"""simple docstring"""






import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Optional[int]		,			_SCREAMING_SNAKE_CASE	:Union[str, Any]		,			_SCREAMING_SNAKE_CASE	:Tuple=0  )				-> List[Any]:
						# Format the message.
						if name is None:
												a_							:			str     		=					None
						else:
												a_							:			Any     		=					"." * max(0		,			spaces - 2  ) + "# {:" + str(50 - spaces  ) + "s}"
												a_							:			Optional[int]     		=					fmt.format(_SCREAMING_SNAKE_CASE  )

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

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Optional[int]		,			_SCREAMING_SNAKE_CASE	:Tuple		,			_SCREAMING_SNAKE_CASE	:List[str]		,			_SCREAMING_SNAKE_CASE	:str  )				-> List[str]:
						# 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_							:			Union[str, Any]     		=					param.size()
						# other versions store [num_heads * num_splits * hidden_size, :]
						a_							:			Dict     		=					(num_heads, num_splits, hidden_size) + input_shape[1:]
						a_							:			str     		=					param.view(*_SCREAMING_SNAKE_CASE  )
						a_							:			Union[str, Any]     		=					param.transpose(0		,			1  ).contiguous()
						a_							:			List[str]     		=					param.view(*_SCREAMING_SNAKE_CASE  )
						return param

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Optional[int]		,			_SCREAMING_SNAKE_CASE	:Any  )				-> Optional[int]:
						a_							:			str     		=					config.n_positions
						a_							:			List[Any]     		=					config.n_layer
						a_							:			Optional[int]     		=					config.vocab_size
						a_							:			Tuple     		=					config.n_head
						a_							:			Optional[Any]     		=					config.n_embd // config.n_head

						a_							:			List[str]     		=					sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
						a_							:			List[str]     		=					{
						    "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_							:			Tuple     		=					"model.language_model.encoder.layers."
						for i in range(_SCREAMING_SNAKE_CASE  ):
												a_							:			Union[str, Any]     		=					torch.tril(torch.ones((n_positions, n_positions)		,			dtype=torch.bool  )  )
												a_							:			int     		=					causal_mask.view(1		,			1		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
												a_							:			Dict     		=					causal_mask
												a_							:			List[Any]     		=					torch.tensor(-1E4		,			dtype=torch.bfloataa  )

												a_							:			Optional[int]     		=					sd_megatron[F'''{pf}{i}.input_layernorm.weight''']
												a_							:			int     		=					sd_megatron[F'''{pf}{i}.input_layernorm.bias''']

												a_							:			Tuple     		=					sd_megatron[F'''{pf}{i}.self_attention.query_key_value.weight''']
												a_							:			Any     		=					fix_query_key_value_ordering(_SCREAMING_SNAKE_CASE		,			3		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
												a_							:			Tuple     		=					vala.transpose(0		,			1  ).contiguous()

												a_							:			List[Any]     		=					sd_megatron[F'''{pf}{i}.self_attention.query_key_value.bias''']
												a_							:			List[Any]     		=					fix_query_key_value_ordering(_SCREAMING_SNAKE_CASE		,			3		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
												a_							:			Any     		=					vala

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

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

						return sd_hf

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:int		,			_SCREAMING_SNAKE_CASE	:Optional[int]  )				-> str:
						a_							:			Optional[Any]     		=					64000
						a_							:			int     		=					config_megatron["encoder_seq_length"]
						a_							:			List[str]     		=					config_megatron["hidden_size"]
						a_							:			Optional[Any]     		=					config_megatron["num_layers"]
						a_							:			Optional[int]     		=					config_megatron["num_attention_heads"]
						a_							:			Union[str, Any]     		=					config_megatron["ffn_hidden_size"]
						a_							:			Any     		=					"gelu"
						a_							:			List[str]     		=					0.1
						a_							:			Optional[Any]     		=					0.1
						a_							:			int     		=					0.1
						a_							:			List[Any]     		=					config_megatron["layernorm_epsilon"]  # 1e-5
						a_							:			int     		=					config_megatron["init_method_std"]  # 0.02
						a_							:			int     		=					config_megatron["apply_query_key_layer_scaling"]  # True
						a_							:			str     		=					True
						a_							:			List[Any]     		=					True

