





'''simple docstring'''






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase       		=      logging.get_logger(__name__)

lowerCAmelCase       		=      {
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}
class 		lowerCamelCase  (      _A       ):
 snake_case_      =   "deta"
 snake_case_      =   {
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
 }



 def __init__(   self		,      a_=None		,      a_=900		,      a_=2_048		,      a_=6		,      a_=2_048		,      a_=8		,      a_=6		,      a_=1_024		,      a_=8		,      a_=0.0		,      a_=True		,      a_="relu"		,      a_=256		,      a_=0.1		,      a_=0.0		,      a_=0.0		,      a_=0.02		,      a_=1.0		,      a_=True		,      a_=False		,      a_="sine"		,      a_=5		,      a_=4		,      a_=4		,      a_=True		,      a_=300		,      a_=True		,      a_=True		,      a_=1		,      a_=5		,      a_=2		,      a_=1		,      a_=1		,      a_=5		,      a_=2		,      a_=0.1		,      a_=0.25		,      **a_		,      ):
   if backbone_config is None:
     logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."	)
     lowerCAmelCase     :    Dict						=   CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]	)
   else:
     if isinstance(a_		,      a_	):
       lowerCAmelCase     :    Any						=   backbone_config.pop("model_type"	)
       lowerCAmelCase     :    Dict						=   CONFIG_MAPPING[backbone_model_type]
       lowerCAmelCase     :    int						=   config_class.from_dict(a_	)

   lowerCAmelCase     :    Optional[int]						=   backbone_config
   lowerCAmelCase     :    Dict						=   num_queries
   lowerCAmelCase     :    Optional[int]						=   max_position_embeddings
   lowerCAmelCase     :    List[Any]						=   d_model
   lowerCAmelCase     :    Optional[int]						=   encoder_ffn_dim
   lowerCAmelCase     :    Optional[int]						=   encoder_layers
   lowerCAmelCase     :    Optional[Any]						=   encoder_attention_heads
   lowerCAmelCase     :    List[str]						=   decoder_ffn_dim
   lowerCAmelCase     :    Optional[Any]						=   decoder_layers
   lowerCAmelCase     :    Tuple						=   decoder_attention_heads
   lowerCAmelCase     :    Optional[int]						=   dropout
   lowerCAmelCase     :    Dict						=   attention_dropout
   lowerCAmelCase     :    Optional[int]						=   activation_dropout
   lowerCAmelCase     :    Optional[Any]						=   activation_function
   lowerCAmelCase     :    Dict						=   init_std
   lowerCAmelCase     :    Tuple						=   init_xavier_std
   lowerCAmelCase     :    List[str]						=   encoder_layerdrop
   lowerCAmelCase     :    Dict						=   auxiliary_loss
   lowerCAmelCase     :    Any						=   position_embedding_type
   # deformable attributes
   lowerCAmelCase     :    int						=   num_feature_levels
   lowerCAmelCase     :    int						=   encoder_n_points
   lowerCAmelCase     :    Optional[int]						=   decoder_n_points
   lowerCAmelCase     :    Union[str, Any]						=   two_stage
   lowerCAmelCase     :    List[Any]						=   two_stage_num_proposals
   lowerCAmelCase     :    Union[str, Any]						=   with_box_refine
   lowerCAmelCase     :    Dict						=   assign_first_stage
   if two_stage is True and with_box_refine is False:
     raise ValueError("If two_stage is True, with_box_refine must be True."	)
   # Hungarian matcher
   lowerCAmelCase     :    str						=   class_cost
   lowerCAmelCase     :    Optional[Any]						=   bbox_cost
   lowerCAmelCase     :    Tuple						=   giou_cost
   # Loss coefficients
   lowerCAmelCase     :    Dict						=   mask_loss_coefficient
   lowerCAmelCase     :    Tuple						=   dice_loss_coefficient
   lowerCAmelCase     :    int						=   bbox_loss_coefficient
   lowerCAmelCase     :    Optional[int]						=   giou_loss_coefficient
   lowerCAmelCase     :    Any						=   eos_coefficient
   lowerCAmelCase     :    Tuple						=   focal_alpha
   super().__init__(is_encoder_decoder=a_		,      **a_	)



 @property
 def   _lowerCamelCase    (   self	):
   return self.encoder_attention_heads



 @property
 def   _lowerCamelCase    (   self	):
   return self.d_model





 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    List[str]						=   copy.deepcopy(self.__dict__	)
   lowerCAmelCase     :    Tuple						=   self.backbone_config.to_dict()
   lowerCAmelCase     :    Any						=   self.__class__.model_type
   return output










'''simple docstring'''






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

import datasets


lowerCAmelCase       		=      """\
@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}
}
"""


lowerCAmelCase       		=      """\
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.
"""


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



