



"""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  lowerCamelCase_						():
    _UpperCAmelCase							:  Optional[Any]											=    '''https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'''
    _UpperCAmelCase							:  int											=    Image.open(requests.get(UpperCamelCase__     ,       stream=UpperCamelCase__							).raw							).convert('''RGB'''							)

    return image





def  lowerCamelCase_						(UpperCamelCase__		:		Any							):
    _UpperCAmelCase							:  int											=    []
    # 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  lowerCamelCase_						(UpperCamelCase__		:		List[Any]     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		int							):
    _UpperCAmelCase							:  List[Any]											=    dct.pop(UpperCamelCase__							)
    _UpperCAmelCase							:  Dict											=    val





def  lowerCamelCase_						(UpperCamelCase__		:		Any     ,       UpperCamelCase__		:		str							):
    for i in range(config.vision_config.num_hidden_layers							):
        # read in original q and v biases
        _UpperCAmelCase							:  Tuple											=    state_dict.pop(F'visual_encoder.blocks.{i}.attn.q_bias'							)
        _UpperCAmelCase							:  Optional[int]											=    state_dict.pop(F'visual_encoder.blocks.{i}.attn.v_bias'							)

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





def  lowerCamelCase_						(UpperCamelCase__		:		Dict							):
    _UpperCAmelCase							:  Union[str, Any]											=    364 if '''coco''' in model_name else 224
    _UpperCAmelCase							:  Tuple											=    InstructBlipVisionConfig(image_size=UpperCamelCase__							).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:
        _UpperCAmelCase							:  Any											=    TaConfig.from_pretrained('''google/flan-t5-xl'''     ,       dense_act_fn='''gelu'''     ,       bos_token_id=1							).to_dict()
    elif "t5-xxl" in model_name:
        _UpperCAmelCase							:  Union[str, Any]											=    TaConfig.from_pretrained('''google/flan-t5-xxl'''     ,       dense_act_fn='''gelu'''     ,       bos_token_id=1							).to_dict()
    elif "vicuna-7b" in model_name:
        _UpperCAmelCase							:  Any											=    LlamaConfig.from_pretrained('''decapoda-research/llama-7b-hf'''     ,       vocab_size=3_2001							).to_dict()
    elif "vicuna-13b" in model_name:
        _UpperCAmelCase							:  Optional[int]											=    LlamaConfig.from_pretrained('''decapoda-research/llama-13b-hf'''     ,       vocab_size=3_2001							).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
    _UpperCAmelCase							:  Dict											=    InstructBlipQFormerConfig(vocab_size=3_0523							).to_dict()
    _UpperCAmelCase							:  Tuple											=    InstructBlipConfig(vision_config=UpperCamelCase__     ,       text_config=UpperCamelCase__     ,       qformer_config=UpperCamelCase__							)

    return config, image_size





@torch.no_grad()
def  lowerCamelCase_						(UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		Dict=None     ,       UpperCamelCase__		:		str=False							):
    _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''bert-base-uncased'''     ,       truncation_side='''left'''							)
    qformer_tokenizer.add_special_tokens({'''bos_token''': '''[DEC]'''}							)

    if "t5" in model_name:
        _UpperCAmelCase							:  Tuple											=    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>"})
        _UpperCAmelCase							:  Any											=    LlamaTokenizerFast.from_pretrained(
            '''huggyllama/llama-7b'''     ,       truncation_side='''left'''     ,       bos_token='''</s>'''     ,       unk_token='''</s>'''							)
        tokenizer.add_special_tokens({'''pad_token''': '''[PAD]'''}							)

    _UpperCAmelCase							,				_UpperCAmelCase							:  int											=    get_blipa_config(UpperCamelCase__							)
    _UpperCAmelCase							:  List[str]											=    InstructBlipForConditionalGeneration(UpperCamelCase__							).eval()

    _UpperCAmelCase							:  int											=    {
        '''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'''),
    }

