





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
)
A    :					Tuple =      logging.getLogger(__name__)







def    __lowerCamelCase					(							__a   :Optional[int]    ,     __a   :List[str]    )		->				Tuple:






		"""simple docstring"""

		A__        =   np.argmax(__a    ,     axis=1    )
		return np.sum(outputs == labels    )







def    __lowerCamelCase					(							__a   :Tuple    )		->				Dict:






		"""simple docstring"""

		with open(__a    ,     encoding="""utf_8"""    ) as f:
				A__        =   csv.reader(__a    )
				A__        =   []
				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    __lowerCamelCase					(							__a   :Optional[int]    ,     __a   :List[Any]    ,     __a   :Dict    ,     __a   :Optional[Any]    ,     __a   :Optional[Any]    ,     __a   :int    )		->				Union[str, Any]:






		"""simple docstring"""

		A__        =   []
		for dataset in encoded_datasets:
				A__        =   len(__a    )
				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_0_0    ,     dtype=np.intaa    )
				A__        =   np.zeros((n_batch,)    ,     dtype=np.intaa    )
				for (
				    i,
				    (story, conta, conta, mc_label),
				) in enumerate(__a    ):
						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(__a    ) - 1
						A__        =   len(__a    ) - 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(__a    ) for t in all_inputs    )    )
		return tensor_datasets







def    __lowerCamelCase					(							)		->				Union[str, Any]:






		"""simple docstring"""

		A__        =   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."""    )
		A__        =   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    )

		A__        =   torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""    )
		A__        =   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
		A__        =   ["""_start_""", """_delimiter_""", """_classify_"""]
		A__        =   OpenAIGPTTokenizer.from_pretrained(args.model_name    )
		tokenizer.add_tokens(__a    )
		A__        =   tokenizer.convert_tokens_to_ids(__a    )
		A__        =   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   :Tuple    ):
				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..."""    )
		A__        =   load_rocstories_dataset(args.train_dataset    )
		A__        =   load_rocstories_dataset(args.eval_dataset    )
		A__        =   (train_dataset, eval_dataset)
		A__        =   tokenize_and_encode(__a    )

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

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

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

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

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

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

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

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

				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(__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()










from sklearn.metrics import fa_score

import datasets


A    :					Any =      '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


A    :					List[Any] =      '''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


A    :					List[Any] =      '''
@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}
'''


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,     _KWARGS_DESCRIPTION    )
class   A			(datasets.Metric    ):
			'''simple docstring'''




			def    a_       (			self   :    Optional[int]   )	->	List[Any]:







					"""simple docstring"""






					return datasets.MetricInfo(
					    description=_DESCRIPTION							,	citation=_CITATION							,	inputs_description=_KWARGS_DESCRIPTION							,	features=datasets.Features(
					        {
					            """predictions""": datasets.Sequence(datasets.Value("""int32"""   )   ),
					            """references""": datasets.Sequence(datasets.Value("""int32"""   )   ),
					        }
					        if self.config_name == """multilabel"""
					        else {
					            """predictions""": datasets.Value("""int32"""   ),
					            """references""": datasets.Value("""int32"""   ),
					        }   )							,	reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"""]							,	)







			def    a_       (			self   :    Any							,	__lowerCAmelCase   :    Tuple							,	__lowerCAmelCase   :    Union[str, Any]							,	__lowerCAmelCase   :    Dict=None							,	__lowerCAmelCase   :    List[str]=1							,	__lowerCAmelCase   :    Any="binary"							,	__lowerCAmelCase   :    Optional[int]=None   )	->	List[Any]:







					"""simple docstring"""






					A__        =   fa_score(
					    __lowerCAmelCase							,	__lowerCAmelCase							,	labels=__lowerCAmelCase							,	pos_label=__lowerCAmelCase							,	average=__lowerCAmelCase							,	sample_weight=__lowerCAmelCase   )
					return {"f1": float(__lowerCAmelCase   ) if score.size == 1 else score}



