

'''simple docstring'''




import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
_lowerCAmelCase         =						logging.getLogger(__name__)




def 						UpperCamelCase	(			a		,						a			)				->     Dict:






     '''simple docstring'''
     __magic_name__             =    np.argmax(__lowerCAmelCase		,						axis=1			)
     return np.sum(outputs == labels			)




def 						UpperCamelCase	(			a			)				->     Tuple:






     '''simple docstring'''
     with open(__lowerCAmelCase		,						encoding='''utf_8'''			) as f:
          __magic_name__             =    csv.reader(__lowerCAmelCase			)
          __magic_name__             =    []
          next(__lowerCAmelCase			)  # skip the first line
          for line in tqdm(__lowerCAmelCase			):
               output.append((''' '''.join(line[1:5]			), line[5], line[6], int(line[-1]			) - 1)			)
     return output




def 						UpperCamelCase	(			a		,						a		,						a		,						a		,						a		,						a			)				->     Dict:






     '''simple docstring'''
     __magic_name__             =    []
     for dataset in encoded_datasets:
          __magic_name__             =    len(__lowerCAmelCase			)
          __magic_name__             =    np.zeros((n_batch, 2, input_len)		,						dtype=np.intaa			)
          __magic_name__             =    np.zeros((n_batch, 2)		,						dtype=np.intaa			)
          __magic_name__             =    np.full((n_batch, 2, input_len)		,						fill_value=-100		,						dtype=np.intaa			)
          __magic_name__             =    np.zeros((n_batch,)		,						dtype=np.intaa			)
          for (
              i,
              (story, conta, conta, mc_label),
          ) in enumerate(__lowerCAmelCase			):
               __magic_name__             =    [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               __magic_name__             =    [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               __magic_name__             =    with_conta
               __magic_name__             =    with_conta
               __magic_name__             =    len(__lowerCAmelCase			) - 1
               __magic_name__             =    len(__lowerCAmelCase			) - 1
               __magic_name__             =    with_conta
               __magic_name__             =    with_conta
               __magic_name__             =    mc_label
          __magic_name__             =    (input_ids, mc_token_ids, lm_labels, mc_labels)
          tensor_datasets.append(tuple(torch.tensor(__lowerCAmelCase			) for t in all_inputs			)			)
     return tensor_datasets




def 						UpperCamelCase	(			)				->     Dict:






     '''simple docstring'''
     __magic_name__             =    argparse.ArgumentParser()
     parser.add_argument('''--model_name'''		,						type=__lowerCAmelCase		,						default='''openai-gpt'''		,						help='''pretrained model name'''			)
     parser.add_argument('''--do_train'''		,						action='''store_true'''		,						help='''Whether to run training.'''			)
     parser.add_argument('''--do_eval'''		,						action='''store_true'''		,						help='''Whether to run eval on the dev set.'''			)
     parser.add_argument(
         '''--output_dir'''		,						default=__lowerCAmelCase		,						type=__lowerCAmelCase		,						required=__lowerCAmelCase		,						help='''The output directory where the model predictions and checkpoints will be written.'''		,						)
     parser.add_argument('''--train_dataset'''		,						type=__lowerCAmelCase		,						default=''''''			)
     parser.add_argument('''--eval_dataset'''		,						type=__lowerCAmelCase		,						default=''''''			)
     parser.add_argument('''--seed'''		,						type=__lowerCAmelCase		,						default=42			)
     parser.add_argument('''--num_train_epochs'''		,						type=__lowerCAmelCase		,						default=3			)
     parser.add_argument('''--train_batch_size'''		,						type=__lowerCAmelCase		,						default=8			)
     parser.add_argument('''--eval_batch_size'''		,						type=__lowerCAmelCase		,						default=16			)
     parser.add_argument('''--adam_epsilon'''		,						default=1e-8		,						type=__lowerCAmelCase		,						help='''Epsilon for Adam optimizer.'''			)
     parser.add_argument('''--max_grad_norm'''		,						type=__lowerCAmelCase		,						default=1			)
     parser.add_argument(
         '''--max_steps'''		,						default=-1		,						type=__lowerCAmelCase		,						help=(
             '''If > 0: set total number of training                         steps to perform. Override num_train_epochs.'''
         )		,						)
     parser.add_argument(
         '''--gradient_accumulation_steps'''		,						type=__lowerCAmelCase		,						default=1		,						help='''Number of updates steps to accumulate before                        performing a backward/update pass.'''		,						)
     parser.add_argument('''--learning_rate'''		,						type=__lowerCAmelCase		,						default=6.25e-5			)
     parser.add_argument('''--warmup_steps'''		,						default=0		,						type=__lowerCAmelCase		,						help='''Linear warmup over warmup_steps.'''			)
     parser.add_argument('''--lr_schedule'''		,						type=__lowerCAmelCase		,						default='''warmup_linear'''			)
     parser.add_argument('''--weight_decay'''		,						type=__lowerCAmelCase		,						default=0.01			)
     parser.add_argument('''--lm_coef'''		,						type=__lowerCAmelCase		,						default=0.9			)
     parser.add_argument('''--n_valid'''		,						type=__lowerCAmelCase		,						default=374			)

