

import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		List[Any]			, _SCREAMING_SNAKE_CASE       :		Union[str, Any]			, _SCREAMING_SNAKE_CASE       :		List[str]=0							):





    """simple docstring"""


    if name is None:
        SCREAMING_SNAKE_CASE_						=    None
    else:
        SCREAMING_SNAKE_CASE_						=    '.' * max(0			, spaces - 2							) + '# {:' + str(50 - spaces							) + 's}'
        SCREAMING_SNAKE_CASE_						=    fmt.format(_SCREAMING_SNAKE_CASE							)

    # Print and recurse (if needed).
    if isinstance(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							):
        if msg is not None:
            print(_SCREAMING_SNAKE_CASE							)
        for k in val.keys():
            recursive_print(_SCREAMING_SNAKE_CASE			, val[k]			, spaces + 2							)
    elif isinstance(_SCREAMING_SNAKE_CASE			, torch.Tensor							):
        print(_SCREAMING_SNAKE_CASE			, ':'			, val.size()							)
    else:
        print(_SCREAMING_SNAKE_CASE			, ':'			, _SCREAMING_SNAKE_CASE							)


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Optional[Any]			, _SCREAMING_SNAKE_CASE       :		Any			, _SCREAMING_SNAKE_CASE       :		Tuple			, _SCREAMING_SNAKE_CASE       :		str							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    SCREAMING_SNAKE_CASE_						=    (num_heads, num_splits, hidden_size) + input_shape[1:]
    SCREAMING_SNAKE_CASE_						=    param.view(*_SCREAMING_SNAKE_CASE							)
    SCREAMING_SNAKE_CASE_						=    param.transpose(0			, 1							).contiguous()
    SCREAMING_SNAKE_CASE_						=    param.view(*_SCREAMING_SNAKE_CASE							)
    return param


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		int			, _SCREAMING_SNAKE_CASE       :		Tuple							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    config.n_positions
    SCREAMING_SNAKE_CASE_						=    config.n_layer
    SCREAMING_SNAKE_CASE_						=    config.vocab_size
    SCREAMING_SNAKE_CASE_						=    config.n_head
    SCREAMING_SNAKE_CASE_						=    config.n_embd // config.n_head

    SCREAMING_SNAKE_CASE_						=    sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
    SCREAMING_SNAKE_CASE_						=    {
        'transformer.wte.weight': word_embeddings,
        'transformer.wpe.weight': sd_megatron['model.language_model.embedding.position_embeddings.weight'],
        'transformer.ln_f.weight': sd_megatron['model.language_model.encoder.final_layernorm.weight'],
        'transformer.ln_f.bias': sd_megatron['model.language_model.encoder.final_layernorm.bias'],
    }

    SCREAMING_SNAKE_CASE_						=    'model.language_model.encoder.layers.'
    for i in range(_SCREAMING_SNAKE_CASE							):
        SCREAMING_SNAKE_CASE_						=    torch.tril(torch.ones((n_positions, n_positions)			, dtype=torch.bool							)							)
        SCREAMING_SNAKE_CASE_						=    causal_mask.view(1			, 1			, _SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
        SCREAMING_SNAKE_CASE_						=    causal_mask
        SCREAMING_SNAKE_CASE_						=    torch.tensor(-1E4			, dtype=torch.bfloataa							)

        SCREAMING_SNAKE_CASE_						=    sd_megatron[f"""{pf}{i}.input_layernorm.weight"""]
        SCREAMING_SNAKE_CASE_						=    sd_megatron[f"""{pf}{i}.input_layernorm.bias"""]

        SCREAMING_SNAKE_CASE_						=    sd_megatron[f"""{pf}{i}.self_attention.query_key_value.weight"""]
        SCREAMING_SNAKE_CASE_						=    fix_query_key_value_ordering(_SCREAMING_SNAKE_CASE			, 3			, _SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
        SCREAMING_SNAKE_CASE_						=    vala.transpose(0			, 1							).contiguous()

        SCREAMING_SNAKE_CASE_						=    sd_megatron[f"""{pf}{i}.self_attention.query_key_value.bias"""]
        SCREAMING_SNAKE_CASE_						=    fix_query_key_value_ordering(_SCREAMING_SNAKE_CASE			, 3			, _SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
        SCREAMING_SNAKE_CASE_						=    vala

        SCREAMING_SNAKE_CASE_						=    sd_megatron[f"""{pf}{i}.self_attention.dense.weight"""].transpose(
            0			, 1							)
        SCREAMING_SNAKE_CASE_						=    sd_megatron[f"""{pf}{i}.self_attention.dense.bias"""]
        SCREAMING_SNAKE_CASE_						=    sd_megatron[f"""{pf}{i}.post_attention_layernorm.weight"""]
        SCREAMING_SNAKE_CASE_						=    sd_megatron[f"""{pf}{i}.post_attention_layernorm.bias"""]
        SCREAMING_SNAKE_CASE_						=    sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.weight"""].transpose(0			, 1							)
        SCREAMING_SNAKE_CASE_						=    sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.bias"""]
        SCREAMING_SNAKE_CASE_						=    sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.weight"""].transpose(
            0			, 1							)
        SCREAMING_SNAKE_CASE_						=    sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.bias"""]

