import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig







def     A__	(       __A ,			__A ,			__A=0						):







    '''simple docstring'''
    # Format the message.
    if name is None:
        _lowerCamelCase  :		Tuple			    =    None
    else:
        _lowerCamelCase  :		Optional[int]			    =    """.""" * max(0 ,			spaces - 2						) + """# {:""" + str(50 - spaces						) + """s}"""
        _lowerCamelCase  :		List[str]			    =    fmt.format(__A						)

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







def     A__	(       __A ,			__A ,			__A ,			__A						):







    '''simple docstring'''
    # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
    # for compatibility with later versions of NVIDIA Megatron-LM.
    # The inverse operation is performed inside Megatron-LM to read checkpoints:
    # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
    # If param is the weight tensor of the self-attention block, the returned tensor
    # will have to be transposed one more time to be read by HuggingFace GPT2.
    _lowerCamelCase  :		List[str]			    =    param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    _lowerCamelCase  :		Tuple			    =    (num_heads, num_splits, hidden_size) + input_shape[1:]
    _lowerCamelCase  :		Tuple			    =    param.view(*__A						)
    _lowerCamelCase  :		str			    =    param.transpose(0 ,			1						).contiguous()
    _lowerCamelCase  :		Any			    =    param.view(*__A						)
    return param







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		Union[str, Any]			    =    config.n_positions
    _lowerCamelCase  :		str			    =    config.n_layer
    _lowerCamelCase  :		List[str]			    =    config.vocab_size
    _lowerCamelCase  :		Optional[int]			    =    config.n_head
    _lowerCamelCase  :		Dict			    =    config.n_embd // config.n_head

    _lowerCamelCase  :		List[Any]			    =    sd_megatron["""model.language_model.embedding.word_embeddings.weight"""][:vocab_size, :]
    _lowerCamelCase  :		Optional[int]			    =    {
        """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"""],
    }

    _lowerCamelCase  :		Union[str, Any]			    =    """model.language_model.encoder.layers."""
    for i in range(__A						):
        _lowerCamelCase  :		str			    =    torch.tril(torch.ones((n_positions, n_positions) ,			dtype=torch.bool						)						)
        _lowerCamelCase  :		Optional[Any]			    =    causal_mask.view(1 ,			1 ,			__A ,			__A						)
        _lowerCamelCase  :		Optional[Any]			    =    causal_mask
        _lowerCamelCase  :		str			    =    torch.tensor(-1E4 ,			dtype=torch.bfloataa						)

        _lowerCamelCase  :		Optional[Any]			    =    sd_megatron[F"""{pf}{i}.input_layernorm.weight"""]
        _lowerCamelCase  :		int			    =    sd_megatron[F"""{pf}{i}.input_layernorm.bias"""]

        _lowerCamelCase  :		Union[str, Any]			    =    sd_megatron[F"""{pf}{i}.self_attention.query_key_value.weight"""]
        _lowerCamelCase  :		Union[str, Any]			    =    fix_query_key_value_ordering(__A ,			3 ,			__A ,			__A						)
        _lowerCamelCase  :		Any			    =    vala.transpose(0 ,			1						).contiguous()

        _lowerCamelCase  :		List[str]			    =    sd_megatron[F"""{pf}{i}.self_attention.query_key_value.bias"""]
        _lowerCamelCase  :		Any			    =    fix_query_key_value_ordering(__A ,			3 ,			__A ,			__A						)
        _lowerCamelCase  :		Union[str, Any]			    =    vala

        _lowerCamelCase  :		Optional[int]			    =    sd_megatron[F"""{pf}{i}.self_attention.dense.weight"""].transpose(
            0 ,			1						)
        _lowerCamelCase  :		List[str]			    =    sd_megatron[F"""{pf}{i}.self_attention.dense.bias"""]
        _lowerCamelCase  :		List[str]			    =    sd_megatron[F"""{pf}{i}.post_attention_layernorm.weight"""]
        _lowerCamelCase  :		Optional[int]			    =    sd_megatron[F"""{pf}{i}.post_attention_layernorm.bias"""]
        _lowerCamelCase  :		Any			    =    sd_megatron[F"""{pf}{i}.mlp.dense_h_to_4h.weight"""].transpose(0 ,			1						)
        _lowerCamelCase  :		Optional[Any]			    =    sd_megatron[F"""{pf}{i}.mlp.dense_h_to_4h.bias"""]
        _lowerCamelCase  :		Union[str, Any]			    =    sd_megatron[F"""{pf}{i}.mlp.dense_4h_to_h.weight"""].transpose(
            0 ,			1						)
        _lowerCamelCase  :		str			    =    sd_megatron[F"""{pf}{i}.mlp.dense_4h_to_h.bias"""]

