





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig



def _a							(      lowerCamelCase:  Optional[int]   ,							lowerCamelCase:  Optional[Any]   ,							lowerCamelCase:  Optional[int]=0					)      ->		Dict:







      '''simple docstring'''






      if name is None:
            __A			  =							None
      else:
            __A			  =							'''.''' * max(0   ,							spaces - 2					) + '''# {:''' + str(50 - spaces					) + '''s}'''
            __A			  =							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:  Optional[Any]   ,							lowerCamelCase:  Dict   ,							lowerCamelCase:  str   ,							lowerCamelCase:  List[str]					)      ->		Dict:







      '''simple docstring'''






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



def _a							(      lowerCamelCase:  List[str]   ,							lowerCamelCase:  Optional[int]					)      ->		List[str]:







      '''simple docstring'''






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

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

            __A			  =							sd_megatron[F"""{pf}{i}.input_layernorm.weight"""]
            __A			  =							sd_megatron[F"""{pf}{i}.input_layernorm.bias"""]

            __A			  =							sd_megatron[F"""{pf}{i}.self_attention.query_key_value.weight"""]
            __A			  =							fix_query_key_value_ordering(lowerCamelCase   ,							3   ,							lowerCamelCase   ,							lowerCamelCase					)
            __A			  =							vala.transpose(0   ,							1					).contiguous()

            __A			  =							sd_megatron[F"""{pf}{i}.self_attention.query_key_value.bias"""]
            __A			  =							fix_query_key_value_ordering(lowerCamelCase   ,							3   ,							lowerCamelCase   ,							lowerCamelCase					)
            __A			  =							vala

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

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

      return sd_hf



def _a							(      lowerCamelCase:  List[str]   ,							lowerCamelCase:  List[str]					)      ->		str:







      '''simple docstring'''






      __A			  =							6_40_00
      __A			  =							config_megatron['''encoder_seq_length''']
      __A			  =							config_megatron['''hidden_size''']
      __A			  =							config_megatron['''num_layers''']
      __A			  =							config_megatron['''num_attention_heads''']
      __A			  =							config_megatron['''ffn_hidden_size''']
      __A			  =							'''gelu'''
      __A			  =							0.1
      __A			  =							0.1
      __A			  =							0.1
      __A			  =							config_megatron['''layernorm_epsilon''']  # 1e-5
      __A			  =							config_megatron['''init_method_std''']  # 0.02
      __A			  =							config_megatron['''apply_query_key_layer_scaling''']  # True
      __A			  =							True
      __A			  =							True

      # This identifies the 6.7B (7B) model which uses a different tokenizer
      if config_megatron["hidden_size"] == 40_96:
            __A			  =							1  # <|endoftext|>
            __A			  =							1  # <|endoftext|>
            __A			  =							0  # <unk>
      else:
            __A			  =							2  # <s>
            __A			  =							3  # <|endoftext|>
            __A			  =							0  # <pad>

      return config_hf



def _a							(      lowerCamelCase:  Dict					)      ->		Tuple:







      '''simple docstring'''






      print(lowerCamelCase					)

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

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

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

      __A			  =							checkpoint['''state_dict''']

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

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

      __A			  =							'''GPTSw3Tokenizer'''

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

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


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












def _a							(      lowerCamelCase:  list					)      ->		list:







      '''simple docstring'''






      __A			  =							len(lowerCamelCase					)
      for cycle_start in range(0   ,							array_len - 1					):
            __A			  =							array[cycle_start]

            __A			  =							cycle_start
            for i in range(cycle_start + 1   ,							lowerCamelCase					):
                  if array[i] < item:
                        pos += 1

            if pos == cycle_start:
                  continue

            while item == array[pos]:
                  pos += 1

            __A  ,		__A			  =							item, array[pos]
            while pos != cycle_start:
                  __A			  =							cycle_start
                  for i in range(cycle_start + 1   ,							lowerCamelCase					):
                        if array[i] < item:
                              pos += 1

                  while item == array[pos]:
                        pos += 1

                  __A  ,		__A			  =							item, array[pos]

      return array


if __name__ == "__main__":
       assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
       assert cycle_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]





