"""
    #################################################################################################################################################################################################
    # 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 MegatronBertConfig
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.tokenizer_type = ds_args.tokenizer_type
        config.vocab_size = ds_args.padded_vocab_size
        config.max_position_embeddings = ds_args.max_position_embeddings
        config.hidden_size = ds_args.hidden_size
        config.num_hidden_layers = ds_args.num_layers
        config.num_attention_heads = ds_args.num_attention_heads
        config.intermediate_size = ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
    heads = config.num_attention_heads
    hidden_size_per_head = config.hidden_size // heads
    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["bert.embeddings.word_embeddings.weight"] = word_embeddings
    pos_embeddings = embeddings["position_embeddings"]["weight"]
    assert pos_embeddings.size(0) == config.max_position_embeddings and pos_embeddings.size(1) == config.hidden_size
    output_state_dict["bert.embeddings.position_embeddings.weight"] = pos_embeddings
    tokentype_embeddings = embeddings["tokentype_embeddings"]["weight"]
    output_state_dict["bert.embeddings.token_type_embeddings.weight"] = tokentype_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': '.attention.output.dense.', 'self_attention.dense': '.attention.output.dense.', 'mlp.dense_h_to_4h': '.intermediate.dense.', 'mlp.dense_4h_to_h': '.output.dense.'}
    attention_qkv_weight = None
    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"bert.encoder.layer.{layer_idx}"
        if op_name.endswith("layernorm"):
            ln_name = "attention.ln" if op_name.startswith("input") else "ln"
            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":
            assert attention_qkv_weight is None, ""
            out_val = fix_query_key_value_ordering(val, checkpoint_version, 3, heads, hidden_size_per_head)
            attention_qkv_weight = out_val
        elif (op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value") and weight_or_bias == "bias":
            assert attention_qkv_weight is not None, ""
            q = attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
            k = attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
            v = attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]
            out_val = fix_query_key_value_ordering(val, checkpoint_version, 3, heads, hidden_size_per_head)
            q_bias = out_val[0 * config.hidden_size : 1 * config.hidden_size]
            k_bias = out_val[1 * config.hidden_size : 2 * config.hidden_size]
            v_bias = out_val[2 * config.hidden_size : 3 * config.hidden_size]
            output_state_dict[f"{layer_name}.attention.self.query.weight"] = q
            output_state_dict[f"{layer_name}.attention.self.query.bias"] = q_bias
            output_state_dict[f"{layer_name}.attention.self.key.weight"] = k
            output_state_dict[f"{layer_name}.attention.self.key.bias"] = k_bias
            output_state_dict[f"{layer_name}.attention.self.value.weight"] = v
            output_state_dict[f"{layer_name}.attention.self.value.bias"] = v_bias
            attention_qkv_weight = None
        elif weight_or_bias in ["weight", "bias"]:
            out_name = megatron_to_transformers[op_name]
            output_state_dict[layer_name + out_name + weight_or_bias] = val
    output_state_dict["bert.encoder.ln.weight"] = transformer["final_layernorm.weight"]
    output_state_dict["bert.encoder.ln.bias"] = transformer["final_layernorm.bias"]
    pooler = lm["pooler"]
    output_state_dict["bert.pooler.dense.weight"] = pooler["dense.weight"]
    output_state_dict["bert.pooler.dense.bias"] = pooler["dense.bias"]
    lm_head = model["lm_head"]
    output_state_dict["cls.predictions.transform.dense.weight"] = lm_head["dense.weight"]
    output_state_dict["cls.predictions.transform.dense.bias"] = lm_head["dense.bias"]
    output_state_dict["cls.predictions.transform.LayerNorm.weight"] = lm_head["layernorm.weight"]
    output_state_dict["cls.predictions.transform.LayerNorm.bias"] = lm_head["layernorm.bias"]
    output_state_dict["cls.predictions.decoder.weight"] = word_embeddings
    output_state_dict["cls.predictions.bias"] = lm_head["bias"]
    binary_head = model["binary_head"]
    output_state_dict["cls.seq_relationship.weight"] = binary_head["weight"]
    output_state_dict["cls.seq_relationship.bias"] = binary_head["bias"]
    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 ZIP file containing the checkpoint")
    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")
    if args.config_file == "":
        config = MegatronBertConfig()
        config.vocab_size = input_state_dict["model"]["lm_head"]["bias"].numel()
    else: config = MegatronBertConfig.from_json_file(args.config_file)
    print("Converting")
    output_state_dict = convert_megatron_checkpoint(args, input_state_dict, config)
    if args.print_checkpoint_structure: recursive_print(None, output_state_dict)
    print("Saving config")
    config.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.                                                                 #
    #################################################################################################################################################################################################
"""

