

import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig

def   lowerCamelCase__       (      a__			,      a__			,      a__=0)		->    List[str]:



    """simple docstring"""







    if name is None:
        _snake_case : int												=		None
    else:
        _snake_case : Any												=		'.' * max(0			,      spaces - 2) + '# {:' + str(5_0 - spaces) + 's}'
        _snake_case : Dict												=		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   lowerCamelCase__       (      a__			,      a__			,      a__			,      a__)		->    List[str]:



    """simple docstring"""







    _snake_case : Dict												=		param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    _snake_case : str												=		(num_heads, num_splits, hidden_size) + input_shape[1:]
    _snake_case : List[Any]												=		param.view(*a__)
    _snake_case : int												=		param.transpose(0			,      1).contiguous()
    _snake_case : str												=		param.view(*a__)
    return param

def   lowerCamelCase__       (      a__			,      a__)		->    int:



    """simple docstring"""







    _snake_case : Dict												=		config.n_positions
    _snake_case : int												=		config.n_layer
    _snake_case : List[str]												=		config.vocab_size
    _snake_case : Dict												=		config.n_head
    _snake_case : List[Any]												=		config.n_embd // config.n_head

    _snake_case : Dict												=		sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
    _snake_case : Tuple												=		{
        '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'],
    }

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

        _snake_case : str												=		sd_megatron[F"""{pf}{i}.input_layernorm.weight"""]
        _snake_case : int												=		sd_megatron[F"""{pf}{i}.input_layernorm.bias"""]

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

        _snake_case : Dict												=		sd_megatron[F"""{pf}{i}.self_attention.query_key_value.bias"""]
        _snake_case : int												=		fix_query_key_value_ordering(a__			,      3			,      a__			,      a__)
        _snake_case : int												=		vala

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

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

    return sd_hf

def   lowerCamelCase__       (      a__			,      a__)		->    Any:



    """simple docstring"""







    _snake_case : Optional[int]												=		6_4_0_0_0
    _snake_case : Optional[int]												=		config_megatron['encoder_seq_length']
    _snake_case : Optional[int]												=		config_megatron['hidden_size']
    _snake_case : List[Any]												=		config_megatron['num_layers']
    _snake_case : List[Any]												=		config_megatron['num_attention_heads']
    _snake_case : Any												=		config_megatron['ffn_hidden_size']
    _snake_case : Dict												=		'gelu'
    _snake_case : List[str]												=		0.1
    _snake_case : str												=		0.1
    _snake_case : Tuple												=		0.1
    _snake_case : str												=		config_megatron['layernorm_epsilon']  # 1e-5
    _snake_case : List[Any]												=		config_megatron['init_method_std']  # 0.02
    _snake_case : Tuple												=		config_megatron['apply_query_key_layer_scaling']  # True
    _snake_case : List[str]												=		True
    _snake_case : str												=		True

    # This identifies the 6.7B (7B) model which uses a different tokenizer
    if config_megatron["hidden_size"] == 4_0_9_6:
        _snake_case : Union[str, Any]												=		1  # <|endoftext|>
        _snake_case : Union[str, Any]												=		1  # <|endoftext|>
        _snake_case : Tuple												=		0  # <unk>
    else:
        _snake_case : Dict												=		2  # <s>
        _snake_case : Dict												=		3  # <|endoftext|>
        _snake_case : Optional[int]												=		0  # <pad>

    return config_hf

def   lowerCamelCase__       (      a__)		->    Optional[Any]:



    """simple docstring"""







    print(a__)

    _snake_case : str												=		args.checkpoint_path
    _snake_case : Tuple												=		args.save_path
    if isfile(a__):
        raise FileNotFoundError(F"""ERROR! could not find file {checkpoint_path}""")

    # Load the model.
    _snake_case : Optional[int]												=		torch.load(a__			,      map_location='cpu')

    # Load the config.
    _snake_case : str												=		checkpoint['hyper_parameters']['cfg']
    _snake_case : Any												=		GPTaConfig()
    _snake_case : List[str]												=		copy_config(config_hf=a__			,      config_megatron=a__)
    _snake_case : int												=		['GPT2LMHeadModel']

    _snake_case : List[Any]												=		checkpoint['state_dict']

    # Convert.
    print('Converting')
    _snake_case : Optional[Any]												=		convert_megatron_checkpoint(a__			,      a__)

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

    _snake_case : int												=		'GPTSw3Tokenizer'

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

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_    =   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")
   SCREAMING_SNAKE_CASE_    =   parser.parse_args()
   main(_args)






'''simple docstring'''



from __future__ import annotations

from collections import Counter

def   lowerCamelCase__       (      a__)		->    list[int]:



    """simple docstring"""







    _snake_case : Optional[int]												=		[True] * n
    _snake_case : List[str]												=		False
    _snake_case : int												=		False
    _snake_case : Tuple												=		True

    for i in range(3			,      int(n**0.5 + 1)			,      2):
        _snake_case : int												=		i * 2
        while index < n:
            _snake_case : List[str]												=		False
            _snake_case : Union[str, Any]												=		index + i

    _snake_case : List[str]												=		[2]

    for i in range(3			,      a__			,      2):
        if is_prime[i]:
            primes.append(a__)

    return primes

def   lowerCamelCase__       (      a__)		->    list[list[int]]:



    """simple docstring"""







    _snake_case : Tuple												=		str(a__)
    _snake_case : str												=		[]
    _snake_case : Union[str, Any]												=		['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']

    for duplicate in Counter(a__) - Counter(set(a__)):
        _snake_case : List[Any]												=		[int(number_str.replace(a__			,      a__)) for digit in digits]
        replacements.append(a__)

    return replacements

def   lowerCamelCase__       (      a__ = 8)		->    int:



    """simple docstring"""







    _snake_case : Any												=		set()

    # Filter primes with less than 3 replaceable digits
    _snake_case : List[Any]												=		{
        x for x in set(prime_sieve(1_0_0_0_0_0_0)) if len(str(a__)) - len(set(str(a__))) >= 3
    }

    for prime in primes:
        if prime in numbers_checked:
            continue

        _snake_case : int												=		digit_replacements(a__)

        for family in replacements:
            numbers_checked.update(a__)
            _snake_case : Union[str, Any]												=		primes.intersection(a__)

            if len(a__) != family_length:
                continue

            return min(a__)

    return -1


if __name__ == "__main__":
   print(solution())



