"""
    #################################################################################################################################################################################################
    # 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 gc
import json
import os
import shutil
import warnings
from typing import List
import torch
from sapiens_transformers import GenerationConfig, LlamaConfig, LlamaForCausalLM, LlamaTokenizer, PreTrainedTokenizerFast
from sapiens_transformers.convert_slow_tokenizer import TikTokenConverter
try: from sapiens_transformers import LlamaTokenizerFast
except ImportError as e:
    warnings.warn(e)
    warnings.warn("The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion")
    LlamaTokenizerFast = None
NUM_SHARDS = {'7B': 1, '8B': 1, '8Bf': 1, '7Bf': 1, '13B': 2, '13Bf': 2, '34B': 4, '30B': 4, '65B': 8, '70B': 8, '70Bf': 8, '405B': 8, '405B-MP16': 16}
CONTEXT_LENGTH_FOR_VERSION = {"3.1": 131072, "3": 8192, "2": 4096, "1": 2048}
def compute_intermediate_size(n, ffn_dim_multiplier=1, multiple_of=256): return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)
def read_json(path):
    with open(path, "r") as f: return json.load(f)
def write_json(text, path):
    with open(path, "w") as f: json.dump(text, f)
def write_model(model_path, input_base_path, model_size=None, safe_serialization=True, llama_version="1", vocab_size=None, num_shards=None, instruct=False):
    os.makedirs(model_path, exist_ok=True)
    tmp_model_path = os.path.join(model_path, "tmp")
    os.makedirs(tmp_model_path, exist_ok=True)
    params = read_json(os.path.join(input_base_path, "params.json"))
    num_shards = NUM_SHARDS[model_size] if num_shards is None else num_shards
    params = params.get("model", params)
    n_layers = params["n_layers"]
    n_heads = params["n_heads"]
    n_heads_per_shard = n_heads // num_shards
    dim = params["dim"]
    dims_per_head = dim // n_heads
    base = params.get("rope_theta", 10000.0)
    inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
    if base > 10000.0 and float(llama_version) < 3: max_position_embeddings = 16384
    else: max_position_embeddings = CONTEXT_LENGTH_FOR_VERSION[llama_version]
    if params.get("n_kv_heads", None) is not None:
        num_key_value_heads = params["n_kv_heads"]
        num_key_value_heads_per_shard = num_key_value_heads // num_shards
        key_value_dim = dims_per_head * num_key_value_heads
    else:
        num_key_value_heads = n_heads
        num_key_value_heads_per_shard = n_heads_per_shard
        key_value_dim = dim
    def permute(w, n_heads, dim1=dim, dim2=dim): return w.view(n_heads, dim1 // n_heads // 2, 2, dim2).transpose(1, 2).reshape(dim1, dim2)
    print(f"Fetching all parameters from the checkpoint at {input_base_path}.")
    if num_shards == 1: loaded = torch.load(os.path.join(input_base_path, "consolidated.00.pth"), map_location="cpu")
    else:
        checkpoint_list = sorted([file for file in os.listdir(input_base_path) if file.endswith(".pth")])
        print("Loading in order:", checkpoint_list)
        loaded = [torch.load(os.path.join(input_base_path, file), map_location="cpu") for file in checkpoint_list]
    param_count = 0
    index_dict = {"weight_map": {}}
    for layer_i in range(n_layers):
        filename = f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
        if num_shards == 1:
            state_dict = {f"model.layers.{layer_i}.self_attn.q_proj.weight": permute(loaded[f"layers.{layer_i}.attention.wq.weight"], n_heads=n_heads),
            f"model.layers.{layer_i}.self_attn.k_proj.weight": permute(loaded[f"layers.{layer_i}.attention.wk.weight"], n_heads=num_key_value_heads, dim1=key_value_dim),
            f"model.layers.{layer_i}.self_attn.v_proj.weight": loaded[f"layers.{layer_i}.attention.wv.weight"], f"model.layers.{layer_i}.self_attn.o_proj.weight": loaded[f"layers.{layer_i}.attention.wo.weight"],
            f"model.layers.{layer_i}.mlp.gate_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w1.weight"], f"model.layers.{layer_i}.mlp.down_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w2.weight"],
            f"model.layers.{layer_i}.mlp.up_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w3.weight"], f"model.layers.{layer_i}.input_layernorm.weight": loaded[f"layers.{layer_i}.attention_norm.weight"],
            f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[f"layers.{layer_i}.ffn_norm.weight"]}
        else:
            state_dict = {f"model.layers.{layer_i}.input_layernorm.weight": loaded[0][f"layers.{layer_i}.attention_norm.weight"].clone(),
            f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[0][f"layers.{layer_i}.ffn_norm.weight"].clone()}
            state_dict[f"model.layers.{layer_i}.self_attn.q_proj.weight"] = permute(torch.cat([loaded[i][f"layers.{layer_i}.attention.wq.weight"].view(n_heads_per_shard, dims_per_head, dim)
            for i in range(len(loaded))], dim=0).reshape(dim, dim), n_heads=n_heads)
            state_dict[f"model.layers.{layer_i}.self_attn.k_proj.weight"] = permute(torch.cat([loaded[i][f"layers.{layer_i}.attention.wk.weight"].view(num_key_value_heads_per_shard, dims_per_head, dim)
            for i in range(len(loaded))], dim=0).reshape(key_value_dim, dim), num_key_value_heads, key_value_dim, dim)
            state_dict[f"model.layers.{layer_i}.self_attn.v_proj.weight"] = torch.cat([loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(num_key_value_heads_per_shard, dims_per_head, dim)
            for i in range(len(loaded))], dim=0).reshape(key_value_dim, dim)
            state_dict[f"model.layers.{layer_i}.self_attn.o_proj.weight"] = torch.cat([loaded[i][f"layers.{layer_i}.attention.wo.weight"] for i in range(len(loaded))], dim=1)
            state_dict[f"model.layers.{layer_i}.mlp.gate_proj.weight"] = torch.cat([loaded[i][f"layers.{layer_i}.feed_forward.w1.weight"] for i in range(len(loaded))], dim=0)
            state_dict[f"model.layers.{layer_i}.mlp.down_proj.weight"] = torch.cat([loaded[i][f"layers.{layer_i}.feed_forward.w2.weight"] for i in range(len(loaded))], dim=1)
            state_dict[f"model.layers.{layer_i}.mlp.up_proj.weight"] = torch.cat([loaded[i][f"layers.{layer_i}.feed_forward.w3.weight"] for i in range(len(loaded))], dim=0)
        state_dict[f"model.layers.{layer_i}.self_attn.rotary_emb.inv_freq"] = inv_freq
        for k, v in state_dict.items():
            index_dict["weight_map"][k] = filename
            param_count += v.numel()
        torch.save(state_dict, os.path.join(tmp_model_path, filename))
    filename = f"pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"
    if num_shards == 1: state_dict = {"model.embed_tokens.weight": loaded["tok_embeddings.weight"], "model.norm.weight": loaded["norm.weight"], "lm_head.weight": loaded["output.weight"]}
    else:
        concat_dim = 0 if llama_version in ["3", "3.1"] else 1
        state_dict = {"model.norm.weight": loaded[0]["norm.weight"], "model.embed_tokens.weight": torch.cat([loaded[i]["tok_embeddings.weight"] for i in range(len(loaded))], dim=concat_dim),
        "lm_head.weight": torch.cat([loaded[i]["output.weight"] for i in range(len(loaded))], dim=0)}
    for k, v in state_dict.items():
        index_dict["weight_map"][k] = filename
        param_count += v.numel()
    torch.save(state_dict, os.path.join(tmp_model_path, filename))
    index_dict["metadata"] = {"total_size": param_count * 2}
    write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))
    ffn_dim_multiplier = params["ffn_dim_multiplier"] if "ffn_dim_multiplier" in params else 1
    multiple_of = params["multiple_of"] if "multiple_of" in params else 256
    if llama_version in ["3", "3.1"]:
        bos_token_id = 128000
        if instruct: eos_token_id = [128001, 128008, 128009]
        else: eos_token_id = 128001
    else:
        bos_token_id = 1
        eos_token_id = 2
    config = LlamaConfig(hidden_size=dim, intermediate_size=compute_intermediate_size(dim, ffn_dim_multiplier, multiple_of), num_attention_heads=params["n_heads"],
    num_hidden_layers=params["n_layers"], rms_norm_eps=params["norm_eps"], num_key_value_heads=num_key_value_heads, vocab_size=vocab_size, rope_theta=base,
    max_position_embeddings=max_position_embeddings, bos_token_id=bos_token_id, eos_token_id=eos_token_id)
    config.save_pretrained(tmp_model_path)
    if instruct:
        generation_config = GenerationConfig(do_sample=True, temperature=0.6, top_p=0.9, bos_token_id=bos_token_id, eos_token_id=eos_token_id)
        generation_config.save_pretrained(tmp_model_path)
    del state_dict
    del loaded
    gc.collect()
    print("Loading the checkpoint in a Llama model.")
    model = LlamaForCausalLM.from_pretrained(tmp_model_path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True)
    del model.config._name_or_path
    model.config.torch_dtype = torch.float16
    print("Saving in the Transformers format.")