    _UpperCAmelCase							,				_UpperCAmelCase							:  Tuple											=    model_name_to_original[model_name]

    # load original model
    print('''Loading original model...'''							)
    _UpperCAmelCase							:  List[str]											=    '''cuda:1''' if torch.cuda.is_available() else '''cpu'''
    _UpperCAmelCase							:  Optional[int]											=    '''cuda:2''' if torch.cuda.is_available() else '''cpu'''
    _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  List[Any]											=    load_model_and_preprocess(
        name=UpperCamelCase__     ,       model_type=UpperCamelCase__     ,       is_eval=UpperCamelCase__     ,       device=UpperCamelCase__							)
    original_model.eval()
    print('''Done!'''							)

    # update state dict keys
    _UpperCAmelCase							:  int											=    original_model.state_dict()
    _UpperCAmelCase							:  str											=    create_rename_keys(UpperCamelCase__							)
    for src, dest in rename_keys:
        rename_key(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)

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

    # read in qv biases
    read_in_q_v_bias(UpperCamelCase__     ,       UpperCamelCase__							)

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

    _UpperCAmelCase							:  Optional[Any]											=    load_demo_image()
    _UpperCAmelCase							:  Optional[Any]											=    '''What is unusual about this image?'''

    # create processor
    _UpperCAmelCase							:  Union[str, Any]											=    BlipImageProcessor(
        size={'''height''': image_size, '''width''': image_size}     ,       image_mean=UpperCamelCase__     ,       image_std=UpperCamelCase__							)
    _UpperCAmelCase							:  Optional[Any]											=    InstructBlipProcessor(
        image_processor=UpperCamelCase__     ,       tokenizer=UpperCamelCase__     ,       qformer_tokenizer=UpperCamelCase__     ,       )
    _UpperCAmelCase							:  Optional[int]											=    processor(images=UpperCamelCase__     ,       text=UpperCamelCase__     ,       return_tensors='''pt'''							).to(UpperCamelCase__							)

    # make sure processor creates exact same pixel values
    _UpperCAmelCase							:  int											=    vis_processors['''eval'''](UpperCamelCase__							).unsqueeze(0							).to(UpperCamelCase__							)
    _UpperCAmelCase							:  Dict											=    inputs.pixel_values
    assert torch.allclose(original_pixel_values.to(pixel_values.device							)     ,       UpperCamelCase__							)

    original_model.to(UpperCamelCase__							)
    hf_model.to(UpperCamelCase__							)
    with torch.no_grad():
        if "vicuna" in model_name:
            _UpperCAmelCase							:  Dict											=    original_model({'''image''': original_pixel_values, '''text_input''': [prompt]}							).logits
            _UpperCAmelCase							:  Optional[int]											=    hf_model(**UpperCamelCase__							).logits
        else:
            _UpperCAmelCase							:  List[str]											=    original_model(
                {'''image''': original_pixel_values, '''text_input''': [prompt], '''text_output''': ['''\n''']}							).logits
            _UpperCAmelCase							:  Tuple											=    tokenizer('''\n'''     ,       return_tensors='''pt'''							).input_ids.to(UpperCamelCase__							)
            _UpperCAmelCase							:  Any											=    label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id     ,       -100							)
            _UpperCAmelCase							:  Tuple											=    hf_model(**UpperCamelCase__     ,       labels=UpperCamelCase__							).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
    _UpperCAmelCase							:  Union[str, Any]											=    1E-4 if '''vicuna''' in model_name else 1E-5
    assert torch.allclose(original_logits.to(logits.device							)     ,       UpperCamelCase__     ,       atol=UpperCamelCase__							)
    print('''Looks ok!'''							)

    print('''Generating with original model...'''							)
    _UpperCAmelCase							:  Union[str, Any]											=    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...'''							)
    _UpperCAmelCase							:  Optional[int]											=    hf_model.generate(
        **UpperCamelCase__     ,       do_sample=UpperCamelCase__     ,       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?
        _UpperCAmelCase							:  str											=    2
    print('''Original generation:'''     ,       UpperCamelCase__							)
    _UpperCAmelCase							:  Dict											=    processor.batch_decode(UpperCamelCase__     ,       skip_special_tokens=UpperCamelCase__							)
    _UpperCAmelCase							:  int											=    [text.strip() for text in output_text]
    print('''HF generation:'''     ,       UpperCamelCase__							)

