





import math






class        lowercase__			:

   '''simple docstring'''


   def  UpperCamelCase__    (       self,						__magic_name__,						__magic_name__				)					-> Dict:






       """simple docstring"""
       UpperCamelCase__				:							Optional[int]			  = 0.0
       UpperCamelCase__				:							Union[str, Any]			  = 0.0
       for i in range(len(__magic_name__				)				):
           da += math.pow((sample[i] - weights[0][i]),						2				)
           da += math.pow((sample[i] - weights[1][i]),						2				)
           return 0 if da > da else 1
       return 0




   def  UpperCamelCase__    (       self,						__magic_name__,						__magic_name__,						__magic_name__,						__magic_name__				)					-> Dict:






       """simple docstring"""
       for i in range(len(__magic_name__				)				):
           weights[j][i] += alpha * (sample[i] - weights[j][i])
       return weights




def 						lowerCAmelCase_       (    ) ->      None:
    UpperCamelCase__				:							Union[str, Any]			  = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

    # weight initialization ( n, C )
    UpperCamelCase__				:							Tuple			  = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

    # training
    UpperCamelCase__				:							Optional[int]			  = SelfOrganizingMap()
    UpperCamelCase__				:							str			  = 3
    UpperCamelCase__				:							int			  = 0.5

    for _ in range(SCREAMING_SNAKE_CASE__ ):
        for j in range(len(SCREAMING_SNAKE_CASE__ ) ):
            # training sample
            UpperCamelCase__				:							str			  = training_samples[j]

            # Compute the winning vector
            UpperCamelCase__				:							Tuple			  = self_organizing_map.get_winner(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__ )

            # Update the winning vector
            UpperCamelCase__				:							List[str]			  = self_organizing_map.update(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__ )

    # classify test sample
    UpperCamelCase__				:							List[str]			  = [0, 0, 0, 1]
    UpperCamelCase__				:							Union[str, Any]			  = self_organizing_map.get_winner(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__ )

    # results
    print(f"Clusters that the test sample belongs to : {winner}" )
    print(f"Weights that have been trained : {weights}" )


# running the main() function
if __name__ == "__main__":
  main()






"""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 		__snake_case		( )    ->  Tuple:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[int]														=       "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
     _UpperCAmelCase     :      int														=       Image.open(requests.get(SCREAMING_SNAKE_CASE__		,    stream=SCREAMING_SNAKE_CASE__							).raw							).convert("RGB"							)

     return image

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      List[Any]														=       []
     # 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 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[str]		,    SCREAMING_SNAKE_CASE__      :		Dict		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[int]														=       dct.pop(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Tuple														=       val

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[Any]:





     '''simple docstring'''

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

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

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str		,    SCREAMING_SNAKE_CASE__      :		Any							)    ->  str:





     '''simple docstring'''

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

     _UpperCAmelCase     :      Optional[Any]														=       BlipaConfig(vision_config=SCREAMING_SNAKE_CASE__		,    text_config=SCREAMING_SNAKE_CASE__							)

     return config, image_size

@torch.no_grad()
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		Tuple=None		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]=False							)    ->  Optional[Any]:





     '''simple docstring'''

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

     _UpperCAmelCase     :      int														=       BlipaForConditionalGeneration(SCREAMING_SNAKE_CASE__							).eval()

     _UpperCAmelCase     :      Optional[int]														=       {
         "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     :      str														=       model_name_to_original[model_name]

     # load original model
     print("Loading original model..."							)
     _UpperCAmelCase     :      List[Any]														=       "cuda" if torch.cuda.is_available() else "cpu"
     _UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       load_model_and_preprocess(
         name=SCREAMING_SNAKE_CASE__		,    model_type=SCREAMING_SNAKE_CASE__		,    is_eval=SCREAMING_SNAKE_CASE__		,    device=SCREAMING_SNAKE_CASE__							)
     original_model.eval()
     print("Done!"							)

