




# 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 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase=0				)      ->		List[Any]:





  """simple docstring"""





  if name is None:
    _lowercase:			Optional[int]      =     None
  else:
    _lowercase:			Dict      =     '.' * max(0       ,  spaces - 2				) + '# {:' + str(50 - spaces				) + 's}'
    _lowercase:			List[str]      =     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       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		List[str]:





  """simple docstring"""





  _lowercase:			Optional[int]      =     param.size()
  if checkpoint_version == 1.0:
    # version 1.0 stores [num_heads * hidden_size * num_splits, :]
    _lowercase:			Optional[int]      =     (num_heads, hidden_size, num_splits) + input_shape[1:]
    _lowercase:			Any      =     param.view(*UpperCamelCase				)
    _lowercase:			Tuple      =     param.transpose(0       ,  2				)
    _lowercase:			List[str]      =     param.transpose(1       ,  2				).contiguous()
  elif checkpoint_version >= 2.0:
    # other versions store [num_heads * num_splits * hidden_size, :]
    _lowercase:			Union[str, Any]      =     (num_heads, num_splits, hidden_size) + input_shape[1:]
    _lowercase:			Any      =     param.view(*UpperCamelCase				)
    _lowercase:			List[str]      =     param.transpose(0       ,  1				).contiguous()
  _lowercase:			Optional[int]      =     param.view(*UpperCamelCase				)
  return param


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  _lowercase:			Dict      =     {}

  # old versions did not store training args
  _lowercase:			Optional[int]      =     input_state_dict.get('args'       ,  UpperCamelCase				)
  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))

    _lowercase:			Tuple      =     ds_args.tokenizer_type
    _lowercase:			Any      =     ds_args.padded_vocab_size
    _lowercase:			Any      =     ds_args.max_position_embeddings
    _lowercase:			Optional[int]      =     ds_args.hidden_size
    _lowercase:			Union[str, Any]      =     ds_args.num_layers
    _lowercase:			Union[str, Any]      =     ds_args.num_attention_heads
    _lowercase:			Optional[int]      =     ds_args.ffn_hidden_size if 'ffn_hidden_size' in ds_args else 4 * ds_args.hidden_size
    # pprint(config)

  # The number of heads.
  _lowercase:			Union[str, Any]      =     config.num_attention_heads
  # The hidden_size per head.
  _lowercase:			Tuple      =     config.hidden_size // heads
  # Megatron-LM checkpoint version
  if "checkpoint_version" in input_state_dict.keys():
    _lowercase:			Dict      =     input_state_dict['checkpoint_version']
  else:
    _lowercase:			str      =     0.0

  # The model.
  _lowercase:			Dict      =     input_state_dict['model']
  # The language model.
  _lowercase:			List[Any]      =     model['language_model']
  # The embeddings.
  _lowercase:			Optional[Any]      =     lm['embedding']

  # The word embeddings.
  _lowercase:			Any      =     embeddings['word_embeddings']['weight']
  # Truncate the embedding table to vocab_size rows.
  _lowercase:			Dict      =     word_embeddings[: config.vocab_size, :]
  # Store the word embeddings.
  _lowercase:			Union[str, Any]      =     word_embeddings

  # The position embeddings.
  _lowercase:			List[str]      =     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.
  _lowercase:			List[str]      =     pos_embeddings

  # The token-type embeddings.
  _lowercase:			Tuple      =     embeddings['tokentype_embeddings']['weight']
  # Store the position embeddings.
  _lowercase:			str      =     tokentype_embeddings

  # The transformer.
  _lowercase:			Union[str, Any]      =     lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

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

  # The simple map of names for "automated" rules.
  _lowercase:			Optional[int]      =     {
      '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.
  _lowercase:			str      =     None

  # Extract the layers.
  for key, val in transformer.items():
    # Match the name.
    _lowercase:			List[str]      =     layer_re.match(UpperCamelCase				)

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

    # The index of the layer.
    _lowercase:			Union[str, Any]      =     int(m.group(1				)				)
    # The name of the operation.
    _lowercase:			List[str]      =     m.group(2				)
    # Is it a weight or a bias?
    _lowercase:			List[Any]      =     m.group(3				)

    # The name of the layer.
    _lowercase:			Any      =     F'bert.encoder.layer.{layer_idx}'

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

      _lowercase:			List[Any]      =     fix_query_key_value_ordering(UpperCamelCase       ,  UpperCamelCase       ,  3       ,  UpperCamelCase       ,  UpperCamelCase				)
      # Store the tensor as we need the bias as well to interleave QKV and biases.
      _lowercase:			List[Any]      =     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.
      _lowercase:			Union[str, Any]      =     attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
      _lowercase:			Union[str, Any]      =     attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
      _lowercase:			str      =     attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

