'''simple docstring'''




import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig




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

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




def 				lowerCamelCase      ( UpperCAmelCase__			:   Dict , UpperCAmelCase__			:   Dict , UpperCAmelCase__			:   Optional[Any] , UpperCAmelCase__			:   Any					)     -> Tuple:
							# 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.
							lowercase_   :     int									=			param.size()
							# other versions store [num_heads * num_splits * hidden_size, :]
							lowercase_   :     Any									=			(num_heads, num_splits, hidden_size) + input_shape[1:]
							lowercase_   :     int									=			param.view(*UpperCAmelCase__					)
							lowercase_   :     Any									=			param.transpose(0 , 1					).contiguous()
							lowercase_   :     int									=			param.view(*UpperCAmelCase__					)
							return param




def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[int] , UpperCAmelCase__			:   Optional[Any]					)     -> str:
							lowercase_   :     List[str]									=			config.n_positions
							lowercase_   :     List[Any]									=			config.n_layer
							lowercase_   :     int									=			config.vocab_size
							lowercase_   :     Optional[int]									=			config.n_head
							lowercase_   :     List[Any]									=			config.n_embd // config.n_head

							lowercase_   :     Optional[int]									=			sd_megatron["""model.language_model.embedding.word_embeddings.weight"""][:vocab_size, :]
							lowercase_   :     Tuple									=			{
							    """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"""],
							}

							lowercase_   :     Optional[int]									=			"""model.language_model.encoder.layers."""
							for i in range(UpperCAmelCase__					):
														lowercase_   :     Union[str, Any]									=			torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.bool					)					)
														lowercase_   :     List[Any]									=			causal_mask.view(1 , 1 , UpperCAmelCase__ , UpperCAmelCase__					)
														lowercase_   :     Optional[Any]									=			causal_mask
														lowercase_   :     Optional[Any]									=			torch.tensor(-1e4 , dtype=torch.bfloataa					)

														lowercase_   :     Tuple									=			sd_megatron[F'''{pf}{i}.input_layernorm.weight''']
														lowercase_   :     Any									=			sd_megatron[F'''{pf}{i}.input_layernorm.bias''']

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

														lowercase_   :     List[Any]									=			sd_megatron[F'''{pf}{i}.self_attention.query_key_value.bias''']
														lowercase_   :     Tuple									=			fix_query_key_value_ordering(UpperCAmelCase__ , 3 , UpperCAmelCase__ , UpperCAmelCase__					)
														lowercase_   :     Dict									=			vala

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

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

							return sd_hf




def 				lowerCamelCase      ( UpperCAmelCase__			:   Tuple , UpperCAmelCase__			:   List[str]					)     -> List[str]:
							lowercase_   :     List[Any]									=			64000
							lowercase_   :     str									=			config_megatron["""encoder_seq_length"""]
							lowercase_   :     Optional[Any]									=			config_megatron["""hidden_size"""]
							lowercase_   :     Dict									=			config_megatron["""num_layers"""]
							lowercase_   :     Tuple									=			config_megatron["""num_attention_heads"""]
							lowercase_   :     Optional[Any]									=			config_megatron["""ffn_hidden_size"""]
							lowercase_   :     List[Any]									=			"""gelu"""
							lowercase_   :     List[Any]									=			0.1
							lowercase_   :     List[Any]									=			0.1
							lowercase_   :     Dict									=			0.1
							lowercase_   :     Dict									=			config_megatron["""layernorm_epsilon"""]  # 1e-5
							lowercase_   :     Any									=			config_megatron["""init_method_std"""]  # 0.02
							lowercase_   :     List[str]									=			config_megatron["""apply_query_key_layer_scaling"""]  # True
							lowercase_   :     Union[str, Any]									=			True
							lowercase_   :     List[Any]									=			True

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

							return config_hf




def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[int]					)     -> Optional[int]:
							print(UpperCAmelCase__					)

							lowercase_   :     Optional[Any]									=			args.checkpoint_path
							lowercase_   :     Dict									=			args.save_path
							if isfile(UpperCAmelCase__					):
														raise FileNotFoundError(F'''ERROR! could not find file {checkpoint_path}'''					)

