


"""simple docstring"""






import math
from decimal import Decimal, getcontext



def 			A__		(					_UpperCAmelCase    :      int = 4_00_00_00	)							->	int:

	'''simple docstring'''


	try:
		snake_case__  :						Optional[Any]		=      int(_UpperCAmelCase	)
	except (TypeError, ValueError):
		raise TypeError("Parameter n must be int or castable to int."	)
	if n <= 0:
		raise ValueError("Parameter n must be greater than or equal to one."	)
	snake_case__  :						List[str]		=      1_00
	snake_case__  :						Tuple		=      (Decimal(5	) ** Decimal(0.5	) + 1) / Decimal(2	)

	snake_case__  :						str		=      (math.floor(math.log(n * (phi + 2)				,    _UpperCAmelCase	) - 1	) // 3) * 3 + 2
	snake_case__  :						List[str]		=      Decimal(round(phi ** Decimal(index + 1	)	)	) / (phi + 2)
	snake_case__  :						List[Any]		=      num // 2
	return int(_UpperCAmelCase	)


if __name__ == "__main__":
					print(f"{solution() = }")











# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig







def       _a (		SCREAMING_SNAKE_CASE_				:							Any , SCREAMING_SNAKE_CASE_				:							List[str] , SCREAMING_SNAKE_CASE_				:							List[str]=0	):
					# Format the message.
					if name is None:
										__lowerCAmelCase							  =			None
					else:
										__lowerCAmelCase							  =			"." * max(0 , spaces - 2	) + "# {:" + str(50 - spaces	) + "s}"
										__lowerCAmelCase							  =			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       _a (		SCREAMING_SNAKE_CASE_				:							Optional[int] , SCREAMING_SNAKE_CASE_				:							Dict , SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							str , SCREAMING_SNAKE_CASE_				:							Any	):
					# 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 BERT.
					__lowerCAmelCase							  =			param.size()
					if checkpoint_version == 1.0:
										# version 1.0 stores [num_heads * hidden_size * num_splits, :]
										__lowerCAmelCase							  =			(num_heads, hidden_size, num_splits) + input_shape[1:]
										__lowerCAmelCase							  =			param.view(*SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			param.transpose(0 , 2	)
										__lowerCAmelCase							  =			param.transpose(1 , 2	).contiguous()
					elif checkpoint_version >= 2.0:
										# other versions store [num_heads * num_splits * hidden_size, :]
										__lowerCAmelCase							  =			(num_heads, num_splits, hidden_size) + input_shape[1:]
										__lowerCAmelCase							  =			param.view(*SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			param.transpose(0 , 1	).contiguous()
					__lowerCAmelCase							  =			param.view(*SCREAMING_SNAKE_CASE_	)
					return param







def       _a (		SCREAMING_SNAKE_CASE_				:							Any , SCREAMING_SNAKE_CASE_				:							Any , SCREAMING_SNAKE_CASE_				:							Optional[Any]	):
					# The converted output model.
					__lowerCAmelCase							  =			{}

					# old versions did not store training args
					__lowerCAmelCase							  =			input_state_dict.get("args" , SCREAMING_SNAKE_CASE_	)
					if ds_args is not None:
										# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
										# from pprint import pprint
										# pprint(vars(ds_args))

										__lowerCAmelCase							  =			ds_args.tokenizer_type
										__lowerCAmelCase							  =			ds_args.padded_vocab_size
										__lowerCAmelCase							  =			ds_args.max_position_embeddings
										__lowerCAmelCase							  =			ds_args.hidden_size
										__lowerCAmelCase							  =			ds_args.num_layers
										__lowerCAmelCase							  =			ds_args.num_attention_heads
										__lowerCAmelCase							  =			ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
										# pprint(config)

					# The number of heads.
					__lowerCAmelCase							  =			config.num_attention_heads
					# The hidden_size per head.
					__lowerCAmelCase							  =			config.hidden_size // heads
					# Megatron-LM checkpoint version
					if "checkpoint_version" in input_state_dict.keys():
										__lowerCAmelCase							  =			input_state_dict["checkpoint_version"]
					else:
										__lowerCAmelCase							  =			0.0

