

import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A_		:			str										=   logging.getLogger(__name__)
@dataclass
class 					A_   :


    '''simple docstring'''






    a__     							=       field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}						)
    a__     							=       field(
        default=_a ,     metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
    a__     							=       field(
        default=_a ,     metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}						)
    a__     							=       field(
        default=_a ,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,     )






@dataclass
class 					A_   :


    '''simple docstring'''






    a__     							=       field(
        metadata={"help": "The name of the task to train selected in the list: " + ", ".join(hans_processors.keys()						)}						)
    a__     							=       field(
        metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}						)
    a__     							=       field(
        default=1_28 ,     metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        } ,     )
    a__     							=       field(
        default=_a ,     metadata={"help": "Overwrite the cached training and evaluation sets"}						)






def       __a       (   SCREAMING_SNAKE_CASE      )      ->     Dict[str, torch.Tensor]:






    '''simple docstring'''
    __UpperCAmelCase									=			default_data_collator(SCREAMING_SNAKE_CASE      )
    __UpperCAmelCase									=			batch.pop('''pairID'''       ,   SCREAMING_SNAKE_CASE      )
    return batch


def       __a       (   )      ->     Dict:






    '''simple docstring'''
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    __UpperCAmelCase									=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
    __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase									=			parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir      )
        and os.listdir(training_args.output_dir      )
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
            ''' --overwrite_output_dir to overcome.'''      )

    # Setup logging
    logging.basicConfig(
        format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''       ,   datefmt='''%m/%d/%Y %H:%M:%S'''       ,   level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN       ,   )
    logger.warning(
        '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''       ,   training_args.local_rank       ,   training_args.device       ,   training_args.n_gpu       ,   bool(training_args.local_rank != -1      )       ,   training_args.fpaa       ,   )
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank      ):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info('''Training/evaluation parameters %s'''       ,   SCREAMING_SNAKE_CASE      )

    # Set seed
    set_seed(training_args.seed      )

    try:
        __UpperCAmelCase									=			hans_tasks_num_labels[data_args.task_name]
    except KeyError:
        raise ValueError('''Task not found: %s''' % (data_args.task_name)      )

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    __UpperCAmelCase									=			AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,   num_labels=SCREAMING_SNAKE_CASE       ,   finetuning_task=data_args.task_name       ,   cache_dir=model_args.cache_dir       ,   )
    __UpperCAmelCase									=			AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,   cache_dir=model_args.cache_dir       ,   )
    __UpperCAmelCase									=			AutoModelForSequenceClassification.from_pretrained(
        model_args.model_name_or_path       ,   from_tf=bool('''.ckpt''' in model_args.model_name_or_path      )       ,   config=SCREAMING_SNAKE_CASE       ,   cache_dir=model_args.cache_dir       ,   )

    # Get datasets
    __UpperCAmelCase									=			(
        HansDataset(
            data_dir=data_args.data_dir       ,   tokenizer=SCREAMING_SNAKE_CASE       ,   task=data_args.task_name       ,   max_seq_length=data_args.max_seq_length       ,   overwrite_cache=data_args.overwrite_cache       ,   )
        if training_args.do_train
        else None
    )
    __UpperCAmelCase									=			(
        HansDataset(
            data_dir=data_args.data_dir       ,   tokenizer=SCREAMING_SNAKE_CASE       ,   task=data_args.task_name       ,   max_seq_length=data_args.max_seq_length       ,   overwrite_cache=data_args.overwrite_cache       ,   evaluate=SCREAMING_SNAKE_CASE       ,   )
        if training_args.do_eval
        else None
    )

    # Initialize our Trainer
    __UpperCAmelCase									=			Trainer(
        model=SCREAMING_SNAKE_CASE       ,   args=SCREAMING_SNAKE_CASE       ,   train_dataset=SCREAMING_SNAKE_CASE       ,   eval_dataset=SCREAMING_SNAKE_CASE       ,   data_collator=SCREAMING_SNAKE_CASE       ,   )

    # Training
    if training_args.do_train:
        trainer.train(
            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir      )

