


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

import datasets


snake_case_						:			Union[str, Any]													=     "\\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"


snake_case_						:			Optional[int]													=     "\\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"


snake_case_						:			str													=     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 						A__							(		datasets.Metric  ):




						def 							__UpperCamelCase				(       self					:      Any  )							->     Any:


										"""simple docstring"""





										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 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      Any				,    _a					:      int  )							->     int:


										"""simple docstring"""





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



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


snake_case_						:			int													=							logging.get_logger(__name__)

snake_case_						:			Optional[Any]													=							{
    '''SCUT-DLVCLab/lilt-roberta-en-base''': (
        '''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
    ),
}

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"lilt"




						def __init__(       self					:      int				,    _a					:      Dict=3_0522				,    _a					:      str=768				,    _a					:      Optional[int]=12				,    _a					:      str=12				,    _a					:      List[str]=3072				,    _a					:      Any="gelu"				,    _a					:      int=0.1				,    _a					:      Any=0.1				,    _a					:      Union[str, Any]=512				,    _a					:      str=2				,    _a					:      List[Any]=0.02				,    _a					:      str=1E-12				,    _a					:      str=0				,    _a					:      str="absolute"				,    _a					:      Any=None				,    _a					:      Dict=4				,    _a					:      int=1024				,    **_a					:      List[Any]				,    )							->     Any:


										"""simple docstring"""





										super().__init__(pad_token_id=_a				,    **_a  )

										_SCREAMING_SNAKE_CASE			  =vocab_size
										_SCREAMING_SNAKE_CASE			  =hidden_size
										_SCREAMING_SNAKE_CASE			  =num_hidden_layers
										_SCREAMING_SNAKE_CASE			  =num_attention_heads
										_SCREAMING_SNAKE_CASE			  =hidden_act
										_SCREAMING_SNAKE_CASE			  =intermediate_size
										_SCREAMING_SNAKE_CASE			  =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE			  =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE			  =max_position_embeddings
										_SCREAMING_SNAKE_CASE			  =type_vocab_size
										_SCREAMING_SNAKE_CASE			  =initializer_range
										_SCREAMING_SNAKE_CASE			  =layer_norm_eps
										_SCREAMING_SNAKE_CASE			  =position_embedding_type
										_SCREAMING_SNAKE_CASE			  =classifier_dropout
										_SCREAMING_SNAKE_CASE			  =channel_shrink_ratio
										_SCREAMING_SNAKE_CASE			  =max_ad_position_embeddings