



"""simple docstring"""


from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""EncodecFeatureExtractor"""
			UpperCamelCase__			      =	("""T5Tokenizer""", """T5TokenizerFast""")



			def __init__(		self     :    List[str]				,   __UpperCamelCase     :    Tuple				,   __UpperCamelCase     :    Tuple   )->Dict:
							super().__init__(__UpperCamelCase				,   __UpperCamelCase   )
							_UpperCAmelCase					=    self.feature_extractor
							_UpperCAmelCase					=    False



			def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    Optional[int]=None				,   __UpperCamelCase     :    Optional[int]=None				,   __UpperCamelCase     :    List[Any]=True   )->int:
							return self.tokenizer.get_decoder_prompt_ids(task=__UpperCamelCase				,   language=__UpperCamelCase				,   no_timestamps=__UpperCamelCase   )



			def __call__(		self     :    Any				,   *__UpperCamelCase     :    Dict				,   **__UpperCamelCase     :    Tuple   )->str:
							# For backward compatibility
							if self._in_target_context_manager:
											return self.current_processor(*__UpperCamelCase				,   **__UpperCamelCase   )

							_UpperCAmelCase					=    kwargs.pop('''audio'''				,   __UpperCamelCase   )
							_UpperCAmelCase					=    kwargs.pop('''sampling_rate'''				,   __UpperCamelCase   )
							_UpperCAmelCase					=    kwargs.pop('''text'''				,   __UpperCamelCase   )
							if len(__UpperCamelCase   ) > 0:
											_UpperCAmelCase					=    args[0]
											_UpperCAmelCase					=    args[1:]

							if audio is None and text is None:
											raise ValueError('''You need to specify either an `audio` or `text` input to process.'''   )

							if text is not None:
											_UpperCAmelCase					=    self.tokenizer(__UpperCamelCase				,   **__UpperCamelCase   )

							if audio is not None:
											_UpperCAmelCase					=    self.feature_extractor(__UpperCamelCase				,   *__UpperCamelCase				,   sampling_rate=__UpperCamelCase				,   **__UpperCamelCase   )

							if audio is None:
											return inputs

							elif text is None:
											return audio_inputs

							else:
											_UpperCAmelCase					=    audio_inputs['''input_values''']
											if "padding_mask" in audio_inputs:
															_UpperCAmelCase					=    audio_inputs['''padding_mask''']
											return inputs



			def  lowercase__						(		self     :    Any				,   *__UpperCamelCase     :    Tuple				,   **__UpperCamelCase     :    Dict   )->List[Any]:
							_UpperCAmelCase					=    kwargs.pop('''audio'''				,   __UpperCamelCase   )
							_UpperCAmelCase					=    kwargs.pop('''padding_mask'''				,   __UpperCamelCase   )

							if len(__UpperCamelCase   ) > 0:
											_UpperCAmelCase					=    args[0]
											_UpperCAmelCase					=    args[1:]

							if audio_values is not None:
											return self._decode_audio(__UpperCamelCase				,   padding_mask=__UpperCamelCase   )
							else:
											return self.tokenizer.batch_decode(*__UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    str				,   *__UpperCamelCase     :    Optional[Any]				,   **__UpperCamelCase     :    Union[str, Any]   )->Dict:
							return self.tokenizer.decode(*__UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    Optional[Any]				,   __UpperCamelCase     :    Optional = None   )->List[np.ndarray]:
							_UpperCAmelCase					=    to_numpy(__UpperCamelCase   )
							_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    audio_values.shape

							if padding_mask is None:
											return list(__UpperCamelCase   )

							_UpperCAmelCase					=    to_numpy(__UpperCamelCase   )

							# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
							# token (so that the generated audio values are **not** treated as padded tokens)
							_UpperCAmelCase					=    seq_len - padding_mask.shape[-1]
							_UpperCAmelCase					=    1 - self.feature_extractor.padding_value
							_UpperCAmelCase					=    np.pad(__UpperCamelCase				,   ((0, 0), (0, difference))				,   '''constant'''				,   constant_values=__UpperCamelCase   )

							_UpperCAmelCase					=    audio_values.tolist()
							for i in range(__UpperCamelCase   ):
											_UpperCAmelCase					=    np.asarray(audio_values[i]   )[
											    padding_mask[i][None, :] != self.feature_extractor.padding_value
											]
											_UpperCAmelCase					=    sliced_audio.reshape(__UpperCamelCase				,   -1   )

							return audio_values








"""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,
							}



