



import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def     __lowerCamelCase							(   __magic_name__						:	str  ,			__magic_name__						:	Optional[int]  ,			__magic_name__						:	Tuple=0    ):

							if name is None:
														a__:							Any									=None
							else:
														a__:							Tuple									="." * max(0  ,			spaces - 2    ) + "# {:" + str(50 - spaces    ) + "s}"
														a__:							Any									=fmt.format(__magic_name__    )

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





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





def     __lowerCamelCase							(   __magic_name__						:	List[Any]  ,			__magic_name__						:	List[Any]    ):
							a__:							Tuple									=config.n_positions
							a__:							List[str]									=config.n_layer
							a__:							Optional[Any]									=config.vocab_size
							a__:							int									=config.n_head
							a__:							Union[str, Any]									=config.n_embd // config.n_head

							a__:							str									=sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
							a__:							str									={
							    "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"],
							}

							a__:							Tuple									="model.language_model.encoder.layers."
							for i in range(__magic_name__    ):
														a__:							List[Any]									=torch.tril(torch.ones((n_positions, n_positions)  ,			dtype=torch.bool    )    )
														a__:							str									=causal_mask.view(1  ,			1  ,			__magic_name__  ,			__magic_name__    )
														a__:							int									=causal_mask
														a__:							Union[str, Any]									=torch.tensor(-1e4  ,			dtype=torch.bfloataa    )

														a__:							Dict									=sd_megatron[F"{pf}{i}.input_layernorm.weight"]
														a__:							List[str]									=sd_megatron[F"{pf}{i}.input_layernorm.bias"]

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

														a__:							Tuple									=sd_megatron[F"{pf}{i}.self_attention.query_key_value.bias"]
														a__:							Optional[int]									=fix_query_key_value_ordering(__magic_name__  ,			3  ,			__magic_name__  ,			__magic_name__    )
														a__:							Optional[Any]									=vala

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

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

							return sd_hf





def     __lowerCamelCase							(   __magic_name__						:	str  ,			__magic_name__						:	str    ):
							a__:							Any									=64_000
							a__:							Optional[int]									=config_megatron["encoder_seq_length"]
							a__:							Optional[int]									=config_megatron["hidden_size"]
							a__:							Dict									=config_megatron["num_layers"]
							a__:							List[str]									=config_megatron["num_attention_heads"]
							a__:							Dict									=config_megatron["ffn_hidden_size"]
							a__:							List[str]									="gelu"
							a__:							int									=0.1
							a__:							str									=0.1
							a__:							Optional[int]									=0.1
							a__:							Union[str, Any]									=config_megatron["layernorm_epsilon"]  # 1e-5
							a__:							Tuple									=config_megatron["init_method_std"]  # 0.02
							a__:							int									=config_megatron["apply_query_key_layer_scaling"]  # True
							a__:							Optional[Any]									=True
							a__:							str									=True

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

							return config_hf





def     __lowerCamelCase							(   __magic_name__						:	Optional[Any]    ):
							print(__magic_name__    )

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

							# Load the model.
							a__:							Union[str, Any]									=torch.load(__magic_name__  ,			map_location="cpu"    )

							# Load the config.
							a__:							Optional[Any]									=checkpoint["hyper_parameters"]["cfg"]
							a__:							Tuple									=GPTaConfig()
							a__:							Optional[int]									=copy_config(config_hf=__magic_name__  ,			config_megatron=__magic_name__    )
							a__:							str									=["GPT2LMHeadModel"]

							a__:							str									=checkpoint["state_dict"]

							# Convert.
							print("Converting"    )
							a__:							Any									=convert_megatron_checkpoint(__magic_name__  ,			__magic_name__    )

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

							a__:							Union[str, Any]									="GPTSw3Tokenizer"

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

							# Store the state_dict to file.
							a__:							List[str]									=os.path.join(__magic_name__  ,			"pytorch_model.bin"    )
							print(F"Saving checkpoint to \"{output_checkpoint_file}\""    )
							torch.save(__magic_name__  ,			__magic_name__    )


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













"""simple docstring"""





def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						int:
    """simple docstring"""

    if not isinstance(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       ):
        raise ValueError('double_factorial() only accepts integral values'       )
    if num < 0:
        raise ValueError('double_factorial() not defined for negative values'       )
    UpperCamelCase__								=     1
    for i in range(SCREAMING_SNAKE_CASE			,						0			,						-2       ):
        value *= i
    return value


if __name__ == "__main__":
  import doctest

  doctest.testmod()




