






'''simple docstring'''

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_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def     __lowercase  ()   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				"""https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"""
     __lowerCamelCase						:       List[str]				   =				Image.open(requests.get(_lowercase,						stream=_lowercase     ).raw     ).convert("""RGB"""     )

     return image
def     __lowercase  (_lowercase     )   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				[]
     # 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.embeddings.layernorm.weight""")     )
     rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.bias""", """qformer.embeddings.layernorm.bias""")     )

     # fmt: on
     return rename_keys
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     int:


     """simple docstring"""
     __lowerCamelCase						:       List[Any]				   =				dct.pop(_lowercase     )
     __lowerCamelCase						:       Union[str, Any]				   =				val
def     __lowercase  (_lowercase,						_lowercase     )   ->     Dict:


     """simple docstring"""
     for i in range(config.vision_config.num_hidden_layers     ):
          # read in original q and v biases
          __lowerCamelCase						:       Optional[Any]				   =				state_dict.pop(f"visual_encoder.blocks.{i}.attn.q_bias"     )
          __lowerCamelCase						:       List[Any]				   =				state_dict.pop(f"visual_encoder.blocks.{i}.attn.v_bias"     )

          # next, set bias in the state dict
          __lowerCamelCase						:       int				   =				torch.cat((q_bias, torch.zeros_like(_lowercase,						requires_grad=_lowercase     ), v_bias)     )
          __lowerCamelCase						:       Any				   =				qkv_bias
def     __lowercase  (_lowercase     )   ->     Union[str, Any]:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				364 if """coco""" in model_name else 224
     __lowerCamelCase						:       List[Any]				   =				InstructBlipVisionConfig(image_size=_lowercase     ).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 "t5-xl" in model_name:
          __lowerCamelCase						:       Optional[Any]				   =				TaConfig.from_pretrained("""google/flan-t5-xl""",						dense_act_fn="""gelu""",						bos_token_id=1     ).to_dict()
     elif "t5-xxl" in model_name:
          __lowerCamelCase						:       Optional[int]				   =				TaConfig.from_pretrained("""google/flan-t5-xxl""",						dense_act_fn="""gelu""",						bos_token_id=1     ).to_dict()
     elif "vicuna-7b" in model_name:
          __lowerCamelCase						:       Tuple				   =				LlamaConfig.from_pretrained("""decapoda-research/llama-7b-hf""",						vocab_size=32_001     ).to_dict()
     elif "vicuna-13b" in model_name:
          __lowerCamelCase						:       Tuple				   =				LlamaConfig.from_pretrained("""decapoda-research/llama-13b-hf""",						vocab_size=32_001     ).to_dict()
     else:
          raise ValueError("""Model name not supported"""     )

     # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
     __lowerCamelCase						:       str				   =				InstructBlipQFormerConfig(vocab_size=30_523     ).to_dict()
     __lowerCamelCase						:       Union[str, Any]				   =				InstructBlipConfig(vision_config=_lowercase,						text_config=_lowercase,						qformer_config=_lowercase     )

     return config, image_size
@torch.no_grad()
def     __lowercase  (_lowercase,						_lowercase=None,						_lowercase=False     )   ->     Optional[int]:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				AutoTokenizer.from_pretrained("""bert-base-uncased""",						truncation_side="""left"""     )
     qformer_tokenizer.add_special_tokens({"""bos_token""": """[DEC]"""}     )

     if "t5" in model_name:
          __lowerCamelCase						:       int				   =				TaTokenizerFast.from_pretrained("""google/flan-t5-xl""",						truncation_side="""left"""     )
     elif "vicuna" in model_name:
          # the following was used in the original implementation:
          # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
          # tokenizer.add_special_tokens({"pad_token": "[PAD]"})
          # tokenizer.add_special_tokens({"bos_token": "</s>"})
          # tokenizer.add_special_tokens({"eos_token": "</s>"})
          # tokenizer.add_special_tokens({"unk_token": "</s>"})
          __lowerCamelCase						:       int				   =				LlamaTokenizerFast.from_pretrained(
              """huggyllama/llama-7b""",						truncation_side="""left""",						bos_token="""</s>""",						unk_token="""</s>"""     )
          tokenizer.add_special_tokens({"""pad_token""": """[PAD]"""}     )

     __lowerCamelCase   ,      __lowerCamelCase						:       str				   =				get_blipa_config(_lowercase     )
     __lowerCamelCase						:       List[Any]				   =				InstructBlipForConditionalGeneration(_lowercase     ).eval()

     __lowerCamelCase						:       Any				   =				{
         """instructblip-vicuna-7b""": ("""blip2_vicuna_instruct""", """vicuna7b"""),
         """instructblip-vicuna-13b""": ("""blip2_vicuna_instruct""", """vicuna13b"""),
         """instructblip-flan-t5-xl""": ("""blip2_t5_instruct""", """flant5xl"""),
         """instructblip-flan-t5-xxl""": ("""blip2_t5_instruct""", """flant5xxl"""),
     }

