


"""simple docstring"""







import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase=0			):





						"""simple docstring"""

						if name is None:
												UpperCAmelCase__												=     None
						else:
												UpperCAmelCase__												=     '''.''' * max(0						,      spaces - 2			) + '''# {:''' + str(50 - spaces			) + '''s}'''
												UpperCAmelCase__												=     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        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase			):





						"""simple docstring"""

						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(*_UpperCamelCase			)
						UpperCAmelCase__												=     param.transpose(0						,      1			).contiguous()
						UpperCAmelCase__												=     param.view(*_UpperCamelCase			)
						return param






def        a__			(							_UpperCamelCase						,      _UpperCamelCase			):





						"""simple docstring"""

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

												UpperCAmelCase__												=     sd_megatron[F"""{pf}{i}.self_attention.query_key_value.bias"""]
												UpperCAmelCase__												=     fix_query_key_value_ordering(_UpperCamelCase						,      3						,      _UpperCamelCase						,      _UpperCamelCase			)
												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__			(							_UpperCamelCase						,      _UpperCamelCase			):





						"""simple docstring"""

						UpperCAmelCase__												=     64000
						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        a__			(							_UpperCamelCase			):





						"""simple docstring"""

						print(_UpperCamelCase			)

						UpperCAmelCase__												=     args.checkpoint_path
						UpperCAmelCase__												=     args.save_path
						if isfile(_UpperCamelCase			):
												raise FileNotFoundError(F"""ERROR! could not find file {checkpoint_path}"""			)

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

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

						UpperCAmelCase__												=     checkpoint['''state_dict''']

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

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

						UpperCAmelCase__												=     '''GPTSw3Tokenizer'''

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

						# Store the state_dict to file.
						UpperCAmelCase__												=     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"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


__a      			= {"configuration_bert_generation": ["BertGenerationConfig"]}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= ["BertGenerationTokenizer"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= [
						    "BertGenerationDecoder",
						    "BertGenerationEncoder",
						    "BertGenerationPreTrainedModel",
						    "load_tf_weights_in_bert_generation",
						]


if TYPE_CHECKING:
						from .configuration_bert_generation import BertGenerationConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bert_generation import BertGenerationTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bert_generation import (
												    BertGenerationDecoder,
												    BertGenerationEncoder,
												    BertGenerationPreTrainedModel,
												    load_tf_weights_in_bert_generation,
												)

else:
						import sys

						__a      			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)