'''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
)
__UpperCAmelCase												=logging.getLogger(__name__)






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   List[str]:
    __lowerCamelCase          =  np.argmax(UpperCamelCase__    ,  axis=1    )
    return np.sum(outputs == labels    )






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   Optional[Any]:
    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 __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   List[Any]:
    __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=-1_00    ,  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 __lowerCAmelCase			(							)     ->   int:
    __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=0.0_1    )
    parser.add_argument('''--lm_coef'''    ,  type=UpperCamelCase__    ,  default=0.9    )
    parser.add_argument('''--n_valid'''    ,  type=UpperCamelCase__    ,  default=3_74    )

    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__    ):
        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()

'''simple docstring'''




__UpperCAmelCase												="ABCDEFGHIJKLMNOPQRSTUVWXYZ"






def __lowerCAmelCase			(							)     ->   None:
    __lowerCamelCase          =  input('''Enter message: '''    )
    __lowerCamelCase          =  input('''Enter key [alphanumeric]: '''    )
    __lowerCamelCase          =  input('''Encrypt/Decrypt [e/d]: '''    )

    if mode.lower().startswith('''e'''    ):
        __lowerCamelCase          =  '''encrypt'''
        __lowerCamelCase          =  encrypt_message(UpperCamelCase__    ,  UpperCamelCase__    )
    elif mode.lower().startswith('''d'''    ):
        __lowerCamelCase          =  '''decrypt'''
        __lowerCamelCase          =  decrypt_message(UpperCamelCase__    ,  UpperCamelCase__    )

    print(f"""\n{mode.title()}ed message:"""    )
    print(UpperCamelCase__    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   str:
    return translate_message(UpperCamelCase__    ,  UpperCamelCase__    ,  '''encrypt'''    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   str:
    return translate_message(UpperCamelCase__    ,  UpperCamelCase__    ,  '''decrypt'''    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   str:
    __lowerCamelCase          =  []
    __lowerCamelCase          =  0
    __lowerCamelCase          =  key.upper()

    for symbol in message:
        __lowerCamelCase          =  LETTERS.find(symbol.upper()    )
        if num != -1:
            if mode == "encrypt":
                num += LETTERS.find(key[key_index]    )
            elif mode == "decrypt":
                num -= LETTERS.find(key[key_index]    )

            num %= len(UpperCamelCase__    )

            if symbol.isupper():
                translated.append(LETTERS[num]    )
            elif symbol.islower():
                translated.append(LETTERS[num].lower()    )

            key_index += 1
            if key_index == len(UpperCamelCase__    ):
                __lowerCamelCase          =  0
        else:
            translated.append(UpperCamelCase__    )
    return "".join(UpperCamelCase__    )


if __name__ == "__main__":
  main()
