


import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig
def        __lowerCamelCase		(				UpperCAmelCase_		:	Dict				,						UpperCAmelCase_		:	Union[str, Any]				,						UpperCAmelCase_		:	List[Any]=0		):




      """simple docstring"""





      if name is None:
            a		:Optional[int]											= None
      else:
            a		:int											= '''.''' * max(0				,						spaces - 2		) + '''# {:''' + str(50 - spaces		) + '''s}'''
            a		: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        __lowerCamelCase		(				UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	List[str]				,						UpperCAmelCase_		:	Optional[Any]				,						UpperCAmelCase_		:	List[Any]		):




      """simple docstring"""





      a		:Union[str, Any]											= param.size()
      # other versions store [num_heads * num_splits * hidden_size, :]
      a		:Optional[int]											= (num_heads, num_splits, hidden_size) + input_shape[1:]
      a		:Dict											= param.view(*UpperCAmelCase_		)
      a		:List[str]											= param.transpose(0				,						1		).contiguous()
      a		:Optional[Any]											= param.view(*UpperCAmelCase_		)
      return param
def        __lowerCamelCase		(				UpperCAmelCase_		:	Optional[int]				,						UpperCAmelCase_		:	Dict		):




      """simple docstring"""





      a		:Optional[int]											= config.n_positions
      a		:str											= config.n_layer
      a		:List[str]											= config.vocab_size
      a		:Optional[int]											= config.n_head
      a		:Optional[Any]											= config.n_embd // config.n_head

      a		:str											= sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
      a		:Optional[int]											= {
          '''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		:Dict											= '''model.language_model.encoder.layers.'''
      for i in range(UpperCAmelCase_		):
            a		:Dict											= torch.tril(torch.ones((n_positions, n_positions)				,						dtype=torch.bool		)		)
            a		:List[Any]											= causal_mask.view(1				,						1				,						UpperCAmelCase_				,						UpperCAmelCase_		)
            a		:List[Any]											= causal_mask
            a		:str											= torch.tensor(-1E4				,						dtype=torch.bfloataa		)

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

            a		:Any											= sd_megatron[F'''{pf}{i}.self_attention.query_key_value.weight''']
            a		:Optional[int]											= fix_query_key_value_ordering(UpperCAmelCase_				,						3				,						UpperCAmelCase_				,						UpperCAmelCase_		)
            a		:Any											= vala.transpose(0				,						1		).contiguous()

            a		:Any											= sd_megatron[F'''{pf}{i}.self_attention.query_key_value.bias''']
            a		:Optional[Any]											= fix_query_key_value_ordering(UpperCAmelCase_				,						3				,						UpperCAmelCase_				,						UpperCAmelCase_		)
            a		:Union[str, Any]											= vala

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

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

      return sd_hf
def        __lowerCamelCase		(				UpperCAmelCase_		:	int				,						UpperCAmelCase_		:	Tuple		):




      """simple docstring"""





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

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

      return config_hf
def        __lowerCamelCase		(				UpperCAmelCase_		:	Any		):




      """simple docstring"""





      print(UpperCAmelCase_		)

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

      # Load the model.
      a		:Any											= torch.load(UpperCAmelCase_				,						map_location='''cpu'''		)

      # Load the config.
      a		:List[Any]											= checkpoint['''hyper_parameters''']['''cfg''']
      a		:Optional[Any]											= GPTaConfig()
      a		:int											= copy_config(config_hf=UpperCAmelCase_				,						config_megatron=UpperCAmelCase_		)
      a		:Any											= ['''GPT2LMHeadModel''']

      a		:Dict											= checkpoint['''state_dict''']

      # Convert.
      print('''Converting'''		)
      a		:Dict											= convert_megatron_checkpoint(UpperCAmelCase_				,						UpperCAmelCase_		)

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

      a		:Any											= '''GPTSw3Tokenizer'''

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

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


if __name__ == "__main__":
     snake_case       :					Tuple        =  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       :					Tuple        =  parser.parse_args()
     main(_args)









import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


snake_case       :					Tuple        =  logging.get_logger(__name__)







class 	_snake_case						(   _snake_case							):




 def __init__(	self      ,						*_lowerCamelCase      ,						**_lowerCamelCase					):
       warnings.warn(
           '''The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
           ''' Please use ConvNextImageProcessor instead.'''      ,						_lowerCamelCase      ,						)
       super().__init__(*_lowerCamelCase      ,						**_lowerCamelCase					)