    # Evaluation
    if training_args.do_eval:
        logger.info('''*** Evaluate ***'''      )

        __UpperCAmelCase									=			trainer.predict(SCREAMING_SNAKE_CASE      )
        __UpperCAmelCase									=			output.predictions
        __UpperCAmelCase									=			np.argmax(SCREAMING_SNAKE_CASE       ,   axis=1      )

        __UpperCAmelCase									=			[ex.pairID for ex in eval_dataset]
        __UpperCAmelCase									=			os.path.join(training_args.output_dir       ,   '''hans_predictions.txt'''      )
        __UpperCAmelCase									=			eval_dataset.get_labels()
        if trainer.is_world_master():
            with open(SCREAMING_SNAKE_CASE       ,   '''w'''      ) as writer:
                writer.write('''pairID,gold_label\n'''      )
                for pid, pred in zip(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      ):
                    writer.write('''ex''' + str(SCREAMING_SNAKE_CASE      ) + ''',''' + label_list[int(SCREAMING_SNAKE_CASE      )] + '''\n'''      )

        trainer._log(output.metrics      )


def       __a       (   SCREAMING_SNAKE_CASE      )      ->     Tuple:






    '''simple docstring'''
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
       main()







import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
A_		:			Any										=   '\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n'

# TODO: Add description of the dataset here
# You can copy an official description
A_		:			Optional[Any]										=   '\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n'

# TODO: Add a link to an official homepage for the dataset here
A_		:			Dict										=   ''

# TODO: Add the licence for the dataset here if you can find it
A_		:			Dict										=   ''

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
A_		:			Union[str, Any]										=   {
    'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
    'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
}
class 					A_   (      datasets.GeneratorBasedBuilder						):


    '''simple docstring'''






    a__     							=       datasets.Version("1.1.0"						)

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    a__     							=       [
        datasets.BuilderConfig(name="first_domain" ,     version=_a ,     description="This part of my dataset covers a first domain"						),
        datasets.BuilderConfig(name="second_domain" ,     version=_a ,     description="This part of my dataset covers a second domain"						),
    ]

    a__     							=       "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.


    def      lowerCAmelCase_						(self  )     ->      Tuple:
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
            __UpperCAmelCase									=			datasets.Features(
                {
                    '''sentence''': datasets.Value('''string'''  ),
                    '''option1''': datasets.Value('''string'''  ),
                    '''answer''': datasets.Value('''string'''  )
                    # These are the features of your dataset like images, labels ...
                }  )
        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
            __UpperCAmelCase									=			datasets.Features(
                {
                    '''sentence''': datasets.Value('''string'''  ),
                    '''option2''': datasets.Value('''string'''  ),
                    '''second_domain_answer''': datasets.Value('''string'''  )
                    # These are the features of your dataset like images, labels ...
                }  )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION     ,      features=lowercase__     ,      homepage=_HOMEPAGE     ,      license=_LICENSE     ,      citation=_CITATION     ,      )


    def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        __UpperCAmelCase									=			_URLS[self.config.name]
        __UpperCAmelCase									=			dl_manager.download_and_extract(lowercase__  )
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN     ,      gen_kwargs={
                    '''filepath''': os.path.join(lowercase__     ,      '''train.jsonl'''  ),
                    '''split''': '''train''',
                }     ,      ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION     ,      gen_kwargs={
                    '''filepath''': os.path.join(lowercase__     ,      '''dev.jsonl'''  ),
                    '''split''': '''dev''',
                }     ,      ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST     ,      gen_kwargs={
                    '''filepath''': os.path.join(lowercase__     ,      '''test.jsonl'''  ),
                    '''split''': '''test'''
                }     ,      ),
        ]






    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__  )     ->      Tuple:
        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(lowercase__     ,      encoding='''utf-8'''  ) as f:
            for key, row in enumerate(lowercase__  ):
                __UpperCAmelCase									=			json.loads(lowercase__  )
                if self.config.name == "first_domain":
                    # Yields examples as (key, example) tuples
                    yield key, {
                        "sentence": data["sentence"],
                        "option1": data["option1"],
                        "answer": "" if split == "test" else data["answer"],
                    }
                else:
                    yield key, {
                        "sentence": data["sentence"],
                        "option2": data["option2"],
                        "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
                    }




