code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  import os
  from os.path import isfile
  
  import torch
  
  from transformers import GPTaConfig
  def _A		(		lowercase ,					lowercase ,					lowercase=0	):
  
  
  
  
  
  
  
      """simple docstring"""
      # Format the message.
      if name is None:
          a					=None
      else:
          a					='''.''' * max(0 ,					spaces - 2	) + '''# {:''' + str(50 - spaces	) + '''s}'''
          a					=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 _A		(		lowercase ,					lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      # 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.
      a					=param.size()
      # other versions store [num_heads * num_splits * hidden_size, :]
      a					=(num_heads, num_splits, hidden_size) + input_shape[1:]
      a					=param.view(*lowercase	)
      a					=param.transpose(0 ,					1	).contiguous()
      a					=param.view(*lowercase	)
      return param
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=config.n_positions
      a					=config.n_layer
      a					=config.vocab_size
      a					=config.n_head
      a					=config.n_embd // config.n_head
  
      a					=sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
      a					={
          '''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					='''model.language_model.encoder.layers.'''
      for i in range(lowercase	):
          a					=torch.tril(torch.ones((n_positions, n_positions) ,					dtype=torch.bool	)	)
          a					=causal_mask.view(1 ,					1 ,					lowercase ,					lowercase	)
          a					=causal_mask
          a					=torch.tensor(-1E4 ,					dtype=torch.bfloataa	)
  
          a					=sd_megatron[f'''{pf}{i}.input_layernorm.weight''']
          a					=sd_megatron[f'''{pf}{i}.input_layernorm.bias''']
  
          a					=sd_megatron[f'''{pf}{i}.self_attention.query_key_value.weight''']
          a					=fix_query_key_value_ordering(lowercase ,					3 ,					lowercase ,					lowercase	)
          a					=vala.transpose(0 ,					1	).contiguous()
  
          a					=sd_megatron[f'''{pf}{i}.self_attention.query_key_value.bias''']
          a					=fix_query_key_value_ordering(lowercase ,					3 ,					lowercase ,					lowercase	)
          a					=vala
  
          a					=sd_megatron[f'''{pf}{i}.self_attention.dense.weight'''].transpose(
              0 ,					1	)
          a					=sd_megatron[f'''{pf}{i}.self_attention.dense.bias''']
          a					=sd_megatron[f'''{pf}{i}.post_attention_layernorm.weight''']
          a					=sd_megatron[f'''{pf}{i}.post_attention_layernorm.bias''']
          a					=sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.weight'''].transpose(0 ,					1	)
          a					=sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.bias''']
          a					=sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.weight'''].transpose(
              0 ,					1	)
          a					=sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.bias''']
  
      # For LM head, transformers' wants the matrix to weight embeddings.
      a					=word_embeddings
  
      return sd_hf
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=6_40_00
      a					=config_megatron['''encoder_seq_length''']
      a					=config_megatron['''hidden_size''']
      a					=config_megatron['''num_layers''']
      a					=config_megatron['''num_attention_heads''']
      a					=config_megatron['''ffn_hidden_size''']
      a					='''gelu'''
      a					=0.1
      a					=0.1
      a					=0.1
      a					=config_megatron['''layernorm_epsilon''']  # 1e-5
      a					=config_megatron['''init_method_std''']  # 0.02
      a					=config_megatron['''apply_query_key_layer_scaling''']  # True
      a					=True
      a					=True
  
      # This identifies the 6.7B (7B) model which uses a different tokenizer
      if config_megatron["hidden_size"] == 40_96:
          a					=1  # <|endoftext|>
          a					=1  # <|endoftext|>
          a					=0  # <unk>
      else:
          a					=2  # <s>
          a					=3  # <|endoftext|>
          a					=0  # <pad>
  
      return config_hf
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      print(lowercase	)
  
      a					=args.checkpoint_path
      a					=args.save_path
      if isfile(lowercase	):
          raise FileNotFoundError(f'''ERROR! could not find file {checkpoint_path}'''	)
  
      # Load the model.
      a					=torch.load(lowercase ,					map_location='''cpu'''	)
  
      # Load the config.
      a					=checkpoint['''hyper_parameters''']['''cfg''']
      a					=GPTaConfig()
      a					=copy_config(config_hf=lowercase ,					config_megatron=lowercase	)
      a					=['''GPT2LMHeadModel''']
  
      a					=checkpoint['''state_dict''']
  
      # Convert.
      print('''Converting'''	)
      a					=convert_megatron_checkpoint(lowercase ,					lowercase	)
  
      # Print the structure of converted state dict.
      if args.print_checkpoint_structure:
          recursive_print(lowercase ,					lowercase	)
  
      a					='''GPTSw3Tokenizer'''
  
      # Store the config to file.
      print('''Saving config'''	)
      config_hf.save_pretrained(lowercase	)
  
      # Store the state_dict to file.
      a					=os.path.join(lowercase ,					'''pytorch_model.bin'''	)
      print(f'''Saving checkpoint to "{output_checkpoint_file}"'''	)
      torch.save(lowercase ,					lowercase	)
  
  
  if __name__ == "__main__":
         lowerCamelCase_     :     Dict    		=						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_     :     int    		=						parser.parse_args()
         main(_args)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from packaging import version
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  lowerCamelCase_     :     Any    		=						logging.get_logger(__name__)
  
  lowerCamelCase_     :     Tuple    		=						{
      """apple/mobilevitv2-1.0""": """https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json""",
  }
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"mobilevitv2"
  
  
   def __init__(	self				,					__A=3				,					__A=256				,					__A=2				,					__A=2.0				,					__A="swish"				,					__A=3				,					__A=32				,					__A=0.1				,					__A=0.02				,					__A=1E-5				,					__A=512				,					__A=[6, 12, 18]				,					__A=0.1				,					__A=255				,					__A=[2, 4, 3]				,					__A=[128, 192, 256]				,					__A=1.0				,					__A=2				,					__A=0.0				,					__A=0.0				,					**__A				,					)      ->  int:
       super().__init__(**__A		)
  
       a					=num_channels
       a					=image_size
       a					=patch_size
       a					=expand_ratio
       a					=hidden_act
       a					=conv_kernel_size
       a					=output_stride
       a					=initializer_range
       a					=layer_norm_eps
       a					=n_attn_blocks
       a					=base_attn_unit_dims
       a					=width_multiplier
       a					=ffn_multiplier
       a					=ffn_dropout
       a					=attn_dropout
       a					=classifier_dropout_prob
  
       # decode head attributes for semantic segmentation
       a					=aspp_out_channels
       a					=atrous_rates
       a					=aspp_dropout_prob
       a					=semantic_loss_ignore_index
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						version.parse("1.11"    )
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Mapping[str, Mapping[int, str]]:
       return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})]		)
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Mapping[str, Mapping[int, str]]:
       if self.task == "image-classification":
           return OrderedDict([('''logits''', {0: '''batch'''})]		)
       else:
           return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]		)
  
  
  
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  float:
       return 1E-4