     parser.add_argument('''--server_ip'''		,						type=__lowerCAmelCase		,						default=''''''		,						help='''Can be used for distant debugging.'''			)
     parser.add_argument('''--server_port'''		,						type=__lowerCAmelCase		,						default=''''''		,						help='''Can be used for distant debugging.'''			)
     __magic_name__             =    parser.parse_args()
     print(__lowerCAmelCase			)

     if args.server_ip and args.server_port:
          # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
          import ptvsd

          print('''Waiting for debugger attach'''			)
          ptvsd.enable_attach(address=(args.server_ip, args.server_port)		,						redirect_output=__lowerCAmelCase			)
          ptvsd.wait_for_attach()

     random.seed(args.seed			)
     np.random.seed(args.seed			)
     torch.manual_seed(args.seed			)
     torch.cuda.manual_seed_all(args.seed			)

     __magic_name__             =    torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''			)
     __magic_name__             =    torch.cuda.device_count()
     logger.info('''device: {}, n_gpu {}'''.format(__lowerCAmelCase		,						__lowerCAmelCase			)			)

     if not args.do_train and not args.do_eval:
          raise ValueError('''At least one of `do_train` or `do_eval` must be True.'''			)

     if not os.path.exists(args.output_dir			):
          os.makedirs(args.output_dir			)

     # Load tokenizer and model
     # This loading functions also add new tokens and embeddings called `special tokens`
     # These new embeddings will be fine-tuned on the RocStories dataset
     __magic_name__             =    ['''_start_''', '''_delimiter_''', '''_classify_''']
     __magic_name__             =    OpenAIGPTTokenizer.from_pretrained(args.model_name			)
     tokenizer.add_tokens(__lowerCAmelCase			)
     __magic_name__             =    tokenizer.convert_tokens_to_ids(__lowerCAmelCase			)
     __magic_name__             =    OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name			)
     model.resize_token_embeddings(len(__lowerCAmelCase			)			)
     model.to(__lowerCAmelCase			)

     # Load and encode the datasets
     def tokenize_and_encode(a			):
          if isinstance(__lowerCAmelCase		,						__lowerCAmelCase			):
               return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(__lowerCAmelCase			)			)
          elif isinstance(__lowerCAmelCase		,						__lowerCAmelCase			):
               return obj
          return [tokenize_and_encode(__lowerCAmelCase			) for o in obj]

     logger.info('''Encoding dataset...'''			)
     __magic_name__             =    load_rocstories_dataset(args.train_dataset			)
     __magic_name__             =    load_rocstories_dataset(args.eval_dataset			)
     __magic_name__             =    (train_dataset, eval_dataset)
     __magic_name__             =    tokenize_and_encode(__lowerCAmelCase			)