     __lowerCamelCase   ,      __lowerCamelCase						:       str				   =				model_name_to_original[model_name]

     # load original model
     print("""Loading original model..."""     )
     __lowerCamelCase						:       List[str]				   =				"""cuda:1""" if torch.cuda.is_available() else """cpu"""
     __lowerCamelCase						:       Any				   =				"""cuda:2""" if torch.cuda.is_available() else """cpu"""
     __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase						:       Dict				   =				load_model_and_preprocess(
         name=_lowercase,						model_type=_lowercase,						is_eval=_lowercase,						device=_lowercase     )
     original_model.eval()
     print("""Done!"""     )

     # update state dict keys
     __lowerCamelCase						:       str				   =				original_model.state_dict()
     __lowerCamelCase						:       Union[str, Any]				   =				create_rename_keys(_lowercase     )
     for src, dest in rename_keys:
          rename_key(_lowercase,						_lowercase,						_lowercase     )

     # some keys can be renamed efficiently
     for key, val in state_dict.copy().items():
          __lowerCamelCase						:       Optional[Any]				   =				state_dict.pop(_lowercase     )
          if key.startswith("""Qformer.bert"""     ):
               __lowerCamelCase						:       List[str]				   =				key.replace("""Qformer.bert""",						"""qformer"""     )
          if "attention.self" in key:
               __lowerCamelCase						:       List[Any]				   =				key.replace("""self""",						"""attention"""     )
          if "llm_proj" in key:
               __lowerCamelCase						:       List[str]				   =				key.replace("""llm_proj""",						"""language_projection"""     )
          if "t5_proj" in key:
               __lowerCamelCase						:       Any				   =				key.replace("""t5_proj""",						"""language_projection"""     )
          if key.startswith("""llm_model"""     ):
               __lowerCamelCase						:       int				   =				key.replace("""llm_model""",						"""language_model"""     )
          if key.startswith("""t5"""     ):
               __lowerCamelCase						:       Union[str, Any]				   =				key.replace("""t5""",						"""language"""     )
          __lowerCamelCase						:       List[Any]				   =				val

     # read in qv biases
     read_in_q_v_bias(_lowercase,						_lowercase     )

     # note: weights get loaded in torch.float32 by default
     hf_model.load_state_dict(_lowercase,						strict=_lowercase     )

     __lowerCamelCase						:       Optional[Any]				   =				load_demo_image()
     __lowerCamelCase						:       Dict				   =				"""What is unusual about this image?"""

     # create processor
     __lowerCamelCase						:       Dict				   =				BlipImageProcessor(
         size={"""height""": image_size, """width""": image_size},						image_mean=_lowercase,						image_std=_lowercase     )
     __lowerCamelCase						:       List[Any]				   =				InstructBlipProcessor(
         image_processor=_lowercase,						tokenizer=_lowercase,						qformer_tokenizer=_lowercase,						)
     __lowerCamelCase						:       Tuple				   =				processor(images=_lowercase,						text=_lowercase,						return_tensors="""pt"""     ).to(_lowercase     )

     # make sure processor creates exact same pixel values
     __lowerCamelCase						:       List[Any]				   =				vis_processors["""eval"""](_lowercase     ).unsqueeze(0     ).to(_lowercase     )
     __lowerCamelCase						:       Union[str, Any]				   =				inputs.pixel_values
     assert torch.allclose(original_pixel_values.to(pixel_values.device     ),						_lowercase     )

     original_model.to(_lowercase     )
     hf_model.to(_lowercase     )
     with torch.no_grad():
          if "vicuna" in model_name:
               __lowerCamelCase						:       List[str]				   =				original_model({"""image""": original_pixel_values, """text_input""": [prompt]}     ).logits
               __lowerCamelCase						:       Dict				   =				hf_model(**_lowercase     ).logits
          else:
               __lowerCamelCase						:       List[str]				   =				original_model(
                   {"""image""": original_pixel_values, """text_input""": [prompt], """text_output""": ["""\n"""]}     ).logits
               __lowerCamelCase						:       str				   =				tokenizer("""\n""",						return_tensors="""pt"""     ).input_ids.to(_lowercase     )
               __lowerCamelCase						:       Tuple				   =				label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id,						-100     )
               __lowerCamelCase						:       Tuple				   =				hf_model(**_lowercase,						labels=_lowercase     ).logits

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

     # assert values
     assert original_logits.shape == logits.shape
     __lowerCamelCase						:       List[str]				   =				1e-4 if """vicuna""" in model_name else 1e-5
     assert torch.allclose(original_logits.to(logits.device     ),						_lowercase,						atol=_lowercase     )
     print("""Looks ok!"""     )

     print("""Generating with original model..."""     )
     __lowerCamelCase						:       List[str]				   =				original_model.generate({"""image""": original_pixel_values, """prompt""": prompt},						num_beams=5     )