					# The model.
					__lowerCAmelCase							  =			input_state_dict["model"]
					# The language model.
					__lowerCAmelCase							  =			model["language_model"]
					# The embeddings.
					__lowerCAmelCase							  =			lm["embedding"]

					# The word embeddings.
					__lowerCAmelCase							  =			embeddings["word_embeddings"]["weight"]
					# Truncate the embedding table to vocab_size rows.
					__lowerCAmelCase							  =			word_embeddings[: config.vocab_size, :]
					# Store the word embeddings.
					__lowerCAmelCase							  =			word_embeddings

					# The position embeddings.
					__lowerCAmelCase							  =			embeddings["position_embeddings"]["weight"]
					assert pos_embeddings.size(0	) == config.max_position_embeddings and pos_embeddings.size(1	) == config.hidden_size
					# Store the position embeddings.
					__lowerCAmelCase							  =			pos_embeddings

					# The token-type embeddings.
					__lowerCAmelCase							  =			embeddings["tokentype_embeddings"]["weight"]
					# Store the position embeddings.
					__lowerCAmelCase							  =			tokentype_embeddings

					# The transformer.
					__lowerCAmelCase							  =			lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

					# The regex to extract layer names.
					__lowerCAmelCase							  =			re.compile(R"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"	)

					# The simple map of names for "automated" rules.
					__lowerCAmelCase							  =			{
					    "attention.dense": ".attention.output.dense.",
					    "self_attention.dense": ".attention.output.dense.",
					    "mlp.dense_h_to_4h": ".intermediate.dense.",
					    "mlp.dense_4h_to_h": ".output.dense.",
					}

					# Keep track of the attention/query/value tensor.
					__lowerCAmelCase							  =			None

					# Extract the layers.
					for key, val in transformer.items():
										# Match the name.
										__lowerCAmelCase							  =			layer_re.match(SCREAMING_SNAKE_CASE_	)

										# Stop if that's not a layer
										if m is None:
															break

										# The index of the layer.
										__lowerCAmelCase							  =			int(m.group(1	)	)
										# The name of the operation.
										__lowerCAmelCase							  =			m.group(2	)
										# Is it a weight or a bias?
										__lowerCAmelCase							  =			m.group(3	)

										# The name of the layer.
										__lowerCAmelCase							  =			F"""bert.encoder.layer.{layer_idx}"""

										# For layernorm(s), simply store the layer norm.
										if op_name.endswith("layernorm"	):
															__lowerCAmelCase							  =			"attention.ln" if op_name.startswith("input"	) else "ln"
															__lowerCAmelCase							  =			val

										# Transpose the QKV matrix.
										elif (
										    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
										) and weight_or_bias == "weight":
															# Make sure the QKV pointer is nil.
															assert attention_qkv_weight is None, ""

															__lowerCAmelCase							  =			fix_query_key_value_ordering(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , 3 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
															# Store the tensor as we need the bias as well to interleave QKV and biases.
															__lowerCAmelCase							  =			out_val

										# Transpose the bias.
										elif (
										    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
										) and weight_or_bias == "bias":
															# Make sure we read the weight tensor.
															assert attention_qkv_weight is not None, ""

															# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
															__lowerCAmelCase							  =			attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
															__lowerCAmelCase							  =			attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
															__lowerCAmelCase							  =			attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

															__lowerCAmelCase							  =			fix_query_key_value_ordering(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , 3 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
															# Split the bias.
															__lowerCAmelCase							  =			out_val[0 * config.hidden_size : 1 * config.hidden_size]
															__lowerCAmelCase							  =			out_val[1 * config.hidden_size : 2 * config.hidden_size]
															__lowerCAmelCase							  =			out_val[2 * config.hidden_size : 3 * config.hidden_size]