     # Compute the max input length for the Transformer
     __magic_name__             =    model.config.n_positions // 2 - 2
     __magic_name__             =    max(
         len(story[:max_length]			) + max(len(conta[:max_length]			)		,						len(conta[:max_length]			)			) + 3
         for dataset in encoded_datasets
         for story, conta, conta, _ in dataset			)
     __magic_name__             =    min(__lowerCAmelCase		,						model.config.n_positions			)  # Max size of input for the pre-trained model

     # Prepare inputs tensors and dataloaders
     __magic_name__             =    pre_process_datasets(__lowerCAmelCase		,						__lowerCAmelCase		,						__lowerCAmelCase		,						*__lowerCAmelCase			)
     __magic_name__       ,  __magic_name__             =    tensor_datasets[0], tensor_datasets[1]

     __magic_name__             =    TensorDataset(*__lowerCAmelCase			)
     __magic_name__             =    RandomSampler(__lowerCAmelCase			)
     __magic_name__             =    DataLoader(__lowerCAmelCase		,						sampler=__lowerCAmelCase		,						batch_size=args.train_batch_size			)

     __magic_name__             =    TensorDataset(*__lowerCAmelCase			)
     __magic_name__             =    SequentialSampler(__lowerCAmelCase			)
     __magic_name__             =    DataLoader(__lowerCAmelCase		,						sampler=__lowerCAmelCase		,						batch_size=args.eval_batch_size			)

     # Prepare optimizer
     if args.do_train:
          if args.max_steps > 0:
               __magic_name__             =    args.max_steps
               __magic_name__             =    args.max_steps // (len(__lowerCAmelCase			) // args.gradient_accumulation_steps) + 1
          else:
               __magic_name__             =    len(__lowerCAmelCase			) // args.gradient_accumulation_steps * args.num_train_epochs

          __magic_name__             =    list(model.named_parameters()			)
          __magic_name__             =    ['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
          __magic_name__             =    [
              {
                  '''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay			)],
                  '''weight_decay''': args.weight_decay,
              },
              {'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay			)], '''weight_decay''': 0.0},
          ]
          __magic_name__             =    AdamW(__lowerCAmelCase		,						lr=args.learning_rate		,						eps=args.adam_epsilon			)
          __magic_name__             =    get_linear_schedule_with_warmup(
              __lowerCAmelCase		,						num_warmup_steps=args.warmup_steps		,						num_training_steps=__lowerCAmelCase			)

     if args.do_train:
          __magic_name__       ,  __magic_name__       ,  __magic_name__             =    0, 0, None
          model.train()
          for _ in trange(int(args.num_train_epochs			)		,						desc='''Epoch'''			):
               __magic_name__             =    0
               __magic_name__             =    0
               __magic_name__             =    tqdm(__lowerCAmelCase		,						desc='''Training'''			)
               for step, batch in enumerate(__lowerCAmelCase			):
                    __magic_name__             =    tuple(t.to(__lowerCAmelCase			) for t in batch			)
                    __magic_name__       ,  __magic_name__       ,  __magic_name__       ,  __magic_name__             =    batch
                    __magic_name__             =    model(__lowerCAmelCase		,						mc_token_ids=__lowerCAmelCase		,						lm_labels=__lowerCAmelCase		,						mc_labels=__lowerCAmelCase			)
                    __magic_name__             =    args.lm_coef * losses[0] + losses[1]
                    loss.backward()
                    optimizer.step()
                    scheduler.step()
                    optimizer.zero_grad()
                    tr_loss += loss.item()
                    __magic_name__             =    (
                        loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                    )
                    nb_tr_steps += 1
                    __magic_name__             =    '''Training loss: {:.2e} lr: {:.2e}'''.format(__lowerCAmelCase		,						scheduler.get_lr()[0]			)

    # Save a trained model
     if args.do_train:
          # Save a trained model, configuration and tokenizer
          __magic_name__             =    model.module if hasattr(__lowerCAmelCase		,						'''module'''			) else model  # Only save the model itself

