





'''simple docstring'''




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

import datasets


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


_A:					List[str]							=				"""\
This metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.
It first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.
"""


_A:					Any							=				r"""
Calculates accuracy after canonicalizing inputs.

Args:
    predictions: list of predictions to score. Each prediction
        is a string that contains natural language and LaTex.
    references: list of reference for each prediction. Each
        reference is a string that contains natural language
        and LaTex.
Returns:
    accuracy: accuracy after canonicalizing inputs
        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")

Examples:
    >>> metric = datasets.load_metric(\"competition_math\")
    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])
    >>> print(results)
    {'accuracy': 1.0}
"""






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



    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     ,			__A     ,			__A					):
     __UpperCAmelCase					=   0.0
     for i, j in zip(__A     ,			__A					):
      n_correct += 1.0 if math_equivalence.is_equiv(__A     ,			__A					) else 0.0
     __UpperCAmelCase					=   n_correct / len(__A					)
     return {
         "accuracy": accuracy,
     }












'''simple docstring'''




from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A:					str							=				logging.get_logger(__name__)

_A:					Optional[Any]							=				{
    """huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json""",
}






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			Tuple						   =       """autoformer"""
    _A :			str						   =       {
        """hidden_size""": """d_model""",
        """num_attention_heads""": """encoder_attention_heads""",
        """num_hidden_layers""": """encoder_layers""",
    }



    def __init__(  self     ,			__A = None     ,			__A = None     ,			__A = "student_t"     ,			__A = "nll"     ,			__A = 1     ,			__A = [1, 2, 3, 4, 5, 6, 7]     ,			__A = True     ,			__A = 0     ,			__A = 0     ,			__A = 0     ,			__A = 0     ,			__A = None     ,			__A = None     ,			__A = 64     ,			__A = 2     ,			__A = 2     ,			__A = 2     ,			__A = 2     ,			__A = 32     ,			__A = 32     ,			__A = "gelu"     ,			__A = 0.1     ,			__A = 0.1     ,			__A = 0.1     ,			__A = 0.1     ,			__A = 0.1     ,			__A = 100     ,			__A = 0.0_2     ,			__A = True     ,			__A=True     ,			__A = 10     ,			__A = 25     ,			__A = 3     ,			**__A     ,			):
     # time series specific configuration
     __UpperCAmelCase					=   prediction_length
     __UpperCAmelCase					=   context_length if context_length is not None else prediction_length
     __UpperCAmelCase					=   distribution_output
     __UpperCAmelCase					=   loss
     __UpperCAmelCase					=   input_size
     __UpperCAmelCase					=   num_time_features
     __UpperCAmelCase					=   lags_sequence
     __UpperCAmelCase					=   scaling
     __UpperCAmelCase					=   num_dynamic_real_features
     __UpperCAmelCase					=   num_static_real_features
     __UpperCAmelCase					=   num_static_categorical_features
     if cardinality is not None and num_static_categorical_features > 0:
      if len(__A					) != num_static_categorical_features:
       raise ValueError(
           'The cardinality should be a list of the same length as `num_static_categorical_features`'					)
      __UpperCAmelCase					=   cardinality
     else:
      __UpperCAmelCase					=   [0]
     if embedding_dimension is not None and num_static_categorical_features > 0:
      if len(__A					) != num_static_categorical_features:
       raise ValueError(
           'The embedding dimension should be a list of the same length as `num_static_categorical_features`'					)
      __UpperCAmelCase					=   embedding_dimension
     else:
      __UpperCAmelCase					=   [min(50     ,			(cat + 1) // 2					) for cat in self.cardinality]
     __UpperCAmelCase					=   num_parallel_samples

     # Transformer architecture configuration
     __UpperCAmelCase					=   input_size * len(self.lags_sequence					) + self._number_of_features
     __UpperCAmelCase					=   d_model
     __UpperCAmelCase					=   encoder_attention_heads
     __UpperCAmelCase					=   decoder_attention_heads
     __UpperCAmelCase					=   encoder_ffn_dim
     __UpperCAmelCase					=   decoder_ffn_dim
     __UpperCAmelCase					=   encoder_layers
     __UpperCAmelCase					=   decoder_layers

     __UpperCAmelCase					=   dropout
     __UpperCAmelCase					=   attention_dropout
     __UpperCAmelCase					=   activation_dropout
     __UpperCAmelCase					=   encoder_layerdrop
     __UpperCAmelCase					=   decoder_layerdrop

     __UpperCAmelCase					=   activation_function
     __UpperCAmelCase					=   init_std

     __UpperCAmelCase					=   use_cache

     # Autoformer
     __UpperCAmelCase					=   label_length
     __UpperCAmelCase					=   moving_average
     __UpperCAmelCase					=   autocorrelation_factor

     super().__init__(is_encoder_decoder=__A     ,			**__A					)




    @property
    def 					__lowerCamelCase						(  self					):
     return (
         sum(self.embedding_dimension					)
         + self.num_dynamic_real_features
         + self.num_time_features
         + self.num_static_real_features
         + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
     )





