"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import argparse
import requests
import torch
from lavis.models import load_model_and_preprocess
from PIL import Image
from sapiens_transformers import (AutoTokenizer, BlipImageProcessor, InstructBlipConfig, InstructBlipForConditionalGeneration, InstructBlipProcessor, InstructBlipQFormerConfig,
InstructBlipVisionConfig, LlamaConfig, LlamaTokenizerFast, T5Config, T5TokenizerFast)
from sapiens_transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def load_demo_image():
    url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
    image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
    return image
def create_rename_keys(config):
    rename_keys = []
    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"))
    rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.embeddings.layernorm.weight"))
    rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.embeddings.layernorm.bias"))
    return rename_keys
def rename_key(dct, old, new):
    val = dct.pop(old)
    dct[new] = val
def read_in_q_v_bias(state_dict, config):
    for i in range(config.vision_config.num_hidden_layers):
        q_bias = state_dict.pop(f"visual_encoder.blocks.{i}.attn.q_bias")
        v_bias = state_dict.pop(f"visual_encoder.blocks.{i}.attn.v_bias")
        qkv_bias = torch.cat((q_bias, torch.zeros_like(v_bias, requires_grad=False), v_bias))
        state_dict[f"vision_model.encoder.layers.{i}.self_attn.qkv.bias"] = qkv_bias
def get_blip2_config(model_name):
    image_size = 364 if "coco" in model_name else 224
    vision_config = InstructBlipVisionConfig(image_size=image_size).to_dict()
    if "t5-xl" in model_name: text_config = T5Config.from_pretrained("google/flan-t5-xl", dense_act_fn="gelu", bos_token_id=1).to_dict()
    elif "t5-xxl" in model_name: text_config = T5Config.from_pretrained("google/flan-t5-xxl", dense_act_fn="gelu", bos_token_id=1).to_dict()
    elif "vicuna-7b" in model_name: text_config = LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf", vocab_size=32001).to_dict()
    elif "vicuna-13b" in model_name: text_config = LlamaConfig.from_pretrained("decapoda-research/llama-13b-hf", vocab_size=32001).to_dict()
    else: raise ValueError("Model name not supported")
    qformer_config = InstructBlipQFormerConfig(vocab_size=30523).to_dict()
    config = InstructBlipConfig(vision_config=vision_config, text_config=text_config, qformer_config=qformer_config)
    return config, image_size
@torch.no_grad()
def convert_blip2_checkpoint(model_name, pytorch_dump_folder_path=None, push_to_hub=False):
    qformer_tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased", truncation_side="left")
    qformer_tokenizer.add_special_tokens({"bos_token": "[DEC]"})
    if "t5" in model_name: tokenizer = T5TokenizerFast.from_pretrained("google/flan-t5-xl", truncation_side="left")
    elif "vicuna" in model_name:
        tokenizer = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b", truncation_side="left", bos_token="</s>", unk_token="</s>")
        tokenizer.add_special_tokens({"pad_token": "[PAD]"})
    config, image_size = get_blip2_config(model_name)
    hf_model = InstructBlipForConditionalGeneration(config).eval()
    model_name_to_original = {'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')}
    name, type = model_name_to_original[model_name]
    print("Loading original model...")
    hf_model_device = "cuda:1" if torch.cuda.is_available() else "cpu"
    lavis_device = "cuda:2" if torch.cuda.is_available() else "cpu"
    original_model, vis_processors, _ = load_model_and_preprocess(name=name, model_type=type, is_eval=True, device=lavis_device)
    original_model.eval()
    print("Done!")
    state_dict = original_model.state_dict()
    rename_keys = create_rename_keys(config)
    for src, dest in rename_keys: rename_key(state_dict, src, dest)
    for key, val in state_dict.copy().items():
        val = state_dict.pop(key)
        if key.startswith("Qformer.bert"): key = key.replace("Qformer.bert", "qformer")
        if "attention.self" in key: key = key.replace("self", "attention")
        if "llm_proj" in key: key = key.replace("llm_proj", "language_projection")
        if "t5_proj" in key: key = key.replace("t5_proj", "language_projection")
        if key.startswith("llm_model"): key = key.replace("llm_model", "language_model")
        if key.startswith("t5"): key = key.replace("t5", "language")
        state_dict[key] = val
    read_in_q_v_bias(state_dict, config)
    hf_model.load_state_dict(state_dict, strict=True)
    image = load_demo_image()
    prompt = "What is unusual about this image?"
    image_processor = BlipImageProcessor(size={"height": image_size, "width": image_size}, image_mean=OPENAI_CLIP_MEAN, image_std=OPENAI_CLIP_STD)
    processor = InstructBlipProcessor(image_processor=image_processor, tokenizer=tokenizer, qformer_tokenizer=qformer_tokenizer)
    inputs = processor(images=image, text=prompt, return_tensors="pt").to(hf_model_device)
    original_pixel_values = vis_processors["eval"](image).unsqueeze(0).to(lavis_device)
    pixel_values = inputs.pixel_values
    assert torch.allclose(original_pixel_values.to(pixel_values.device), pixel_values)
    original_model.to(lavis_device)
    hf_model.to(hf_model_device)
    with torch.no_grad():
        if "vicuna" in model_name:
            original_logits = original_model({"image": original_pixel_values, "text_input": [prompt]}).logits
            logits = hf_model(**inputs).logits
        else:
            original_logits = original_model({"image": original_pixel_values, "text_input": [prompt], "text_output": ["\n"]}).logits
            label_input_ids = tokenizer("\n", return_tensors="pt").input_ids.to(hf_model_device)
            labels = label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id, -100)
            logits = hf_model(**inputs, labels=labels).logits
    print("First values of original logits:", original_logits[0, :3, :3])
    print("First values of HF logits:", logits[0, :3, :3])
    assert original_logits.shape == logits.shape
    atol = 1e-4 if "vicuna" in model_name else 1e-5
    assert torch.allclose(original_logits.to(logits.device), logits, atol=atol)
    print("Looks ok!")
    print("Generating with original model...")
    original_outputs = original_model.generate({"image": original_pixel_values, "prompt": prompt}, num_beams=5)
    print("Generating with HF model...")
    outputs = hf_model.generate(**inputs, do_sample=False, 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: outputs[outputs == 0] = 2
    print("Original generation:", original_outputs)
    output_text = processor.batch_decode(outputs, skip_special_tokens=True)
    output_text = [text.strip() for text in output_text]
    print("HF generation:", output_text)
    if pytorch_dump_folder_path is not None:
        processor.save_pretrained(pytorch_dump_folder_path)
        hf_model.save_pretrained(pytorch_dump_folder_path)
    if push_to_hub:
        processor.push_to_hub(f"Salesforce/{model_name}")
        hf_model.push_to_hub(f"Salesforce/{model_name}")
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    choices = ["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")
    args = parser.parse_args()
    convert_blip2_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