    # For LM head, transformers' wants the matrix to weight embeddings.
    SCREAMING_SNAKE_CASE_						=    word_embeddings

    return sd_hf


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		List[str]			, _SCREAMING_SNAKE_CASE       :		List[str]							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    64_000
    SCREAMING_SNAKE_CASE_						=    config_megatron['encoder_seq_length']
    SCREAMING_SNAKE_CASE_						=    config_megatron['hidden_size']
    SCREAMING_SNAKE_CASE_						=    config_megatron['num_layers']
    SCREAMING_SNAKE_CASE_						=    config_megatron['num_attention_heads']
    SCREAMING_SNAKE_CASE_						=    config_megatron['ffn_hidden_size']
    SCREAMING_SNAKE_CASE_						=    'gelu'
    SCREAMING_SNAKE_CASE_						=    0.1
    SCREAMING_SNAKE_CASE_						=    0.1
    SCREAMING_SNAKE_CASE_						=    0.1
    SCREAMING_SNAKE_CASE_						=    config_megatron['layernorm_epsilon']  # 1e-5
    SCREAMING_SNAKE_CASE_						=    config_megatron['init_method_std']  # 0.02
    SCREAMING_SNAKE_CASE_						=    config_megatron['apply_query_key_layer_scaling']  # True
    SCREAMING_SNAKE_CASE_						=    True
    SCREAMING_SNAKE_CASE_						=    True

    # This identifies the 6.7B (7B) model which uses a different tokenizer
    if config_megatron["hidden_size"] == 4_096:
        SCREAMING_SNAKE_CASE_						=    1  # <|endoftext|>
        SCREAMING_SNAKE_CASE_						=    1  # <|endoftext|>
        SCREAMING_SNAKE_CASE_						=    0  # <unk>
    else:
        SCREAMING_SNAKE_CASE_						=    2  # <s>
        SCREAMING_SNAKE_CASE_						=    3  # <|endoftext|>
        SCREAMING_SNAKE_CASE_						=    0  # <pad>

    return config_hf


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Any							):





    """simple docstring"""


    print(_SCREAMING_SNAKE_CASE							)

    SCREAMING_SNAKE_CASE_						=    args.checkpoint_path
    SCREAMING_SNAKE_CASE_						=    args.save_path
    if isfile(_SCREAMING_SNAKE_CASE							):
        raise FileNotFoundError(f"""ERROR! could not find file {checkpoint_path}"""							)

    # Load the model.
    SCREAMING_SNAKE_CASE_						=    torch.load(_SCREAMING_SNAKE_CASE			, map_location='cpu'							)

    # Load the config.
    SCREAMING_SNAKE_CASE_						=    checkpoint['hyper_parameters']['cfg']
    SCREAMING_SNAKE_CASE_						=    GPTaConfig()
    SCREAMING_SNAKE_CASE_						=    copy_config(config_hf=_SCREAMING_SNAKE_CASE			, config_megatron=_SCREAMING_SNAKE_CASE							)
    SCREAMING_SNAKE_CASE_						=    ['GPT2LMHeadModel']

    SCREAMING_SNAKE_CASE_						=    checkpoint['state_dict']

    # Convert.
    print('Converting'							)
    SCREAMING_SNAKE_CASE_						=    convert_megatron_checkpoint(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)

    # Print the structure of converted state dict.
    if args.print_checkpoint_structure:
        recursive_print(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)

    SCREAMING_SNAKE_CASE_						=    'GPTSw3Tokenizer'

    # Store the config to file.
    print('Saving config'							)
    config_hf.save_pretrained(_SCREAMING_SNAKE_CASE							)

    # Store the state_dict to file.
    SCREAMING_SNAKE_CASE_						=    os.path.join(_SCREAMING_SNAKE_CASE			, 'pytorch_model.bin'							)
    print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""							)
    torch.save(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
       UpperCamelCase__	:   str								=     argparse.ArgumentParser()
       parser.add_argument(
           "--checkpoint_path",
           type=str,
           required=True,
           help="e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000",
       )
       parser.add_argument("--save_path", type=str, required=True, help="e.g. /home/user/gpt-sw3/hf")
       parser.add_argument("--print-checkpoint-structure", action="store_true")
       UpperCamelCase__	:   Any								=     parser.parse_args()
       main(_args)





def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list							):





    """simple docstring"""


    if not isinstance(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							):
        raise ValueError('Input series is not valid, valid series - [2, 4, 8]'							)
    if len(_SCREAMING_SNAKE_CASE							) == 0:
        raise ValueError('Input list must be a non empty list'							)
    if len(_SCREAMING_SNAKE_CASE							) == 1:
        return True
    try:
        SCREAMING_SNAKE_CASE_						=    series[1] / series[0]
        for index in range(len(_SCREAMING_SNAKE_CASE							) - 1							):
            if series[index + 1] / series[index] != common_ratio:
                return False
    except ZeroDivisionError:
        return False
    return True


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list							):





    """simple docstring"""


    if not isinstance(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							):
        raise ValueError('Input series is not valid, valid series - [2, 4, 8]'							)
    if len(_SCREAMING_SNAKE_CASE							) == 0:
        raise ValueError('Input list must be a non empty list'							)
    SCREAMING_SNAKE_CASE_						=    1
    for value in series:
        answer *= value
    return pow(_SCREAMING_SNAKE_CASE			, 1 / len(_SCREAMING_SNAKE_CASE							)							)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


