


# 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_gpt2/convert_megatron_gpt2_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 AutoTokenizer, GPTaConfig







def 						lowerCamelCase(   a__	,a__	,a__=0):
				# Format the message.
				if name is None:
								_SCREAMING_SNAKE_CASE			  =None
				else:
								_SCREAMING_SNAKE_CASE			  ='''.''' * max(0	,spaces - 2) + '''# {:''' + str(50 - spaces) + '''s}'''
								_SCREAMING_SNAKE_CASE			  =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 						lowerCamelCase(   a__	,a__	,a__	,a__	,a__):
				# 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.
				_SCREAMING_SNAKE_CASE			  =param.size()
				if checkpoint_version == 1.0:
								# version 1.0 stores [num_heads * hidden_size * num_splits, :]
								_SCREAMING_SNAKE_CASE			  =(num_heads, hidden_size, num_splits) + input_shape[1:]
								_SCREAMING_SNAKE_CASE			  =param.view(*a__)
								_SCREAMING_SNAKE_CASE			  =param.transpose(0	,2)
								_SCREAMING_SNAKE_CASE			  =param.transpose(1	,2).contiguous()
				elif checkpoint_version >= 2.0:
								# other versions store [num_heads * num_splits * hidden_size, :]
								_SCREAMING_SNAKE_CASE			  =(num_heads, num_splits, hidden_size) + input_shape[1:]
								_SCREAMING_SNAKE_CASE			  =param.view(*a__)
								_SCREAMING_SNAKE_CASE			  =param.transpose(0	,1).contiguous()
				_SCREAMING_SNAKE_CASE			  =param.view(*a__)
				return param







def 						lowerCamelCase(   a__	,a__	,a__):
				# The converted output model.
				_SCREAMING_SNAKE_CASE			  ={}

				# old versions did not store training args
				_SCREAMING_SNAKE_CASE			  =input_state_dict.get('''args'''	,a__)
				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))

								_SCREAMING_SNAKE_CASE			  =ds_args.padded_vocab_size
								_SCREAMING_SNAKE_CASE			  =ds_args.max_position_embeddings
								_SCREAMING_SNAKE_CASE			  =ds_args.hidden_size
								_SCREAMING_SNAKE_CASE			  =ds_args.num_layers
								_SCREAMING_SNAKE_CASE			  =ds_args.num_attention_heads
								_SCREAMING_SNAKE_CASE			  =ds_args.ffn_hidden_size
								# pprint(config)

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

				# The model.
				_SCREAMING_SNAKE_CASE			  =input_state_dict['''model''']
				# The language model.
				_SCREAMING_SNAKE_CASE			  =model['''language_model''']
				# The embeddings.
				_SCREAMING_SNAKE_CASE			  =lm['''embedding''']

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

				# The position embeddings.
				_SCREAMING_SNAKE_CASE			  =embeddings['''position_embeddings''']['''weight''']
				# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
				_SCREAMING_SNAKE_CASE			  =pos_embeddings.size(0)
				if n_positions != config.n_positions:
								raise ValueError(
								    f"pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match")
				# Store the position embeddings.
				_SCREAMING_SNAKE_CASE			  =pos_embeddings

				# The transformer.
				_SCREAMING_SNAKE_CASE			  =lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

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

				# The simple map of names for "automated" rules.
				_SCREAMING_SNAKE_CASE			  ={
				    '''attention.dense''': '''.attn.c_proj.''',
				    '''self_attention.dense''': '''.attn.c_proj.''',
				    '''mlp.dense_h_to_4h''': '''.mlp.c_fc.''',
				    '''mlp.dense_4h_to_h''': '''.mlp.c_proj.''',
				}

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

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

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

								# The name of the layer.
								_SCREAMING_SNAKE_CASE			  =f"transformer.h.{layer_idx}"

								# For layernorm(s), simply store the layer norm.
								if op_name.endswith('''layernorm'''):
												_SCREAMING_SNAKE_CASE			  ='''ln_1''' if op_name.startswith('''input''') else '''ln_2'''
												_SCREAMING_SNAKE_CASE			  =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":
												# Insert a tensor of 1x1xDxD bias.
												_SCREAMING_SNAKE_CASE			  =torch.tril(torch.ones((n_positions, n_positions)	,dtype=torch.floataa)).view(
												    1	,1	,a__	,a__)
												_SCREAMING_SNAKE_CASE			  =causal_mask

												# Insert a "dummy" tensor for masked_bias.
												_SCREAMING_SNAKE_CASE			  =torch.tensor(-1e4	,dtype=torch.floataa)
												_SCREAMING_SNAKE_CASE			  =masked_bias

												_SCREAMING_SNAKE_CASE			  =fix_query_key_value_ordering(a__	,a__	,3	,a__	,a__)
												# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
												_SCREAMING_SNAKE_CASE			  =out_val.transpose(0	,1).contiguous()
												# Store.
												_SCREAMING_SNAKE_CASE			  =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":
												_SCREAMING_SNAKE_CASE			  =fix_query_key_value_ordering(a__	,a__	,3	,a__	,a__)
												# Store. No change of shape.
												_SCREAMING_SNAKE_CASE			  =out_val

