] ,      s_end.T      ).sum(1      ).softmax(0      )

        if p_starts is not None:
         UpperCAmelCase__							 =						torch.vstack((p_starts, p_start)      )
         UpperCAmelCase__							 =						torch.vstack((p_ends, p_end)      )
        else:
         UpperCAmelCase__							 =						p_start
         UpperCAmelCase__							 =						p_end

       return p_starts, p_ends





