



'''simple docstring'''







import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE=0     ):



		if name is None:
				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      None
		else:
				SCREAMING_SNAKE_CASE_						:Tuple										=      '.' * max(0 ,		spaces - 2     ) + '# {:' + str(50 - spaces     ) + 's}'
				SCREAMING_SNAKE_CASE_						:List[Any]										=      fmt.format(_lowercase     )

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







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      param.size()
		# other versions store [num_heads * num_splits * hidden_size, :]
		SCREAMING_SNAKE_CASE_						:Tuple										=      (num_heads, num_splits, hidden_size) + input_shape[1:]
		SCREAMING_SNAKE_CASE_						:Dict										=      param.view(*_lowercase     )
		SCREAMING_SNAKE_CASE_						:Any										=      param.transpose(0 ,		1     ).contiguous()
		SCREAMING_SNAKE_CASE_						:str										=      param.view(*_lowercase     )
		return param







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Any										=      config.n_positions
		SCREAMING_SNAKE_CASE_						:Dict										=      config.n_layer
		SCREAMING_SNAKE_CASE_						:int										=      config.vocab_size
		SCREAMING_SNAKE_CASE_						:Any										=      config.n_head
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      config.n_embd // config.n_head

		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
		SCREAMING_SNAKE_CASE_						:Union[str, 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'],
		}

		SCREAMING_SNAKE_CASE_						:Optional[int]										=      'model.language_model.encoder.layers.'
		for i in range(_lowercase     ):
				SCREAMING_SNAKE_CASE_						:int										=      torch.tril(torch.ones((n_positions, n_positions) ,		dtype=torch.bool     )     )
				SCREAMING_SNAKE_CASE_						:List[str]										=      causal_mask.view(1 ,		1 ,		_lowercase ,		_lowercase     )
				SCREAMING_SNAKE_CASE_						:Tuple										=      causal_mask
				SCREAMING_SNAKE_CASE_						:int										=      torch.tensor(-1E4 ,		dtype=torch.bfloataa     )

				SCREAMING_SNAKE_CASE_						:Optional[int]										=      sd_megatron[F'{pf}{i}.input_layernorm.weight']
				SCREAMING_SNAKE_CASE_						:Any										=      sd_megatron[F'{pf}{i}.input_layernorm.bias']

				SCREAMING_SNAKE_CASE_						:List[str]										=      sd_megatron[F'{pf}{i}.self_attention.query_key_value.weight']
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      fix_query_key_value_ordering(_lowercase ,		3 ,		_lowercase ,		_lowercase     )
				SCREAMING_SNAKE_CASE_						:str										=      vala.transpose(0 ,		1     ).contiguous()

				SCREAMING_SNAKE_CASE_						:str										=      sd_megatron[F'{pf}{i}.self_attention.query_key_value.bias']
				SCREAMING_SNAKE_CASE_						:str										=      fix_query_key_value_ordering(_lowercase ,		3 ,		_lowercase ,		_lowercase     )
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      vala

				SCREAMING_SNAKE_CASE_						:List[str]										=      sd_megatron[F'{pf}{i}.self_attention.dense.weight'].transpose(
				    0 ,		1     )
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      sd_megatron[F'{pf}{i}.self_attention.dense.bias']
				SCREAMING_SNAKE_CASE_						:Dict										=      sd_megatron[F'{pf}{i}.post_attention_layernorm.weight']
				SCREAMING_SNAKE_CASE_						:Any										=      sd_megatron[F'{pf}{i}.post_attention_layernorm.bias']
				SCREAMING_SNAKE_CASE_						:List[Any]										=      sd_megatron[F'{pf}{i}.mlp.dense_h_to_4h.weight'].transpose(0 ,		1     )
				SCREAMING_SNAKE_CASE_						:Tuple										=      sd_megatron[F'{pf}{i}.mlp.dense_h_to_4h.bias']
				SCREAMING_SNAKE_CASE_						:int										=      sd_megatron[F'{pf}{i}.mlp.dense_4h_to_h.weight'].transpose(
				    0 ,		1     )
				SCREAMING_SNAKE_CASE_						:Tuple										=      sd_megatron[F'{pf}{i}.mlp.dense_4h_to_h.bias']

