




import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Any       ,     __UpperCamelCase			:						Union[str, Any]       ,     __UpperCamelCase			:						Dict=0      )  ->							int:






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

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




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						int       ,     __UpperCamelCase			:						Optional[Any]       ,     __UpperCamelCase			:						str       ,     __UpperCamelCase			:						Union[str, Any]      )  ->							Any:
    UpperCAmelCase_							 =     param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    UpperCAmelCase_							 =     (num_heads, num_splits, hidden_size) + input_shape[1:]
    UpperCAmelCase_							 =     param.view(*lowercase_      )
    UpperCAmelCase_							 =     param.transpose(0       ,     1      ).contiguous()
    UpperCAmelCase_							 =     param.view(*lowercase_      )
    return param




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						List[Any]       ,     __UpperCamelCase			:						Tuple      )  ->							List[Any]:
    UpperCAmelCase_							 =     config.n_positions
    UpperCAmelCase_							 =     config.n_layer
    UpperCAmelCase_							 =     config.vocab_size
    UpperCAmelCase_							 =     config.n_head
    UpperCAmelCase_							 =     config.n_embd // config.n_head

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

    UpperCAmelCase_							 =     '''model.language_model.encoder.layers.'''
    for i in range(lowercase_      ):
        UpperCAmelCase_							 =     torch.tril(torch.ones((n_positions, n_positions)       ,     dtype=torch.bool      )      )
        UpperCAmelCase_							 =     causal_mask.view(1       ,     1       ,     lowercase_       ,     lowercase_      )
        UpperCAmelCase_							 =     causal_mask
        UpperCAmelCase_							 =     torch.tensor(-1e4       ,     dtype=torch.bfloataa      )

        UpperCAmelCase_							 =     sd_megatron[f'{pf}{i}.input_layernorm.weight']
        UpperCAmelCase_							 =     sd_megatron[f'{pf}{i}.input_layernorm.bias']

        UpperCAmelCase_							 =     sd_megatron[f'{pf}{i}.self_attention.query_key_value.weight']
        UpperCAmelCase_							 =     fix_query_key_value_ordering(lowercase_       ,     3       ,     lowercase_       ,     lowercase_      )
        UpperCAmelCase_							 =     vala.transpose(0       ,     1      ).contiguous()

        UpperCAmelCase_							 =     sd_megatron[f'{pf}{i}.self_attention.query_key_value.bias']
        UpperCAmelCase_							 =     fix_query_key_value_ordering(lowercase_       ,     3       ,     lowercase_       ,     lowercase_      )
        UpperCAmelCase_							 =     vala

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

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

    return sd_hf




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Dict       ,     __UpperCamelCase			:						Union[str, Any]      )  ->							str:
    UpperCAmelCase_							 =     6_4000
    UpperCAmelCase_							 =     config_megatron['''encoder_seq_length''']
    UpperCAmelCase_							 =     config_megatron['''hidden_size''']
    UpperCAmelCase_							 =     config_megatron['''num_layers''']
    UpperCAmelCase_							 =     config_megatron['''num_attention_heads''']
    UpperCAmelCase_							 =     config_megatron['''ffn_hidden_size''']
    UpperCAmelCase_							 =     '''gelu'''
    UpperCAmelCase_							 =     0.1
    UpperCAmelCase_							 =     0.1
    UpperCAmelCase_							 =     0.1
    UpperCAmelCase_							 =     config_megatron['''layernorm_epsilon''']  # 1e-5
    UpperCAmelCase_							 =     config_megatron['''init_method_std''']  # 0.02
    UpperCAmelCase_							 =     config_megatron['''apply_query_key_layer_scaling''']  # True
    UpperCAmelCase_							 =     True
    UpperCAmelCase_							 =     True

    # This identifies the 6.7B (7B) model which uses a different tokenizer
    if config_megatron["hidden_size"] == 4096:
        UpperCAmelCase_							 =     1  # <|endoftext|>
        UpperCAmelCase_							 =     1  # <|endoftext|>
        UpperCAmelCase_							 =     0  # <unk>
    else:
        UpperCAmelCase_							 =     2  # <s>
        UpperCAmelCase_							 =     3  # <|endoftext|>
        UpperCAmelCase_							 =     0  # <pad>

    return config_hf




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Any      )  ->							Union[str, Any]:
    print(lowercase_      )

    UpperCAmelCase_							 =     args.checkpoint_path
    UpperCAmelCase_							 =     args.save_path
    if isfile(lowercase_      ):
        raise FileNotFoundError(f'ERROR! could not find file {checkpoint_path}'      )

    # Load the model.
    UpperCAmelCase_							 =     torch.load(lowercase_       ,     map_location='''cpu'''      )

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

    UpperCAmelCase_							 =     checkpoint['''state_dict''']

    # Convert.
    print('''Converting'''      )
    UpperCAmelCase_							 =     convert_megatron_checkpoint(lowercase_       ,     lowercase_      )

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

    UpperCAmelCase_							 =     '''GPTSw3Tokenizer'''

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

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


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












"""simple docstring"""





import numpy as np




def 		lowerCAmelCase_	(  lowercase_   :							str		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Dict												=       np.exp(lowercase_		)

 # Add up the all the exponentials
 __SCREAMING_SNAKE_CASE :			Tuple												=       np.sum(lowercase_		)

 # Divide every exponent by the sum of all exponents
 __SCREAMING_SNAKE_CASE :			List[Any]												=       exponent_vector / sum_of_exponents

 return softmax_vector


if __name__ == "__main__":
    print(softmax((0,)))

