


'''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     :  List[str]											=    logging.getLogger(__name__)






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				str       =							np.argmax(_A ,    axis=1       )
       return np.sum(outputs == labels       )






def       lowercase					(_A       ):







       """simple docstring"""







       with open(_A ,    encoding='utf_8'       ) as f:
              _lowerCAmelCase     :				Union[str, Any]       =							csv.reader(_A       )
              _lowerCAmelCase     :				Tuple       =							[]
              next(_A       )  # skip the first line
              for line in tqdm(_A       ):
                     output.append((' '.join(line[1:5]       ), line[5], line[6], int(line[-1]       ) - 1)       )
       return output






def       lowercase					(_A ,    _A ,    _A ,    _A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[int]       =							[]
       for dataset in encoded_datasets:
              _lowerCAmelCase     :				str       =							len(_A       )
              _lowerCAmelCase     :				Tuple       =							np.zeros((n_batch, 2, input_len) ,    dtype=np.intaa       )
              _lowerCAmelCase     :				Optional[int]       =							np.zeros((n_batch, 2) ,    dtype=np.intaa       )
              _lowerCAmelCase     :				Dict       =							np.full((n_batch, 2, input_len) ,    fill_value=-1_0_0 ,    dtype=np.intaa       )
              _lowerCAmelCase     :				Any       =							np.zeros((n_batch,) ,    dtype=np.intaa       )
              for (
                  i,
                  (story, conta, conta, mc_label),
              ) in enumerate(_A       ):
                     _lowerCAmelCase     :				Dict       =							[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     _lowerCAmelCase     :				List[str]       =							[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     _lowerCAmelCase     :				Optional[int]       =							with_conta
                     _lowerCAmelCase     :				List[str]       =							with_conta
                     _lowerCAmelCase     :				Optional[Any]       =							len(_A       ) - 1
                     _lowerCAmelCase     :				List[str]       =							len(_A       ) - 1
                     _lowerCAmelCase     :				List[str]       =							with_conta
                     _lowerCAmelCase     :				Dict       =							with_conta
                     _lowerCAmelCase     :				Any       =							mc_label
              _lowerCAmelCase     :				List[str]       =							(input_ids, mc_token_ids, lm_labels, mc_labels)
              tensor_datasets.append(tuple(torch.tensor(_A       ) for t in all_inputs       )       )
       return tensor_datasets






def       lowercase					():







       """simple docstring"""







       _lowerCAmelCase     :				Dict       =							argparse.ArgumentParser()
       parser.add_argument('--model_name' ,    type=_A ,    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=_A ,    type=_A ,    required=_A ,    help='The output directory where the model predictions and checkpoints will be written.' ,    )
       parser.add_argument('--train_dataset' ,    type=_A ,    default=''       )
       parser.add_argument('--eval_dataset' ,    type=_A ,    default=''       )
       parser.add_argument('--seed' ,    type=_A ,    default=4_2       )
       parser.add_argument('--num_train_epochs' ,    type=_A ,    default=3       )
       parser.add_argument('--train_batch_size' ,    type=_A ,    default=8       )
       parser.add_argument('--eval_batch_size' ,    type=_A ,    default=1_6       )
       parser.add_argument('--adam_epsilon' ,    default=1E-8 ,    type=_A ,    help='Epsilon for Adam optimizer.'       )
       parser.add_argument('--max_grad_norm' ,    type=_A ,    default=1       )
       parser.add_argument(
           '--max_steps' ,    default=-1 ,    type=_A ,    help=(
               'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
           ) ,    )
       parser.add_argument(
           '--gradient_accumulation_steps' ,    type=_A ,    default=1 ,    help='Number of updates steps to accumulate before                        performing a backward/update pass.' ,    )
       parser.add_argument('--learning_rate' ,    type=_A ,    default=6.25E-5       )
       parser.add_argument('--warmup_steps' ,    default=0 ,    type=_A ,    help='Linear warmup over warmup_steps.'       )
       parser.add_argument('--lr_schedule' ,    type=_A ,    default='warmup_linear'       )
       parser.add_argument('--weight_decay' ,    type=_A ,    default=0.01       )
       parser.add_argument('--lm_coef' ,    type=_A ,    default=0.9       )
       parser.add_argument('--n_valid' ,    type=_A ,    default=3_7_4       )

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

       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=_A       )
              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       )

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

       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
       _lowerCAmelCase     :				Union[str, Any]       =							['_start_', '_delimiter_', '_classify_']
       _lowerCAmelCase     :				str       =							OpenAIGPTTokenizer.from_pretrained(args.model_name       )
       tokenizer.add_tokens(_A       )
       _lowerCAmelCase     :				Tuple       =							tokenizer.convert_tokens_to_ids(_A       )
       _lowerCAmelCase     :				Optional[int]       =							OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name       )
       model.resize_token_embeddings(len(_A       )       )
       model.to(_A       )

