


"""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
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		(		)      ->		Optional[Any]:


    """simple docstring"""





    snake_case       : List[str]    =       "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
    snake_case       : str    =       Image.open(requests.get(lowercase		,				stream=lowercase					).raw					).convert("RGB"					)

    return image







def     __lowerCAmelCase		(		lowercase    :    List[str]					)      ->		int:


    """simple docstring"""





    snake_case       : List[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.layernorm.weight")					)
    rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias")					)

    # fmt: on
    return rename_keys







def     __lowerCAmelCase		(		lowercase    :    Dict		,				lowercase    :    Any		,				lowercase    :    Dict					)      ->		str:


    """simple docstring"""





    snake_case       : Any    =       dct.pop(lowercase					)
    snake_case       : Any    =       val







def     __lowerCAmelCase		(		lowercase    :    List[str]		,				lowercase    :    Dict					)      ->		List[str]:


    """simple docstring"""





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

        # next, set bias in the state dict
        snake_case       : Union[str, Any]    =       torch.cat((q_bias, torch.zeros_like(lowercase		,				requires_grad=lowercase					), v_bias)					)
        snake_case       : List[Any]    =       qkv_bias







def     __lowerCAmelCase		(		lowercase    :    List[str]		,				lowercase    :    Optional[Any]					)      ->		int:


    """simple docstring"""





    snake_case       : List[str]    =       364 if "coco" in model_name else 224
    snake_case       : List[str]    =       BlipaVisionConfig(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 "opt-2.7b" in model_name:
        snake_case       : Union[str, Any]    =       OPTConfig.from_pretrained("facebook/opt-2.7b"		,				eos_token_id=lowercase					).to_dict()
    elif "opt-6.7b" in model_name:
        snake_case       : Optional[Any]    =       OPTConfig.from_pretrained("facebook/opt-6.7b"		,				eos_token_id=lowercase					).to_dict()
    elif "t5-xl" in model_name:
        snake_case       : Union[str, Any]    =       TaConfig.from_pretrained("google/flan-t5-xl"		,				dense_act_fn="gelu"		,				bos_token_id=1					).to_dict()
    elif "t5-xxl" in model_name:
        snake_case       : Optional[int]    =       TaConfig.from_pretrained("google/flan-t5-xxl"		,				dense_act_fn="gelu"		,				bos_token_id=1					).to_dict()

    snake_case       : List[Any]    =       BlipaConfig(vision_config=lowercase		,				text_config=lowercase					)

    return config, image_size







@torch.no_grad()
def     __lowerCAmelCase		(		lowercase    :    Optional[Any]		,				lowercase    :    Optional[int]=None		,				lowercase    :    Optional[int]=False					)      ->		List[Any]:


    """simple docstring"""





    snake_case       : Optional[Any]    =       (
        AutoTokenizer.from_pretrained("facebook/opt-2.7b"					)
        if "opt" in model_name
        else AutoTokenizer.from_pretrained("google/flan-t5-xl"					)
    )
    snake_case       : Dict    =       tokenizer("\n"		,				add_special_tokens=lowercase					).input_ids[0]
    snake_case   ,snake_case       : Tuple    =       get_blipa_config(lowercase		,				eos_token_id=lowercase					)

    snake_case       : Any    =       BlipaForConditionalGeneration(lowercase					).eval()

    snake_case       : List[str]    =       {
        "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"),
    }

    snake_case   ,snake_case       : Dict    =       model_name_to_original[model_name]

    # load original model
    print("Loading original model..."					)
    snake_case       : Tuple    =       "cuda" if torch.cuda.is_available() else "cpu"
    snake_case   ,snake_case   ,snake_case       : Union[str, Any]    =       load_model_and_preprocess(
        name=lowercase		,				model_type=lowercase		,				is_eval=lowercase		,				device=lowercase					)
    original_model.eval()
    print("Done!"					)

    # update state dict keys
    snake_case       : List[Any]    =       original_model.state_dict()
    snake_case       : List[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():
        snake_case       : Optional[int]    =       state_dict.pop(lowercase					)
        if key.startswith("Qformer.bert"					):
            snake_case       : List[Any]    =       key.replace("Qformer.bert"		,				"qformer"					)
        if "attention.self" in key:
            snake_case       : str    =       key.replace("self"		,				"attention"					)
        if "opt_proj" in key:
            snake_case       : Union[str, Any]    =       key.replace("opt_proj"		,				"language_projection"					)
        if "t5_proj" in key:
            snake_case       : Dict    =       key.replace("t5_proj"		,				"language_projection"					)
        if key.startswith("opt"					):
            snake_case       : Union[str, Any]    =       key.replace("opt"		,				"language"					)
        if key.startswith("t5"					):
            snake_case       : Dict    =       key.replace("t5"		,				"language"					)
        snake_case       : Any    =       val

    # read in qv biases
    read_in_q_v_bias(lowercase		,				lowercase					)

    snake_case   ,snake_case       : Dict    =       hf_model.load_state_dict(lowercase		,				strict=lowercase					)
    assert len(lowercase					) == 0
    assert unexpected_keys == ["qformer.embeddings.position_ids"]

    snake_case       : List[Any]    =       load_demo_image()
    snake_case       : Any    =       vis_processors["eval"](lowercase					).unsqueeze(0					).to(lowercase					)
    snake_case       : int    =       tokenizer(["\n"]		,				return_tensors="pt"					).input_ids.to(lowercase					)

