code: 
  
  import argparse
  import os
  from os.path import isfile
  
  import torch
  
  from transformers import GPTaConfig
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase=0				)					-> Dict:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      if name is None:
          lowerCAmelCase				:			Tuple    			=      None
      else:
          lowerCAmelCase				:			Any    			=      '.' * max(0,      spaces - 2				) + '# {:' + str(50 - spaces				) + 's}'
          lowerCAmelCase				:			Union[str, Any]    			=      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 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> List[Any]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			List[str]    			=      param.size()
      # other versions store [num_heads * num_splits * hidden_size, :]
      lowerCAmelCase				:			Tuple    			=      (num_heads, num_splits, hidden_size) + input_shape[1:]
      lowerCAmelCase				:			List[str]    			=      param.view(*_UpperCAmelCase				)
      lowerCAmelCase				:			Union[str, Any]    			=      param.transpose(0,      1				).contiguous()
      lowerCAmelCase				:			List[str]    			=      param.view(*_UpperCAmelCase				)
      return param
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> str:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			int    			=      config.n_positions
      lowerCAmelCase				:			str    			=      config.n_layer
      lowerCAmelCase				:			Union[str, Any]    			=      config.vocab_size
      lowerCAmelCase				:			List[Any]    			=      config.n_head
      lowerCAmelCase				:			int    			=      config.n_embd // config.n_head
  
      lowerCAmelCase				:			str    			=      sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
      lowerCAmelCase				:			List[Any]    			=      {
          '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				:			Optional[Any]    			=      'model.language_model.encoder.layers.'
      for i in range(_UpperCAmelCase				):
          lowerCAmelCase				:			str    			=      torch.tril(torch.ones((n_positions, n_positions),      dtype=torch.bool				)				)
          lowerCAmelCase				:			Optional[int]    			=      causal_mask.view(1,      1,      _UpperCAmelCase,      _UpperCAmelCase				)
          lowerCAmelCase				:			Dict    			=      causal_mask
          lowerCAmelCase				:			str    			=      torch.tensor(-1e4,      dtype=torch.bfloataa				)
  
          lowerCAmelCase				:			List[str]    			=      sd_megatron[f"{pf}{i}.input_layernorm.weight"]
          lowerCAmelCase				:			Optional[Any]    			=      sd_megatron[f"{pf}{i}.input_layernorm.bias"]
  
          lowerCAmelCase				:			Any    			=      sd_megatron[f"{pf}{i}.self_attention.query_key_value.weight"]
          lowerCAmelCase				:			Dict    			=      fix_query_key_value_ordering(_UpperCAmelCase,      3,      _UpperCAmelCase,      _UpperCAmelCase				)
          lowerCAmelCase				:			List[str]    			=      vala.transpose(0,      1				).contiguous()
  
          lowerCAmelCase				:			Union[str, Any]    			=      sd_megatron[f"{pf}{i}.self_attention.query_key_value.bias"]
          lowerCAmelCase				:			Tuple    			=      fix_query_key_value_ordering(_UpperCAmelCase,      3,      _UpperCAmelCase,      _UpperCAmelCase				)
          lowerCAmelCase				:			Union[str, Any]    			=      vala
  
          lowerCAmelCase				:			Optional[int]    			=      sd_megatron[f"{pf}{i}.self_attention.dense.weight"].transpose(
              0,      1				)
          lowerCAmelCase				:			Union[str, Any]    			=      sd_megatron[f"{pf}{i}.self_attention.dense.bias"]
          lowerCAmelCase				:			Dict    			=      sd_megatron[f"{pf}{i}.post_attention_layernorm.weight"]
          lowerCAmelCase				:			List[str]    			=      sd_megatron[f"{pf}{i}.post_attention_layernorm.bias"]
          lowerCAmelCase				:			Union[str, Any]    			=      sd_megatron[f"{pf}{i}.mlp.dense_h_to_4h.weight"].transpose(0,      1				)
          lowerCAmelCase				:			Optional[Any]    			=      sd_megatron[f"{pf}{i}.mlp.dense_h_to_4h.bias"]
          lowerCAmelCase				:			Any    			=      sd_megatron[f"{pf}{i}.mlp.dense_4h_to_h.weight"].transpose(
              0,      1				)
          lowerCAmelCase				:			Tuple    			=      sd_megatron[f"{pf}{i}.mlp.dense_4h_to_h.bias"]
  