    # For LM head, transformers' wants the matrix to weight embeddings.
    _lowerCamelCase  :		Union[str, Any]			    =    word_embeddings

    return sd_hf







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		Union[str, Any]			    =    64_000
    _lowerCamelCase  :		List[Any]			    =    config_megatron["""encoder_seq_length"""]
    _lowerCamelCase  :		int			    =    config_megatron["""hidden_size"""]
    _lowerCamelCase  :		Optional[int]			    =    config_megatron["""num_layers"""]
    _lowerCamelCase  :		str			    =    config_megatron["""num_attention_heads"""]
    _lowerCamelCase  :		List[Any]			    =    config_megatron["""ffn_hidden_size"""]
    _lowerCamelCase  :		List[str]			    =    """gelu"""
    _lowerCamelCase  :		Optional[Any]			    =    0.1
    _lowerCamelCase  :		str			    =    0.1
    _lowerCamelCase  :		str			    =    0.1
    _lowerCamelCase  :		Union[str, Any]			    =    config_megatron["""layernorm_epsilon"""]  # 1e-5
    _lowerCamelCase  :		Tuple			    =    config_megatron["""init_method_std"""]  # 0.02
    _lowerCamelCase  :		Optional[int]			    =    config_megatron["""apply_query_key_layer_scaling"""]  # True
    _lowerCamelCase  :		Union[str, Any]			    =    True
    _lowerCamelCase  :		List[Any]			    =    True

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

    return config_hf







def     A__	(       __A						):







    '''simple docstring'''
    print(__A						)

    _lowerCamelCase  :		int			    =    args.checkpoint_path
    _lowerCamelCase  :		Optional[Any]			    =    args.save_path
    if isfile(__A						):
        raise FileNotFoundError(F"""ERROR! could not find file {checkpoint_path}"""						)

    # Load the model.
    _lowerCamelCase  :		Optional[int]			    =    torch.load(__A ,			map_location="""cpu"""						)

    # Load the config.
    _lowerCamelCase  :		List[Any]			    =    checkpoint["""hyper_parameters"""]["""cfg"""]
    _lowerCamelCase  :		Tuple			    =    GPTaConfig()
    _lowerCamelCase  :		Union[str, Any]			    =    copy_config(config_hf=__A ,			config_megatron=__A						)
    _lowerCamelCase  :		Optional[Any]			    =    ["""GPT2LMHeadModel"""]

    _lowerCamelCase  :		Optional[int]			    =    checkpoint["""state_dict"""]

    # Convert.
    print("""Converting"""						)
    _lowerCamelCase  :		Any			    =    convert_megatron_checkpoint(__A ,			__A						)

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

    _lowerCamelCase  :		Dict			    =    """GPTSw3Tokenizer"""

    # Store the config to file.
    print("""Saving config"""						)
    config_hf.save_pretrained(__A						)

    # Store the state_dict to file.
    _lowerCamelCase  :		Any			    =    os.path.join(__A ,			"""pytorch_model.bin"""						)
    print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""						)
    torch.save(__A ,			__A						)


if __name__ == "__main__":
    lowerCAmelCase      :				Any     			=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")
    lowerCAmelCase      :				Tuple     			=parser.parse_args()
    main(_args)





from __future__ import annotations

import sys







def     A__	(       __A						):







    '''simple docstring'''
    if n < 0:
        raise ValueError("""Negative arguments are not supported"""						)
    return _fib(__A						)[0]







def     A__	(       __A						):







    '''simple docstring'''
    if n == 0:  # (F(0), F(1))
        return (0, 1)

    # F(2n) = F(n)[2F(n+1) − F(n)]
    # F(2n+1) = F(n+1)^2+F(n)^2
    _lowerCamelCase    ,				_lowerCamelCase  :		List[Any]			    =    _fib(n // 2						)
    _lowerCamelCase  :		List[Any]			    =    a * (b * 2 - a)
    _lowerCamelCase  :		Optional[int]			    =    a * a + b * b
    return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
    lowerCAmelCase      :				Union[str, Any]     			=int(sys.argv[1])
    print(F"""fibonacci({n}) is {fibonacci(n)}""")