      _lowercase:			List[Any]      =     fix_query_key_value_ordering(UpperCamelCase       ,  UpperCamelCase       ,  3       ,  UpperCamelCase       ,  UpperCamelCase				)
      # Split the bias.
      _lowercase:			Optional[Any]      =     out_val[0 * config.hidden_size : 1 * config.hidden_size]
      _lowercase:			str      =     out_val[1 * config.hidden_size : 2 * config.hidden_size]
      _lowercase:			Optional[int]      =     out_val[2 * config.hidden_size : 3 * config.hidden_size]

      # Store.
      _lowercase:			Tuple      =     q
      _lowercase:			Tuple      =     q_bias
      _lowercase:			Tuple      =     k
      _lowercase:			Optional[int]      =     k_bias
      _lowercase:			List[Any]      =     v
      _lowercase:			Any      =     v_bias

      # Clear the stored tensor.
      _lowercase:			int      =     None

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

    # The final layernorm.
  _lowercase:			Dict      =     transformer['final_layernorm.weight']
  _lowercase:			int      =     transformer['final_layernorm.bias']

  # The pooler.
  _lowercase:			int      =     lm['pooler']

  # Store the matrix and the bias.
  _lowercase:			int      =     pooler['dense.weight']
  _lowercase:			Optional[Any]      =     pooler['dense.bias']

  # The LM head from Megatron (for RACE).
  _lowercase:			int      =     model['lm_head']

  # The transform matrix.
  _lowercase:			Optional[Any]      =     lm_head['dense.weight']
  _lowercase:			List[str]      =     lm_head['dense.bias']

  # The transform LN.
  _lowercase:			Optional[int]      =     lm_head['layernorm.weight']
  _lowercase:			int      =     lm_head['layernorm.bias']

  # For the decoder, we replicate the weights.
  _lowercase:			Optional[int]      =     word_embeddings
  _lowercase:			List[str]      =     lm_head['bias']

  # The classifier from Megatron (for MLNI).
  _lowercase:			Optional[int]      =     model['binary_head']

  # Store the classifier.
  _lowercase:			Dict      =     binary_head['weight']
  _lowercase:			List[str]      =     binary_head['bias']

  # It should be done!
  return output_state_dict


def 		_lowerCamelCase					(       )      ->		Any:





  """simple docstring"""





  _lowercase:			Optional[Any]      =     argparse.ArgumentParser()
  parser.add_argument('--print-checkpoint-structure'       ,  action='store_true'				)
  parser.add_argument('path_to_checkpoint'       ,  type=UpperCamelCase       ,  help='Path to the ZIP file containing the checkpoint'				)
  parser.add_argument(
      '--config_file'       ,  default=''       ,  type=UpperCamelCase       ,  help='An optional config json file describing the pre-trained model.'       ,  )
  _lowercase:			Dict      =     parser.parse_args()

  # Extract the basename.
  _lowercase:			List[Any]      =     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:
        _lowercase:			int      =     torch.load(UpperCamelCase       ,  map_location='cpu'				)
  else:
    _lowercase:			Tuple      =     torch.load(args.path_to_checkpoint       ,  map_location='cpu'				)

  if args.config_file == "":
    # Default config of megatron-bert 345m
    _lowercase:			List[Any]      =     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
    _lowercase:			Tuple      =     input_state_dict['model']['lm_head']['bias'].numel()
  else:
    _lowercase:			Tuple      =     MegatronBertConfig.from_json_file(args.config_file				)

  # Convert.
  print('Converting'				)
  _lowercase:			Dict      =     convert_megatron_checkpoint(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)

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

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

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


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

if __name__ == "__main__":
   main()

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






from __future__ import annotations

class   UpperCAmelCase    :






   def __init__(						self :							List[Any]    ,       UpperCamelCase_ :							int  )->      None:
     _lowercase:			Optional[Any]      =     data
     _lowercase:			Optional[int]      =     None

class   UpperCAmelCase    :






   def __init__(						self :							Optional[Any]  )->      Union[str, Any]:
     _lowercase:			List[str]      =     None






   def 			_a    (						self :							List[Any]    ,       UpperCamelCase_ :							int  )->      int:
     _lowercase:			Any      =     Node(UpperCamelCase_  )
     _lowercase:			List[Any]      =     self.head
     _lowercase:			List[str]      =     new_node
     return self.head.data






   def 			_a    (						self :							Tuple  )->      int | None:
     _lowercase:			List[str]      =     self.head
     _lowercase:			str      =     self.head
     if self.head:
       while fast_pointer and fast_pointer.next:
         _lowercase:			Optional[int]      =     fast_pointer.next.next
         _lowercase:			Tuple      =     slow_pointer.next
       return slow_pointer.data
     else:
       print('No element found.'  )
       return None


if __name__ == "__main__":
   UpperCAmelCase__    :							Union[str, Any]       		=     LinkedList()
   for _ in range(int(input().strip())):
      UpperCAmelCase__    :							List[Any]       		=     int(input().strip())
      link.push(data)
   print(link.middle_element())
