






from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
  from ..models.auto import AutoTokenizer
class 							A_    :






 '''simple docstring'''




 def  snake_case__				(	self							,  _A) ->		Union[str, Any]:

     """simple docstring"""



     raise NotImplementedError()


 def  snake_case__				(	self) ->		str:

     """simple docstring"""



     raise NotImplementedError()



class 							A_    (						__lowercase			):






 '''simple docstring'''




 def __init__(	self							,  _A							,  _A = False							,  **_A) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				tokenizer
     _UpperCAmelCase				:      List[Any]			  =				skip_prompt
     _UpperCAmelCase				:      Dict			  =				decode_kwargs

     # variables used in the streaming process
     _UpperCAmelCase				:      Optional[Any]			  =				[]
     _UpperCAmelCase				:      Optional[int]			  =				0
     _UpperCAmelCase				:      Dict			  =				True


 def  snake_case__				(	self							,  _A) ->		Optional[int]:

     """simple docstring"""



     if len(value.shape) > 1 and value.shape[0] > 1:
         raise ValueError('''TextStreamer only supports batch size 1''')
     elif len(value.shape) > 1:
         _UpperCAmelCase				:      List[str]			  =				value[0]

     if self.skip_prompt and self.next_tokens_are_prompt:
         _UpperCAmelCase				:      Optional[Any]			  =				False
         return

     # Add the new token to the cache and decodes the entire thing.
     self.token_cache.extend(value.tolist())
     _UpperCAmelCase				:      Optional[Any]			  =				self.tokenizer.decode(self.token_cache							,  **self.decode_kwargs)

     # After the symbol for a new line, we flush the cache.
     if text.endswith('''\n'''):
         _UpperCAmelCase				:      Union[str, Any]			  =				text[self.print_len :]
         _UpperCAmelCase				:      Optional[Any]			  =				[]
         _UpperCAmelCase				:      int			  =				0
     # If the last token is a CJK character, we print the characters.
     elif len(_lowerCAmelCase) > 0 and self._is_chinese_char(ord(text[-1])):
         _UpperCAmelCase				:      Tuple			  =				text[self.print_len :]
         self.print_len += len(_lowerCAmelCase)
     # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
     # which may change with the subsequent token -- there are probably smarter ways to do this!)
     else:
         _UpperCAmelCase				:      List[str]			  =				text[self.print_len : text.rfind(''' ''') + 1]
         self.print_len += len(_lowerCAmelCase)

     self.on_finalized_text(_lowerCAmelCase)


 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     if len(self.token_cache) > 0:
         _UpperCAmelCase				:      str			  =				self.tokenizer.decode(self.token_cache							,  **self.decode_kwargs)
         _UpperCAmelCase				:      int			  =				text[self.print_len :]
         _UpperCAmelCase				:      List[Any]			  =				[]
         _UpperCAmelCase				:      Tuple			  =				0
     else:
         _UpperCAmelCase				:      Tuple			  =				''''''

     _UpperCAmelCase				:      Union[str, Any]			  =				True
     self.on_finalized_text(_lowerCAmelCase							,  stream_end=_lowerCAmelCase)


 def  snake_case__				(	self							,  _A							,  _A = False) ->		List[Any]:

     """simple docstring"""



     print(_lowerCAmelCase							,  flush=_lowerCAmelCase							,  end='''''' if not stream_end else None)


 def  snake_case__				(	self							,  _A) ->		str:

     """simple docstring"""



     if (
         (cp >= 0X4e_00 and cp <= 0X9f_ff)
         or (cp >= 0X34_00 and cp <= 0X4d_bf)  #
         or (cp >= 0X2_00_00 and cp <= 0X2_a6_df)  #
         or (cp >= 0X2_a7_00 and cp <= 0X2_b7_3f)  #
         or (cp >= 0X2_b7_40 and cp <= 0X2_b8_1f)  #
         or (cp >= 0X2_b8_20 and cp <= 0X2_ce_af)  #
         or (cp >= 0Xf9_00 and cp <= 0Xfa_ff)
         or (cp >= 0X2_f8_00 and cp <= 0X2_fa_1f)  #
     ):  #
         return True

     return False



class 							A_    (						__lowercase			):






 '''simple docstring'''




 def __init__(	self							,  _A							,  _A = False							,  _A = None							,  **_A) ->		Dict:

     """simple docstring"""



     super().__init__(_lowerCAmelCase							,  _lowerCAmelCase							,  **_lowerCAmelCase)
     _UpperCAmelCase				:      Tuple			  =				Queue()
     _UpperCAmelCase				:      Any			  =				None
     _UpperCAmelCase				:      Union[str, Any]			  =				timeout


 def  snake_case__				(	self							,  _A							,  _A = False) ->		List[str]:

     """simple docstring"""



     self.text_queue.put(_lowerCAmelCase							,  timeout=self.timeout)
     if stream_end:
         self.text_queue.put(self.stop_signal							,  timeout=self.timeout)


 def __iter__(	self) ->		List[Any]:

     """simple docstring"""



     return self


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				self.text_queue.get(timeout=self.timeout)
     if value == self.stop_signal:
         raise StopIteration()
     else:
         return value












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

import datasets


SCREAMING_SNAKE_CASE 							=						'\\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 							=						'\\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 							=						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  snake_case__				(	self) ->		Optional[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  snake_case__				(	self							,  _A							,  _A) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				0.0
     for i, j in zip(_A							,  _A):
         n_correct += 1.0 if math_equivalence.is_equiv(_A							,  _A) else 0.0
     _UpperCAmelCase				:      Tuple			  =				n_correct / len(_A)
     return {
         "accuracy": accuracy,
     }




