



import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig

def       A_   (						__a  :					Union[str, Any]			,		__a  :					List[str]			,		__a  :					Union[str, Any]=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  :					List[Any]			,		__a  :					Dict			,		__a  :					List[Any]			,		__a  :					int		):

				"""simple docstring"""
				# 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__					   =		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[Any]			,		__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  :					List[str]			,		__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  :					Union[str, 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							(      lowerCamelCase:  List[Any]					)      ->		Optional[Any]:







      '''simple docstring'''






      __A			  =							[7_01, 3_01, 1_32, 57, 23, 10, 4, 1]
      for gap in gaps:
            for i in range(lowerCamelCase   ,							len(lowerCamelCase					)					):
                  __A			  =							collection[i]
                  __A			  =							i
                  while j >= gap and collection[j - gap] > insert_value:
                        __A			  =							collection[j - gap]
                        j -= gap
                  if j != i:
                        __A			  =							insert_value
      return collection


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

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





