




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

import datasets


__A        =    "\\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"


__A        =    "\\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"


__A        =    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       lowercase_    (      self							)    ->      Union[str, 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       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    0.0
							for i, j in zip(lowerCamelCase__			,						lowerCamelCase__							):
											n_correct += 1.0 if math_equivalence.is_equiv(lowerCamelCase__			,						lowerCamelCase__							) else 0.0
							__lowerCamelCase		       =    n_correct / len(lowerCamelCase__							)
							return {
							    "accuracy": accuracy,
							}








import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


__A        =    logging.get_logger(__name__)

__A        =    {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__A        =    {
    "tokenizer_file": {
        "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
    },
}

__A        =    {
    "gpt-neox-20b": 20_48,
}







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       VOCAB_FILES_NAMES
			snake_case_   		=       PRETRAINED_VOCAB_FILES_MAP
			snake_case_   		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case_   		=       ['''input_ids''', '''attention_mask''']


			def __init__(      self			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						lowerCamelCase__="<|endoftext|>"			,						lowerCamelCase__="<|endoftext|>"			,						lowerCamelCase__="<|endoftext|>"			,						lowerCamelCase__=False			,						**lowerCamelCase__			,						)    ->      int:



							'''simple docstring'''
							super().__init__(
							    lowerCamelCase__			,						lowerCamelCase__			,						tokenizer_file=lowerCamelCase__			,						unk_token=lowerCamelCase__			,						bos_token=lowerCamelCase__			,						eos_token=lowerCamelCase__			,						add_prefix_space=lowerCamelCase__			,						**lowerCamelCase__			,						)

							__lowerCamelCase		       =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
							if pre_tok_state.get('add_prefix_space'			,						lowerCamelCase__							) != add_prefix_space:
											__lowerCamelCase		       =    getattr(lowerCamelCase__			,						pre_tok_state.pop('type'							)							)
											__lowerCamelCase		       =    add_prefix_space
											__lowerCamelCase		       =    pre_tok_class(**lowerCamelCase__							)

							__lowerCamelCase		       =    add_prefix_space


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__ = None							)    ->      Tuple[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    self._tokenizer.model.save(lowerCamelCase__			,						name=lowerCamelCase__							)
							return tuple(lowerCamelCase__							)






			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      List[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    []
							for is_user, text in conversation.iter_texts():
											input_ids.extend(self.encode(lowerCamelCase__			,						add_special_tokens=lowerCamelCase__							) + [self.eos_token_id]							)

							if len(lowerCamelCase__							) > self.model_max_length:
											__lowerCamelCase		       =    input_ids[-self.model_max_length :]
							return input_ids