          # If we save using the predefined names, we can load using `from_pretrained`
          __magic_name__             =    os.path.join(args.output_dir		,						__lowerCAmelCase			)
          __magic_name__             =    os.path.join(args.output_dir		,						__lowerCAmelCase			)

          torch.save(model_to_save.state_dict()		,						__lowerCAmelCase			)
          model_to_save.config.to_json_file(__lowerCAmelCase			)
          tokenizer.save_vocabulary(args.output_dir			)

          # Load a trained model and vocabulary that you have fine-tuned
          __magic_name__             =    OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir			)
          __magic_name__             =    OpenAIGPTTokenizer.from_pretrained(args.output_dir			)
          model.to(__lowerCAmelCase			)

     if args.do_eval:
          model.eval()
          __magic_name__       ,  __magic_name__             =    0, 0
          __magic_name__       ,  __magic_name__             =    0, 0
          for batch in tqdm(__lowerCAmelCase		,						desc='''Evaluating'''			):
               __magic_name__             =    tuple(t.to(__lowerCAmelCase			) for t in batch			)
               __magic_name__       ,  __magic_name__       ,  __magic_name__       ,  __magic_name__             =    batch
               with torch.no_grad():
                    __magic_name__       ,  __magic_name__       ,  __magic_name__       ,  __magic_name__             =    model(
                        __lowerCAmelCase		,						mc_token_ids=__lowerCAmelCase		,						lm_labels=__lowerCAmelCase		,						mc_labels=__lowerCAmelCase			)

               __magic_name__             =    mc_logits.detach().cpu().numpy()
               __magic_name__             =    mc_labels.to('''cpu'''			).numpy()
               __magic_name__             =    accuracy(__lowerCAmelCase		,						__lowerCAmelCase			)

               eval_loss += mc_loss.mean().item()
               eval_accuracy += tmp_eval_accuracy

               nb_eval_examples += input_ids.size(0			)
               nb_eval_steps += 1

          __magic_name__             =    eval_loss / nb_eval_steps
          __magic_name__             =    eval_accuracy / nb_eval_examples
          __magic_name__             =    tr_loss / nb_tr_steps if args.do_train else None
          __magic_name__             =    {'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}

          __magic_name__             =    os.path.join(args.output_dir		,						'''eval_results.txt'''			)
          with open(__lowerCAmelCase		,						'''w'''			) as writer:
               logger.info('''***** Eval results *****'''			)
               for key in sorted(result.keys()			):
                    logger.info('''  %s = %s'''		,						__lowerCAmelCase		,						str(result[key]			)			)
                    writer.write('''%s = %s\n''' % (key, str(result[key]			))			)


if __name__ == "__main__":
      main()









'''simple docstring'''




from __future__ import annotations

import math
import random
from typing import Any






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   Union[str, Any]      ):
      __magic_name__						       =    []
      __magic_name__						       =    0
      __magic_name__						       =    0



 def 			snake_case__  (   self		:   int      ):
      return self.head == self.tail



 def 			snake_case__  (   self		:   int			,				a__		:   Any      ):
      self.data.append(a__      )
      __magic_name__						       =    self.tail + 1



 def 			snake_case__  (   self		:   Tuple      ):
      __magic_name__						       =    self.data[self.head]
      __magic_name__						       =    self.head + 1
      return ret



 def 			snake_case__  (   self		:   Optional[Any]      ):
      return self.tail - self.head



 def 			snake_case__  (   self		:   List[Any]      ):
      print(self.data      )
      print('''**************'''      )
      print(self.data[self.head : self.tail]      )






