
'''simple docstring'''







import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig

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

def 				_snake_case (		lowercase			,    lowercase						)       ->       Dict:
				__a       :     Optional[int]								=	config.n_positions
				__a       :     Optional[int]								=	config.n_layer
				__a       :     List[Any]								=	config.vocab_size
				__a       :     Optional[Any]								=	config.n_head
				__a       :     Dict								=	config.n_embd // config.n_head

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

								__a       :     str								=	sd_megatron[F"""{pf}{i}.input_layernorm.weight"""]
								__a       :     List[Any]								=	sd_megatron[F"""{pf}{i}.input_layernorm.bias"""]

								__a       :     Optional[Any]								=	sd_megatron[F"""{pf}{i}.self_attention.query_key_value.weight"""]
								__a       :     Optional[int]								=	fix_query_key_value_ordering(lowercase			,    3			,    lowercase			,    lowercase						)
								__a       :     List[str]								=	vala.transpose(0			,    1						).contiguous()

								__a       :     Any								=	sd_megatron[F"""{pf}{i}.self_attention.query_key_value.bias"""]
								__a       :     Union[str, Any]								=	fix_query_key_value_ordering(lowercase			,    3			,    lowercase			,    lowercase						)
								__a       :     Any								=	vala

								__a       :     str								=	sd_megatron[F"""{pf}{i}.self_attention.dense.weight"""].transpose(
								    0			,    1						)
								__a       :     List[Any]								=	sd_megatron[F"""{pf}{i}.self_attention.dense.bias"""]
								__a       :     Optional[int]								=	sd_megatron[F"""{pf}{i}.post_attention_layernorm.weight"""]
								__a       :     Any								=	sd_megatron[F"""{pf}{i}.post_attention_layernorm.bias"""]
								__a       :     Union[str, Any]								=	sd_megatron[F"""{pf}{i}.mlp.dense_h_to_4h.weight"""].transpose(0			,    1						)
								__a       :     List[str]								=	sd_megatron[F"""{pf}{i}.mlp.dense_h_to_4h.bias"""]
								__a       :     str								=	sd_megatron[F"""{pf}{i}.mlp.dense_4h_to_h.weight"""].transpose(
								    0			,    1						)
								__a       :     Optional[int]								=	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 				_snake_case (		lowercase			,    lowercase						)       ->       Union[str, Any]:
				__a       :     Tuple								=	6_4_0_0_0
				__a       :     Any								=	config_megatron["""encoder_seq_length"""]
				__a       :     Optional[int]								=	config_megatron["""hidden_size"""]
				__a       :     Tuple								=	config_megatron["""num_layers"""]
				__a       :     List[Any]								=	config_megatron["""num_attention_heads"""]
				__a       :     int								=	config_megatron["""ffn_hidden_size"""]
				__a       :     Dict								=	"""gelu"""
				__a       :     List[str]								=	0.1
				__a       :     List[Any]								=	0.1
				__a       :     Dict								=	0.1
				__a       :     Tuple								=	config_megatron["""layernorm_epsilon"""]  # 1e-5
				__a       :     List[str]								=	config_megatron["""init_method_std"""]  # 0.02
				__a       :     str								=	config_megatron["""apply_query_key_layer_scaling"""]  # True
				__a       :     List[Any]								=	True
				__a       :     Dict								=	True

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

				return config_hf

def 				_snake_case (		lowercase						)       ->       List[str]:
				print(lowercase						)

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

				# Load the model.
				__a       :     Any								=	torch.load(lowercase			,    map_location="""cpu"""						)

				# Load the config.
				__a       :     Optional[Any]								=	checkpoint["""hyper_parameters"""]["""cfg"""]
				__a       :     Dict								=	GPTaConfig()
				__a       :     Optional[Any]								=	copy_config(config_hf=lowercase			,    config_megatron=lowercase						)
				__a       :     List[Any]								=	["""GPT2LMHeadModel"""]

				__a       :     Dict								=	checkpoint["""state_dict"""]

				# Convert.
				print("""Converting"""						)
				__a       :     List[str]								=	convert_megatron_checkpoint(lowercase			,    lowercase						)

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

				__a       :     Dict								=	"""GPTSw3Tokenizer"""

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

				# Store the state_dict to file.
				__a       :     Tuple								=	os.path.join(lowercase			,    """pytorch_model.bin"""						)
				print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""						)
				torch.save(lowercase			,    lowercase						)


if __name__ == "__main__":
					__SCREAMING_SNAKE_CASE		:  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')
					__SCREAMING_SNAKE_CASE		:  List[Any]        =      parser.parse_args()
					main(_args)

'''simple docstring'''







def 				_snake_case (		lowercase						)       ->       Tuple:
				__a       :     Union[str, Any]								=	[7_0_1, 3_0_1, 1_3_2, 5_7, 2_3, 1_0, 4, 1]
				for gap in gaps:
								for i in range(lowercase			,    len(lowercase						)						):
												__a       :     Tuple								=	collection[i]
												__a       :     Optional[int]								=	i
												while j >= gap and collection[j - gap] > insert_value:
																__a       :     Tuple								=	collection[j - gap]
																j -= gap
												if j != i:
																__a       :     Dict								=	insert_value
				return collection


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					__SCREAMING_SNAKE_CASE		:  Any        =      input('Enter numbers separated by a comma:\n').strip()
					__SCREAMING_SNAKE_CASE		:  Optional[int]        =      [int(item) for item in user_input.split(',')]
					print(shell_sort(unsorted))