


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			=							logging.getLogger(__name__)




def        snake_case_					(  snake_case      ,							snake_case					)  ->    Optional[int]:
       lowercase__:       Optional[int]        =       np.argmax(snake_case      ,							axis=1					)
       return np.sum(outputs == labels					)




def        snake_case_					(  snake_case					)  ->    Dict:
       with open(snake_case      ,							encoding='utf_8'					) as f:
              lowercase__:       str        =       csv.reader(snake_case					)
              lowercase__:       int        =       []
              next(snake_case					)  # skip the first line
              for line in tqdm(snake_case					):
                     output.append((' '.join(line[1:5]					), line[5], line[6], int(line[-1]					) - 1)					)
       return output




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case					)  ->    Tuple:
       lowercase__:       List[Any]        =       []
       for dataset in encoded_datasets:
              lowercase__:       Dict        =       len(snake_case					)
              lowercase__:       int        =       np.zeros((n_batch, 2, input_len)      ,							dtype=np.intaa					)
              lowercase__:       int        =       np.zeros((n_batch, 2)      ,							dtype=np.intaa					)
              lowercase__:       Optional[int]        =       np.full((n_batch, 2, input_len)      ,							fill_value=-1_00      ,							dtype=np.intaa					)
              lowercase__:       Optional[Any]        =       np.zeros((n_batch,)      ,							dtype=np.intaa					)
              for (
                  i,
                  (story, conta, conta, mc_label),
              ) in enumerate(snake_case					):
                     lowercase__:       List[Any]        =       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     lowercase__:       List[Any]        =       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     lowercase__:       Union[str, Any]        =       with_conta
                     lowercase__:       List[Any]        =       with_conta
                     lowercase__:       Any        =       len(snake_case					) - 1
                     lowercase__:       Dict        =       len(snake_case					) - 1
                     lowercase__:       Optional[Any]        =       with_conta
                     lowercase__:       Tuple        =       with_conta
                     lowercase__:       int        =       mc_label
              lowercase__:       Any        =       (input_ids, mc_token_ids, lm_labels, mc_labels)
              tensor_datasets.append(tuple(torch.tensor(snake_case					) for t in all_inputs					)					)
       return tensor_datasets




def        snake_case_					(  )  ->    Union[str, Any]:
       lowercase__:       Optional[Any]        =       argparse.ArgumentParser()
       parser.add_argument('--model_name'      ,							type=snake_case      ,							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=snake_case      ,							type=snake_case      ,							required=snake_case      ,							help='The output directory where the model predictions and checkpoints will be written.'      ,							)
       parser.add_argument('--train_dataset'      ,							type=snake_case      ,							default=''					)
       parser.add_argument('--eval_dataset'      ,							type=snake_case      ,							default=''					)
       parser.add_argument('--seed'      ,							type=snake_case      ,							default=42					)
       parser.add_argument('--num_train_epochs'      ,							type=snake_case      ,							default=3					)
       parser.add_argument('--train_batch_size'      ,							type=snake_case      ,							default=8					)
       parser.add_argument('--eval_batch_size'      ,							type=snake_case      ,							default=16					)
       parser.add_argument('--adam_epsilon'      ,							default=1e-8      ,							type=snake_case      ,							help='Epsilon for Adam optimizer.'					)
       parser.add_argument('--max_grad_norm'      ,							type=snake_case      ,							default=1					)
       parser.add_argument(
           '--max_steps'      ,							default=-1      ,							type=snake_case      ,							help=(
               'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
           )      ,							)
       parser.add_argument(
           '--gradient_accumulation_steps'      ,							type=snake_case      ,							default=1      ,							help='Number of updates steps to accumulate before                        performing a backward/update pass.'      ,							)
       parser.add_argument('--learning_rate'      ,							type=snake_case      ,							default=6.25e-5					)
       parser.add_argument('--warmup_steps'      ,							default=0      ,							type=snake_case      ,							help='Linear warmup over warmup_steps.'					)
       parser.add_argument('--lr_schedule'      ,							type=snake_case      ,							default='warmup_linear'					)
       parser.add_argument('--weight_decay'      ,							type=snake_case      ,							default=0.0_1					)
       parser.add_argument('--lm_coef'      ,							type=snake_case      ,							default=0.9					)
       parser.add_argument('--n_valid'      ,							type=snake_case      ,							default=3_74					)

       parser.add_argument('--server_ip'      ,							type=snake_case      ,							default=''      ,							help='Can be used for distant debugging.'					)
       parser.add_argument('--server_port'      ,							type=snake_case      ,							default=''      ,							help='Can be used for distant debugging.'					)
       lowercase__:       List[str]        =       parser.parse_args()
       print(snake_case					)

       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=snake_case					)
              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					)

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

       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
       lowercase__:       Any        =       ['_start_', '_delimiter_', '_classify_']
       lowercase__:       Any        =       OpenAIGPTTokenizer.from_pretrained(args.model_name					)
       tokenizer.add_tokens(snake_case					)
       lowercase__:       int        =       tokenizer.convert_tokens_to_ids(snake_case					)
       lowercase__:       int        =       OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name					)
       model.resize_token_embeddings(len(snake_case					)					)
       model.to(snake_case					)