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   List[str]			,				a__		:   Any      ):
      __magic_name__						       =    data
      __magic_name__						       =    None
      __magic_name__						       =    None
      __magic_name__						       =    1



 def 			snake_case__  (   self		:   Optional[int]      ):
      return self.data



 def 			snake_case__  (   self		:   List[Any]      ):
      return self.left



 def 			snake_case__  (   self		:   Tuple      ):
      return self.right



 def 			snake_case__  (   self		:   Any      ):
      return self.height



 def 			snake_case__  (   self		:   Optional[Any]			,				a__		:   Any      ):
      __magic_name__						       =    data



 def 			snake_case__  (   self		:   int			,				a__		:   MyNode | None      ):
      __magic_name__						       =    node



 def 			snake_case__  (   self		:   Tuple			,				a__		:   MyNode | None      ):
      __magic_name__						       =    node



 def 			snake_case__  (   self		:   List[str]			,				a__		:   int      ):
      __magic_name__						       =    height




def 						UpperCamelCase	(			a			)				->     int:






     '''simple docstring'''
     if node is None:
          return 0
     return node.get_height()




def 						UpperCamelCase	(			a		,						a			)				->     int:






     '''simple docstring'''
     if a > b:
          return a
     return b




def 						UpperCamelCase	(			a			)				->     MyNode:






     '''simple docstring'''
     print('''left rotation node:'''		,						node.get_data()			)
     __magic_name__						       =    node.get_left()
     assert ret is not None
     node.set_left(ret.get_right()			)
     ret.set_right(a			)
     __magic_name__						       =    my_max(get_height(node.get_right()			)		,						get_height(node.get_left()			)			) + 1
     node.set_height(a			)
     __magic_name__						       =    my_max(get_height(ret.get_right()			)		,						get_height(ret.get_left()			)			) + 1
     ret.set_height(a			)
     return ret




def 						UpperCamelCase	(			a			)				->     MyNode:






     '''simple docstring'''
     print('''right rotation node:'''		,						node.get_data()			)
     __magic_name__						       =    node.get_right()
     assert ret is not None
     node.set_right(ret.get_left()			)
     ret.set_left(a			)
     __magic_name__						       =    my_max(get_height(node.get_right()			)		,						get_height(node.get_left()			)			) + 1
     node.set_height(a			)
     __magic_name__						       =    my_max(get_height(ret.get_right()			)		,						get_height(ret.get_left()			)			) + 1
     ret.set_height(a			)
     return ret




def 						UpperCamelCase	(			a			)				->     MyNode:






     '''simple docstring'''
     __magic_name__						       =    node.get_left()
     assert left_child is not None
     node.set_left(left_rotation(a			)			)
     return right_rotation(a			)




def 						UpperCamelCase	(			a			)				->     MyNode:






     '''simple docstring'''
     __magic_name__						       =    node.get_right()
     assert right_child is not None
     node.set_right(right_rotation(a			)			)
     return left_rotation(a			)




def 						UpperCamelCase	(			a		,						a			)				->     MyNode | None:






     '''simple docstring'''
     if node is None:
          return MyNode(a			)
     if data < node.get_data():
          node.set_left(insert_node(node.get_left()		,						a			)			)
          if (
              get_height(node.get_left()			) - get_height(node.get_right()			) == 2
          ):  # an unbalance detected
               __magic_name__						       =    node.get_left()
               assert left_child is not None
               if (
                   data < left_child.get_data()
               ):  # new node is the left child of the left child
                    __magic_name__						       =    right_rotation(a			)
               else:
                    __magic_name__						       =    lr_rotation(a			)
     else:
          node.set_right(insert_node(node.get_right()		,						a			)			)
          if get_height(node.get_right()			) - get_height(node.get_left()			) == 2:
               __magic_name__						       =    node.get_right()
               assert right_child is not None
               if data < right_child.get_data():
                    __magic_name__						       =    rl_rotation(a			)
               else:
                    __magic_name__						       =    left_rotation(a			)
     __magic_name__						       =    my_max(get_height(node.get_right()			)		,						get_height(node.get_left()			)			) + 1
     node.set_height(a			)
     return node




def 						UpperCamelCase	(			a			)				->     Any:






     '''simple docstring'''
     while True:
          __magic_name__						       =    root.get_right()
          if right_child is None:
               break
          __magic_name__						       =    right_child
     return root.get_data()




def 						UpperCamelCase	(			a			)				->     Any:






     '''simple docstring'''
     while True:
          __magic_name__						       =    root.get_left()
          if left_child is None:
               break
          __magic_name__						       =    left_child
     return root.get_data()




def 						UpperCamelCase	(			a		,						a			)				->     MyNode | None:






     '''simple docstring'''
     __magic_name__						       =    root.get_left()
     __magic_name__						       =    root.get_right()
     if root.get_data() == data:
          if left_child is not None and right_child is not None:
               __magic_name__						       =    get_left_most(a			)
               root.set_data(a			)
               root.set_right(del_node(a		,						a			)			)
          elif left_child is not None:
               __magic_name__						       =    left_child
          elif right_child is not None:
               __magic_name__						       =    right_child
          else:
               return None
     elif root.get_data() > data:
          if left_child is None:
               print('''No such data'''			)
               return root
          else:
               root.set_left(del_node(a		,						a			)			)
     else:  # root.get_data() < data
          if right_child is None:
               return root
          else:
               root.set_right(del_node(a		,						a			)			)

     if get_height(a			) - get_height(a			) == 2:
          assert right_child is not None
          if get_height(right_child.get_right()			) > get_height(right_child.get_left()			):
               __magic_name__						       =    left_rotation(a			)
          else:
               __magic_name__						       =    rl_rotation(a			)
     elif get_height(a			) - get_height(a			) == -2:
          assert left_child is not None
          if get_height(left_child.get_left()			) > get_height(left_child.get_right()			):
               __magic_name__						       =    right_rotation(a			)
          else:
               __magic_name__						       =    lr_rotation(a			)
     __magic_name__						       =    my_max(get_height(root.get_right()			)		,						get_height(root.get_left()			)			) + 1
     root.set_height(a			)
     return root






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   List[Any]      ):
      __magic_name__						       =    None



 def 			snake_case__  (   self		:   List[Any]      ):
      return get_height(self.root      )



 def 			snake_case__  (   self		:   Optional[int]			,				a__		:   Any      ):
      print('''insert:''' + str(a__      )      )
      __magic_name__						       =    insert_node(self.root			,				a__      )



 def 			snake_case__  (   self		:   Dict			,				a__		:   Any      ):
      print('''delete:''' + str(a__      )      )
      if self.root is None:
           print('''Tree is empty!'''      )
           return
      __magic_name__						       =    del_node(self.root			,				a__      )



 def __str__(   self		:   Optional[Any]			,				):  # a level traversale, gives a more intuitive look on the tree
      __magic_name__						       =    ''''''
      __magic_name__						       =    MyQueue()
      q.push(self.root      )
      __magic_name__						       =    self.get_height()
      if layer == 0:
           return output
      __magic_name__						       =    0
      while not q.is_empty():
           __magic_name__						       =    q.pop()
           __magic_name__						       =    ''' ''' * int(math.pow(2			,				layer - 1      )      )
           output += space
           if node is None:
                output += "*"
                q.push(a__      )
                q.push(a__      )
           else:
                output += str(node.get_data()      )
                q.push(node.get_left()      )
                q.push(node.get_right()      )
           output += space
           __magic_name__						       =    cnt + 1
           for i in range(100      ):
                if cnt == math.pow(2			,				a__      ) - 1:
                     __magic_name__						       =    layer - 1
                     if layer == 0:
                          output += "\n*************************************"
                          return output
                     output += "\n"
                     break
      output += "\n*************************************"
      return output




def 						UpperCamelCase	(			)				->     None:






     '''simple docstring'''
     import doctest

     doctest.testmod()


if __name__ == "__main__":
      _test()
      _lowerCAmelCase         =						AVLtree()
      _lowerCAmelCase         =						list(range(10))
      random.shuffle(lst)
      for i in lst:
            t.insert(i)
            print(str(t))
      random.shuffle(lst)
      for i in lst:
            t.del_node(i)
            print(str(t))






