






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
)
__snake_case      :      Union[str, Any]             =       logging.getLogger(__name__)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   Any						)					->				str:

						"""simple docstring"""



						lowerCAmelCase__        =  np.argmax(UpperCamelCase_      ,    axis=1						)
						return np.sum(outputs == labels						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict						)					->				List[str]:

						"""simple docstring"""



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







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   Union[str, Any]      ,    UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   List[str]      ,    UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   List[str]						)					->				List[Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  []
						for dataset in encoded_datasets:
												lowerCAmelCase__        =  len(UpperCamelCase_						)
												lowerCAmelCase__        =  np.zeros((n_batch, 2, input_len)      ,    dtype=np.intaa						)
												lowerCAmelCase__        =  np.zeros((n_batch, 2)      ,    dtype=np.intaa						)
												lowerCAmelCase__        =  np.full((n_batch, 2, input_len)      ,    fill_value=-100      ,    dtype=np.intaa						)
												lowerCAmelCase__        =  np.zeros((n_batch,)      ,    dtype=np.intaa						)
												for (
												    i,
												    (story, conta, conta, mc_label),
												) in enumerate(UpperCamelCase_						):
																		lowerCAmelCase__        =  [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
																		lowerCAmelCase__        =  [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
																		lowerCAmelCase__        =  with_conta
																		lowerCAmelCase__        =  with_conta
																		lowerCAmelCase__        =  len(UpperCamelCase_						) - 1
																		lowerCAmelCase__        =  len(UpperCamelCase_						) - 1
																		lowerCAmelCase__        =  with_conta
																		lowerCAmelCase__        =  with_conta
																		lowerCAmelCase__        =  mc_label
												lowerCAmelCase__        =  (input_ids, mc_token_ids, lm_labels, mc_labels)
												tensor_datasets.append(tuple(torch.tensor(UpperCamelCase_						) for t in all_inputs						)						)
						return tensor_datasets







def 		_UpperCamelCase				(      )					->				Optional[Any]:

						"""simple docstring"""



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

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

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

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

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

						logger.info('Encoding dataset...'						)
						lowerCAmelCase__        =  load_rocstories_dataset(args.train_dataset						)
						lowerCAmelCase__        =  load_rocstories_dataset(args.eval_dataset						)
						lowerCAmelCase__        =  (train_dataset, eval_dataset)
						lowerCAmelCase__        =  tokenize_and_encode(UpperCamelCase_						)

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

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

						lowerCAmelCase__        =  TensorDataset(*UpperCamelCase_						)
						lowerCAmelCase__        =  RandomSampler(UpperCamelCase_						)
						lowerCAmelCase__        =  DataLoader(UpperCamelCase_      ,    sampler=UpperCamelCase_      ,    batch_size=args.train_batch_size						)

						lowerCAmelCase__        =  TensorDataset(*UpperCamelCase_						)
						lowerCAmelCase__        =  SequentialSampler(UpperCamelCase_						)
						lowerCAmelCase__        =  DataLoader(UpperCamelCase_      ,    sampler=UpperCamelCase_      ,    batch_size=args.eval_batch_size						)

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

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

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

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

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

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

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

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

																		lowerCAmelCase__        =  mc_logits.detach().cpu().numpy()
																		lowerCAmelCase__        =  mc_labels.to('cpu'						).numpy()
																		lowerCAmelCase__        =  accuracy(UpperCamelCase_      ,    UpperCamelCase_						)

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

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

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

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


if __name__ == "__main__":
					main()













import colorsys

from PIL import Image  # type: ignore







def 		_UpperCamelCase				(      UpperCamelCase_    :   float      ,    UpperCamelCase_    :   float      ,    UpperCamelCase_    :   int						)					->				float:

						"""simple docstring"""



						lowerCAmelCase__        =  x
						lowerCAmelCase__        =  y
						for step in range(UpperCamelCase_						):  # noqa: B007
												lowerCAmelCase__        =  a * a - b * b + x
												lowerCAmelCase__        =  2 * a * b + y
												lowerCAmelCase__        =  a_new

												# divergence happens for all complex number with an absolute value
												# greater than 4
												if a * a + b * b > 4:
																		break
						return step / (max_step - 1)







def 		_UpperCamelCase				(      UpperCamelCase_    :   float						)					->				tuple:

						"""simple docstring"""



						if distance == 1:
												return (0, 0, 0)
						else:
												return (255, 255, 255)







def 		_UpperCamelCase				(      UpperCamelCase_    :   float						)					->				tuple:

						"""simple docstring"""



						if distance == 1:
												return (0, 0, 0)
						else:
												return tuple(round(i * 255						) for i in colorsys.hsv_to_rgb(UpperCamelCase_      ,    1      ,    1						)						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   int = 800      ,    UpperCamelCase_    :   int = 600      ,    UpperCamelCase_    :   float = -0.6      ,    UpperCamelCase_    :   float = 0      ,    UpperCamelCase_    :   float = 3.2      ,    UpperCamelCase_    :   int = 50      ,    UpperCamelCase_    :   bool = True      ,    )					->				Image.Image:

						"""simple docstring"""



						lowerCAmelCase__        =  Image.new('RGB'      ,    (image_width, image_height)						)
						lowerCAmelCase__        =  img.load()

						# loop through the image-coordinates
						for image_x in range(UpperCamelCase_						):
												for image_y in range(UpperCamelCase_						):
																		# determine the figure-coordinates based on the image-coordinates
																		lowerCAmelCase__        =  figure_width / image_width * image_height
																		lowerCAmelCase__        =  figure_center_x + (image_x / image_width - 0.5) * figure_width
																		lowerCAmelCase__        =  figure_center_y + (image_y / image_height - 0.5) * figure_height

																		lowerCAmelCase__        =  get_distance(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)

																		# color the corresponding pixel based on the selected coloring-function
																		if use_distance_color_coding:
																								lowerCAmelCase__        =  get_color_coded_rgb(UpperCamelCase_						)
																		else:
																								lowerCAmelCase__        =  get_black_and_white_rgb(UpperCamelCase_						)

						return img


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					# colored version, full figure
					__snake_case      :      str             =       get_image()

					# uncomment for colored version, different section, zoomed in
					# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
					# figure_width = 0.8)

					# uncomment for black and white version, full figure
					# img = get_image(use_distance_color_coding = False)

					# uncomment to save the image
					# img.save("mandelbrot.png")

					img.show()