							# Load the model.
							lowercase_   :     str									=			torch.load(UpperCAmelCase__ , map_location="""cpu"""					)

							# Load the config.
							lowercase_   :     str									=			checkpoint["""hyper_parameters"""]["""cfg"""]
							lowercase_   :     Optional[Any]									=			GPTaConfig()
							lowercase_   :     Tuple									=			copy_config(config_hf=UpperCAmelCase__ , config_megatron=UpperCAmelCase__					)
							lowercase_   :     int									=			["""GPT2LMHeadModel"""]

							lowercase_   :     Tuple									=			checkpoint["""state_dict"""]

							# Convert.
							print("""Converting"""					)
							lowercase_   :     Tuple									=			convert_megatron_checkpoint(UpperCAmelCase__ , UpperCAmelCase__					)

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

							lowercase_   :     Tuple									=			"""GPTSw3Tokenizer"""

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

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


if __name__ == "__main__":
						_lowercase		:					Tuple													=		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")
						_lowercase		:					Optional[int]													=		parser.parse_args()
						main(_args)






'''simple docstring'''




from __future__ import annotations





class        __magic_name__		:







			def __init__(      self							:    List[str]							,     lowercase_							:    int       ):
										lowercase_   :     List[str]									=			vertices
										lowercase_   :     Optional[Any]									=			[[0] * vertices for _ in range(lowercase_       )]







			def       SCREAMING_SNAKE_CASE_	(      self							:    int							,     lowercase_							:    list[int]       ):
										print("""Vertex \t Distance from Source"""       )
										for vertex in range(self.vertices       ):
																	print(lowercase_							,     """\t\t"""							,     distances_from_source[vertex]       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    str							,     lowercase_							:    list[int]							,     lowercase_							:    list[bool]       ):
										lowercase_   :     Dict									=			1E7
										lowercase_   :     int									=			0

										# Search not nearest vertex not in the shortest path tree
										for vertex in range(self.vertices       ):
																	if distances_from_source[vertex] < minimum and visited[vertex] is False:
																								lowercase_   :     Tuple									=			distances_from_source[vertex]
																								lowercase_   :     str									=			vertex
										return min_index







			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]							,     lowercase_							:    int       ):
										lowercase_   :     Optional[Any]									=			[int(1E7       )] * self.vertices  # distances from the source
										lowercase_   :     List[Any]									=			0
										lowercase_   :     Any									=			[False] * self.vertices

										for _ in range(self.vertices       ):
																	lowercase_   :     Tuple									=			self.minimum_distance(lowercase_							,     lowercase_       )
																	lowercase_   :     Union[str, Any]									=			True

																	# Update dist value of the adjacent vertices
																	# of the picked vertex only if the current
																	# distance is greater than new distance and
																	# the vertex in not in the shortest path tree
																	for v in range(self.vertices       ):
																								if (
																								    self.graph[u][v] > 0
																								    and visited[v] is False
																								    and distances[v] > distances[u] + self.graph[u][v]
																								):
																															lowercase_   :     Any									=			distances[u] + self.graph[u][v]

										self.print_solution(lowercase_       )


if __name__ == "__main__":
						_lowercase		:					Any													=		Graph(9)
						_lowercase		:					Any													=		[
						    [0, 4, 0, 0, 0, 0, 0, 8, 0],
						    [4, 0, 8, 0, 0, 0, 0, 11, 0],
						    [0, 8, 0, 7, 0, 4, 0, 0, 2],
						    [0, 0, 7, 0, 9, 14, 0, 0, 0],
						    [0, 0, 0, 9, 0, 10, 0, 0, 0],
						    [0, 0, 4, 14, 10, 0, 2, 0, 0],
						    [0, 0, 0, 0, 0, 2, 0, 1, 6],
						    [8, 11, 0, 0, 0, 0, 1, 0, 7],
						    [0, 0, 2, 0, 0, 0, 6, 7, 0],
						]
						graph.dijkstra(0)





