



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
)
UpperCAmelCase_		 =					logging.getLogger(__name__)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int	,   _snake_case	:str				)  ->     List[str]:
   _A      =       np.argmax(_snake_case	,   axis=1				)
   return np.sum(outputs == labels				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Dict				)  ->     List[str]:
   with open(_snake_case	,   encoding='''utf_8'''				) as f:
      _A      =       csv.reader(_snake_case				)
      _A      =       []
      next(_snake_case				)  # skip the first line
      for line in tqdm(_snake_case				):
         output.append((''' '''.join(line[1:5]				), line[5], line[6], int(line[-1]				) - 1)				)
   return output

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]	,   _snake_case	:str	,   _snake_case	:List[str]	,   _snake_case	:Tuple	,   _snake_case	:int	,   _snake_case	:List[str]				)  ->     Tuple:
   _A      =       []
   for dataset in encoded_datasets:
      _A      =       len(_snake_case				)
      _A      =       np.zeros((n_batch, 2, input_len)	,   dtype=np.intaa				)
      _A      =       np.zeros((n_batch, 2)	,   dtype=np.intaa				)
      _A      =       np.full((n_batch, 2, input_len)	,   fill_value=-100	,   dtype=np.intaa				)
      _A      =       np.zeros((n_batch,)	,   dtype=np.intaa				)
      for (
          i,
          (story, conta, conta, mc_label),
      ) in enumerate(_snake_case				):
         _A      =       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
         _A      =       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
         _A      =       with_conta
         _A      =       with_conta
         _A      =       len(_snake_case				) - 1
         _A      =       len(_snake_case				) - 1
         _A      =       with_conta
         _A      =       with_conta
         _A      =       mc_label
      _A      =       (input_ids, mc_token_ids, lm_labels, mc_labels)
      tensor_datasets.append(tuple(torch.tensor(_snake_case				) for t in all_inputs				)				)
   return tensor_datasets

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Union[str, Any]:
   _A      =       argparse.ArgumentParser()
   parser.add_argument('''--model_name'''	,   type=_snake_case	,   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=_snake_case	,   type=_snake_case	,   required=_snake_case	,   help='''The output directory where the model predictions and checkpoints will be written.'''	,   )
   parser.add_argument('''--train_dataset'''	,   type=_snake_case	,   default=''''''				)
   parser.add_argument('''--eval_dataset'''	,   type=_snake_case	,   default=''''''				)
   parser.add_argument('''--seed'''	,   type=_snake_case	,   default=42				)
   parser.add_argument('''--num_train_epochs'''	,   type=_snake_case	,   default=3				)
   parser.add_argument('''--train_batch_size'''	,   type=_snake_case	,   default=8				)
   parser.add_argument('''--eval_batch_size'''	,   type=_snake_case	,   default=16				)
   parser.add_argument('''--adam_epsilon'''	,   default=1E-8	,   type=_snake_case	,   help='''Epsilon for Adam optimizer.'''				)
   parser.add_argument('''--max_grad_norm'''	,   type=_snake_case	,   default=1				)
   parser.add_argument(
       '''--max_steps'''	,   default=-1	,   type=_snake_case	,   help=(
           '''If > 0: set total number of training                         steps to perform. Override num_train_epochs.'''
       )	,   )
   parser.add_argument(
       '''--gradient_accumulation_steps'''	,   type=_snake_case	,   default=1	,   help='''Number of updates steps to accumulate before                        performing a backward/update pass.'''	,   )
   parser.add_argument('''--learning_rate'''	,   type=_snake_case	,   default=6.25E-5				)
   parser.add_argument('''--warmup_steps'''	,   default=0	,   type=_snake_case	,   help='''Linear warmup over warmup_steps.'''				)
   parser.add_argument('''--lr_schedule'''	,   type=_snake_case	,   default='''warmup_linear'''				)
   parser.add_argument('''--weight_decay'''	,   type=_snake_case	,   default=0.01				)
   parser.add_argument('''--lm_coef'''	,   type=_snake_case	,   default=0.9				)
   parser.add_argument('''--n_valid'''	,   type=_snake_case	,   default=374				)

