

'''simple docstring'''


import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase=0	)     ->     str:
	# Format the message.
	if name is None:
		UpperCAmelCase_		:   Optional[int]													=       None
	else:
		UpperCAmelCase_		:   Optional[Any]													=       '.' * max(0 ,spaces - 2	) + '# {:' + str(5_0 - spaces	) + 's}'
		UpperCAmelCase_		:   List[str]													=       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     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     int:
	UpperCAmelCase_		:   Optional[int]													=       param.size()
	# other versions store [num_heads * num_splits * hidden_size, :]
	UpperCAmelCase_		:   Dict													=       (num_heads, num_splits, hidden_size) + input_shape[1:]
	UpperCAmelCase_		:   List[str]													=       param.view(*__a	)
	UpperCAmelCase_		:   List[Any]													=       param.transpose(0 ,1	).contiguous()
	UpperCAmelCase_		:   Optional[int]													=       param.view(*__a	)
	return param







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase	)     ->     List[str]:
	UpperCAmelCase_		:   List[Any]													=       config.n_positions
	UpperCAmelCase_		:   int													=       config.n_layer
	UpperCAmelCase_		:   Dict													=       config.vocab_size
	UpperCAmelCase_		:   Dict													=       config.n_head
	UpperCAmelCase_		:   Optional[Any]													=       config.n_embd // config.n_head

	UpperCAmelCase_		:   Tuple													=       sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
	UpperCAmelCase_		:   Dict													=       {
	    '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_		:   int													=       'model.language_model.encoder.layers.'
	for i in range(__a	):
		UpperCAmelCase_		:   Tuple													=       torch.tril(torch.ones((n_positions, n_positions) ,dtype=torch.bool	)	)
		UpperCAmelCase_		:   List[Any]													=       causal_mask.view(1 ,1 ,__a ,__a	)
		UpperCAmelCase_		:   List[Any]													=       causal_mask
		UpperCAmelCase_		:   Optional[Any]													=       torch.tensor(-1e4 ,dtype=torch.bfloataa	)

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

		UpperCAmelCase_		:   Union[str, Any]													=       sd_megatron[f"""{pf}{i}.self_attention.query_key_value.weight"""]
		UpperCAmelCase_		:   Dict													=       fix_query_key_value_ordering(__a ,3 ,__a ,__a	)
		UpperCAmelCase_		:   Tuple													=       vala.transpose(0 ,1	).contiguous()

		UpperCAmelCase_		:   Optional[Any]													=       sd_megatron[f"""{pf}{i}.self_attention.query_key_value.bias"""]
		UpperCAmelCase_		:   Optional[int]													=       fix_query_key_value_ordering(__a ,3 ,__a ,__a	)
		UpperCAmelCase_		:   Any													=       vala

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

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

	return sd_hf







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase	)     ->     Union[str, Any]:
	UpperCAmelCase_		:   Any													=       6_4_0_0_0
	UpperCAmelCase_		:   Dict													=       config_megatron['encoder_seq_length']
	UpperCAmelCase_		:   int													=       config_megatron['hidden_size']
	UpperCAmelCase_		:   int													=       config_megatron['num_layers']
	UpperCAmelCase_		:   Optional[Any]													=       config_megatron['num_attention_heads']
	UpperCAmelCase_		:   Dict													=       config_megatron['ffn_hidden_size']
	UpperCAmelCase_		:   List[Any]													=       'gelu'
	UpperCAmelCase_		:   Any													=       0.1
	UpperCAmelCase_		:   List[Any]													=       0.1
	UpperCAmelCase_		:   Dict													=       0.1
	UpperCAmelCase_		:   List[Any]													=       config_megatron['layernorm_epsilon']  # 1e-5
	UpperCAmelCase_		:   Optional[int]													=       config_megatron['init_method_std']  # 0.02
	UpperCAmelCase_		:   Optional[int]													=       config_megatron['apply_query_key_layer_scaling']  # True
	UpperCAmelCase_		:   List[str]													=       True
	UpperCAmelCase_		:   str													=       True

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

	return config_hf







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     Optional[int]:
	print(__a	)

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

	# Load the model.
	UpperCAmelCase_		:   Optional[Any]													=       torch.load(__a ,map_location='cpu'	)

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

	UpperCAmelCase_		:   Union[str, Any]													=       checkpoint['state_dict']

	# Convert.
	print('Converting'	)
	UpperCAmelCase_		:   Tuple													=       convert_megatron_checkpoint(__a ,__a	)

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

	UpperCAmelCase_		:   Dict													=       'GPTSw3Tokenizer'

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

	# Store the state_dict to file.
	UpperCAmelCase_		:   Optional[Any]													=       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)











from __future__ import annotations

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_ = 0		):
											a__					   =		value
											a__					   =		None
											a__					   =		None




def       A_   (						__a  :					Node | None			,		__a  :					Node | None		):

				"""simple docstring"""
				if treea is None:
								return treea
				if treea is None:
								return treea

				a__					   =		treea.value + treea.value
				a__					   =		merge_two_binary_trees(treea.left			,		treea.left		)
				a__					   =		merge_two_binary_trees(treea.right			,		treea.right		)
				return treea

def       A_   (						__a  :					Node | None		):

				"""simple docstring"""
				if root:
								print(root.value		)
								print_preorder(root.left		)
								print_preorder(root.right		)


if __name__ == "__main__":
	UpperCAmelCase							=       Node(1)
	UpperCAmelCase							=       Node(2)
	UpperCAmelCase							=       Node(3)
	UpperCAmelCase							=       Node(4)

	UpperCAmelCase							=       Node(2)
	UpperCAmelCase							=       Node(4)
	UpperCAmelCase							=       Node(6)
	UpperCAmelCase							=       Node(9)
	UpperCAmelCase							=       Node(5)

	print("""Tree1 is: """)
	print_preorder(treea)
	print("""Tree2 is: """)
	print_preorder(treea)
	UpperCAmelCase							=       merge_two_binary_trees(treea, treea)
	print("""Merged Tree is: """)
	print_preorder(merged_tree)

