'''simple docstring'''





import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


SCREAMING_SNAKE_CASE_:     Optional[Any]  =logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_:     int  ={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE_:     Optional[int]  ={
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

SCREAMING_SNAKE_CASE_:     List[str]  ={
    'allenai/led-base-16384': 1_63_84,
}




class        __A      ( UpperCamelCase__	):
    a__       :						Optional[int]          =  VOCAB_FILES_NAMES
    a__       :						int          =  PRETRAINED_VOCAB_FILES_MAP
    a__       :						Optional[Any]          =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__       :						List[str]          =  LEDTokenizer
    a__       :						Tuple          =  ["""input_ids""", """attention_mask"""]



    def __init__(self    :    List[str]   ,			__a    :    Union[str, Any]=None   ,			__a    :    Optional[int]=None   ,			__a    :    Tuple=None   ,			__a    :    Optional[int]="replace"   ,			__a    :    Union[str, Any]="<s>"   ,			__a    :    Tuple="</s>"   ,			__a    :    List[Any]="</s>"   ,			__a    :    Optional[Any]="<s>"   ,			__a    :    List[str]="<unk>"   ,			__a    :    Optional[int]="<pad>"   ,			__a    :    int="<mask>"   ,			__a    :    Union[str, Any]=False   ,			__a    :    List[str]=True   ,			**__a    :    Union[str, Any]   ,			):
         super().__init__(
             __a   ,			__a   ,			tokenizer_file=__a   ,			errors=__a   ,			bos_token=__a   ,			eos_token=__a   ,			sep_token=__a   ,			cls_token=__a   ,			unk_token=__a   ,			pad_token=__a   ,			mask_token=__a   ,			add_prefix_space=__a   ,			trim_offsets=__a   ,			**__a   ,			)

         UpperCAmelCase_           =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
         if pre_tok_state.get("add_prefix_space"   ,			__a ) != add_prefix_space:
              UpperCAmelCase_           =   getattr(__a   ,			pre_tok_state.pop("type" ) )
              UpperCAmelCase_           =   add_prefix_space
              UpperCAmelCase_           =   pre_tok_class(**__a )

         UpperCAmelCase_           =   add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         UpperCAmelCase_           =   "post_processor"
         UpperCAmelCase_           =   getattr(self.backend_tokenizer   ,			__a   ,			__a )
         if tokenizer_component_instance:
              UpperCAmelCase_           =   json.loads(tokenizer_component_instance.__getstate__() )

              # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
              if "sep" in state:
                   UpperCAmelCase_           =   tuple(state["sep"] )
              if "cls" in state:
                   UpperCAmelCase_           =   tuple(state["cls"] )

              UpperCAmelCase_           =   False

              if state.get("add_prefix_space"   ,			__a ) != add_prefix_space:
                   UpperCAmelCase_           =   add_prefix_space
                   UpperCAmelCase_           =   True

              if state.get("trim_offsets"   ,			__a ) != trim_offsets:
                   UpperCAmelCase_           =   trim_offsets
                   UpperCAmelCase_           =   True

              if changes_to_apply:
                   UpperCAmelCase_           =   getattr(__a   ,			state.pop("type" ) )
                   UpperCAmelCase_           =   component_class(**__a )
                   setattr(self.backend_tokenizer   ,			__a   ,			__a )



    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    def 					_lowercase						(self    :    Optional[Any] ):
         if self._mask_token is None:
              if self.verbose:
                   logger.error("Using mask_token, but it is not set yet." )
              return None
         return str(self._mask_token )



    @mask_token.setter
    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    str ):
         UpperCAmelCase_           =   AddedToken(__a   ,			lstrip=__a   ,			rstrip=__a ) if isinstance(__a   ,			__a ) else value
         UpperCAmelCase_           =   value



    def 					_lowercase						(self    :    Union[str, Any]   ,			*__a    :    Union[str, Any]   ,			**__a    :    int ):
         UpperCAmelCase_           =   kwargs.get("is_split_into_words"   ,			__a )

         if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                  "to use it with pretokenized inputs." )

         return super()._batch_encode_plus(*__a   ,			**__a )



    def 					_lowercase						(self    :    Dict   ,			*__a    :    Tuple   ,			**__a    :    str ):
         UpperCAmelCase_           =   kwargs.get("is_split_into_words"   ,			__a )

         if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                  "to use it with pretokenized inputs." )

         return super()._encode_plus(*__a   ,			**__a )



    def 					_lowercase						(self    :    Dict   ,			__a    :    str   ,			__a    :    Optional[str] = None ):
         UpperCAmelCase_           =   self._tokenizer.model.save(__a   ,			name=__a )
         return tuple(__a )



    def 					_lowercase						(self    :    str   ,			__a    :    List[str]   ,			__a    :    Dict=None ):
         UpperCAmelCase_           =   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
              return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



    def 					_lowercase						(self    :    Any   ,			__a    :    List[int]   ,			__a    :    Optional[List[int]] = None ):
         UpperCAmelCase_           =   [self.sep_token_id]
         UpperCAmelCase_           =   [self.cls_token_id]

         if token_ids_a is None:
              return len(cls + token_ids_a + sep ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]






    def 					_lowercase						(self    :    Any   ,			__a    :    Union[Dict[str, EncodedInput], BatchEncoding]   ,			__a    :    Optional[int] = None   ,			__a    :    PaddingStrategy = PaddingStrategy.DO_NOT_PAD   ,			__a    :    Optional[int] = None   ,			__a    :    Optional[bool] = None   ,			):
         UpperCAmelCase_           =   super()._pad(
             encoded_inputs=__a   ,			max_length=__a   ,			padding_strategy=__a   ,			pad_to_multiple_of=__a   ,			return_attention_mask=__a   ,			)

         # Load from model defaults
         if return_attention_mask is None:
              UpperCAmelCase_           =   "attention_mask" in self.model_input_names

         if return_attention_mask and "global_attention_mask" in encoded_inputs:
              UpperCAmelCase_           =   encoded_inputs[self.model_input_names[0]]
              # `global_attention_mask` need to have the same length as other (sequential) inputs.
              UpperCAmelCase_           =   len(encoded_inputs["global_attention_mask"] ) != len(__a )

              if needs_to_be_padded:
                   UpperCAmelCase_           =   len(__a ) - len(encoded_inputs["global_attention_mask"] )

                   if self.padding_side == "right":
                        # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                        UpperCAmelCase_           =   (
                            encoded_inputs["global_attention_mask"] + [-1] * difference
                        )
                   elif self.padding_side == "left":
                        UpperCAmelCase_           =   [-1] * difference + encoded_inputs[
                            "global_attention_mask"
                        ]
                   else:
                        raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )

         return encoded_inputs





'''simple docstring'''





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

import datasets


SCREAMING_SNAKE_CASE_:     Optional[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'


SCREAMING_SNAKE_CASE_:     Union[str, Any]  ='\\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'


SCREAMING_SNAKE_CASE_:     List[Any]  =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 					_lowercase						(self    :    Optional[Any] ):
         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 					_lowercase						(self    :    Tuple   ,			__a    :    Optional[int]   ,			__a    :    List[Any] ):
         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,
         }