    # create processor
    snake_case       : int    =       BlipImageProcessor(
        size={"height": image_size, "width": image_size}		,				image_mean=lowercase		,				image_std=lowercase					)
    snake_case       : Optional[Any]    =       BlipaProcessor(image_processor=lowercase		,				tokenizer=lowercase					)
    snake_case       : Optional[Any]    =       processor(images=lowercase		,				return_tensors="pt"					).pixel_values.to(lowercase					)

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

    original_model.to(lowercase					)
    hf_model.to(lowercase					)
    with torch.no_grad():
        if "opt" in model_name:
            snake_case       : str    =       original_model({"image": original_pixel_values, "text_input": [""]}					).logits
            snake_case       : List[Any]    =       hf_model(lowercase		,				lowercase					).logits
        else:
            snake_case       : Any    =       original_model(
                {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}					).logits
            snake_case       : str    =       input_ids.masked_fill(input_ids == tokenizer.pad_token_id		,				-100					)
            snake_case       : Any    =       hf_model(lowercase		,				lowercase		,				labels=lowercase					).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":
        snake_case       : List[Any]    =       torch.tensor(
            [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]		,				device=lowercase					)
        assert torch.allclose(logits[0, :3, :3]		,				lowercase		,				atol=1e-4					)
    elif model_name == "blip2-flan-t5-xl-coco":
        snake_case       : Optional[int]    =       torch.tensor(
            [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]		,				device=lowercase					)
    else:
        # cast to same type
        snake_case       : List[Any]    =       logits.dtype
        assert torch.allclose(original_logits.to(lowercase					)		,				lowercase		,				atol=1e-2					)
    print("Looks ok!"					)

    print("Generating a caption..."					)
    snake_case       : int    =       ""
    snake_case       : Tuple    =       tokenizer(lowercase		,				return_tensors="pt"					).input_ids.to(lowercase					)

    snake_case       : List[str]    =       original_model.generate({"image": original_pixel_values}					)
    snake_case       : Tuple    =       hf_model.generate(
        lowercase		,				lowercase		,				do_sample=lowercase		,				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:"		,				lowercase					)
    snake_case       : Optional[Any]    =       input_ids.shape[1]
    snake_case       : Optional[Any]    =       processor.batch_decode(outputs[:, prompt_length:]		,				skip_special_tokens=lowercase					)
    snake_case       : List[str]    =       [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'nielsr/{model_name}'					)
        hf_model.push_to_hub(F'nielsr/{model_name}'					)


if __name__ == "__main__":
  __snake_case       =       argparse.ArgumentParser()
  __snake_case       =       [
      """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""",
  )

  __snake_case       =       parser.parse_args()

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









"""simple docstring"""



__snake_case       =       """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""







def     __lowerCAmelCase		(		)      ->		None:


    """simple docstring"""





    snake_case       : str    =       input("Enter message: "					)
    snake_case       : Tuple    =       input("Enter key [alphanumeric]: "					)
    snake_case       : Union[str, Any]    =       input("Encrypt/Decrypt [e/d]: "					)

    if mode.lower().startswith("e"					):
        snake_case       : str    =       "encrypt"
        snake_case       : Optional[int]    =       encrypt_message(lowercase		,				lowercase					)
    elif mode.lower().startswith("d"					):
        snake_case       : List[Any]    =       "decrypt"
        snake_case       : Tuple    =       decrypt_message(lowercase		,				lowercase					)

    print(F'\n{mode.title()}ed message:'					)
    print(lowercase					)







def     __lowerCAmelCase		(		lowercase    :    str		,				lowercase    :    str					)      ->		str:


    """simple docstring"""





    return translate_message(lowercase		,				lowercase		,				"encrypt"					)







def     __lowerCAmelCase		(		lowercase    :    str		,				lowercase    :    str					)      ->		str:


    """simple docstring"""





    return translate_message(lowercase		,				lowercase		,				"decrypt"					)







def     __lowerCAmelCase		(		lowercase    :    str		,				lowercase    :    str		,				lowercase    :    str					)      ->		str:


    """simple docstring"""





    snake_case       : List[Any]    =       []
    snake_case       : List[str]    =       0
    snake_case       : List[Any]    =       key.upper()

    for symbol in message:
        snake_case       : Optional[int]    =       LETTERS.find(symbol.upper()					)
        if num != -1:
            if mode == "encrypt":
                num += LETTERS.find(key[key_index]					)
            elif mode == "decrypt":
                num -= LETTERS.find(key[key_index]					)

            num %= len(lowercase					)

            if symbol.isupper():
                translated.append(LETTERS[num]					)
            elif symbol.islower():
                translated.append(LETTERS[num].lower()					)

            key_index += 1
            if key_index == len(lowercase					):
                snake_case       : List[str]    =       0
        else:
            translated.append(lowercase					)
    return "".join(lowercase					)


if __name__ == "__main__":
  main()





