
"""simple docstring"""





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase=0    ):
   # Format the message.
   if name is None:
      UpperCAmelCase_   :   str     	=       None
   else:
      UpperCAmelCase_   :   Tuple     	=       "." * max(0,						spaces - 2    ) + "# {:" + str(50 - spaces    ) + "s}"
      UpperCAmelCase_   :   Optional[int]     	=       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,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
   # for compatibility with later versions of NVIDIA Megatron-LM.
   # The inverse operation is performed inside Megatron-LM to read checkpoints:
   # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
   # If param is the weight tensor of the self-attention block, the returned tensor
   # will have to be transposed one more time to be read by HuggingFace GPT2.
   UpperCAmelCase_   :   Any     	=       param.size()
   # other versions store [num_heads * num_splits * hidden_size, :]
   UpperCAmelCase_   :   List[Any]     	=       (num_heads, num_splits, hidden_size) + input_shape[1:]
   UpperCAmelCase_   :   List[Any]     	=       param.view(*__lowerCamelCase    )
   UpperCAmelCase_   :   List[str]     	=       param.transpose(0,						1    ).contiguous()
   UpperCAmelCase_   :   Union[str, Any]     	=       param.view(*__lowerCamelCase    )
   return param







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Dict     	=       config.n_positions
   UpperCAmelCase_   :   Optional[int]     	=       config.n_layer
   UpperCAmelCase_   :   Any     	=       config.vocab_size
   UpperCAmelCase_   :   Optional[Any]     	=       config.n_head
   UpperCAmelCase_   :   Optional[int]     	=       config.n_embd // config.n_head

   UpperCAmelCase_   :   Union[str, Any]     	=       sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
   UpperCAmelCase_   :   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"],
   }

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

      UpperCAmelCase_   :   List[str]     	=       sd_megatron[f"""{pf}{i}.input_layernorm.weight"""]
      UpperCAmelCase_   :   Union[str, Any]     	=       sd_megatron[f"""{pf}{i}.input_layernorm.bias"""]

      UpperCAmelCase_   :   List[Any]     	=       sd_megatron[f"""{pf}{i}.self_attention.query_key_value.weight"""]
      UpperCAmelCase_   :   List[Any]     	=       fix_query_key_value_ordering(__lowerCamelCase,						3,						__lowerCamelCase,						__lowerCamelCase    )
      UpperCAmelCase_   :   Optional[Any]     	=       vala.transpose(0,						1    ).contiguous()

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

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

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

   return sd_hf







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   int     	=       6_4000
   UpperCAmelCase_   :   List[Any]     	=       config_megatron["encoder_seq_length"]
   UpperCAmelCase_   :   Dict     	=       config_megatron["hidden_size"]
   UpperCAmelCase_   :   List[Any]     	=       config_megatron["num_layers"]
   UpperCAmelCase_   :   Optional[Any]     	=       config_megatron["num_attention_heads"]
   UpperCAmelCase_   :   Optional[Any]     	=       config_megatron["ffn_hidden_size"]
   UpperCAmelCase_   :   List[str]     	=       "gelu"
   UpperCAmelCase_   :   Tuple     	=       0.1
   UpperCAmelCase_   :   Any     	=       0.1
   UpperCAmelCase_   :   Dict     	=       0.1
   UpperCAmelCase_   :   Optional[Any]     	=       config_megatron["layernorm_epsilon"]  # 1e-5
   UpperCAmelCase_   :   Dict     	=       config_megatron["init_method_std"]  # 0.02
   UpperCAmelCase_   :   Optional[int]     	=       config_megatron["apply_query_key_layer_scaling"]  # True
   UpperCAmelCase_   :   List[Any]     	=       True
   UpperCAmelCase_   :   str     	=       True

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

   return config_hf







def       __a	(						__lowerCamelCase    ):
   print(__lowerCamelCase    )

   UpperCAmelCase_   :   Optional[int]     	=       args.checkpoint_path
   UpperCAmelCase_   :   Dict     	=       args.save_path
   if isfile(__lowerCamelCase    ):
      raise FileNotFoundError(f"""ERROR! could not find file {checkpoint_path}"""    )

   # Load the model.
   UpperCAmelCase_   :   List[Any]     	=       torch.load(__lowerCamelCase,						map_location="cpu"    )

   # Load the config.
   UpperCAmelCase_   :   List[str]     	=       checkpoint["hyper_parameters"]["cfg"]
   UpperCAmelCase_   :   str     	=       GPTaConfig()
   UpperCAmelCase_   :   Tuple     	=       copy_config(config_hf=__lowerCamelCase,						config_megatron=__lowerCamelCase    )
   UpperCAmelCase_   :   List[str]     	=       ["GPT2LMHeadModel"]

   UpperCAmelCase_   :   int     	=       checkpoint["state_dict"]

   # Convert.
   print("Converting"    )
   UpperCAmelCase_   :   Optional[Any]     	=       convert_megatron_checkpoint(__lowerCamelCase,						__lowerCamelCase    )

