code: 
  
  
  
  
  import argparse
  import os
  from os.path import isfile
  
  import torch
  
  from transformers import GPTaConfig
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE=0   ) ->       Dict:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			if name is None:
  						SCREAMING_SNAKE_CASE_			=	None
  			else:
  						SCREAMING_SNAKE_CASE_			=	'.' * max(0					,						spaces - 2   ) + '# {:' + str(50 - spaces   ) + 's}'
  						SCREAMING_SNAKE_CASE_			=	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 lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       List[Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	param.size()
  			# other versions store [num_heads * num_splits * hidden_size, :]
  			SCREAMING_SNAKE_CASE_			=	(num_heads, num_splits, hidden_size) + input_shape[1:]
  			SCREAMING_SNAKE_CASE_			=	param.view(*_UpperCAmelCase   )
  			SCREAMING_SNAKE_CASE_			=	param.transpose(0					,						1   ).contiguous()
  			SCREAMING_SNAKE_CASE_			=	param.view(*_UpperCAmelCase   )
  			return param
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       str:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	config.n_positions
  			SCREAMING_SNAKE_CASE_			=	config.n_layer
  			SCREAMING_SNAKE_CASE_			=	config.vocab_size
  			SCREAMING_SNAKE_CASE_			=	config.n_head
  			SCREAMING_SNAKE_CASE_			=	config.n_embd // config.n_head
  
  			SCREAMING_SNAKE_CASE_			=	sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
  			SCREAMING_SNAKE_CASE_			=	{
  			    '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_			=	'model.language_model.encoder.layers.'
  			for i in range(_UpperCAmelCase   ):
  						SCREAMING_SNAKE_CASE_			=	torch.tril(torch.ones((n_positions, n_positions)					,						dtype=torch.bool   )   )
  						SCREAMING_SNAKE_CASE_			=	causal_mask.view(1					,						1					,						_UpperCAmelCase					,						_UpperCAmelCase   )
  						SCREAMING_SNAKE_CASE_			=	causal_mask
  						SCREAMING_SNAKE_CASE_			=	torch.tensor(-1E4					,						dtype=torch.bfloataa   )
  
  						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.input_layernorm.weight']
  						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.input_layernorm.bias']
  
  						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.self_attention.query_key_value.weight']
  						SCREAMING_SNAKE_CASE_			=	fix_query_key_value_ordering(_UpperCAmelCase					,						3					,						_UpperCAmelCase					,						_UpperCAmelCase   )
  						SCREAMING_SNAKE_CASE_			=	vala.transpose(0					,						1   ).contiguous()
  
  						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.self_attention.query_key_value.bias']
  						SCREAMING_SNAKE_CASE_			=	fix_query_key_value_ordering(_UpperCAmelCase					,						3					,						_UpperCAmelCase					,						_UpperCAmelCase   )
  						SCREAMING_SNAKE_CASE_			=	vala
  
  						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.self_attention.dense.weight'].transpose(
  						    0					,						1   )
  						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.self_attention.dense.bias']
  						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.post_attention_layernorm.weight']
  						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.post_attention_layernorm.bias']
  						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.mlp.dense_h_to_4h.weight'].transpose(0					,						1   )
  						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.mlp.dense_h_to_4h.bias']
  						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.mlp.dense_4h_to_h.weight'].transpose(
  						    0					,						1   )
  						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.mlp.dense_4h_to_h.bias']
  