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

   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=_snake_case				)
      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				)

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

   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
   _A      =       ['''_start_''', '''_delimiter_''', '''_classify_''']
   _A      =       OpenAIGPTTokenizer.from_pretrained(args.model_name				)
   tokenizer.add_tokens(_snake_case				)
   _A      =       tokenizer.convert_tokens_to_ids(_snake_case				)
   _A      =       OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name				)
   model.resize_token_embeddings(len(_snake_case				)				)
   model.to(_snake_case				)

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

   logger.info('''Encoding dataset...'''				)
   _A      =       load_rocstories_dataset(args.train_dataset				)
   _A      =       load_rocstories_dataset(args.eval_dataset				)
   _A      =       (train_dataset, eval_dataset)
   _A      =       tokenize_and_encode(_snake_case				)

   # Compute the max input length for the Transformer
   _A      =       model.config.n_positions // 2 - 2
   _A      =       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				)
   _A      =       min(_snake_case	,   model.config.n_positions				)  # Max size of input for the pre-trained model

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

   _A      =       TensorDataset(*_snake_case				)
   _A      =       RandomSampler(_snake_case				)
   _A      =       DataLoader(_snake_case	,   sampler=_snake_case	,   batch_size=args.train_batch_size				)

   _A      =       TensorDataset(*_snake_case				)
   _A      =       SequentialSampler(_snake_case				)
   _A      =       DataLoader(_snake_case	,   sampler=_snake_case	,   batch_size=args.eval_batch_size				)

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

      _A      =       list(model.named_parameters()				)
      _A      =       ['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
      _A      =       [
          {
              '''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},
      ]
      _A      =       AdamW(_snake_case	,   lr=args.learning_rate	,   eps=args.adam_epsilon				)
      _A      =       get_linear_schedule_with_warmup(
          _snake_case	,   num_warmup_steps=args.warmup_steps	,   num_training_steps=_snake_case				)

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

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

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

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

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

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

         _A      =       mc_logits.detach().cpu().numpy()
         _A      =       mc_labels.to('''cpu'''				).numpy()
         _A      =       accuracy(_snake_case	,   _snake_case				)

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

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

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

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


if __name__ == "__main__":
   main()








from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

UpperCAmelCase_		 =					TypeVar("""T""")

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   return (position - 1) // 2

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   return (2 * position) + 1

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   return (2 * position) + 2


class 	lowerCamelCase__	(       Generic[T]):



 """simple docstring"""

 def __init__(    self					:					Optional[int]      )   ->	None:
    _A      =       []
    _A      =       {}
    _A      =       0

 def __len__(    self					:					str      )   ->	int:
    return self.elements

 def __repr__(    self					:					Optional[int]      )   ->	str:
    return str(self.heap      )

 def     snake_case_      (    self					:					str      )   ->	bool:
    # Check if the priority queue is empty
    return self.elements == 0

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					int      )   ->	None:
    # Add an element with given priority to the queue
    self.heap.append((elem, weight)      )
    _A      =       self.elements
    self.elements += 1
    self._bubble_up(__lowerCAmelCase      )

 def     snake_case_      (    self					:					Tuple      )   ->	T:
    # Remove and return the element with lowest weight (highest priority)
    if self.elements > 1:
       self._swap_nodes(0			,						self.elements - 1      )
    _A  ,    _A      =       self.heap.pop()
    del self.position_map[elem]
    self.elements -= 1
    if self.elements > 0:
       _A  ,    _A      =       self.heap[0]
       self._bubble_down(__lowerCAmelCase      )
    return elem