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

						return config_hf

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Tuple  )				-> Union[str, Any]:
						print(_SCREAMING_SNAKE_CASE  )

						a_							:			Any     		=					args.checkpoint_path
						a_							:			List[Any]     		=					args.save_path
						if isfile(_SCREAMING_SNAKE_CASE  ):
												raise FileNotFoundError(F'''ERROR! could not find file {checkpoint_path}'''  )

						# Load the model.
						a_							:			Dict     		=					torch.load(_SCREAMING_SNAKE_CASE		,			map_location="cpu"  )

						# Load the config.
						a_							:			List[Any]     		=					checkpoint["hyper_parameters"]["cfg"]
						a_							:			Union[str, Any]     		=					GPTaConfig()
						a_							:			Dict     		=					copy_config(config_hf=_SCREAMING_SNAKE_CASE		,			config_megatron=_SCREAMING_SNAKE_CASE  )
						a_							:			Tuple     		=					["GPT2LMHeadModel"]

						a_							:			str     		=					checkpoint["state_dict"]

						# Convert.
						print("Converting"  )
						a_							:			int     		=					convert_megatron_checkpoint(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )

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

						a_							:			Optional[int]     		=					"GPTSw3Tokenizer"

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

						# Store the state_dict to file.
						a_							:			Union[str, Any]     		=					os.path.join(_SCREAMING_SNAKE_CASE		,			"pytorch_model.bin"  )
						print(F'''Saving checkpoint to "{output_checkpoint_file}"'''  )
						torch.save(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )


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





"""simple docstring"""






import numpy as np

UpperCamelCase     					=     [
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]
class   UpperCAmelCase__				:


	"""simple docstring"""







	def __init__(			self )    ->	None:
							a_							:			List[Any]     		=					np.array(_SCREAMING_SNAKE_CASE )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	np.ndarray:
							a_		,					a_							:			List[str]     		=					np.where(letter == self.SQUARE )
							a_							:			Union[str, Any]     		=					np.concatenate([indexa + 1, indexa + 1] )
							return indexes







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	str:
							return self.SQUARE[indexa - 1, indexa - 1]







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	str:
							a_							:			List[str]     		=					message.lower()
							a_							:			Union[str, Any]     		=					message.replace("j"       ,     "i" )

							a_							:			Tuple     		=					""
							for letter_index in range(len(_SCREAMING_SNAKE_CASE ) ):
													if message[letter_index] != " ":
																			a_							:			Dict     		=					self.letter_to_numbers(message[letter_index] )
																			a_							:			str     		=					encoded_message + str(numbers[0] ) + str(numbers[1] )
													elif message[letter_index] == " ":
																			a_							:			Any     		=					encoded_message + " "

							return encoded_message







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	str:
							a_							:			Dict     		=					message.replace(" "       ,     "  " )
							a_							:			str     		=					""
							for numbers_index in range(int(len(_SCREAMING_SNAKE_CASE ) / 2 ) ):
													if message[numbers_index * 2] != " ":
																			a_							:			str     		=					message[numbers_index * 2]
																			a_							:			str     		=					message[numbers_index * 2 + 1]

																			a_							:			Optional[Any]     		=					self.numbers_to_letter(int(_SCREAMING_SNAKE_CASE )       ,     int(_SCREAMING_SNAKE_CASE ) )
																			a_							:			Optional[Any]     		=					decoded_message + letter
													elif message[numbers_index * 2] == " ":
																			a_							:			str     		=					decoded_message + " "

							return decoded_message




