



"""simple docstring"""


import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
					import torch
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""char"""
			UpperCamelCase__			      =	"""bpe"""
			UpperCamelCase__			      =	"""wp"""



__A   :							Union[str, Any]										=   (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	["""image_processor""", """char_tokenizer"""]
			UpperCamelCase__			      =	"""ViTImageProcessor"""
			UpperCamelCase__			      =	"""MgpstrTokenizer"""



			def __init__(		self     :    List[str]				,   __UpperCamelCase     :    Dict=None				,   __UpperCamelCase     :    int=None				,   **__UpperCamelCase     :    Dict   )->Union[str, Any]:
							_UpperCAmelCase					=    None
							if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.'''				,   __UpperCamelCase				,   )
											_UpperCAmelCase					=    kwargs.pop('''feature_extractor'''   )

							_UpperCAmelCase					=    image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
											raise ValueError('''You need to specify an `image_processor`.'''   )
							if tokenizer is None:
											raise ValueError('''You need to specify a `tokenizer`.'''   )

							_UpperCAmelCase					=    tokenizer
							_UpperCAmelCase					=    AutoTokenizer.from_pretrained('''gpt2'''   )
							_UpperCAmelCase					=    AutoTokenizer.from_pretrained('''bert-base-uncased'''   )

							super().__init__(__UpperCamelCase				,   __UpperCamelCase   )



			def __call__(		self     :    Any				,   __UpperCamelCase     :    str=None				,   __UpperCamelCase     :    Tuple=None				,   __UpperCamelCase     :    Tuple=None				,   **__UpperCamelCase     :    List[str]   )->List[Any]:
							if images is None and text is None:
											raise ValueError('''You need to specify either an `images` or `text` input to process.'''   )

							if images is not None:
											_UpperCAmelCase					=    self.image_processor(__UpperCamelCase				,   return_tensors=__UpperCamelCase				,   **__UpperCamelCase   )
							if text is not None:
											_UpperCAmelCase					=    self.char_tokenizer(__UpperCamelCase				,   return_tensors=__UpperCamelCase				,   **__UpperCamelCase   )

							if text is None:
											return inputs
							elif images is None:
											return encodings
							else:
											_UpperCAmelCase					=    encodings['''input_ids''']
											return inputs



			def  lowercase__						(		self     :    str				,   __UpperCamelCase     :    Dict   )->str:
							_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    sequences
							_UpperCAmelCase					=    char_preds.size(0   )

							_UpperCAmelCase		,						_UpperCAmelCase					=    self._decode_helper(__UpperCamelCase				,   '''char'''   )
							_UpperCAmelCase		,						_UpperCAmelCase					=    self._decode_helper(__UpperCamelCase				,   '''bpe'''   )
							_UpperCAmelCase		,						_UpperCAmelCase					=    self._decode_helper(__UpperCamelCase				,   '''wp'''   )

							_UpperCAmelCase					=    []
							_UpperCAmelCase					=    []
							for i in range(__UpperCamelCase   ):
											_UpperCAmelCase					=    [char_scores[i], bpe_scores[i], wp_scores[i]]
											_UpperCAmelCase					=    [char_strs[i], bpe_strs[i], wp_strs[i]]
											_UpperCAmelCase					=    scores.index(max(__UpperCamelCase   )   )
											final_strs.append(strs[max_score_index]   )
											final_scores.append(scores[max_score_index]   )

							_UpperCAmelCase					=    {}
							_UpperCAmelCase					=    final_strs
							_UpperCAmelCase					=    final_scores
							_UpperCAmelCase					=    char_strs
							_UpperCAmelCase					=    bpe_strs
							_UpperCAmelCase					=    wp_strs
							return out



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    Union[str, Any]   )->List[str]:
							if format == DecodeType.CHARACTER:
											_UpperCAmelCase					=    self.char_decode
											_UpperCAmelCase					=    1
											_UpperCAmelCase					=    '''[s]'''
							elif format == DecodeType.BPE:
											_UpperCAmelCase					=    self.bpe_decode
											_UpperCAmelCase					=    2
											_UpperCAmelCase					=    '''#'''
							elif format == DecodeType.WORDPIECE:
											_UpperCAmelCase					=    self.wp_decode
											_UpperCAmelCase					=    1_0_2
											_UpperCAmelCase					=    '''[SEP]'''
							else:
											raise ValueError(F'Format {format} is not supported.'   )

							_UpperCAmelCase		,						_UpperCAmelCase					=    [], []
							_UpperCAmelCase					=    pred_logits.size(0   )
							_UpperCAmelCase					=    pred_logits.size(1   )
							_UpperCAmelCase		,						_UpperCAmelCase					=    pred_logits.topk(1				,   dim=-1				,   largest=__UpperCamelCase				,   sorted=__UpperCamelCase   )
							_UpperCAmelCase					=    preds_index.view(-1				,   __UpperCamelCase   )[:, 1:]
							_UpperCAmelCase					=    decoder(__UpperCamelCase   )
							_UpperCAmelCase		,						_UpperCAmelCase					=    torch.nn.functional.softmax(__UpperCamelCase				,   dim=2   ).max(dim=2   )
							_UpperCAmelCase					=    preds_max_prob[:, 1:]

							for index in range(__UpperCamelCase   ):
											_UpperCAmelCase					=    preds_str[index].find(__UpperCamelCase   )
											_UpperCAmelCase					=    preds_str[index][:pred_eos]
											_UpperCAmelCase					=    preds_index[index].cpu().tolist()
											_UpperCAmelCase					=    pred_index.index(__UpperCamelCase   ) if eos_token in pred_index else -1
											_UpperCAmelCase					=    preds_max_prob[index][: pred_eos_index + 1]
											_UpperCAmelCase					=    pred_max_prob.cumprod(dim=0   )[-1] if pred_max_prob.nelement() != 0 else 0.0
											dec_strs.append(__UpperCamelCase   )
											conf_scores.append(__UpperCamelCase   )

							return dec_strs, conf_scores



			def  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    Union[str, Any]   )->List[Any]:
							_UpperCAmelCase					=    [seq.replace(''' '''				,   ''''''   ) for seq in self.char_tokenizer.batch_decode(__UpperCamelCase   )]
							return decode_strs



			def  lowercase__						(		self     :    List[Any]				,   __UpperCamelCase     :    Optional[int]   )->Union[str, Any]:
							return self.bpe_tokenizer.batch_decode(__UpperCamelCase   )



			def  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    List[str]   )->Tuple:
							_UpperCAmelCase					=    [seq.replace(''' '''				,   ''''''   ) for seq in self.wp_tokenizer.batch_decode(__UpperCamelCase   )]
							return decode_strs








"""simple docstring"""


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

import datasets


__A   :							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"


__A   :							Optional[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"


__A   :							Tuple										=   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):
			"""simple docstring"""



			def  lowercase__						(		self     :    List[Any]   )->Dict:
							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     :    int				,   __UpperCamelCase     :    List[str]				,   __UpperCamelCase     :    Union[str, Any]   )->Tuple:
							_UpperCAmelCase					=    0.0
							for i, j in zip(__UpperCamelCase				,   __UpperCamelCase   ):
											n_correct += 1.0 if math_equivalence.is_equiv(__UpperCamelCase				,   __UpperCamelCase   ) else 0.0
							_UpperCAmelCase					=    n_correct / len(__UpperCamelCase   )
							return {
							    "accuracy": accuracy,
							}