								# Transpose the weights.
								elif weight_or_bias == "weight":
												_SCREAMING_SNAKE_CASE			  =megatron_to_transformers[op_name]
												_SCREAMING_SNAKE_CASE			  =val.transpose(0	,1)

								# Copy the bias.
								elif weight_or_bias == "bias":
												_SCREAMING_SNAKE_CASE			  =megatron_to_transformers[op_name]
												_SCREAMING_SNAKE_CASE			  =val

    # DEBUG.
				assert config.n_layer == layer_idx + 1

				# The final layernorm.
				_SCREAMING_SNAKE_CASE			  =transformer['''final_layernorm.weight''']
				_SCREAMING_SNAKE_CASE			  =transformer['''final_layernorm.bias''']

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

				# It should be done!
				return output_state_dict







def 						lowerCamelCase(   ):
				# Create the argument parser.
				_SCREAMING_SNAKE_CASE			  =argparse.ArgumentParser()
				parser.add_argument('''--print-checkpoint-structure'''	,action='''store_true''')
				parser.add_argument(
				    '''path_to_checkpoint'''	,type=a__	,help='''Path to the checkpoint file (.zip archive or direct .pt file)'''	,)
				parser.add_argument(
				    '''--config_file'''	,default=''''''	,type=a__	,help='''An optional config json file describing the pre-trained model.'''	,)
				_SCREAMING_SNAKE_CASE			  =parser.parse_args()

				# Extract the basename.
				_SCREAMING_SNAKE_CASE			  =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:
																_SCREAMING_SNAKE_CASE			  =torch.load(a__	,map_location='''cpu''')
				else:
								_SCREAMING_SNAKE_CASE			  =torch.load(args.path_to_checkpoint	,map_location='''cpu''')

				_SCREAMING_SNAKE_CASE			  =input_state_dict.get('''args'''	,a__)

				# Read the config, or default to the model released by NVIDIA.
				if args.config_file == "":
								if ds_args is not None:
												if ds_args.bias_gelu_fusion:
																_SCREAMING_SNAKE_CASE			  ='''gelu_fast'''
												elif ds_args.openai_gelu:
																_SCREAMING_SNAKE_CASE			  ='''gelu_new'''
												else:
																_SCREAMING_SNAKE_CASE			  ='''gelu'''
								else:
												# in the very early days this used to be "gelu_new"
												_SCREAMING_SNAKE_CASE			  ='''gelu_new'''

								# Spell out all parameters in case the defaults change.
								_SCREAMING_SNAKE_CASE			  =GPTaConfig(
								    vocab_size=5_0257	,n_positions=1024	,n_embd=1024	,n_layer=24	,n_head=16	,n_inner=4096	,activation_function=a__	,resid_pdrop=0.1	,embd_pdrop=0.1	,attn_pdrop=0.1	,layer_norm_epsilon=1e-5	,initializer_range=0.02	,summary_type='''cls_index'''	,summary_use_proj=a__	,summary_activation=a__	,summary_proj_to_labels=a__	,summary_first_dropout=0.1	,scale_attn_weights=a__	,use_cache=a__	,bos_token_id=5_0256	,eos_token_id=5_0256	,)
				else:
								_SCREAMING_SNAKE_CASE			  =GPTaConfig.from_json_file(args.config_file)

				_SCREAMING_SNAKE_CASE			  =['''GPT2LMHeadModel''']

				# Convert.
				print('''Converting''')
				_SCREAMING_SNAKE_CASE			  =convert_megatron_checkpoint(a__	,a__	,a__)

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

				# Add tokenizer class info to config
				# see https://github.com/huggingface/transformers/issues/13906)
				if ds_args is not None:
								_SCREAMING_SNAKE_CASE			  =ds_args.tokenizer_type
								if tokenizer_type == "GPT2BPETokenizer":
												_SCREAMING_SNAKE_CASE			  ='''gpt2'''
								elif tokenizer_type == "PretrainedFromHF":
												_SCREAMING_SNAKE_CASE			  =ds_args.tokenizer_name_or_path
								else:
												raise ValueError(f"Unrecognized tokenizer_type {tokenizer_type}")
				else:
								_SCREAMING_SNAKE_CASE			  ='''gpt2'''

				_SCREAMING_SNAKE_CASE			  =AutoTokenizer.from_pretrained(a__)
				_SCREAMING_SNAKE_CASE			  =type(a__).__name__
				_SCREAMING_SNAKE_CASE			  =tokenizer_class

				# Store the config to file.
				print('''Saving config''')
				config.save_pretrained(a__)

				# Save tokenizer based on args
				print(f"Adding {tokenizer_class} tokenizer files")
				tokenizer.save_pretrained(a__)

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


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

if __name__ == "__main__":
							main()

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



from collections.abc import Generator







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =0, 1
				while True:
								_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =b, a + b
								yield b







def 						lowerCamelCase(   a__ = 1000):
				_SCREAMING_SNAKE_CASE			  =1
				_SCREAMING_SNAKE_CASE			  =fibonacci_generator()
				while len(str(next(a__))) < n:
								answer += 1
				return answer + 1


if __name__ == "__main__":
							print(solution(int(str(input()).strip())))