"""
    #################################################################################################################################################################################################
    # 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 re
import zipfile
import torch
from sapiens_transformers import AutoTokenizer, GPT2Config
def recursive_print(name, val, spaces=0):
    if name is None: msg = None
    else:
        fmt = "." * max(0, spaces - 2) + "# {:" + str(50 - spaces) + "s}"
        msg = fmt.format(name)
    if isinstance(val, dict):
        if msg is not None: print(msg)
        for k in val.keys(): recursive_print(k, val[k], spaces + 2)
    elif isinstance(val, torch.Tensor): print(msg, ":", val.size())
    else: print(msg, ":", val)
def fix_query_key_value_ordering(param, checkpoint_version, num_splits, num_heads, hidden_size):
    input_shape = param.size()
    if checkpoint_version == 1.0:
        saved_shape = (num_heads, hidden_size, num_splits) + input_shape[1:]
        param = param.view(*saved_shape)
        param = param.transpose(0, 2)
        param = param.transpose(1, 2).contiguous()
    elif checkpoint_version >= 2.0:
        saved_shape = (num_heads, num_splits, hidden_size) + input_shape[1:]
        param = param.view(*saved_shape)
        param = param.transpose(0, 1).contiguous()
    param = param.view(*input_shape)
    return param
def convert_megatron_checkpoint(args, input_state_dict, config):
    output_state_dict = {}
    ds_args = input_state_dict.get("args", None)
    if ds_args is not None:
        config.vocab_size = ds_args.padded_vocab_size
        config.n_positions = ds_args.max_position_embeddings
        config.n_embd = ds_args.hidden_size
        config.n_layer = ds_args.num_layers
        config.n_head = ds_args.num_attention_heads
        config.n_inner = ds_args.ffn_hidden_size
    heads = config.n_head
    hidden_size_per_head = config.n_embd // config.n_head
    if "checkpoint_version" in input_state_dict.keys(): checkpoint_version = input_state_dict["checkpoint_version"]
    else: checkpoint_version = 0.0
    model = input_state_dict["model"]
    lm = model["language_model"]
    embeddings = lm["embedding"]
    word_embeddings = embeddings["word_embeddings"]["weight"]
    word_embeddings = word_embeddings[: config.vocab_size, :]
    output_state_dict["transformer.wte.weight"] = word_embeddings
    pos_embeddings = embeddings["position_embeddings"]["weight"]
    n_positions = pos_embeddings.size(0)
    if n_positions != config.n_positions: raise ValueError(f"pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match")
    output_state_dict["transformer.wpe.weight"] = pos_embeddings
    transformer = lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]
    layer_re = re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)")
    megatron_to_transformers = {'attention.dense': '.attn.c_proj.', 'self_attention.dense': '.attn.c_proj.', 'mlp.dense_h_to_4h': '.mlp.c_fc.', 'mlp.dense_4h_to_h': '.mlp.c_proj.'}
    for key, val in transformer.items():
        m = layer_re.match(key)
        if m is None: break
        layer_idx = int(m.group(1))
        op_name = m.group(2)
        weight_or_bias = m.group(3)
        layer_name = f"transformer.h.{layer_idx}"
        if op_name.endswith("layernorm"):
            ln_name = "ln_1" if op_name.startswith("input") else "ln_2"
            output_state_dict[layer_name + "." + ln_name + "." + weight_or_bias] = val
        elif (op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value") and weight_or_bias == "weight":
            causal_mask = torch.tril(torch.ones((n_positions, n_positions), dtype=torch.float16)).view(1, 1, n_positions, n_positions)
            output_state_dict[layer_name + ".attn.bias"] = causal_mask
            masked_bias = torch.tensor(-1e4, dtype=torch.float16)
            output_state_dict[layer_name + ".attn.masked_bias"] = masked_bias
            out_val = fix_query_key_value_ordering(val, checkpoint_version, 3, heads, hidden_size_per_head)
            out_val = out_val.transpose(0, 1).contiguous()
            output_state_dict[layer_name + ".attn.c_attn.weight"] = out_val
        elif (op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value") and weight_or_bias == "bias":
            out_val = fix_query_key_value_ordering(val, checkpoint_version, 3, heads, hidden_size_per_head)
            output_state_dict[layer_name + ".attn.c_attn.bias"] = out_val
        elif weight_or_bias == "weight":
            out_name = megatron_to_transformers[op_name]
            output_state_dict[layer_name + out_name + "weight"] = val.transpose(0, 1)
        elif weight_or_bias == "bias":
            out_name = megatron_to_transformers[op_name]
            output_state_dict[layer_name + out_name + "bias"] = val
    assert config.n_layer == layer_idx + 1
    output_state_dict["transformer.ln_f.weight"] = transformer["final_layernorm.weight"]
    output_state_dict["transformer.ln_f.bias"] = transformer["final_layernorm.bias"]
    output_state_dict["lm_head.weight"] = word_embeddings
    return output_state_dict
def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--print-checkpoint-structure", action="store_true")
    parser.add_argument("path_to_checkpoint", type=str, help="Path to the checkpoint file (.zip archive or direct .pt file)")
    parser.add_argument("--config_file", default="", type=str, help="An optional config json file describing the pre-trained model.")