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

       logger.info('Encoding dataset...'       )
       _lowerCAmelCase     :				int       =							load_rocstories_dataset(args.train_dataset       )
       _lowerCAmelCase     :				Optional[int]       =							load_rocstories_dataset(args.eval_dataset       )
       _lowerCAmelCase     :				Tuple       =							(train_dataset, eval_dataset)
       _lowerCAmelCase     :				int       =							tokenize_and_encode(_A       )

       # Compute the max input length for the Transformer
       _lowerCAmelCase     :				Dict       =							model.config.n_positions // 2 - 2
       _lowerCAmelCase     :				Any       =							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       )
       _lowerCAmelCase     :				List[str]       =							min(_A ,    model.config.n_positions       )  # Max size of input for the pre-trained model

       # Prepare inputs tensors and dataloaders
       _lowerCAmelCase     :				List[Any]       =							pre_process_datasets(_A ,    _A ,    _A ,    *_A       )
       _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[int]       =							tensor_datasets[0], tensor_datasets[1]

       _lowerCAmelCase     :				List[str]       =							TensorDataset(*_A       )
       _lowerCAmelCase     :				Any       =							RandomSampler(_A       )
       _lowerCAmelCase     :				int       =							DataLoader(_A ,    sampler=_A ,    batch_size=args.train_batch_size       )

       _lowerCAmelCase     :				List[str]       =							TensorDataset(*_A       )
       _lowerCAmelCase     :				str       =							SequentialSampler(_A       )
       _lowerCAmelCase     :				Optional[int]       =							DataLoader(_A ,    sampler=_A ,    batch_size=args.eval_batch_size       )

       # Prepare optimizer
       if args.do_train:
              if args.max_steps > 0:
                     _lowerCAmelCase     :				str       =							args.max_steps
                     _lowerCAmelCase     :				Any       =							args.max_steps // (len(_A       ) // args.gradient_accumulation_steps) + 1
              else:
                     _lowerCAmelCase     :				List[Any]       =							len(_A       ) // args.gradient_accumulation_steps * args.num_train_epochs

              _lowerCAmelCase     :				Any       =							list(model.named_parameters()       )
              _lowerCAmelCase     :				Optional[Any]       =							['bias', 'LayerNorm.bias', 'LayerNorm.weight']
              _lowerCAmelCase     :				Dict       =							[
                  {
                      '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},
              ]
              _lowerCAmelCase     :				Optional[int]       =							AdamW(_A ,    lr=args.learning_rate ,    eps=args.adam_epsilon       )
              _lowerCAmelCase     :				int       =							get_linear_schedule_with_warmup(
                  _A ,    num_warmup_steps=args.warmup_steps ,    num_training_steps=_A       )

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

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

              # If we save using the predefined names, we can load using `from_pretrained`
              _lowerCAmelCase     :				List[str]       =							os.path.join(args.output_dir ,    _A       )
              _lowerCAmelCase     :				str       =							os.path.join(args.output_dir ,    _A       )

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

              # Load a trained model and vocabulary that you have fine-tuned
              _lowerCAmelCase     :				int       =							OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir       )
              _lowerCAmelCase     :				List[str]       =							OpenAIGPTTokenizer.from_pretrained(args.output_dir       )
              model.to(_A       )

       if args.do_eval:
              model.eval()
              _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[Any]       =							0, 0
              _lowerCAmelCase      ,  _lowerCAmelCase     :				str       =							0, 0
              for batch in tqdm(_A ,    desc='Evaluating'       ):
                     _lowerCAmelCase     :				Tuple       =							tuple(t.to(_A       ) for t in batch       )
                     _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				Tuple       =							batch
                     with torch.no_grad():
                            _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				int       =							model(
                                _A ,    mc_token_ids=_A ,    lm_labels=_A ,    mc_labels=_A       )

                     _lowerCAmelCase     :				Optional[int]       =							mc_logits.detach().cpu().numpy()
                     _lowerCAmelCase     :				Optional[int]       =							mc_labels.to('cpu'       ).numpy()
                     _lowerCAmelCase     :				List[str]       =							accuracy(_A ,    _A       )

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

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

              _lowerCAmelCase     :				List[str]       =							eval_loss / nb_eval_steps
              _lowerCAmelCase     :				str       =							eval_accuracy / nb_eval_examples
              _lowerCAmelCase     :				List[Any]       =							tr_loss / nb_tr_steps if args.do_train else None
              _lowerCAmelCase     :				Dict       =							{'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

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


if __name__ == "__main__":
       main()





'''simple docstring'''







from math import pi, sqrt, tan






def       lowercase					(_A       ):







       """simple docstring"""







       if side_length < 0:
              raise ValueError('surface_area_cube() only accepts non-negative values'       )
       return 6 * side_length**2






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       if length < 0 or breadth < 0 or height < 0:
              raise ValueError('surface_area_cuboid() only accepts non-negative values'       )
       return 2 * ((length * breadth) + (breadth * height) + (length * height))






def       lowercase					(_A       ):







