"""simple docstring"""






import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig



def     a_		(   lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase=0			):
				# Format the message.
				if name is None:
								UpperCAmelCase__			 =				None
				else:
								UpperCAmelCase__			 =				'.' * max(0   ,     spaces - 2			) + '# {:' + str(5_0 - spaces			) + 's}'
								UpperCAmelCase__			 =				fmt.format(lowerCamelCase			)

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



def     a_		(   lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase			):
				# 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.
				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(*lowerCamelCase			)
				UpperCAmelCase__			 =				param.transpose(0   ,     1			).contiguous()
				UpperCAmelCase__			 =				param.view(*lowerCamelCase			)
				return param



def     a_		(   lowerCamelCase   ,     lowerCamelCase			):
				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(lowerCamelCase			):
								UpperCAmelCase__			 =				torch.tril(torch.ones((n_positions, n_positions)   ,     dtype=torch.bool			)			)
								UpperCAmelCase__			 =				causal_mask.view(1   ,     1   ,     lowerCamelCase   ,     lowerCamelCase			)
								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(lowerCamelCase   ,     3   ,     lowerCamelCase   ,     lowerCamelCase			)
								UpperCAmelCase__			 =				vala.transpose(0   ,     1			).contiguous()

								UpperCAmelCase__			 =				sd_megatron[f'''{pf}{i}.self_attention.query_key_value.bias''']
								UpperCAmelCase__			 =				fix_query_key_value_ordering(lowerCamelCase   ,     3   ,     lowerCamelCase   ,     lowerCamelCase			)
								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     a_		(   lowerCamelCase   ,     lowerCamelCase			):
				UpperCAmelCase__			 =				6_4_0_0_0
				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"] == 4_0_9_6:
								UpperCAmelCase__			 =				1  # <|endoftext|>
								UpperCAmelCase__			 =				1  # <|endoftext|>
								UpperCAmelCase__			 =				0  # <unk>
				else:
								UpperCAmelCase__			 =				2  # <s>
								UpperCAmelCase__			 =				3  # <|endoftext|>
								UpperCAmelCase__			 =				0  # <pad>

				return config_hf



def     a_		(   lowerCamelCase			):
				print(lowerCamelCase			)

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

				# Load the model.
				UpperCAmelCase__			 =				torch.load(lowerCamelCase   ,     map_location='cpu'			)

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

				UpperCAmelCase__			 =				checkpoint['state_dict']

				# Convert.
				print('Converting'			)
				UpperCAmelCase__			 =				convert_megatron_checkpoint(lowerCamelCase   ,     lowerCamelCase			)

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

				UpperCAmelCase__			 =				'GPTSw3Tokenizer'

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

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


if __name__ == "__main__":
						lowerCAmelCase__    :    Dict			=      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__    :    Any			=      parser.parse_args()
						main(_args)




"""simple docstring"""






def     a_		(   lowerCamelCase			):
				if num < 0:
								return -1
				return 1 if num else 0



def     a_		(   ):
				assert signum(5			) == 1
				assert signum(-5			) == -1
				assert signum(0			) == 0


if __name__ == "__main__":
						print(signum(12))
						print(signum(-12))
						print(signum(0))



