





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def   lowerCamelCase__   (   UpperCamelCase__	:				Tuple				,			UpperCamelCase__	:				Union[str, Any]				,			UpperCamelCase__	:				Tuple=0  )	->       Optional[Any]:



					'''simple docstring'''




					if name is None:
										_snake_case													= None
					else:
										_snake_case													= '.' * max(0				,			spaces - 2  ) + '# {:' + str(50 - spaces  ) + 's}'
										_snake_case													= 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   lowerCamelCase__   (   UpperCamelCase__	:				Tuple				,			UpperCamelCase__	:				List[Any]				,			UpperCamelCase__	:				Dict				,			UpperCamelCase__	:				List[str]  )	->       Union[str, Any]:



					'''simple docstring'''




					_snake_case													= param.size()
					# other versions store [num_heads * num_splits * hidden_size, :]
					_snake_case													= (num_heads, num_splits, hidden_size) + input_shape[1:]
					_snake_case													= param.view(*__lowerCAmelCase  )
					_snake_case													= param.transpose(0				,			1  ).contiguous()
					_snake_case													= param.view(*__lowerCAmelCase  )
					return param





def   lowerCamelCase__   (   UpperCamelCase__	:				List[Any]				,			UpperCamelCase__	:				List[str]  )	->       Optional[Any]:



					'''simple docstring'''




					_snake_case													= config.n_positions
					_snake_case													= config.n_layer
					_snake_case													= config.vocab_size
					_snake_case													= config.n_head
					_snake_case													= config.n_embd // config.n_head

					_snake_case													= sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
					_snake_case													= {
					    '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													= 'model.language_model.encoder.layers.'
					for i in range(__lowerCAmelCase  ):
										_snake_case													= torch.tril(torch.ones((n_positions, n_positions)				,			dtype=torch.bool  )  )
										_snake_case													= causal_mask.view(1				,			1				,			__lowerCAmelCase				,			__lowerCAmelCase  )
										_snake_case													= causal_mask
										_snake_case													= torch.tensor(-1e4				,			dtype=torch.bfloataa  )

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

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

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

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

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

					return sd_hf





def   lowerCamelCase__   (   UpperCamelCase__	:				Union[str, Any]				,			UpperCamelCase__	:				int  )	->       str:



					'''simple docstring'''




					_snake_case													= 64_000
					_snake_case													= config_megatron['encoder_seq_length']
					_snake_case													= config_megatron['hidden_size']
					_snake_case													= config_megatron['num_layers']
					_snake_case													= config_megatron['num_attention_heads']
					_snake_case													= config_megatron['ffn_hidden_size']
					_snake_case													= 'gelu'
					_snake_case													= 0.1
					_snake_case													= 0.1
					_snake_case													= 0.1
					_snake_case													= config_megatron['layernorm_epsilon']  # 1e-5
					_snake_case													= config_megatron['init_method_std']  # 0.02
					_snake_case													= config_megatron['apply_query_key_layer_scaling']  # True
					_snake_case													= True
					_snake_case													= True

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

					return config_hf





def   lowerCamelCase__   (   UpperCamelCase__	:				Dict  )	->       List[Any]:



					'''simple docstring'''




					print(__lowerCAmelCase  )

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

					# Load the model.
					_snake_case													= torch.load(__lowerCAmelCase				,			map_location='cpu'  )

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

					_snake_case													= checkpoint['state_dict']

					# Convert.
					print('Converting'  )
					_snake_case													= convert_megatron_checkpoint(__lowerCAmelCase				,			__lowerCAmelCase  )

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

					_snake_case													= 'GPTSw3Tokenizer'

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

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


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_ (					__lowerCAmelCase				)							->					str:




						'''simple docstring'''



						lowerCamelCase__					=1
						lowerCamelCase__					=0
						lowerCamelCase__					=1
						lowerCamelCase__					=1
						lowerCamelCase__					=3
						lowerCamelCase__					=3
						lowerCamelCase__					=limit
						lowerCamelCase__					=0

						lowerCamelCase__					=""
						while counter != decimal + 1:
												if 4 * q + r - t < n * t:
																		result += str(__lowerCAmelCase				)
																		if counter == 0:
																								result += "."

																		if decimal == counter:
																								break

																		counter += 1
																		lowerCamelCase__					=10 * (r - n * t)
																		lowerCamelCase__					=((10 * (3 * q + r)) // t) - 10 * n
																		q *= 10
																		lowerCamelCase__					=nr
												else:
																		lowerCamelCase__					=(2 * q + r) * l
																		lowerCamelCase__					=(q * (7 * k) + 2 + (r * l)) // (t * l)
																		q *= k
																		t *= l
																		l += 2
																		k += 1
																		lowerCamelCase__					=nn
																		lowerCamelCase__					=nr
						return result





def 			lowerCamelCase_ (					)							->					None:




						'''simple docstring'''



						print(F'''{calculate_pi(50				) = }'''				)
						import doctest

						doctest.testmod()


if __name__ == "__main__":
	main()