 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					int      )   ->	None:
    # Update the weight of the given key
    _A      =       self.position_map[elem]
    _A      =       (elem, weight)
    if position > 0:
       _A      =       get_parent_position(__lowerCAmelCase      )
       _A  ,    _A      =       self.heap[parent_position]
       if parent_weight > weight:
          self._bubble_up(__lowerCAmelCase      )
       else:
          self._bubble_down(__lowerCAmelCase      )
    else:
       self._bubble_down(__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					T      )   ->	None:
    # Place a node at the proper position (upward movement) [to be used internally
    # only]
    _A      =       self.position_map[elem]
    if curr_pos == 0:
       return None
    _A      =       get_parent_position(__lowerCAmelCase      )
    _A  ,    _A      =       self.heap[curr_pos]
    _A  ,    _A      =       self.heap[parent_position]
    if parent_weight > weight:
       self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
       return self._bubble_up(__lowerCAmelCase      )
    return None

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					T      )   ->	None:
    # Place a node at the proper position (downward movement) [to be used
    # internally only]
    _A      =       self.position_map[elem]
    _A  ,    _A      =       self.heap[curr_pos]
    _A      =       get_child_left_position(__lowerCAmelCase      )
    _A      =       get_child_right_position(__lowerCAmelCase      )
    if child_left_position < self.elements and child_right_position < self.elements:
       _A  ,    _A      =       self.heap[child_left_position]
       _A  ,    _A      =       self.heap[child_right_position]
       if child_right_weight < child_left_weight and child_right_weight < weight:
          self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
          return self._bubble_down(__lowerCAmelCase      )
    if child_left_position < self.elements:
       _A  ,    _A      =       self.heap[child_left_position]
       if child_left_weight < weight:
          self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
          return self._bubble_down(__lowerCAmelCase      )
    else:
       return None
    if child_right_position < self.elements:
       _A  ,    _A      =       self.heap[child_right_position]
       if child_right_weight < weight:
          self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
          return self._bubble_down(__lowerCAmelCase      )
    return None

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					int      )   ->	None:
    # Swap the nodes at the given positions
    _A      =       self.heap[nodea_pos][0]
    _A      =       self.heap[nodea_pos][0]
    _A  ,    _A      =       (
        self.heap[nodea_pos],
        self.heap[nodea_pos],
    )
    _A      =       nodea_pos
    _A      =       nodea_pos


class 	lowerCamelCase__	(       Generic[T]):



 """simple docstring"""

 def __init__(    self					:					str      )   ->	None:
    _A      =       {}
    _A      =       0

 def __repr__(    self					:					str      )   ->	str:
    return str(self.connections      )

 def __len__(    self					:					Dict      )   ->	int:
    return self.nodes

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					T      )   ->	None:
    # Add a node in the graph if it is not in the graph
    if node not in self.connections:
       _A      =       {}
       self.nodes += 1

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					int      )   ->	None:
    # Add an edge between 2 nodes in the graph
    self.add_node(__lowerCAmelCase      )
    self.add_node(__lowerCAmelCase      )
    _A      =       weight
    _A      =       weight

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:GraphUndirectedWeighted[T]	,   )  ->     tuple[dict[T, int], dict[T, T | None]]:
   _A      =       {node: maxsize for node in graph.connections}
   _A      =       {node: None for node in graph.connections}

   _A      =       MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(_snake_case	,   _snake_case				)

   if priority_queue.is_empty():
      return dist, parent

   # initialization
   _A      =       priority_queue.extract_min()
   _A      =       0
   for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
         _A      =       dist[node] + graph.connections[node][neighbour]
         priority_queue.update_key(_snake_case	,   dist[neighbour]				)
         _A      =       node

    # running prim's algorithm
   while not priority_queue.is_empty():
      _A      =       priority_queue.extract_min()
      for neighbour in graph.connections[node]:
         if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            _A      =       dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(_snake_case	,   dist[neighbour]				)
            _A      =       node
   return dist, parent



