

'''simple docstring'''



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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()

def 	lowerCamelCase__							(   a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   a__ = None      ,   )		->     Optional[int]:



    """simple docstring"""







    _snake_case			:			List[Any]				       =		{}

    if train_file is not None:
        _snake_case			:			Dict				       =		[train_file]
    if eval_file is not None:
        _snake_case			:			Dict				       =		[eval_file]
    if test_file is not None:
        _snake_case			:			int				       =		[test_file]

    _snake_case			:			Tuple				       =		datasets.load_dataset('csv'      ,   data_files=snake_case__)
    _snake_case			:			List[str]				       =		list(ds[list(files.keys())[0]].features.keys())
    _snake_case			:			str				       =		features_name.pop(snake_case__)
    _snake_case			:			Dict				       =		list(set(ds[list(files.keys())[0]][label_name]))
    _snake_case			:			Any				       =		{label: i for i, label in enumerate(snake_case__)}
    _snake_case			:			Union[str, Any]				       =		tokenizer.model_input_names
    _snake_case			:			Dict				       =		{}

    if len(snake_case__) == 1:
        for k in files.keys():
            _snake_case			:			Optional[int]				       =		ds[k].map(
                lambda a__: tokenizer.batch_encode_plus(
                    example[features_name[0]]      ,   truncation=snake_case__      ,   max_length=snake_case__      ,   padding='max_length')      ,   batched=snake_case__      ,   )
    elif len(snake_case__) == 2:
        for k in files.keys():
            _snake_case			:			Optional[int]				       =		ds[k].map(
                lambda a__: tokenizer.batch_encode_plus(
                    (example[features_name[0]], example[features_name[1]])      ,   truncation=snake_case__      ,   max_length=snake_case__      ,   padding='max_length'      ,   )      ,   batched=snake_case__      ,   )

    def gen_train():
        for ex in transformed_ds[datasets.Split.TRAIN]:
            _snake_case			:			int				       =		{k: v for k, v in ex.items() if k in input_names}
            _snake_case			:			Dict				       =		labelaid[ex[label_name]]
            yield (d, label)

    def gen_val():
        for ex in transformed_ds[datasets.Split.VALIDATION]:
            _snake_case			:			Optional[Any]				       =		{k: v for k, v in ex.items() if k in input_names}
            _snake_case			:			Tuple				       =		labelaid[ex[label_name]]
            yield (d, label)

    def gen_test():
        for ex in transformed_ds[datasets.Split.TEST]:
            _snake_case			:			Optional[Any]				       =		{k: v for k, v in ex.items() if k in input_names}
            _snake_case			:			Optional[int]				       =		labelaid[ex[label_name]]
            yield (d, label)

    _snake_case			:			List[Any]				       =		(
        tf.data.Dataset.from_generator(
            snake_case__      ,   ({k: tf.intaa for k in input_names}, tf.intaa)      ,   ({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([]))      ,   )
        if datasets.Split.TRAIN in transformed_ds
        else None
    )

    if train_ds is not None:
        _snake_case			:			Union[str, Any]				       =		train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN])))

    _snake_case			:			Tuple				       =		(
        tf.data.Dataset.from_generator(
            snake_case__      ,   ({k: tf.intaa for k in input_names}, tf.intaa)      ,   ({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([]))      ,   )
        if datasets.Split.VALIDATION in transformed_ds
        else None
    )

    if val_ds is not None:
        _snake_case			:			Optional[int]				       =		val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION])))

    _snake_case			:			str				       =		(
        tf.data.Dataset.from_generator(
            snake_case__      ,   ({k: tf.intaa for k in input_names}, tf.intaa)      ,   ({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([]))      ,   )
        if datasets.Split.TEST in transformed_ds
        else None
    )

    if test_ds is not None:
        _snake_case			:			Tuple				       =		test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST])))

    return train_ds, val_ds, test_ds, labelaid


SCREAMING_SNAKE_CASE_     = logging.getLogger(__name__)

@dataclass
class       SCREAMING_SNAKE_CASE       :






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__			:					int     =					field(metadata={'''help''': '''Which column contains the label'''}       )
      SCREAMING_SNAKE_CASE__			:					str     =					field(default=_SCREAMING_SNAKE_CASE   ,metadata={'''help''': '''The path of the training file'''}       )
      SCREAMING_SNAKE_CASE__			:					Optional[str]     =					field(default=_SCREAMING_SNAKE_CASE   ,metadata={'''help''': '''The path of the development file'''}       )
      SCREAMING_SNAKE_CASE__			:					Optional[str]     =					field(default=_SCREAMING_SNAKE_CASE   ,metadata={'''help''': '''The path of the test file'''}       )
      SCREAMING_SNAKE_CASE__			:					int     =					field(
          default=128   ,metadata={
              '''help''': (
                  '''The maximum total input sequence length after tokenization. Sequences longer '''
                  '''than this will be truncated, sequences shorter will be padded.'''
              )
          }   ,)
      SCREAMING_SNAKE_CASE__			:					bool     =					field(
          default=_SCREAMING_SNAKE_CASE   ,metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}       )



@dataclass
class       SCREAMING_SNAKE_CASE       :






