





'''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							:	str			=logging.getLogger(__name__)





def 						UpperCamelCase				(							_lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					Dict       ):
    A__				=			np.argmax(_lowerCamelCase						,       axis=1       )
    return np.sum(outputs == labels       )





def 						UpperCamelCase				(							_lowerCamelCase					:					Optional[int]       ):
    with open(_lowerCamelCase						,       encoding="utf_8"       ) as f:
        A__				=			csv.reader(_lowerCamelCase       )
        A__				=			[]
        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				(							_lowerCamelCase					:					Optional[Any]						,       _lowerCamelCase					:					int						,       _lowerCamelCase					:					List[Any]						,       _lowerCamelCase					:					str						,       _lowerCamelCase					:					List[Any]						,       _lowerCamelCase					:					Union[str, Any]       ):
    A__				=			[]
    for dataset in encoded_datasets:
        A__				=			len(_lowerCamelCase       )
        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=-1_00						,       dtype=np.intaa       )
        A__				=			np.zeros((n_batch,)						,       dtype=np.intaa       )
        for (
            i,
            (story, conta, conta, mc_label),
        ) in enumerate(_lowerCamelCase       ):
            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(_lowerCamelCase       ) - 1
            A__				=			len(_lowerCamelCase       ) - 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(_lowerCamelCase       ) for t in all_inputs       )       )
    return tensor_datasets





def 						UpperCamelCase				(							):
    A__				=			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=0.0_1       )
    parser.add_argument("--lm_coef"						,       type=_lowerCamelCase						,       default=0.9       )
    parser.add_argument("--n_valid"						,       type=_lowerCamelCase						,       default=3_74       )

    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."       )
    A__				=			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       )

    A__				=			torch.device("cuda" if torch.cuda.is_available() else "cpu"       )
    A__				=			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
    A__				=			["_start_", "_delimiter_", "_classify_"]
    A__				=			OpenAIGPTTokenizer.from_pretrained(args.model_name       )
    tokenizer.add_tokens(_lowerCamelCase       )
    A__				=			tokenizer.convert_tokens_to_ids(_lowerCamelCase       )
    A__				=			OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name       )
    model.resize_token_embeddings(len(_lowerCamelCase       )       )
    model.to(_lowerCamelCase       )

    # Load and encode the datasets
    def tokenize_and_encode(_lowerCamelCase					:					Any       ):
        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..."       )
    A__				=			load_rocstories_dataset(args.train_dataset       )
    A__				=			load_rocstories_dataset(args.eval_dataset       )
    A__				=			(train_dataset, eval_dataset)
    A__				=			tokenize_and_encode(_lowerCamelCase       )

    # 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(_lowerCamelCase						,       model.config.n_positions       )  # Max size of input for the pre-trained model

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

    A__				=			TensorDataset(*_lowerCamelCase       )
    A__				=			RandomSampler(_lowerCamelCase       )
    A__				=			DataLoader(_lowerCamelCase						,       sampler=_lowerCamelCase						,       batch_size=args.train_batch_size       )

    A__				=			TensorDataset(*_lowerCamelCase       )
    A__				=			SequentialSampler(_lowerCamelCase       )
    A__				=			DataLoader(_lowerCamelCase						,       sampler=_lowerCamelCase						,       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(_lowerCamelCase       ) // args.gradient_accumulation_steps) + 1
        else:
            A__				=			len(_lowerCamelCase       ) // 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(_lowerCamelCase						,       lr=args.learning_rate						,       eps=args.adam_epsilon       )
        A__				=			get_linear_schedule_with_warmup(
            _lowerCamelCase						,       num_warmup_steps=args.warmup_steps						,       num_training_steps=_lowerCamelCase       )

    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(_lowerCamelCase						,       desc="Training"       )
            for step, batch in enumerate(_lowerCamelCase       ):
                A__				=			tuple(t.to(_lowerCamelCase       ) for t in batch       )
                A__,	A__,	A__,	A__				=			batch
                A__				=			model(_lowerCamelCase						,       mc_token_ids=_lowerCamelCase						,       lm_labels=_lowerCamelCase						,       mc_labels=_lowerCamelCase       )
                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(_lowerCamelCase						,       scheduler.get_lr()[0]       )

    # Save a trained model
    if args.do_train:
        # Save a trained model, configuration and tokenizer
        A__				=			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`
        A__				=			os.path.join(args.output_dir						,       _lowerCamelCase       )
        A__				=			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
        A__				=			OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir       )
        A__				=			OpenAIGPTTokenizer.from_pretrained(args.output_dir       )
        model.to(_lowerCamelCase       )

    if args.do_eval:
        model.eval()
        A__,	A__				=			0, 0
        A__,	A__				=			0, 0
        for batch in tqdm(_lowerCamelCase						,       desc="Evaluating"       ):
            A__				=			tuple(t.to(_lowerCamelCase       ) for t in batch       )
            A__,	A__,	A__,	A__				=			batch
            with torch.no_grad():
                A__,	A__,	A__,	A__				=			model(
                    _lowerCamelCase						,       mc_token_ids=_lowerCamelCase						,       lm_labels=_lowerCamelCase						,       mc_labels=_lowerCamelCase       )