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

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


if __name__ == "__main__":
  _lowerCAmelCase    :str     =					argparse.ArgumentParser()
  _lowerCAmelCase    :List[str]     =					[
      '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',
  )

  _lowerCAmelCase    :str     =					parser.parse_args()

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







"""simple docstring"""
from __future__ import annotations

import os
from collections.abc import Mapping

_lowerCAmelCase    :Tuple     =					tuple[int, int]
class 							_UpperCAmelCase							:





     '''simple docstring'''



     def __init__(					self       ,	A       ,	A		)							->					None:
         _UpperCAmelCase							:  set[int]											=    vertices
         _UpperCAmelCase							:  dict[EdgeT, int]											=    {
             (min(A		), max(A		)): weight for edge, weight in edges.items()
         }





     def  __lowerCAmelCase	(					self       ,	A       ,	A		)							->					None:
         self.vertices.add(edge[0]		)
         self.vertices.add(edge[1]		)
         _UpperCAmelCase							:  List[Any]											=    weight





     def  __lowerCAmelCase	(					self		)							->					Graph:
         _UpperCAmelCase							:  Graph											=    Graph({min(self.vertices		)}       ,	{}		)
         _UpperCAmelCase							:  EdgeT
         _UpperCAmelCase							:  int
         _UpperCAmelCase							:  EdgeT
         _UpperCAmelCase							:  int

         while len(subgraph.vertices		) < len(self.vertices		):
             _UpperCAmelCase							:  Any											=    max(self.edges.values()		) + 1
             for edge, weight in self.edges.items():
                 if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                     if weight < min_weight:
                         _UpperCAmelCase							:  Tuple											=    edge
                         _UpperCAmelCase							:  Optional[int]											=    weight

             subgraph.add_edge(A       ,	A		)

         return subgraph





def  lowerCamelCase_						(UpperCamelCase__		:		str = "p107_network.txt"							):
    _UpperCAmelCase							:  str											=    os.path.abspath(os.path.dirname(UpperCamelCase__							)							)
    _UpperCAmelCase							:  str											=    os.path.join(UpperCamelCase__     ,       UpperCamelCase__							)
    _UpperCAmelCase							:  dict[EdgeT, int]											=    {}
    _UpperCAmelCase							:  list[str]
    _UpperCAmelCase							:  int
    _UpperCAmelCase							:  int

    with open(UpperCamelCase__							) as f:
        _UpperCAmelCase							:  str											=    f.read().strip().split('''\n'''							)

    _UpperCAmelCase							:  List[Any]											=    [line.split(''','''							) for line in data]

    for edgea in range(1     ,       len(UpperCamelCase__							)							):
        for edgea in range(UpperCamelCase__							):
            if adjaceny_matrix[edgea][edgea] != "-":
                _UpperCAmelCase							:  Optional[Any]											=    int(adjaceny_matrix[edgea][edgea]							)

    _UpperCAmelCase							:  Graph											=    Graph(set(range(len(UpperCamelCase__							)							)							)     ,       UpperCamelCase__							)

    _UpperCAmelCase							:  Graph											=    graph.prims_algorithm()

    _UpperCAmelCase							:  int											=    sum(graph.edges.values()							)
    _UpperCAmelCase							:  int											=    sum(subgraph.edges.values()							)

    return initial_total - optimal_total


if __name__ == "__main__":
  print(f"{solution() = }")