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

   UpperCAmelCase_   :   Tuple     	=       "GPTSw3Tokenizer"

   # Store the config to file.
   print("Saving config"    )
   config_hf.save_pretrained(__lowerCamelCase    )

   # Store the state_dict to file.
   UpperCAmelCase_   :   Tuple     	=       os.path.join(__lowerCamelCase,						"pytorch_model.bin"    )
   print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""    )
   torch.save(__lowerCamelCase,						__lowerCamelCase    )


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






'''simple docstring'''






import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a_       :     List[str] 							=     logging.get_logger(__name__)

a_       :     Union[str, Any] 							=     {
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}




class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						"""oneformer"""
 _lowerCAmelCase			       =						{"""hidden_size""": """hidden_dim"""}
 def __init__(			self   ,					__magic_name__ = None   ,					__magic_name__ = 2_55   ,					__magic_name__ = 1_50   ,					__magic_name__ = 0.1   ,					__magic_name__ = 2.0   ,					__magic_name__ = 5.0   ,					__magic_name__ = 5.0   ,					__magic_name__ = 0.5   ,					__magic_name__ = 0.0_7   ,					__magic_name__ = 1_25_44   ,					__magic_name__ = 3.0   ,					__magic_name__ = 0.7_5   ,					__magic_name__ = 0.0_2   ,					__magic_name__ = 1.0   ,					__magic_name__ = 1e-05   ,					__magic_name__ = False   ,					__magic_name__ = True   ,					__magic_name__ = True   ,					__magic_name__ = [4, 8, 16, 32]   ,					__magic_name__ = 77   ,					__magic_name__ = 2_56   ,					__magic_name__ = 77   ,					__magic_name__ = 6   ,					__magic_name__ = 4_94_08   ,					__magic_name__ = 2   ,					__magic_name__ = 16   ,					__magic_name__ = 2_56   ,					__magic_name__ = 2_56   ,					__magic_name__ = 2_56   ,					__magic_name__ = 10_24   ,					__magic_name__ = "GN"   ,					__magic_name__ = 6   ,					__magic_name__ = 10   ,					__magic_name__ = True   ,					__magic_name__ = 8   ,					__magic_name__ = 0.1   ,					__magic_name__ = 20_48   ,					__magic_name__ = False   ,					__magic_name__ = False   ,					__magic_name__ = 2   ,					__magic_name__ = 4   ,					**__magic_name__   ,					)       ->				Dict:
      if backbone_config is None:
           logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'    )
           _a         =				CONFIG_MAPPING['swin'](
               image_size=2_24   ,					in_channels=3   ,					patch_size=4   ,					embed_dim=96   ,					depths=[2, 2, 6, 2]   ,					num_heads=[3, 6, 12, 24]   ,					window_size=7   ,					drop_path_rate=0.3   ,					use_absolute_embeddings=__magic_name__   ,					out_features=['stage1', 'stage2', 'stage3', 'stage4']   ,					)
      elif isinstance(__magic_name__   ,					__magic_name__    ):
           _a         =				backbone_config.get('model_type'    )
           _a         =				CONFIG_MAPPING[backbone_model_type]
           _a         =				config_class.from_dict(__magic_name__    )

      _a         =				backbone_config

      _a         =				ignore_value
      _a         =				num_queries
      _a         =				no_object_weight
      _a         =				class_weight
      _a         =				mask_weight
      _a         =				dice_weight
      _a         =				contrastive_weight
      _a         =				contrastive_temperature
      _a         =				train_num_points
      _a         =				oversample_ratio
      _a         =				importance_sample_ratio
      _a         =				init_std
      _a         =				init_xavier_std
      _a         =				layer_norm_eps
      _a         =				is_training
      _a         =				use_auxiliary_loss
      _a         =				output_auxiliary_logits
      _a         =				strides
      _a         =				task_seq_len
      _a         =				text_encoder_width
      _a         =				text_encoder_context_length
      _a         =				text_encoder_num_layers
      _a         =				text_encoder_vocab_size
      _a         =				text_encoder_proj_layers
      _a         =				text_encoder_n_ctx
      _a         =				conv_dim
      _a         =				mask_dim
      _a         =				hidden_dim
      _a         =				encoder_feedforward_dim
      _a         =				norm
      _a         =				encoder_layers
      _a         =				decoder_layers
      _a         =				use_task_norm
      _a         =				num_attention_heads
      _a         =				dropout
      _a         =				dim_feedforward
      _a         =				pre_norm
      _a         =				enforce_input_proj
      _a         =				query_dec_layers
      _a         =				common_stride
      _a         =				decoder_layers

      super().__init__(**__magic_name__    )




 def 				__UpperCAmelCase		(			self    )       ->				Dict[str, any]:
      _a         =				copy.deepcopy(self.__dict__    )
      _a         =				self.backbone_config.to_dict()
      _a         =				self.__class__.model_type
      return output