            A__				=			mc_logits.detach().cpu().numpy()
            A__				=			mc_labels.to("cpu"       ).numpy()
            A__				=			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

        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(_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'''

import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm
  import torch_xla.debug.metrics as met






class    UpperCAmelCase	(       UpperCamelCase__				):
 def __init__(				self      :Optional[Any]						,       *lowercase_      :int						,       lowercase_      :Any=None						,       lowercase_      :List[str]=None						,       **lowercase_      :Any							)->							Any:
     super().__init__(*lowercase_						,       **lowercase_							)
     A__				=			eval_examples
     A__				=			post_process_function
 def      UpperCAmelCase_							(				self      :str						,       lowercase_      :str=None						,       lowercase_      :Optional[int]=None						,       lowercase_      :Optional[int]=None						,       lowercase_      :str = "eval"							)->							Union[str, Any]:
     A__				=			self.eval_dataset if eval_dataset is None else eval_dataset
     A__				=			self.get_eval_dataloader(lowercase_							)
     A__				=			self.eval_examples if eval_examples is None else eval_examples

     # Temporarily disable metric computation, we will do it in the loop here.
     A__				=			self.compute_metrics
     A__				=			None
     A__				=			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     A__				=			time.time()
     try:
         A__				=			eval_loop(
             lowercase_						,       description="Evaluation"						,       prediction_loss_only=True if compute_metrics is None else None						,       ignore_keys=lowercase_						,       metric_key_prefix=lowercase_						,       )
     finally:
         A__				=			compute_metrics
     A__				=			self.args.eval_batch_size * self.args.world_size
     if F"{metric_key_prefix}_jit_compilation_time" in output.metrics:
         start_time += output.metrics[F"{metric_key_prefix}_jit_compilation_time"]
     output.metrics.update(
         speed_metrics(
             lowercase_						,       lowercase_						,       num_samples=output.num_samples						,       num_steps=math.ceil(output.num_samples / total_batch_size							)						,       )							)
     if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
         # Only the main node write the results by default
         A__				=			self.post_process_function(lowercase_						,       lowercase_						,       output.predictions							)
         A__				=			self.compute_metrics(lowercase_							)

         # Prefix all keys with metric_key_prefix + '_'
         for key in list(metrics.keys()							):
             if not key.startswith(F"{metric_key_prefix}_"							):
                 A__				=			metrics.pop(lowercase_							)
         metrics.update(output.metrics							)
     else:
         A__				=			output.metrics

     if self.args.should_log:
         # Only the main node log the results by default
         self.log(lowercase_							)

     if self.args.tpu_metrics_debug or self.args.debug:
         # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
         xm.master_print(met.metrics_report()							)

     A__				=			self.callback_handler.on_evaluate(self.args						,       self.state						,       self.control						,       lowercase_							)
     return metrics



 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :List[Any]						,       lowercase_      :str						,       lowercase_      :Any=None						,       lowercase_      :str = "test"							)->							List[Any]:
     A__				=			self.get_test_dataloader(lowercase_							)

     # Temporarily disable metric computation, we will do it in the loop here.
     A__				=			self.compute_metrics
     A__				=			None
     A__				=			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     A__				=			time.time()
     try:
         A__				=			eval_loop(
             lowercase_						,       description="Prediction"						,       prediction_loss_only=True if compute_metrics is None else None						,       ignore_keys=lowercase_						,       metric_key_prefix=lowercase_						,       )
     finally:
         A__				=			compute_metrics
     A__				=			self.args.eval_batch_size * self.args.world_size
     if F"{metric_key_prefix}_jit_compilation_time" in output.metrics:
         start_time += output.metrics[F"{metric_key_prefix}_jit_compilation_time"]
     output.metrics.update(
         speed_metrics(
             lowercase_						,       lowercase_						,       num_samples=output.num_samples						,       num_steps=math.ceil(output.num_samples / total_batch_size							)						,       )							)

     if self.post_process_function is None or self.compute_metrics is None:
         return output

     A__				=			self.post_process_function(lowercase_						,       lowercase_						,       output.predictions						,       "predict"							)
     A__				=			self.compute_metrics(lowercase_							)

     # Prefix all keys with metric_key_prefix + '_'
     for key in list(metrics.keys()							):
         if not key.startswith(F"{metric_key_prefix}_"							):
             A__				=			metrics.pop(lowercase_							)
     metrics.update(output.metrics							)
     return PredictionOutput(predictions=predictions.predictions						,       label_ids=predictions.label_ids						,       metrics=lowercase_							)