    model.save_pretrained(model_path, safe_serialization=safe_serialization)
    shutil.rmtree(tmp_model_path, ignore_errors=True)
class Llama3Converter(TikTokenConverter):
    def __init__(self, vocab_file, special_tokens=None, instruct=False, model_max_length=None, **kwargs):
        super().__init__(vocab_file, additional_special_tokens=special_tokens, **kwargs)
        tokenizer = self.converted()
        chat_template = (
            "{% set loop_messages = messages %}"
            "{% for message in loop_messages %}"
            "{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}"
            "{% if loop.index0 == 0 %}"
            "{% set content = bos_token + content %}"
            "{% endif %}"
            "{{ content }}"
            "{% endfor %}"
            "{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}"
        )
        self.tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer, bos_token="<|begin_of_text|>", eos_token="<|end_of_text|>" if not instruct else "<|eot_id|>",
        chat_template=chat_template if instruct else None, model_input_names=["input_ids", "attention_mask"], model_max_length=model_max_length)
def write_tokenizer(tokenizer_path, input_tokenizer_path, llama_version="2", special_tokens=None, instruct=False):
    tokenizer_class = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
    if llama_version in ["3", "3.1"]: tokenizer = Llama3Converter(input_tokenizer_path, special_tokens, instruct, model_max_length=CONTEXT_LENGTH_FOR_VERSION[llama_version]).tokenizer
    else: tokenizer = tokenizer_class(input_tokenizer_path)
    print(f"Saving a {tokenizer_class.__name__} to {tokenizer_path}.")
    tokenizer.save_pretrained(tokenizer_path)
    return tokenizer
DEFAULT_LLAMA_SPECIAL_TOKENS = {"3": ["<|begin_of_text|>", "<|end_of_text|>", "<|reserved_special_token_0|>", "<|reserved_special_token_1|>", "<|reserved_special_token_2|>", "<|reserved_special_token_3|>",
"<|start_header_id|>", "<|end_header_id|>", "<|reserved_special_token_4|>", "<|eot_id|>"] + [f"<|reserved_special_token_{i}|>" for i in range(5, 256 - 5)],
"3.1": ["<|begin_of_text|>", "<|end_of_text|>", "<|reserved_special_token_0|>", "<|reserved_special_token_1|>", "<|finetune_right_pad_id|>", "<|reserved_special_token_2|>",
"<|start_header_id|>", "<|end_header_id|>", "<|eom_id|>", "<|eot_id|>", "<|python_tag|>"] + [f"<|reserved_special_token_{i}|>" for i in range(3, 256 - 8)]}
def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--input_dir", help="Location of LLaMA weights, which contains tokenizer.model and model folders")
    parser.add_argument("--model_size", default=None, help="'f' Deprecated in favor of `num_shards`: models correspond to the finetuned versions, and are specific to the Llama2 official release. For more details on Llama2, checkout the original repo: https://huggingface.co/meta-llama")
    parser.add_argument("--output_dir", help="Location to write HF model and tokenizer")
    parser.add_argument("--safe_serialization", default=True, type=bool, help="Whether or not to save using `safetensors`.")
    parser.add_argument("--llama_version", choices=["1", "2", "3", "3.1"], default="1", type=str, help="Version of the Llama model to convert. Currently supports Llama1 and Llama2. Controls the context size")
    parser.add_argument("--num_shards", default=None, type=int, help="The number of individual shards used for the model. Does not have to be the same as the number of consolidated_xx.pth")
    parser.add_argument("--special_tokens", default=None, type=List[str], help="The list of special tokens that should be added to the model.")
    parser.add_argument("--instruct", default=False, type=bool, help="Whether the model is an instruct model or not. Will affect special tokens for llama 3.1.")
    args = parser.parse_args()
    if args.model_size is None and args.num_shards is None: raise ValueError("You have to set at least `num_shards` if you are not giving the `model_size`")
    if args.special_tokens is None: args.special_tokens = DEFAULT_LLAMA_SPECIAL_TOKENS.get(str(args.llama_version), [])
    spm_path = os.path.join(args.input_dir, "tokenizer.model")
    vocab_size = len(write_tokenizer(args.output_dir, spm_path, llama_version=args.llama_version, special_tokens=args.special_tokens, instruct=args.instruct))
    if args.model_size != "tokenizer_only":
        write_model(model_path=args.output_dir, input_base_path=args.input_dir, model_size=args.model_size, safe_serialization=args.safe_serialization, llama_version=args.llama_version,
        vocab_size=vocab_size, num_shards=args.num_shards, instruct=args.instruct)
if __name__ == "__main__": main()
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
