





'''simple docstring'''

import math

import flax.linen as nn
import jax.numpy as jnp







def    a__							(							lowercase   :	jnp.ndarray,   lowercase   :	int,   lowercase   :	float = 1,   lowercase   :	float = 1,   lowercase   :	float = 1.0e4,   lowercase   :	bool = False,   lowercase   :	float = 1.0,   )						->						jnp.ndarray:





     """simple docstring"""

     assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
     assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
     _UpperCamelCase							       =			float(embedding_dim // 2 )
     _UpperCamelCase							       =			math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
     _UpperCamelCase							       =			min_timescale * jnp.exp(jnp.arange(lowercase,   dtype=jnp.floataa ) * -log_timescale_increment )
     _UpperCamelCase							       =			jnp.expand_dims(lowercase,   1 ) * jnp.expand_dims(lowercase,   0 )

     # scale embeddings
     _UpperCamelCase							       =			scale * emb

     if flip_sin_to_cos:
          _UpperCamelCase							       =			jnp.concatenate([jnp.cos(lowercase ), jnp.sin(lowercase )],   axis=1 )
     else:
          _UpperCamelCase							       =			jnp.concatenate([jnp.sin(lowercase ), jnp.cos(lowercase )],   axis=1 )
     _UpperCamelCase							       =			jnp.reshape(lowercase,   [jnp.shape(lowercase )[0], embedding_dim] )
     return signal
class 							__lowerCAmelCase	(       nn.Module					):






       """simple docstring"""
       _snake_case      :	int									=       3_2
       _snake_case      :	jnp.dtype									=       jnp.floataa





       @nn.compact
       def __call__(				self  :						List[Any]			,					lowerCAmelCase__  :						List[str]	)       ->	int:

            '''simple docstring'''




            _UpperCamelCase							       =			nn.Dense(self.time_embed_dim			,					dtype=self.dtype			,					name='''linear_1'''	)(lowerCAmelCase__	)
            _UpperCamelCase							       =			nn.silu(lowerCAmelCase__	)
            _UpperCamelCase							       =			nn.Dense(self.time_embed_dim			,					dtype=self.dtype			,					name='''linear_2'''	)(lowerCAmelCase__	)
            return temb

class 							__lowerCAmelCase	(       nn.Module					):






       """simple docstring"""
       _snake_case      :	int									=       3_2
       _snake_case      :	bool									=       False
       _snake_case      :	float									=       1





       @nn.compact
       def __call__(				self  :						List[str]			,					lowerCAmelCase__  :						List[str]	)       ->	Union[str, Any]:

            '''simple docstring'''




            return get_sinusoidal_embeddings(
                lowerCAmelCase__			,					embedding_dim=self.dim			,					flip_sin_to_cos=self.flip_sin_to_cos			,					freq_shift=self.freq_shift	)










'''simple docstring'''

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

import datasets


lowercase__		:     str			=							'\\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'


lowercase__		:     str			=							'\\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'


lowercase__		:     Dict			=							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 							__lowerCAmelCase	(       datasets.Metric					):






       """simple docstring"""





       def snake_case__		(				self  :						Dict	)       ->	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 snake_case__		(				self  :						List[Any]			,					lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						int	)       ->	List[str]:

            '''simple docstring'''




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



