

'''simple docstring'''

import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig

def      __magic_name__							(			A       , A       , A=0				)      ->		Dict:




	if name is None:
		snake_case							=					None
	else:
		snake_case							=					'.' * max(0       , spaces - 2				) + '# {:' + str(5_0 - spaces				) + 's}'
		snake_case							=					fmt.format(A				)

	# Print and recurse (if needed).
	if isinstance(A       , A				):
		if msg is not None:
			print(A				)
		for k in val.keys():
			recursive_print(A       , val[k]       , spaces + 2				)
	elif isinstance(A       , torch.Tensor				):
		print(A       , ':'       , val.size()				)
	else:
		print(A       , ':'       , A				)

def      __magic_name__							(			A       , A       , A       , A				)      ->		Optional[int]:
	snake_case							=					param.size()
	# other versions store [num_heads * num_splits * hidden_size, :]
	snake_case							=					(num_heads, num_splits, hidden_size) + input_shape[1:]
	snake_case							=					param.view(*A				)
	snake_case							=					param.transpose(0       , 1				).contiguous()
	snake_case							=					param.view(*A				)
	return param

def      __magic_name__							(			A       , A				)      ->		int:
	snake_case							=					config.n_positions
	snake_case							=					config.n_layer
	snake_case							=					config.vocab_size
	snake_case							=					config.n_head
	snake_case							=					config.n_embd // config.n_head

	snake_case							=					sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
	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'],
	}

	snake_case							=					'model.language_model.encoder.layers.'
	for i in range(A				):
		snake_case							=					torch.tril(torch.ones((n_positions, n_positions)       , dtype=torch.bool				)				)
		snake_case							=					causal_mask.view(1       , 1       , A       , A				)
		snake_case							=					causal_mask
		snake_case							=					torch.tensor(-1E4       , dtype=torch.bfloataa				)

		snake_case							=					sd_megatron[F'''{pf}{i}.input_layernorm.weight''']
		snake_case							=					sd_megatron[F'''{pf}{i}.input_layernorm.bias''']

		snake_case							=					sd_megatron[F'''{pf}{i}.self_attention.query_key_value.weight''']
		snake_case							=					fix_query_key_value_ordering(A       , 3       , A       , A				)
		snake_case							=					vala.transpose(0       , 1				).contiguous()

		snake_case							=					sd_megatron[F'''{pf}{i}.self_attention.query_key_value.bias''']
		snake_case							=					fix_query_key_value_ordering(A       , 3       , A       , A				)
		snake_case							=					vala

		snake_case							=					sd_megatron[F'''{pf}{i}.self_attention.dense.weight'''].transpose(
		    0       , 1				)
		snake_case							=					sd_megatron[F'''{pf}{i}.self_attention.dense.bias''']
		snake_case							=					sd_megatron[F'''{pf}{i}.post_attention_layernorm.weight''']
		snake_case							=					sd_megatron[F'''{pf}{i}.post_attention_layernorm.bias''']
		snake_case							=					sd_megatron[F'''{pf}{i}.mlp.dense_h_to_4h.weight'''].transpose(0       , 1				)
		snake_case							=					sd_megatron[F'''{pf}{i}.mlp.dense_h_to_4h.bias''']
		snake_case							=					sd_megatron[F'''{pf}{i}.mlp.dense_4h_to_h.weight'''].transpose(
		    0       , 1				)
		snake_case							=					sd_megatron[F'''{pf}{i}.mlp.dense_4h_to_h.bias''']

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

	return sd_hf

def      __magic_name__							(			A       , A				)      ->		Tuple:
	snake_case							=					6_4_0_0_0
	snake_case							=					config_megatron['encoder_seq_length']
	snake_case							=					config_megatron['hidden_size']
	snake_case							=					config_megatron['num_layers']
	snake_case							=					config_megatron['num_attention_heads']
	snake_case							=					config_megatron['ffn_hidden_size']
	snake_case							=					'gelu'
	snake_case							=					0.1
	snake_case							=					0.1
	snake_case							=					0.1
	snake_case							=					config_megatron['layernorm_epsilon']  # 1e-5
	snake_case							=					config_megatron['init_method_std']  # 0.02
	snake_case							=					config_megatron['apply_query_key_layer_scaling']  # True
	snake_case							=					True
	snake_case							=					True

	# This identifies the 6.7B (7B) model which uses a different tokenizer
	if config_megatron["hidden_size"] == 4_0_9_6:
		snake_case							=					1  # <|endoftext|>
		snake_case							=					1  # <|endoftext|>
		snake_case							=					0  # <unk>
	else:
		snake_case							=					2  # <s>
		snake_case							=					3  # <|endoftext|>
		snake_case							=					0  # <pad>

	return config_hf

def      __magic_name__							(			A				)      ->		Dict:
	print(A				)

	snake_case							=					args.checkpoint_path
	snake_case							=					args.save_path
	if isfile(A				):
		raise FileNotFoundError(F'''ERROR! could not find file {checkpoint_path}'''				)

	# Load the model.
	snake_case							=					torch.load(A       , map_location='cpu'				)

	# Load the config.
	snake_case							=					checkpoint['hyper_parameters']['cfg']
	snake_case							=					GPTaConfig()
	snake_case							=					copy_config(config_hf=A       , config_megatron=A				)
	snake_case							=					['GPT2LMHeadModel']

	snake_case							=					checkpoint['state_dict']

	# Convert.
	print('Converting'				)
	snake_case							=					convert_megatron_checkpoint(A       , A				)

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

	snake_case							=					'GPTSw3Tokenizer'

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

	# Store the state_dict to file.
	snake_case							=					os.path.join(A       , 'pytorch_model.bin'				)
	print(F'''Saving checkpoint to \"{output_checkpoint_file}\"'''				)
	torch.save(A       , A				)


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











def       lowerCAmelCase_      (	lowerCamelCase:							str    )       ->      int:


      '''simple docstring'''




      lowerCamelCase__   	=					53_81
      for x in s:
            lowerCamelCase__   	=					((hash_value << 5) + hash_value) + ord(lowerCamelCase    )
      return hash_value & 0Xffff_ffff