





"""simple docstring"""




import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=0   ):






  """simple docstring"""





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






  """simple docstring"""





  A_    :			Tuple									=    param.size()
  # other versions store [num_heads * num_splits * hidden_size, :]
  A_    :			Optional[Any]									=    (num_heads, num_splits, hidden_size) + input_shape[1:]
  A_    :			int									=    param.view(*_UpperCAmelCase   )
  A_    :			Optional[int]									=    param.transpose(0					,   1   ).contiguous()
  A_    :			int									=    param.view(*_UpperCAmelCase   )
  return param





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			List[str]									=    config.n_positions
  A_    :			Any									=    config.n_layer
  A_    :			str									=    config.vocab_size
  A_    :			int									=    config.n_head
  A_    :			Union[str, Any]									=    config.n_embd // config.n_head

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

    A_    :			Optional[Any]									=    sd_megatron[f"""{pf}{i}.input_layernorm.weight"""]
    A_    :			Optional[int]									=    sd_megatron[f"""{pf}{i}.input_layernorm.bias"""]

    A_    :			str									=    sd_megatron[f"""{pf}{i}.self_attention.query_key_value.weight"""]
    A_    :			Optional[int]									=    fix_query_key_value_ordering(_UpperCAmelCase					,   3					,   _UpperCAmelCase					,   _UpperCAmelCase   )
    A_    :			int									=    vala.transpose(0					,   1   ).contiguous()

    A_    :			int									=    sd_megatron[f"""{pf}{i}.self_attention.query_key_value.bias"""]
    A_    :			List[Any]									=    fix_query_key_value_ordering(_UpperCAmelCase					,   3					,   _UpperCAmelCase					,   _UpperCAmelCase   )
    A_    :			str									=    vala

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

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

  return sd_hf





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Union[str, Any]									=    64000
  A_    :			List[Any]									=    config_megatron['encoder_seq_length']
  A_    :			List[str]									=    config_megatron['hidden_size']
  A_    :			List[str]									=    config_megatron['num_layers']
  A_    :			List[Any]									=    config_megatron['num_attention_heads']
  A_    :			List[Any]									=    config_megatron['ffn_hidden_size']
  A_    :			Optional[int]									=    'gelu'
  A_    :			List[Any]									=    0.1
  A_    :			List[Any]									=    0.1
  A_    :			Tuple									=    0.1
  A_    :			int									=    config_megatron['layernorm_epsilon']  # 1e-5
  A_    :			int									=    config_megatron['init_method_std']  # 0.02
  A_    :			List[str]									=    config_megatron['apply_query_key_layer_scaling']  # True
  A_    :			int									=    True
  A_    :			Union[str, Any]									=    True

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

  return config_hf





def 		UpperCAmelCase__						(	_UpperCAmelCase   ):






  """simple docstring"""





  print(_UpperCAmelCase   )

  A_    :			List[str]									=    args.checkpoint_path
  A_    :			Union[str, Any]									=    args.save_path
  if isfile(_UpperCAmelCase   ):
    raise FileNotFoundError(f"""ERROR! could not find file {checkpoint_path}"""   )

  # Load the model.
  A_    :			List[Any]									=    torch.load(_UpperCAmelCase					,   map_location='cpu'   )

  # Load the config.
  A_    :			Any									=    checkpoint['hyper_parameters']['cfg']
  A_    :			Union[str, Any]									=    GPTaConfig()
  A_    :			Union[str, Any]									=    copy_config(config_hf=_UpperCAmelCase					,   config_megatron=_UpperCAmelCase   )
  A_    :			Optional[Any]									=    ['GPT2LMHeadModel']

  A_    :			Tuple									=    checkpoint['state_dict']

  # Convert.
  print('Converting'   )
  A_    :			int									=    convert_megatron_checkpoint(_UpperCAmelCase					,   _UpperCAmelCase   )

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

  A_    :			Optional[int]									=    'GPTSw3Tokenizer'

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

  # Store the state_dict to file.
  A_    :			Optional[Any]									=    os.path.join(_UpperCAmelCase					,   'pytorch_model.bin'   )
  print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""   )
  torch.save(_UpperCAmelCase					,   _UpperCAmelCase   )


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







"""simple docstring"""





from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like






def 							A(    snake_case_		,  snake_case_=3						):
       """simple docstring"""





       lowercase__:							Tuple         =     int(mask / 2						)
       # copy image size
       lowercase__:							str         =     zeros_like(snake_case_						)
       for i in range(snake_case_		,  gray_img.shape[0] - bd						):
              for j in range(snake_case_		,  gray_img.shape[1] - bd						):
                     # get mask according with mask
                     lowercase__:							Optional[int]         =     ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]						)
                     # calculate mask median
                     lowercase__:							Union[str, Any]         =     sort(snake_case_						)[inta(divide((multiply(snake_case_		,  snake_case_						))		,  2						) + 1						)]
                     lowercase__:							Optional[Any]         =     median
       return median_img


if __name__ == "__main__":
  # read original image
  UpperCamelCase					=     imread("""../image_data/lena.jpg""")
  # turn image in gray scale value
  UpperCamelCase					=     cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  UpperCamelCase					=     median_filter(gray, 3)
  UpperCamelCase					=     median_filter(gray, 5)

  # show result images
  imshow("""median filter with 3x3 mask""", medianaxa)
  imshow("""median filter with 5x5 mask""", medianaxa)
  waitKey(0)






