"""
    #################################################################################################################################################################################################
    # 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 os
import warnings
import torch
from sapiens_accelerator import init_empty_weights
from sapiens_transformers import GemmaTokenizer, RecurrentGemmaConfig, RecurrentGemmaForCausalLM
try: from sapiens_transformers import GemmaTokenizerFast
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")
    GemmaTokenizerFast = None
import regex as re
gemma_2b_config = RecurrentGemmaConfig(num_attention_heads=10, num_key_value_heads=1, hidden_size=2560, intermediate_size=15360, vocab_size=256000, num_hidden_layers=26)
gemma_7b_config = RecurrentGemmaConfig()
CONFIG_MAPPING = {"2B": gemma_2b_config, "7B": gemma_7b_config}
LAYER_NAME_MAPPING = {"embedder.weight": "model.embed_tokens.weight"}
def write_model(save_path, input_base_path, config, safe_serialization=True, push_to_hub=False, dtype=torch.float32):
    print(f"Fetching all parameters from the checkpoint at '{input_base_path}'")
    model_state_dict = torch.load(input_base_path, map_location="cpu")
    REPLACEMENT = {'blocks.': 'layers.', '.ffw_down.b': '.down_proj.b', '.ffw_down.w': '.down_proj.w', '.ffw_up.b': '.up_proj.bias', '.ffw_up.w': '.up_proj.weight',
    'recurrent_block': 'temporal_block', 'attention_block': 'temporal_block', 'temporal_block.proj_final': 'temporal_block.out_proj', 'norm.scale': 'norm.weight',
    '.proj_k': '.k_proj', '.proj_q': '.q_proj', '.proj_v': '.v_proj', '.proj_final': '.o_proj', 'embedder.input_embedding': 'embed_tokens.weight', 'conv_1d.w': 'conv_1d.weight',
    'conv_1d.b': 'conv_1d.bias', 'input_gate.w': 'input_gate.weight', 'input_gate.b': 'input_gate.bias', 'a_param': 'recurrent_param', 'a_gate.b': 'recurrent_gate.bias',
    'a_gate.w': 'recurrent_gate.weight'}
    state_dict = {}
    for k, v in model_state_dict.items():
        k = "model." + k
        pattern = re.compile("|".join(map(re.escape, REPLACEMENT.keys())))
        key = pattern.sub(lambda match: REPLACEMENT[match.group(0)], k)
        if "conv_1d.weight" in key: v = v[:, None, :].transpose(0, 2)
        if "up_proj.weight" in key:
            state_dict[key.replace("up_proj", "gate_proj")] = v[0].T.contiguous()
            v = v[1].T.contiguous()
        if "up_proj.bias" in key:
            state_dict[key.replace("up_proj", "gate_proj")] = v[0, 0, 0].clone()
            v = v[1, 0, 0].contiguous()
        if "recurrent_gate.bias" in key: state_dict[key.replace("gate.", "gate_")] = v.contiguous().clone()
        elif "recurrent_gate.weight" in key: state_dict[key.replace("gate.", "gate_")] = v.contiguous().clone()
        elif "input_gate.b" in key: state_dict[key.replace("gate.", "gate_")] = v.contiguous().clone()
        elif "input_gate.w" in key: state_dict[key.replace("gate.", "gate_")] = v.contiguous().clone()
        elif "embed_tokens" in key:
            state_dict[key] = v[: config.vocab_size, :].contiguous().clone()
            state_dict["lm_head.weight"] = v[: config.vocab_size, :].contiguous().clone()
        else: state_dict[key] = v.contiguous()
    torch.set_default_dtype(dtype)
    print("Loading the checkpoint in a Gemma model.")
    with init_empty_weights(): model = RecurrentGemmaForCausalLM(config)
    model.load_state_dict(state_dict, assign=True, strict=True)
    model.config.torch_dtype = torch.float32
    del model.config._name_or_path
    print("Saving in the Transformers format.")
    if push_to_hub: print(f"pushing the model to {save_path}")
    else: model.save_pretrained(save_path, safe_serialization=safe_serialization)
def write_tokenizer(input_tokenizer_path, save_path, push_to_hub=False):
    tokenizer_class = GemmaTokenizer if GemmaTokenizerFast is None else GemmaTokenizerFast
    print(f"Saving a {tokenizer_class.__name__} to {save_path}.")
    tokenizer = tokenizer_class(input_tokenizer_path)
    if push_to_hub: tokenizer.push_to_hub(save_path)
    else: tokenizer.save_pretrained(save_path)
def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--input_checkpoint", help="Absolute path to the target Gemma weights.", default="/home/arthur/transformers_recurrentgemma/google/recurrent-gemma-2b-it/ToBeDeleted/2b-it.pt")
    parser.add_argument("--tokenizer_checkpoint", help="Location of Gemma tokenizer model")
    parser.add_argument("--model_size", default="2B", choices=["2B", "7B", "tokenizer_only"], help="'f' models correspond to the finetuned versions, and are specific to the Gemma2 official release. For more details on Gemma2, checkout the original repo: https://huggingface.co/google/gemma-7b")
    parser.add_argument("--output_dir", default="google/recurrent-gemma-2b-it-hf", help="Location to write HF model and tokenizer")
    parser.add_argument("--pickle_serialization", help="Whether or not to save using `safetensors`.", action="store_true", default=False)
    parser.add_argument("--convert_tokenizer", help="Whether or not to convert the tokenizer as well.", action="store_true", default=False)
    parser.add_argument("--push_to_hub", help="Whether or not to push the model to the hub at `output_dir` instead of saving it locally.", action="store_true", default=False)
    parser.add_argument("--dtype", default="float32", help="Target dtype of the converted model")
    args = parser.parse_args()
    if args.convert_tokenizer:
        if args.tokenizer_checkpoint is None: raise ValueError("Path to the tokenizer is required when passing --convert_tokenizer")
        spm_path = os.path.join(args.tokenizer_checkpoint)
        write_tokenizer(spm_path, args.output_dir, args.push_to_hub)
    config = CONFIG_MAPPING[args.model_size]
    dtype = getattr(torch, args.dtype)
    write_model(config=config, input_base_path=args.input_checkpoint, save_path=args.output_dir, safe_serialization=not args.pickle_serialization, push_to_hub=args.push_to_hub, dtype=dtype)
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.                                                                 #
    #################################################################################################################################################################################################
"""