      # For LM head, transformers' wants the matrix to weight embeddings.
      lowerCAmelCase				:			List[Any]    			=      word_embeddings
  
      return sd_hf
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> Optional[int]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			List[Any]    			=      64_000
      lowerCAmelCase				:			List[str]    			=      config_megatron['encoder_seq_length']
      lowerCAmelCase				:			Any    			=      config_megatron['hidden_size']
      lowerCAmelCase				:			Dict    			=      config_megatron['num_layers']
      lowerCAmelCase				:			Union[str, Any]    			=      config_megatron['num_attention_heads']
      lowerCAmelCase				:			Union[str, Any]    			=      config_megatron['ffn_hidden_size']
      lowerCAmelCase				:			Optional[int]    			=      'gelu'
      lowerCAmelCase				:			str    			=      0.1
      lowerCAmelCase				:			Optional[Any]    			=      0.1
      lowerCAmelCase				:			Union[str, Any]    			=      0.1
      lowerCAmelCase				:			Optional[int]    			=      config_megatron['layernorm_epsilon']  # 1e-5
      lowerCAmelCase				:			List[Any]    			=      config_megatron['init_method_std']  # 0.02
      lowerCAmelCase				:			Optional[Any]    			=      config_megatron['apply_query_key_layer_scaling']  # True
      lowerCAmelCase				:			Dict    			=      True
      lowerCAmelCase				:			Union[str, Any]    			=      True
  
      # This identifies the 6.7B (7B) model which uses a different tokenizer
      if config_megatron["hidden_size"] == 4_096:
          lowerCAmelCase				:			int    			=      1  # <|endoftext|>
          lowerCAmelCase				:			str    			=      1  # <|endoftext|>
          lowerCAmelCase				:			str    			=      0  # <unk>
      else:
          lowerCAmelCase				:			Any    			=      2  # <s>
          lowerCAmelCase				:			int    			=      3  # <|endoftext|>
          lowerCAmelCase				:			int    			=      0  # <pad>
  
      return config_hf
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> Optional[int]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      print(_UpperCAmelCase				)
  
      lowerCAmelCase				:			Tuple    			=      args.checkpoint_path
      lowerCAmelCase				:			Any    			=      args.save_path
      if isfile(_UpperCAmelCase				):
          raise FileNotFoundError(f"ERROR! could not find file {checkpoint_path}"				)
  
      # Load the model.
      lowerCAmelCase				:			List[Any]    			=      torch.load(_UpperCAmelCase,      map_location='cpu'				)
  
      # Load the config.
      lowerCAmelCase				:			List[Any]    			=      checkpoint['hyper_parameters']['cfg']
      lowerCAmelCase				:			Optional[int]    			=      GPTaConfig()
      lowerCAmelCase				:			Optional[int]    			=      copy_config(config_hf=_UpperCAmelCase,      config_megatron=_UpperCAmelCase				)
      lowerCAmelCase				:			List[str]    			=      ['GPT2LMHeadModel']
  
      lowerCAmelCase				:			Tuple    			=      checkpoint['state_dict']
  
      # Convert.
      print('Converting'				)
      lowerCAmelCase				:			str    			=      convert_megatron_checkpoint(_UpperCAmelCase,      _UpperCAmelCase				)
  
      # Print the structure of converted state dict.
      if args.print_checkpoint_structure:
          recursive_print(_UpperCAmelCase,      _UpperCAmelCase				)
  
      lowerCAmelCase				:			str    			=      'GPTSw3Tokenizer'
  
      # Store the config to file.
      print('Saving config'				)
      config_hf.save_pretrained(_UpperCAmelCase				)
  
      # Store the state_dict to file.
      lowerCAmelCase				:			Dict    			=      os.path.join(_UpperCAmelCase,      'pytorch_model.bin'				)
      print(f"Saving checkpoint to \"{output_checkpoint_file}\""				)
      torch.save(_UpperCAmelCase,      _UpperCAmelCase				)
  
  
  if __name__ == "__main__":
   __A   :     int      =			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   :     Union[str, Any]      =			parser.parse_args()
   main(_args)
  
  
  
  
  
  
style_context: 
  
  from __future__ import annotations
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> set[int]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			List[str]    			=      {1}
      lowerCAmelCase				:			int    			=      [1]
      for _ in range(2,      depth + 1				):
          lowerCAmelCase				:			str    			=      [*previous_coefficients, 0]
          lowerCAmelCase				:			Optional[int]    			=      [0, *previous_coefficients]
          lowerCAmelCase				:			Tuple    			=      []
          for x, y in zip(_UpperCAmelCase,      _UpperCAmelCase				):
              coefficients.add(x + y				)
              previous_coefficients.append(x + y				)
      return coefficients
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> set[int]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			str    			=      set()
      for number in unique_coefficients:
          lowerCAmelCase				:			Dict    			=      2
          lowerCAmelCase				:			Tuple    			=      number
          while divisor**2 <= copy_number:
              lowerCAmelCase				:			List[str]    			=      0
              while copy_number % divisor == 0:
                  copy_number //= divisor
                  multiplicity += 1
              if multiplicity >= 2:
                  non_squarefrees.add(_UpperCAmelCase				)
                  break
              divisor += 1
  
      return unique_coefficients.difference(_UpperCAmelCase				)
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase = 51				)					-> int:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			int    			=      get_pascal_triangle_unique_coefficients(_UpperCAmelCase				)
      lowerCAmelCase				:			Optional[int]    			=      get_squarefrees(_UpperCAmelCase				)
      return sum(_UpperCAmelCase				)
  
  
  if __name__ == "__main__":
   print(F'{solution() = }')
  
  
  
  
  
  