     # important: we need to cast the weights of the HF model to the appropriate type
     print("""Generating with HF model..."""     )
     __lowerCamelCase						:       Dict				   =				hf_model.generate(
         **_lowercase,						do_sample=_lowercase,						num_beams=5,						max_length=256,						min_length=1,						top_p=0.9,						repetition_penalty=1.5,						length_penalty=1.0,						temperature=1,						)
     if "vicuna" in model_name:
          # convert output id 0 to 2 (eos_token_id)
          # TODO add this in the generate method?
          __lowerCamelCase						:       int				   =				2
     print("""Original generation:""",						_lowercase     )
     __lowerCamelCase						:       Tuple				   =				processor.batch_decode(_lowercase,						skip_special_tokens=_lowercase     )
     __lowerCamelCase						:       int				   =				[text.strip() for text in output_text]
     print("""HF generation:""",						_lowercase     )

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

     if push_to_hub:
          processor.push_to_hub(f"Salesforce/{model_name}"     )
          hf_model.push_to_hub(f"Salesforce/{model_name}"     )


if __name__ == "__main__":
   UpperCAmelCase__						:Any          =			argparse.ArgumentParser()
   UpperCAmelCase__						:Any          =			[
       """instructblip-vicuna-7b""",
       """instructblip-vicuna-13b""",
       """instructblip-flan-t5-xl""",
       """instructblip-flan-t5-xxl""",
   ]
   parser.add_argument(
       """--model_name""",
       default="""instructblip-flan-t5-xl""",
       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""",
   )

   UpperCAmelCase__						:Any          =			parser.parse_args()

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











'''simple docstring'''

from __future__ import annotations

from random import random







class 	SCREAMING_SNAKE_CASE	:




       def __init__(					self      : Tuple	,    A__      : int | None = None					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				value
            __lowerCamelCase						:       Dict				   =				random()
            __lowerCamelCase						:       Node | None				   =				None
            __lowerCamelCase						:       Node | None				   =				None




       def __repr__(					self      : Any					):


            """simple docstring"""







            from pprint import pformat

            if self.left is None and self.right is None:
                 return f"'{self.value}: {self.prior:.5}'"
            else:
                 return pformat(
                     {f"{self.value}: {self.prior:.5}": (self.left, self.right)}	,    indent=1					)




       def __str__(					self      : List[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				str(self.value					) + """ """
            __lowerCamelCase						:       Any				   =				str(self.left or """"""					)
            __lowerCamelCase						:       List[str]				   =				str(self.right or """"""					)
            return value + left + right

def     __lowercase  (_lowercase,						_lowercase     )   ->     tuple[Node | None, Node | None]:


     """simple docstring"""
     if root is None:  # None tree is split into 2 Nones
          return None, None
     elif root.value is None:
          return None, None
     else:
          if value < root.value:
               __lowerCamelCase   ,      __lowerCamelCase						:       int				   =				split(root.left,						_lowercase     )
               return left, root
          else:
               __lowerCamelCase   ,      __lowerCamelCase						:       Union[str, Any]				   =				split(root.right,						_lowercase     )
               return root, right
def     __lowercase  (_lowercase,						_lowercase     )   ->     Node | None:


     """simple docstring"""
     if (not left) or (not right):  # If one node is None, return the other
          return left or right
     elif left.prior < right.prior:
          __lowerCamelCase						:       Tuple				   =				merge(left.right,						_lowercase     )
          return left
     else:
          __lowerCamelCase						:       int				   =				merge(_lowercase,						right.left     )
          return right
def     __lowercase  (_lowercase,						_lowercase     )   ->     Node | None:


     """simple docstring"""
     __lowerCamelCase						:       List[str]				   =				Node(_lowercase     )
     __lowerCamelCase   ,      __lowerCamelCase						:       List[Any]				   =				split(_lowercase,						_lowercase     )
     return merge(merge(_lowercase,						_lowercase     ),						_lowercase     )
def     __lowercase  (_lowercase,						_lowercase     )   ->     Node | None:


     """simple docstring"""
     __lowerCamelCase   ,      __lowerCamelCase						:       int				   =				split(_lowercase,						value - 1     )
     __lowerCamelCase   ,      __lowerCamelCase						:       List[str]				   =				split(_lowercase,						_lowercase     )
     return merge(_lowercase,						_lowercase     )
def     __lowercase  (_lowercase     )   ->     None:


     """simple docstring"""
     if not root:  # None
          return
     else:
          inorder(root.left     )
          print(root.value,						end=""","""     )
          inorder(root.right     )
def     __lowercase  (_lowercase,						_lowercase     )   ->     Node | None:


     """simple docstring"""
     for arg in args.split():
          if arg[0] == "+":
               __lowerCamelCase						:       Dict				   =				insert(_lowercase,						int(arg[1:]     )     )

          elif arg[0] == "-":
               __lowerCamelCase						:       List[str]				   =				erase(_lowercase,						int(arg[1:]     )     )

          else:
               print("""Unknown command"""     )

     return root
def     __lowercase  ()   ->     None:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				None
     print(
         """enter numbers to create a tree, + value to add value into treap, """
         """- value to erase all nodes with value. 'q' to quit. """     )

     __lowerCamelCase						:       int				   =				input()
     while args != "q":
          __lowerCamelCase						:       List[Any]				   =				interact_treap(_lowercase,						_lowercase     )
          print(_lowercase     )
          __lowerCamelCase						:       List[Any]				   =				input()

     print("""good by!"""     )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()