       """simple docstring"""







       if radius < 0:
              raise ValueError('surface_area_sphere() only accepts non-negative values'       )
       return 4 * pi * radius**2






def       lowercase					(_A       ):







       """simple docstring"""







       if radius < 0:
              raise ValueError('surface_area_hemisphere() only accepts non-negative values'       )
       return 3 * pi * radius**2






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       if radius < 0 or height < 0:
              raise ValueError('surface_area_cone() only accepts non-negative values'       )
       return pi * radius * (radius + (height**2 + radius**2) ** 0.5)






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       if radius_a < 0 or radius_a < 0 or height < 0:
              raise ValueError(
                  'surface_area_conical_frustum() only accepts non-negative values'       )
       _lowerCAmelCase     :				int       =							(height**2 + (radius_a - radius_a) ** 2) ** 0.5
       return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       if radius < 0 or height < 0:
              raise ValueError('surface_area_cylinder() only accepts non-negative values'       )
       return 2 * pi * radius * (height + radius)






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       if torus_radius < 0 or tube_radius < 0:
              raise ValueError('surface_area_torus() only accepts non-negative values'       )
       if torus_radius < tube_radius:
              raise ValueError(
                  'surface_area_torus() does not support spindle or self intersecting tori'       )
       return 4 * pow(_A ,    2       ) * torus_radius * tube_radius






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       if length < 0 or width < 0:
              raise ValueError('area_rectangle() only accepts non-negative values'       )
       return length * width






def       lowercase					(_A       ):







       """simple docstring"""







       if side_length < 0:
              raise ValueError('area_square() only accepts non-negative values'       )
       return side_length**2






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       if base < 0 or height < 0:
              raise ValueError('area_triangle() only accepts non-negative values'       )
       return (base * height) / 2






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       if sidea < 0 or sidea < 0 or sidea < 0:
              raise ValueError('area_triangle_three_sides() only accepts non-negative values'       )
       elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
              raise ValueError('Given three sides do not form a triangle'       )
       _lowerCAmelCase     :				Tuple       =							(sidea + sidea + sidea) / 2
       _lowerCAmelCase     :				Dict       =							sqrt(
           semi_perimeter
           * (semi_perimeter - sidea)
           * (semi_perimeter - sidea)
           * (semi_perimeter - sidea)       )
       return area






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       if base < 0 or height < 0:
              raise ValueError('area_parallelogram() only accepts non-negative values'       )
       return base * height






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       if basea < 0 or basea < 0 or height < 0:
              raise ValueError('area_trapezium() only accepts non-negative values'       )
       return 1 / 2 * (basea + basea) * height






def       lowercase					(_A       ):







       """simple docstring"""







       if radius < 0:
              raise ValueError('area_circle() only accepts non-negative values'       )
       return pi * radius**2






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       if radius_x < 0 or radius_y < 0:
              raise ValueError('area_ellipse() only accepts non-negative values'       )
       return pi * radius_x * radius_y






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       if diagonal_a < 0 or diagonal_a < 0:
              raise ValueError('area_rhombus() only accepts non-negative values'       )
       return 1 / 2 * diagonal_a * diagonal_a






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       if not isinstance(_A ,    _A       ) or sides < 3:
              raise ValueError(
                  'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides'       )
       elif length < 0:
              raise ValueError(
                  'area_reg_polygon() only accepts non-negative values as \
length of a side'       )
       return (sides * length**2) / (4 * tan(pi / sides       ))
       return (sides * length**2) / (4 * tan(pi / sides       ))


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

       print("""[DEMO] Areas of various geometric shapes: \n""")
       print(F'''Rectangle: {area_rectangle(10, 20) = }''')
       print(F'''Square: {area_square(10) = }''')
       print(F'''Triangle: {area_triangle(10, 10) = }''')
       print(F'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
       print(F'''Parallelogram: {area_parallelogram(10, 20) = }''')
       print(F'''Rhombus: {area_rhombus(10, 20) = }''')
       print(F'''Trapezium: {area_trapezium(10, 20, 30) = }''')
       print(F'''Circle: {area_circle(20) = }''')
       print(F'''Ellipse: {area_ellipse(10, 20) = }''')
       print("""\nSurface Areas of various geometric shapes: \n""")
       print(F'''Cube: {surface_area_cube(20) = }''')
       print(F'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
       print(F'''Sphere: {surface_area_sphere(20) = }''')
       print(F'''Hemisphere: {surface_area_hemisphere(20) = }''')
       print(F'''Cone: {surface_area_cone(10, 20) = }''')
       print(F'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
       print(F'''Cylinder: {surface_area_cylinder(10, 20) = }''')
       print(F'''Torus: {surface_area_torus(20, 10) = }''')
       print(F'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
       print(F'''Square: {area_reg_polygon(4, 10) = }''')
       print(F'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''')