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__			:					str     =					field(
          metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}       )
      SCREAMING_SNAKE_CASE__			:					Optional[str]     =					field(
          default=_SCREAMING_SNAKE_CASE   ,metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}       )
      SCREAMING_SNAKE_CASE__			:					Optional[str]     =					field(
          default=_SCREAMING_SNAKE_CASE   ,metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}       )
      SCREAMING_SNAKE_CASE__			:					bool     =					field(default=_SCREAMING_SNAKE_CASE   ,metadata={'''help''': '''Set this flag to use fast tokenization.'''}       )
      # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
      # or just modify its tokenizer_config.json.
      SCREAMING_SNAKE_CASE__			:					Optional[str]     =					field(
          default=_SCREAMING_SNAKE_CASE   ,metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}   ,)



def 	lowerCamelCase__							(   )		->     List[Any]:



    """simple docstring"""







    _snake_case			:			str				       =		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
    _snake_case			:			int				       =		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      ,   )
    logger.info(
        F"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1)}, """
        F"""16-bits training: {training_args.fpaa}""")
    logger.info(F"""Training/evaluation parameters {training_args}""")

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

    _snake_case			:			str				       =		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      ,   )

    _snake_case			:			Dict				       =		get_tfds(
        train_file=data_args.train_file      ,   eval_file=data_args.dev_file      ,   test_file=data_args.test_file      ,   tokenizer=snake_case__      ,   label_column_id=data_args.label_column_id      ,   max_seq_length=data_args.max_seq_length      ,   )

    _snake_case			:			Union[str, Any]				       =		AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,   num_labels=len(snake_case__)      ,   labelaid=snake_case__      ,   idalabel={id: label for label, id in labelaid.items()}      ,   finetuning_task='text-classification'      ,   cache_dir=model_args.cache_dir      ,   )

    with training_args.strategy.scope():
        _snake_case			:			Any				       =		TFAutoModelForSequenceClassification.from_pretrained(
            model_args.model_name_or_path      ,   from_pt=bool('.bin' in model_args.model_name_or_path)      ,   config=snake_case__      ,   cache_dir=model_args.cache_dir      ,   )

    def compute_metrics(a__) -> Dict:
        _snake_case			:			Any				       =		np.argmax(p.predictions      ,   axis=1)

        return {"acc": (preds == p.label_ids).mean()}

    # Initialize our Trainer
    _snake_case			:			Any				       =		TFTrainer(
        model=snake_case__      ,   args=snake_case__      ,   train_dataset=snake_case__      ,   eval_dataset=snake_case__      ,   compute_metrics=snake_case__      ,   )

    # Training
    if training_args.do_train:
        trainer.train()
        trainer.save_model()
        tokenizer.save_pretrained(training_args.output_dir)

    # Evaluation
    _snake_case			:			str				       =		{}
    if training_args.do_eval:
        logger.info('*** Evaluate ***')
        _snake_case			:			Any				       =		trainer.evaluate()
        _snake_case			:			Optional[Any]				       =		os.path.join(training_args.output_dir      ,   'eval_results.txt')

        with open(snake_case__      ,   'w') as writer:
            logger.info('***** Eval results *****')

            for key, value in result.items():
                logger.info(F"""  {key} = {value}""")
                writer.write(F"""{key} = {value}\n""")

            results.update(snake_case__)

    return results


if __name__ == "__main__":
   main()











"""simple docstring"""
import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


_lowerCAmelCase       =				"\\n@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the MATH Dataset},\n  author={Dan Hendrycks\n    and Collin Burns\n    and Saurav Kadavath\n    and Akul Arora\n    and Steven Basart\n    and Eric Tang\n    and Dawn Song\n    and Jacob Steinhardt},\n  journal={arXiv preprint arXiv:2103.03874},\n  year={2021}\n}\n"


_lowerCAmelCase       =				"\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.\n"


_lowerCAmelCase       =				r"\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")\n\nExamples:\n    >>> metric = datasets.load_metric(\"competition_math\")\n    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])\n    >>> print(results)\n    {'accuracy': 1.0}\n"



@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION							,							_KWARGS_DESCRIPTION    )
class   SCREAMING_SNAKE_CASE	(					datasets.Metric    ):





	"""simple docstring"""




	def  lowerCamelCase(self				):
		return datasets.MetricInfo(
		    description=_DESCRIPTION  ,  citation=_CITATION  ,  inputs_description=_KWARGS_DESCRIPTION  ,  features=datasets.Features(
		        {
		            """predictions""": datasets.Value("""string"""				),
		            """references""": datasets.Value("""string"""				),
		        }				)  ,  homepage="""https://github.com/hendrycks/math"""  ,  codebase_urls=["""https://github.com/hendrycks/math"""]  ,  )





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Optional[Any]						     = 0.0
		for i, j in zip(lowerCAmelCase_  ,  lowerCAmelCase_				):
			n_correct += 1.0 if math_equivalence.is_equiv(lowerCAmelCase_  ,  lowerCAmelCase_				) else 0.0
		A_		:    List[str]						     = n_correct / len(lowerCAmelCase_				)
		return {
		    "accuracy": accuracy,
		}