															# Store.
															__lowerCAmelCase							  =			q
															__lowerCAmelCase							  =			q_bias
															__lowerCAmelCase							  =			k
															__lowerCAmelCase							  =			k_bias
															__lowerCAmelCase							  =			v
															__lowerCAmelCase							  =			v_bias

															# Clear the stored tensor.
															__lowerCAmelCase							  =			None

										# Copy weights and biases as is.
										elif weight_or_bias in ["weight", "bias"]:
															__lowerCAmelCase							  =			megatron_to_transformers[op_name]
															__lowerCAmelCase							  =			val

    # The final layernorm.
					__lowerCAmelCase							  =			transformer["final_layernorm.weight"]
					__lowerCAmelCase							  =			transformer["final_layernorm.bias"]

					# The pooler.
					__lowerCAmelCase							  =			lm["pooler"]

					# Store the matrix and the bias.
					__lowerCAmelCase							  =			pooler["dense.weight"]
					__lowerCAmelCase							  =			pooler["dense.bias"]

					# The LM head from Megatron (for RACE).
					__lowerCAmelCase							  =			model["lm_head"]

					# The transform matrix.
					__lowerCAmelCase							  =			lm_head["dense.weight"]
					__lowerCAmelCase							  =			lm_head["dense.bias"]

					# The transform LN.
					__lowerCAmelCase							  =			lm_head["layernorm.weight"]
					__lowerCAmelCase							  =			lm_head["layernorm.bias"]

					# For the decoder, we replicate the weights.
					__lowerCAmelCase							  =			word_embeddings
					__lowerCAmelCase							  =			lm_head["bias"]

					# The classifier from Megatron (for MLNI).
					__lowerCAmelCase							  =			model["binary_head"]

					# Store the classifier.
					__lowerCAmelCase							  =			binary_head["weight"]
					__lowerCAmelCase							  =			binary_head["bias"]

					# It should be done!
					return output_state_dict







def       _a (		):
					# Create the argument parser.
					__lowerCAmelCase							  =			argparse.ArgumentParser()
					parser.add_argument("--print-checkpoint-structure" , action="store_true"	)
					parser.add_argument("path_to_checkpoint" , type=SCREAMING_SNAKE_CASE_ , help="Path to the ZIP file containing the checkpoint"	)
					parser.add_argument(
					    "--config_file" , default="" , type=SCREAMING_SNAKE_CASE_ , help="An optional config json file describing the pre-trained model." , )
					__lowerCAmelCase							  =			parser.parse_args()

					# Extract the basename.
					__lowerCAmelCase							  =			os.path.dirname(args.path_to_checkpoint	)

					# Load the model.
					# the .zip is very optional, let's keep it for backward compatibility
					print(F"""Extracting PyTorch state dictionary from \"{args.path_to_checkpoint}\""""	)
					if args.path_to_checkpoint.endswith(".zip"	):
										with zipfile.ZipFile(args.path_to_checkpoint , "r"	) as checkpoint:
															with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"	) as pytorch_dict:
																				__lowerCAmelCase							  =			torch.load(SCREAMING_SNAKE_CASE_ , map_location="cpu"	)
					else:
										__lowerCAmelCase							  =			torch.load(args.path_to_checkpoint , map_location="cpu"	)

					if args.config_file == "":
										# Default config of megatron-bert 345m
										__lowerCAmelCase							  =			MegatronBertConfig()

										# different megatron-bert-*-345m models have different vocab sizes, so override the default
										# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
										__lowerCAmelCase							  =			input_state_dict["model"]["lm_head"]["bias"].numel()
					else:
										__lowerCAmelCase							  =			MegatronBertConfig.from_json_file(args.config_file	)

					# Convert.
					print("Converting"	)
					__lowerCAmelCase							  =			convert_megatron_checkpoint(SCREAMING_SNAKE_CASE_ , 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_	)

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

					# Store the state_dict to file.
					__lowerCAmelCase							  =			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__":
					main()

####################################################################################################