  			# For LM head, transformers' wants the matrix to weight embeddings.
  			SCREAMING_SNAKE_CASE_			=	word_embeddings
  
  			return sd_hf
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Optional[int]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	6_40_00
  			SCREAMING_SNAKE_CASE_			=	config_megatron['encoder_seq_length']
  			SCREAMING_SNAKE_CASE_			=	config_megatron['hidden_size']
  			SCREAMING_SNAKE_CASE_			=	config_megatron['num_layers']
  			SCREAMING_SNAKE_CASE_			=	config_megatron['num_attention_heads']
  			SCREAMING_SNAKE_CASE_			=	config_megatron['ffn_hidden_size']
  			SCREAMING_SNAKE_CASE_			=	'gelu'
  			SCREAMING_SNAKE_CASE_			=	0.1
  			SCREAMING_SNAKE_CASE_			=	0.1
  			SCREAMING_SNAKE_CASE_			=	0.1
  			SCREAMING_SNAKE_CASE_			=	config_megatron['layernorm_epsilon']  # 1e-5
  			SCREAMING_SNAKE_CASE_			=	config_megatron['init_method_std']  # 0.02
  			SCREAMING_SNAKE_CASE_			=	config_megatron['apply_query_key_layer_scaling']  # True
  			SCREAMING_SNAKE_CASE_			=	True
  			SCREAMING_SNAKE_CASE_			=	True
  
  			# This identifies the 6.7B (7B) model which uses a different tokenizer
  			if config_megatron["hidden_size"] == 40_96:
  						SCREAMING_SNAKE_CASE_			=	1  # <|endoftext|>
  						SCREAMING_SNAKE_CASE_			=	1  # <|endoftext|>
  						SCREAMING_SNAKE_CASE_			=	0  # <unk>
  			else:
  						SCREAMING_SNAKE_CASE_			=	2  # <s>
  						SCREAMING_SNAKE_CASE_			=	3  # <|endoftext|>
  						SCREAMING_SNAKE_CASE_			=	0  # <pad>
  
  			return config_hf
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Optional[int]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			print(_UpperCAmelCase   )
  
  			SCREAMING_SNAKE_CASE_			=	args.checkpoint_path
  			SCREAMING_SNAKE_CASE_			=	args.save_path
  			if isfile(_UpperCAmelCase   ):
  						raise FileNotFoundError(F'ERROR! could not find file {checkpoint_path}'   )
  
  			# Load the model.
  			SCREAMING_SNAKE_CASE_			=	torch.load(_UpperCAmelCase					,						map_location='cpu'   )
  
  			# Load the config.
  			SCREAMING_SNAKE_CASE_			=	checkpoint['hyper_parameters']['cfg']
  			SCREAMING_SNAKE_CASE_			=	GPTaConfig()
  			SCREAMING_SNAKE_CASE_			=	copy_config(config_hf=_UpperCAmelCase					,						config_megatron=_UpperCAmelCase   )
  			SCREAMING_SNAKE_CASE_			=	['GPT2LMHeadModel']
  
  			SCREAMING_SNAKE_CASE_			=	checkpoint['state_dict']
  
  			# Convert.
  			print('Converting'   )
  			SCREAMING_SNAKE_CASE_			=	convert_megatron_checkpoint(_UpperCAmelCase					,						_UpperCAmelCase   )
  
  			# Print the structure of converted state dict.
  			if args.print_checkpoint_structure:
  						recursive_print(_UpperCAmelCase					,						_UpperCAmelCase   )
  
  			SCREAMING_SNAKE_CASE_			=	'GPTSw3Tokenizer'
  
  			# Store the config to file.
  			print('Saving config'   )
  			config_hf.save_pretrained(_UpperCAmelCase   )
  
  			# Store the state_dict to file.
  			SCREAMING_SNAKE_CASE_			=	os.path.join(_UpperCAmelCase					,						'pytorch_model.bin'   )
  			print(F'Saving checkpoint to \"{output_checkpoint_file}\"'   )
  			torch.save(_UpperCAmelCase					,						_UpperCAmelCase   )
  
  
  if __name__ == "__main__":
  				SCREAMING_SNAKE_CASE__    :    int								=					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__    :    Union[str, Any]								=					parser.parse_args()
  				main(_args)
  
style_context: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					{"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    int								=					["DetrFeatureExtractor"]
  				SCREAMING_SNAKE_CASE__    :    Any								=					["DetrImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    List[Any]								=					[
  				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "DetrForObjectDetection",
  				    "DetrForSegmentation",
  				    "DetrModel",
  				    "DetrPreTrainedModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_detr import DetrFeatureExtractor
  								from .image_processing_detr import DetrImageProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_detr import (
  								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    DetrForObjectDetection,
  								    DetrForSegmentation,
  								    DetrModel,
  								    DetrPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				SCREAMING_SNAKE_CASE__    :    Tuple								=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  