    args = parser.parse_args()
    basename = os.path.dirname(args.path_to_checkpoint)
    print(f"Extracting PyTorch state dictionary from {args.path_to_checkpoint}")
    if args.path_to_checkpoint.endswith(".zip"):
        with zipfile.ZipFile(args.path_to_checkpoint, "r") as checkpoint:
            with checkpoint.open("release/mp_rank_00/model_optim_rng.pt") as pytorch_dict: input_state_dict = torch.load(pytorch_dict, map_location="cpu")
    else: input_state_dict = torch.load(args.path_to_checkpoint, map_location="cpu")
    ds_args = input_state_dict.get("args", None)
    if args.config_file == "":
        if ds_args is not None:
            if ds_args.bias_gelu_fusion: activation_function = "gelu_fast"
            elif ds_args.openai_gelu: activation_function = "gelu_new"
            else: activation_function = "gelu"
        else: activation_function = "gelu_new"
        config = GPT2Config(vocab_size=50257, n_positions=1024, n_embd=1024, n_layer=24, n_head=16, n_inner=4096, activation_function=activation_function, resid_pdrop=0.1,
        embd_pdrop=0.1, attn_pdrop=0.1, layer_norm_epsilon=1e-5, initializer_range=0.02, summary_type="cls_index", summary_use_proj=True, summary_activation=None,
        summary_proj_to_labels=True, summary_first_dropout=0.1, scale_attn_weights=True, use_cache=True, bos_token_id=50256, eos_token_id=50256)
    else: config = GPT2Config.from_json_file(args.config_file)
    config.architectures = ["GPT2LMHeadModel"]
    print("Converting")
    output_state_dict = convert_megatron_checkpoint(args, input_state_dict, config)
    if args.print_checkpoint_structure: recursive_print(None, output_state_dict)
    if ds_args is not None:
        tokenizer_type = ds_args.tokenizer_type
        if tokenizer_type == "GPT2BPETokenizer": tokenizer_model_name = "openai-community/gpt2"
        elif tokenizer_type == "PretrainedFromHF": tokenizer_model_name = ds_args.tokenizer_name_or_path
        else: raise ValueError(f"Unrecognized tokenizer_type {tokenizer_type}")
    else: tokenizer_model_name = "openai-community/gpt2"
    tokenizer = AutoTokenizer.from_pretrained(tokenizer_model_name)
    tokenizer_class = type(tokenizer).__name__
    config.tokenizer_class = tokenizer_class
    print("Saving config")
    config.save_pretrained(basename)
    print(f"Adding {tokenizer_class} tokenizer files")
    tokenizer.save_pretrained(basename)
    output_checkpoint_file = os.path.join(basename, "pytorch_model.bin")
    print(f'Saving checkpoint to "{output_checkpoint_file}"')
    torch.save(output_state_dict, output_checkpoint_file)
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.                                                                 #
    #################################################################################################################################################################################################
"""

