
"""simple docstring"""


import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase=0       )				-> Union[str, Any]:
	# Format the message.
	if name is None:
		_lowerCAmelCase								=None
	else:
		_lowerCAmelCase								=""".""" * max(0    , spaces - 2       ) + """# {:""" + str(50 - spaces       ) + """s}"""
		_lowerCAmelCase								=fmt.format(__UpperCamelCase       )

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



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> str:
	# 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								=param.size()
	# other versions store [num_heads * num_splits * hidden_size, :]
	_lowerCAmelCase								=(num_heads, num_splits, hidden_size) + input_shape[1:]
	_lowerCAmelCase								=param.view(*__UpperCamelCase       )
	_lowerCAmelCase								=param.transpose(0    , 1       ).contiguous()
	_lowerCAmelCase								=param.view(*__UpperCamelCase       )
	return param



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase       )				-> Any:
	_lowerCAmelCase								=config.n_positions
	_lowerCAmelCase								=config.n_layer
	_lowerCAmelCase								=config.vocab_size
	_lowerCAmelCase								=config.n_head
	_lowerCAmelCase								=config.n_embd // config.n_head

	_lowerCAmelCase								=sd_megatron["""model.language_model.embedding.word_embeddings.weight"""][:vocab_size, :]
	_lowerCAmelCase								={
	    """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								="""model.language_model.encoder.layers."""
	for i in range(__UpperCamelCase       ):
		_lowerCAmelCase								=torch.tril(torch.ones((n_positions, n_positions)    , dtype=torch.bool       )       )
		_lowerCAmelCase								=causal_mask.view(1    , 1    , __UpperCamelCase    , __UpperCamelCase       )
		_lowerCAmelCase								=causal_mask
		_lowerCAmelCase								=torch.tensor(-1E4    , dtype=torch.bfloataa       )

		_lowerCAmelCase								=sd_megatron[F'''{pf}{i}.input_layernorm.weight''']
		_lowerCAmelCase								=sd_megatron[F'''{pf}{i}.input_layernorm.bias''']

		_lowerCAmelCase								=sd_megatron[F'''{pf}{i}.self_attention.query_key_value.weight''']
		_lowerCAmelCase								=fix_query_key_value_ordering(__UpperCamelCase    , 3    , __UpperCamelCase    , __UpperCamelCase       )
		_lowerCAmelCase								=vala.transpose(0    , 1       ).contiguous()

		_lowerCAmelCase								=sd_megatron[F'''{pf}{i}.self_attention.query_key_value.bias''']
		_lowerCAmelCase								=fix_query_key_value_ordering(__UpperCamelCase    , 3    , __UpperCamelCase    , __UpperCamelCase       )
		_lowerCAmelCase								=vala

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

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

	return sd_hf



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase       )				-> int:
	_lowerCAmelCase								=64000
	_lowerCAmelCase								=config_megatron["""encoder_seq_length"""]
	_lowerCAmelCase								=config_megatron["""hidden_size"""]
	_lowerCAmelCase								=config_megatron["""num_layers"""]
	_lowerCAmelCase								=config_megatron["""num_attention_heads"""]
	_lowerCAmelCase								=config_megatron["""ffn_hidden_size"""]
	_lowerCAmelCase								="""gelu"""
	_lowerCAmelCase								=0.1
	_lowerCAmelCase								=0.1
	_lowerCAmelCase								=0.1
	_lowerCAmelCase								=config_megatron["""layernorm_epsilon"""]  # 1e-5
	_lowerCAmelCase								=config_megatron["""init_method_std"""]  # 0.02
	_lowerCAmelCase								=config_megatron["""apply_query_key_layer_scaling"""]  # True
	_lowerCAmelCase								=True
	_lowerCAmelCase								=True

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

	return config_hf



def     _lowerCamelCase(__UpperCamelCase       )				-> Optional[int]:
	print(__UpperCamelCase       )

	_lowerCAmelCase								=args.checkpoint_path
	_lowerCAmelCase								=args.save_path
	if isfile(__UpperCamelCase       ):
		raise FileNotFoundError(F'''ERROR! could not find file {checkpoint_path}'''       )

	# Load the model.
	_lowerCAmelCase								=torch.load(__UpperCamelCase    , map_location="""cpu"""       )

	# Load the config.
	_lowerCAmelCase								=checkpoint["""hyper_parameters"""]["""cfg"""]
	_lowerCAmelCase								=GPTaConfig()
	_lowerCAmelCase								=copy_config(config_hf=__UpperCamelCase    , config_megatron=__UpperCamelCase       )
	_lowerCAmelCase								=["""GPT2LMHeadModel"""]

	_lowerCAmelCase								=checkpoint["""state_dict"""]

	# Convert.
	print("""Converting"""       )
	_lowerCAmelCase								=convert_megatron_checkpoint(__UpperCamelCase    , __UpperCamelCase       )

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

	_lowerCAmelCase								="""GPTSw3Tokenizer"""

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

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


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





"""simple docstring"""


def     _lowerCamelCase(__UpperCamelCase       )				-> list:
	_lowerCAmelCase								=[True for i in range(n + 1       )]
	_lowerCAmelCase								=[i - 1 for i in range(n + 1       )]
	_lowerCAmelCase								=[]
	for i in range(2    , n + 1       ):
		if is_prime[i]:
			primes.append(__UpperCamelCase       )
		for j in range(0    , len(__UpperCamelCase       )       ):
			if i * primes[j] >= n:
				break
			_lowerCAmelCase								=False

			if i % primes[j] == 0:
				_lowerCAmelCase								=totients[i] * primes[j]
				break

			_lowerCAmelCase								=totients[i] * (primes[j] - 1)

	return totients


if __name__ == "__main__":
			import doctest

			doctest.testmod()