		# For LM head, transformers' wants the matrix to weight embeddings.
		SCREAMING_SNAKE_CASE_						:List[Any]										=      word_embeddings

		return sd_hf







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:List[str]										=      6_4000
		SCREAMING_SNAKE_CASE_						:Tuple										=      config_megatron['encoder_seq_length']
		SCREAMING_SNAKE_CASE_						:List[str]										=      config_megatron['hidden_size']
		SCREAMING_SNAKE_CASE_						:int										=      config_megatron['num_layers']
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      config_megatron['num_attention_heads']
		SCREAMING_SNAKE_CASE_						:Dict										=      config_megatron['ffn_hidden_size']
		SCREAMING_SNAKE_CASE_						:List[Any]										=      'gelu'
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      0.1
		SCREAMING_SNAKE_CASE_						:int										=      0.1
		SCREAMING_SNAKE_CASE_						:Any										=      0.1
		SCREAMING_SNAKE_CASE_						:List[Any]										=      config_megatron['layernorm_epsilon']  # 1e-5
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      config_megatron['init_method_std']  # 0.02
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      config_megatron['apply_query_key_layer_scaling']  # True
		SCREAMING_SNAKE_CASE_						:str										=      True
		SCREAMING_SNAKE_CASE_						:Any										=      True

		# This identifies the 6.7B (7B) model which uses a different tokenizer
		if config_megatron["hidden_size"] == 4096:
				SCREAMING_SNAKE_CASE_						:Tuple										=      1  # <|endoftext|>
				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      1  # <|endoftext|>
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      0  # <unk>
		else:
				SCREAMING_SNAKE_CASE_						:List[str]										=      2  # <s>
				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      3  # <|endoftext|>
				SCREAMING_SNAKE_CASE_						:Any										=      0  # <pad>

		return config_hf







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		print(_lowercase     )

		SCREAMING_SNAKE_CASE_						:Dict										=      args.checkpoint_path
		SCREAMING_SNAKE_CASE_						:List[str]										=      args.save_path
		if isfile(_lowercase     ):
				raise FileNotFoundError(F'ERROR! could not find file {checkpoint_path}'     )

		# Load the model.
		SCREAMING_SNAKE_CASE_						:int										=      torch.load(_lowercase ,		map_location='cpu'     )

		# Load the config.
		SCREAMING_SNAKE_CASE_						:Dict										=      checkpoint['hyper_parameters']['cfg']
		SCREAMING_SNAKE_CASE_						:Dict										=      GPTaConfig()
		SCREAMING_SNAKE_CASE_						:Tuple										=      copy_config(config_hf=_lowercase ,		config_megatron=_lowercase     )
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      ['GPT2LMHeadModel']

		SCREAMING_SNAKE_CASE_						:Tuple										=      checkpoint['state_dict']

		# Convert.
		print('Converting'     )
		SCREAMING_SNAKE_CASE_						:str										=      convert_megatron_checkpoint(_lowercase ,		_lowercase     )

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

		SCREAMING_SNAKE_CASE_						:Optional[int]										=      'GPTSw3Tokenizer'

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

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__     : List[Any]				=   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""")
							SCREAMING_SNAKE_CASE__     : Optional[Any]				=   parser.parse_args()
							main(_args)








import math
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						math.log(2,   10   ) * number
    _lowercase     			=						round((log_number - int(_lowercase   )),   15   )

    return difference
def 							UpperCAmelCase   ( _lowercase = 678910   ):


    '''simple docstring'''



    _lowercase     			=						90
    _lowercase     			=						0

    _lowercase     			=						math.log(1.23,   10   )
    _lowercase     			=						math.log(1.24,   10   )
    _lowercase     			=						0

    while position < number:
        _lowercase     			=						log_difference(_lowercase   )

        if difference >= upper_limit:
            power_iterator += 93

        elif difference < lower_limit:
            power_iterator += 196

        else:
            _lowercase     			=						power_iterator
            power_iterator += 196
            position += 1

    return previous_power


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(f"{solution() = }")
