



import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig

def      A_ (		__a       :			Optional[Any]	,						__a       :			str	,						__a       :			Dict=0						):

				"""simple docstring"""
				# Format the message.
				if name is None:
								a__			 =						None
				else:
								a__			 =						""".""" * max(0	,						spaces - 2						) + """# {:""" + str(50 - spaces						) + """s}"""
								a__			 =						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      A_ (		__a       :			Optional[int]	,						__a       :			int	,						__a       :			Dict	,						__a       :			Dict						):

				"""simple docstring"""
				a__			 =						param.size()
				# other versions store [num_heads * num_splits * hidden_size, :]
				a__			 =						(num_heads, num_splits, hidden_size) + input_shape[1:]
				a__			 =						param.view(*__a						)
				a__			 =						param.transpose(0	,						1						).contiguous()
				a__			 =						param.view(*__a						)
				return param

def      A_ (		__a       :			Optional[int]	,						__a       :			Dict						):

				"""simple docstring"""
				a__			 =						config.n_positions
				a__			 =						config.n_layer
				a__			 =						config.vocab_size
				a__			 =						config.n_head
				a__			 =						config.n_embd // config.n_head

				a__			 =						sd_megatron["""model.language_model.embedding.word_embeddings.weight"""][:vocab_size, :]
				a__			 =						{
				    """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__			 =						"""model.language_model.encoder.layers."""
				for i in range(__a						):
								a__			 =						torch.tril(torch.ones((n_positions, n_positions)	,						dtype=torch.bool						)						)
								a__			 =						causal_mask.view(1	,						1	,						__a	,						__a						)
								a__			 =						causal_mask
								a__			 =						torch.tensor(-1e4	,						dtype=torch.bfloataa						)

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

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

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

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

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

				return sd_hf

def      A_ (		__a       :			Tuple	,						__a       :			List[Any]						):

				"""simple docstring"""
				a__			 =						64_000
				a__			 =						config_megatron["""encoder_seq_length"""]
				a__			 =						config_megatron["""hidden_size"""]
				a__			 =						config_megatron["""num_layers"""]
				a__			 =						config_megatron["""num_attention_heads"""]
				a__			 =						config_megatron["""ffn_hidden_size"""]
				a__			 =						"""gelu"""
				a__			 =						0.1
				a__			 =						0.1
				a__			 =						0.1
				a__			 =						config_megatron["""layernorm_epsilon"""]  # 1e-5
				a__			 =						config_megatron["""init_method_std"""]  # 0.02
				a__			 =						config_megatron["""apply_query_key_layer_scaling"""]  # True
				a__			 =						True
				a__			 =						True

				# This identifies the 6.7B (7B) model which uses a different tokenizer
				if config_megatron["hidden_size"] == 4_096:
								a__			 =						1  # <|endoftext|>
								a__			 =						1  # <|endoftext|>
								a__			 =						0  # <unk>
				else:
								a__			 =						2  # <s>
								a__			 =						3  # <|endoftext|>
								a__			 =						0  # <pad>

				return config_hf

def      A_ (		__a       :			Any						):

				"""simple docstring"""
				print(__a						)

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

				# Load the model.
				a__			 =						torch.load(__a	,						map_location="""cpu"""						)

				# Load the config.
				a__			 =						checkpoint["""hyper_parameters"""]["""cfg"""]
				a__			 =						GPTaConfig()
				a__			 =						copy_config(config_hf=__a	,						config_megatron=__a						)
				a__			 =						["""GPT2LMHeadModel"""]

				a__			 =						checkpoint["""state_dict"""]

				# Convert.
				print("""Converting"""						)
				a__			 =						convert_megatron_checkpoint(__a	,						__a						)

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

				a__			 =						"""GPTSw3Tokenizer"""

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

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


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)






def      A_ (		__a       :			int						):

				"""simple docstring"""
				if number != int(__a						):
								raise ValueError("""factorial() only accepts integral values"""						)
				if number < 0:
								raise ValueError("""factorial() not defined for negative values"""						)
				a__			 =						1
				for i in range(1	,						number + 1						):
								value *= i
				return value

def      A_ (		__a       :			int						):

				"""simple docstring"""
				if not isinstance(__a	,						__a						):
								raise ValueError("""factorial() only accepts integral values"""						)
				if n < 0:
								raise ValueError("""factorial() not defined for negative values"""						)
				return 1 if n == 0 or n == 1 else n * factorial(n - 1						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	UpperCAmelCase								=   int(input("""Enter a positive integer: """).strip() or 0)
	print(f"""factorial{n} is {factorial(n)}""")