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

     # some keys can be renamed efficiently
     for key, val in state_dict.copy().items():
          _UpperCAmelCase     :      Tuple														=       state_dict.pop(SCREAMING_SNAKE_CASE__							)
          if key.startswith("Qformer.bert"							):
               _UpperCAmelCase     :      List[Any]														=       key.replace("Qformer.bert"		,    "qformer"							)
          if "attention.self" in key:
               _UpperCAmelCase     :      Dict														=       key.replace("self"		,    "attention"							)
          if "opt_proj" in key:
               _UpperCAmelCase     :      Union[str, Any]														=       key.replace("opt_proj"		,    "language_projection"							)
          if "t5_proj" in key:
               _UpperCAmelCase     :      Optional[Any]														=       key.replace("t5_proj"		,    "language_projection"							)
          if key.startswith("opt"							):
               _UpperCAmelCase     :      Dict														=       key.replace("opt"		,    "language"							)
          if key.startswith("t5"							):
               _UpperCAmelCase     :      Union[str, Any]														=       key.replace("t5"		,    "language"							)
          _UpperCAmelCase     :      List[str]														=       val

     # read in qv biases
     read_in_q_v_bias(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       hf_model.load_state_dict(SCREAMING_SNAKE_CASE__		,    strict=SCREAMING_SNAKE_CASE__							)
     assert len(SCREAMING_SNAKE_CASE__							) == 0
     assert unexpected_keys == ["qformer.embeddings.position_ids"]

     _UpperCAmelCase     :      Dict														=       load_demo_image()
     _UpperCAmelCase     :      List[Any]														=       vis_processors["eval"](SCREAMING_SNAKE_CASE__							).unsqueeze(0							).to(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Optional[Any]														=       tokenizer(["\n"]		,    return_tensors="pt"							).input_ids.to(SCREAMING_SNAKE_CASE__							)

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

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

     original_model.to(SCREAMING_SNAKE_CASE__							)
     hf_model.to(SCREAMING_SNAKE_CASE__							)
     with torch.no_grad():
          if "opt" in model_name:
               _UpperCAmelCase     :      str														=       original_model({"image": original_pixel_values, "text_input": [""]}							).logits
               _UpperCAmelCase     :      Optional[int]														=       hf_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							).logits
          else:
               _UpperCAmelCase     :      List[Any]														=       original_model(
                   {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}							).logits
               _UpperCAmelCase     :      List[str]														=       input_ids.masked_fill(input_ids == tokenizer.pad_token_id		,    -100							)
               _UpperCAmelCase     :      Union[str, Any]														=       hf_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    labels=SCREAMING_SNAKE_CASE__							).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     :      List[str]														=       torch.tensor(
              [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]]		,    device=SCREAMING_SNAKE_CASE__							)
          assert torch.allclose(logits[0, :3, :3]		,    SCREAMING_SNAKE_CASE__		,    atol=1E-4							)
     elif model_name == "blip2-flan-t5-xl-coco":
          _UpperCAmelCase     :      Any														=       torch.tensor(
              [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]]		,    device=SCREAMING_SNAKE_CASE__							)
     else:
          # cast to same type
          _UpperCAmelCase     :      List[str]														=       logits.dtype
          assert torch.allclose(original_logits.to(SCREAMING_SNAKE_CASE__							)		,    SCREAMING_SNAKE_CASE__		,    atol=1E-2							)
     print("Looks ok!"							)

     print("Generating a caption..."							)
     _UpperCAmelCase     :      int														=       ""
     _UpperCAmelCase     :      Any														=       tokenizer(SCREAMING_SNAKE_CASE__		,    return_tensors="pt"							).input_ids.to(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      List[Any]														=       original_model.generate({"image": original_pixel_values}							)
     _UpperCAmelCase     :      int														=       hf_model.generate(
         SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    do_sample=SCREAMING_SNAKE_CASE__		,    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:"		,    SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      str														=       input_ids.shape[1]
     _UpperCAmelCase     :      List[Any]														=       processor.batch_decode(outputs[:, prompt_length:]		,    skip_special_tokens=SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      int														=       [text.strip() for text in output_text]
     print("HF generation:"		,    SCREAMING_SNAKE_CASE__							)

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

     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							:   Union[str, Any]				=				argparse.ArgumentParser()
 _lowerCAmelCase							:   List[Any]				=				[
     "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							:   str				=				parser.parse_args()

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