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

       logger.info('Encoding dataset...'					)
       lowercase__:       Dict        =       load_rocstories_dataset(args.train_dataset					)
       lowercase__:       Dict        =       load_rocstories_dataset(args.eval_dataset					)
       lowercase__:       str        =       (train_dataset, eval_dataset)
       lowercase__:       Any        =       tokenize_and_encode(snake_case					)

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

       # Prepare inputs tensors and dataloaders
       lowercase__:       str        =       pre_process_datasets(snake_case      ,							snake_case      ,							snake_case      ,							*snake_case					)
       lowercase__      ,						lowercase__:       Optional[Any]        =       tensor_datasets[0], tensor_datasets[1]

       lowercase__:       List[str]        =       TensorDataset(*snake_case					)
       lowercase__:       Dict        =       RandomSampler(snake_case					)
       lowercase__:       Optional[int]        =       DataLoader(snake_case      ,							sampler=snake_case      ,							batch_size=args.train_batch_size					)

       lowercase__:       str        =       TensorDataset(*snake_case					)
       lowercase__:       str        =       SequentialSampler(snake_case					)
       lowercase__:       Optional[Any]        =       DataLoader(snake_case      ,							sampler=snake_case      ,							batch_size=args.eval_batch_size					)

       # Prepare optimizer
       if args.do_train:
              if args.max_steps > 0:
                     lowercase__:       Union[str, Any]        =       args.max_steps
                     lowercase__:       Tuple        =       args.max_steps // (len(snake_case					) // args.gradient_accumulation_steps) + 1
              else:
                     lowercase__:       Optional[Any]        =       len(snake_case					) // args.gradient_accumulation_steps * args.num_train_epochs

              lowercase__:       str        =       list(model.named_parameters()					)
              lowercase__:       Any        =       ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
              lowercase__:       str        =       [
                  {
                      '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},
              ]
              lowercase__:       Tuple        =       AdamW(snake_case      ,							lr=args.learning_rate      ,							eps=args.adam_epsilon					)
              lowercase__:       Tuple        =       get_linear_schedule_with_warmup(
                  snake_case      ,							num_warmup_steps=args.warmup_steps      ,							num_training_steps=snake_case					)

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

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

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

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

              # Load a trained model and vocabulary that you have fine-tuned
              lowercase__:       Optional[Any]        =       OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir					)
              lowercase__:       Any        =       OpenAIGPTTokenizer.from_pretrained(args.output_dir					)
              model.to(snake_case					)

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

                     lowercase__:       Dict        =       mc_logits.detach().cpu().numpy()
                     lowercase__:       Tuple        =       mc_labels.to('cpu'					).numpy()
                     lowercase__:       Dict        =       accuracy(snake_case      ,							snake_case					)

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

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

              lowercase__:       Optional[int]        =       eval_loss / nb_eval_steps
              lowercase__:       Optional[int]        =       eval_accuracy / nb_eval_examples
              lowercase__:       int        =       tr_loss / nb_tr_steps if args.do_train else None
              lowercase__:       Optional[Any]        =       {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

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


if __name__ == "__main__":
 main()






import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


__lowerCAmelCase			=							'''\
@inproceedings{popovic-2015-chrf,
    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W15-3049",
    doi = "10.18653/v1/W15-3049",
    pages = "392--395",
}
@inproceedings{popovic-2017-chrf,
    title = "chr{F}++: words helping character n-grams",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-4770",
    doi = "10.18653/v1/W17-4770",
    pages = "612--618",
}
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

__lowerCAmelCase			=							'''\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
'''

__lowerCAmelCase			=							'''
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    \'score\' (float): The chrF (chrF++) score,
    \'char_order\' (int): The character n-gram order,
    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    \'beta\' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}
'''





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,	_KWARGS_DESCRIPTION				)
class   __a      (   datasets.Metric				):
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        if version.parse(scb.__version__			) < version.parse('1.4.12'			):
               raise ImportWarning(
                   'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
                   'You can install it with `pip install "sacrebleu>=1.4.12"`.'			)
        return datasets.MetricInfo(
            description=_DESCRIPTION    ,			citation=_CITATION    ,			homepage='https://github.com/mjpost/sacreBLEU#chrf--chrf'    ,			inputs_description=_KWARGS_DESCRIPTION    ,			features=datasets.Features(
                {
                    'predictions': datasets.Value('string'    ,			id='sequence'			),
                    'references': datasets.Sequence(datasets.Value('string'    ,			id='sequence'			)    ,			id='references'			),
                }			)    ,			codebase_urls=['https://github.com/mjpost/sacreBLEU#chrf--chrf']    ,			reference_urls=[
                'https://github.com/m-popovic/chrF',
            ]    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = CHRF.CHAR_ORDER    ,			lowerCAmelCase__ = CHRF.WORD_ORDER    ,			lowerCAmelCase__ = CHRF.BETA    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = False    ,			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       str        =       len(references[0]			)
        if any(len(lowerCAmelCase__			) != references_per_prediction for refs in references			):
               raise ValueError('Sacrebleu requires the same number of references for each prediction'			)
        lowercase__:       List[str]        =       [[refs[i] for refs in references] for i in range(lowerCAmelCase__			)]

        lowercase__:       Union[str, Any]        =       CHRF(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			)
        lowercase__:       str        =       sb_chrf.corpus_score(lowerCAmelCase__    ,			lowerCAmelCase__			)

        return {
            "score": output.score,
            "char_order": output.char_order,
            "word_order": output.word_order,
            "beta": output.beta,
        }


